Time series analysis of wind speed using VAR and the generalized impulse response technique

Energy Technology Data Exchange (ETDEWEB)

Ewing, Bradley T. [Area of Information Systems and Quantitative Sciences, Rawls College of Business and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX 79409-2101 (United States); Kruse, Jamie Brown [Center for Natural Hazard Research, East Carolina University, Greenville, NC (United States); Schroeder, John L. [Department of Geosciences and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States); Smith, Douglas A. [Department of Civil Engineering and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States)

2007-03-15

This research examines the interdependence in time series wind speed data measured in the same location at four different heights. A multiple-equation system known as a vector autoregression is proposed for characterizing the time series dynamics of wind. Additionally, the recently developed method of generalized impulse response analysis provides insight into the cross-effects of the wind series and their responses to shocks. Findings are based on analysis of contemporaneous wind speed time histories taken at 13, 33, 70 and 160 ft above ground level with a sampling rate of 10 Hz. The results indicate that wind speeds measured at 70 ft was the most variable. Further, the turbulence persisted longer at the 70-ft measurement than at the other heights. The greatest interdependence is observed at 13 ft. Gusts at 160 ft led to the greatest persistence to an 'own' shock and led to greatest persistence in the responses of the other wind series. (author)

Transition Icons for Time-Series Visualization and Exploratory Analysis.

Science.gov (United States)

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Analysis of archaeological pieces with nuclear techniques

International Nuclear Information System (INIS)

Tenorio, D.

2002-01-01

In this work nuclear techniques such as Neutron Activation Analysis, PIXE, X-ray fluorescence analysis, Metallography, Uranium series, Rutherford Backscattering for using in analysis of archaeological specimens and materials are described. Also some published works and thesis about analysis of different Mexican and Meso american archaeological sites are referred. (Author)

The Timeseries Toolbox - A Web Application to Enable Accessible, Reproducible Time Series Analysis

Science.gov (United States)

Veatch, W.; Friedman, D.; Baker, B.; Mueller, C.

2017-12-01

The vast majority of data analyzed by climate researchers are repeated observations of physical process or time series data. This data lends itself of a common set of statistical techniques and models designed to determine trends and variability (e.g., seasonality) of these repeated observations. Often, these same techniques and models can be applied to a wide variety of different time series data. The Timeseries Toolbox is a web application designed to standardize and streamline these common approaches to time series analysis and modeling with particular attention to hydrologic time series used in climate preparedness and resilience planning and design by the U. S. Army Corps of Engineers. The application performs much of the pre-processing of time series data necessary for more complex techniques (e.g. interpolation, aggregation). With this tool, users can upload any dataset that conforms to a standard template and immediately begin applying these techniques to analyze their time series data.

Statistical techniques applied to aerial radiometric surveys (STAARS): series introduction and the principal-components-analysis method

International Nuclear Information System (INIS)

Pirkle, F.L.

1981-04-01

STAARS is a new series which is being published to disseminate information concerning statistical procedures for interpreting aerial radiometric data. The application of a particular data interpretation technique to geologic understanding for delineating regions favorable to uranium deposition is the primary concern of STAARS. Statements concerning the utility of a technique on aerial reconnaissance data as well as detailed aerial survey data will be included

Time series analysis methods and applications for flight data

CERN Document Server

Zhang, Jianye

2017-01-01

This book focuses on different facets of flight data analysis, including the basic goals, methods, and implementation techniques. As mass flight data possesses the typical characteristics of time series, the time series analysis methods and their application for flight data have been illustrated from several aspects, such as data filtering, data extension, feature optimization, similarity search, trend monitoring, fault diagnosis, and parameter prediction, etc. An intelligent information-processing platform for flight data has been established to assist in aircraft condition monitoring, training evaluation and scientific maintenance. The book will serve as a reference resource for people working in aviation management and maintenance, as well as researchers and engineers in the fields of data analysis and data mining.

Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

DEFF Research Database (Denmark)

Nonejad, Nima

This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast...... and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where...

Handbook of Time Series Analysis Recent Theoretical Developments and Applications

CERN Document Server

Schelter, Björn; Timmer, Jens

2006-01-01

This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest de

Review and classification of variability analysis techniques with clinical applications

Science.gov (United States)

2011-01-01

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis. PMID:21985357

Review and classification of variability analysis techniques with clinical applications.

Science.gov (United States)

Bravi, Andrea; Longtin, André; Seely, Andrew J E

2011-10-10

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.

Gold analysis by the gamma absorption technique

International Nuclear Information System (INIS)

Kurtoglu, Arzu; Tugrul, A.B.

2003-01-01

Gold (Au) analyses are generally performed using destructive techniques. In this study, the Gamma Absorption Technique has been employed for gold analysis. A series of different gold alloys of known gold content were analysed and a calibration curve was obtained. This curve was then used for the analysis of unknown samples. Gold analyses can be made non-destructively, easily and quickly by the gamma absorption technique. The mass attenuation coefficients of the alloys were measured around the K-shell absorption edge of Au. Theoretical mass attenuation coefficient values were obtained using the WinXCom program and comparison of the experimental results with the theoretical values showed generally good and acceptable agreement

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates.

Science.gov (United States)

Xia, Li C; Steele, Joshua A; Cram, Jacob A; Cardon, Zoe G; Simmons, Sheri L; Vallino, Joseph J; Fuhrman, Jed A; Sun, Fengzhu

2011-01-01

The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

On statistical inference in time series analysis of the evolution of road safety.

Science.gov (United States)

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Photoplethismographic Signal Processed with Nonlinear Time Series Analysis Tools

International Nuclear Information System (INIS)

Hernandez Caceres, Jose Luis; Hong, Rolando; Garcia Lanz, Abel; Garcia Dominguez, Luis; Cabannas, Karelia

2001-01-01

Finger photoplethismography (PPG) signals were submitted to nonlinear time series analysis. The applied analytical techniques were: (i) High degree polynomial fitting for baseline estimation; (ii) FFT analysis for estimating power spectra; (iii) fractal dimension estimation via the Higuchi's time-domain method, and (iv) kernel nonparametric estimation for reconstructing noise free-attractors and also for estimating signal's stochastic components

An operator expansion technique for path integral analysis

International Nuclear Information System (INIS)

Tsvetkov, I.V.

1995-01-01

A new method of path integral analysis in the framework of a power series technique is presented. The method is based on the operator expansion of an exponential. A regular procedure to calculate the correction terms is found. (orig.)

Applied time series analysis

CERN Document Server

Woodward, Wayne A; Elliott, Alan C

2011-01-01

""There is scarcely a standard technique that the reader will find left out … this book is highly recommended for those requiring a ready introduction to applicable methods in time series and serves as a useful resource for pedagogical purposes.""-International Statistical Review (2014), 82""Current time series theory for practice is well summarized in this book.""-Emmanuel Parzen, Texas A&M University""What an extraordinary range of topics covered, all very insightfully. I like [the authors'] innovations very much, such as the AR factor table.""-David Findley, U.S. Census Bureau (retired)""…

Assessing Spontaneous Combustion Instability with Nonlinear Time Series Analysis

Science.gov (United States)

Eberhart, C. J.; Casiano, M. J.

2015-01-01

Considerable interest lies in the ability to characterize the onset of spontaneous instabilities within liquid propellant rocket engine (LPRE) combustion devices. Linear techniques, such as fast Fourier transforms, various correlation parameters, and critical damping parameters, have been used at great length for over fifty years. Recently, nonlinear time series methods have been applied to deduce information pertaining to instability incipiency hidden in seemingly stochastic combustion noise. A technique commonly used in biological sciences known as the Multifractal Detrended Fluctuation Analysis has been extended to the combustion dynamics field, and is introduced here as a data analysis approach complementary to linear ones. Advancing, a modified technique is leveraged to extract artifacts of impending combustion instability that present themselves a priori growth to limit cycle amplitudes. Analysis is demonstrated on data from J-2X gas generator testing during which a distinct spontaneous instability was observed. Comparisons are made to previous work wherein the data were characterized using linear approaches. Verification of the technique is performed by examining idealized signals and comparing two separate, independently developed tools.

Chaotic time series analysis in economics: Balance and perspectives

International Nuclear Information System (INIS)

Faggini, Marisa

2014-01-01

The aim of the paper is not to review the large body of work concerning nonlinear time series analysis in economics, about which much has been written, but rather to focus on the new techniques developed to detect chaotic behaviours in economic data. More specifically, our attention will be devoted to reviewing some of these techniques and their application to economic and financial data in order to understand why chaos theory, after a period of growing interest, appears now not to be such an interesting and promising research area

Chaotic time series analysis in economics: Balance and perspectives

Energy Technology Data Exchange (ETDEWEB)

Faggini, Marisa, E-mail: mfaggini@unisa.it [Dipartimento di Scienze Economiche e Statistiche, Università di Salerno, Fisciano 84084 (Italy)

2014-12-15

The aim of the paper is not to review the large body of work concerning nonlinear time series analysis in economics, about which much has been written, but rather to focus on the new techniques developed to detect chaotic behaviours in economic data. More specifically, our attention will be devoted to reviewing some of these techniques and their application to economic and financial data in order to understand why chaos theory, after a period of growing interest, appears now not to be such an interesting and promising research area.

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

The Recoverability of P-Technique Factor Analysis

Science.gov (United States)

Molenaar, Peter C. M.; Nesselroade, John R.

2009-01-01

It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even…

Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

Science.gov (United States)

Yujun, Yang; Jianping, Li; Yimei, Yang

This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

Complexity testing techniques for time series data: A comprehensive literature review

International Nuclear Information System (INIS)

Tang, Ling; Lv, Huiling; Yang, Fengmei; Yu, Lean

2015-01-01

Highlights: • A literature review of complexity testing techniques for time series data is provided. • Complexity measurements can generally fall into fractality, methods derived from nonlinear dynamics and entropy. • Different types investigate time series data from different perspectives. • Measures, applications and future studies for each type are presented. - Abstract: Complexity may be one of the most important measurements for analysing time series data; it covers or is at least closely related to different data characteristics within nonlinear system theory. This paper provides a comprehensive literature review examining the complexity testing techniques for time series data. According to different features, the complexity measurements for time series data can be divided into three primary groups, i.e., fractality (mono- or multi-fractality) for self-similarity (or system memorability or long-term persistence), methods derived from nonlinear dynamics (via attractor invariants or diagram descriptions) for attractor properties in phase-space, and entropy (structural or dynamical entropy) for the disorder state of a nonlinear system. These estimations analyse time series dynamics from different perspectives but are closely related to or even dependent on each other at the same time. In particular, a weaker self-similarity, a more complex structure of attractor, and a higher-level disorder state of a system consistently indicate that the observed time series data are at a higher level of complexity. Accordingly, this paper presents a historical tour of the important measures and works for each group, as well as ground-breaking and recent applications and future research directions.

Contributions to fuzzy polynomial techniques for stability analysis and control

OpenAIRE

Pitarch Pérez, José Luis

2014-01-01

The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees...

Independent component analysis: A new possibility for analysing series of electron energy loss spectra

International Nuclear Information System (INIS)

Bonnet, Nogl; Nuzillard, Danielle

2005-01-01

A complementary approach is proposed for analysing series of electron energy-loss spectra that can be recorded with the spectrum-line technique, across an interface for instance. This approach, called blind source separation (BSS) or independent component analysis (ICA), complements two existing methods: the spatial difference approach and multivariate statistical analysis. The principle of the technique is presented and illustrations are given through one simulated example and one real example

Identification of human operator performance models utilizing time series analysis

Science.gov (United States)

Holden, F. M.; Shinners, S. M.

1973-01-01

The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.

Notes on economic time series analysis system theoretic perspectives

CERN Document Server

Aoki, Masanao

1983-01-01

In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor­ ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of wha...

TIME SERIES ANALYSIS ON STOCK MARKET FOR TEXT MINING CORRELATION OF ECONOMY NEWS

Directory of Open Access Journals (Sweden)

Sadi Evren SEKER

2014-01-01

Full Text Available This paper proposes an information retrieval methodfor the economy news. Theeffect of economy news, are researched in the wordlevel and stock market valuesare considered as the ground proof.The correlation between stock market prices and economy news is an already ad-dressed problem for most of the countries. The mostwell-known approach is ap-plying the text mining approaches to the news and some time series analysis tech-niques over stock market closing values in order toapply classification or cluster-ing algorithms over the features extracted. This study goes further and tries to askthe question what are the available time series analysis techniques for the stockmarket closing values and which one is the most suitable? In this study, the newsand their dates are collected into a database and text mining is applied over thenews, the text mining part has been kept simple with only term frequency – in-verse document frequency method. For the time series analysis part, we havestudied 10 different methods such as random walk, moving average, acceleration,Bollinger band, price rate of change, periodic average, difference, momentum orrelative strength index and their variation. In this study we have also explainedthese techniques in a comparative way and we have applied the methods overTurkish Stock Market closing values for more than a2 year period. On the otherhand, we have applied the term frequency – inversedocument frequency methodon the economy news of one of the high-circulatingnewspapers in Turkey.

Discontinuous conduction mode analysis of phase-modulated series ...

Indian Academy of Sciences (India)

modulated dc–dc series resonant converter (SRC) operating in discontinuous conduction mode (DCM). The conventional fundamental harmonic approximation technique is extended for a non-ideal series resonant tank to clarify the limitations of ...

Biological time series analysis using a context free language: applicability to pulsatile hormone data.

Directory of Open Access Journals (Sweden)

Dennis A Dean

Full Text Available We present a novel approach for analyzing biological time-series data using a context-free language (CFL representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals.

A technique for filling gaps in time series with complicated power spectra

International Nuclear Information System (INIS)

Brown, T.M.

1984-01-01

Fahlman and Ulrych (1982) describe a method for estimating the power and phase spectra of gapped time series, using a maximum-entropy reconstruction of the data in the gaps. It has proved difficult to apply this technique to solar oscillations data, because of the great complexity of the solar oscillations spectrum. We describe a means for avoiding this difficulty, and report the results of a series of blind tests of the modified technique. The main results of these tests are: 1. Gap-filling gives good results, provided that the signal-to-noise ration in the original data is large enough, and provided the gaps are short enough. For low-noise data, the duty cycle of the observations should not be less than about 50%. 2. The frequencies and widths of narrow spectrum features are well reproduced by the technique. 3. The technique systematically reduces the apparent amplitudes of small features in the spectrum relative to large ones. (orig.)

Visibility Graph Based Time Series Analysis.

Science.gov (United States)

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Visibility Graph Based Time Series Analysis.

Directory of Open Access Journals (Sweden)

Mutua Stephen

Full Text Available Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

The "Chaos Theory" and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease?

Science.gov (United States)

Krstacic, Goran; Krstacic, Antonija; Smalcelj, Anton; Milicic, Davor; Jembrek-Gostovic, Mirjana

2007-04-01

Dynamic analysis techniques may quantify abnormalities in heart rate variability (HRV) based on nonlinear and fractal analysis (chaos theory). The article emphasizes clinical and prognostic significance of dynamic changes in short-time series applied on patients with coronary heart disease (CHD) during the exercise electrocardiograph (ECG) test. The subjects were included in the series after complete cardiovascular diagnostic data. Series of R-R and ST-T intervals were obtained from exercise ECG data after sampling digitally. The range rescaled analysis method determined the fractal dimension of the intervals. To quantify fractal long-range correlation's properties of heart rate variability, the detrended fluctuation analysis technique was used. Approximate entropy (ApEn) was applied to quantify the regularity and complexity of time series, as well as unpredictability of fluctuations in time series. It was found that the short-term fractal scaling exponent (alpha(1)) is significantly lower in patients with CHD (0.93 +/- 0.07 vs 1.09 +/- 0.04; P chaos theory during the exercise ECG test point out the multifractal time series in CHD patients who loss normal fractal characteristics and regularity in HRV. Nonlinear analysis technique may complement traditional ECG analysis.

Clinical and epidemiological rounds. Time series

Directory of Open Access Journals (Sweden)

León-Álvarez, Alba Luz

2016-07-01

Full Text Available Analysis of time series is a technique that implicates the study of individuals or groups observed in successive moments in time. This type of analysis allows the study of potential causal relationships between different variables that change over time and relate to each other. It is the most important technique to make inferences about the future, predicting, on the basis or what has happened in the past and it is applied in different disciplines of knowledge. Here we discuss different components of time series, the analysis technique and specific examples in health research.

A Novel Generation Method for the PV Power Time Series Combining the Decomposition Technique and Markov Chain Theory

DEFF Research Database (Denmark)

Xu, Shenzhi; Ai, Xiaomeng; Fang, Jiakun

2017-01-01

Photovoltaic (PV) power generation has made considerable developments in recent years. But its intermittent and volatility of its output has seriously affected the security operation of the power system. In order to better understand the PV generation and provide sufficient data support...... for analysis the impacts, a novel generation method for PV power time series combining decomposition technique and Markov chain theory is presented in this paper. It digs important factors from historical data from existing PV plants and then reproduce new data with similar patterns. In detail, the proposed...... method first decomposes the PV power time series into ideal output curve, amplitude parameter series and random fluctuating component three parts. Then generating daily ideal output curve by the extraction of typical daily data, amplitude parameter series based on the Markov chain Monte Carlo (MCMC...

Management of odontogenic cysts by endonasal endoscopic techniques: A systematic review and case series.

Science.gov (United States)

Marino, Michael J; Luong, Amber; Yao, William C; Citardi, Martin J

2018-01-01

Odontogenic cysts and tumors of the maxilla may be amendable to management by endonasal endoscopic techniques, which may reduce the morbidity associated with open procedures and avoid difficult reconstruction. To perform a systematic review that evaluates the feasibility and outcomes of endoscopic techniques in the management of different odontogenic cysts. A case series of our experience with these minimally invasive techniques was assembled for insight into the technical aspects of these procedures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used to identify English-language studies that reported the use of endoscopic techniques in the management of odontogenic cysts. Several medical literature data bases were searched for all occurrences in the title or abstract of the terms "odontogenic" and "endoscopic" between January 1, 1950, and October 1, 2016. Publications were evaluated for the technique used, histopathology, complications, recurrences, and the follow-up period. A case series of patients who presented to a tertiary rhinology clinic and who underwent treatment of odontogenic cysts by an endoscopic technique was included. A systematic review identified 16 case reports or series that described the use of endoscopic techniques for the treatment of odontogenic cysts, including 45 total patients. Histopathologies encountered were radicular (n = 16) and dentigerous cysts (n = 10), and keratocystic odontogenic tumor (n = 12). There were no reported recurrences or major complications for a mean follow-up of 29 months. A case series of patients in our institution identified seven patients without recurrence for a mean follow-up of 10 months. Endonasal endoscopic treatment of various odontogenic cysts are described in the literature and are associated with effective treatment of these lesions for an average follow-up period of >2 years. These techniques have the potential to reduce morbidity associated with the resection of these

On-line condition monitoring of nuclear systems via symbolic time series analysis

International Nuclear Information System (INIS)

Rajagopalan, V.; Ray, A.; Garcia, H. E.

2006-01-01

This paper provides a symbolic time series analysis approach to fault diagnostics and condition monitoring. The proposed technique is built upon concepts from wavelet theory, symbolic dynamics and pattern recognition. Various aspects of the methodology such as wavelet selection, choice of alphabet and determination of depth of D-Markov Machine are explained in the paper. The technique is validated with experiments performed in a Machine Condition Monitoring (MCM) test bed at the Idaho National Laboratory. (authors)

TIME SERIES ANALYSIS USING A UNIQUE MODEL OF TRANSFORMATION

Directory of Open Access Journals (Sweden)

Goran Klepac

2007-12-01

Full Text Available REFII1 model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series. An advantage of this approach of time series analysis is the linkage of different methods for time series analysis, linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. It is worth mentioning that REFII model is not a closed system, which means that we have a finite set of methods. At first, this is a model for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in a domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analysis. The advantage of REFII model is its possible application in many different areas such as finance, medicine, voice recognition, face recognition and text mining.

A Reception Analysis on the Youth Audiences of TV Series in Marivan

Directory of Open Access Journals (Sweden)

Omid Karimi

2014-03-01

Full Text Available The aim of this article is to describe the role of foreign media as the agitators of popular culture. For that with reception analysis it’s pay to describe decoding of youth audiences about this series. Globalization theory and Reception in Communication theory are formed the theoretical system of current article. The methodology in this research is qualitative one, and two techniques as in-depth interview and observation are used for data collection. The results show different people based on individual features, social and cultural backgrounds have inclination toward special characters and identify with them. This inclination so far the audience fallow the series because of his/her favorite character. Also there is a great compatibility between audience backgrounds and their receptions. A number of audience have criticized the series and point out the negative consequences on its society. However, seeing the series continue; really they prefer watching series enjoying to risks of it.

Time Series Analysis Forecasting and Control

CERN Document Server

Box, George E P; Reinsel, Gregory C

2011-01-01

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, cl

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

Water temperature forecasting and estimation using fourier series and communication theory techniques

International Nuclear Information System (INIS)

Long, L.L.

1976-01-01

Fourier series and statistical communication theory techniques are utilized in the estimation of river water temperature increases caused by external thermal inputs. An example estimate assuming a constant thermal input is demonstrated. A regression fit of the Fourier series approximation of temperature is then used to forecast daily average water temperatures. Also, a 60-day prediction of daily average water temperature is made with the aid of the Fourier regression fit by using significant Fourier components

Analysis of archaeological pieces with nuclear techniques; Analisis de piezas arqueologicas con tecnicas nucleares

Energy Technology Data Exchange (ETDEWEB)

Tenorio, D [Instituto Nacional de Investigaciones Nucleares, A.P. 18-1027, 11801 Mexico D.F. (Mexico)

2002-07-01

In this work nuclear techniques such as Neutron Activation Analysis, PIXE, X-ray fluorescence analysis, Metallography, Uranium series, Rutherford Backscattering for using in analysis of archaeological specimens and materials are described. Also some published works and thesis about analysis of different Mexican and Meso american archaeological sites are referred. (Author)

Disentangling Time-series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis

Energy Technology Data Exchange (ETDEWEB)

Czekala, Ian [Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 (United States); Mandel, Kaisey S.; Andrews, Sean M.; Dittmann, Jason A. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Ghosh, Sujit K. [Department of Statistics, NC State University, 2311 Stinson Drive, Raleigh, NC 27695 (United States); Montet, Benjamin T. [Department of Astronomy and Astrophysics, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 (United States); Newton, Elisabeth R., E-mail: iczekala@stanford.edu [Massachusetts Institute of Technology, Cambridge, MA 02138 (United States)

2017-05-01

Measurements of radial velocity variations from the spectroscopic monitoring of stars and their companions are essential for a broad swath of astrophysics; these measurements provide access to the fundamental physical properties that dictate all phases of stellar evolution and facilitate the quantitative study of planetary systems. The conversion of those measurements into both constraints on the orbital architecture and individual component spectra can be a serious challenge, however, especially for extreme flux ratio systems and observations with relatively low sensitivity. Gaussian processes define sampling distributions of flexible, continuous functions that are well-motivated for modeling stellar spectra, enabling proficient searches for companion lines in time-series spectra. We introduce a new technique for spectral disentangling, where the posterior distributions of the orbital parameters and intrinsic, rest-frame stellar spectra are explored simultaneously without needing to invoke cross-correlation templates. To demonstrate its potential, this technique is deployed on red-optical time-series spectra of the mid-M-dwarf binary LP661-13. We report orbital parameters with improved precision compared to traditional radial velocity analysis and successfully reconstruct the primary and secondary spectra. We discuss potential applications for other stellar and exoplanet radial velocity techniques and extensions to time-variable spectra. The code used in this analysis is freely available as an open-source Python package.

Time series analysis in the social sciences the fundamentals

CERN Document Server

Shin, Youseop

2017-01-01

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

Elements of nonlinear time series analysis and forecasting

CERN Document Server

De Gooijer, Jan G

2017-01-01

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

Science.gov (United States)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

Pipe-anchor discontinuity analysis utilizing power series solutions, Bessel functions, and Fourier series

International Nuclear Information System (INIS)

Williams, Dennis K.; Ranson, William F.

2003-01-01

One of the paradigmatic classes of problems that frequently arise in piping stress analysis discipline is the effect of local stresses created by supports and restraints attachments. Over the past 20 years, concerns have been identified by both regulatory agencies in the nuclear power industry and others in the process and chemicals industries concerning the effect of various stiff clamping arrangements on the expected life of the pipe and its various piping components. In many of the commonly utilized geometries and arrangements of pipe clamps, the elasticity problem becomes the axisymmetric stress and deformation determination in a hollow cylinder (pipe) subjected to the appropriate boundary conditions and respective loads per se. One of the geometries that serve as a pipe anchor is comprised of two pipe clamps that are bolted tightly to the pipe and affixed to a modified shoe-type arrangement. The shoe is employed for the purpose of providing an immovable base that can be easily attached either by bolting or welding to a structural steel pipe rack. Over the past 50 years, the computational tools available to the piping analyst have changed dramatically and thereby have caused the implementation of solutions to the basic problems of elasticity to change likewise. The need to obtain closed form elasticity solutions, however, has always been a driving force in engineering. The employment of symbolic calculus that is currently available through numerous software packages makes closed form solutions very economical. This paper briefly traces the solutions over the past 50 years to a variety of axisymmetric stress problems involving hollow circular cylinders employing a Fourier series representation. In the present example, a properly chosen Fourier series represent the mathematical simulation of the imposed axial displacements on the outside diametrical surface. A general solution technique is introduced for the axisymmetric discontinuity stresses resulting from an

All-phase MR angiography using independent component analysis of dynamic contrast enhanced MRI time series. φ-MRA

International Nuclear Information System (INIS)

Suzuki, Kiyotaka; Matsuzawa, Hitoshi; Watanabe, Masaki; Nakada, Tsutomu; Nakayama, Naoki; Kwee, I.L.

2003-01-01

Dynamic contrast enhanced magnetic resonance imaging (dynamic MRI) represents a MRI version of non-diffusible tracer methods, the main clinical use of which is the physiological construction of what is conventionally referred to as perfusion images. The raw data utilized for constructing MRI perfusion images are time series of pixel signal alterations associated with the passage of a gadolinium containing contrast agent. Such time series are highly compatible with independent component analysis (ICA), a novel statistical signal processing technique capable of effectively separating a single mixture of multiple signals into their original independent source signals (blind separation). Accordingly, we applied ICA to dynamic MRI time series. The technique was found to be powerful, allowing for hitherto unobtainable assessment of regional cerebral hemodynamics in vivo. (author)

Time series analysis for psychological research: examining and forecasting change.

Science.gov (United States)

Jebb, Andrew T; Tay, Louis; Wang, Wei; Huang, Qiming

2015-01-01

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials.

Time series analysis for psychological research: examining and forecasting change

Science.gov (United States)

Jebb, Andrew T.; Tay, Louis; Wang, Wei; Huang, Qiming

2015-01-01

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials. PMID:26106341

Analysis of Heavy-Tailed Time Series

DEFF Research Database (Denmark)

Xie, Xiaolei

This thesis is about analysis of heavy-tailed time series. We discuss tail properties of real-world equity return series and investigate the possibility that a single tail index is shared by all return series of actively traded equities in a market. Conditions for this hypothesis to be true...... are identified. We study the eigenvalues and eigenvectors of sample covariance and sample auto-covariance matrices of multivariate heavy-tailed time series, and particularly for time series with very high dimensions. Asymptotic approximations of the eigenvalues and eigenvectors of such matrices are found...... and expressed in terms of the parameters of the dependence structure, among others. Furthermore, we study an importance sampling method for estimating rare-event probabilities of multivariate heavy-tailed time series generated by matrix recursion. We show that the proposed algorithm is efficient in the sense...

Charting the trends in nuclear techniques for analysis of inorganic environmental pollutants

International Nuclear Information System (INIS)

Braun, T.

1986-01-01

Publications in Analytical Abstracts in the period 1975-1984 and papers presented at the Modern Trends in Activation Analysis international conferences series in the period 1961-1986 have been used as an empirical basis for assessing general trends in research and publication activity. Some ebbs and flows in the speciality of instrumental techniques for analysis of environmental trace pollutants are revealed by a statistical analysis of the publications. (author)

Mathematical foundations of time series analysis a concise introduction

CERN Document Server

Beran, Jan

2017-01-01

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

Applications of soft computing in time series forecasting simulation and modeling techniques

CERN Document Server

Singh, Pritpal

2016-01-01

This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and governmen...

The analysis of time series: an introduction

National Research Council Canada - National Science Library

Chatfield, Christopher

1989-01-01

.... A variety of practical examples are given to support the theory. The book covers a wide range of time-series topics, including probability models for time series, Box-Jenkins forecasting, spectral analysis, linear systems and system identification...

Time series analysis of reference crop evapotranspiration using soft computing techniques for Ganjam District, Odisha, India

Science.gov (United States)

Patra, S. R.

2017-12-01

Evapotranspiration (ET0) influences water resources and it is considered as a vital process in aridic hydrologic frameworks. It is one of the most important measure in finding the drought condition. Therefore, time series forecasting of evapotranspiration is very important in order to help the decision makers and water system mangers build up proper systems to sustain and manage water resources. Time series considers that -history repeats itself, hence by analysing the past values, better choices, or forecasts, can be carried out for the future. Ten years of ET0 data was used as a part of this study to make sure a satisfactory forecast of monthly values. In this study, three models: (ARIMA) mathematical model, artificial neural network model, support vector machine model are presented. These three models are used for forecasting monthly reference crop evapotranspiration based on ten years of past historical records (1991-2001) of measured evaporation at Ganjam region, Odisha, India without considering the climate data. The developed models will allow water resource managers to predict up to 12 months, making these predictions very useful to optimize the resources needed for effective water resources management. In this study multistep-ahead prediction is performed which is more complex and troublesome than onestep ahead. Our investigation proposed that nonlinear relationships may exist among the monthly indices, so that the ARIMA model might not be able to effectively extract the full relationship hidden in the historical data. Support vector machines are potentially helpful time series forecasting strategies on account of their strong nonlinear mapping capability and resistance to complexity in forecasting data. SVMs have great learning capability in time series modelling compared to ANN. For instance, the SVMs execute the structural risk minimization principle, which allows in better generalization as compared to neural networks that use the empirical risk

Identifying secondary series for stepwise common singular spectrum ...

African Journals Online (AJOL)

Abstract. Common singular spectrum analysis is a technique which can be used to forecast a pri- mary time series by using the information from a secondary series. Not all secondary series, however, provide useful information. A first contribution in this paper is to point out the properties which a secondary series should ...

Adventures in Modern Time Series Analysis: From the Sun to the Crab Nebula and Beyond

Science.gov (United States)

Scargle, Jeffrey

2014-01-01

With the generation of long, precise, and finely sampled time series the Age of Digital Astronomy is uncovering and elucidating energetic dynamical processes throughout the Universe. Fulfilling these opportunities requires data effective analysis techniques rapidly and automatically implementing advanced concepts. The Time Series Explorer, under development in collaboration with Tom Loredo, provides tools ranging from simple but optimal histograms to time and frequency domain analysis for arbitrary data modes with any time sampling. Much of this development owes its existence to Joe Bredekamp and the encouragement he provided over several decades. Sample results for solar chromospheric activity, gamma-ray activity in the Crab Nebula, active galactic nuclei and gamma-ray bursts will be displayed.

Affirmative Action. Module Number 16. Work Experience Program Modules. Coordination Techniques Series.

Science.gov (United States)

Shawhan, Carl; Morley, Ray

This self-instructional module, the last in a series of 16 on techniques for coordinating work experience programs, deals with affirmative action. Addressed in the module are the following topics: the nature of affirmative action legislation and regulations, the role of the teacher-coordinator as a resource person for affirmative action…

Reliability analysis of large scaled structures by optimization technique

International Nuclear Information System (INIS)

Ishikawa, N.; Mihara, T.; Iizuka, M.

1987-01-01

This paper presents a reliability analysis based on the optimization technique using PNET (Probabilistic Network Evaluation Technique) method for the highly redundant structures having a large number of collapse modes. This approach makes the best use of the merit of the optimization technique in which the idea of PNET method is used. The analytical process involves the minimization of safety index of the representative mode, subjected to satisfaction of the mechanism condition and of the positive external work. The procedure entails the sequential performance of a series of the NLP (Nonlinear Programming) problems, where the correlation condition as the idea of PNET method pertaining to the representative mode is taken as an additional constraint to the next analysis. Upon succeeding iterations, the final analysis is achieved when a collapse probability at the subsequent mode is extremely less than the value at the 1st mode. The approximate collapse probability of the structure is defined as the sum of the collapse probabilities of the representative modes classified by the extent of correlation. Then, in order to confirm the validity of the proposed method, the conventional Monte Carlo simulation is also revised by using the collapse load analysis. Finally, two fairly large structures were analyzed to illustrate the scope and application of the approach. (orig./HP)

Surgical techniques for the treatment of ankyloglossia in children: a case series

Directory of Open Access Journals (Sweden)

Marina Azevedo JUNQUEIRA

2014-06-01

Full Text Available This paper reports a series of clinical cases of ankyloglossia in children, which were approached by different techniques: frenotomy and frenectomy with the use of one hemostat, two hemostats, a groove director or laser. Information on the indications, contraindications, advantages and disadvantages of the techniques was also presented. Children diagnosed with ankyloglossia were subjected to different surgical procedures. The choice of the techniques was based on the age of the patient, length of the frenulum and availability of the instruments and equipment. All the techniques presented are successful for the treatment of ankyloglossia and require a skilled professional. Laser may be considered a simple and safe alternative for children while reducing the amount of local anesthetics needed, the bleeding and the chances of infection, swelling and discomfort.

Time series analysis of soil Radon-222 recorded at Kutch region, Gujarat, India

International Nuclear Information System (INIS)

Madhusudan Rao, K.; Rastogi, B.K.; Barman, Chiranjib; Chaudhuri, Hirok

2013-01-01

Kutch region in Gujarat lies in a seismic vulnerable zone (seismic zone-v). After the devastating Bhuj earthquake (7.7M) of January 26, 2001 in the Kutch region several researcher focused their attention to monitor geophysical and geochemical precursors for earthquakes in the region. In order to find out the possible geochemical precursory signals for earthquake events, we monitored radioactive gas radon-222 in sub surface soil gas at Kutch region. We have analysed the recorded soil radon-222 time series by means of nonlinear techniques such as FFT power spectral analysis, empirical mode decomposition, multi-fractal analysis along with other linear statistical methods. Some fascinating and fruitful results originated out the nonlinear analysis of the said time series have been discussed in the present paper. The entire analytical method aided us to recognize the nature and pattern of soil radon-222 emanation process. Moreover the recording and statistical and non-linear analysis of soil radon data at Kutch region will assist us to understand the preparation phase of an imminent seismic event in the region. (author)

Time Series Analysis of Insar Data: Methods and Trends

Science.gov (United States)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Image Analysis Technique for Material Behavior Evaluation in Civil Structures

Science.gov (United States)

Moretti, Michele; Rossi, Gianluca

2017-01-01

The article presents a hybrid monitoring technique for the measurement of the deformation field. The goal is to obtain information about crack propagation in existing structures, for the purpose of monitoring their state of health. The measurement technique is based on the capture and analysis of a digital image set. Special markers were used on the surface of the structures that can be removed without damaging existing structures as the historical masonry. The digital image analysis was done using software specifically designed in Matlab to follow the tracking of the markers and determine the evolution of the deformation state. The method can be used in any type of structure but is particularly suitable when it is necessary not to damage the surface of structures. A series of experiments carried out on masonry walls of the Oliverian Museum (Pesaro, Italy) and Palazzo Silvi (Perugia, Italy) have allowed the validation of the procedure elaborated by comparing the results with those derived from traditional measuring techniques. PMID:28773129

Network structure of multivariate time series.

Science.gov (United States)

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Analysis of cyclical behavior in time series of stock market returns

Science.gov (United States)

Stratimirović, Djordje; Sarvan, Darko; Miljković, Vladimir; Blesić, Suzana

2018-01-01

In this paper we have analyzed scaling properties and cyclical behavior of the three types of stock market indexes (SMI) time series: data belonging to stock markets of developed economies, emerging economies, and of the underdeveloped or transitional economies. We have used two techniques of data analysis to obtain and verify our findings: the wavelet transform (WT) spectral analysis to identify cycles in the SMI returns data, and the time-dependent detrended moving average (tdDMA) analysis to investigate local behavior around market cycles and trends. We found cyclical behavior in all SMI data sets that we have analyzed. Moreover, the positions and the boundaries of cyclical intervals that we found seam to be common for all markets in our dataset. We list and illustrate the presence of nine such periods in our SMI data. We report on the possibilities to differentiate between the level of growth of the analyzed markets by way of statistical analysis of the properties of wavelet spectra that characterize particular peak behaviors. Our results show that measures like the relative WT energy content and the relative WT amplitude of the peaks in the small scales region could be used to partially differentiate between market economies. Finally, we propose a way to quantify the level of development of a stock market based on estimation of local complexity of market's SMI series. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index, which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups.

Nonlinear time series analysis of the human electrocardiogram

International Nuclear Information System (INIS)

Perc, Matjaz

2005-01-01

We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method

Social network analysis of character interaction in the Stargate and Star Trek television series

Science.gov (United States)

Tan, Melody Shi Ai; Ujum, Ephrance Abu; Ratnavelu, Kuru

This paper undertakes a social network analysis of two science fiction television series, Stargate and Star Trek. Television series convey stories in the form of character interaction, which can be represented as “character networks”. We connect each pair of characters that exchanged spoken dialogue in any given scene demarcated in the television series transcripts. These networks are then used to characterize the overall structure and topology of each series. We find that the character networks of both series have similar structure and topology to that found in previous work on mythological and fictional networks. The character networks exhibit the small-world effects but found no significant support for power-law. Since the progression of an episode depends to a large extent on the interaction between each of its characters, the underlying network structure tells us something about the complexity of that episode’s storyline. We assessed the complexity using techniques from spectral graph theory. We found that the episode networks are structured either as (1) closed networks, (2) those containing bottlenecks that connect otherwise disconnected clusters or (3) a mixture of both.

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

Energy Technology Data Exchange (ETDEWEB)

Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

Improved Sectional Image Analysis Technique for Evaluating Fiber Orientations in Fiber-Reinforced Cement-Based Materials.

Science.gov (United States)

Lee, Bang Yeon; Kang, Su-Tae; Yun, Hae-Bum; Kim, Yun Yong

2016-01-12

The distribution of fiber orientation is an important factor in determining the mechanical properties of fiber-reinforced concrete. This study proposes a new image analysis technique for improving the evaluation accuracy of fiber orientation distribution in the sectional image of fiber-reinforced concrete. A series of tests on the accuracy of fiber detection and the estimation performance of fiber orientation was performed on artificial fiber images to assess the validity of the proposed technique. The validation test results showed that the proposed technique estimates the distribution of fiber orientation more accurately than the direct measurement of fiber orientation by image analysis.

Time series analysis of temporal networks

Science.gov (United States)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

A Fast Multi-layer Subnetwork Connection Method for Time Series InSAR Technique

Directory of Open Access Journals (Sweden)

WU Hong'an

2016-10-01

Full Text Available Nowadays, times series interferometric synthetic aperture radar (InSAR technique has been widely used in ground deformation monitoring, especially in urban areas where lots of stable point targets can be detected. However, in standard time series InSAR technique, affected by atmospheric correlation distance and the threshold of linear model coherence, the Delaunay triangulation for connecting point targets can be easily separated into many discontinuous subnetworks. Thus it is difficult to retrieve ground deformation in non-urban areas. In order to monitor ground deformation in large areas efficiently, a novel multi-layer subnetwork connection (MLSC method is proposed for connecting all subnetworks. The advantage of the method is that it can quickly reduce the number of subnetworks with valid edges layer-by-layer. This method is compared with the existing complex network connecting mehod. The experimental results demonstrate that the data processing time of the proposed method is only 32.56% of the latter one.

Koopman Operator Framework for Time Series Modeling and Analysis

Science.gov (United States)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India

Directory of Open Access Journals (Sweden)

Gautam Ratnesh

2016-09-01

Full Text Available Evapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR and moving average (MA, autoregressive moving average (ARMA, autoregressive integrated moving average (ARIMA, Thomas Feiring, etc. Out of these models ARIMA model has been found to be more suitable for analysis and forecasting of hydrological events. Therefore, in this study ARIMA models have been used for forecasting of mean monthly reference crop evapotranspiration by stochastic analysis. The data series of 102 years i.e. 1224 months of Bokaro District were used for analysis and forecasting. Different order of ARIMA model was selected on the basis of autocorrelation function (ACF and partial autocorrelation (PACF of data series. Maximum likelihood method was used for determining the parameters of the models. To see the statistical parameter of model, best fitted model is ARIMA (0, 1, 4 (0, 1, 112.

Time series modeling, computation, and inference

CERN Document Server

Prado, Raquel

2010-01-01

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2015-01-01

Praise for the First Edition ""…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics."" -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.    Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both

Analysis of series resonant converter with series-parallel connection

Science.gov (United States)

Lin, Bor-Ren; Huang, Chien-Lan

2011-02-01

In this study, a parallel inductor-inductor-capacitor (LLC) resonant converter series-connected on the primary side and parallel-connected on the secondary side is presented for server power supply systems. Based on series resonant behaviour, the power metal-oxide-semiconductor field-effect transistors are turned on at zero voltage switching and the rectifier diodes are turned off at zero current switching. Thus, the switching losses on the power semiconductors are reduced. In the proposed converter, the primary windings of the two LLC converters are connected in series. Thus, the two converters have the same primary currents to ensure that they can supply the balance load current. On the output side, two LLC converters are connected in parallel to share the load current and to reduce the current stress on the secondary windings and the rectifier diodes. In this article, the principle of operation, steady-state analysis and design considerations of the proposed converter are provided and discussed. Experiments with a laboratory prototype with a 24 V/21 A output for server power supply were performed to verify the effectiveness of the proposed converter.

A Smoothing Technique for the Multifractal Analysis of a Medium Voltage Feeders Electric Current

Science.gov (United States)

de Santis, Enrico; Sadeghian, Alireza; Rizzi, Antonello

2017-12-01

The current paper presents a data-driven detrending technique allowing to smooth complex sinusoidal trends from a real-world electric load time series before applying the Detrended Multifractal Fluctuation Analysis (MFDFA). The algorithm we call Smoothed Sort and Cut Fourier Detrending (SSC-FD) is based on a suitable smoothing of high power periodicities operating directly in the Fourier spectrum through a polynomial fitting technique of the DFT. The main aim consists of disambiguating the characteristic slow varying periodicities, that can impair the MFDFA analysis, from the residual signal in order to study its correlation properties. The algorithm performances are evaluated on a simple benchmark test consisting of a persistent series where the Hurst exponent is known, with superimposed ten sinusoidal harmonics. Moreover, the behavior of the algorithm parameters is assessed computing the MFDFA on the well-known sunspot data, whose correlation characteristics are reported in literature. In both cases, the SSC-FD method eliminates the apparent crossover induced by the synthetic and natural periodicities. Results are compared with some existing detrending methods within the MFDFA paradigm. Finally, a study of the multifractal characteristics of the electric load time series detrendended by the SSC-FD algorithm is provided, showing a strong persistent behavior and an appreciable amplitude of the multifractal spectrum that allows to conclude that the series at hand has multifractal characteristics.

Nonlinear time series analysis with R

CERN Document Server

Huffaker, Ray; Rosa, Rodolfo

2017-01-01

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjec...

DIY Solar Market Analysis Webinar Series: Solar Resource and Technical

Science.gov (United States)

Series: Solar Resource and Technical Potential DIY Solar Market Analysis Webinar Series: Solar Resource and Technical Potential Wednesday, June 11, 2014 As part of a Do-It-Yourself Solar Market Analysis Potential | State, Local, and Tribal Governments | NREL DIY Solar Market Analysis Webinar

Allan deviation analysis of financial return series

Science.gov (United States)

Hernández-Pérez, R.

2012-05-01

We perform a scaling analysis for the return series of different financial assets applying the Allan deviation (ADEV), which is used in the time and frequency metrology to characterize quantitatively the stability of frequency standards since it has demonstrated to be a robust quantity to analyze fluctuations of non-stationary time series for different observation intervals. The data used are opening price daily series for assets from different markets during a time span of around ten years. We found that the ADEV results for the return series at short scales resemble those expected for an uncorrelated series, consistent with the efficient market hypothesis. On the other hand, the ADEV results for absolute return series for short scales (first one or two decades) decrease following approximately a scaling relation up to a point that is different for almost each asset, after which the ADEV deviates from scaling, which suggests that the presence of clustering, long-range dependence and non-stationarity signatures in the series drive the results for large observation intervals.

Time series modeling in traffic safety research.

Science.gov (United States)

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

Entropic Analysis of Electromyography Time Series

Science.gov (United States)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Applicability of contact angle techniques used in the analysis of contact lenses, part 1: comparative methodologies.

Science.gov (United States)

Campbell, Darren; Carnell, Sarah Maria; Eden, Russell John

2013-05-01

Contact angle, as a representative measure of surface wettability, is often employed to interpret contact lens surface properties. The literature is often contradictory and can lead to confusion. This literature review is part of a series regarding the analysis of hydrogel contact lenses using contact angle techniques. Here we present an overview of contact angle terminology, methodology, and analysis. Having discussed this background material, subsequent parts of the series will discuss the analysis of contact lens contact angles and evaluate differences in published laboratory results. The concepts of contact angle, wettability and wetting are presented as an introduction. Contact angle hysteresis is outlined and highlights the advantages in using dynamic analytical techniques over static methods. The surface free energy of a material illustrates how contact angle analysis is capable of providing supplementary surface characterization. Although single values are able to distinguish individual material differences, surface free energy and dynamic methods provide an improved understanding of material behavior. The frequently used sessile drop, captive bubble, and Wilhelmy plate techniques are discussed. Their use as both dynamic and static methods, along with the advantages and disadvantages of each technique, is explained. No single contact angle technique fully characterizes the wettability of a material surface, and the application of complimenting methods allows increased characterization. At present, there is not an ISO standard method designed for soft materials. It is important that each contact angle technique has a standard protocol, as small protocol differences between laboratories often contribute to a variety of published data that are not easily comparable.

The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

Simultaneous determination of radionuclides separable into natural decay series by use of time-interval analysis

International Nuclear Information System (INIS)

Hashimoto, Tetsuo; Sanada, Yukihisa; Uezu, Yasuhiro

2004-01-01

A delayed coincidence method, time-interval analysis (TIA), has been applied to successive α-α decay events on the millisecond time-scale. Such decay events are part of the 220 Rn→ 216 Po (T 1/2 145 ms) (Th-series) and 219 Rn→ 215 Po (T 1/2 1.78 ms) (Ac-series). By using TIA in addition to measurement of 226 Ra (U-series) from α-spectrometry by liquid scintillation counting (LSC), two natural decay series could be identified and separated. The TIA detection efficiency was improved by using the pulse-shape discrimination technique (PSD) to reject β-pulses, by solvent extraction of Ra combined with simple chemical separation, and by purging the scintillation solution with dry N 2 gas. The U- and Th-series together with the Ac-series were determined, respectively, from alpha spectra and TIA carried out immediately after Ra-extraction. Using the 221 Fr→ 217 At (T 1/2 32.3 ms) decay process as a tracer, overall yields were estimated from application of TIA to the 225 Ra (Np-decay series) at the time of maximum growth. The present method has proven useful for simultaneous determination of three radioactive decay series in environmental samples. (orig.)

Nonlinear techniques for forecasting solar activity directly from its time series

Science.gov (United States)

Ashrafi, S.; Roszman, L.; Cooley, J.

1993-01-01

This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Maximum Bandwidth Enhancement of Current Mirror using Series-Resistor and Dynamic Body Bias Technique

Directory of Open Access Journals (Sweden)

V. Niranjan

2014-09-01

Full Text Available This paper introduces a new approach for enhancing the bandwidth of a low voltage CMOS current mirror. The proposed approach is based on utilizing body effect in a MOS transistor by connecting its gate and bulk terminals together for signal input. This results in boosting the effective transconductance of MOS transistor along with reduction of the threshold voltage. The proposed approach does not affect the DC gain of the current mirror. We demonstrate that the proposed approach features compatibility with widely used series-resistor technique for enhancing the current mirror bandwidth and both techniques have been employed simultaneously for maximum bandwidth enhancement. An important consequence of using both techniques simultaneously is the reduction of the series-resistor value for achieving the same bandwidth. This reduction in value is very attractive because a smaller resistor results in smaller chip area and less noise. PSpice simulation results using 180 nm CMOS technology from TSMC are included to prove the unique results. The proposed current mirror operates at 1Volt consuming only 102 µW and maximum bandwidth extension ratio of 1.85 has been obtained using the proposed approach. Simulation results are in good agreement with analytical predictions.

[Liver transplantation--indications, surgical technique, results--the analysis of a clinical series of 200 cases].

Science.gov (United States)

Popescu, I; Ionescu, M; Braşoveanu, V; Hrehoreţ, D; Matei, E; Dorobantu, B; Zamfir, R; Alexandrescu, S; Grigorie, M; Tulbure, D; Popa, L; Ungureanu, M; Tomescu, D; Droc, G; Popescu, H; Cristea, A; Gheorghe, L; Iacob, S; Gheorghe, C; Boroş, M; Lupescu, I; Vlad, L; Herlea, V; Croitoru, M; Platon, P; Alloub, A

2010-01-01

Initially considered experimental, liver transplantation (LT) has become the treatment of choice for the patients with end-stage liver diseases. Between April 2000 and October 2009, 200 LTs (10 reLTs) were performed in 190 patients, this study being retrospective. There were transplanted 110 men and 80 women, 159 adults and 31 children with the age between 1 and 64 years old (mean age--39.9). The main indication in the adult group was represented by viral cirrhosis, while the pediatric series the etiology was mainly glycogenosis and biliary atresia. There were performed 143 whole graft LTs, 46 living donor LTs, 6 split LTs, 4 reduced LTs and one domino LT RESULTS: The postoperative survival was 90% (170 patients). The patient and graft one-year and five-year survivals were 76.9%, 73.6% and 71%, 68.2%, respectively. The early complications occurred in 127 patients (67%). The late complications were recorded in 71 patients (37.3%). The intraoperative and early postoperative mortality rate was 9.5% (18 patients). The Romanian liver transplantation program from Fundeni includes all types of current surgical techniques and the results are comparable with those from other international centers.

Parametric time series analysis of geoelectrical signals: an application to earthquake forecasting in Southern Italy

Directory of Open Access Journals (Sweden)

V. Tramutoli

1996-06-01

Full Text Available An autoregressive model was selected to describe geoelectrical time series. An objective technique was subsequently applied to analyze and discriminate values above (below an a priorifixed threshold possibly related to seismic events. A complete check of the model and the main guidelines to estimate the occurrence probability of extreme events are reported. A first application of the proposed technique is discussed through the analysis of the experimental data recorded by an automatic station located in Tito, a small town on the Apennine chain in Southern Italy. This region was hit by the November 1980 Irpinia-Basilicata earthquake and it is one of most active areas of the Mediterranean region. After a preliminary filtering procedure to reduce the influence of external parameters (i.e. the meteo-climatic effects, it was demonstrated that the geoelectrical residual time series are well described by means of a second order autoregressive model. Our findings outline a statistical methodology to evaluate the efficiency of electrical seismic precursors.

Studies on time series applications in environmental sciences

CERN Document Server

Bărbulescu, Alina

2016-01-01

Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. .

A reference data set for validating vapor pressure measurement techniques: homologous series of polyethylene glycols

Science.gov (United States)

Krieger, Ulrich K.; Siegrist, Franziska; Marcolli, Claudia; Emanuelsson, Eva U.; Gøbel, Freya M.; Bilde, Merete; Marsh, Aleksandra; Reid, Jonathan P.; Huisman, Andrew J.; Riipinen, Ilona; Hyttinen, Noora; Myllys, Nanna; Kurtén, Theo; Bannan, Thomas; Percival, Carl J.; Topping, David

2018-01-01

To predict atmospheric partitioning of organic compounds between gas and aerosol particle phase based on explicit models for gas phase chemistry, saturation vapor pressures of the compounds need to be estimated. Estimation methods based on functional group contributions require training sets of compounds with well-established saturation vapor pressures. However, vapor pressures of semivolatile and low-volatility organic molecules at atmospheric temperatures reported in the literature often differ by several orders of magnitude between measurement techniques. These discrepancies exceed the stated uncertainty of each technique which is generally reported to be smaller than a factor of 2. At present, there is no general reference technique for measuring saturation vapor pressures of atmospherically relevant compounds with low vapor pressures at atmospheric temperatures. To address this problem, we measured vapor pressures with different techniques over a wide temperature range for intercomparison and to establish a reliable training set. We determined saturation vapor pressures for the homologous series of polyethylene glycols (H - (O - CH2 - CH2)n - OH) for n = 3 to n = 8 ranging in vapor pressure at 298 K from 10-7 to 5×10-2 Pa and compare them with quantum chemistry calculations. Such a homologous series provides a reference set that covers several orders of magnitude in saturation vapor pressure, allowing a critical assessment of the lower limits of detection of vapor pressures for the different techniques as well as permitting the identification of potential sources of systematic error. Also, internal consistency within the series allows outlying data to be rejected more easily. Most of the measured vapor pressures agreed within the stated uncertainty range. Deviations mostly occurred for vapor pressure values approaching the lower detection limit of a technique. The good agreement between the measurement techniques (some of which are sensitive to the mass

Stochastic time series analysis of hydrology data for water resources

Science.gov (United States)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

Series Resistance Analysis of Passivated Emitter Rear Contact Cells Patterned Using Inkjet Printing

Directory of Open Access Journals (Sweden)

Martha A. T. Lenio

2012-01-01

Full Text Available For higher-efficiency solar cell structures, such as the Passivated Emitter Rear Contact (PERC cells, to be fabricated in a manufacturing environment, potentially low-cost techniques such as inkjet printing and metal plating are desirable. A common problem that is experienced when fabricating PERC cells is low fill factors due to high series resistance. This paper identifies and attempts to quantify sources of series resistance in inkjet-patterned PERC cells that employ electroless or light-induced nickel-plating techniques followed by copper light-induced plating. Photoluminescence imaging is used to determine locations of series resistance losses in these inkjet-patterned and plated PERC cells.

Interpretation of engine cycle-to-cycle variation by chaotic time series analysis

Energy Technology Data Exchange (ETDEWEB)

Daw, C.S.; Kahl, W.K.

1990-01-01

In this paper we summarize preliminary results from applying a new mathematical technique -- chaotic time series analysis (CTSA) -- to cylinder pressure data from a spark-ignition (SI) four-stroke engine fueled with both methanol and iso-octane. Our objective is to look for the presence of deterministic chaos'' dynamics in peak pressure variations and to investigate the potential usefulness of CTSA as a diagnostic tool. Our results suggest that sequential peak cylinder pressures exhibit some characteristic features of deterministic chaos and that CTSA can extract previously unrecognized information from such data. 18 refs., 11 figs., 2 tabs.

Analysis of radiation-induced microchemical evolution in 300 series stainless steel

International Nuclear Information System (INIS)

Brager, H.R.; Garner, F.A.

1980-03-01

The irradiation of 300 series stainless steel by fast neutrons leads to an evolution of alloy microstructures that involves not only the formation of voids and dislocations, but also an extensive repartitioning of elements between various phases. This latter evolution has been shown to be the primary determinant of the alloy behavior in response to the large number of variables which influence void swelling and irradiation creep. The combined use of scanning transmission electron microscopy and energy-dispersive x-ray analysis has been the key element in the study of this phenomenon. Problems associated with the analysis of radioactive specimens are resolved by minor equipment modifications. Problems associated with spatial resolution limitations and the complexity and heterogeneity of the microchemical evolution have been overcome by using several data acquisition techniques. These include the measurement of compositional profiles near sinks, the use of foil-edge analysis, and the statistical sampling of many matrix and precipitate volumes

Interpolation techniques used for data quality control and calculation of technical series: an example of a Central European daily time series

Czech Academy of Sciences Publication Activity Database

Štěpánek, P.; Zahradníček, P.; Huth, Radan

2011-01-01

Roč. 115, 1-2 (2011), s. 87-98 ISSN 0324-6329 R&D Projects: GA ČR GA205/08/1619 Institutional research plan: CEZ:AV0Z30420517 Keywords : data quality control * filling missing values * interpolation techniques * climatological time series Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.364, year: 2011 http://www.met.hu/en/ismeret-tar/kiadvanyok/idojaras/index.php?id=34

Inorganic chemical analysis of environmental materials—A lecture series

Science.gov (United States)

Crock, J.G.; Lamothe, P.J.

2011-01-01

At the request of the faculty of the Colorado School of Mines, Golden, Colorado, the authors prepared and presented a lecture series to the students of a graduate level advanced instrumental analysis class. The slides and text presented in this report are a compilation and condensation of this series of lectures. The purpose of this report is to present the slides and notes and to emphasize the thought processes that should be used by a scientist submitting samples for analyses in order to procure analytical data to answer a research question. First and foremost, the analytical data generated can be no better than the samples submitted. The questions to be answered must first be well defined and the appropriate samples collected from the population that will answer the question. The proper methods of analysis, including proper sample preparation and digestion techniques, must then be applied. Care must be taken to achieve the required limits of detection of the critical analytes to yield detectable analyte concentration (above "action" levels) for the majority of the study's samples and to address what portion of those analytes answer the research question-total or partial concentrations. To guarantee a robust analytical result that answers the research question(s), a well-defined quality assurance and quality control (QA/QC) plan must be employed. This QA/QC plan must include the collection and analysis of field and laboratory blanks, sample duplicates, and matrix-matched standard reference materials (SRMs). The proper SRMs may include in-house materials and/or a selection of widely available commercial materials. A discussion of the preparation and applicability of in-house reference materials is also presented. Only when all these analytical issues are sufficiently addressed can the research questions be answered with known certainty.

Time averaging, ageing and delay analysis of financial time series

Science.gov (United States)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Time-Series Analysis of Remotely-Sensed SeaWiFS Chlorophyll in River-Influenced Coastal Regions

Science.gov (United States)

Acker, James G.; McMahon, Erin; Shen, Suhung; Hearty, Thomas; Casey, Nancy

2009-01-01

The availability of a nearly-continuous record of remotely-sensed chlorophyll a data (chl a) from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission, now longer than ten years, enables examination of time-series trends for multiple global locations. Innovative data analysis technology available on the World Wide Web facilitates such analyses. In coastal regions influenced by river outflows, chl a is not always indicative of actual trends in phytoplankton chlorophyll due to the interference of colored dissolved organic matter and suspended sediments; significant chl a timeseries trends for coastal regions influenced by river outflows may nonetheless be indicative of important alterations of the hydrologic and coastal environment. Chl a time-series analysis of nine marine regions influenced by river outflows demonstrates the simplicity and usefulness of this technique. The analyses indicate that coastal time-series are significantly influenced by unusual flood events. Major river systems in regions with relatively low human impact did not exhibit significant trends. Most river systems with demonstrated human impact exhibited significant negative trends, with the noteworthy exception of the Pearl River in China, which has a positive trend.

Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of Space Geodetic Time Series

Science.gov (United States)

Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano

2015-04-01

A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources

Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis

Directory of Open Access Journals (Sweden)

S. P. Arunachalam

2018-01-01

Full Text Available Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR and atrial fibrillation (AF was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

Adsorption of proteins from plasma to a series of hydrophilic-hydrophobic copolymers. I. Analysis with the in situ radioiodination technique

International Nuclear Information System (INIS)

Horbett, T.A.; Weathersby, P.K.

1981-01-01

The adsorption of proteins affects cellular interactions with foreign surfaces and thus plays an important role in determining the biocompatibility of implants. Previous studies have indicated differences in the affinity of various proteins for a given polymer, and differences in the affinity of fibrinogen for a series of polymers varying in hydrophilicity. These studies suggest that differences in the composition of the protein layer adsorbed to polymers from plasma might exist. To examine this hypothesis, the proteins adsorbed from plasma to a series of polymers varying in hydrophilicity were analyzed with the iodogram technique. Copolymers of hydroxyethyl methacrylate and ethyl methacrylate made by the radiation grafting technique were exposed to plasma for 0.5 or 150 min. The adsorbed proteins were iodinated, eluted with SDS, and separated with polyacrylamide gel electrophoresis. Fibrinogen, immunoglobulin G, hemoglobin, and a peak tentatively ascribed to prothrombin were the major proteins detected. Very little iodine was incorporated into adsorbed albumin, even though it was shown to be present by a separate experiment using dye binding. The fraction of total radioactivity associated with each of nine proteins was found to vary markedly and systematically among the surfaces. The distribution of radioactivity into the proteins was very different on 0.5 and 150-min plasma exposed polymers. The results reflect both compositional differences in the adsorbed protein layer on the polymers and differences in the accessibility of proteins to the labeling reagent in the adsorbed state. Differences in the organization of the adsorbed protein layer may play a key role in determining whether cell surface receptors can come in contact with the specific plasma protein able to further stimulate the cell

Interrupted time-series analysis: studying trends in neurosurgery.

Science.gov (United States)

Wong, Ricky H; Smieliauskas, Fabrice; Pan, I-Wen; Lam, Sandi K

2015-12-01

OBJECT Neurosurgery studies traditionally have evaluated the effects of interventions on health care outcomes by studying overall changes in measured outcomes over time. Yet, this type of linear analysis is limited due to lack of consideration of the trend's effects both pre- and postintervention and the potential for confounding influences. The aim of this study was to illustrate interrupted time-series analysis (ITSA) as applied to an example in the neurosurgical literature and highlight ITSA's potential for future applications. METHODS The methods used in previous neurosurgical studies were analyzed and then compared with the methodology of ITSA. RESULTS The ITSA method was identified in the neurosurgical literature as an important technique for isolating the effect of an intervention (such as a policy change or a quality and safety initiative) on a health outcome independent of other factors driving trends in the outcome. The authors determined that ITSA allows for analysis of the intervention's immediate impact on outcome level and on subsequent trends and enables a more careful measure of the causal effects of interventions on health care outcomes. CONCLUSIONS ITSA represents a significant improvement over traditional observational study designs in quantifying the impact of an intervention. ITSA is a useful statistical procedure to understand, consider, and implement as the field of neurosurgery evolves in sophistication in big-data analytics, economics, and health services research.

Runoff Potentiality of a Watershed through SCS and Functional Data Analysis Technique

Directory of Open Access Journals (Sweden)

M. I. Adham

2014-01-01

Full Text Available Runoff potentiality of a watershed was assessed based on identifying curve number (CN, soil conservation service (SCS, and functional data analysis (FDA techniques. Daily discrete rainfall data were collected from weather stations in the study area and analyzed through lowess method for smoothing curve. As runoff data represents a periodic pattern in each watershed, Fourier series was introduced to fit the smooth curve of eight watersheds. Seven terms of Fourier series were introduced for the watersheds 5 and 8, while 8 terms of Fourier series were used for the rest of the watersheds for the best fit of data. Bootstrapping smooth curve analysis reveals that watersheds 1, 2, 3, 6, 7, and 8 are with monthly mean runoffs of 29, 24, 22, 23, 26, and 27 mm, respectively, and these watersheds would likely contribute to surface runoff in the study area. The purpose of this study was to transform runoff data into a smooth curve for representing the surface runoff pattern and mean runoff of each watershed through statistical method. This study provides information of runoff potentiality of each watershed and also provides input data for hydrological modeling.

Runoff Potentiality of a Watershed through SCS and Functional Data Analysis Technique

Science.gov (United States)

Adham, M. I.; Shirazi, S. M.; Othman, F.; Rahman, S.; Yusop, Z.; Ismail, Z.

2014-01-01

Runoff potentiality of a watershed was assessed based on identifying curve number (CN), soil conservation service (SCS), and functional data analysis (FDA) techniques. Daily discrete rainfall data were collected from weather stations in the study area and analyzed through lowess method for smoothing curve. As runoff data represents a periodic pattern in each watershed, Fourier series was introduced to fit the smooth curve of eight watersheds. Seven terms of Fourier series were introduced for the watersheds 5 and 8, while 8 terms of Fourier series were used for the rest of the watersheds for the best fit of data. Bootstrapping smooth curve analysis reveals that watersheds 1, 2, 3, 6, 7, and 8 are with monthly mean runoffs of 29, 24, 22, 23, 26, and 27 mm, respectively, and these watersheds would likely contribute to surface runoff in the study area. The purpose of this study was to transform runoff data into a smooth curve for representing the surface runoff pattern and mean runoff of each watershed through statistical method. This study provides information of runoff potentiality of each watershed and also provides input data for hydrological modeling. PMID:25152911

Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China

Directory of Open Access Journals (Sweden)

Liying Geng

2014-03-01

Full Text Available More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR (Global Inventory Modeling and Map Studies (GIMMS and Pathfinder AVHRR Land (PAL, Satellite Pour l’ Observation de la Terre (SPOT VEGETATION (VGT, and Moderate Resolution Imaging Spectroradiometer (MODIS (Terra with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE technique, the Savitzky-Golay (S-G technique, the mean value iteration filter (MVI technique, the asymmetric Gaussian (A-G technique, the double logistic (D-L technique, the changing-weight filter (CW technique, the interpolation for data reconstruction (IDR technique, and the Whittaker smoother (WS technique. These techniques were evaluated by calculating the root mean square error (RMSE, the Akaike Information Criterion (AIC, and the Bayesian Information Criterion (BIC. The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study

Laurent series expansion of sunrise-type diagrams using configuration space techniques

International Nuclear Information System (INIS)

Groote, S.; Koerner, J.G.; Pivovarov, A.A.

2004-01-01

We show that configuration space techniques can be used to efficiently calculate the complete Laurent series ε-expansion of sunrise-type diagrams to any loop order in D-dimensional space-time for any external momentum and for arbitrary mass configurations. For negative powers of ε the results are obtained in analytical form. For positive powers of ε including the finite ε 0 contribution the result is obtained numerically in terms of low-dimensional integrals. We present general features of the calculation and provide exemplary results up to five-loop order which are compared to available results in the literature. (orig.)

Characteristic vector analysis of inflection ratio spectra: New technique for analysis of ocean color data

Science.gov (United States)

Grew, G. W.

1985-01-01

Characteristic vector analysis applied to inflection ratio spectra is a new approach to analyzing spectral data. The technique applied to remote data collected with the multichannel ocean color sensor (MOCS), a passive sensor, simultaneously maps the distribution of two different phytopigments, chlorophyll alpha and phycoerythrin, the ocean. The data set presented is from a series of warm core ring missions conducted during 1982. The data compare favorably with a theoretical model and with data collected on the same mission by an active sensor, the airborne oceanographic lidar (AOL).

Rainfall Prediction of Indian Peninsula: Comparison of Time Series Based Approach and Predictor Based Approach using Machine Learning Techniques

Science.gov (United States)

Dash, Y.; Mishra, S. K.; Panigrahi, B. K.

2017-12-01

Prediction of northeast/post monsoon rainfall which occur during October, November and December (OND) over Indian peninsula is a challenging task due to the dynamic nature of uncertain chaotic climate. It is imperative to elucidate this issue by examining performance of different machine leaning (ML) approaches. The prime objective of this research is to compare between a) statistical prediction using historical rainfall observations and global atmosphere-ocean predictors like Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and b) empirical prediction based on a time series analysis of past rainfall data without using any other predictors. Initially, ML techniques have been applied on SST and SLP data (1948-2014) obtained from NCEP/NCAR reanalysis monthly mean provided by the NOAA ESRL PSD. Later, this study investigated the applicability of ML methods using OND rainfall time series for 1948-2014 and forecasted up to 2018. The predicted values of aforementioned methods were verified using observed time series data collected from Indian Institute of Tropical Meteorology and the result revealed good performance of ML algorithms with minimal error scores. Thus, it is found that both statistical and empirical methods are useful for long range climatic projections.

Comparative study between the PIXE technique and neutron activation analysis for Zinc determination

International Nuclear Information System (INIS)

Cruvinel, Paulo Estevao; Crestana, Silvio; Artaxo Netto, Paulo Eduardo

1997-01-01

This work presents a comparative study between the PIXE, proton beams and neutron activation analysis (NAA) techniques, for determination of total zinc concentration. Particularly, soil samples from the Pindorama, Instituto Agronomico de Campinas, Sao Paulo State, Brazil, experimental station have been analysed and measuring the zinc contents in μg/g. The results presented good correlation between the mentioned techniques. The PIXE and NAA analyses have been carried out by using the series S, 2.4 MeV proton beams Pelletron accelerator and the IPEN/CNEN-IEA-R1 reactor, both installed at the Sao Paulo - Brazil university

Visibility graph analysis of heart rate time series and bio-marker of congestive heart failure

Science.gov (United States)

Bhaduri, Anirban; Bhaduri, Susmita; Ghosh, Dipak

2017-09-01

Study of RR interval time series for Congestive Heart Failure had been an area of study with different methods including non-linear methods. In this article the cardiac dynamics of heart beat are explored in the light of complex network analysis, viz. visibility graph method. Heart beat (RR Interval) time series data taken from Physionet database [46, 47] belonging to two groups of subjects, diseased (congestive heart failure) (29 in number) and normal (54 in number) are analyzed with the technique. The overall results show that a quantitative parameter can significantly differentiate between the diseased subjects and the normal subjects as well as different stages of the disease. Further, the data when split into periods of around 1 hour each and analyzed separately, also shows the same consistent differences. This quantitative parameter obtained using the visibility graph analysis thereby can be used as a potential bio-marker as well as a subsequent alarm generation mechanism for predicting the onset of Congestive Heart Failure.

Avoid Filling Swiss Cheese with Whipped Cream; Imputation Techniques and Evaluation Procedures for Cross-Country Time Series

OpenAIRE

Michael Weber; Michaela Denk

2011-01-01

International organizations collect data from national authorities to create multivariate cross-sectional time series for their analyses. As data from countries with not yet well-established statistical systems may be incomplete, the bridging of data gaps is a crucial challenge. This paper investigates data structures and missing data patterns in the cross-sectional time series framework, reviews missing value imputation techniques used for micro data in official statistics, and discusses the...

Metagenomics meets time series analysis: unraveling microbial community dynamics

NARCIS (Netherlands)

Faust, K.; Lahti, L.M.; Gonze, D.; Vos, de W.M.; Raes, J.

2015-01-01

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic

MCNP perturbation technique for criticality analysis

International Nuclear Information System (INIS)

McKinney, G.W.; Iverson, J.L.

1995-01-01

The differential operator perturbation technique has been incorporated into the Monte Carlo N-Particle transport code MCNP and will become a standard feature of future releases. This feature includes first and/or second order terms of the Taylor Series expansion for response perturbations related to cross-section data (i.e., density, composition, etc.). Criticality analyses can benefit from this technique in that predicted changes in the track-length tally estimator of K eff may be obtained for multiple perturbations in a single run. A key advantage of this method is that a precise estimate of a small change in response (i.e., < 1%) is easily obtained. This technique can also offer acceptable accuracy, to within a few percent, for up to 20-30% changes in a response

Applied time series analysis and innovative computing

CERN Document Server

Ao, Sio-Iong

2010-01-01

This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research.

Detection and Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of GPS Time Series

Science.gov (United States)

Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

2014-12-01

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies. Indeed, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here we present the application of the vbICA technique to GPS position time series. First, we use vbICA on synthetic data that simulate a seismic cycle

Multiresolution analysis of Bursa Malaysia KLCI time series

Science.gov (United States)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Dynamical analysis and visualization of tornadoes time series.

Directory of Open Access Journals (Sweden)

António M Lopes

Full Text Available In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Dynamical analysis and visualization of tornadoes time series.

Science.gov (United States)

Lopes, António M; Tenreiro Machado, J A

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Use of recurrence plot and recurrence quantification analysis in Taiwan unemployment rate time series

Science.gov (United States)

Chen, Wei-Shing

2011-04-01

The aim of the article is to answer the question if the Taiwan unemployment rate dynamics is generated by a non-linear deterministic dynamic process. This paper applies a recurrence plot and recurrence quantification approach based on the analysis of non-stationary hidden transition patterns of the unemployment rate of Taiwan. The case study uses the time series data of the Taiwan’s unemployment rate during the period from 1978/01 to 2010/06. The results show that recurrence techniques are able to identify various phases in the evolution of unemployment transition in Taiwan.

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

Science.gov (United States)

Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James

2013-01-01

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. VI. BAYESIAN BLOCK REPRESENTATIONS

Energy Technology Data Exchange (ETDEWEB)

Scargle, Jeffrey D. [Space Science and Astrobiology Division, MS 245-3, NASA Ames Research Center, Moffett Field, CA 94035-1000 (United States); Norris, Jay P. [Physics Department, Boise State University, 2110 University Drive, Boise, ID 83725-1570 (United States); Jackson, Brad [The Center for Applied Mathematics and Computer Science, Department of Mathematics, San Jose State University, One Washington Square, MH 308, San Jose, CA 95192-0103 (United States); Chiang, James, E-mail: jeffrey.d.scargle@nasa.gov [W. W. Hansen Experimental Physics Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics and SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94305 (United States)

2013-02-20

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by Arias-Castro et al. In the spirit of Reproducible Research all of the code and data necessary to reproduce all of the figures in this paper are included as supplementary material.

STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. VI. BAYESIAN BLOCK REPRESENTATIONS

International Nuclear Information System (INIS)

Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James

2013-01-01

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it—an improved and generalized version of Bayesian Blocks—that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by Arias-Castro et al. In the spirit of Reproducible Research all of the code and data necessary to reproduce all of the figures in this paper are included as supplementary material.

Time Series Analysis of the Quasar PKS 1749+096

Science.gov (United States)

Lam, Michael T.; Balonek, T. J.

2011-01-01

Multiple timescales of variability are observed in quasars at a variety of wavelengths, the nature of which is not fully understood. In 2007 and 2008, the quasar 1749+096 underwent two unprecedented optical outbursts, reaching a brightness never before seen in our twenty years of monitoring. Much lower level activity had been seen prior to these two outbursts. We present an analysis of the timescales of variability over the two regimes using a variety of statistical techniques. An IDL software package developed at Colgate University over the summer of 2010, the Quasar User Interface (QUI), provides effective computation of four time series functions for analyzing underlying trends present in generic, discretely sampled data sets. Using the Autocorrelation Function, Structure Function, and Power Spectrum, we are able to quickly identify possible variability timescales. QUI is also capable of computing the Cross-Correlation Function for comparing variability at different wavelengths. We apply these algorithms to 1749+096 and present our analysis of the timescales for this object. Funding for this project was received from Colgate University, the Justus and Jayne Schlichting Student Research Fund, and the NASA / New York Space Grant.

Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

Science.gov (United States)

Koeppen, W. C.; Pilger, E.; Wright, R.

2011-07-01

We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

Time-series analysis to study the impact of an intersection on dispersion along a street canyon.

Science.gov (United States)

Richmond-Bryant, Jennifer; Eisner, Alfred D; Hahn, Intaek; Fortune, Christopher R; Drake-Richman, Zora E; Brixey, Laurie A; Talih, M; Wiener, Russell W; Ellenson, William D

2009-12-01

This paper presents data analysis from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study to assess the transport of ultrafine particulate matter (PM) across urban intersections. Experiments were performed in a street canyon perpendicular to a highway in Brooklyn, NY, USA. Real-time ultrafine PM samplers were positioned on either side of an intersection at multiple locations along a street to collect time-series number concentration data. Meteorology equipment was positioned within the street canyon and at an upstream background site to measure wind speed and direction. Time-series analysis was performed on the PM data to compute a transport velocity along the direction of the street for the cases where background winds were parallel and perpendicular to the street. The data were analyzed for sampler pairs located (1) on opposite sides of the intersection and (2) on the same block. The time-series analysis demonstrated along-street transport, including across the intersection when background winds were parallel to the street canyon and there was minimal transport and no communication across the intersection when background winds were perpendicular to the street canyon. Low but significant values of the cross-correlation function (CCF) underscore the turbulent nature of plume transport along the street canyon. The low correlations suggest that flow switching around corners or traffic-induced turbulence at the intersection may have aided dilution of the PM plume from the highway. This observation supports similar findings in the literature. Furthermore, the time-series analysis methodology applied in this study is introduced as a technique for studying spatiotemporal variation in the urban microscale environment.

Fourier analysis of time series an introduction

CERN Document Server

Bloomfield, Peter

2000-01-01

A new, revised edition of a yet unrivaled work on frequency domain analysis Long recognized for his unique focus on frequency domain methods for the analysis of time series data as well as for his applied, easy-to-understand approach, Peter Bloomfield brings his well-known 1976 work thoroughly up to date. With a minimum of mathematics and an engaging, highly rewarding style, Bloomfield provides in-depth discussions of harmonic regression, harmonic analysis, complex demodulation, and spectrum analysis. All methods are clearly illustrated using examples of specific data sets, while ample

Time series analysis of ozone data in Isfahan

Science.gov (United States)

Omidvari, M.; Hassanzadeh, S.; Hosseinibalam, F.

2008-07-01

Time series analysis used to investigate the stratospheric ozone formation and decomposition processes. Different time series methods are applied to detect the reason for extreme high ozone concentrations for each season. Data was convert into seasonal component and frequency domain, the latter has been evaluated by using the Fast Fourier Transform (FFT), spectral analysis. The power density spectrum estimated from the ozone data showed peaks at cycle duration of 22, 20, 36, 186, 365 and 40 days. According to seasonal component analysis most fluctuation was in 1999 and 2000, but the least fluctuation was in 2003. The best correlation between ozone and sun radiation was found in 2000. Other variables which are not available cause to this fluctuation in the 1999 and 2001. The trend of ozone is increasing in 1999 and is decreasing in other years.

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

Science.gov (United States)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify

Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction

Directory of Open Access Journals (Sweden)

Helmut Thome

2015-07-01

Full Text Available Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality. To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other. The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling.

Time series analysis and its applications with R examples

CERN Document Server

Shumway, Robert H

2017-01-01

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonli...

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

Science.gov (United States)

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Comparison of the Performance of Two Advanced Spectral Methods for the Analysis of Times Series in Paleoceanography

Directory of Open Access Journals (Sweden)

Eulogio Pardo-Igúzquiza

2015-08-01

Full Text Available Many studies have revealed the cyclicity of past ocean/atmosphere dynamics at a wide range of time scales (from decadal to millennial time scales, based on the spectral analysis of time series of climate proxies obtained from deep sea sediment cores. Among the many techniques available for spectral analysis, the maximum entropy method and the Thomson multitaper approach have frequently been used because of their good statistical properties and high resolution with short time series. The novelty of the present study is that we compared the two methods by according to the performance of their statistical tests to assess the statistical significance of their power spectrum estimates. The statistical significance of maximum entropy estimates was assessed by a random permutation test (Pardo-Igúzquiza and Rodríguez-Tovar, 2000, while the statistical significance of the Thomson multitaper method was assessed by an F-test (Thomson, 1982. We compared the results obtained in a case study using simulated data where the spectral content of the time series was known and in a case study with real data. In both cases the results are similar: while the cycles identified as significant by maximum entropy and the permutation test have a clear physical interpretation, the F-test with the Thomson multitaper estimator tends to find as no significant the peaks in the low frequencies and tends to give as significant more spurious peaks in the middle and high frequencies. Nevertheless, the best strategy is to use both techniques and to use the advantages of each of them.

PREFACE: 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2011)

Science.gov (United States)

Teodorescu, Liliana; Britton, David; Glover, Nigel; Heinrich, Gudrun; Lauret, Jérôme; Naumann, Axel; Speer, Thomas; Teixeira-Dias, Pedro

2012-06-01

ACAT2011 This volume of Journal of Physics: Conference Series is dedicated to scientific contributions presented at the 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2011) which took place on 5-7 September 2011 at Brunel University, UK. The workshop series, which began in 1990 in Lyon, France, brings together computer science researchers and practitioners, and researchers from particle physics and related fields in order to explore and confront the boundaries of computing and of automatic data analysis and theoretical calculation techniques. It is a forum for the exchange of ideas among the fields, exploring and promoting cutting-edge computing, data analysis and theoretical calculation techniques in fundamental physics research. This year's edition of the workshop brought together over 100 participants from all over the world. 14 invited speakers presented key topics on computing ecosystems, cloud computing, multivariate data analysis, symbolic and automatic theoretical calculations as well as computing and data analysis challenges in astrophysics, bioinformatics and musicology. Over 80 other talks and posters presented state-of-the art developments in the areas of the workshop's three tracks: Computing Technologies, Data Analysis Algorithms and Tools, and Computational Techniques in Theoretical Physics. Panel and round table discussions on data management and multivariate data analysis uncovered new ideas and collaboration opportunities in the respective areas. This edition of ACAT was generously sponsored by the Science and Technology Facility Council (STFC), the Institute for Particle Physics Phenomenology (IPPP) at Durham University, Brookhaven National Laboratory in the USA and Dell. We would like to thank all the participants of the workshop for the high level of their scientific contributions and for the enthusiastic participation in all its activities which were, ultimately, the key factors in the

A taylor series approach to survival analysis

International Nuclear Information System (INIS)

Brodsky, J.B.; Groer, P.G.

1984-09-01

A method of survival analysis using hazard functions is developed. The method uses the well known mathematical theory for Taylor Series. Hypothesis tests of the adequacy of many statistical models, including proportional hazards and linear and/or quadratic dose responses, are obtained. A partial analysis of leukemia mortality in the Life Span Study cohort is used as an example. Furthermore, a relatively robust estimation procedure for the proportional hazards model is proposed. (author)

Seasonal and annual precipitation time series trend analysis in North Carolina, United States

Science.gov (United States)

Sayemuzzaman, Mohammad; Jha, Manoj K.

2014-02-01

The present study performs the spatial and temporal trend analysis of the annual and seasonal time-series of a set of uniformly distributed 249 stations precipitation data across the state of North Carolina, United States over the period of 1950-2009. The Mann-Kendall (MK) test, the Theil-Sen approach (TSA) and the Sequential Mann-Kendall (SQMK) test were applied to quantify the significance of trend, magnitude of trend, and the trend shift, respectively. Regional (mountain, piedmont and coastal) precipitation trends were also analyzed using the above-mentioned tests. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation of precipitation data series. The application of the above-mentioned procedures has shown very notable statewide increasing trend for winter and decreasing trend for fall precipitation. Statewide mixed (increasing/decreasing) trend has been detected in annual, spring, and summer precipitation time series. Significant trends (confidence level ≥ 95%) were detected only in 8, 7, 4 and 10 nos. of stations (out of 249 stations) in winter, spring, summer, and fall, respectively. Magnitude of the highest increasing (decreasing) precipitation trend was found about 4 mm/season (- 4.50 mm/season) in fall (summer) season. Annual precipitation trend magnitude varied between - 5.50 mm/year and 9 mm/year. Regional trend analysis found increasing precipitation in mountain and coastal regions in general except during the winter. Piedmont region was found to have increasing trends in summer and fall, but decreasing trend in winter, spring and on an annual basis. The SQMK test on "trend shift analysis" identified a significant shift during 1960 - 70 in most parts of the state. Finally, the comparison between winter (summer) precipitations with the North Atlantic Oscillation (Southern Oscillation) indices concluded that the variability and trend of precipitation can be explained by the

The role of alternative (advanced) conscious sedation techniques in dentistry for adult patients: a series of cases.

Science.gov (United States)

Robb, N

2014-03-01

The basic techniques of conscious sedation have been found to be safe and effective for the management of anxiety in adult dental patients requiring sedation to allow them to undergo dental treatment. There remains great debate within the profession as to the role of the so called advanced sedation techniques. This paper presents a series of nine patients who were managed with advanced sedation techniques where the basic techniques were either inappropriate or had previously failed to provide adequate relief of anxiety. In these cases, had there not been the availability of advanced sedation techniques, the most likely recourse would have been general anaesthesia--a treatment modality that current guidance indicates should not be used where there is an appropriate alternative. The sedation techniques used have provided that appropriate alternative management strategy.

Lecture notes for Advanced Time Series Analysis

DEFF Research Database (Denmark)

Madsen, Henrik; Holst, Jan

1997-01-01

A first version of this notes was used at the lectures in Grenoble, and they are now extended and improved (together with Jan Holst), and used in Ph.D. courses on Advanced Time Series Analysis at IMM and at the Department of Mathematical Statistics, University of Lund, 1994, 1997, ...

Modeling of human operator dynamics in simple manual control utilizing time series analysis. [tracking (position)

Science.gov (United States)

Agarwal, G. C.; Osafo-Charles, F.; Oneill, W. D.; Gottlieb, G. L.

1982-01-01

Time series analysis is applied to model human operator dynamics in pursuit and compensatory tracking modes. The normalized residual criterion is used as a one-step analytical tool to encompass the processes of identification, estimation, and diagnostic checking. A parameter constraining technique is introduced to develop more reliable models of human operator dynamics. The human operator is adequately modeled by a second order dynamic system both in pursuit and compensatory tracking modes. In comparing the data sampling rates, 100 msec between samples is adequate and is shown to provide better results than 200 msec sampling. The residual power spectrum and eigenvalue analysis show that the human operator is not a generator of periodic characteristics.

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

Science.gov (United States)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

Science.gov (United States)

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Surface analysis the principal techniques

CERN Document Server

Vickerman, John C

2009-01-01

This completely updated and revised second edition of Surface Analysis: The Principal Techniques, deals with the characterisation and understanding of the outer layers of substrates, how they react, look and function which are all of interest to surface scientists. Within this comprehensive text, experts in each analysis area introduce the theory and practice of the principal techniques that have shown themselves to be effective in both basic research and in applied surface analysis. Examples of analysis are provided to facilitate the understanding of this topic and to show readers how they c

Time series analysis of barometric pressure data

International Nuclear Information System (INIS)

La Rocca, Paola; Riggi, Francesco; Riggi, Daniele

2010-01-01

Time series of atmospheric pressure data, collected over a period of several years, were analysed to provide undergraduate students with educational examples of application of simple statistical methods of analysis. In addition to basic methods for the analysis of periodicities, a comparison of two forecast models, one based on autoregression algorithms, and the other making use of an artificial neural network, was made. Results show that the application of artificial neural networks may give slightly better results compared to traditional methods.

Non-linear forecasting in high-frequency financial time series

Science.gov (United States)

Strozzi, F.; Zaldívar, J. M.

2005-08-01

A new methodology based on state space reconstruction techniques has been developed for trading in financial markets. The methodology has been tested using 18 high-frequency foreign exchange time series. The results are in apparent contradiction with the efficient market hypothesis which states that no profitable information about future movements can be obtained by studying the past prices series. In our (off-line) analysis positive gain may be obtained in all those series. The trading methodology is quite general and may be adapted to other financial time series. Finally, the steps for its on-line application are discussed.

Series of Bessel and Kummer-type functions

CERN Document Server

Baricz, Arpad; Pogány, Tibor K

2017-01-01

This book is devoted to the study of certain integral representations for Neumann, Kapteyn, Schlömilch, Dini and Fourier series of Bessel and other special functions, such as Struve and von Lommel functions. The aim is also to find the coefficients of the Neumann and Kapteyn series, as well as closed-form expressions and summation formulas for the series of Bessel functions considered. Some integral representations are deduced using techniques from the theory of differential equations. The text is aimed at a mathematical audience, including graduate students and those in the scientific community who are interested in a new perspective on Fourier–Bessel series, and their manifold and polyvalent applications, mainly in general classical analysis, applied mathematics and mathematical physics.

Time-series analysis of Nigeria rice supply and demand: Error ...

African Journals Online (AJOL)

The study examined a time-series analysis of Nigeria rice supply and demand with a view to determining any long-run equilibrium between them using the Error Correction Model approach (ECM). The data used for the study represents the annual series of 1960-2007 (47 years) for rice supply and demand in Nigeria, ...

Time Series Factor Analysis with an Application to Measuring Money

NARCIS (Netherlands)

Gilbert, Paul D.; Meijer, Erik

2005-01-01

Time series factor analysis (TSFA) and its associated statistical theory is developed. Unlike dynamic factor analysis (DFA), TSFA obviates the need for explicitly modeling the process dynamics of the underlying phenomena. It also differs from standard factor analysis (FA) in important respects: the

A novel water quality data analysis framework based on time-series data mining.

Science.gov (United States)

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of series resistance effects on forward I - V and C - V characteristics of mis type diodes

International Nuclear Information System (INIS)

Altindal, S.; Tekeli, Z.; Karadeniz, S.; Tugluoglu, N.; Ercan, I.

2002-01-01

In order to determine the series resistance R s , we have followed Lie et al., Cheung et al. and Kang et al., from the plot of I vs dV/dLn(I) which was linear curve over a wide range of current values at each temperature. The values of Rs were obtained from the slope of the linear parts of the curves and then the series resistance at each temperature has been evaluated at Ln(I) vs (V-IR s ) curves. The curves are linear over a wide range of voltage. The most reliable values of ideality factor n and reverse saturation current Is were then determined. In addition to role of series resistance on the C-V and G-V characteristics of diode have been investigated. Both C-V and G-V measurements show that the measured capacitance and conductance seriously varies with applied bias and frequency due to presence of R s . The density of interface states, barrier height and series resistance from the forward bias I-V characteristics using this method agrees very well with that obtained from the capacitance technique. It is clear that ignoring the series resistance (device with high series resistance) can lead to significant errors in the analysis of the I-V-T, C-V-f and G-V-f characteristics

Spectral Unmixing Analysis of Time Series Landsat 8 Images

Science.gov (United States)

Zhuo, R.; Xu, L.; Peng, J.; Chen, Y.

2018-05-01

Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compared to independent image analyses. Moreover, different land cover types may demonstrate different temporal patterns, which can aid the discrimination of different natures. Therefore, this letter presents time series K-P-Means, a new solution to the problem of unmixing time series Landsat imagery. The proposed approach is to obtain the "purified" pixels in order to achieve optimal unmixing performance. The vertex component analysis (VCA) is used to extract endmembers for endmember initialization. First, nonnegative least square (NNLS) is used to estimate abundance maps by using the endmember. Then, the estimated endmember is the mean value of "purified" pixels, which is the residual of the mixed pixel after excluding the contribution of all nondominant endmembers. Assembling two main steps (abundance estimation and endmember update) into the iterative optimization framework generates the complete algorithm. Experiments using both simulated and real Landsat 8 images show that the proposed "joint unmixing" approach provides more accurate endmember and abundance estimation results compared with "separate unmixing" approach.

Nonlinear Time Series Analysis via Neural Networks

Science.gov (United States)

Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

PREFACE: 15th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT2013)

Science.gov (United States)

Wang, Jianxiong

2014-06-01

This volume of Journal of Physics: Conference Series is dedicated to scientific contributions presented at the 15th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2013) which took place on 16-21 May 2013 at the Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China. The workshop series brings together computer science researchers and practitioners, and researchers from particle physics and related fields to explore and confront the boundaries of computing and of automatic data analysis and theoretical calculation techniques. This year's edition of the workshop brought together over 120 participants from all over the world. 18 invited speakers presented key topics on the universe in computer, Computing in Earth Sciences, multivariate data analysis, automated computation in Quantum Field Theory as well as computing and data analysis challenges in many fields. Over 70 other talks and posters presented state-of-the-art developments in the areas of the workshop's three tracks: Computing Technologies, Data Analysis Algorithms and Tools, and Computational Techniques in Theoretical Physics. The round table discussions on open-source, knowledge sharing and scientific collaboration stimulate us to think over the issue in the respective areas. ACAT 2013 was generously sponsored by the Chinese Academy of Sciences (CAS), National Natural Science Foundation of China (NFSC), Brookhaven National Laboratory in the USA (BNL), Peking University (PKU), Theoretical Physics Cernter for Science facilities of CAS (TPCSF-CAS) and Sugon. We would like to thank all the participants for their scientific contributions and for the en- thusiastic participation in all its activities of the workshop. Further information on ACAT 2013 can be found at http://acat2013.ihep.ac.cn. Professor Jianxiong Wang Institute of High Energy Physics Chinese Academy of Science Details of committees and sponsors are available in the PDF

Recent advances in the instrumental techniques for the analysis of modern materials (II)

International Nuclear Information System (INIS)

Ahmed, M.

1990-01-01

Inductively Coupled Plasma Mass Spectrometry ICP-MS a logical development of equally established sister technique of ICP-AEA discussed in part-1 of this series of article on modern analytical techniques. The rapid adaptation of argon plasma as ion source for time of flight quadrupole mass analyser has led to the development of truly integrated instrumental technique for analysis of solutions and slurries. The powerful combination with laser ablation device has made the direct analysis of geological, geochemical and other complex conducting and non conducting samples possible in days rather months at sub ppm levels. Parallel development in computer hardware and software has made the instrumental optimization easy enabling the generation of meaningful analytical data a matter of routine. The limitations imposed by spectroscopic and non restricted the variety of matrices and materials covered by ICP-MS of LA-ICP-MS. The technique has provided it formidable analytical power in wide areas of industrial environmental, social, biological and break through advanced materials used in space mass communication, transportation and general areas of advanced analytical chemistry. It is expected that in combination with other instrumental methods as HPLC, ETC, ion chromatography. ICP-MS shall continue to dominate well into the 21st century. (author)

Growth And Export Expansion In Mauritius - A Time Series Analysis ...

African Journals Online (AJOL)

Growth And Export Expansion In Mauritius - A Time Series Analysis. ... RV Sannassee, R Pearce ... Using Granger Causality tests, the short-run analysis results revealed that there is significant reciprocal causality between real export earnings ...

Soil analysis. Modern instrumental technique

International Nuclear Information System (INIS)

Smith, K.A.

1993-01-01

This book covers traditional methods of analysis and specialist monographs on individual instrumental techniques, which are usually not written with soil or plant analysis specifically in mind. The principles of the techniques are combined with discussions of sample preparation and matrix problems, and critical reviews of applications in soil science and related disciplines. Individual chapters are processed separately for inclusion in the appropriate data bases

Improvement on Exoplanet Detection Methods and Analysis via Gaussian Process Fitting Techniques

Science.gov (United States)

Van Ross, Bryce; Teske, Johanna

2018-01-01

Planetary signals in radial velocity (RV) data are often accompanied by signals coming solely from stellar photo- or chromospheric variation. Such variation can reduce the precision of planet detection and mass measurements, and cause misidentification of planetary signals. Recently, several authors have demonstrated the utility of Gaussian Process (GP) regression for disentangling planetary signals in RV observations (Aigrain et al. 2012; Angus et al. 2017; Czekala et al. 2017; Faria et al. 2016; Gregory 2015; Haywood et al. 2014; Rajpaul et al. 2015; Foreman-Mackey et al. 2017). GP models the covariance of multivariate data to make predictions about likely underlying trends in the data, which can be applied to regions where there are no existing observations. The potency of GP has been used to infer stellar rotation periods; to model and disentangle time series spectra; and to determine physical aspects, populations, and detection of exoplanets, among other astrophysical applications. Here, we implement similar analysis techniques to times series of the Ca-2 H and K activity indicator measured simultaneously with RVs in a small sample of stars from the large Keck/HIRES RV planet search program. Our goal is to characterize the pattern(s) of non-planetary variation to be able to know what is/ is not a planetary signal. We investigated ten different GP kernels and their respective hyperparameters to determine the optimal combination (e.g., the lowest Bayesian Information Criterion value) in each stellar data set. To assess the hyperparameters’ error, we sampled their posterior distributions using Markov chain Monte Carlo (MCMC) analysis on the optimized kernels. Our results demonstrate how GP analysis of stellar activity indicators alone can contribute to exoplanet detection in RV data, and highlight the challenges in applying GP analysis to relatively small, irregularly sampled time series.

Fourier series analysis of a cylindrical pressure vessel subjected to axial end load and external pressure

International Nuclear Information System (INIS)

Brar, Gurinder Singh; Hari, Yogeshwar; Williams, Dennis K.

2013-01-01

This paper presents the comparison of a reliability technique that employs a Fourier series representation of random axisymmetric and asymmetric imperfections in a cylindrical pressure vessel subjected to an axial end load and external pressure, with evaluations prescribed by the ASME Boiler and Pressure Vessel Code, Section VIII, Division 2 Rules. The ultimate goal of the reliability technique described herein is to predict the critical buckling load associated with the subject cylindrical pressure vessel. Initial geometric imperfections are shown to have a significant effect on the calculated load carrying capacity of the vessel. Fourier decomposition was employed to interpret imperfections as structural features that can be easily related to various other types of defined imperfections. The initial functional description of the imperfections consists of an axisymmetric portion and a deviant portion, which are availed in the form of a double Fourier series. Fifty simulated shells generated by the Monte Carlo technique are employed in the final prediction of the critical buckling load. The representation of initial geometrical imperfections in the cylindrical pressure vessel requires the determination of respective Fourier coefficients. Multi-mode analyses are expanded to evaluate a large number of potential buckling modes for both predefined geometries in combination with asymmetric imperfections as a function of position within the given cylindrical shell. The probability of the ultimate buckling stress exceeding a predefined threshold stress is also calculated. The method and results described herein are in stark contrast to the “knockdown factor” approach as applied to compressive stress evaluations currently utilized in industry. Further effort is needed to improve on the current design rules regarding column buckling of large diameter pressure vessels subjected to an axial end load and external pressure designed in accordance with ASME Boiler and

A Generalized Technique in Numerical Integration

Science.gov (United States)

Safouhi, Hassan

2018-02-01

Integration by parts is one of the most popular techniques in the analysis of integrals and is one of the simplest methods to generate asymptotic expansions of integral representations. The product of the technique is usually a divergent series formed from evaluating boundary terms; however, sometimes the remaining integral is also evaluated. Due to the successive differentiation and anti-differentiation required to form the series or the remaining integral, the technique is difficult to apply to problems more complicated than the simplest. In this contribution, we explore a generalized and formalized integration by parts to create equivalent representations to some challenging integrals. As a demonstrative archetype, we examine Bessel integrals, Fresnel integrals and Airy functions.

Renal transplant lithiasis: analysis of our series and review of the literature.

Science.gov (United States)

Stravodimos, Konstantinos G; Adamis, Stefanos; Tyritzis, Stavros; Georgios, Zavos; Constantinides, Constantinos A

2012-01-01

Renal transplant lithiasis represents a rather uncommon complication. Even rare, it can result in significant morbidity and a devastating loss of renal function if obstruction occurs. We present our experience with graft lithiasis in our series of renal transplantations and review the literature regarding the epidemiology, pathophysiology, and current therapeutic strategies in the management of renal transplant lithiasis. In a retrospective analysis of a consecutive series of 1525 renal transplantations that were performed between January 1983 and March 2007, 7 patients were found to have allograft lithiasis. In five cases, the calculi were localized in the renal unit, and in two cases, in the ureter. A review in the English language was also performed of the Medline and PubMed databases using the keywords renal transplant lithiasis, donor-gifted lithiasis, and urological complications after kidney transplantation. Several retrospective studies regarding the incidence, etiology, as well as predisposing factors for graft lithiasis were reviewed. Data regarding the current therapeutic strategies for graft lithiasis were also evaluated, and outcomes were compared with the results of our series. Most studies report a renal transplant lithiasis incidence of 0.4% to 1%. In our series, incidence of graft lithiasis was 0.46% (n=7). Of the seven patients, three were treated via percutaneous nephrolithotripsy (PCNL); in three patients, shockwave lithotripsy (SWL) was performed; and in a single case, spontaneous passage of a urinary calculus was observed. All patients are currently stone free but still remain under close urologic surveillance. Renal transplant lithiasis requires vigilance, a high index of suspicion, prompt recognition, and management. Treatment protocols should mimic those for solitary kidneys. Minimally invasive techniques are available to remove graft calculi. Long-term follow-up is essential to determine the outcome, as well as to prevent recurrence.

Time Series Analysis of Wheat Futures Reward in China

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

Different from the fact that the main researches are focused on single futures contract and lack of the comparison of different periods, this paper described the statistical characteristics of wheat futures reward time series of Zhengzhou Commodity Exchange in recent three years. Besides the basic statistic analysis, the paper used the GARCH and EGARCH model to describe the time series which had the ARCH effect and analyzed the persistence of volatility shocks and the leverage effect. The results showed that compared with that of normal one,wheat futures reward series were abnormality, leptokurtic and thick tail distribution. The study also found that two-part of the reward series had no autocorrelation. Among the six correlative series, three ones presented the ARCH effect. By using of the Auto-regressive Distributed Lag Model, GARCH model and EGARCH model, the paper demonstrates the persistence of volatility shocks and the leverage effect on the wheat futures reward time series. The results reveal that on the one hand, the statistical characteristics of the wheat futures reward are similar to the aboard mature futures market as a whole. But on the other hand, the results reflect some shortages such as the immatureness and the over-control by the government in the Chinese future market.

Effective Feature Preprocessing for Time Series Forecasting

DEFF Research Database (Denmark)

Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

2006-01-01

Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

Biostatistics series module 9: Survival analysis

Directory of Open Access Journals (Sweden)

Avijit Hazra

2017-01-01

Full Text Available Survival analysis is concerned with “time to event“ data. Conventionally, it dealt with cancer death as the event in question, but it can handle any event occurring over a time frame, and this need not be always adverse in nature. When the outcome of a study is the time to an event, it is often not possible to wait until the event in question has happened to all the subjects, for example, until all are dead. In addition, subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. The data set is thus an assemblage of times to the event in question and times after which no more information on the individual is available. Survival analysis methods are the only techniques capable of handling censored observations without treating them as missing data. They also make no assumption regarding normal distribution of time to event data. Descriptive methods for exploring survival times in a sample include life table and Kaplan–Meier techniques as well as various kinds of distribution fitting as advanced modeling techniques. The Kaplan–Meier cumulative survival probability over time plot has become the signature plot for biomedical survival analysis. Several techniques are available for comparing the survival experience in two or more groups – the log-rank test is popularly used. This test can also be used to produce an odds ratio as an estimate of risk of the event in the test group; this is called hazard ratio (HR. Limitations of the traditional log-rank test have led to various modifications and enhancements. Finally, survival analysis offers different regression models for estimating the impact of multiple predictors on survival. Cox's proportional hazard model is the most general of the regression methods that allows the hazard function to be modeled on a set of explanatory variables without making restrictive assumptions concerning the

17th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2016)

International Nuclear Information System (INIS)

2016-01-01

Preface The 2016 version of the International Workshop on Advanced Computing and Analysis Techniques in Physics Research took place on January 18-22, 2016, at the Universidad Técnica Federico Santa Maria -UTFSM- in Valparaiso, Chile. The present volume of IOP Conference Series is devoted to the selected scientific contributions presented at the workshop. In order to guarantee the scientific quality of the Proceedings all papers were thoroughly peer-reviewed by an ad-hoc Editorial Committee with the help of many careful reviewers. The ACAT Workshop series has a long tradition starting in 1990 (Lyon, France), and takes place in intervals of a year and a half. Formerly these workshops were known under the name AIHENP (Artificial Intelligence for High Energy and Nuclear Physics). Each edition brings together experimental and theoretical physicists and computer scientists/experts, from particle and nuclear physics, astronomy and astrophysics in order to exchange knowledge and experience in computing and data analysis in physics. Three tracks cover the main topics: Computing technology: languages and system architectures. Data analysis: algorithms and tools. Theoretical Physics: techniques and methods. Although most contributions and discussions are related to particle physics and computing, other fields like condensed matter physics, earth physics, biophysics are often addressed in the hope to share our approaches and visions. It created a forum for exchanging ideas among fields, exploring and promoting cutting-edge computing technologies and debating hot topics. (paper)

Vibration impact acoustic emission technique for identification and analysis of defects in carbon steel tubes: Part B Cluster analysis

Energy Technology Data Exchange (ETDEWEB)

Halim, Zakiah Abd [Universiti Teknikal Malaysia Melaka (Malaysia); Jamaludin, Nordin; Junaidi, Syarif [Faculty of Engineering and Built, Universiti Kebangsaan Malaysia, Bangi (Malaysia); Yahya, Syed Yusainee Syed [Universiti Teknologi MARA, Shah Alam (Malaysia)

2015-04-15

Current steel tubes inspection techniques are invasive, and the interpretation and evaluation of inspection results are manually done by skilled personnel. Part A of this work details the methodology involved in the newly developed non-invasive, non-destructive tube inspection technique based on the integration of vibration impact (VI) and acoustic emission (AE) systems known as the vibration impact acoustic emission (VIAE) technique. AE signals have been introduced into a series of ASTM A179 seamless steel tubes using the impact hammer. Specifically, a good steel tube as the reference tube and four steel tubes with through-hole artificial defect at different locations were used in this study. The AEs propagation was captured using a high frequency sensor of AE systems. The present study explores the cluster analysis approach based on autoregressive (AR) coefficients to automatically interpret the AE signals. The results from the cluster analysis were graphically illustrated using a dendrogram that demonstrated the arrangement of the natural clusters of AE signals. The AR algorithm appears to be the more effective method in classifying the AE signals into natural groups. This approach has successfully classified AE signals for quick and confident interpretation of defects in carbon steel tubes.

Vibration impact acoustic emission technique for identification and analysis of defects in carbon steel tubes: Part B Cluster analysis

International Nuclear Information System (INIS)

Halim, Zakiah Abd; Jamaludin, Nordin; Junaidi, Syarif; Yahya, Syed Yusainee Syed

2015-01-01

Current steel tubes inspection techniques are invasive, and the interpretation and evaluation of inspection results are manually done by skilled personnel. Part A of this work details the methodology involved in the newly developed non-invasive, non-destructive tube inspection technique based on the integration of vibration impact (VI) and acoustic emission (AE) systems known as the vibration impact acoustic emission (VIAE) technique. AE signals have been introduced into a series of ASTM A179 seamless steel tubes using the impact hammer. Specifically, a good steel tube as the reference tube and four steel tubes with through-hole artificial defect at different locations were used in this study. The AEs propagation was captured using a high frequency sensor of AE systems. The present study explores the cluster analysis approach based on autoregressive (AR) coefficients to automatically interpret the AE signals. The results from the cluster analysis were graphically illustrated using a dendrogram that demonstrated the arrangement of the natural clusters of AE signals. The AR algorithm appears to be the more effective method in classifying the AE signals into natural groups. This approach has successfully classified AE signals for quick and confident interpretation of defects in carbon steel tubes.

Application of functional analysis techniques to supervisory systems

International Nuclear Information System (INIS)

Lambert, Manuel; Riera, Bernard; Martel, Gregory

1999-01-01

The aim of this paper is to apply firstly two interesting functional analysis techniques for the design of supervisory systems for complex processes, and secondly to discuss the strength and the weaknesses of each of them. Two functional analysis techniques have been applied, SADT (Structured Analysis and Design Technique) and FAST (Functional Analysis System Technique) on a process, an example of a Water Supply Process Control (WSPC) system. These techniques allow a functional description of industrial processes. The paper briefly discusses the functions of a supervisory system and some advantages of the application of functional analysis for the design of a 'human' centered supervisory system. Then the basic principles of the two techniques applied on the WSPC system are presented. Finally, the different results obtained from the two techniques are discussed

Topological data analysis of financial time series: Landscapes of crashes

Science.gov (United States)

Gidea, Marian; Katz, Yuri

2018-02-01

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

Time series analysis in astronomy: Limits and potentialities

DEFF Research Database (Denmark)

Vio, R.; Kristensen, N.R.; Madsen, Henrik

2005-01-01

In this paper we consider the problem of the limits concerning the physical information that can be extracted from the analysis of one or more time series ( light curves) typical of astrophysical objects. On the basis of theoretical considerations and numerical simulations, we show that with no a...

Multi-granular trend detection for time-series analysis

NARCIS (Netherlands)

van Goethem, A.I.; Staals, F.; Löffler, M.; Dykes, J.; Speckmann, B.

2017-01-01

Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data

ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

Directory of Open Access Journals (Sweden)

Z.-G. Zhou

2016-06-01

Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

Methods for removal of unwanted signals from gravity time-series: Comparison using linear techniques complemented with analysis of system dynamics

Science.gov (United States)

Valencio, Arthur; Grebogi, Celso; Baptista, Murilo S.

2017-10-01

The presence of undesirable dominating signals in geophysical experimental data is a challenge in many subfields. One remarkable example is surface gravimetry, where frequencies from Earth tides correspond to time-series fluctuations up to a thousand times larger than the phenomena of major interest, such as hydrological gravity effects or co-seismic gravity changes. This work discusses general methods for the removal of unwanted dominating signals by applying them to 8 long-period gravity time-series of the International Geodynamics and Earth Tides Service, equivalent to the acquisition from 8 instruments in 5 locations representative of the network. We compare three different conceptual approaches for tide removal: frequency filtering, physical modelling, and data-based modelling. Each approach reveals a different limitation to be considered depending on the intended application. Vestiges of tides remain in the residues for the modelling procedures, whereas the signal was distorted in different ways by the filtering and data-based procedures. The linear techniques employed were power spectral density, spectrogram, cross-correlation, and classical harmonics decomposition, while the system dynamics was analysed by state-space reconstruction and estimation of the largest Lyapunov exponent. Although the tides could not be completely eliminated, they were sufficiently reduced to allow observation of geophysical events of interest above the 10 nm s-2 level, exemplified by a hydrology-related event of 60 nm s-2. The implementations adopted for each conceptual approach are general, so that their principles could be applied to other kinds of data affected by undesired signals composed mainly by periodic or quasi-periodic components.

Time series analysis of nuclear instrumentation in EBR-II

International Nuclear Information System (INIS)

Imel, G.R.

1996-01-01

Results of a time series analysis of the scaler count data from the 3 wide range nuclear detectors in the Experimental Breeder Reactor-II are presented. One of the channels was replaced, and it was desired to determine if there was any statistically significant change (ie, improvement) in the channel's response after the replacement. Data were collected from all 3 channels for 16-day periods before and after detector replacement. Time series analysis and statistical tests showed that there was no significant change after the detector replacement. Also, there were no statistically significant differences among the 3 channels, either before or after the replacement. Finally, it was determined that errors in the reactivity change inferred from subcritical count monitoring during fuel handling would be on the other of 20-30 cents for single count intervals

Employ Simulation Techniques. Second Edition. Module C-5 of Category C--Instructional Execution. Professional Teacher Education Module Series.

Science.gov (United States)

Ohio State Univ., Columbus. National Center for Research in Vocational Education.

One of a series of performance-based teacher education learning packages focusing upon specific professional competencies of vocational teachers, this learning module deals with employing simulation techniques. It consists of an introduction and four learning experiences. Covered in the first learning experience are various types of simulation…

Development of indicators of vegetation recovery based on time series analysis of SPOT Vegetation data

Science.gov (United States)

Lhermitte, S.; Tips, M.; Verbesselt, J.; Jonckheere, I.; Van Aardt, J.; Coppin, Pol

2005-10-01

Large-scale wild fires have direct impacts on natural ecosystems and play a major role in the vegetation ecology and carbon budget. Accurate methods for describing post-fire development of vegetation are therefore essential for the understanding and monitoring of terrestrial ecosystems. Time series analysis of satellite imagery offers the potential to quantify these parameters with spatial and temporal accuracy. Current research focuses on the potential of time series analysis of SPOT Vegetation S10 data (1999-2001) to quantify the vegetation recovery of large-scale burns detected in the framework of GBA2000. The objective of this study was to provide quantitative estimates of the spatio-temporal variation of vegetation recovery based on remote sensing indicators. Southern Africa was used as a pilot study area, given the availability of ground and satellite data. An automated technique was developed to extract consistent indicators of vegetation recovery from the SPOT-VGT time series. Reference areas were used to quantify the vegetation regrowth by means of Regeneration Indices (RI). Two kinds of recovery indicators (time and value- based) were tested for RI's of NDVI, SR, SAVI, NDWI, and pure band information. The effects of vegetation structure and temporal fire regime features on the recovery indicators were subsequently analyzed. Statistical analyses were conducted to assess whether the recovery indicators were different for different vegetation types and dependent on timing of the burning season. Results highlighted the importance of appropriate reference areas and the importance of correct normalization of the SPOT-VGT data.

Development of a new analysis technique to measure low radial-order p modes in spatially-resolved helioseismic data

Energy Technology Data Exchange (ETDEWEB)

Salabert, David; Leibacher, John W [National Solar Observatory, 950 North Cherry Avenue, Tucson, AZ 85719 (United States); Appourchaux, Thierry [Institut d' Astrophysique Spatiale, CNRS-Universite Paris XI UMR 8617, 91405 Orsay Cedex (France)], E-mail: dsalabert@nso.edu

2008-10-15

In order to take full advantage of the long time series collected by the GONG and MDI helioseismic projects, we present here an adaptation of the rotation-corrected m-averaged spectrum technique in order to observe low radial-order solar p modes. Modeled profiles of the solar rotation demonstrated the potential advantage of such a technique. Here we develop a new analysis procedure which finds the best estimates of the shift of each m of a given (n, {iota}) multiplet, commonly expressed as an expansion in a set of orthogonal polynomials, which yield the narrowest mode in the m-averaged spectrum. We apply the technique to the GONG data for modes with 1 {<=} {iota} {<=} 25 and show that it allows us to measure lower-frequency modes than with classic peak-fitting analysis of the individual-m spectra.

Time series analysis in chaotic diode resonator circuit

Energy Technology Data Exchange (ETDEWEB)

Hanias, M.P. [TEI of Chalkis, GR 34400, Evia, Chalkis (Greece)] e-mail: mhanias@teihal.gr; Giannaris, G. [TEI of Chalkis, GR 34400, Evia, Chalkis (Greece); Spyridakis, A. [TEI of Chalkis, GR 34400, Evia, Chalkis (Greece); Rigas, A. [TEI of Chalkis, GR 34400, Evia, Chalkis (Greece)

2006-01-01

A diode resonator chaotic circuit is presented. Multisim is used to simulate the circuit and show the presence of chaos. Time series analysis performed by the method proposed by Grasberger and Procaccia. The correlation and minimum embedding dimension {nu} and m {sub min}, respectively, were calculated. Also the corresponding Kolmogorov entropy was calculated.

Time series analysis in chaotic diode resonator circuit

International Nuclear Information System (INIS)

Hanias, M.P.; Giannaris, G.; Spyridakis, A.; Rigas, A.

2006-01-01

A diode resonator chaotic circuit is presented. Multisim is used to simulate the circuit and show the presence of chaos. Time series analysis performed by the method proposed by Grasberger and Procaccia. The correlation and minimum embedding dimension ν and m min , respectively, were calculated. Also the corresponding Kolmogorov entropy was calculated

An evaluation of dynamic mutuality measurements and methods in cyclic time series

Science.gov (United States)

Xia, Xiaohua; Huang, Guitian; Duan, Na

2010-12-01

Several measurements and techniques have been developed to detect dynamic mutuality and synchronicity of time series in econometrics. This study aims to compare the performances of five methods, i.e., linear regression, dynamic correlation, Markov switching models, concordance index and recurrence quantification analysis, through numerical simulations. We evaluate the abilities of these methods to capture structure changing and cyclicity in time series and the findings of this paper would offer guidance to both academic and empirical researchers. Illustration examples are also provided to demonstrate the subtle differences of these techniques.

Event-sequence time series analysis in ground-based gamma-ray astronomy

International Nuclear Information System (INIS)

Barres de Almeida, U.; Chadwick, P.; Daniel, M.; Nolan, S.; McComb, L.

2008-01-01

The recent, extreme episodes of variability detected from Blazars by the leading atmospheric Cerenkov experiments motivate the development and application of specialized statistical techniques that enable the study of this rich data set to its furthest extent. The identification of the shortest variability timescales supported by the data and the actual variability structure observed in the light curves of these sources are some of the fundamental aspects being studied, that answers can bring new developments on the understanding of the physics of these objects and on the mechanisms of production of VHE gamma-rays in the Universe. Some of our efforts in studying the time variability of VHE sources involve the application of dynamic programming algorithms to the problem of detecting change-points in a Poisson sequence. In this particular paper we concentrate on the more primary issue of the applicability of counting statistics to the analysis of time-series on VHE gamma-ray astronomy.

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

International Nuclear Information System (INIS)

Tang You-Fu; Liu Shu-Lin; Jiang Rui-Hong; Liu Ying-Hui

2013-01-01

We study the correlation between detrended fluctuation analysis (DFA) and the Lempel-Ziv complexity (LZC) in nonlinear time series analysis in this paper. Typical dynamic systems including a logistic map and a Duffing model are investigated. Moreover, the influence of Gaussian random noise on both the DFA and LZC are analyzed. The results show a high correlation between the DFA and LZC, which can quantify the non-stationarity and the nonlinearity of the time series, respectively. With the enhancement of the random component, the exponent a and the normalized complexity index C show increasing trends. In addition, C is found to be more sensitive to the fluctuation in the nonlinear time series than α. Finally, the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve, and an effective fault diagnosis result is obtained

Hybrid analysis for indicating patients with breast cancer using temperature time series.

Science.gov (United States)

Silva, Lincoln F; Santos, Alair Augusto S M D; Bravo, Renato S; Silva, Aristófanes C; Muchaluat-Saade, Débora C; Conci, Aura

2016-07-01

Breast cancer is the most common cancer among women worldwide. Diagnosis and treatment in early stages increase cure chances. The temperature of cancerous tissue is generally higher than that of healthy surrounding tissues, making thermography an option to be considered in screening strategies of this cancer type. This paper proposes a hybrid methodology for analyzing dynamic infrared thermography in order to indicate patients with risk of breast cancer, using unsupervised and supervised machine learning techniques, which characterizes the methodology as hybrid. The dynamic infrared thermography monitors or quantitatively measures temperature changes on the examined surface, after a thermal stress. In the dynamic infrared thermography execution, a sequence of breast thermograms is generated. In the proposed methodology, this sequence is processed and analyzed by several techniques. First, the region of the breasts is segmented and the thermograms of the sequence are registered. Then, temperature time series are built and the k-means algorithm is applied on these series using various values of k. Clustering formed by k-means algorithm, for each k value, is evaluated using clustering validation indices, generating values treated as features in the classification model construction step. A data mining tool was used to solve the combined algorithm selection and hyperparameter optimization (CASH) problem in classification tasks. Besides the classification algorithm recommended by the data mining tool, classifiers based on Bayesian networks, neural networks, decision rules and decision tree were executed on the data set used for evaluation. Test results support that the proposed analysis methodology is able to indicate patients with breast cancer. Among 39 tested classification algorithms, K-Star and Bayes Net presented 100% classification accuracy. Furthermore, among the Bayes Net, multi-layer perceptron, decision table and random forest classification algorithms, an

Harmonic analysis of occupational-accident time-series as a part of the quantified risk evaluation in worksites: Application on electric power industry and construction sector

International Nuclear Information System (INIS)

Marhavilas, P.K.; Koulouriotis, D.E.; Spartalis, S.H.

2013-01-01

The development of an integrated risk analysis scheme, which will combine a well-considered selection of widespread techniques, would enable the companies to achieve efficient results on risk assessment. In this study, we develop a methodological framework (as a part of the quantified risk evaluation), by incorporating a new technique, that is implemented by the harmonic-analysis of time-series of occupational-accidents (called as HATS). Our objective is therefore, twofold: (i) the development of a new risk assessment framework (HATS technique) and the subsequent application of HATS on the worksites of electric power industry and construction sector, and (ii) the enrichment of the harmonic-analysis theoretical background, as far as the significance-level of spectral peaks is concerned, with fully-completed practical tables, that they have been produced by using the scientific literature. In fact, we apply HATS on occupational-accident time-series, which were (a) observed in the worksites of the Greek Public electric Power Corporation (PPC) and the Greek construction-companies (GCCs), and (b) recorded in great statistical-databases of PPC, and IKA (the Greek Social Insurance Institute/Ministry of Health), respectively. The results of HATS were tested statistically by using Shimshoni's significance-test. Moreover, the results of the comparative time/frequency-domain analysis of the accident time-series in PPC (for 1993–2009) and GCCs (for 1999–2007), prove that they are characterized by the existence of a periodic factor which (a) constitutes a permanent feature for the dynamic behavior of PPC's and GCCs' OHSS (occupational health and safety system), and (b) could be taken into account by risk managers in risk assessment, i.e., immediate suppressive measures must be taken place to abolish the danger source which is originated from the quasi-periodic appearance of the most important hazard sources

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

Science.gov (United States)

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

Directory of Open Access Journals (Sweden)

John P Marken

Full Text Available Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

Probabilistic risk assessment course documentation. Volume 5. System reliability and analysis techniques Session D - quantification

International Nuclear Information System (INIS)

Lofgren, E.V.

1985-08-01

This course in System Reliability and Analysis Techniques focuses on the probabilistic quantification of accident sequences and the link between accident sequences and consequences. Other sessions in this series focus on the quantification of system reliability and the development of event trees and fault trees. This course takes the viewpoint that event tree sequences or combinations of system failures and success are available and that Boolean equations for system fault trees have been developed and are available. 93 figs., 11 tabs

Sensitivity analysis of machine-learning models of hydrologic time series

Science.gov (United States)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Mapping air temperature using time series analysis of LST : The SINTESI approach

NARCIS (Netherlands)

Alfieri, S.M.; De Lorenzi, F.; Menenti, M.

2013-01-01

This paper presents a new procedure to map time series of air temperature (Ta) at fine spatial resolution using time series analysis of satellite-derived land surface temperature (LST) observations. The method assumes that air temperature is known at a single (reference) location such as in gridded

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature.

Science.gov (United States)

Bhandari, Siddhartha; Bergmann, Neil; Jurdak, Raja; Kusy, Branislav

2017-05-26

Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.

The physiology analysis system: an integrated approach for warehousing, management and analysis of time-series physiology data.

Science.gov (United States)

McKenna, Thomas M; Bawa, Gagandeep; Kumar, Kamal; Reifman, Jaques

2007-04-01

The physiology analysis system (PAS) was developed as a resource to support the efficient warehousing, management, and analysis of physiology data, particularly, continuous time-series data that may be extensive, of variable quality, and distributed across many files. The PAS incorporates time-series data collected by many types of data-acquisition devices, and it is designed to free users from data management burdens. This Web-based system allows both discrete (attribute) and time-series (ordered) data to be manipulated, visualized, and analyzed via a client's Web browser. All processes occur on a server, so that the client does not have to download data or any application programs, and the PAS is independent of the client's computer operating system. The PAS contains a library of functions, written in different computer languages that the client can add to and use to perform specific data operations. Functions from the library are sequentially inserted into a function chain-based logical structure to construct sophisticated data operators from simple function building blocks, affording ad hoc query and analysis of time-series data. These features support advanced mining of physiology data.

Mathematical methods in time series analysis and digital image processing

CERN Document Server

Kurths, J; Maass, P; Timmer, J

2008-01-01

The aim of this volume is to bring together research directions in theoretical signal and imaging processing developed rather independently in electrical engineering, theoretical physics, mathematics and the computer sciences. In particular, mathematically justified algorithms and methods, the mathematical analysis of these algorithms, and methods as well as the investigation of connections between methods from time series analysis and image processing are reviewed. An interdisciplinary comparison of these methods, drawing upon common sets of test problems from medicine and geophysical/enviromental sciences, is also addressed. This volume coherently summarizes work carried out in the field of theoretical signal and image processing. It focuses on non-linear and non-parametric models for time series as well as on adaptive methods in image processing.

Bulk analysis using nuclear techniques

International Nuclear Information System (INIS)

Borsaru, M.; Holmes, R.J.; Mathew, P.J.

1983-01-01

Bulk analysis techniques developed for the mining industry are reviewed. Using penetrating neutron and #betta#-radiations, measurements are obtained directly from a large volume of sample (3-30 kg) #betta#-techniques were used to determine the grade of iron ore and to detect shale on conveyor belts. Thermal neutron irradiation was developed for the simultaneous determination of iron and aluminium in iron ore on a conveyor belt. Thermal-neutron activation analysis includes the determination of alumina in bauxite, and manganese and alumina in manganese ore. Fast neutron activation analysis is used to determine silicon in iron ores, and alumina and silica in bauxite. Fast and thermal neutron activation has been used to determine the soil in shredded sugar cane. (U.K.)

Reliability analysis techniques in power plant design

International Nuclear Information System (INIS)

Chang, N.E.

1981-01-01

An overview of reliability analysis techniques is presented as applied to power plant design. The key terms, power plant performance, reliability, availability and maintainability are defined. Reliability modeling, methods of analysis and component reliability data are briefly reviewed. Application of reliability analysis techniques from a design engineering approach to improving power plant productivity is discussed. (author)

Fractal analysis and nonlinear forecasting of indoor 222Rn time series

International Nuclear Information System (INIS)

Pausch, G.; Bossew, P.; Hofmann, W.; Steger, F.

1998-01-01

Fractal analyses of indoor 222 Rn time series were performed using different chaos theory based measurements such as time delay method, Hurst's rescaled range analysis, capacity (fractal) dimension, and Lyapunov exponent. For all time series we calculated only positive Lyapunov exponents which is a hint to chaos, while the Hurst exponents were well below 0.5, indicating antipersistent behaviour (past trends tend to reverse in the future). These time series were also analyzed with a nonlinear prediction method which allowed an estimation of the embedding dimensions with some restrictions, limiting the prediction to about three relative time steps. (orig.)

GPS Position Time Series @ JPL

Science.gov (United States)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Nuclear analysis techniques and environmental sciences

International Nuclear Information System (INIS)

1997-10-01

31 theses are collected in this book. It introduced molecular activation analysis micro-PIXE and micro-probe analysis, x-ray fluorescence analysis and accelerator mass spectrometry. The applications about these nuclear analysis techniques are presented and reviewed for environmental sciences

A Comparison of Pseudo-Maximum Likelihood and Asymptotically Distribution-Free Dynamic Factor Analysis Parameter Estimation in Fitting Covariance-Structure Models to Block-Toeplitz Representing Single-Subject Multivariate Time-Series

NARCIS (Netherlands)

Molenaar, P.C.M.; Nesselroade, J.R.

1998-01-01

The study of intraindividual variability pervades empirical inquiry in virtually all subdisciplines of psychology. The statistical analysis of multivariate time-series data - a central product of intraindividual investigations - requires special modeling techniques. The dynamic factor model (DFM),

Statistical evaluation of vibration analysis techniques

Science.gov (United States)

Milner, G. Martin; Miller, Patrice S.

1987-01-01

An evaluation methodology is presented for a selection of candidate vibration analysis techniques applicable to machinery representative of the environmental control and life support system of advanced spacecraft; illustrative results are given. Attention is given to the statistical analysis of small sample experiments, the quantification of detection performance for diverse techniques through the computation of probability of detection versus probability of false alarm, and the quantification of diagnostic performance.

Assessing error sources for Landsat time series analysis for tropical test sites in Viet Nam and Ethiopia

Science.gov (United States)

Schultz, Michael; Verbesselt, Jan; Herold, Martin; Avitabile, Valerio

2013-10-01

Researchers who use remotely sensed data can spend half of their total effort analysing prior data. If this data preprocessing does not match the application, this time spent on data analysis can increase considerably and can lead to inaccuracies. Despite the existence of a number of methods for pre-processing Landsat time series, each method has shortcomings, particularly for mapping forest changes under varying illumination, data availability and atmospheric conditions. Based on the requirements of mapping forest changes as defined by the United Nations (UN) Reducing Emissions from Forest Degradation and Deforestation (REDD) program, the accurate reporting of the spatio-temporal properties of these changes is necessary. We compared the impact of three fundamentally different radiometric preprocessing techniques Moderate Resolution Atmospheric TRANsmission (MODTRAN), Second Simulation of a Satellite Signal in the Solar Spectrum (6S) and simple Dark Object Subtraction (DOS) on mapping forest changes using Landsat time series data. A modification of Breaks For Additive Season and Trend (BFAST) monitor was used to jointly map the spatial and temporal agreement of forest changes at test sites in Ethiopia and Viet Nam. The suitability of the pre-processing methods for the occurring forest change drivers was assessed using recently captured Ground Truth and high resolution data (1000 points). A method for creating robust generic forest maps used for the sampling design is presented. An assessment of error sources has been performed identifying haze as a major source for time series analysis commission error.

Techniques for sensitivity analysis of SYVAC results

International Nuclear Information System (INIS)

Prust, J.O.

1985-05-01

Sensitivity analysis techniques may be required to examine the sensitivity of SYVAC model predictions to the input parameter values, the subjective probability distributions assigned to the input parameters and to the relationship between dose and the probability of fatal cancers plus serious hereditary disease in the first two generations of offspring of a member of the critical group. This report mainly considers techniques for determining the sensitivity of dose and risk to the variable input parameters. The performance of a sensitivity analysis technique may be improved by decomposing the model and data into subsets for analysis, making use of existing information on sensitivity and concentrating sampling in regions the parameter space that generates high doses or risks. A number of sensitivity analysis techniques are reviewed for their application to the SYVAC model including four techniques tested in an earlier study by CAP Scientific for the SYVAC project. This report recommends the development now of a method for evaluating the derivative of dose and parameter value and extending the Kruskal-Wallis technique to test for interactions between parameters. It is also recommended that the sensitivity of the output of each sub-model of SYVAC to input parameter values should be examined. (author)

Techniques involving extreme environment, nondestructive techniques, computer methods in metals research, and data analysis

International Nuclear Information System (INIS)

Bunshah, R.F.

1976-01-01

A number of different techniques which range over several different aspects of materials research are covered in this volume. They are concerned with property evaluation of 4 0 K and below, surface characterization, coating techniques, techniques for the fabrication of composite materials, computer methods, data evaluation and analysis, statistical design of experiments and non-destructive test techniques. Topics covered in this part include internal friction measurements; nondestructive testing techniques; statistical design of experiments and regression analysis in metallurgical research; and measurement of surfaces of engineering materials

Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum

Directory of Open Access Journals (Sweden)

L.F.P. Franca

2003-01-01

Full Text Available This contribution presents an investigation on noise sensitivity of some of the most disseminated techniques employed to estimate Lyapunov exponents from time series. Since noise contamination is unavoidable in cases of data acquisition, it is important to recognize techniques that could be employed for a correct identification of chaos. State space reconstruction and the determination of Lyapunov exponents are carried out to investigate the response of a nonlinear pendulum. Signals are generated by numerical integration of the mathematical model, selecting a single variable of the system as a time series. In order to simulate experimental data sets, a random noise is introduced in the signal. Basically, the analyses of periodic and chaotic motions are carried out. Results obtained from mathematical model are compared with the one obtained from time series analysis, evaluating noise sensitivity. This procedure allows the identification of the best techniques to be employed in the analysis of experimental data.

ECO-TECHNIQUE OF SEWER RENOVATION USING COMPOSITE SHELLS: STRUCTURAL ANALYSIS

Directory of Open Access Journals (Sweden)

B. Attaf

2015-07-01

Full Text Available An eco-technical renovation of the sewage system is developed in this paper; this technique involves incorporating into the existing sewer a series of jointed prefabricated sandwich or composite shells. The purpose of his study is to determine the structural shell deflection, the high displacement areas and to validate the non-failure criterion for each ply constituting the inner and outer laminate facings. The numerical results were obtained at low cost by using the finite element method. Studies have focused on structural analysis of a typical shell unit with an ovoid form (egg-shaped section when it is subjected, during annular space filling operation, to pressure forces generated by wet concrete. To ensure the safety of the composite shell structure, Tsai-Hill criterion function is applied and results are presented for the most stressed plies

Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

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Heiko Balzter

2015-03-01

Full Text Available Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v is provided. The results show that the temporal scales of the current climate (1960–2014 are different from the long-term average (1850–1960. For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the

Time series analysis of gold production in Malaysia

Science.gov (United States)

Muda, Nora; Hoon, Lee Yuen

2012-05-01

Gold is a soft, malleable, bright yellow metallic element and unaffected by air or most reagents. It is highly valued as an asset or investment commodity and is extensively used in jewellery, industrial application, dentistry and medical applications. In Malaysia, gold mining is limited in several areas such as Pahang, Kelantan, Terengganu, Johor and Sarawak. The main purpose of this case study is to obtain a suitable model for the production of gold in Malaysia. The model can also be used to predict the data of Malaysia's gold production in the future. Box-Jenkins time series method was used to perform time series analysis with the following steps: identification, estimation, diagnostic checking and forecasting. In addition, the accuracy of prediction is tested using mean absolute percentage error (MAPE). From the analysis, the ARIMA (3,1,1) model was found to be the best fitted model with MAPE equals to 3.704%, indicating the prediction is very accurate. Hence, this model can be used for forecasting. This study is expected to help the private and public sectors to understand the gold production scenario and later plan the gold mining activities in Malaysia.

Applications of Electromigration Techniques: Applications of Electromigration Techniques in Food Analysis

Science.gov (United States)

Wieczorek, Piotr; Ligor, Magdalena; Buszewski, Bogusław

Electromigration techniques, including capillary electrophoresis (CE), are widely used for separation and identification of compounds present in food products. These techniques may also be considered as alternate and complementary with respect to commonly used analytical techniques, such as high-performance liquid chromatography (HPLC), or gas chromatography (GC). Applications of CE concern the determination of high-molecular compounds, like polyphenols, including flavonoids, pigments, vitamins, food additives (preservatives, antioxidants, sweeteners, artificial pigments) are presented. Also, the method developed for the determination of proteins and peptides composed of amino acids, which are basic components of food products, are studied. Other substances such as carbohydrates, nucleic acids, biogenic amines, natural toxins, and other contaminations including pesticides and antibiotics are discussed. The possibility of CE application in food control laboratories, where analysis of the composition of food and food products are conducted, is of great importance. CE technique may be used during the control of technological processes in the food industry and for the identification of numerous compounds present in food. Due to the numerous advantages of the CE technique it is successfully used in routine food analysis.

Flow analysis techniques for phosphorus: an overview.

Science.gov (United States)

Estela, José Manuel; Cerdà, Víctor

2005-04-15

A bibliographical review on the implementation and the results obtained in the use of different flow analytical techniques for the determination of phosphorus is carried out. The sources, occurrence and importance of phosphorus together with several aspects regarding the analysis and terminology used in the determination of this element are briefly described. A classification as well as a brief description of the basis, advantages and disadvantages of the different existing flow techniques, namely; segmented flow analysis (SFA), flow injection analysis (FIA), sequential injection analysis (SIA), all injection analysis (AIA), batch injection analysis (BIA), multicommutated FIA (MCFIA), multisyringe FIA (MSFIA) and multipumped FIA (MPFIA) is also carried out. The most relevant manuscripts regarding the analysis of phosphorus by means of flow techniques are herein classified according to the detection instrumental technique used with the aim to facilitate their study and obtain an overall scope. Finally, the analytical characteristics of numerous flow-methods reported in the literature are provided in the form of a table and their applicability to samples with different matrixes, namely water samples (marine, river, estuarine, waste, industrial, drinking, etc.), soils leachates, plant leaves, toothpaste, detergents, foodstuffs (wine, orange juice, milk), biological samples, sugars, fertilizer, hydroponic solutions, soils extracts and cyanobacterial biofilms are tabulated.

Bridge flap technique as a single-step solution to mucogingival problems: A case series

Directory of Open Access Journals (Sweden)

Vivek Gupta

2011-01-01

Full Text Available Shallow vestibule, gingival recession, inadequate width of attached gingiva (AG and aberrant frenum pull are an array of mucogingival problems for which several independent and effective surgical solutions are reported in the literature. This case series reports the effectiveness of the bridge flap technique as a single-step surgical entity for increasing the depth of the vestibule, root coverage, increasing the width of the AG and solving the problem of abnormal frenum pull. Eight patients with 18 teeth altogether having Millers class I, II or III recession along with problems of shallow vestibule, inadequate width of AG and with or without frenum pull underwent this surgical procedure and were followed-up till 9 months post-operatively. The mean root coverage obtained was 55% and the mean average gain in width of the AG was 3.5 mm. The mean percentage gain in clinical attachment level was 41%. The bridge flap technique can be an effective single-step solution for the aforementioned mucogingival problems if present simultaneously in any case, and offers considerable advantages over other mucogingival surgical techniques in terms of simplicity, limited chair-time for the patient and the operator, single surgical intervention for manifold mucogingival problems and low morbidity because of the absence of palatal donor tissue.

Various Techniques to Increase Keratinized Tissue for Implant Supported Overdentures: Retrospective Case Series

Directory of Open Access Journals (Sweden)

Ahmed Elkhaweldi

2015-01-01

Full Text Available Purpose. The purpose of this retrospective case series is to describe and compare different surgical techniques that can be utilized to augment the keratinized soft tissue around implant-supported overdentures. Materials and Methods. The data set was extracted as deidentified information from the routine treatment of patients at the Ashman Department of Periodontology and Implant Dentistry at New York University College of Dentistry. Eight edentulous patients were selected to be included in this study. Patients were treated for lack of keratinized tissue prior to implant placement, during the second stage surgery, and after delivery of the final prosthesis. Results. All 8 patients in this study were wearing a complete maxillary and/or mandibular denture for at least a year before the time of the surgery. One of the following surgical techniques was utilized to increase the amount of keratinized tissue: apically positioned flap (APF, pedicle graft (PG, connective tissue graft (CTG, or free gingival graft (FGG. Conclusions. The amount of keratinized tissue should be taken into consideration when planning for implant-supported overdentures. The apical repositioning flap is an effective approach to increase the width of keratinized tissue prior to the implant placement.

An evaluation of directional analysis techniques for multidirectional, partially reflected waves .1. numerical investigations

DEFF Research Database (Denmark)

Ilic, C; Chadwick, A; Helm-Petersen, Jacob

2000-01-01

, non-phased locked methods are more appropriate. In this paper, the accuracy of two non-phased locked methods of directional analysis, the maximum likelihood method (MLM) and the Bayesian directional method (BDM) have been quantitatively evaluated using numerical simulations for the case...... of multidirectional waves with partial reflections. It is shown that the results are influenced by the ratio of distance from the reflector (L) to the length of the time series (S) used in the spectral analysis. Both methods are found to be capable of determining the incident and reflective wave fields when US > 0......Recent studies of advanced directional analysis techniques have mainly centred on incident wave fields. In the study of coastal structures, however, partially reflective wave fields are commonly present. In the near structure field, phase locked methods can be successfully applied. In the far field...

Predicting chaotic time series

International Nuclear Information System (INIS)

Farmer, J.D.; Sidorowich, J.J.

1987-01-01

We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

Fourier techniques in X-ray timing

NARCIS (Netherlands)

van der Klis, M.

1988-01-01

Basic principles of Fourier techniques often used in X-ray time series analysis are reviewed. The relation between the discrete Fourier transform and the continuous Fourier transform is discussed to introduce the concepts of windowing and aliasing. The relation is derived between the power spectrum

Absolute high-resolution Se+ photoionization cross-section measurements with Rydberg-series analysis

International Nuclear Information System (INIS)

Esteves, D. A.; Bilodeau, R. C.; Sterling, N. C.; Phaneuf, R. A.; Kilcoyne, A. L. D.; Red, E. C.; Aguilar, A.

2011-01-01

Absolute single photoionization cross-section measurements for Se + ions were performed at the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory using the photo-ion merged-beams technique. Measurements were made at a photon energy resolution of 5.5 meV from 17.75 to 21.85 eV spanning the 4s 2 4p 3 4 S 3/2 o ground-state ionization threshold and the 2 P 3/2 o , 2 P 1/2 o , 2 D 5/2 o , and 2 D 3/2 o metastable state thresholds. Extensive analysis of the complex resonant structure in this region identified numerous Rydberg series of resonances and obtained the Se 2+ 4s 2 4p 23 P 2 and 4s 2 4p 21 S 0 state energies. In addition, particular attention was given to removing significant effects in the measurements due to a small percentage of higher-order undulator radiation.

Analysis and implementation of LLC-T series parallel resonant ...

African Journals Online (AJOL)

A prototype 300 W, 100 kHz converter is designed and built to experimentally demonstrate, dynamic and steady state performance for the LLC-T series parallel resonant converter. A comparative study is performed between experimental results and the simulation studies. The analysis shows that the output of converter is ...

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

International Nuclear Information System (INIS)

Munoz-Diosdado, A

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

Energy Technology Data Exchange (ETDEWEB)

Munoz-Diosdado, A [Department of Mathematics, Unidad Profesional Interdisciplinaria de Biotecnologia, Instituto Politecnico Nacional, Av. Acueducto s/n, 07340, Mexico City (Mexico)

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems.

TV content analysis techniques and applications

CERN Document Server

Kompatsiaris, Yiannis

2012-01-01

The rapid advancement of digital multimedia technologies has not only revolutionized the production and distribution of audiovisual content, but also created the need to efficiently analyze TV programs to enable applications for content managers and consumers. Leaving no stone unturned, TV Content Analysis: Techniques and Applications provides a detailed exploration of TV program analysis techniques. Leading researchers and academics from around the world supply scientifically sound treatment of recent developments across the related subject areas--including systems, architectures, algorithms,

Nonlinear analysis of magnetospheric data Part I. Geometric characteristics of the AE index time series and comparison with nonlinear surrogate data

Directory of Open Access Journals (Sweden)

G. P. Pavlos

1999-01-01

Full Text Available A long AE index time series is used as a crucial magnetospheric quantity in order to study the underlying dynainics. For this purpose we utilize methods of nonlinear and chaotic analysis of time series. Two basic components of this analysis are the reconstruction of the experimental tiine series state space trajectory of the underlying process and the statistical testing of an null hypothesis. The null hypothesis against which the experimental time series are tested is that the observed AE index signal is generated by a linear stochastic signal possibly perturbed by a static nonlinear distortion. As dis ' ' ating statistics we use geometrical characteristics of the reconstructed state space (Part I, which is the work of this paper and dynamical characteristics (Part II, which is the work a separate paper, and "nonlinear" surrogate data, generated by two different techniques which can mimic the original (AE index signal. lie null hypothesis is tested for geometrical characteristics which are the dimension of the reconstructed trajectory and some new geometrical parameters introduced in this work for the efficient discrimination between the nonlinear stochastic surrogate data and the AE index. Finally, the estimated geometric characteristics of the magnetospheric AE index present new evidence about the nonlinear and low dimensional character of the underlying magnetospheric dynamics for the AE index.

Detection of Moving Targets Based on Doppler Spectrum Analysis Technique for Passive Coherent Radar

Directory of Open Access Journals (Sweden)

Zhao Yao-dong

2013-06-01

Full Text Available A novel method of moving targets detection taking Doppler spectrum analysis technique for Passive Coherent Radar (PCR is provided. After dividing the receiving signals into segments as pulse series, it utilizes the technique of pulse compress and Doppler processing to detect and locate the targets. Based on the algorithm for Pulse-Doppler (PD radar, the equipollence between continuous and pulsed wave in match filtering is proved and details of this method are introduced. To compare it with the traditional method of Cross-Ambiguity Function (CAF calculation, the relationship and mathematical modes of them are analyzed, with some suggestions on parameters choosing. With little influence to the gain of targets, the method can greatly promote the processing efficiency. The validity of the proposed method is demonstrated by offline processing real collected data sets and simulation results.

PHOTOGRAMMETRIC TECHNIQUES FOR ROAD SURFACE ANALYSIS

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V. A. Knyaz

2016-06-01

Full Text Available The quality and condition of a road surface is of great importance for convenience and safety of driving. So the investigations of the behaviour of road materials in laboratory conditions and monitoring of existing roads are widely fulfilled for controlling a geometric parameters and detecting defects in the road surface. Photogrammetry as accurate non-contact measuring method provides powerful means for solving different tasks in road surface reconstruction and analysis. The range of dimensions concerned in road surface analysis can have great variation from tenths of millimetre to hundreds meters and more. So a set of techniques is needed to meet all requirements of road parameters estimation. Two photogrammetric techniques for road surface analysis are presented: for accurate measuring of road pavement and for road surface reconstruction based on imagery obtained from unmanned aerial vehicle. The first technique uses photogrammetric system based on structured light for fast and accurate surface 3D reconstruction and it allows analysing the characteristics of road texture and monitoring the pavement behaviour. The second technique provides dense 3D model road suitable for road macro parameters estimation.

Improvements of the two-dimensional FDTD method for the simulation of normal- and superconducting planar waveguides using time series analysis

International Nuclear Information System (INIS)

Hofschen, S.; Wolff, I.

1996-01-01

Time-domain simulation results of two-dimensional (2-D) planar waveguide finite-difference time-domain (FDTD) analysis are normally analyzed using Fourier transform. The introduced method of time series analysis to extract propagation and attenuation constants reduces the desired computation time drastically. Additionally, a nonequidistant discretization together with an adequate excitation technique is used to reduce the number of spatial grid points. Therefore, it is possible to reduce the number of spatial grid points. Therefore, it is possible to simulate normal- and superconducting planar waveguide structures with very thin conductors and small dimensions, as they are used in MMIC technology. The simulation results are compared with measurements and show good agreement

Improvements of the two-dimensional FDTD method for the simulation of normal- and superconducting planar waveguides using time series analysis

Energy Technology Data Exchange (ETDEWEB)

Hofschen, S.; Wolff, I. [Gerhard Mercator Univ. of Duisburg (Germany). Dept. of Electrical Engineering

1996-08-01

Time-domain simulation results of two-dimensional (2-D) planar waveguide finite-difference time-domain (FDTD) analysis are normally analyzed using Fourier transform. The introduced method of time series analysis to extract propagation and attenuation constants reduces the desired computation time drastically. Additionally, a nonequidistant discretization together with an adequate excitation technique is used to reduce the number of spatial grid points. Therefore, it is possible to reduce the number of spatial grid points. Therefore, it is possible to simulate normal- and superconducting planar waveguide structures with very thin conductors and small dimensions, as they are used in MMIC technology. The simulation results are compared with measurements and show good agreement.

Land Subsidence Monitoring by InSAR Time Series Technique Derived From ALOS-2 PALSAR-2 over Surabaya City, Indonesia

Science.gov (United States)

Aditiya, A.; Takeuchi, W.; Aoki, Y.

2017-12-01

Surabaya is the second largest city in Indonesia and the capital of East Java Province with rapid population and industrialization. The impact of urbanization in the big city can suffer potential disasters either nature or anthropogenic such as land subsidence and flood. The pattern of land subsidence need to be mapped for the purposes of planning and structuring the city as well as taking appropriate policy in anticipating and mitigating the impact. This research has used interferometric Synthetic Aperture Radar (InSAR) Small Baseline Subset (SBAS) technique and applied time series analysis to investigate land subsidence occured. The technique includes the process of focusing the SAR data, incorporating the precise orbit, generating interferogram and phase unwrapping using SNAPHU algorithms. The results showed land subsidence has been detected during 2014-2017 over Surabaya city area using ALOS-2/PALSAR-2 images data. These results reveal the subsidence has observed in several area in Surabaya in particular northern part reach up to ∼2 cm/year. The fastest subsidence occurs in highly populated areas suffer vulnerable to flooding and sea level rise impact. In urban areas we found a correlation between land subsidence with residential or industrial land use. It concludes that land subsidence is mainly caused by ground water consumption for industrial and residential use respectively.

Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

Science.gov (United States)

Kalantari, Mahdi; Yarmohammadi, Masoud; Hassani, Hossein; Silva, Emmanuel Sirimal

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the L1 norm-based version of Singular Spectrum Analysis (SSA), namely L1-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially L1-SSA can provide better imputation in comparison to other methods.

Constrained principal component analysis and related techniques

CERN Document Server

Takane, Yoshio

2013-01-01

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concre

Analysis of historical series of industrial demand of energy; Analisi delle serie storiche dei consumi energetici dell`industria

Energy Technology Data Exchange (ETDEWEB)

Moauro, F. [ENEA, Centro Ricerche Casaccia, Rome (Italy). Dip. Energia

1995-03-01

This paper reports a short term analysis of the Italian demand for energy fonts and a check of a statistic model supposing the industrial demand for energy fonts as a function of prices and production, according to neoclassic neoclassic micro economic theory. To this pourpose monthly time series of industrial consumption of main energy fonts in 6 sectors, industrial production indexes in the same sectors and indexes of energy prices (coal, natural gas, oil products, electricity) have been used. The statistic methodology refers to modern analysis of time series and specifically to transfer function models. These ones permit rigorous identification and representation of the most important dynamic relations between dependent variables (production and prices), as relation of an input-output system. The results have shown an important positive correlation between energy consumption with prices. Furthermore, it has been shown the reliability of forecasts and their use as monthly energy indicators.

Time Series Analysis of Wheat flour Price Shocks in Pakistan: A Case Analysis

OpenAIRE

Asad Raza Abdi; Ali Hassan Halepoto; Aisha Bashir Shah; Faiz M. Shaikh

2013-01-01

The current research investigates the wheat flour Price Shocks in Pakistan: A case analysis. Data was collected by using secondary sources by using Time series Analysis, and data were analyzed by using SPSS-20 version. It was revealed that the price of wheat flour increases from last four decades, and trend of price shocks shows that due to certain market variation and supply and demand shocks also play a positive relationship in price shocks in the wheat prices. It was further revealed th...

Time series clustering analysis of health-promoting behavior

Science.gov (United States)

Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng

2013-10-01

Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.

Fractal time series analysis of postural stability in elderly and control subjects

Directory of Open Access Journals (Sweden)

Doussot Michel

2007-05-01

Full Text Available Abstract Background The study of balance using stabilogram analysis is of particular interest in the study of falls. Although simple statistical parameters derived from the stabilogram have been shown to predict risk of falls, such measures offer little insight into the underlying control mechanisms responsible for degradation in balance. In contrast, fractal and non-linear time-series analysis of stabilograms, such as estimations of the Hurst exponent (H, may provide information related to the underlying motor control strategies governing postural stability. In order to be adapted for a home-based follow-up of balance, such methods need to be robust, regardless of the experimental protocol, while producing time-series that are as short as possible. The present study compares two methods of calculating H: Detrended Fluctuation Analysis (DFA and Stabilogram Diffusion Analysis (SDA for elderly and control subjects, as well as evaluating the effect of recording duration. Methods Centre of pressure signals were obtained from 90 young adult subjects and 10 elderly subjects. Data were sampled at 100 Hz for 30 s, including stepping onto and off the force plate. Estimations of H were made using sliding windows of 10, 5, and 2.5 s durations, with windows slid forward in 1-s increments. Multivariate analysis of variance was used to test for the effect of time, age and estimation method on the Hurst exponent, while the intra-class correlation coefficient (ICC was used as a measure of reliability. Results Both SDA and DFA methods were able to identify differences in postural stability between control and elderly subjects for time series as short as 5 s, with ICC values as high as 0.75 for DFA. Conclusion Both methods would be well-suited to non-invasive longitudinal assessment of balance. In addition, reliable estimations of H were obtained from time series as short as 5 s.

Bayesian near-boundary analysis in basic macroeconomic time series models

NARCIS (Netherlands)

M.D. de Pooter (Michiel); F. Ravazzolo (Francesco); R. Segers (René); H.K. van Dijk (Herman)

2008-01-01

textabstractSeveral lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor; Valenzuela, Olga

2017-01-01

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting.  It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of comput...

Arab drama series content analysis from a transnational Arab identity perspective

Directory of Open Access Journals (Sweden)

Joelle Chamieh

2016-04-01

Full Text Available The scientific contribution in deciphering drama series falls under the discipline of understanding the narratology of distinctive cultures and traditions within specific contexts of certain societies. This article spells out the interferences deployed by the provocations that are induced through the functions of values in modeling societies which are projected through the transmission of media. The proposed operational model consists of providing an à priori design of common Arab values assimilated into an innovative grid analysis code book that has enabled the execution of a systematic and reliable approach to the quantitative content analysis performance. Additionally, a more thorough qualitative content analysis has been implemented in terms of narratolgy where actions have been evaluated based on the grid analysis code book for a clearer perception of Arab values depicted in terms of their context within the Arab drama milieu. This approach has been deployed on four Arab drama series covering the transnational/national and non-divisive/divisive media aspects in the intention of extracting the transmitted values from a common identity perspective for cause of divulging Arab people’s expectancies.

Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

Science.gov (United States)

Wang, Na; Li, Dong; Wang, Qiwen

2012-12-01

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government

Flood Frequency Analysis For Partial Duration Series In Ganjiang River Basin

Science.gov (United States)

zhangli, Sun; xiufang, Zhu; yaozhong, Pan

2016-04-01

Accurate estimation of flood frequency is key to effective, nationwide flood damage abatement programs. The partial duration series (PDS) method is widely used in hydrologic studies because it considers all events above a certain threshold level as compared to the annual maximum series (AMS) method, which considers only the annual maximum value. However, the PDS has a drawback in that it is difficult to define the thresholds and maintain an independent and identical distribution of the partial duration time series; this drawback is discussed in this paper. The Ganjiang River is the seventh largest tributary of the Yangtze River, the longest river in China. The Ganjiang River covers a drainage area of 81,258 km2 at the Wanzhou hydrologic station as the basin outlet. In this work, 56 years of daily flow data (1954-2009) from the Wanzhou station were used to analyze flood frequency, and the Pearson-III model was employed as the hydrologic probability distribution. Generally, three tasks were accomplished: (1) the threshold of PDS by percentile rank of daily runoff was obtained; (2) trend analysis of the flow series was conducted using PDS; and (3) flood frequency analysis was conducted for partial duration flow series. The results showed a slight upward trend of the annual runoff in the Ganjiang River basin. The maximum flow with a 0.01 exceedance probability (corresponding to a 100-year flood peak under stationary conditions) was 20,000 m3/s, while that with a 0.1 exceedance probability was 15,000 m3/s. These results will serve as a guide to hydrological engineering planning, design, and management for policymakers and decision makers associated with hydrology.

SPI Trend Analysis of New Zealand Applying the ITA Technique

Directory of Open Access Journals (Sweden)

Tommaso Caloiero

2018-03-01

Full Text Available A natural temporary imbalance of water availability, consisting of persistent lower-than-average or higher-than-average precipitation, can cause extreme dry and wet conditions that adversely impact agricultural yields, water resources, infrastructure, and human systems. In this study, dry and wet periods in New Zealand were expressed using the Standardized Precipitation Index (SPI. First, both the short term (3 and 6 months and the long term (12 and 24 months SPI were estimated, and then, possible trends in the SPI values were detected by means of a new graphical technique, the Innovative Trend Analysis (ITA, which allows the trend identification of the low, medium, and high values of a series. Results show that, in every area currently subject to drought, an increase in this phenomenon can be expected. Specifically, the results of this paper highlight that agricultural regions on the eastern side of the South Island, as well as the north-eastern regions of the North Island, are the most consistently vulnerable areas. In fact, in these regions, the trend analysis mainly showed a general reduction in all the values of the SPI: that is, a tendency toward heavier droughts and weaker wet periods.

Cluster analysis of activity-time series in motor learning

DEFF Research Database (Denmark)

Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

2002-01-01

Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

Time Series in Education: The Analysis of Daily Attendance in Two High Schools

Science.gov (United States)

Koopmans, Matthijs

2011-01-01

This presentation discusses the use of a time series approach to the analysis of daily attendance in two urban high schools over the course of one school year (2009-10). After establishing that the series for both schools were stationary, they were examined for moving average processes, autoregression, seasonal dependencies (weekly cycles),…

The investigation of trapped thickness shear modes in a contoured AT-cut quartz plate using the power series expansion technique

Science.gov (United States)

Li, Peng; Jin, Feng

2018-01-01

The dynamic model about the anti-plane vibration of a contoured quartz plate with thickness changing continuously is established by ignoring the effect of small elastic constant c 56. The governing equation is solved using the power series expansion technique, and the trapped thickness shear modes caused by bulge thickness are revealed. Theoretically, the proposed method is more general, which can be capable of handling various thickness profiles defined mathematically. After the convergence of the series is demonstrated and the correctness is numerically validated with the aid of finite element method results, systematic parametric studies are subsequently carried out to quantify the effects of the geometry parameter upon the trapped modes, including resonant frequency and mode shape. After that, the band structures of thickness shear waves propagation in a periodically contoured quartz plate, as well as the power transmission spectra, are obtained based on the power series expansion technique. It is revealed that broad stop bands below cut-off frequency exist owing to the trapped modes excited by the geometry inhomogeneity, which has little relationship with the structural periodicity, and its physical mechanism is different from the Bragg scattering effect. The outcome is widely applicable, and can be utilized to provide theoretical and practical guidance for the design and manufacturing of quartz resonators and wave filters.

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Science.gov (United States)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Automated classification of Permanent Scatterers time-series based on statistical characterization tests

Science.gov (United States)

Berti, Matteo; Corsini, Alessandro; Franceschini, Silvia; Iannacone, Jean Pascal

2013-04-01

The application of space borne synthetic aperture radar interferometry has progressed, over the last two decades, from the pioneer use of single interferograms for analyzing changes on the earth's surface to the development of advanced multi-interferogram techniques to analyze any sort of natural phenomena which involves movements of the ground. The success of multi-interferograms techniques in the analysis of natural hazards such as landslides and subsidence is widely documented in the scientific literature and demonstrated by the consensus among the end-users. Despite the great potential of this technique, radar interpretation of slope movements is generally based on the sole analysis of average displacement velocities, while the information embraced in multi interferogram time series is often overlooked if not completely neglected. The underuse of PS time series is probably due to the detrimental effect of residual atmospheric errors, which make the PS time series characterized by erratic, irregular fluctuations often difficult to interpret, and also to the difficulty of performing a visual, supervised analysis of the time series for a large dataset. In this work is we present a procedure for automatic classification of PS time series based on a series of statistical characterization tests. The procedure allows to classify the time series into six distinctive target trends (0=uncorrelated; 1=linear; 2=quadratic; 3=bilinear; 4=discontinuous without constant velocity; 5=discontinuous with change in velocity) and retrieve for each trend a series of descriptive parameters which can be efficiently used to characterize the temporal changes of ground motion. The classification algorithms were developed and tested using an ENVISAT datasets available in the frame of EPRS-E project (Extraordinary Plan of Environmental Remote Sensing) of the Italian Ministry of Environment (track "Modena", Northern Apennines). This dataset was generated using standard processing, then the

A comparison of various forecasting techniques applied to mean hourly wind speed time series

Energy Technology Data Exchange (ETDEWEB)

Sfetsos, A. [7 Pirsou Street, Athens (Greece)

2000-09-01

This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Neural Logic Networks. The developed models are evaluated for their ability to produce accurate and fast forecasts. (Author)

Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

Time series analysis of monthly pulpwood use in the Northeast

Science.gov (United States)

James T. Bones

1980-01-01

Time series analysis was used to develop a model that depicts pulpwood use in the Northeast. The model is useful in forecasting future pulpwood requirements (short term) or monitoring pulpwood-use activity in relation to past use patterns. The model predicted a downturn in use during 1980.

Time Series Analysis Based on Running Mann Whitney Z Statistics

Science.gov (United States)

A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...

Time Series Analysis of 3D Coordinates Using Nonstochastic Observations

NARCIS (Netherlands)

Velsink, H.

2016-01-01

Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to formulate constraints on

Time Series Analysis of 3D Coordinates Using Nonstochastic Observations

NARCIS (Netherlands)

Hiddo Velsink

2016-01-01

From the article: Abstract Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

Science.gov (United States)

Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

2016-04-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

Optimizing Availability of a Framework in Series Configuration Utilizing Markov Model and Monte Carlo Simulation Techniques

Directory of Open Access Journals (Sweden)

Mansoor Ahmed Siddiqui

2017-06-01

Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.

Statistical analysis of yearly series of maximum daily rainfall in Spain. Analisis estadistico de las series anuales de maximas lluvias diarias en Espaa

Energy Technology Data Exchange (ETDEWEB)

Ferrer Polo, J.; Ardiles Lopez, K. L. (CEDEX, Ministerio de Obras Publicas, Transportes y Medio ambiente, Madrid (Spain))

1994-01-01

Work on the statistical modelling of maximum daily rainfalls is presented, with a view to estimating the quantiles for different return periods. An index flood approach has been adopted in which the local quantiles are a result of rescaling a regional law using the mean of each series of values, that is utilized as a local scale factor. The annual maximum series have been taken from 1.545 meteorological stations over a 30 year period, and these have been classified into 26 regions defined according to meteorological criteria, the homogeneity of wich has been checked by means of a statistical analysis of the coefficients of variation of the samples,using the. An estimation has been made of the parameters for the following four distribution models: Two Component Extreme Value (TCEV); General Extreme Value (GEV); Log-Pearson III (LP3); and SQRT-Exponential Type Distribution of Maximum. The analysis of the quantiles obtained reveals slight differences in the results thus detracting from the importance of the model selection. The last of the above-mentioned distribution has been finally chosen, on the basis of the following: it is defined with fewer parameters it is the only that was proposed specifically for the analysis of daily rainfall maximums; it yields more conservative results than the traditional Gumbel distribution for the high return periods; and it is capable of providing a good description of the main sampling statistics concerning the right-hand tail of the distribution, a fact that has been checked with Montecarlo's simulation techniques. The choice of a distribution model with only two parameters has led to the selection of the regional coefficient of variation as the only determining parameter for the regional quantiles. This has permitted the elimination of the quantiles discontinuity of the classical regional approach, thus smoothing the values of that coefficient by means of an isoline plan on a national scale.

Trend analysis and change point detection of annual and seasonal temperature series in Peninsular Malaysia

Science.gov (United States)

Suhaila, Jamaludin; Yusop, Zulkifli

2017-06-01

Most of the trend analysis that has been conducted has not considered the existence of a change point in the time series analysis. If these occurred, then the trend analysis will not be able to detect an obvious increasing or decreasing trend over certain parts of the time series. Furthermore, the lack of discussion on the possible factors that influenced either the decreasing or the increasing trend in the series needs to be addressed in any trend analysis. Hence, this study proposes to investigate the trends, and change point detection of mean, maximum and minimum temperature series, both annually and seasonally in Peninsular Malaysia and determine the possible factors that could contribute to the significance trends. In this study, Pettitt and sequential Mann-Kendall (SQ-MK) tests were used to examine the occurrence of any abrupt climate changes in the independent series. The analyses of the abrupt changes in temperature series suggested that most of the change points in Peninsular Malaysia were detected during the years 1996, 1997 and 1998. These detection points captured by Pettitt and SQ-MK tests are possibly related to climatic factors, such as El Niño and La Niña events. The findings also showed that the majority of the significant change points that exist in the series are related to the significant trend of the stations. Significant increasing trends of annual and seasonal mean, maximum and minimum temperatures in Peninsular Malaysia were found with a range of 2-5 °C/100 years during the last 32 years. It was observed that the magnitudes of the increasing trend in minimum temperatures were larger than the maximum temperatures for most of the studied stations, particularly at the urban stations. These increases are suspected to be linked with the effect of urban heat island other than El Niño event.

Time domain series system definition and gear set reliability modeling

International Nuclear Information System (INIS)

Xie, Liyang; Wu, Ningxiang; Qian, Wenxue

2016-01-01

Time-dependent multi-configuration is a typical feature for mechanical systems such as gear trains and chain drives. As a series system, a gear train is distinct from a traditional series system, such as a chain, in load transmission path, system-component relationship, system functioning manner, as well as time-dependent system configuration. Firstly, the present paper defines time-domain series system to which the traditional series system reliability model is not adequate. Then, system specific reliability modeling technique is proposed for gear sets, including component (tooth) and subsystem (tooth-pair) load history description, material priori/posterior strength expression, time-dependent and system specific load-strength interference analysis, as well as statistically dependent failure events treatment. Consequently, several system reliability models are developed for gear sets with different tooth numbers in the scenario of tooth root material ultimate tensile strength failure. The application of the models is discussed in the last part, and the differences between the system specific reliability model and the traditional series system reliability model are illustrated by virtue of several numerical examples. - Highlights: • A new type of series system, i.e. time-domain multi-configuration series system is defined, that is of great significance to reliability modeling. • Multi-level statistical analysis based reliability modeling method is presented for gear transmission system. • Several system specific reliability models are established for gear set reliability estimation. • The differences between the traditional series system reliability model and the new model are illustrated.

Comparison of laser fluorimetry, high resolution gamma-ray spectrometry and neutron activation analysis techniques for determination of uranium content in soil samples

International Nuclear Information System (INIS)

Ghods, A.; Asgharizadeh, F.; Salimi, B.; Abbasi, A.

2004-01-01

Much more concern is given nowadays for exposure of the world population to natural radiation especially to uranium since 57% of that exposure is due to radon-222, which is a member of uranium decay series. Most of the methods used for uranium determination is low concentration require either tedious separation and preconcentration or the accessibility to special instrumentation for detection of uranium at this low level. this study compares three techniques and methods for uranium analysis among different soil sample with variable uranium contents. Two of these techniques, neutron activation analysis and high resolution gamma-ray spectrometry , are non-destructive while the other, laser fluorimetry is done via chemical extraction of uranium. Analysis of standard materials is done also to control the quality and accuracy of the work. In spite of having quite variable ranges of detection limit, results obtained by high resolution gamma-ray spectrometry based on the assumption of having secular equilibrium between uranium and its daughters, which causes deviation whenever this condition was missed. For samples with reasonable uranium content, neutron activation analysis would be a rapid and reliable technique, while for low uranium content laser fluorimetry would be the most appropriate and accurate technique

Regional Land Subsidence Analysis in Eastern Beijing Plain by InSAR Time Series and Wavelet Transforms

Directory of Open Access Journals (Sweden)

Mingliang Gao

2018-02-01

Full Text Available Land subsidence is the disaster phenomenon of environmental geology with regionally surface altitude lowering caused by the natural or man-made factors. Beijing, the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence rates of more than 100 mm/year. In this study, we employ two SAR datasets acquired by Envisat and TerraSAR-X satellites to investigate the surface deformation in Beijing Plain from 2003 to 2013 based on the multi-temporal InSAR technique. Furthermore, we also use observation wells to provide in situ hydraulic head levels to perform the evolution of land subsidence and spatial-temporal changes of groundwater level. Then, we analyze the accumulated displacement and hydraulic head level time series using continuous wavelet transform to separate periodic signal components. Finally, cross wavelet transform (XWT and wavelet transform coherence (WTC are implemented to analyze the relationship between the accumulated displacement and hydraulic head level time series. The results show that the subsidence centers in the northern Beijing Plain is spatially consistent with the groundwater drop funnels. According to the analysis of well based results located in different areas, the long-term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction, while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics (seasonal characteristics as the groundwater level changes. Moreover, according to the wavelet transformation, the land subsidence time series at monitoring well site lags several months behind the groundwater level change.

Causes of failure with Szabo technique – An analysis of nine cases

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Rajendra Kumar Jain

2013-05-01

Conclusion: This case series shows that the Szabo technique, in spite of some difficulties like wire entanglement, stent dislodgement and resistance during stent advancement, is a simple and feasible method for treating variety of ostial lesions precisely compared to conventional angioplasty.

Aerodynamic analysis of S series wind turbine airfoils by using X foil technique

International Nuclear Information System (INIS)

Zaheer, M.A.; Munir, M.A.; Zahid, I.; Rizwan, M.

2015-01-01

In order to attain supreme energy from wind turbine economically, blade profile enactment must be acquired. For extracting extreme power from wind, it is necessary to develop rotor models of wind turbine which have high rotation rates and power coefficients. Maximum power can also be haul out by using suitable airfoils at root and tip sections of wind turbine blades. In this research four different S-series airfoils have been selected to study their behavior for maximum power extraction from wind. The wind conditions during the research were scertained from the wind speeds over Kallar Kahar Pakistan. In order to study the wind turbine operation, the extremely important parameters are lift and drag forces. Therefore an endeavor to study lift force and drag force at various sections of wind turbine blade is shown in current research. In order to acquire the utmost power from wind turbine, highest value of sliding ratio is prerequisite. At various wind speeds, performance of several blade profiles was analyzed and for every wind speed, the appropriate blade profile is ascertained grounded on the utmost sliding ratio. For every airfoil, prime angle of attack is resolute at numerous wind speeds. (author)

Fourier Series Formalization in ACL2(r

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Cuong K. Chau

2015-09-01

Full Text Available We formalize some basic properties of Fourier series in the logic of ACL2(r, which is a variant of ACL2 that supports reasoning about the real and complex numbers by way of non-standard analysis. More specifically, we extend a framework for formally evaluating definite integrals of real-valued, continuous functions using the Second Fundamental Theorem of Calculus. Our extended framework is also applied to functions containing free arguments. Using this framework, we are able to prove the orthogonality relationships between trigonometric functions, which are the essential properties in Fourier series analysis. The sum rule for definite integrals of indexed sums is also formalized by applying the extended framework along with the First Fundamental Theorem of Calculus and the sum rule for differentiation. The Fourier coefficient formulas of periodic functions are then formalized from the orthogonality relations and the sum rule for integration. Consequently, the uniqueness of Fourier sums is a straightforward corollary. We also present our formalization of the sum rule for definite integrals of infinite series in ACL2(r. Part of this task is to prove the Dini Uniform Convergence Theorem and the continuity of a limit function under certain conditions. A key technique in our proofs of these theorems is to apply the overspill principle from non-standard analysis.

INTERNAL ENVIRONMENT ANALYSIS TECHNIQUES

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Caescu Stefan Claudiu

2011-12-01

Full Text Available Theme The situation analysis, as a separate component of the strategic planning, involves collecting and analysing relevant types of information on the components of the marketing environment and their evolution on the one hand and also on the organization’s resources and capabilities on the other. Objectives of the Research The main purpose of the study of the analysis techniques of the internal environment is to provide insight on those aspects that are of strategic importance to the organization. Literature Review The marketing environment consists of two distinct components, the internal environment that is made from specific variables within the organization and the external environment that is made from variables external to the organization. Although analysing the external environment is essential for corporate success, it is not enough unless it is backed by a detailed analysis of the internal environment of the organization. The internal environment includes all elements that are endogenous to the organization, which are influenced to a great extent and totally controlled by it. The study of the internal environment must answer all resource related questions, solve all resource management issues and represents the first step in drawing up the marketing strategy. Research Methodology The present paper accomplished a documentary study of the main techniques used for the analysis of the internal environment. Results The special literature emphasizes that the differences in performance from one organization to another is primarily dependant not on the differences between the fields of activity, but especially on the differences between the resources and capabilities and the ways these are capitalized on. The main methods of analysing the internal environment addressed in this paper are: the analysis of the organizational resources, the performance analysis, the value chain analysis and the functional analysis. Implications Basically such

The Fourier decomposition method for nonlinear and non-stationary time series analysis.

Science.gov (United States)

Singh, Pushpendra; Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-03-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

A case study of the sensitivity of forecast skill to data and data analysis techniques

Science.gov (United States)

Baker, W. E.; Atlas, R.; Halem, M.; Susskind, J.

1983-01-01

A series of experiments have been conducted to examine the sensitivity of forecast skill to various data and data analysis techniques for the 0000 GMT case of January 21, 1979. These include the individual components of the FGGE observing system, the temperatures obtained with different satellite retrieval methods, and the method of vertical interpolation between the mandatory pressure analysis levels and the model sigma levels. It is found that NESS TIROS-N infrared retrievals seriously degrade a rawinsonde-only analysis over land, resulting in a poorer forecast over North America. Less degradation in the 72-hr forecast skill at sea level and some improvement at 500 mb is noted, relative to the control with TIROS-N retrievals produced with a physical inversion method which utilizes a 6-hr forecast first guess. NESS VTPR oceanic retrievals lead to an improved forecast over North America when added to the control.

Development of analysis software for radiation time-series data with the use of visual studio 2005

International Nuclear Information System (INIS)

Hohara, Sin-ya; Horiguchi, Tetsuo; Ito, Shin

2008-01-01

Time-Series Analysis supplies a new vision that conventional analysis methods such as energy spectroscopy haven't achieved ever. However, application of time-series analysis to radiation measurements needs much effort in software and hardware development. By taking advantage of Visual Studio 2005, we developed an analysis software, 'ListFileConverter', for time-series radiation measurement system called as 'MPA-3'. The software is based on graphical user interface (GUI) architecture that enables us to save a large amount of operation time in the analysis, and moreover to make an easy-access to special file structure of MPA-3 data. In this paper, detailed structure of ListFileConverter is fully explained, and experimental results for counting capability of MPA-3 hardware system and those for neutron measurements with our UTR-KINKI reactor are also given. (author)

Validation of non-stationary precipitation series for site-specific impact assessment: comparison of two statistical downscaling techniques

Science.gov (United States)

Mullan, Donal; Chen, Jie; Zhang, Xunchang John

2016-02-01

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

Determination and identification of naturally occurring decay series using milli-second order pulse time interval analysis (TIA)

International Nuclear Information System (INIS)

Hashimoto, T.; Sanada, Y.; Uezu, Y.

2003-01-01

A delayed coincidence method, called a time interval analysis (TIA) method, has been successfully applied to selective determination of the correlated α-α decay events in millisecond order life-time. A main decay process applicable to TIA-treatment is 220 Rn → 216 Po(T 1/2 :145ms) → {Th-series}. The TIA is fundamentally based on the difference of time interval distribution between non-correlated decay events and other events such as background or random events when they were compiled the time interval data within a fixed time (for example, a tenth of concerned half lives). The sensitivity of the TIA-analysis due to correlated α-α decay events could be subsequently improved in respect of background elimination using the pulse shape discrimination technique (PSD with PERALS counter) to reject β/γ-pulses, purging of nitrogen gas into extra scintillator, and applying solvent extraction of Ra. (author)

Applications of neutron activation analysis technique

International Nuclear Information System (INIS)

Jonah, S. A.

2000-07-01

The technique was developed as far back as 1936 by G. Hevesy and H. Levy for the analysis of Dy using an isotopic source. Approximately 40 elements can be analyzed by instrumental neutron activation analysis (INNA) technique with neutrons from a nuclear reactor. By applying radiochemical separation, the number of elements that can be analysed may be increased to almost 70. Compared with other analytical methods used in environmental and industrial research, NAA has some unique features. These are multi-element capability, rapidity, reproducibility of results, complementarity to other methods, freedom from analytical blank and independency of chemical state of elements. There are several types of neutron sources namely: nuclear reactors, accelerator-based and radioisotope-based sources, but nuclear reactors with high fluxes of neutrons from the fission of 235 U give the most intense irradiation, and hence the highest available sensitivities for NAA. In this paper, the applications of NAA of socio-economic importance are discussed. The benefits of using NAA and related nuclear techniques for on-line applications in industrial process control are highlighted. A brief description of the NAA set-ups at CERT is enumerated. Finally, NAA is compared with other leading analytical techniques

Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction.

Science.gov (United States)

Carleton, W Christopher; Campbell, David; Collard, Mark

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating-the most common chronometric technique in archaeological and palaeoenvironmental research-creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20-30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence.

Elemental analysis techniques using proton microbeam

International Nuclear Information System (INIS)

Sakai, Takuro; Oikawa, Masakazu; Sato, Takahiro

2005-01-01

Proton microbeam is a powerful tool for two-dimensional elemental analysis. The analysis is based on Particle Induced X-ray Emission (PIXE) and Particle Induced Gamma-ray Emission (PIGE) techniques. The paper outlines the principles and instruments, and describes the dental application has been done in JAERI Takasaki. (author)

16th International workshop on Advanced Computing and Analysis Techniques in physics (ACAT)

CERN Document Server

Lokajicek, M; Tumova, N

2015-01-01

16th International workshop on Advanced Computing and Analysis Techniques in physics (ACAT). The ACAT workshop series, formerly AIHENP (Artificial Intelligence in High Energy and Nuclear Physics), was created back in 1990. Its main purpose is to gather researchers related with computing in physics research together, from both physics and computer science sides, and bring them a chance to communicate with each other. It has established bridges between physics and computer science research, facilitating the advances in our understanding of the Universe at its smallest and largest scales. With the Large Hadron Collider and many astronomy and astrophysics experiments collecting larger and larger amounts of data, such bridges are needed now more than ever. The 16th edition of ACAT aims to bring related researchers together, once more, to explore and confront the boundaries of computing, automatic data analysis and theoretical calculation technologies. It will create a forum for exchanging ideas among the fields an...

Development of evaluation method for software safety analysis techniques

International Nuclear Information System (INIS)

Huang, H.; Tu, W.; Shih, C.; Chen, C.; Yang, W.; Yih, S.; Kuo, C.; Chen, M.

2006-01-01

Full text: Full text: Following the massive adoption of digital Instrumentation and Control (I and C) system for nuclear power plant (NPP), various Software Safety Analysis (SSA) techniques are used to evaluate the NPP safety for adopting appropriate digital I and C system, and then to reduce risk to acceptable level. However, each technique has its specific advantage and disadvantage. If the two or more techniques can be complementarily incorporated, the SSA combination would be more acceptable. As a result, if proper evaluation criteria are available, the analyst can then choose appropriate technique combination to perform analysis on the basis of resources. This research evaluated the applicable software safety analysis techniques nowadays, such as, Preliminary Hazard Analysis (PHA), Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Markov chain modeling, Dynamic Flowgraph Methodology (DFM), and simulation-based model analysis; and then determined indexes in view of their characteristics, which include dynamic capability, completeness, achievability, detail, signal/ noise ratio, complexity, and implementation cost. These indexes may help the decision makers and the software safety analysts to choose the best SSA combination arrange their own software safety plan. By this proposed method, the analysts can evaluate various SSA combinations for specific purpose. According to the case study results, the traditional PHA + FMEA + FTA (with failure rate) + Markov chain modeling (without transfer rate) combination is not competitive due to the dilemma for obtaining acceptable software failure rates. However, the systematic architecture of FTA and Markov chain modeling is still valuable for realizing the software fault structure. The system centric techniques, such as DFM and Simulation-based model analysis, show the advantage on dynamic capability, achievability, detail, signal/noise ratio. However, their disadvantage are the completeness complexity

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Science.gov (United States)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2018-04-01

For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

Energy Technology Data Exchange (ETDEWEB)

Simmhan, Yogesh; Noor, Muhammad Usman

2013-10-09

Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

Phase correction and error estimation in InSAR time series analysis

Science.gov (United States)

Zhang, Y.; Fattahi, H.; Amelung, F.

2017-12-01

During the last decade several InSAR time series approaches have been developed in response to the non-idea acquisition strategy of SAR satellites, such as large spatial and temporal baseline with non-regular acquisitions. The small baseline tubes and regular acquisitions of new SAR satellites such as Sentinel-1 allows us to form fully connected networks of interferograms and simplifies the time series analysis into a weighted least square inversion of an over-determined system. Such robust inversion allows us to focus more on the understanding of different components in InSAR time-series and its uncertainties. We present an open-source python-based package for InSAR time series analysis, called PySAR (https://yunjunz.github.io/PySAR/), with unique functionalities for obtaining unbiased ground displacement time-series, geometrical and atmospheric correction of InSAR data and quantifying the InSAR uncertainty. Our implemented strategy contains several features including: 1) improved spatial coverage using coherence-based network of interferograms, 2) unwrapping error correction using phase closure or bridging, 3) tropospheric delay correction using weather models and empirical approaches, 4) DEM error correction, 5) optimal selection of reference date and automatic outlier detection, 6) InSAR uncertainty due to the residual tropospheric delay, decorrelation and residual DEM error, and 7) variance-covariance matrix of final products for geodetic inversion. We demonstrate the performance using SAR datasets acquired by Cosmo-Skymed and TerraSAR-X, Sentinel-1 and ALOS/ALOS-2, with application on the highly non-linear volcanic deformation in Japan and Ecuador (figure 1). Our result shows precursory deformation before the 2015 eruptions of Cotopaxi volcano, with a maximum uplift of 3.4 cm on the western flank (fig. 1b), with a standard deviation of 0.9 cm (fig. 1a), supporting the finding by Morales-Rivera et al. (2017, GRL); and a post-eruptive subsidence on the same

Advanced Techniques of Stress Analysis

Directory of Open Access Journals (Sweden)

Simion TATARU

2013-12-01

Full Text Available This article aims to check the stress analysis technique based on 3D models also making a comparison with the traditional technique which utilizes a model built directly into the stress analysis program. This comparison of the two methods will be made with reference to the rear fuselage of IAR-99 aircraft, structure with a high degree of complexity which allows a meaningful evaluation of both approaches. Three updated databases are envisaged: the database having the idealized model obtained using ANSYS and working directly on documentation, without automatic generation of nodes and elements (with few exceptions, the rear fuselage database (performed at this stage obtained with Pro/ ENGINEER and the one obtained by using ANSYS with the second database. Then, each of the three databases will be used according to arising necessities.The main objective is to develop the parameterized model of the rear fuselage using the computer aided design software Pro/ ENGINEER. A review of research regarding the use of virtual reality with the interactive analysis performed by the finite element method is made to show the state- of- the-art achieved in this field.

Time-series modeling: applications to long-term finfish monitoring data

International Nuclear Information System (INIS)

Bireley, L.E.

1985-01-01

The growing concern and awareness that developed during the 1970's over the effects that industry had on the environment caused the electric utility industry in particular to develop monitoring programs. These programs generate long-term series of data that are not very amenable to classical normal-theory statistical analysis. The monitoring data collected from three finfish programs (impingement, trawl and seine) at the Millstone Nuclear Power Station were typical of such series and thus were used to develop methodology that used the full extent of the information in the series. The basis of the methodology was classic Box-Jenkins time-series modeling; however, the models also included deterministic components that involved flow, season and time as predictor variables. Time entered into the models as harmonic regression terms. Of the 32 models fitted to finfish catch data, 19 were found to account for more than 70% of the historical variation. The models were than used to forecast finfish catches a year in advance and comparisons were made to actual data. Usually the confidence intervals associated with the forecasts encompassed most of the observed data. The technique can provide the basis for intervention analysis in future impact assessments

Recurrence Density Enhanced Complex Networks for Nonlinear Time Series Analysis

Science.gov (United States)

Costa, Diego G. De B.; Reis, Barbara M. Da F.; Zou, Yong; Quiles, Marcos G.; Macau, Elbert E. N.

We introduce a new method, which is entitled Recurrence Density Enhanced Complex Network (RDE-CN), to properly analyze nonlinear time series. Our method first transforms a recurrence plot into a figure of a reduced number of points yet preserving the main and fundamental recurrence properties of the original plot. This resulting figure is then reinterpreted as a complex network, which is further characterized by network statistical measures. We illustrate the computational power of RDE-CN approach by time series by both the logistic map and experimental fluid flows, which show that our method distinguishes different dynamics sufficiently well as the traditional recurrence analysis. Therefore, the proposed methodology characterizes the recurrence matrix adequately, while using a reduced set of points from the original recurrence plots.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

Science.gov (United States)

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis

Directory of Open Access Journals (Sweden)

C. Nagarajan

2012-09-01

Full Text Available This paper presents a Closed Loop CLL-T (capacitor inductor inductor Series Parallel Resonant Converter (SPRC has been simulated and the performance is analysised. A three element CLL-T SPRC working under load independent operation (voltage type and current type load is presented in this paper. The Steady state Stability Analysis of CLL-T SPRC has been developed using State Space technique and the regulation of output voltage is done by using Fuzzy controller. The simulation study indicates the superiority of fuzzy control over the conventional control methods. The proposed approach is expected to provide better voltage regulation for dynamic load conditions. A prototype 300 W, 100 kHz converter is designed and built to experimentally demonstrate, dynamic and steady state performance for the CLL-T SPRC are compared from the simulation studies.

Data Rods: High Speed, Time-Series Analysis of Massive Cryospheric Data Sets Using Object-Oriented Database Methods

Science.gov (United States)

Liang, Y.; Gallaher, D. W.; Grant, G.; Lv, Q.

2011-12-01

Change over time, is the central driver of climate change detection. The goal is to diagnose the underlying causes, and make projections into the future. In an effort to optimize this process we have developed the Data Rod model, an object-oriented approach that provides the ability to query grid cell changes and their relationships to neighboring grid cells through time. The time series data is organized in time-centric structures called "data rods." A single data rod can be pictured as the multi-spectral data history at one grid cell: a vertical column of data through time. This resolves the long-standing problem of managing time-series data and opens new possibilities for temporal data analysis. This structure enables rapid time- centric analysis at any grid cell across multiple sensors and satellite platforms. Collections of data rods can be spatially and temporally filtered, statistically analyzed, and aggregated for use with pattern matching algorithms. Likewise, individual image pixels can be extracted to generate multi-spectral imagery at any spatial and temporal location. The Data Rods project has created a series of prototype databases to store and analyze massive datasets containing multi-modality remote sensing data. Using object-oriented technology, this method overcomes the operational limitations of traditional relational databases. To demonstrate the speed and efficiency of time-centric analysis using the Data Rods model, we have developed a sea ice detection algorithm. This application determines the concentration of sea ice in a small spatial region across a long temporal window. If performed using traditional analytical techniques, this task would typically require extensive data downloads and spatial filtering. Using Data Rods databases, the exact spatio-temporal data set is immediately available No extraneous data is downloaded, and all selected data querying occurs transparently on the server side. Moreover, fundamental statistical

Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

Science.gov (United States)

Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

2013-01-01

This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

Image/Time Series Mining Algorithms: Applications to Developmental Biology, Document Processing and Data Streams

Science.gov (United States)

Tataw, Oben Moses

2013-01-01

Interdisciplinary research in computer science requires the development of computational techniques for practical application in different domains. This usually requires careful integration of different areas of technical expertise. This dissertation presents image and time series analysis algorithms, with practical interdisciplinary applications…

Science in Drama: Using Television Programmes to Teach Concepts and Techniques

Science.gov (United States)

Rutter, Gordon

2011-01-01

By using a specific episode of the popular television cartoon series "The Simpsons," a range of techniques can be communicated, including microscope setup and use, simple chemical analysis, observation, and interpretation. Knowledge of blood groups and typing, morphological comparison of hair samples, fingerprint analysis, and DNA fingerprinting…

New analysis methods to push the boundaries of diagnostic techniques in the environmental sciences

International Nuclear Information System (INIS)

Lungaroni, M.; Peluso, E.; Gelfusa, M.; Malizia, A.; Talebzadeh, S.; Gaudio, P.; Murari, A.; Vega, J.

2016-01-01

In the last years, new and more sophisticated measurements have been at the basis of the major progress in various disciplines related to the environment, such as remote sensing and thermonuclear fusion. To maximize the effectiveness of the measurements, new data analysis techniques are required. First data processing tasks, such as filtering and fitting, are of primary importance, since they can have a strong influence on the rest of the analysis. Even if Support Vector Regression is a method devised and refined at the end of the 90s, a systematic comparison with more traditional non parametric regression methods has never been reported. In this paper, a series of systematic tests is described, which indicates how SVR is a very competitive method of non-parametric regression that can usefully complement and often outperform more consolidated approaches. The performance of Support Vector Regression as a method of filtering is investigated first, comparing it with the most popular alternative techniques. Then Support Vector Regression is applied to the problem of non-parametric regression to analyse Lidar surveys for the environments measurement of particulate matter due to wildfires. The proposed approach has given very positive results and provides new perspectives to the interpretation of the data.

A new analysis technique for microsamples

International Nuclear Information System (INIS)

Boyer, R.; Journoux, J.P.; Duval, C.

1989-01-01

For many decades, isotopic analysis of Uranium or Plutonium has been performed by mass spectrometry. The most recent analytical techniques, using the counting method or a plasma torch combined with a mass spectrometer (ICP.MS) have not yet to reach a greater degree of precision than the older methods in this field. The two means of ionization for isotopic analysis - by electronic bombardment of atoms or molecules (source of gas ions) and - by thermal effect (thermoionic source) are compared revealing some inconsistency between the quantity of sample necessary for analysis and the luminosity. In fact, the quantity of sample necessary for the gas source mass spectrometer is 10 to 20 times greater than that for the thermoionization spectrometer, while the sample consumption is between 10 5 to 10 6 times greater. This proves that almost the entire sample is not necessary for the measurement; it is only required because of the system of introduction for the gas spectrometer. The new analysis technique referred to as ''Microfluorination'' corrects this anomaly and exploits the advantages of the electron bombardment method of ionization

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

Science.gov (United States)

Cossalter, Michele; Mengshoel, Ole J.; Selker, Ted

2013-01-01

Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

Handbook of Qualitative Research Techniques and Analysis in Entrepreneurship

DEFF Research Database (Denmark)

One of the most challenging tasks in the research design process is choosing the most appropriate data collection and analysis techniques. This Handbook provides a detailed introduction to five qualitative data collection and analysis techniques pertinent to exploring entreprneurial phenomena....

Innovative techniques to analyze time series of geomagnetic activity indices

Science.gov (United States)

Balasis, Georgios; Papadimitriou, Constantinos; Daglis, Ioannis A.; Potirakis, Stelios M.; Eftaxias, Konstantinos

2016-04-01

Magnetic storms are undoubtedly among the most important phenomena in space physics and also a central subject of space weather. The non-extensive Tsallis entropy has been recently introduced, as an effective complexity measure for the analysis of the geomagnetic activity Dst index. The Tsallis entropy sensitively shows the complexity dissimilarity among different "physiological" (normal) and "pathological" states (intense magnetic storms). More precisely, the Tsallis entropy implies the emergence of two distinct patterns: (i) a pattern associated with the intense magnetic storms, which is characterized by a higher degree of organization, and (ii) a pattern associated with normal periods, which is characterized by a lower degree of organization. Other entropy measures such as Block Entropy, T-Complexity, Approximate Entropy, Sample Entropy and Fuzzy Entropy verify the above mentioned result. Importantly, the wavelet spectral analysis in terms of Hurst exponent, H, also shows the existence of two different patterns: (i) a pattern associated with the intense magnetic storms, which is characterized by a fractional Brownian persistent behavior (ii) a pattern associated with normal periods, which is characterized by a fractional Brownian anti-persistent behavior. Finally, we observe universality in the magnetic storm and earthquake dynamics, on a basis of a modified form of the Gutenberg-Richter law for the Tsallis statistics. This finding suggests a common approach to the interpretation of both phenomena in terms of the same driving physical mechanism. Signatures of discrete scale invariance in Dst time series further supports the aforementioned proposal.

Quality assurance techniques for activation analysis

International Nuclear Information System (INIS)

Becker, D.A.

1984-01-01

The principles and techniques of quality assurance are applied to the measurement method of activation analysis. Quality assurance is defined to include quality control and quality assessment. Plans for quality assurance include consideration of: personnel; facilities; analytical design; sampling and sample preparation; the measurement process; standards; and documentation. Activation analysis concerns include: irradiation; chemical separation; counting/detection; data collection, and analysis; and calibration. Types of standards discussed include calibration materials and quality assessment materials

Revisit of series-SSHI with comparisons to other interfacing circuits in piezoelectric energy harvesting

International Nuclear Information System (INIS)

Lien, I C; Shu, Y C; Shiu, S M; Lin, H C; Wu, W J

2010-01-01

SSHI (synchronized switch harvesting on inductor) techniques have been demonstrated to be capable of boosting power in vibration-based piezoelectric energy harvesters. However, the effect of frequency deviation from resonance on the electrical response of an SSHI system has not been taken into account from the original analysis. Here an improved analysis accounting for such an effect is proposed to investigate the electrical behavior of a series-SSHI system. The analytic expression of harvested power is proposed and validated numerically. Its performance evaluation is carried out and compared with the piezoelectric systems using either the standard or parallel-SSHI electronic interfaces. The result shows that the electrical response of an ideal series-SSHI system is in sharp contrast to that of an ideal parallel-SSHI system. The former is similar to a strongly coupled electromechanical standard system operated at the open circuit resonance, while the latter is analogous to that operated at the short circuit resonance with different magnitudes of matching impedance. In addition, the performance degradation due to non-ideal voltage inversion is also discussed. It shows that a series-SSHI system avails against the standard technique in the case of medium coupling, since its peak power is close to the ideal optimal power and the reduction in power is less sensitive to frequency deviation. However, the consideration of inevitable diode loss in practical devices favors the parallel-SSHI technique, since the frequency-insensitive feature is much more pronounced in parallel-SSHI systems than in series-SSHI systems

The effect of the series resistance in dye-sensitized solar cells explored by electron transport and back reaction using electrical and optical modulation techniques

International Nuclear Information System (INIS)

Liu Weiqing; Hu Linhua; Dai Songyuan; Guo Lei; Jiang Nianquan; Kou Dongxing

2010-01-01

The influence of the series resistance on the electron transport and recombination processes in dye-sensitized solar cells (DSC) has been investigated. The series resistances induced by some parts of DSC, such as the transparent conductive oxide (TCO), the electrolyte layer and the counter electrode, influence the performance of DSC. By combining three frequency-domain techniques, specifically electrochemical impedance spectroscopy (EIS), intensity modulated photocurrent spectroscopy (IMPS) and intensity modulated photovoltage spectroscopy (IMVS), we studied the relationship between the series resistance and the dynamic response of DSC. The results show that the series resistance induced by the TCO or counter electrode predominantly affects the electron transport under short circuit conditions and has no significant influence on the recombination under open circuit conditions. However, the resistance related to the electrolyte layer not only limits the carrier transport but also influences the recombination. Possible reasons for the influence of the series resistance on the electron transport and recombination processes in DSC are also discussed.

The application of complex network time series analysis in turbulent heated jets

International Nuclear Information System (INIS)

Charakopoulos, A. K.; Karakasidis, T. E.; Liakopoulos, A.; Papanicolaou, P. N.

2014-01-01

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics

Plate Fixation With Autogenous Calcaneal Dowel Grafting Proximal Fourth and Fifth Metatarsal Fractures: Technique and Case Series.

Science.gov (United States)

Seidenstricker, Chad L; Blahous, Edward G; Bouché, Richard T; Saxena, Amol

Metaphyseal and proximal diaphyseal fractures of the lateral column metatarsals can have problems with healing. In particular, those involving the fifth metatarsal have been associated with a high nonunion rate with nonoperative treatment. Although intramedullary screw fixation results in a high union rate, delayed healing and complications can occur. We describe an innovative technique to treat both acute and chronic injuries involving the metatarsal base from the metaphysis to the proximal diaphyseal bone of the fourth and fifth metatarsals. The surgical technique involves evacuation of sclerotic bone at the fracture site, packing the fracture site with compact cancellous bone, and plate fixation. In our preliminary results, 4 patients displayed 100% radiographic union at a mean of 4.75 (range 4 to 6) weeks with no incidence of refracture, at a mean follow-up point of 3.5 (range 1 to 5) years. The early results with our small series suggest that this technique is a useful treatment choice for metaphyseal and proximal diaphyseal fractures of the fourth and fifth metatarsals. Copyright © 2017 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Properties of Asymmetric Detrended Fluctuation Analysis in the time series of RR intervals

Science.gov (United States)

Piskorski, J.; Kosmider, M.; Mieszkowski, D.; Krauze, T.; Wykretowicz, A.; Guzik, P.

2018-02-01

Heart rate asymmetry is a phenomenon by which the accelerations and decelerations of heart rate behave differently, and this difference is consistent and unidirectional, i.e. in most of the analyzed recordings the inequalities have the same directions. So far, it has been established for variance and runs based types of descriptors of RR intervals time series. In this paper we apply the newly developed method of Asymmetric Detrended Fluctuation Analysis, which so far has mainly been used with economic time series, to the set of 420 stationary 30 min time series of RR intervals from young, healthy individuals aged between 20 and 40. This asymmetric approach introduces separate scaling exponents for rising and falling trends. We systematically study the presence of asymmetry in both global and local versions of this method. In this study global means "applying to the whole time series" and local means "applying to windows jumping along the recording". It is found that the correlation structure of the fluctuations left over after detrending in physiological time series shows strong asymmetric features in both magnitude, with α+ physiological data after shuffling or with a group of symmetric synthetic time series.

A Comparison of Missing-Data Procedures for Arima Time-Series Analysis

Science.gov (United States)

Velicer, Wayne F.; Colby, Suzanne M.

2005-01-01

Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated…

Evaluation of random errors in Williams’ series coefficients obtained with digital image correlation

International Nuclear Information System (INIS)

Lychak, Oleh V; Holyns’kiy, Ivan S

2016-01-01

The use of the Williams’ series parameters for fracture analysis requires valid information about their error values. The aim of this investigation is the development of the method for estimation of the standard deviation of random errors of the Williams’ series parameters, obtained from the measured components of the stress field. Also, the criteria for choosing the optimal number of terms in the truncated Williams’ series for derivation of their parameters with minimal errors is proposed. The method was used for the evaluation of the Williams’ parameters, obtained from the data, and measured by the digital image correlation technique for testing a three-point bending specimen. (paper)

Techniques for the thermal/hydraulic analysis of LMFBR check valves

International Nuclear Information System (INIS)

Cho, S.M.; Kane, R.S.

1979-01-01

A thermal/hydraulic analysis of the check valves in liquid sodium service for LMFBR plants is required to provide temperature data for thermal stress analysis of the valves for specified transient conditions. Because of the complex three-dimensional flow pattern within the valve, the heat transfer analysis techniques for less complicated shapes could not be used. This paper discusses the thermal analysis techniques used to assure that the valve stress analysis is conservative. These techniques include a method for evaluating the recirculating flow patterns and for selecting appropriately conservative heat transfer correlations in various regions of the valve

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics.

Science.gov (United States)

Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina

2015-01-01

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

Small area analysis using micro-diffraction techniques

International Nuclear Information System (INIS)

Goehner, Raymond P.; Tissot, Ralph G. Jr.; Michael, Joseph R.

2000-01-01

An overall trend toward smaller electronic packages and devices makes it increasingly important and difficult to obtain meaningful diffraction information from small areas. X-ray micro-diffraction, electron back-scattered diffraction (EBSD) and Kossel are micro-diffraction techniques used for crystallographic analysis including texture, phase identification and strain measurements. X-ray micro-diffraction primarily is used for phase analysis and residual strain measurements. X-ray micro-diffraction primarily is used for phase analysis and residual strain measurements of areas between 10 microm to 100 microm. For areas this small glass capillary optics are used for producing a usable collimated x-ray beam. These optics are designed to reflect x-rays below the critical angle therefore allowing for larger solid acceptance angle at the x-ray source resulting in brighter smaller x-ray beams. The determination of residual strain using micro-diffraction techniques is very important to the semiconductor industry. Residual stresses have caused voiding of the interconnect metal which then destroys electrical continuity. Being able to determine the residual stress helps industry to predict failures from the aging effects of interconnects due to this stress voiding. Stress measurements would be impossible using a conventional x-ray diffractometer; however, utilizing a 30 microm glass capillary these small areas are readily assessable for analysis. Kossel produces a wide angle diffraction pattern from fluorescent x-rays generated in the sample by an e-beam in a SEM. This technique can yield very precise lattice parameters for determining strain. Fig. 2 shows a Kossel pattern from a Ni specimen. Phase analysis on small areas is also possible using an energy dispersive spectrometer (EBSD) and x-ray micro-diffraction techniques. EBSD has the advantage of allowing the user to observe the area of interest using the excellent imaging capabilities of the SEM. An EDS detector has been

Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.

Science.gov (United States)

Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L

2018-02-01

Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

Analysis of Land Subsidence Monitoring in Mining Area with Time-Series Insar Technology

Science.gov (United States)

Sun, N.; Wang, Y. J.

2018-04-01

Time-series InSAR technology has become a popular land subsidence monitoring method in recent years, because of its advantages such as high accuracy, wide area, low expenditure, intensive monitoring points and free from accessibility restrictions. In this paper, we applied two kinds of satellite data, ALOS PALSAR and RADARSAT-2, to get the subsidence monitoring results of the study area in two time periods by time-series InSAR technology. By analyzing the deformation range, rate and amount, the time-series analysis of land subsidence in mining area was realized. The results show that InSAR technology could be used to monitor land subsidence in large area and meet the demand of subsidence monitoring in mining area.

Predicting linear and nonlinear time series with applications in nuclear safeguards and nonproliferation

International Nuclear Information System (INIS)

Burr, T.L.

1994-04-01

This report is a primer on the analysis of both linear and nonlinear time series with applications in nuclear safeguards and nonproliferation. We analyze eight simulated and two real time series using both linear and nonlinear modeling techniques. The theoretical treatment is brief but references to pertinent theory are provided. Forecasting is our main goal. However, because our most common approach is to fit models to the data, we also emphasize checking model adequacy by analyzing forecast errors for serial correlation or nonconstant variance

Microextraction sample preparation techniques in biomedical analysis.

Science.gov (United States)

Szultka, Malgorzata; Pomastowski, Pawel; Railean-Plugaru, Viorica; Buszewski, Boguslaw

2014-11-01

Biologically active compounds are found in biological samples at relatively low concentration levels. The sample preparation of target compounds from biological, pharmaceutical, environmental, and food matrices is one of the most time-consuming steps in the analytical procedure. The microextraction techniques are dominant. Metabolomic studies also require application of proper analytical technique for the determination of endogenic metabolites present in biological matrix on trace concentration levels. Due to the reproducibility of data, precision, relatively low cost of the appropriate analysis, simplicity of the determination, and the possibility of direct combination of those techniques with other methods (combination types on-line and off-line), they have become the most widespread in routine determinations. Additionally, sample pretreatment procedures have to be more selective, cheap, quick, and environmentally friendly. This review summarizes the current achievements and applications of microextraction techniques. The main aim is to deal with the utilization of different types of sorbents for microextraction and emphasize the use of new synthesized sorbents as well as to bring together studies concerning the systematic approach to method development. This review is dedicated to the description of microextraction techniques and their application in biomedical analysis. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

Science.gov (United States)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Characterizing and estimating noise in InSAR and InSAR time series with MODIS

Science.gov (United States)

Barnhart, William D.; Lohman, Rowena B.

2013-01-01

InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS-based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short-spatial scales (<10 km). Overall, MODIS-inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS-based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ∼6 mm/yr in regions with higher topographic relief.

Segmentation of time series with long-range fractal correlations

Science.gov (United States)

Bernaola-Galván, P.; Oliver, J.L.; Hackenberg, M.; Coronado, A.V.; Ivanov, P.Ch.; Carpena, P.

2012-01-01

Segmentation is a standard method of data analysis to identify change-points dividing a nonstationary time series into homogeneous segments. However, for long-range fractal correlated series, most of the segmentation techniques detect spurious change-points which are simply due to the heterogeneities induced by the correlations and not to real nonstationarities. To avoid this oversegmentation, we present a segmentation algorithm which takes as a reference for homogeneity, instead of a random i.i.d. series, a correlated series modeled by a fractional noise with the same degree of correlations as the series to be segmented. We apply our algorithm to artificial series with long-range correlations and show that it systematically detects only the change-points produced by real nonstationarities and not those created by the correlations of the signal. Further, we apply the method to the sequence of the long arm of human chromosome 21, which is known to have long-range fractal correlations. We obtain only three segments that clearly correspond to the three regions of different G + C composition revealed by means of a multi-scale wavelet plot. Similar results have been obtained when segmenting all human chromosome sequences, showing the existence of previously unknown huge compositional superstructures in the human genome. PMID:23645997

Segmentation of time series with long-range fractal correlations.

Science.gov (United States)

Bernaola-Galván, P; Oliver, J L; Hackenberg, M; Coronado, A V; Ivanov, P Ch; Carpena, P

2012-06-01

Segmentation is a standard method of data analysis to identify change-points dividing a nonstationary time series into homogeneous segments. However, for long-range fractal correlated series, most of the segmentation techniques detect spurious change-points which are simply due to the heterogeneities induced by the correlations and not to real nonstationarities. To avoid this oversegmentation, we present a segmentation algorithm which takes as a reference for homogeneity, instead of a random i.i.d. series, a correlated series modeled by a fractional noise with the same degree of correlations as the series to be segmented. We apply our algorithm to artificial series with long-range correlations and show that it systematically detects only the change-points produced by real nonstationarities and not those created by the correlations of the signal. Further, we apply the method to the sequence of the long arm of human chromosome 21, which is known to have long-range fractal correlations. We obtain only three segments that clearly correspond to the three regions of different G + C composition revealed by means of a multi-scale wavelet plot. Similar results have been obtained when segmenting all human chromosome sequences, showing the existence of previously unknown huge compositional superstructures in the human genome.

Spectral and cross-spectral analysis of uneven time series with the smoothed Lomb-Scargle periodogram and Monte Carlo evaluation of statistical significance

Science.gov (United States)

Pardo-Igúzquiza, Eulogio; Rodríguez-Tovar, Francisco J.

2012-12-01

Many spectral analysis techniques have been designed assuming sequences taken with a constant sampling interval. However, there are empirical time series in the geosciences (sediment cores, fossil abundance data, isotope analysis, …) that do not follow regular sampling because of missing data, gapped data, random sampling or incomplete sequences, among other reasons. In general, interpolating an uneven series in order to obtain a succession with a constant sampling interval alters the spectral content of the series. In such cases it is preferable to follow an approach that works with the uneven data directly, avoiding the need for an explicit interpolation step. The Lomb-Scargle periodogram is a popular choice in such circumstances, as there are programs available in the public domain for its computation. One new computer program for spectral analysis improves the standard Lomb-Scargle periodogram approach in two ways: (1) It explicitly adjusts the statistical significance to any bias introduced by variance reduction smoothing, and (2) it uses a permutation test to evaluate confidence levels, which is better suited than parametric methods when neighbouring frequencies are highly correlated. Another novel program for cross-spectral analysis offers the advantage of estimating the Lomb-Scargle cross-periodogram of two uneven time series defined on the same interval, and it evaluates the confidence levels of the estimated cross-spectra by a non-parametric computer intensive permutation test. Thus, the cross-spectrum, the squared coherence spectrum, the phase spectrum, and the Monte Carlo statistical significance of the cross-spectrum and the squared-coherence spectrum can be obtained. Both of the programs are written in ANSI Fortran 77, in view of its simplicity and compatibility. The program code is of public domain, provided on the website of the journal (http://www.iamg.org/index.php/publisher/articleview/frmArticleID/112/). Different examples (with simulated and

Definition of distance for nonlinear time series analysis of marked point process data

Energy Technology Data Exchange (ETDEWEB)

Iwayama, Koji, E-mail: koji@sat.t.u-tokyo.ac.jp [Research Institute for Food and Agriculture, Ryukoku Univeristy, 1-5 Yokotani, Seta Oe-cho, Otsu-Shi, Shiga 520-2194 (Japan); Hirata, Yoshito; Aihara, Kazuyuki [Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505 (Japan)

2017-01-30

Marked point process data are time series of discrete events accompanied with some values, such as economic trades, earthquakes, and lightnings. A distance for marked point process data allows us to apply nonlinear time series analysis to such data. We propose a distance for marked point process data which can be calculated much faster than the existing distance when the number of marks is small. Furthermore, under some assumptions, the Kullback–Leibler divergences between posterior distributions for neighbors defined by this distance are small. We performed some numerical simulations showing that analysis based on the proposed distance is effective. - Highlights: • A new distance for marked point process data is proposed. • The distance can be computed fast enough for a small number of marks. • The method to optimize parameter values of the distance is also proposed. • Numerical simulations indicate that the analysis based on the distance is effective.

On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

OpenAIRE

Trottini, Mario; Vigo, Isabel; Belda, Santiago

2015-01-01

Given a time series, running trends analysis (RTA) involves evaluating least squares trends over overlapping time windows of L consecutive time points, with overlap by all but one observation. This produces a new series called the “running trends series,” which is used as summary statistics of the original series for further analysis. In recent years, RTA has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that ...

CROSAT: A digital computer program for statistical-spectral analysis of two discrete time series

International Nuclear Information System (INIS)

Antonopoulos Domis, M.

1978-03-01

The program CROSAT computes directly from two discrete time series auto- and cross-spectra, transfer and coherence functions, using a Fast Fourier Transform subroutine. Statistical analysis of the time series is optional. While of general use the program is constructed to be immediately compatible with the ICL 4-70 and H316 computers at AEE Winfrith, and perhaps with minor modifications, with any other hardware system. (author)

Compositional analysis of YBaCuO superconducting films with ion beam analysis techniques

International Nuclear Information System (INIS)

Jones, S.; Timmers, H.; Ophel, T.R.; Elliman, R.G.

1999-01-01

High-T c YBa x Cu y O 7-δ superconducting films are being developed for applications such as superconducting quantum interference devices. The carrier concentration, critical current density J c and critical temperature T c of these films depend sensitively on the oxygen content . Stoichiometry, uniformity with depth, homogeneity across the sample and film thickness are also important quantities for their characterisation. It has been shown, for example, that the stoichiometry of the metallic elements affects the growth characteristics and surface morphology of the films. With the deposit ion techniques used, reproducibility of film properties is difficult. The characterisation of YBa x Cu y O 7-δ films with ion beam analysis techniques is complex. Whereas the three metallic elements can be detected with helium beams and Rutherford Backscattering (RBS), the oxygen signal is generally obscured by that from substrate elements. It can be better detected using resonant backscattering with 3.04MeV 4 He ions or nuclear reaction analysis. Elastic Recoil Detection (ERD) with high-energetic (1MeV/amu), heavy beams (Z > 120), enables all elements to be detected and separated in a single experiment. It is well established that ion bombardment induces vacancies in the oxygen sub-lattice, driving the material to change from crystalline to amorphous, the latter phase having a reduced oxygen content. In previous heavy ion ERD measurements of YBa x Cu yO z films with 200MeV 127 I beams, the opaque films became transparent in the beam spot area, indicative of the amorphous phase. The accuracy of the oxygen measurement is therefore questionable. Indeed, using Raman spectroscopy, distortions of the crystalline structure above a fluence of 5 x 10 11 ion/cm 2 and for higher doses some signatures of a reduction in oxygen content have been observed for such beams. It appears therefore that a correct determination of the oxygen content requires either a drastic reduction in fluence or a

Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin

Directory of Open Access Journals (Sweden)

Seied Yahya Mirzaee

2005-11-01

    One of the important issues that have significant role in study of hydrology of basin is determination of lag time. Lag time has significant role in hydrological studies. Quantity of rainfall related lag time depends on several factors, such as permeability, vegetation cover, catchments slope, rainfall intensity, storm duration and type of rain. Determination of lag time is important parameter in many projects such as dam design and also water resource studies. Lag time of basin could be calculated using various methods. One of these methods is time series analysis of spectral density. The analysis is based on fouries series. The time series is approximated with Sinuous and Cosines functions. In this method harmonically significant quantities with individual frequencies are presented. Spectral density under multiple time series could be used to obtain basin lag time for annual runoff and short-term rainfall fluctuation. A long lag time could be due to snowmelt as well as melting ice due to rainfalls in freezing days. In this research the lag time of Jahanbin basin has been determined using spectral density method. The catchments is subjected to both rainfall and snowfall. For short term rainfall fluctuation with a return period  2, 3, 4 months, the lag times were found 0.18, 0.5 and 0.083 month, respectively.

The Analysis Of Personality Disorder On Two Characters In The Animation Series Black Rock Shooter

OpenAIRE

Ramadhana, Rizki Andrian

2015-01-01

The title of this thesis is The Analysis of Personality Disorder on Two Characters in the Animation Series “Black Rock Shooter” which discusses about the personality disorder of two characters from this series; they are Kagari Izuriha and Yomi Takanashi. The animation series Black Rock Shooter is chosen as the source of data because this animation has psychological genre and represents the complexity of human relationship, especially when build up a friendship. It is because human is a social...

Kinematics analysis technique fouettes 720° classic ballet.

Directory of Open Access Journals (Sweden)

Li Bo

2011-07-01

Full Text Available Athletics practice proved that the more complex the item, the more difficult technique of the exercises. Fouettes at 720° one of the most difficult types of the fouettes. Its implementation is based on high technology during rotation of the performer. To perform this element not only requires good physical condition of the dancer, but also requires possession correct technique dancer. On the basis corresponding kinematic theory in this study, qualitative analysis and quantitative assessment of fouettes at 720 by the best Chinese dancers. For analysis, was taken the method of stereoscopic images and the theoretical analysis.

PENGARUH IKLAN DAN ATRIBUT PRODUK TERHADAP KEPUTUSAN PEMBELIAN SMARTPHONE SAMSUNG SERI GALAXY (SURVEI PADA PELANGGAN ITC ROXY MAS

Directory of Open Access Journals (Sweden)

Basrah Saidani

2013-04-01

Full Text Available Generally, the purpose of this research are: 1 to get more knowledge about Advertising, product attributes and the decision to purchase smartphone Samsung Galaxy series,. 2 to get more knowledge about the influence of Advertising to purchasing decisions smartphone Samsung Galaxy series, 3 to get more knowledge about the influence of product attributes to purchasing decisions smartphone Samsung Galaxy series, 4 to get more knowledge about influence of Advertising and product attributes simultaneously the decision to purchase smartphone Samsung Galaxy series. The observation unit in this research is 100 respondents visitors ITC Roxy Mas – Jakarta to purchase smartphone Samsung Galaxy series. Technique of determining the sample using a convenience sampling technique. Data collection techniques using the questionnaire Liker scale from 1 to 5. Descriptive analysis results showed: 1 the Advertising from Samsung Galaxy series is good enough as a whole but still lacking in message Acceptance and message retention. 2 Attribute a good product overall dimesi dominated by product futures, 3 the decision to purchase is dominated by post-purchase where consumers get satisfaction after buying the Samsung Galaxy series. Hypothesis testing results showed: 1 Advertising have a significant effect on purchasing decisions, 2 product attributes have a significant effect on purchasing decisions, 3 Advertising and product attributes a significant effect on purchasing decisions.

Introduction to Body Composition Assessment Using the Deuterium Dilution Technique with Analysis of Urine Samples by Isotope Ratio Mass Spectrometry

International Nuclear Information System (INIS)

2010-01-01

The IAEA has fostered the more widespread use of a stable isotope technique to assess body composition in different population groups to address priority areas in public health nutrition in Member States. It has done this by supporting national and regional nutrition projects through its technical cooperation programme and coordinated research projects over many years. This publication was developed by an international group of experts to provide practical hands-on guidance in the use of this technique in settings where analysis of stable isotope ratios in biological samples is to be made by isotope ratio mass spectrometry. The publication is targeted at new users of this technique, for example nutritionists, analytical chemists and other professionals. More detailed information on the theoretical background and the practical applications of state of the art methodologies to monitor changes in body composition can be found in IAEA Human Health Series No. 3, Assessment of Body Composition and Total Energy Expenditure in Humans by Stable Isotope Techniques

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

Science.gov (United States)

Qian, Xi-Yuan; Liu, Ya-Min; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

2015-06-01

When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.

Nuclear techniques for bulk and surface analysis of materials

International Nuclear Information System (INIS)

D'Agostino, M.D.; Kamykowski, E.A.; Kuehne, F.J.; Padawer, G.M.; Schneid, E.J.; Schulte, R.L.; Stauber, M.C.; Swanson, F.R.

1978-01-01

A review is presented summarizing several nondestructive bulk and surface analysis nuclear techniques developed in the Grumman Research Laboratories. Bulk analysis techniques include 14-MeV-neutron activation analysis and accelerator-based neutron radiography. The surface analysis techniques include resonant and non-resonant nuclear microprobes for the depth profile analysis of light elements (H, He, Li, Be, C, N, O and F) in the surface of materials. Emphasis is placed on the description and discussion of the unique nuclear microprobe analytical capacibilities of immediate importance to a number of current problems facing materials specialists. The resolution and contrast of neutron radiography was illustrated with an operating heat pipe system. The figure shows that the neutron radiograph has a resolution of better than 0.04 cm with sufficient contrast to indicate Freon 21 on the inner capillaries of the heat pipe and pooling of the liquid at the bottom. (T.G.)

Event tree analysis using artificial intelligence techniques

International Nuclear Information System (INIS)

Dixon, B.W.; Hinton, M.F.

1985-01-01

Artificial Intelligence (AI) techniques used in Expert Systems and Object Oriented Programming are discussed as they apply to Event Tree Analysis. A SeQUence IMPortance calculator, SQUIMP, is presented to demonstrate the implementation of these techniques. Benefits of using AI methods include ease of programming, efficiency of execution, and flexibility of application. The importance of an appropriate user interface is stressed. 5 figs

The development of human behavior analysis techniques

International Nuclear Information System (INIS)

Lee, Jung Woon; Lee, Yong Hee; Park, Geun Ok; Cheon, Se Woo; Suh, Sang Moon; Oh, In Suk; Lee, Hyun Chul; Park, Jae Chang.

1997-07-01

In this project, which is to study on man-machine interaction in Korean nuclear power plants, we developed SACOM (Simulation Analyzer with a Cognitive Operator Model), a tool for the assessment of task performance in the control rooms using software simulation, and also develop human error analysis and application techniques. SACOM was developed to assess operator's physical workload, workload in information navigation at VDU workstations, and cognitive workload in procedural tasks. We developed trip analysis system including a procedure based on man-machine interaction analysis system including a procedure based on man-machine interaction analysis and a classification system. We analyzed a total of 277 trips occurred from 1978 to 1994 to produce trip summary information, and for 79 cases induced by human errors time-lined man-machine interactions. The INSTEC, a database system of our analysis results, was developed. The MARSTEC, a multimedia authoring and representation system for trip information, was also developed, and techniques for human error detection in human factors experiments were established. (author). 121 refs., 38 tabs., 52 figs

The development of human behavior analysis techniques

Energy Technology Data Exchange (ETDEWEB)

Lee, Jung Woon; Lee, Yong Hee; Park, Geun Ok; Cheon, Se Woo; Suh, Sang Moon; Oh, In Suk; Lee, Hyun Chul; Park, Jae Chang

1997-07-01

In this project, which is to study on man-machine interaction in Korean nuclear power plants, we developed SACOM (Simulation Analyzer with a Cognitive Operator Model), a tool for the assessment of task performance in the control rooms using software simulation, and also develop human error analysis and application techniques. SACOM was developed to assess operator`s physical workload, workload in information navigation at VDU workstations, and cognitive workload in procedural tasks. We developed trip analysis system including a procedure based on man-machine interaction analysis system including a procedure based on man-machine interaction analysis and a classification system. We analyzed a total of 277 trips occurred from 1978 to 1994 to produce trip summary information, and for 79 cases induced by human errors time-lined man-machine interactions. The INSTEC, a database system of our analysis results, was developed. The MARSTEC, a multimedia authoring and representation system for trip information, was also developed, and techniques for human error detection in human factors experiments were established. (author). 121 refs., 38 tabs., 52 figs.

Spectroscopic analysis technique for arc-welding process control

Science.gov (United States)

Mirapeix, Jesús; Cobo, Adolfo; Conde, Olga; Quintela, María Ángeles; López-Higuera, José-Miguel

2005-09-01

The spectroscopic analysis of the light emitted by thermal plasmas has found many applications, from chemical analysis to monitoring and control of industrial processes. Particularly, it has been demonstrated that the analysis of the thermal plasma generated during arc or laser welding can supply information about the process and, thus, about the quality of the weld. In some critical applications (e.g. the aerospace sector), an early, real-time detection of defects in the weld seam (oxidation, porosity, lack of penetration, ...) is highly desirable as it can reduce expensive non-destructive testing (NDT). Among others techniques, full spectroscopic analysis of the plasma emission is known to offer rich information about the process itself, but it is also very demanding in terms of real-time implementations. In this paper, we proposed a technique for the analysis of the plasma emission spectrum that is able to detect, in real-time, changes in the process parameters that could lead to the formation of defects in the weld seam. It is based on the estimation of the electronic temperature of the plasma through the analysis of the emission peaks from multiple atomic species. Unlike traditional techniques, which usually involve peak fitting to Voigt functions using the Levenberg-Marquardt recursive method, we employ the LPO (Linear Phase Operator) sub-pixel algorithm to accurately estimate the central wavelength of the peaks (allowing an automatic identification of each atomic species) and cubic-spline interpolation of the noisy data to obtain the intensity and width of the peaks. Experimental tests on TIG-welding using fiber-optic capture of light and a low-cost CCD-based spectrometer, show that some typical defects can be easily detected and identified with this technique, whose typical processing time for multiple peak analysis is less than 20msec. running in a conventional PC.

On the Impact of a Quadratic Acceleration Term in the Analysis of Position Time Series

Science.gov (United States)

Bogusz, Janusz; Klos, Anna; Bos, Machiel Simon; Hunegnaw, Addisu; Teferle, Felix Norman

2016-04-01

The analysis of Global Navigation Satellite System (GNSS) position time series generally assumes that each of the coordinate component series is described by the sum of a linear rate (velocity) and various periodic terms. The residuals, the deviations between the fitted model and the observations, are then a measure of the epoch-to-epoch scatter and have been used for the analysis of the stochastic character (noise) of the time series. Often the parameters of interest in GNSS position time series are the velocities and their associated uncertainties, which have to be determined with the highest reliability. It is clear that not all GNSS position time series follow this simple linear behaviour. Therefore, we have added an acceleration term in the form of a quadratic polynomial function to the model in order to better describe the non-linear motion in the position time series. This non-linear motion could be a response to purely geophysical processes, for example, elastic rebound of the Earth's crust due to ice mass loss in Greenland, artefacts due to deficiencies in bias mitigation models, for example, of the GNSS satellite and receiver antenna phase centres, or any combination thereof. In this study we have simulated 20 time series with different stochastic characteristics such as white, flicker or random walk noise of length of 23 years. The noise amplitude was assumed at 1 mm/y-/4. Then, we added the deterministic part consisting of a linear trend of 20 mm/y (that represents the averaged horizontal velocity) and accelerations ranging from minus 0.6 to plus 0.6 mm/y2. For all these data we estimated the noise parameters with Maximum Likelihood Estimation (MLE) using the Hector software package without taken into account the non-linear term. In this way we set the benchmark to then investigate how the noise properties and velocity uncertainty may be affected by any un-modelled, non-linear term. The velocities and their uncertainties versus the accelerations for

Analysis of trend in temperature and rainfall time series of an Indian arid region: comparative evaluation of salient techniques

Science.gov (United States)

Machiwal, Deepesh; Gupta, Ankit; Jha, Madan Kumar; Kamble, Trupti

2018-04-01

This study investigated trends in 35 years (1979-2013) temperature (maximum, Tmax and minimum, Tmin) and rainfall at annual and seasonal (pre-monsoon, monsoon, post-monsoon, and winter) scales for 31 grid points in a coastal arid region of India. Box-whisker plots of annual temperature and rainfall time series depict systematic spatial gradients. Trends were examined by applying eight tests, such as Kendall rank correlation (KRC), Spearman rank order correlation (SROC), Mann-Kendall (MK), four modified MK tests, and innovative trend analysis (ITA). Trend magnitudes were quantified by Sen's slope estimator, and a new method was adopted to assess the significance of linear trends in MK-test statistics. It was found that the significant serial correlation is prominent in the annual and post-monsoon Tmax and Tmin, and pre-monsoon Tmin. The KRC and MK tests yielded similar results in close resemblance with the SROC test. The performance of two modified MK tests considering variance-correction approaches was found superior to the KRC, MK, modified MK with pre-whitening, and ITA tests. The performance of original MK test is poor due to the presence of serial correlation, whereas the ITA method is over-sensitive in identifying trends. Significantly increasing trends are more prominent in Tmin than Tmax. Further, both the annual and monsoon rainfall time series have a significantly increasing trend of 9 mm year-1. The sequential significance of linear trend in MK test-statistics is very strong (R 2 ≥ 0.90) in the annual and pre-monsoon Tmin (90% grid points), and strong (R 2 ≥ 0.75) in monsoon Tmax (68% grid points), monsoon, post-monsoon, and winter Tmin (respectively 65, 55, and 48% grid points), as well as in the annual and monsoon rainfalls (respectively 68 and 61% grid points). Finally, this study recommends use of variance-corrected MK test for the precise identification of trends. It is emphasized that the rising Tmax may hamper crop growth due to enhanced

Blind source separation problem in GPS time series

Science.gov (United States)

Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

2016-04-01

A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind source separation (BSS) problem, i.e., in recovering and separating the original sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an L2 norm (χ 2), looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic source. We evaluate the ability of the PCA and ICA decomposition

Determining the Number of Factors in P-Technique Factor Analysis

Science.gov (United States)

Lo, Lawrence L.; Molenaar, Peter C. M.; Rovine, Michael

2017-01-01

Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…

Application of Taylor-Series Integration to Reentry Problems with Wind

NARCIS (Netherlands)

Bergsma, Michiel; Mooij, E.

2016-01-01

Taylor-series integration is a numerical integration technique that computes the Taylor series of state variables using recurrence relations and uses this series to propagate the state in time. A Taylor-series integration reentry integrator is developed and compared with the fifth-order

SERI Wind Energy Program

Energy Technology Data Exchange (ETDEWEB)

Noun, R. J.

1983-06-01

The SERI Wind Energy Program manages the areas or innovative research, wind systems analysis, and environmental compatibility for the U.S. Department of Energy. Since 1978, SERI wind program staff have conducted in-house aerodynamic and engineering analyses of novel concepts for wind energy conversion and have managed over 20 subcontracts to determine technical feasibility; the most promising of these concepts is the passive blade cyclic pitch control project. In the area of systems analysis, the SERI program has analyzed the impact of intermittent generation on the reliability of electric utility systems using standard utility planning models. SERI has also conducted methodology assessments. Environmental issues related to television interference and acoustic noise from large wind turbines have been addressed. SERI has identified the causes, effects, and potential control of acoustic noise emissions from large wind turbines.

10th Australian conference on nuclear techniques of analysis. Proceedings

International Nuclear Information System (INIS)

1998-01-01

These proceedings contains abstracts and extended abstracts of 80 lectures and posters presented at the 10th Australian conference on nuclear techniques of analysis hosted by the Australian National University in Canberra, Australia from 24-26 of November 1997. The conference was divided into sessions on the following topics : ion beam analysis and its applications; surface science; novel nuclear techniques of analysis, characterization of thin films, electronic and optoelectronic material formed by ion implantation, nanometre science and technology, plasma science and technology. A special session was dedicated to new nuclear techniques of analysis, future trends and developments. Separate abstracts were prepared for the individual presentation included in this volume

10th Australian conference on nuclear techniques of analysis. Proceedings

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-06-01

These proceedings contains abstracts and extended abstracts of 80 lectures and posters presented at the 10th Australian conference on nuclear techniques of analysis hosted by the Australian National University in Canberra, Australia from 24-26 of November 1997. The conference was divided into sessions on the following topics : ion beam analysis and its applications; surface science; novel nuclear techniques of analysis, characterization of thin films, electronic and optoelectronic material formed by ion implantation, nanometre science and technology, plasma science and technology. A special session was dedicated to new nuclear techniques of analysis, future trends and developments. Separate abstracts were prepared for the individual presentation included in this volume.

Characterization of time series via Rényi complexity-entropy curves

Science.gov (United States)

Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2018-05-01

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective

CERN Document Server

Chen, Shyi-Ming

2013-01-01

Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological a...

Statistical attribution analysis of the nonstationarity of the annual runoff series of the Weihe River.

Science.gov (United States)

Xiong, Lihua; Jiang, Cong; Du, Tao

2014-01-01

Time-varying moments models based on Pearson Type III and normal distributions respectively are built under the generalized additive model in location, scale and shape (GAMLSS) framework to analyze the nonstationarity of the annual runoff series of the Weihe River, the largest tributary of the Yellow River. The detection of nonstationarities in hydrological time series (annual runoff, precipitation and temperature) from 1960 to 2009 is carried out using a GAMLSS model, and then the covariate analysis for the annual runoff series is implemented with GAMLSS. Finally, the attribution of each covariate to the nonstationarity of annual runoff is analyzed quantitatively. The results demonstrate that (1) obvious change-points exist in all three hydrological series, (2) precipitation, temperature and irrigated area are all significant covariates of the annual runoff series, and (3) temperature increase plays the main role in leading to the reduction of the annual runoff series in the study basin, followed by the decrease of precipitation and the increase of irrigated area.

Data mining in time series databases

CERN Document Server

Kandel, Abraham; Bunke, Horst

2004-01-01

Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

Forecasting Enrollments with Fuzzy Time Series.

Science.gov (United States)

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

Energy Technology Data Exchange (ETDEWEB)

Kevin McCarthy; Milos Manic

2012-08-01

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presents an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.

Predicting the Market Potential Using Time Series Analysis

Directory of Open Access Journals (Sweden)

Halmet Bradosti

2015-12-01

Full Text Available The aim of this analysis is to forecast a mini-market sales volume for the period of twelve months starting August 2015 to August 2016. The study is based on the monthly sales in Iraqi Dinar for a private local mini-market for the month of April 2014 to July 2015. As revealed on the graph and of course if the stagnant economic condition continues, the trend of future sales is down-warding. Based on time series analysis, the business may continue to operate and generate small revenues until August 2016. However, due to low sales volume, low profit margin and operating expenses, the revenues may not be adequate enough to produce positive net income and the business may not be able to operate afterward. The principal question rose from this is the forecasting sales in the region will be difficult where the business cycle so dynamic and revolutionary due to systematic risks and unforeseeable future.

Disease management with ARIMA model in time series.

Science.gov (United States)

Sato, Renato Cesar

2013-01-01

The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.

On the deduction of chemical reaction pathways from measurements of time series of concentrations.

Science.gov (United States)

Samoilov, Michael; Arkin, Adam; Ross, John

2001-03-01

We discuss the deduction of reaction pathways in complex chemical systems from measurements of time series of chemical concentrations of reacting species. First we review a technique called correlation metric construction (CMC) and show the construction of a reaction pathway from measurements on a part of glycolysis. Then we present two new improved methods for the analysis of time series of concentrations, entropy metric construction (EMC), and entropy reduction method (ERM), and illustrate (EMC) with calculations on a model reaction system. (c) 2001 American Institute of Physics.

Duality between Time Series and Networks

Science.gov (United States)

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Deviations from uniform power law scaling in nonstationary time series

Science.gov (United States)

Viswanathan, G. M.; Peng, C. K.; Stanley, H. E.; Goldberger, A. L.

1997-01-01

A classic problem in physics is the analysis of highly nonstationary time series that typically exhibit long-range correlations. Here we test the hypothesis that the scaling properties of the dynamics of healthy physiological systems are more stable than those of pathological systems by studying beat-to-beat fluctuations in the human heart rate. We develop techniques based on the Fano factor and Allan factor functions, as well as on detrended fluctuation analysis, for quantifying deviations from uniform power-law scaling in nonstationary time series. By analyzing extremely long data sets of up to N = 10(5) beats for 11 healthy subjects, we find that the fluctuations in the heart rate scale approximately uniformly over several temporal orders of magnitude. By contrast, we find that in data sets of comparable length for 14 subjects with heart disease, the fluctuations grow erratically, indicating a loss of scaling stability.

A numerical technique for reactor subchannel analysis

International Nuclear Information System (INIS)

Fath, Hassan E.S.

1983-01-01

A numerical technique is developed for the solution of the transient boundary layer equations with a moving liquid-vapour interface boundary. The technique uses the finite difference method with the velocity components defined over an Eulerian mesh. A system of interface massless markers is defined where the markers move with the flow field according to a simple kinematic relation between the interface geometry and the fluid velocity. Different applications of nuclear engineering interest are reported with some available results. The present technique is capable of predicting the interface profile near the wall which is important in the reactor subchannel analysis

Photo-degradation of CT-DNA with a series of carbothioamide ruthenium (II) complexes - Synthesis and structural analysis

Science.gov (United States)

Muthuraj, V.; Umadevi, M.

2018-04-01

The present research article is related with the method of preparation, structure and spectroscopic properties of a series of carbothioamide ruthenium (II) complexes with N and S donor ligands namely, 2-((6-chloro-4-oxo-4H-chromen-3-yl)methylene) hydrazine carbothioamide (ClChrTs)/2-((6-methoxy-4-oxo-4H-chromen-3-yl)methylene)hydrazine carbothioamide (MeOChrTS). The synthesized complexes were characterized by several techniques using analytical methods as well as by spectral techniques such as FT-IR, 1HNMR, 13CNMR, ESI mass and thermogravimetry/differential thermal analysis (TG-DTA). The IR spectra shows that the ligand acts as a neutral bidentate with N and S donor atoms. The biological activity of the prepared compounds and metal complexes were tested against cell line of calf-thymus DNA via an intercalation mechanism (MCF-7). In addition, the interaction of Ru(II) complexes and its free ligands with CT-DNA were also investigated by titration with UV-Vis spectra, fluorescence spectra, and Circular dichroism studies. Results suggest that both of the two Ru(II) complexes can bind with calf-thymus DNA via an intercalation mechanism.

Data imputation analysis for Cosmic Rays time series

Science.gov (United States)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

Artificial intelligence techniques used in respiratory sound analysis--a systematic review.

Science.gov (United States)

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

2014-02-01

Artificial intelligence (AI) has recently been established as an alternative method to many conventional methods. The implementation of AI techniques for respiratory sound analysis can assist medical professionals in the diagnosis of lung pathologies. This article highlights the importance of AI techniques in the implementation of computer-based respiratory sound analysis. Articles on computer-based respiratory sound analysis using AI techniques were identified by searches conducted on various electronic resources, such as the IEEE, Springer, Elsevier, PubMed, and ACM digital library databases. Brief descriptions of the types of respiratory sounds and their respective characteristics are provided. We then analyzed each of the previous studies to determine the specific respiratory sounds/pathology analyzed, the number of subjects, the signal processing method used, the AI techniques used, and the performance of the AI technique used in the analysis of respiratory sounds. A detailed description of each of these studies is provided. In conclusion, this article provides recommendations for further advancements in respiratory sound analysis.

The application of a shift theorem analysis technique to multipoint measurements

Directory of Open Access Journals (Sweden)

M. E. Dieckmann

Full Text Available A Fourier domain technique has been proposed previously which, in principle, quantifies the extent to which multipoint in-situ measurements can identify whether or not an observed structure is time stationary in its rest frame. Once a structure, sampled for example by four spacecraft, is shown to be quasi-stationary in its rest frame, the structure's velocity vector can be determined with respect to the sampling spacecraft. We investigate the properties of this technique, which we will refer to as a stationarity test, by applying it to two point measurements of a simulated boundary layer. The boundary layer was evolved using a PIC (particle in cell electromagnetic code. Initial and boundary conditions were chosen such, that two cases could be considered, i.e. a spacecraft pair moving through (1 a time stationary boundary structure and (2 a boundary structure which is evolving (expanding in time. The code also introduces noise in the simulated data time series which is uncorrelated between the two spacecraft. We demonstrate that, provided that the time series is Hanning windowed, the test is effective in determining the relative velocity between the boundary layer and spacecraft and in determining the range of frequencies over which the data can be treated as time stationary or time evolving. This work presents a first step towards understanding the effectiveness of this technique, as required in order for it to be applied to multispacecraft data.

Key words. Electromagnetics (wave propagation · Radio science (waves in plasma · Space plasma physics (active perturbation experiments.

The application of a shift theorem analysis technique to multipoint measurements

Directory of Open Access Journals (Sweden)

M. E. Dieckmann

1999-03-01

Full Text Available A Fourier domain technique has been proposed previously which, in principle, quantifies the extent to which multipoint in-situ measurements can identify whether or not an observed structure is time stationary in its rest frame. Once a structure, sampled for example by four spacecraft, is shown to be quasi-stationary in its rest frame, the structure's velocity vector can be determined with respect to the sampling spacecraft. We investigate the properties of this technique, which we will refer to as a stationarity test, by applying it to two point measurements of a simulated boundary layer. The boundary layer was evolved using a PIC (particle in cell electromagnetic code. Initial and boundary conditions were chosen such, that two cases could be considered, i.e. a spacecraft pair moving through (1 a time stationary boundary structure and (2 a boundary structure which is evolving (expanding in time. The code also introduces noise in the simulated data time series which is uncorrelated between the two spacecraft. We demonstrate that, provided that the time series is Hanning windowed, the test is effective in determining the relative velocity between the boundary layer and spacecraft and in determining the range of frequencies over which the data can be treated as time stationary or time evolving. This work presents a first step towards understanding the effectiveness of this technique, as required in order for it to be applied to multispacecraft data.Key words. Electromagnetics (wave propagation · Radio science (waves in plasma · Space plasma physics (active perturbation experiments.

A methodological comparison of customer service analysis techniques

Science.gov (United States)

James Absher; Alan Graefe; Robert Burns

2003-01-01

Techniques used to analyze customer service data need to be studied. Two primary analysis protocols, importance-performance analysis (IP) and gap score analysis (GA), are compared in a side-by-side comparison using data from two major customer service research projects. A central concern is what, if any, conclusion might be different due solely to the analysis...

Automated thermal mapping techniques using chromatic image analysis

Science.gov (United States)

Buck, Gregory M.

1989-01-01

Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.

Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Benoit Parmentier

2014-12-01

Full Text Available Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI, land surface temperature (LST and albedo (ALB. The analysis involves conducting a seasonal trend analysis (STA to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2 and using principal component analysis (PCA to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1 is characterized by a decrease in mean NDVI (Amplitude 0 with a concurrent increase in albedo (the mean and the annual amplitude and an increase in LST annual variability (Amplitude 1. These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

Modular techniques for dynamic fault-tree analysis

Science.gov (United States)

Patterson-Hine, F. A.; Dugan, Joanne B.

1992-01-01

It is noted that current approaches used to assess the dependability of complex systems such as Space Station Freedom and the Air Traffic Control System are incapable of handling the size and complexity of these highly integrated designs. A novel technique for modeling such systems which is built upon current techniques in Markov theory and combinatorial analysis is described. It enables the development of a hierarchical representation of system behavior which is more flexible than either technique alone. A solution strategy which is based on an object-oriented approach to model representation and evaluation is discussed. The technique is virtually transparent to the user since the fault tree models can be built graphically and the objects defined automatically. The tree modularization procedure allows the two model types, Markov and combinatoric, to coexist and does not require that the entire fault tree be translated to a Markov chain for evaluation. This effectively reduces the size of the Markov chain required and enables solutions with less truncation, making analysis of longer mission times possible. Using the fault-tolerant parallel processor as an example, a model is built and solved for a specific mission scenario and the solution approach is illustrated in detail.

Efficient Algorithms for Segmentation of Item-Set Time Series

Science.gov (United States)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

Study of the dependence of resolution temporal activity for a Philips gemini TF PET/CT scanner by applying a statistical analysis of time series

International Nuclear Information System (INIS)

Sanchez Merino, G.; Cortes Rpdicio, J.; Lope Lope, R.; Martin Gonzalez, T.; Garcia Fidalgo, M. A.

2013-01-01

The aim of the present work is to study the dependence of temporal resolution with the activity using statistical techniques applied to the series of values time series measurements of temporal resolution during daily equipment checks. (Author)

Key-space analysis of double random phase encryption technique

Science.gov (United States)

Monaghan, David S.; Gopinathan, Unnikrishnan; Naughton, Thomas J.; Sheridan, John T.

2007-09-01

We perform a numerical analysis on the double random phase encryption/decryption technique. The key-space of an encryption technique is the set of possible keys that can be used to encode data using that technique. In the case of a strong encryption scheme, many keys must be tried in any brute-force attack on that technique. Traditionally, designers of optical image encryption systems demonstrate only how a small number of arbitrary keys cannot decrypt a chosen encrypted image in their system. However, this type of demonstration does not discuss the properties of the key-space nor refute the feasibility of an efficient brute-force attack. To clarify these issues we present a key-space analysis of the technique. For a range of problem instances we plot the distribution of decryption errors in the key-space indicating the lack of feasibility of a simple brute-force attack.

Improved efficiency of maximum likelihood analysis of time series with temporally correlated errors

Science.gov (United States)

Langbein, John

2017-08-01

Most time series of geophysical phenomena have temporally correlated errors. From these measurements, various parameters are estimated. For instance, from geodetic measurements of positions, the rates and changes in rates are often estimated and are used to model tectonic processes. Along with the estimates of the size of the parameters, the error in these parameters needs to be assessed. If temporal correlations are not taken into account, or each observation is assumed to be independent, it is likely that any estimate of the error of these parameters will be too low and the estimated value of the parameter will be biased. Inclusion of better estimates of uncertainties is limited by several factors, including selection of the correct model for the background noise and the computational requirements to estimate the parameters of the selected noise model for cases where there are numerous observations. Here, I address the second problem of computational efficiency using maximum likelihood estimates (MLE). Most geophysical time series have background noise processes that can be represented as a combination of white and power-law noise, 1/f^{α } with frequency, f. With missing data, standard spectral techniques involving FFTs are not appropriate. Instead, time domain techniques involving construction and inversion of large data covariance matrices are employed. Bos et al. (J Geod, 2013. doi: 10.1007/s00190-012-0605-0) demonstrate one technique that substantially increases the efficiency of the MLE methods, yet is only an approximate solution for power-law indices >1.0 since they require the data covariance matrix to be Toeplitz. That restriction can be removed by simply forming a data filter that adds noise processes rather than combining them in quadrature. Consequently, the inversion of the data covariance matrix is simplified yet provides robust results for a wider range of power-law indices.

Using machine learning to identify structural breaks in single-group interrupted time series designs.

Science.gov (United States)

Linden, Ariel; Yarnold, Paul R

2016-12-01

Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

TECHNIQUE OF THE STATISTICAL ANALYSIS OF INVESTMENT APPEAL OF THE REGION

Directory of Open Access Journals (Sweden)

А. А. Vershinina

2014-01-01

Full Text Available The technique of the statistical analysis of investment appeal of the region is given in scientific article for direct foreign investments. Definition of a technique of the statistical analysis is given, analysis stages reveal, the mathematico-statistical tools are considered.

Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series

Science.gov (United States)

Pal, Mayukha; Madhusudana Rao, P.; Manimaran, P.

2014-12-01

We apply the recently developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation behavior and fractal nature between two non-stationary time series. We analyze the daily return price of gold, West Texas Intermediate and Brent crude oil, foreign exchange rate data, over a period of 18 years. The cross correlation has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, the existence of multifractal cross-correlation between all of these time series is found. We also found that the cross correlation between gold and oil prices possess uncorrelated behavior and the remaining bivariate time series possess persistent behavior. It was observed for five bivariate series that the cross-correlation exponents are less than the calculated average generalized Hurst exponents (GHE) for q0 and for one bivariate series the cross-correlation exponent is greater than GHE for all q values.

Development of fault diagnostic technique using reactor noise analysis

International Nuclear Information System (INIS)

Park, Jin Ho; Kim, J. S.; Oh, I. S.; Ryu, J. S.; Joo, Y. S.; Choi, S.; Yoon, D. B.

1999-04-01

The ultimate goal of this project is to establish the analysis technique to diagnose the integrity of reactor internals using reactor noise. The reactor noise analyses techniques for the PWR and CANDU NPP(Nuclear Power Plants) were established by which the dynamic characteristics of reactor internals and SPND instrumentations could be identified, and the noise database corresponding to each plant(both Korean and foreign one) was constructed and compared. Also the change of dynamic characteristics of the Ulchin 1 and 2 reactor internals were simulated under presumed fault conditions. Additionally portable reactor noise analysis system was developed so that real time noise analysis could directly be able to be performed at plant site. The reactor noise analyses techniques developed and the database obtained from the fault simulation, can be used to establish a knowledge based expert system to diagnose the NPP's abnormal conditions. And the portable reactor noise analysis system may be utilized as a substitute for plant IVMS(Internal Vibration Monitoring System). (author)

Development of fault diagnostic technique using reactor noise analysis

Energy Technology Data Exchange (ETDEWEB)

Park, Jin Ho; Kim, J. S.; Oh, I. S.; Ryu, J. S.; Joo, Y. S.; Choi, S.; Yoon, D. B

1999-04-01

The ultimate goal of this project is to establish the analysis technique to diagnose the integrity of reactor internals using reactor noise. The reactor noise analyses techniques for the PWR and CANDU NPP(Nuclear Power Plants) were established by which the dynamic characteristics of reactor internals and SPND instrumentations could be identified, and the noise database corresponding to each plant(both Korean and foreign one) was constructed and compared. Also the change of dynamic characteristics of the Ulchin 1 and 2 reactor internals were simulated under presumed fault conditions. Additionally portable reactor noise analysis system was developed so that real time noise analysis could directly be able to be performed at plant site. The reactor noise analyses techniques developed and the database obtained from the fault simulation, can be used to establish a knowledge based expert system to diagnose the NPP's abnormal conditions. And the portable reactor noise analysis system may be utilized as a substitute for plant IVMS(Internal Vibration Monitoring System). (author)

A New Linearization Technique Using Multi-sinh Doublet

Directory of Open Access Journals (Sweden)

CEHAN, V.

2009-06-01

Full Text Available In this paper a new linearization technique using multi-sinh doublet, implemented with a second generation current conveyor is presented. This new linearization technique is compared with the one based on multi-tanh doublets with linearization series connected diodes on the branches. The comparative study of the two linearization techniques is carried out using both dynamic range analysis, expressed by linearity error and the THD value calculation of output current, and the noise behavior of the two analyzed doublets. For the multi-sinh linearization technique proposed in the paper a method which assures the increase of the dynamic range, keeping the transconductance value constant is presented. This is done by using two design parameters: the number of series connected diodes N, which specifies the desired linear operating range and the k emitters areas ratio of the input stage transistors, which establishes the transconductance value. In the paper is also shown that if the transconductances of the two analyzed doublets are identical, and for the same values of N and k parameters, respectively, the current consumption of the multi-sinh doublet is always smaller than for the multi-tanh doublet.

Connected to TV series: Quantifying series watching engagement.

Science.gov (United States)

Tóth-Király, István; Bőthe, Beáta; Tóth-Fáber, Eszter; Hága, Győző; Orosz, Gábor

2017-12-01

Background and aims Television series watching stepped into a new golden age with the appearance of online series. Being highly involved in series could potentially lead to negative outcomes, but the distinction between highly engaged and problematic viewers should be distinguished. As no appropriate measure is available for identifying such differences, a short and valid measure was constructed in a multistudy investigation: the Series Watching Engagement Scale (SWES). Methods In Study 1 (N Sample1  = 740 and N Sample2  = 740), exploratory structural equation modeling and confirmatory factor analysis were used to identify the most important facets of series watching engagement. In Study 2 (N = 944), measurement invariance of the SWES was investigated between males and females. In Study 3 (N = 1,520), latent profile analysis (LPA) was conducted to identify subgroups of viewers. Results Five factors of engagement were identified in Study 1 that are of major relevance: persistence, identification, social interaction, overuse, and self-development. Study 2 supported the high levels of equivalence between males and females. In Study 3, three groups of viewers (low-, medium-, and high-engagement viewers) were identified. The highly engaged at-risk group can be differentiated from the other two along key variables of watching time and personality. Discussion The present findings support the overall validity, reliability, and usefulness of the SWES and the results of the LPA showed that it might be useful to identify at-risk viewers before the development of problematic use.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

Analysis of time series and size of equivalent sample

International Nuclear Information System (INIS)

Bernal, Nestor; Molina, Alicia; Pabon, Daniel; Martinez, Jorge

2004-01-01

In a meteorological context, a first approach to the modeling of time series is to use models of autoregressive type. This allows one to take into account the meteorological persistence or temporal behavior, thereby identifying the memory of the analyzed process. This article seeks to pre-sent the concept of the size of an equivalent sample, which helps to identify in the data series sub periods with a similar structure. Moreover, in this article we examine the alternative of adjusting the variance of the series, keeping in mind its temporal structure, as well as an adjustment to the covariance of two time series. This article presents two examples, the first one corresponding to seven simulated series with autoregressive structure of first order, and the second corresponding to seven meteorological series of anomalies of the air temperature at the surface in two Colombian regions

Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series.

Science.gov (United States)

Jiang, Zhixing; Zhang, David; Lu, Guangming

2018-04-19

Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states. Copyright © 2018 Elsevier B.V. All rights reserved.

Preparation of two series of materials with perovskite structure and investigation of their physical properties

International Nuclear Information System (INIS)

Mohamed, H.S.R.

2010-01-01

Results on structural, electric transport and magnetic properties of a series of (Al / In) doped Ca-series and (Al / In) doped Sr-series are presented and discussed.The polycrystalline ceramic samples were prepared by the solid state reaction technique. Elemental analysis showed a reasonable agreement between nominal and actual sample compositions. The grain size (G.S) of the Ca doped series increased with In content (G.S. (x = 0.2) = 79.5 nm and G.S. (x = 0.8) = 95.4 nm). For the Sr-series it has values in the range of 40 - 42 nm.Room temperature structural analysis using the Rietveld refinement technique,showed no structural transitions with the variation of the Al / In ratio. The doped Ca-series had an orthorhombic symmetry with space group Pnma. The Sr -doped series is rhombohedral with space group ( R3C ). In both series the Mn-O bond distance was found to increase whereas the mean Mn-O-Mn bond angle decreased with x. This was ascribed to the size mismatch between the divalent A- site ions and the B- site as a result of the introduction of the large In 3+ ion size. The tolerance factor varies from 0.918-0.933 for the Ca-series and from 0.932 - 0.948 for the Sr-series as x varies from 0.0 to 1.0. The temperature dependence of the magnetic susceptibility and electric resistivity of the Ca-doped series showed distinct ferromagnetic metallic (FMM) to a paramagnetic insulator (PMI) transitions near the Curie point (T C ), which ranges from T C ∼ 210 - 100 K for x = 0.0 to 1.0 respectively. The temperature dependence of the resistivity for the Sr-doped series showed distinct FMM to PMI transitions for samples with x = 0.0, 0.2 and 1.0, whereas samples with x = 0.4, 0.6 and 0.8 showed FMM to PMM. The transition temperature variation is not linear and lies within a narrow temperature range T p ∼ 344 - 367 K The results of the Sr-series showed that the size mismatch between the A- and B- sites is the major factor that controls the magnetic and electric properties

Multivariate Analysis Techniques for Optimal Vision System Design

DEFF Research Database (Denmark)

Sharifzadeh, Sara

The present thesis considers optimization of the spectral vision systems used for quality inspection of food items. The relationship between food quality, vision based techniques and spectral signature are described. The vision instruments for food analysis as well as datasets of the food items...... used in this thesis are described. The methodological strategies are outlined including sparse regression and pre-processing based on feature selection and extraction methods, supervised versus unsupervised analysis and linear versus non-linear approaches. One supervised feature selection algorithm...... (SSPCA) and DCT based characterization of the spectral diffused reflectance images for wavelength selection and discrimination. These methods together with some other state-of-the-art statistical and mathematical analysis techniques are applied on datasets of different food items; meat, diaries, fruits...

Chromatographic Techniques for Rare Earth Elements Analysis

Science.gov (United States)

Chen, Beibei; He, Man; Zhang, Huashan; Jiang, Zucheng; Hu, Bin

2017-04-01

The present capability of rare earth element (REE) analysis has been achieved by the development of two instrumental techniques. The efficiency of spectroscopic methods was extraordinarily improved for the detection and determination of REE traces in various materials. On the other hand, the determination of REEs very often depends on the preconcentration and separation of REEs, and chromatographic techniques are very powerful tools for the separation of REEs. By coupling with sensitive detectors, many ambitious analytical tasks can be fulfilled. Liquid chromatography is the most widely used technique. Different combinations of stationary phases and mobile phases could be used in ion exchange chromatography, ion chromatography, ion-pair reverse-phase chromatography and some other techniques. The application of gas chromatography is limited because only volatile compounds of REEs can be separated. Thin-layer and paper chromatography are techniques that cannot be directly coupled with suitable detectors, which limit their applications. For special demands, separations can be performed by capillary electrophoresis, which has very high separation efficiency.

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor

2016-01-01

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

The Real-time Frequency Spectrum Analysis of Neutron Pulse Signal Series

International Nuclear Information System (INIS)

Tang Yuelin; Ren Yong; Wei Biao; Feng Peng; Mi Deling; Pan Yingjun; Li Jiansheng; Ye Cenming

2009-01-01

The frequency spectrum analysis of neutron pulse signal is a very important method in nuclear stochastic signal processing Focused on the special '0' and '1' of neutron pulse signal series, this paper proposes new rotation-table and realizes a real-time frequency spectrum algorithm under 1G Hz sample rate based on PC with add, address and SSE. The numerical experimental results show that under the count rate of 3X10 6 s -1 , this algorithm is superior to FFTW in time-consumption and can meet the real-time requirement of frequency spectrum analysis. (authors)

Statistical tools for analysis and modeling of cosmic populations and astronomical time series: CUDAHM and TSE

Science.gov (United States)

Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.

2018-01-01

This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.

Lag space estimation in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...

Principal component analysis of MSBAS DInSAR time series from Campi Flegrei, Italy

Science.gov (United States)

Tiampo, Kristy F.; González, Pablo J.; Samsonov, Sergey; Fernández, Jose; Camacho, Antonio

2017-09-01

Because of its proximity to the city of Naples and with a population of nearly 1 million people within its caldera, Campi Flegrei is one of the highest risk volcanic areas in the world. Since the last major eruption in 1538, the caldera has undergone frequent episodes of ground subsidence and uplift accompanied by seismic activity that has been interpreted as the result of a stationary, deeper source below the caldera that feeds shallower eruptions. However, the location and depth of the deeper source is not well-characterized and its relationship to current activity is poorly understood. Recently, a significant increase in the uplift rate has occurred, resulting in almost 13 cm of uplift by 2013 (De Martino et al., 2014; Samsonov et al., 2014b; Di Vito et al., 2016). Here we apply a principal component decomposition to high resolution time series from the region produced by the advanced Multidimensional SBAS DInSAR technique in order to better delineate both the deeper source and the recent shallow activity. We analyzed both a period of substantial subsidence (1993-1999) and a second of significant uplift (2007-2013) and inverted the associated vertical surface displacement for the most likely source models. Results suggest that the underlying dynamics of the caldera changed in the late 1990s, from one in which the primary signal arises from a shallow deflating source above a deeper, expanding source to one dominated by a shallow inflating source. In general, the shallow source lies between 2700 and 3400 m below the caldera while the deeper source lies at 7600 m or more in depth. The combination of principal component analysis with high resolution MSBAS time series data allows for these new insights and confirms the applicability of both to areas at risk from dynamic natural hazards.

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Science.gov (United States)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

Neutron activation analysis: an emerging technique for conservation/preservation

International Nuclear Information System (INIS)

Sayre, E.V.

1976-01-01

The diverse applications of neutron activation in analysis, preservation, and documentation of art works and artifacts are described with illustrations for each application. The uses of this technique to solve problems of attribution and authentication, to reveal the inner structure and composition of art objects, and, in some instances to recreate details of the objects are described. A brief discussion of the theory and techniques of neutron activation analysis is also included

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review.

Science.gov (United States)

Malkin, Zinovy

2016-04-01

The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing the frequency standards deviations. For the past decades, AVAR has increasingly been used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. In addition, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y, and Z can be combined to analyze 3-D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multidimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astrogeodetic time series.

Correlation and multifractality in climatological time series

International Nuclear Information System (INIS)

Pedron, I T

2010-01-01

Climate can be described by statistical analysis of mean values of atmospheric variables over a period. It is possible to detect correlations in climatological time series and to classify its behavior. In this work the Hurst exponent, which can characterize correlation and persistence in time series, is obtained by using the Detrended Fluctuation Analysis (DFA) method. Data series of temperature, precipitation, humidity, solar radiation, wind speed, maximum squall, atmospheric pressure and randomic series are studied. Furthermore, the multifractality of such series is analyzed applying the Multifractal Detrended Fluctuation Analysis (MF-DFA) method. The results indicate presence of correlation (persistent character) in all climatological series and multifractality as well. A larger set of data, and longer, could provide better results indicating the universality of the exponents.

Comparative performance analysis of shunt and series passive filter for LED lamp

Science.gov (United States)

Sarwono, Edi; Facta, Mochammad; Handoko, Susatyo

2018-03-01

Light Emitting Diode lamp or LED lamp nowadays is widely used by consumers as a new innovation in the lighting technologies due to its energy saving for low power consumption lamps for brighter light intensity. How ever, the LED lamp produce an electric pollutant known as harmonics. The harmonics is generated by rectifier as part of LED lamp circuit. The present of harmonics in current or voltage has made the source waveform from the grid is distorted. This distortion may cause inacurrate measurement, mall function, and excessive heating for any element at the grid. This paper present an analysis work of shunt and series filters to suppress the harmonics generated by the LED lamp circuit. The work was initiated by conducting several tests to investigate the harmonic content of voltage and currents. The measurements in this work were carried out by using HIOKI Power Quality Analyzer 3197. The measurement results showed that the harmonics current of tested LED lamps were above the limit of IEEE standard 519-2014. Based on the measurement results shunt and series filters were constructed as low pass filters. The bode analysis were appled during construction and prediction of the filters performance. Based on experimental results, the application of shunt filter at input side of LED lamp has reduced THD current up to 88%. On the other hand, the series filter has significantly reduced THD current up to 92%.

Diffraction analysis of customized illumination technique

Science.gov (United States)

Lim, Chang-Moon; Kim, Seo-Min; Eom, Tae-Seung; Moon, Seung Chan; Shin, Ki S.

2004-05-01

Various enhancement techniques such as alternating PSM, chrome-less phase lithography, double exposure, etc. have been considered as driving forces to lead the production k1 factor towards below 0.35. Among them, a layer specific optimization of illumination mode, so-called customized illumination technique receives deep attentions from lithographers recently. A new approach for illumination customization based on diffraction spectrum analysis is suggested in this paper. Illumination pupil is divided into various diffraction domains by comparing the similarity of the confined diffraction spectrum. Singular imaging property of individual diffraction domain makes it easier to build and understand the customized illumination shape. By comparing the goodness of image in each domain, it was possible to achieve the customized shape of illumination. With the help from this technique, it was found that the layout change would not gives the change in the shape of customized illumination mode.

Application of extended-crystal diffraction techniques to the symmetry and structure analysis of 221-PbBiSrCaCuO

International Nuclear Information System (INIS)

Goodman, P.; Miller, P.

1993-01-01

The discovery of a series of layer-perovskite superconducting compounds by Maeda et al. (1988) presented a challenge for present day electron diffraction techniques, due to their common occurrence as mixed phases, and the existence of complex structural modulations of more than one type. Cowley's (1976) theory developed specifically for describing diffraction effects from layered crystals having a micro-domainal sub-structure seems particularly well suited to the task of solving these structures, while the technique of extended-crystal diffraction is shown here to be capable of providing data of sufficient precision for this analysis. The present study is made on the 221 compound of PbBiSrCaCuO. Using the above diffraction techniques it is shown that the true symmetry of the whole structure is orthorhombic, Amaa, and not monoclinic as previously assumed, and that the superlattice reflections arise as a result of a basic microdomainal constitution, rather than from a uniform and incommensurate modulation. 8 figs

Time series analysis of pressure fluctuation in gas-solid fluidized beds

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C. Alberto S. Felipe

2004-09-01

Full Text Available The purpose of the present work was to study the differentiation of states of typical fluidization (single bubble, multiple bubble and slugging in a gas-solid fluidized bed, using spectral analysis of pressure fluctuation time series. The effects of the method of measuring (differential and absolute pressure fluctuations and the axial position of the probes in the fluidization column on the identification of each of the regimes studied were evaluated. Fast Fourier Transform (FFT was the mathematic tool used to analysing the data of pressure fluctuations, which expresses the behavior of a time series in the frequency domain. Results indicated that the plenum chamber was a place for reliable measurement and that care should be taken in measurement in the dense phase. The method allowed fluid dynamic regimes to be differentiated by their dominant frequency characteristics.

Investigating cardiorespiratory interaction by cross-spectral analysis of event series

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Schäfer, Carsten; Rosenblum, Michael G.; Pikovsky, Arkady S.; Kurths, Jürgen

2000-02-01

The human cardiovascular and respiratory systems interact with each other and show effects of modulation and synchronization. Here we present a cross-spectral technique that specifically considers the event-like character of the heartbeat and avoids typical restrictions of other spectral methods. Using models as well as experimental data, we demonstrate how modulation and synchronization can be distinguished. Finally, we compare the method to traditional techniques and to the analysis of instantaneous phases.

Teaching Earth Signals Analysis Using the Java-DSP Earth Systems Edition: Modern and Past Climate Change

Science.gov (United States)

Ramamurthy, Karthikeyan Natesan; Hinnov, Linda A.; Spanias, Andreas S.

2014-01-01

Modern data collection in the Earth Sciences has propelled the need for understanding signal processing and time-series analysis techniques. However, there is an educational disconnect in the lack of instruction of time-series analysis techniques in many Earth Science academic departments. Furthermore, there are no platform-independent freeware…

Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

Science.gov (United States)

Li, Z.; Ghaith, M.

2017-12-01

Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

Classification of time series patterns from complex dynamic systems

Energy Technology Data Exchange (ETDEWEB)

Schryver, J.C.; Rao, N.

1998-07-01

An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.

Development of an integrated data acquisition and handling system based on digital time series analysis for the measurement of plasma fluctuations

International Nuclear Information System (INIS)

Ghayspoor, R.; Roth, J.R.

1986-01-01

The nonlinear characteristics of data obtained by many plasma diagnostic systems requires the power of modern computers for on-line data processing and reduction. The objective of this work is to develop an integrated data acquisition and handling system based on digital time series analysis techniques. These techniques make it possible to investigate the nature of plasma fluctuations and the physical processes which give rise to them. The approach is to digitize the data, and to generate various spectra by means of Fast Fourier Transforms (FFT). Of particular interest is the computer generated auto-power spectrum, cross-power spectrum, phase spectrum, and squared coherency spectrum. Software programs based on those developed by Jae. Y. Hong at the University of Texas are utilized for these spectra. The LeCroy 3500-SA signal analyzer and VAX 11/780 are used as the data handling and reduction system in this work. In this report, the software required to link these two systems are described

MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

Science.gov (United States)

Koeppen, W. C.; Wright, R.; Pilger, E.

2009-12-01

We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found

The Application of Clustering Techniques to Citation Data. Research Reports Series B No. 6.

Science.gov (United States)

Arms, William Y.; Arms, Caroline

This report describes research carried out as part of the Design of Information Systems in the Social Sciences (DISISS) project. Cluster analysis techniques were applied to a machine readable file of bibliographic data in the form of cited journal titles in order to identify groupings which could be used to structure bibliographic files. Practical…

Decision Analysis Technique

Directory of Open Access Journals (Sweden)

Hammad Dabo Baba

2014-01-01

Full Text Available One of the most significant step in building structure maintenance decision is the physical inspection of the facility to be maintained. The physical inspection involved cursory assessment of the structure and ratings of the identified defects based on expert evaluation. The objective of this paper is to describe present a novel approach to prioritizing the criticality of physical defects in a residential building system using multi criteria decision analysis approach. A residential building constructed in 1985 was considered in this study. Four criteria which includes; Physical Condition of the building system (PC, Effect on Asset (EA, effect on Occupants (EO and Maintenance Cost (MC are considered in the inspection. The building was divided in to nine systems regarded as alternatives. Expert's choice software was used in comparing the importance of the criteria against the main objective, whereas structured Proforma was used in quantifying the defects observed on all building systems against each criteria. The defects severity score of each building system was identified and later multiplied by the weight of the criteria and final hierarchy was derived. The final ranking indicates that, electrical system was considered the most critical system with a risk value of 0.134 while ceiling system scored the lowest risk value of 0.066. The technique is often used in prioritizing mechanical equipment for maintenance planning. However, result of this study indicates that the technique could be used in prioritizing building systems for maintenance planning

Introduction to Body Composition Assessment Using the Deuterium Dilution Technique with Analysis of Urine Samples by Isotope Ratio Mass Spectrometry (French Edition)

International Nuclear Information System (INIS)

2013-01-01

The IAEA has fostered the more widespread use of a stable isotope technique to assess body composition in different population groups to address priority areas in public health nutrition in Member States. It has done this by supporting national and regional nutrition projects through its technical cooperation programme and coordinated research projects over many years. This publication was developed by an international group of experts to provide practical hands-on guidance in the use of this technique in settings where analysis of stable isotope ratios in biological samples is to be made by isotope ratio mass spectrometry. The publication is targeted at new users of this technique, for example nutritionists, analytical chemists and other professionals. More detailed information on the theoretical background and the practical applications of state of the art methodologies to monitor changes in body composition can be found in IAEA Human Health Series No. 3, Assessment of Body Composition and Total Energy Expenditure in Humans by Stable Isotope Techniques

Introduction to Body Composition Assessment Using the Deuterium Dilution Technique with Analysis of Urine Samples by Isotope Ratio Mass Spectrometry (Spanish Edition)

International Nuclear Information System (INIS)

2013-01-01

The IAEA has fostered the more widespread use of a stable isotope technique to assess body composition in different population groups to address priority areas in public health nutrition in Member States. It has done this by supporting national and regional nutrition projects through its technical cooperation programme and coordinated research projects over many years. This publication was developed by an international group of experts to provide practical hands-on guidance in the use of this technique in settings where analysis of stable isotope ratios in biological samples is to be made by isotope ratio mass spectrometry. The publication is targeted at new users of this technique, for example nutritionists, analytical chemists and other professionals. More detailed information on the theoretical background and the practical applications of state of the art methodologies to monitor changes in body composition can be found in IAEA Human Health Series No. 3, Assessment of Body Composition and Total Energy Expenditure in Humans by Stable Isotope Techniques

Causes of failure with Szabo technique - an analysis of nine cases.

Science.gov (United States)

Jain, Rajendra Kumar; Padmanabhan, T N C; Chitnis, Nishad

2013-01-01

The objective of this case series is to identify and define causes of failure of Szabo technique in rapid-exchange monorail system for ostial lesions. From March 2009 to March 2011, 42 patients with an ostial lesion were treated percutaneously at our institution using Szabo technique in a monorail stent system. All patients received unfractionated heparin during intervention. Loading dose of clopidogrel, followed by clopidogrel and aspirin was administered. In 57% of patients, drug-eluting stents were used and in 42.8% patients bare metal stents. The stent was advanced over both wires, the target wire and the anchor wire. The anchor wire, which was passed through the proximal trailing strut of the stent helps to achieve precise stenting. The procedure was considered to be successful if stent was placed precisely covering the lesion and without stent loss or anchor wire prolapsing. Of the total 42 patients, the procedure was successful in 33, while failed in 9. Majority of failures were due to wire entanglement, which was fixed successfully in 3 cases by removing and reinserting the anchor wire. Out of other three failures, in one stent dislodgment occurred, stent could not cross the lesion in one and in another anchor wire got looped and prolapsed into target vessel. This case series shows that the Szabo technique, in spite of some difficulties like wire entanglement, stent dislodgement and resistance during stent advancement, is a simple and feasible method for treating variety of ostial lesions precisely compared to conventional angioplasty. Copyright © 2013 Cardiological Society of India. Published by Elsevier B.V. All rights reserved.

Wet tropospheric delays forecast based on Vienna Mapping Function time series analysis

Science.gov (United States)

Rzepecka, Zofia; Kalita, Jakub

2016-04-01

It is well known that the dry part of the zenith tropospheric delay (ZTD) is much easier to model than the wet part (ZTW). The aim of the research is applying stochastic modeling and prediction of ZTW using time series analysis tools. Application of time series analysis enables closer understanding of ZTW behavior as well as short-term prediction of future ZTW values. The ZTW data used for the studies were obtained from the GGOS service hold by Vienna technical University. The resolution of the data is six hours. ZTW for the years 2010 -2013 were adopted for the study. The International GNSS Service (IGS) permanent stations LAMA and GOPE, located in mid-latitudes, were admitted for the investigations. Initially the seasonal part was separated and modeled using periodic signals and frequency analysis. The prominent annual and semi-annual signals were removed using sines and consines functions. The autocorrelation of the resulting signal is significant for several days (20-30 samples). The residuals of this fitting were further analyzed and modeled with ARIMA processes. For both the stations optimal ARMA processes based on several criterions were obtained. On this basis predicted ZTW values were computed for one day ahead, leaving the white process residuals. Accuracy of the prediction can be estimated at about 3 cm.

Neural networks and traditional time series methods: a synergistic combination in state economic forecasts.

Science.gov (United States)

Hansen, J V; Nelson, R D

1997-01-01

Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments. The pattern finding ability of neural networks gives insightful and alternative views of the seasonal and cyclical components commonly found in economic time series data. Two applications of neural networks to revenue forecasting clearly demonstrate how these models complement traditional time series techniques. In the first, preoccupation with a potential downturn in the economy distracts analysis based on traditional time series methods so that it overlooks an emerging new phenomenon in the data. In this case, neural networks identify the new pattern that then allows modification of the time series models and finally gives more accurate forecasts. In the second application, data structure found by traditional statistical tools allows analysts to provide neural networks with important information that the networks then use to create more accurate models. In summary, for the Utah revenue outlook, the insights that result from a portfolio of forecasts that includes neural networks exceeds the understanding generated from strictly statistical forecasting techniques. In this case, the synergy clearly results in the whole of the portfolio of forecasts being more accurate than the sum of the individual parts.

Introduction to body composition assessment using the deuterium dilution technique with analysis of saliva samples by fourier transform infrared spectrometry

International Nuclear Information System (INIS)

2010-01-01

For many years, the IAEA has fostered the more widespread use of stable isotope techniques to assess body composition in different population groups to address priority areas in public health nutrition in Member States. The objective is to support national and regional nutrition projects through both the IAEA's technical cooperation programme and its coordinated research projects. In particular, during the last few years, the increased access to analyses of deuterium enrichment by Fourier transform infrared (FTIR) spectrometry has increased the application of this technique in Africa, Asia and Latin America. This publication was developed by an international group of experts to provide practical, hands-on guidance in the use of this technique in settings where the analysis of deuterium enrichment in saliva samples will be made by FTIR. It is targeted at new users of this technique, for example nutritionists, analytical chemists and other professionals. More detailed information on the theoretical background and the practical application of state of the art methodologies to monitor changes in body composition can be found in an IAEA publication entitled Assessment of Body Composition and Total Energy Expenditure in Humans by Stable Isotope Techniques (IAEA Human Health Series No. 3)

48 CFR 15.404-1 - Proposal analysis techniques.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Proposal analysis techniques. 15.404-1 Section 15.404-1 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION... assistance of other experts to ensure that an appropriate analysis is performed. (6) Recommendations or...

Fuzzy Linear Regression for the Time Series Data which is Fuzzified with SMRGT Method

Directory of Open Access Journals (Sweden)

Seçil YALAZ

2016-10-01

Full Text Available Our work on regression and classification provides a new contribution to the analysis of time series used in many areas for years. Owing to the fact that convergence could not obtained with the methods used in autocorrelation fixing process faced with time series regression application, success is not met or fall into obligation of changing the models’ degree. Changing the models’ degree may not be desirable in every situation. In our study, recommended for these situations, time series data was fuzzified by using the simple membership function and fuzzy rule generation technique (SMRGT and to estimate future an equation has created by applying fuzzy least square regression (FLSR method which is a simple linear regression method to this data. Although SMRGT has success in determining the flow discharge in open channels and can be used confidently for flow discharge modeling in open canals, as well as in pipe flow with some modifications, there is no clue about that this technique is successful in fuzzy linear regression modeling. Therefore, in order to address the luck of such a modeling, a new hybrid model has been described within this study. In conclusion, to demonstrate our methods’ efficiency, classical linear regression for time series data and linear regression for fuzzy time series data were applied to two different data sets, and these two approaches performances were compared by using different measures.

Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...