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Complexity as a Form of Transition From Dynamics to Thermodynamics: Application to Sociological and Biological Processes.

Description: This dissertation addresses the delicate problem of establishing the statistical mechanical foundation of complex processes. These processes are characterized by a delicate balance of randomness and order, and a correct paradigm for them seems to be the concept of sporadic randomness. First of all, we have studied if it is possible to establish a foundation of these processes on the basis of a generalized version of thermodynamics, of non-extensive nature. A detailed account of this attempt is reported in Ignaccolo and Grigolini (2001), which shows that this approach leads to inconsistencies. It is shown that there is no need to generalize the Kolmogorov-Sinai entropy by means of a non-extensive indicator, and that the anomaly of these processes does not rest on their non-extensive nature, but rather in the fact that the process of transition from dynamics to thermodynamics, this being still extensive, occurs in an exceptionally extended time scale. Even, when the invariant distribution exists, the time necessary to reach the thermodynamic scaling regime is infinite. In the case where no invariant distribution exists, the complex system lives forever in a condition intermediate between dynamics and thermodynamics. This discovery has made it possible to create a new method of analysis of non-stationary time series which is currently applied to problems of sociological and physiological interest.
Date: May 2003
Creator: Ignaccolo, Massimiliano
Partner: UNT Libraries

Water Quality and Streamflow Characteristics, Raritan River Basin, New Jersey

Description: Abstract: The findings of a problem-oriented, river-system investigation of the water quality and streamflow characteristics of the Raritan River, N.J. are described. Information on streamflow duration, time-of-travel measurements, and analyses of chemical, biochemical and physical-water quality are summarized and used to define relations existing between water quality, streamflow, geology, and environmental development in the basin's hydrologic system. Stream quality, particularly in the lower urban areas of the basin, is shown to be deteriorating with time at most of the sampling sites reported. For example, average dissolved-oxygen concentration is reported to be undersaturated at all sampling sites and is decreasing with time at most sites. Biochemical-oxygen demand is increasing at most sites, as are the dissolved-solids content.
Date: June 1974
Creator: Anderson, Peter W. & Faust, Samuel D.
Partner: UNT Libraries Government Documents Department

A System for Simulating Fluctuation Diagnostics for Application to Turbulence Computations

Description: Present-day nonlinear microstability codes are able to compute the saturated fluctuations of a turbulent fluid versus space and time, whether the fluid be liquid, gas, or plasma. They are therefore able to determine turbulence-induced fluid (or particle) and energy fluxes. These codes, however, must be tested against experimental data, not only with respect to transport, but also characteristics of the fluctuations. The latter is challenging because of limitations in the diagnostics (e.g., finite spatial resolution) and the fact that the diagnostics typically do not measure exactly the quantities the codes compute. In this work, we present a system based on IDL{reg_sign} analysis and visualization software in which user-supplied ''diagnostic filters'' are applied to the code outputs to generate simulated diagnostic signals. The same analysis techniques as applied to the measurements, e.g., digital time-series analysis, may then be applied to the synthesized signals. Their statistical properties, such as rms fluctuation level, mean wave numbers, phase and group velocities, correlation lengths and times, and in some cases full S(k,{omega}) spectra can then be compared directly to those of the measurements.
Date: February 21, 2006
Creator: Bravenec, R V & Nevins, W M
Partner: UNT Libraries Government Documents Department

Technical analysis of prospective photovoltaic systems in Utah.

Description: This report explores the technical feasibility of prospective utility-scale photovoltaic system (PV) deployments in Utah. Sandia National Laboratories worked with Rocky Mountain Power (RMP), a division of PacifiCorp operating in Utah, to evaluate prospective 2-megawatt (MW) PV plants in different locations with respect to energy production and possible impact on the RMP system and customers. The study focused on 2-MW{sub AC} nameplate PV systems of different PV technologies and different tracking configurations. Technical feasibility was evaluated at three different potential locations in the RMP distribution system. An advanced distribution simulation tool was used to conduct detailed time-series analysis on each feeder and provide results on the impacts on voltage, demand, voltage regulation equipment operations, and flicker. Annual energy performance was estimated.
Date: February 1, 2012
Creator: Quiroz, Jimmy Edward & Cameron, Christopher P.
Partner: UNT Libraries Government Documents Department

Super synchronization for fused video and time-series neural network training

Description: A key element in establishing neural networks for traffic monitoring is the ground truth data set that verifies the sensor data. The sensors we use have time series data gathered from loop and piezo sensors embedded in the highway. These signals are analyzed and parsed into vehicle events. Features are extracted from these sensors and combine to form the vehicle vectors. The vehicle vectors are combined together with the video data in a data fusion process thereby providing the neural network with its training set. We examine two studies, one by Georgia Tech Research Institute (GTRI) and another by Los Alamos National Laboratory (LANL) that use video information and have had difficulties in establishing the fusion process. That is to say, the correspondence between the video events recorded as the ground truth data and the sensor events has been uncertain. We show that these uncertainties can be removed by establishing a more precise and accurate time measurement for the video events. The principal that the video time information is inherently precise to better than a frame (1/30 s) and that by tracing the factors causing imprecision in the timing of events, we can achieve precisions required for unique vehicle identification we call super synchronization. In the Georgia data study there was an imprecision on the order of 3 seconds and in the LANL study an imprecision of early a second. In both cases, the imprecision had led to lack of proper identification of sensor events. In the case of the Georgia 120 study sensors were placed at various distances downstream, up to 250 meters, from the ground truth camera. The original analysis assumed that there was a fixed time offset corresponding to the downstream location. For this case we show that when we restrict the analysis to passenger cars and ...
Date: June 1, 1996
Creator: Elliott, C.J.; Pepin, J. & Gillmann, R.
Partner: UNT Libraries Government Documents Department

Making business decisions using trend information

Description: Performance Measures, and the trend information that results from their analyses, can help managers in their decision making process. The business decisions that are to be discussed are: Assignment of limited Resources, Funding, Budget; Contractor Rewards/Incentives; Where to focus Process Improvement, Reengineering efforts; When to ask ``What Happened?!!``; Determine if a previous decision was effectively implemented. Trending can provide an input for rational Business Decisions. Key Element is determination of whether or not a significant trend exists - segregating Common Cause from Special Cause. The Control Chart is the tool for accomplishment of trending and determining if you are meeting your Business Objectives. Eliminate Numerical Targets; the goal is Significant Improvement. Profound Knowledge requires integrating data results with gut feeling.
Date: November 24, 1997
Creator: Prevette, S. S.
Partner: UNT Libraries Government Documents Department

International petroleum statistics report, March 1998

Description: The International Petroleum Statistics Report is a monthly publication that provides current international oil data. This report is published for the use of Members of Congress, Federal agencies, State agencies, industry, and the general public. The International Petroleum Statistics Report presents data on international oil production, demand, imports, and stocks. The report has four sections. Section 1 contains time series data on world oil production, and on oil demand and stocks in the Organization for Economic Cooperation and Development (OECD). This section contains annual data beginning in 1985, and monthly data for the most recent two years. Section 2 presents an oil supply/demand balance for the world. This balance is presented in quarterly intervals for the most recent two years. Section 3 presents data on oil imports by OECD countries. This section contains annual data for the most recent year, quarterly data for the most recent two quarters, and monthly data for the most recent twelve months. Section 4 presents annual time series data on world oil production and oil stocks, demand, and trade in OECD countries. World oil production and OECD demand data are for the years 1970 through 1996; OECD stocks from 1973 through 1996; and OECD trade from 1986 through 1996.
Date: March 1, 1998
Partner: UNT Libraries Government Documents Department

Machine and Process System Diagnostics Using One-Step Prediction Maps

Description: This paper describes a method for machine or process system diagnostics that uses one-step prediction maps. The method uses nonlinear time series analysis techniques to form a one-step prediction map that estimates the next time series data point when given a sequence of previously measured time series data point. The difference between the predicted and measured time series values is a measure of the map error. The average value of this error should remain within some bound as long as both the dynamic system and its operating condition remain unchanged. However, changes in the dynamic system or operating condition will cause an increase in average map error. Thus, for a constant operating condition, monitoring the average map error over time should indicate when a change has occurred in the dynamic system. Furthermore, the map error itself forms a time series that can be analyzed to detect changes in system dynamics. The paper provides technical background in the nonlinear analysis techniques used in the diagnostic method, describes the creation of one-step prediction maps and their application to machine or process system diagnostics, and then presents results obtained from applying the diagnostic method to simulated and measured data.
Date: May 10, 1999
Creator: Breeding, J. E.; Damiano, B. & Tucker, R. W., Jr.
Partner: UNT Libraries Government Documents Department

Time Series Based Model Updating in Nonlinear Systems using Singular Value Decomposition

Description: The problem considered is the use of time series data to do model updating in nonlinear structural systems for which the mathematical form of the system nonlinearities is known ahead of time. This work is a departure from most classical model updating work, which utilizes model data to update linear structural dynamics models. In the present application a singular value decomposition (SVD) of the measured data (e.g., m of the N coordinates are measured at n sampling times) is the basis of the updating. The SVD produces a representation of the data as a linear combination of the so-called principal components, which are analogous to modal coordinate time histories in a linear system. The structural dynamics model parameters are updated by minimizing the differences in the SVD's of the experimental data and the model simulations. This method, proposed by Hasselman et al (IMAC 1998), has been applied to both simulated and actual experimental data for low degree of freedom spring-mass systems with cubic nonlinearity and light damping. The main results that will be presented are the following: (1) the SVD updating is robust in the presence of noise, (2) SVD based updating is effective for both linear and nonlinear systems, and (3) in some cases the nonlinear updating problem is actually easier to do than the linear problem because of the additional ''information'' contained in the harmonics produced by the nonlinearity. A possible limitation of the approach is the computing time needed to do the parameter optimization.
Date: June 27, 1999
Creator: Hemez, F.M.; Beardsley, P.; Rhee, W. & Burton, T.D.
Partner: UNT Libraries Government Documents Department

Application of chaotic time series for the characterization and control of fluidization and combustion systems. CRADA final report for CRADA No. ORNL92-0127

Description: Oak Ridge National Laboratory and Babcock and Wilcox, a prominent U.S. boiler manufacturer, have collaborated under this CRADA to investigate the application of deterministic chaos theory to improve the performance of fossil fuel boilers. The types of boilers investigated were coal-fired fluidized beds and more conventional pulverized coal systems. The results of this investigation demonstrate that chaotic time series analysis of boiler signals (e.g., pressure, acoustic and/or optical signals) can be used to characterize the combustion dynamics with greater accuracy than is possible with conventional signal analysis. Further, it is expected that these new analysis techniques will lead to a new generation of boiler diagnostics and control technology that can make U.S. industry more competitive in the world boiler market. Babcock and Wilcox is initiating follow-on internal and external research to further develop the technology.
Date: February 1, 1996
Creator: Daw, C.S.; Fuller, T.A. & Flynn, T.J.
Partner: UNT Libraries Government Documents Department

Chaos, dynamical structure and climate variability

Description: Deterministic chaos in dynamical systems offers a new paradigm for understanding irregular fluctuations. Techniques for identifying deterministic chaos from observed data, without recourse to mathematical models, are being developed. Powerful methods exist for reconstructing multidimensional phase space from an observed time series of a single scalar variable; these methods are invaluable when only a single scalar record of the dynamics is available. However, in some applications multiple concurrent time series may be available for consideration as phase space coordinates. Here the authors propose some basic analytical tools for such multichannel time series data, and illustrate them by applications to a simple synthetic model of chaos, to a low-order model of atmospheric circulation, and to two high-resolution paleoclimate proxy data series. The atmospheric circulation model, originally proposed by Lorenz, has 27 principal unknowns; they establish that the chaotic attractor can be embedded in a subspace of eight dimensions by exhibiting a specific subset of eight unknowns which pass multichannel tests for false nearest neighbors. They also show that one of the principal unknowns in the 27-variable model--the global mean sea surface temperature--is of no discernible usefulness in making short-term forecasts.
Date: September 1, 1995
Creator: Stewart, H.B.
Partner: UNT Libraries Government Documents Department

Initial Evidence for Self-Organized Criticality in Electric Power System Blackouts

Description: We examine correlations in a time series of electric power system blackout sizes using scaled window variance analysis and R/S statistics. The data shows some evidence of long time correlations and has Hurst exponent near 0.7. Large blackouts tend to correlate with further large blackouts after a long time interval. Similar effects are also observed in many other complex systems exhibiting self-organized criticality. We discuss this initial evidence and possible explanations for self-organized criticality in power systems blackouts. Self-organized criticality, if fully confirmed in power systems, would suggest new approaches to understanding and possibly controlling blackouts.
Date: January 4, 2000
Creator: Carreras, B.A.; Dobson, I.; Newman, D.E. & Poole, A.B.
Partner: UNT Libraries Government Documents Department

A Statistical Study of Hard X-Ray Solar Flares

Description: The results of a statistical study of hard x-ray solar flares are presented in this dissertation. Two methods of analysis were used, the Diffusion Entropy (DE) method coupled with an analysis of the data distributions and the Rescaled Range (R/S) Method, sometimes referred to as "Hurst's method". Chapter one provides an introduction to hard x-ray flares within the context of the solar environment and a summary of the statistical paradigms solar astronomers currently work under. Chapter two presents the theory behind the DE and R/S methods. Chapter three presents the results of the two analysis methodologies: most notably important evidence of the conflicting results of the R/S and DE methods, evidence of a Levy statistical signature for the underlying dynamics of the hard x-ray flaring process and a possible separate memory signature for the waiting times. In addition, the stationary and nonstationary characteristics of the waiting times and peak intensities, are revealed. Chapter four provides a concise summary and discussion of the results.
Date: December 2001
Creator: Leddon, Deborah L.
Partner: UNT Libraries

An entropic approach to the analysis of time series.

Description: Statistical analysis of time series. With compelling arguments we show that the Diffusion Entropy Analysis (DEA) is the only method of the literature of the Science of Complexity that correctly determines the scaling hidden within a time series reflecting a Complex Process. The time series is thought of as a source of fluctuations, and the DEA is based on the Shannon entropy of the diffusion process generated by these fluctuations. All traditional methods of scaling analysis, instead, are based on the variance of this diffusion process. The variance methods detect the real scaling only if the Gaussian assumption holds true. We call H the scaling exponent detected by the variance methods and d the real scaling exponent. If the time series is characterized by Fractional Brownian Motion, we have H¹d and the scaling can be safely determined, in this case, by using the variance methods. If, on the contrary, the time series is characterized, for example, by Lévy statistics, H ¹ d and the variance methods cannot be used to detect the true scaling. Lévy walk yields the relation d=1/(3-2H). In the case of Lévy flights, the variance diverges and the exponent H cannot be determined, whereas the scaling d exists and can be established by using the DEA. Therefore, only the joint use of two different scaling analysis methods, the variance scaling analysis and the DEA, can assess the real nature, Gauss or Lévy or something else, of a time series. Moreover, the DEA determines the information content, under the form of Shannon entropy, or of any other convenient entopic indicator, at each time step of the process that, given a sufficiently large number of data, is expected to become diffusion with scaling. This makes it possible to study the regime of transition from dynamics to thermodynamics, non-stationary regimes, ...
Date: December 2001
Creator: Scafetta, Nicola
Partner: UNT Libraries

Ballistic Deposition: Global Scaling and Local Time Series.

Description: Complexity can emerge from extremely simple rules. A paradigmatic example of this is the model of ballistic deposition (BD), a simple model of sedimentary rock growth. In two separate Problem-in-Lieu-of Thesis studies, BD was investigated numerically in (1+1)-D on a lattice. Both studies are combined in this document. For problem I, the global interface roughening (IR) process was studied in terms of effective scaling exponents for a generalized BD model. The model used incorporates a tunable parameter B to change the cooperation between aggregating particles. Scaling was found to depart increasingly from the predictions of Kardar-Parisi-Zhang theory both with decreasing system sizes and with increasing cooperation. For problem II, the local single column evolution during BD rock growth was studied via statistical analysis of time series. Connections were found between single column time series properties and the global IR process.
Date: December 2003
Creator: Schwettmann, Arne
Partner: UNT Libraries

The Induction of Chaos in Electronic Circuits Final Report-October 1, 2001

Description: This project, now known by the name ''Chaos in Electronic Circuits,'' was originally tasked as a two-year project to examine various ''fault'' or ''non-normal'' operational states of common electronic circuits with some focus on determining the feasibility of exploiting these states. Efforts over the two-year duration of this project have been dominated by the study of the chaotic behavior of electronic circuits. These efforts have included setting up laboratory space and hardware for conducting laboratory tests and experiments, acquiring and developing computer simulation and analysis capabilities, conducting literature surveys, developing test circuitry and computer models to exercise and test our capabilities, and experimenting with and studying the use of RF injection as a means of inducing chaotic behavior in electronics. An extensive array of nonlinear time series analysis tools have been developed and integrated into a package named ''After Acquisition'' (AA), including capabilities such as Delayed Coordinate Embedding Mapping (DCEM), Time Resolved (3-D) Fourier Transform, and several other phase space re-creation methods. Many computer models have been developed for Spice and for the ATP (Alternative Transients Program), modeling the several working circuits that have been developed for use in the laboratory. And finally, methods of induction of chaos in electronic circuits have been explored.
Date: April 1, 2003
Creator: R.M.Wheat, Jr.
Partner: UNT Libraries Government Documents Department

State energy data report 1996: Consumption estimates

Description: The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA`s energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs.
Date: February 1, 1999
Partner: UNT Libraries Government Documents Department

Time series modeling of system self-assessment of survival

Description: Self-assessment of survival for a system, subsystem or component is implemented by assessing conditional performance reliability in real-time, which includes modeling and analysis of physical performance data. This paper proposes a time series analysis approach to system self-assessment (prediction) of survival. In the approach, physical performance data are modeled in a time series. The performance forecast is based on the model developed and is converted to the reliability of system survival. In contrast to a standard regression model, a time series model, using on-line data, is suitable for the real-time performance prediction. This paper illustrates an example of time series modeling and survival assessment, regarding an excessive tool edge wear failure mode for a twist drill operation.
Date: June 1, 1999
Creator: Lu, H. & Kolarik, W. J.
Partner: UNT Libraries Government Documents Department

Application of Spectral Analysis to the Cycle Regression Algorithm

Description: Many techniques have been developed to analyze time series. Spectral analysis and cycle regression analysis represent two such techniques. This study combines these two powerful tools to produce two new algorithms; the spectral algorithm and the one-pass algorithm. This research encompasses four objectives. The first objective is to link spectral analysis with cycle regression analysis to determine an initial estimate of the sinusoidal period. The second objective is to determine the best spectral window and truncation point combination to use with cycle regression for the initial estimate of the sinusoidal period. The third is to determine whether the new spectral algorithm performs better than the old T-value algorithm in estimating sinusoidal parameters. The fourth objective is to determine whether the one-pass algorithm can be used to estimate all significant harmonics simultaneously.
Date: August 1984
Creator: Shah, Vivek
Partner: UNT Libraries

Characterization of the fluidization behavior of different solid types based on chaotic time series analysis of pressure signals

Description: Results from a laboratory-scale fluidized bed are presented to show that chaotic time series analysis of pressure-drop measurements can be used to distinguish the behavior patterns of three different fluidizing solids belonging to groups A, B, and D (Geldart classification scheme). The results also show that chaotic time series analysis of pressure measurements can be used to detect transitions in flow regime as gas velocity is increased for a given solid. The results are based on pressure-drop measurements from a room-temperature, atmospheric fluidized bed operated over a range of air flows and a static bed height of 28 cm. In the present study, two different pressure-drop measurements were used. One provided the overall pressure drop and the other provided the pressure drop across a 12.7-cm-high section located near the top of the static bed. In addition to the conventional Fourier analysis, we employ the following chaotic time series analysis tools: principal components analysis of the phase-space trajectory, the mutual information function, and the average Kolmogorov entropy. Our objectives are to show how these analytical tools when applied to fluidized bed pressure-drop measurements can be used to detect transition in fluidization regime as well as to delineate the difference, if any, of the fluidization states in a local or global scale (i.e., spatially distributed) for a given fluidizing solid.
Date: June 1, 1993
Creator: Daw, C. S.; Nguyen, Ke & Skrzycke, D. P.
Partner: UNT Libraries Government Documents Department