He, Ruining; McAuley, Julian
2016-01-01
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underly...
Trend Filtering Techniques for Time Series Analysis
López Arias, Daniel
2016-01-01
Time series can be found almost everywhere in our lives and because of this being capable of analysing them is an important task. Most of the time series we can think of are quite noisy, being this one of the main problems to extract information from them. In this work we use Trend Filtering techniques to try to remove this noise from a series and understand the underlying trend of the series, that gives us information about the behaviour of the series aside from the particular...
Sparse Estimation Techniques for l1 Mean and Trend Filtering
Johan, Ottersten
2015-01-01
It is often desirable to find the underlying trends in time series data. This is a wellknown signal processing problem that has many applications in areas such as financial dataanalysis, climatology, biological and medical sciences etc. Mean filtering finds a piece-wiseconstant trend in the data while trend filtering finds a piece-wise linear trend. When thesignal is noisy, the main difficulty is finding the changing points in the data. These are thepoints where the mean or the trend changes....
An operator model-based filtering scheme
International Nuclear Information System (INIS)
Sawhney, R.S.; Dodds, H.L.; Schryer, J.C.
1990-01-01
This paper presents a diagnostic model developed at Oak Ridge National Laboratory (ORNL) for off-normal nuclear power plant events. The diagnostic model is intended to serve as an embedded module of a cognitive model of the human operator, one application of which could be to assist control room operators in correctly responding to off-normal events by providing a rapid and accurate assessment of alarm patterns and parameter trends. The sequential filter model is comprised of two distinct subsystems --- an alarm analysis followed by an analysis of interpreted plant signals. During the alarm analysis phase, the alarm pattern is evaluated to generate hypotheses of possible initiating events in order of likelihood of occurrence. Each hypothesis is further evaluated through analysis of the current trends of state variables in order to validate/reject (in the form of increased/decreased certainty factor) the given hypothesis. 7 refs., 4 figs
Electricity market modeling trends
International Nuclear Information System (INIS)
Ventosa, Mariano; Baillo, Alvaro; Ramos, Andres; Rivier, Michel
2005-01-01
The trend towards competition in the electricity sector has led to efforts by the research community to develop decision and analysis support models adapted to the new market context. This paper focuses on electricity generation market modeling. Its aim is to help to identify, classify and characterize the somewhat confusing diversity of approaches that can be found in the technical literature on the subject. The paper presents a survey of the most relevant publications regarding electricity market modeling, identifying three major trends: optimization models, equilibrium models and simulation models. It introduces a classification according to their most relevant attributes. Finally, it identifies the most suitable approaches for conducting various types of planning studies or market analysis in this new context
Gradient based filtering of digital elevation models
DEFF Research Database (Denmark)
Knudsen, Thomas; Andersen, Rune Carbuhn
We present a filtering method for digital terrain models (DTMs). The method is based on mathematical morphological filtering within gradient (slope) defined domains. The intention with the filtering procedure is to improbé the cartographic quality of height contours generated from a DTM based on ...
Stochastic and deterministic trend models
Estela Bee Dagum; Camilo Dagum
2008-01-01
In this paper we provide an overview of some trend models formulated for global and local estimation. Global trend models are based on the assumption that the trend or nonstationary mean of a time series can be approximated closely by simple functions of time over the entire span of the series. The most common representation of deterministic and stochastic trend are introduced. In particular, for the former we analyze polynomial and transcendental functions, whereas for the latter we assume t...
Particle filters for random set models
Ristic, Branko
2013-01-01
“Particle Filters for Random Set Models” presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. The resulting algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from navigation and autonomous vehicles to bio-informatics and finance. While particle filters have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. This book...
Abreu, Orlando; Alvear, Daniel
2016-01-01
This book presents an overview of modeling definitions and concepts, theory on human behavior and human performance data, available tools and simulation approaches, model development, and application and validation methods. It considers the data and research efforts needed to develop and incorporate functions for the different parameters into comprehensive escape and evacuation simulations, with a number of examples illustrating different aspects and approaches. After an overview of basic modeling approaches, the book discusses benefits and challenges of current techniques. The representation of evacuees is a central issue, including human behavior and the proper implementation of representational tools. Key topics include the nature and importance of the different parameters involved in ASET and RSET and the interactions between them. A review of the current literature on verification and validation methods is provided, with a set of recommended verification tests and examples of validation tests. The book c...
A latent model for collaborative filtering
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre
Recommender systems based on collaborative filtering have received a great deal of interest over the last decade. Typically, these types of systems either take a user-centered or an item-centered approach when making recommendations, but by employing only one of these two perspectives we may unin...... than other collaborative filtering systems (evaluated on the MovieLens data set). Furthermore, the explicit representation of all users and items allows the model to e.g. make group-based recommendations balancing the preferences of the individual users....
HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.
Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee
2017-08-01
Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.
Modeling branching pore structures in membrane filters
Sanaei, Pejman; Cummings, Linda J.
2016-11-01
Membrane filters are in widespread industrial use, and mathematical models to predict their efficacy are potentially very useful, as such models can suggest design modifications to improve filter performance and lifetime. Many models have been proposed to describe particle capture by membrane filters and the associated fluid dynamics, but most such models are based on a very simple structure in which the pores of the membrane are assumed to be simple circularly-cylindrical tubes spanning the depth of the membrane. Real membranes used in applications usually have much more complex geometry, with interconnected pores which may branch and bifurcate. Pores are also typically larger on the upstream side of the membrane than on the downstream side. We present an idealized mathematical model, in which a membrane consists of a series of bifurcating pores, which decrease in size as the membrane is traversed. Feed solution is forced through the membrane by applied pressure, and particles are removed from the feed either by sieving, or by particle adsorption within pores (which shrinks them). Thus the membrane's permeability decreases as the filtration progresses, ultimately falling to zero. We discuss how filtration efficiency depends on the characteristics of the branching structure. Partial support from NSF DMS 1261596 is gratefully acknowledged.
How to monitor and mitigate stair-casing in l1 trend filtering
Rojas, Cristian R.; Wahlberg, Bo
2014-01-01
In this paper we study the estimation of changing trends in time-series using $\\ell_1$ trend filtering. This method generalizes 1D Total Variation (TV) denoising for detection of step changes in means to detecting changes in trends, and it relies on a convex optimization problem for which there are very efficient numerical algorithms. It is known that TV denoising suffers from the so-called stair-case effect, which leads to detecting false change points. The objective of this paper is to show...
A latent model for collaborative filtering
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre
2012-01-01
Recommender systems based on collaborative filtering have received a great deal of interest over the last two decades. In particular, recently proposed methods based on dimensionality reduction techniques and using a symmetrical representation of users and items have shown promising results. Foll...... on the MovieLens data set). Furthermore, the explicit representation of all users and items allows the model to, e.g., make group-based recommendations balancing the preferences of the individual users....
Dreano, Denis
2015-04-27
A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.
Modelling and measurement of wear particle flow in a dual oil filter system for condition monitoring
DEFF Research Database (Denmark)
Henneberg, Morten; Eriksen, René Lynge; Fich, Jens
2016-01-01
Wear debris is an indicator of the health of machinery, and the availability of accurate methods for characterising debris is important. In this work, a dual filter model for a gear oil system is used in conjunction with operational data to indicate three different system operating states...... boundary condition for particle burst phenomenon, the release of wear particles from a pleated mesh filter is measured in a test rig and included in the model. The findings show that a dual filter model, with startup phenomenon included, can describe trends in the wear particle flow observed in the gear...... oil. Using this model it is possible to draw conclusions on the filtration system performance and wear generation in the gears. Limitations of the proposed model are the lack of ability to describe noise and random burst spikes attributed to measurement error distributions. Trending of gear wear...
Modeling and Measurements of Novel Monolithic Filters
Directory of Open Access Journals (Sweden)
Adalbert Beyer
2008-11-01
Full Text Available This paper presents novel multilayer tuneable high Q-filters based on hairpin resonators including ferroelectric materials. This configuration allows the miniaturization of these filters to a size that makes them suitable for chip and package integration and narrow-band applications. The main focus was miniaturizing filters with coupled loops using multilayer dielectric substrates. A further goal was to increase the quality factor of these distributed filters by embedding high dielectric materials in a multilayer high- and low-k (dielectric constant substrate that is supported by LTCC technology. An improved W-shape bandpass filter was proposed with a wide stopband and approximately 5% bandwidth.
Parsimonious modeling with information filtering networks
Barfuss, Wolfram; Massara, Guido Previde; Di Matteo, T.; Aste, Tomaso
2016-12-01
We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided.
Modeling evaporative loss of oil mist collected by sampling filters.
Raynor, P C; Volckens, J; Leith, D
2000-01-01
Oil mists can cause respiratory distress and have been linked to skin and gastrointestinal cancers in workers. Standard concentration assessment methods call for sampling these mists with fibrous or membrane filters. Previous experimental studies using glass fiber (GF) filters and polyvinyl chloride and polytetrafluoroethylene membrane filters indicate that mist sampled onto filters may volatilize. A model has been developed to predict the evaporation of mist collected on a fibrous sampling filter. Evaporation of retained fluid from membrane filters can be modeled by treating the filter as though it is a fibrous filter. Predictions from the model exhibit good agreement with experimental results. At low mist concentrations, the model indicates that evaporation of retained mineral oil occurs readily. At high mist concentrations, significant evaporation from the filters is not expected because the vapor accompanying the airborne mist is already saturated with the compounds in the oil. The findings from this study indicate that sampling mineral oil mist with filters in accordance with standard methods can lead to estimates of worker exposure to oil mist that are too low.
Transient Heating and Thermomechanical Stress Modeling of Ceramic HEPA Filters
Energy Technology Data Exchange (ETDEWEB)
Bogle, Brandon [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kelly, James [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Haslam, Jeffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-09-29
The purpose of this report is to showcase an initial finite-element analysis model of a ceramic High-Efficiency Particulate (HEPA) Air filter design. Next generation HEPA filter assemblies are being developed at LLNL to withstand high-temperature fire scenarios by use of ceramics and advanced materials. The filters are meant for use in radiological and nuclear facilities, and are required to survive 500°C fires over an hour duration. During such conditions, however, collecting data under varying parameters can be challenging; therefore, a Finite Element Analysis model of the filter was conducted using COMSOL ® Multiphysics to analyze the effects of fire. Finite Element Analysis (FEA) modelling offers several opportunities: researchers can quickly and easily consider impacts of potential design changes, material selection, and flow characterization on filter performance. Specifically, this model provides stress references for the sealant at high temperatures. Modeling of full filter assemblies was deemed inefficient given the computational requirements, so a section of three tubes from the assembly was modeled. The model looked at the transient heating and thermomechanical stress development during a 500°C air flow at 6 CFM. Significant stresses were found at the ceramic-metal interfaces of the filter, and conservative temperature profiles at locations of interest were plotted. The model can be used for the development of sealants that minimize stresses at the ceramic-metal interface. Further work on the model would include the full filter assembly and consider heat losses to make more accurate predictions.
Modeling Flow Past a Tilted Vena Cava Filter
Energy Technology Data Exchange (ETDEWEB)
Singer, M A; Wang, S L
2009-06-29
Inferior vena cava filters are medical devices used to prevent pulmonary embolism (PE) from deep vein thrombosis. In particular, retrievable filters are well-suited for patients who are unresponsive to anticoagulation therapy and whose risk of PE decreased with time. The goal of this work is to use computational fluid dynamics to evaluate the flow past an unoccluded and partially occluded Celect inferior vena cava filter. In particular, the hemodynamic response to thrombus volume and filter tilt is examined, and the results are compared with flow conditions that are known to be thrombogenic. A computer model of the filter inside a model vena cava is constructed using high resolution digital photographs and methods of computer aided design. The models are parameterized using the Overture software framework, and a collection of overlapping grids is constructed to discretize the flow domain. The incompressible Navier-Stokes equations are solved, and the characteristics of the flow (i.e., velocity contours and wall shear stresses) are computed. The volume of stagnant and recirculating flow increases with thrombus volume. In addition, as the filter increases tilt, the cava wall adjacent to the tilted filter is subjected to low velocity flow that gives rise to regions of low wall shear stress. The results demonstrate the ease of IVC filter modeling with the Overture software framework. Flow conditions caused by the tilted Celect filter may elevate the risk of intrafilter thrombosis and facilitate vascular remodeling. This latter condition also increases the risk of penetration and potential incorporation of the hook of the filter into the vena caval wall, thereby complicating filter retrieval. Consequently, severe tilt at the time of filter deployment may warrant early clinical intervention.
Power Consumption Models for Decimation FIR Filters in Multistandard Receivers
Directory of Open Access Journals (Sweden)
Khaled Grati
2012-01-01
Full Text Available Decimation filters are widely used in communication-embedded systems. In fact, decimation filters are useful for implementing channel filtering or selection with low-computation complexity requirements. Many multistandard receiver designs that are required in ubiquitous embedded systems are based on a cascade of decimation filter processing. Filter number and implementation architectures have a significant impact on system performances, such as computation complexity, area, throughput, and power consumption. In this work, we present filter power consumption estimation models for FIR filters. Power consumption models were obtained from a large number of FIR filter syntheses using a direct form. Several curves that estimate power consumption were extracted from these synthesis results. Then, we have evaluated the impact of polyphase decomposition on power consumption of FIR filter and compared it with the direct form results. Some tips regarding power consumption were deduced for the polyphase implementation form. The aim of this work is to help a system designer to select an efficient implementation for FIR in terms of power consumption without having to implement and synthesize the different possible solutions. The proposed method is applied for STMicroelectronics libraries 90 nm and 65 nm low power then validated with a use case of multistandard receiver designing.
Methodology for modeling the microbial contamination of air filters.
Directory of Open Access Journals (Sweden)
Yun Haeng Joe
Full Text Available In this paper, we propose a theoretical model to simulate microbial growth on contaminated air filters and entrainment of bioaerosols from the filters to an indoor environment. Air filter filtration and antimicrobial efficiencies, and effects of dust particles on these efficiencies, were evaluated. The number of bioaerosols downstream of the filter could be characterized according to three phases: initial, transitional, and stationary. In the initial phase, the number was determined by filtration efficiency, the concentration of dust particles entering the filter, and the flow rate. During the transitional phase, the number of bioaerosols gradually increased up to the stationary phase, at which point no further increase was observed. The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively. It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases. The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.
Pyramidal Normalization Filter: Visual Model With Applications To Image Understanding
Schenker, P. S.; Unangst, D. R.; Knaak, T. F.; Huntley, D. T.; Patterson, W. R.
1982-12-01
This paper introduces a new nonlinear filter model which has applications in low-level machine vision. We show that this model, which we designate the normalization filter, is the basis for non-directional, multiple spatial frequency channel resolved detection of image edge structure. We show that the results obtained in this procedure are in close correspondence to the zero-crossing sets of the Marr-Hildreth edge detector.6 By comparison to their model, ours has the additional feature of constant-contrast thresholding, viz., it is spatially brightness adaptive. We describe a highly efficient and flexible realization of the normalization filter based on Burt's algorithm for pyramidal filtering.18 We present illustrative experimental results that we have obtained with a computer implementation of this filter design.
Designing a Wien Filter Model with General Particle Tracer
Mitchell, John; Hofler, Alicia
2017-09-01
The Continuous Electron Beam Accelerator Facility injector employs a beamline component called a Wien filter which is typically used to select charged particles of a certain velocity. The Wien filter is also used to rotate the polarization of a beam for parity violation experiments. The Wien filter consists of perpendicular electric and magnetic fields. The electric field changes the spin orientation, but also imposes a transverse kick which is compensated for by the magnetic field. The focus of this project was to create a simulation of the Wien filter using General Particle Tracer. The results from these simulations were vetted against machine data to analyze the accuracy of the Wien model. Due to the close agreement between simulation and experiment, the data suggest that the Wien filter model is accurate. The model allows a user to input either the desired electric or magnetic field of the Wien filter along with the beam energy as parameters, and is able to calculate the perpendicular field strength required to keep the beam on axis. The updated model will aid in future diagnostic tests of any beamline component downstream of the Wien filter, and allow users to easily calculate the electric and magnetic fields needed for the filter to function properly. Funding support provided by DOE Office of Science's Student Undergraduate Laboratory Internship program.
Data assimilation in integrated hydrological modeling using ensemble Kalman filtering
DEFF Research Database (Denmark)
Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh
2015-01-01
Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...
Energy models: methods and trends
International Nuclear Information System (INIS)
Reuter, A.; Kuehner, R.; Wohlgemuth, N.
1996-01-01
Energy environmental and economical systems do not allow for experimentation since this would be dangerous, too expensive or even impossible. Instead, mathematical models are applied for energy planning. Experimenting is replaced by varying the structure and some parameters of 'energy models', computing the values of depending parameters, comparing variations, and interpreting their outcomings. Energy models are as old as computers. In this article the major new developments in energy modeling will be pointed out. We distinguish between 3 reasons of new developments: progress in computer technology, methodological progress and novel tasks of energy system analysis and planning
Model Adaptation for Prognostics in a Particle Filtering Framework
National Aeronautics and Space Administration — One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated....
Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models
El Gharamti, Mohamad
2010-12-01
Understanding the geology and the hydrology of the subsurface is important to model the fluid flow and the behavior of the contaminant. It is essential to have an accurate knowledge of the movement of the contaminants in the porous media in order to track them and later extract them from the aquifer. A two-dimensional flow model is studied and then applied on a linear contaminant transport model in the same porous medium. Because of possible different sources of uncertainties, the deterministic model by itself cannot give exact estimations for the future contaminant state. Incorporating observations in the model can guide it to the true state. This is usually done using the Kalman filter (KF) when the system is linear and the extended Kalman filter (EKF) when the system is nonlinear. To overcome the high computational cost required by the KF, we use the singular evolutive Kalman filter (SEKF) and the singular evolutive extended Kalman filter (SEEKF) approximations of the KF operating with low-rank covariance matrices. The SEKF can be implemented on large dimensional contaminant problems while the usage of the KF is not possible. Experimental results show that with perfect and imperfect models, the low rank filters can provide as much accurate estimates as the full KF but at much less computational cost. Localization can help the filter analysis as long as there are enough neighborhood data to the point being analyzed. Estimating the permeabilities of the aquifer is successfully tackled using both the EKF and the SEEKF.
Directory of Open Access Journals (Sweden)
Nataliya Chukhrova
2017-05-01
Full Text Available This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving. Most of these state space representations are matrix-based, which complicates their applications. Therefore, to facilitate the implementation of state space models in practice, we present a scalar state space model for cumulative payments, which is an extension of the well-known chain ladder (CL method. The presented model is distribution-free, forms a basis for determining the entire unobservable lower and upper run-off triangles and can easily be applied in practice using the Kalman-filter for prediction, filtering and smoothing of cumulative payments. In addition, the model provides an easy way to find outliers in the data and to determine outlier effects. Finally, an empirical comparison of the scalar state space model, promising prior state space models and some popular stochastic claims reserving methods is performed.
Scalable learning of probabilistic latent models for collaborative filtering
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre
2015-01-01
Collaborative filtering has emerged as a popular way of making user recommendations, but with the increasing sizes of the underlying databases scalability is becoming a crucial issue. In this paper we focus on a recently proposed probabilistic collaborative filtering model that explicitly...... variational Bayes learning and inference algorithm for these types of models. Empirical results show that the proposed algorithm achieves significantly better accuracy results than other straw-men models evaluated on a collection of well-known data sets. We also demonstrate that the algorithm has a highly...
Modeling the sustainability of a ceramic water filter intervention.
Mellor, Jonathan; Abebe, Lydia; Ehdaie, Beeta; Dillingham, Rebecca; Smith, James
2014-02-01
Ceramic water filters (CWFs) are a point-of-use water treatment technology that has shown promise in preventing early childhood diarrhea (ECD) in resource-limited settings. Despite this promise, some researchers have questioned their ability to reduce ECD incidences over the long term since most effectiveness trials conducted to date are less than one year in duration limiting their ability to assess long-term sustainability factors. Most trials also suffer from lack of blinding making them potentially biased. This study uses an agent-based model (ABM) to explore factors related to the long-term sustainability of CWFs in preventing ECD and was based on a three year longitudinal field study. Factors such as filter user compliance, microbial removal effectiveness, filter cleaning and compliance declines were explored. Modeled results indicate that broadly defined human behaviors like compliance and declining microbial effectiveness due to improper maintenance are primary drivers of the outcome metrics of household drinking water quality and ECD rates. The model predicts that a ceramic filter intervention can reduce ECD incidence amongst under two year old children by 41.3%. However, after three years, the average filter is almost entirely ineffective at reducing ECD incidence due to declining filter microbial removal effectiveness resulting from improper maintenance. The model predicts very low ECD rates are possible if compliance rates are 80-90%, filter log reduction efficiency is 3 or greater and there are minimal long-term compliance declines. Cleaning filters at least once every 4 months makes it more likely to achieve very low ECD rates as does the availability of replacement filters for purchase. These results help to understand the heterogeneity seen in previous intervention-control trials and reemphasize the need for researchers to accurately measure confounding variables and ensure that field trials are at least 2-3 years in duration. In summary, the CWF
Model Adaptation for Prognostics in a Particle Filtering Framework
Saha, Bhaskar; Goebel, Kai Frank
2011-01-01
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Model Adaptation for Prognostics in a Particle Filtering Framework
Directory of Open Access Journals (Sweden)
Bhaskar Saha
2011-01-01
Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Model-based auralizations of violin sound trends accompanying plate-bridge tuning or holding.
Bissinger, George; Mores, Robert
2015-04-01
To expose systematic trends in violin sound accompanying "tuning" only the plates or only the bridge, the first structural acoustics-based model auralizations of violin sound were created by passing a bowed-string driving force measured at the bridge of a solid body violin through the dynamic filter (DF) model radiativity profile "filter" RDF(f) (frequency-dependent pressure per unit driving force, free-free suspension, anechoic chamber). DF model auralizations for the more realistic case of a violin held/played in a reverberant auditorium reveal that holding the violin greatly diminishes its low frequency response, an effect only weakly compensated for by auditorium reverberation.
Modeling and Simulation of the Visual Effects of Colored Filters
2015-02-01
AFRL-RH-FS-TR-2015-0006 Modeling and Simulation of the Visual Effects of Colored Filters William R. Brockmeier Michael A. Guevara Thomas K. Kuyk...NUMBER FA8650-14-D-65190 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 0603231F 6. AUTHOR(S) William R. Brockmeier, Michael A. Guevara , Thomas K
Fuel Efficient Diesel Particulate Filter (DPF) Modeling and Development
Energy Technology Data Exchange (ETDEWEB)
Stewart, Mark L.; Gallant, Thomas R.; Kim, Do Heui; Maupin, Gary D.; Zelenyuk, Alla
2010-08-01
The project described in this report seeks to promote effective diesel particulate filter technology with minimum fuel penalty by enhancing fundamental understanding of filtration mechanisms through targeted experiments and computer simulations. The overall backpressure of a filtration system depends upon complex interactions of particulate matter and ash with the microscopic pores in filter media. Better characterization of these phenomena is essential for exhaust system optimization. The acicular mullite (ACM) diesel particulate filter substrate is under continuing development by Dow Automotive. ACM is made up of long mullite crystals which intersect to form filter wall framework and protrude from the wall surface into the DPF channels. ACM filters have been demonstrated to effectively remove diesel exhaust particles while maintaining relatively low backpressure. Modeling approaches developed for more conventional ceramic filter materials, such as silicon carbide and cordierite, have been difficult to apply to ACM because of properties arising from its unique microstructure. Penetration of soot into the high-porosity region of projecting crystal structures leads to a somewhat extended depth filtration mode, but with less dramatic increases in pressure drop than are normally observed during depth filtration in cordierite or silicon carbide filters. Another consequence is greater contact between the soot and solid surfaces, which may enhance the action of some catalyst coatings in filter regeneration. The projecting crystals appear to provide a two-fold benefit for maintaining low backpressures during filter loading: they help prevent soot from being forced into the throats of pores in the lower porosity region of the filter wall, and they also tend to support the forming filter cake, resulting in lower average cake density and higher permeability. Other simulations suggest that soot deposits may also tend to form at the tips of projecting crystals due to the axial
A generalized model via random walks for information filtering
Energy Technology Data Exchange (ETDEWEB)
Ren, Zhuo-Ming, E-mail: zhuomingren@gmail.com [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland); Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, ChongQing, 400714 (China); Kong, Yixiu [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland); Shang, Ming-Sheng, E-mail: msshang@cigit.ac.cn [Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, ChongQing, 400714 (China); Zhang, Yi-Cheng [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland)
2016-08-06
There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation. - Highlights: • We propose a generalized recommendation model employing the random walk dynamics. • The proposed model with single and hybrid of degree information is analyzed. • A strategy with the hybrid degree information improves precision of recommendation.
A generalized model via random walks for information filtering
International Nuclear Information System (INIS)
Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng
2016-01-01
There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation. - Highlights: • We propose a generalized recommendation model employing the random walk dynamics. • The proposed model with single and hybrid of degree information is analyzed. • A strategy with the hybrid degree information improves precision of recommendation.
IIR Filter Modeling Using an Algorithm Inspired on Electromagnetism
Directory of Open Access Journals (Sweden)
Cuevas-Jiménez E.
2013-01-01
Full Text Available Infinite-impulse-response (IIR filtering provides a powerful approach for solving a variety of problems. However, its design represents a very complicated task, since the error surface of IIR filters is generally multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a new method based on the Electromagnetism-Like Optimization Algorithm (EMO is proposed for IIR filter modeling. EMO originates from the electro-magnetism theory of physics by assuming potential solutions as electrically charged particles which spread around the solution space. The charge of each particle depends on its objective function value. This algorithm employs a collective attraction-repulsion mechanism to move the particles towards optimality. The experimental results confirm the high performance of the proposed method in solving various benchmark identification problems.
Human visual modeling and image deconvolution by linear filtering
International Nuclear Information System (INIS)
Larminat, P. de; Barba, D.; Gerber, R.; Ronsin, J.
1978-01-01
The problem is the numerical restoration of images degraded by passing through a known and spatially invariant linear system, and by the addition of a stationary noise. We propose an improvement of the Wiener's filter to allow the restoration of such images. This improvement allows to reduce the important drawbacks of classical Wiener's filter: the voluminous data processing, the lack of consideration of the vision's characteristivs which condition the perception by the observer of the restored image. In a first paragraph, we describe the structure of the visual detection system and a modelling method of this system. In the second paragraph we explain a restoration method by Wiener filtering that takes the visual properties into account and that can be adapted to the local properties of the image. Then the results obtained on TV images or scintigrams (images obtained by a gamma-camera) are commented [fr
New trends in species distribution modelling
Zimmermann, Niklaus E.; Edwards, Thomas C.; Graham, Catherine H.; Pearman, Peter B.; Svenning, Jens-Christian
2010-01-01
Species distribution modelling has its origin in the late 1970s when computing capacity was limited. Early work in the field concentrated mostly on the development of methods to model effectively the shape of a species' response to environmental gradients (Austin 1987, Austin et al. 1990). The methodology and its framework were summarized in reviews 10–15 yr ago (Franklin 1995, Guisan and Zimmermann 2000), and these syntheses are still widely used as reference landmarks in the current distribution modelling literature. However, enormous advancements have occurred over the last decade, with hundreds – if not thousands – of publications on species distribution model (SDM) methodologies and their application to a broad set of conservation, ecological and evolutionary questions. With this special issue, originating from the third of a set of specialized SDM workshops (2008 Riederalp) entitled 'The Utility of Species Distribution Models as Tools for Conservation Ecology', we reflect on current trends and the progress achieved over the last decade.
A comparative study of seven human cochlear filter models.
Saremi, Amin; Beutelmann, Rainer; Dietz, Mathias; Ashida, Go; Kretzberg, Jutta; Verhulst, Sarah
2016-09-01
Auditory models have been developed for decades to simulate characteristics of the human auditory system, but it is often unknown how well auditory models compare to each other or perform in tasks they were not primarily designed for. This study systematically analyzes predictions of seven publicly-available cochlear filter models in response to a fixed set of stimuli to assess their capabilities of reproducing key aspects of human cochlear mechanics. The following features were assessed at frequencies of 0.5, 1, 2, 4, and 8 kHz: cochlear excitation patterns, nonlinear response growth, frequency selectivity, group delays, signal-in-noise processing, and amplitude modulation representation. For each task, the simulations were compared to available physiological data recorded in guinea pigs and gerbils as well as to human psychoacoustics data. The presented results provide application-oriented users with comprehensive information on the advantages, limitations and computation costs of these seven mainstream cochlear filter models.
A generalized model via random walks for information filtering
Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng
2016-08-01
There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation.
Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
. The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported...
A Proposal for a Flexible Trend Specification in DSGE Models
Directory of Open Access Journals (Sweden)
Slanicay Martin
2016-06-01
Full Text Available In this paper I propose a flexible trend specification for estimating DSGE models on log differences. I demonstrate this flexible trend specification on a New Keynesian DSGE model of two economies, which I consequently estimate on data from the Czech economy and the euro area, using Bayesian techniques. The advantage of the trend specification proposed is that the trend component and the cyclical component are modelled jointly in a single model. The proposed trend specification is flexible in the sense that smoothness of the trend can be easily modified by different calibration of some of the trend parameters. The results suggest that this method is capable of finding a very reasonable trend in the data. Moreover, comparison of forecast performance reveals that the proposed specification offers more reliable forecasts than the original variant of the model.
Modeling Alaska boreal forests with a controlled trend surface approach
Mo Zhou; Jingjing Liang
2012-01-01
An approach of Controlled Trend Surface was proposed to simultaneously take into consideration large-scale spatial trends and nonspatial effects. A geospatial model of the Alaska boreal forest was developed from 446 permanent sample plots, which addressed large-scale spatial trends in recruitment, diameter growth, and mortality. The model was tested on two sets of...
Trends in development of innovative business models
Directory of Open Access Journals (Sweden)
Krstić Milan
2016-01-01
Full Text Available The companies doing business in global markets are now compelled to do it in conditions of permanent and turbulent changes. In order to succeed within that kind of environment in the long run, they are to innovate and to continuously strengthen their own innovative strength. Consideration of gaining its own innovative strength becomes top agenda issue of strategic companies. To that purpose, this paper presents the shortened results of a desktop theoretical research that has been undertaken to improve the innovative power of companies. The survey and subsequent analysis identified relevant innovative business models (IBM of companies, some of which briefly presented (CANVAS, SHARE, and WOIS BLUE OCEAN Strategy, which now form the current IBM trend.
Modeling nutrient filtering capacities and export fluxes in macrotidal estuaries
Regnier, P.; Arndt, S.; Savenije, H.; Vanderborght, J.-P.
2009-04-01
A fully transient model of a macrotidal estuary (The Scheldt) has been used to quantify silica and nitrogen filtering capacities and export fluxes to the coastal zone over a period of one year. Results show that in macrotidal estuaries, the seasonally-resolved nutrient fluxes are not only affected by in-situ biogeochemical transformations, but also by nutrient flux imbalances, which result from the time-lagged response of the scalar fields to hydrological perturbations. The estuarine nutrient retention reveals also a strong temporal variability, which is driven by the complex interplay between reaction and transport. As a result, the estuarine filtering capacities cannot be constrained by the freshwater residence alone and, thus, by empirical relationships that have been established between these two parameters. Furthermore, at the seasonal scale, the nutrient export fluxes to the coastal zone cannot be quantified from the riverine loads and the estuarine filtering capacities. More sophisticated approaches to estimate the functioning and response of macrotidal estuaries are thus needed and an alternative methodology, established on the premise that physical forcing mechanisms are the dominant controls on estuarine biogeochemistry at a series of hierarchically related system levels, is briefly outlined.
Nonlinear Kalman Filtering in Affine Term Structure Models
DEFF Research Database (Denmark)
Christoffersen, Peter; Dorion, Christian; Jacobs, Kris
2014-01-01
The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed-income applications. We investigate whether the unscented Kalman filter should be used to capture nonlinearities and compare the performance of the Kalman filter...... performs well when compared with the much more computationally intensive particle filter. These findings suggest that the unscented Kalman filter may be a good approach for a variety of problems in fixed-income pricing....
A modified RRSQRT-filter for assimilating data in atmospheric chemistry models
Segers, A.J.; Heemink, A.W.; Verlaan, M.; Loon, M. van
2000-01-01
The RRSQRT-filter is a special formulation of the Kalman filter for assimilation of data in large scale models. In this formulation, the covariance matrix of the model state is expressed in a limited number of modes. Two modifications have been made to the filter such that it is more robust when
Non-stationary magnetoencephalography by Bayesian filtering of dipole models
Somersalo, E.; Voutilainen, A.; Kaipio, J. P.
2003-10-01
In this paper, we consider the biomagnetic inverse problem of estimating a time-varying source current from magnetic field measurements. It is assumed that the data are severely corrupted by measurement noise. This setting is a model for magnetoencephalography (MEG) when the dynamic nature of the source prevents us from effecting noise reduction by averaging over consecutive measurements. Thus, the potential applications of this approach include the single trial estimation of the brain activity, in particular from the spontaneous MEG data. Our approach is based on non-stationary Bayesian estimation, and we propose the use of particle filters. The source model in this work is either a single dipole or multiple dipole model. Part of the problem consists of the model determination. Numerical simulations are presented.
Modeling Distortion Effects in Class-D Amplifier Filter Inductors
DEFF Research Database (Denmark)
Knott, Arnold; Stegenborg-Andersen, Tore; Thomsen, Ole Cornelius
2010-01-01
Distortion is generally accepted as a quantifier to judge the quality of audio power amplifiers. In switchmode power amplifiers various mechanisms influence this performance measure. After giving an overview of those, this paper focuses on the particular effect of the nonlinearity of the output...... filter components on the audio performance. While the physical reasons for both, the capacitor and the inductor induced distortion are given, the practical in depth demonstration is done for the inductor only. This includes measuring the inductors performance, modeling through fitting and resulting...... into simulation models. The fitted models achieve distortion values between 0.03 % and 0.2 % as a basis to enable the design of a 200 W amplifier....
Optimal Filtering in Mass Transport Modeling From Satellite Gravimetry Data
Ditmar, P.; Hashemi Farahani, H.; Klees, R.
2011-12-01
Monitoring natural mass transport in the Earth's system, which has marked a new era in Earth observation, is largely based on the data collected by the GRACE satellite mission. Unfortunately, this mission is not free from certain limitations, two of which are especially critical. Firstly, its sensitivity is strongly anisotropic: it senses the north-south component of the mass re-distribution gradient much better than the east-west component. Secondly, it suffers from a trade-off between temporal and spatial resolution: a high (e.g., daily) temporal resolution is only possible if the spatial resolution is sacrificed. To make things even worse, the GRACE satellites enter occasionally a phase when their orbit is characterized by a short repeat period, which makes it impossible to reach a high spatial resolution at all. A way to mitigate limitations of GRACE measurements is to design optimal data processing procedures, so that all available information is fully exploited when modeling mass transport. This implies, in particular, that an unconstrained model directly derived from satellite gravimetry data needs to be optimally filtered. In principle, this can be realized with a Wiener filter, which is built on the basis of covariance matrices of noise and signal. In practice, however, a compilation of both matrices (and, therefore, of the filter itself) is not a trivial task. To build the covariance matrix of noise in a mass transport model, it is necessary to start from a realistic model of noise in the level-1B data. Furthermore, a routine satellite gravimetry data processing includes, in particular, the subtraction of nuisance signals (for instance, associated with atmosphere and ocean), for which appropriate background models are used. Such models are not error-free, which has to be taken into account when the noise covariance matrix is constructed. In addition, both signal and noise covariance matrices depend on the type of mass transport processes under
Adaptive kernels in approximate filtering of state-space models
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
2017-01-01
Roč. 31, č. 6 (2017), s. 938-952 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : filtering * nonlinear filters * Bayesian filtering * sequential Monte Carlo * approximate filtering Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 1.708, year: 2016 http://library.utia.cs.cz/separaty/2016/AS/dedecius-0466448.pdf
Modeling defect trends for iterative development
Powell, J. D.; Spanguolo, J. N.
2003-01-01
The Employment of Defects (EoD) approach to measuring and analyzing defects seeks to identify and capture trends and phenomena that are critical to managing software quality in the iterative software development lifecycle at JPL.
Directory of Open Access Journals (Sweden)
Wan Yang
2014-04-01
Full Text Available A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.. Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters--a basic particle filter (PF with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF, and particle Markov chain Monte Carlo (pMCMC--and three ensemble filters--the ensemble Kalman filter (EnKF, the ensemble adjustment Kalman filter (EAKF, and the rank histogram filter (RHF--were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003-2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1-5 weeks in the future; the ensemble filters are more accurate predicting peaks in
On low-frequency errors of uniformly modulated filtered white-noise models for ground motions
Safak, Erdal; Boore, David M.
1988-01-01
Low-frequency errors of a commonly used non-stationary stochastic model (uniformly modulated filtered white-noise model) for earthquake ground motions are investigated. It is shown both analytically and by numerical simulation that uniformly modulated filter white-noise-type models systematically overestimate the spectral response for periods longer than the effective duration of the earthquake, because of the built-in low-frequency errors in the model. The errors, which are significant for low-magnitude short-duration earthquakes, can be eliminated by using the filtered shot-noise-type models (i. e. white noise, modulated by the envelope first, and then filtered).
Modelled long term trends of surface ozone over South Africa
CSIR Research Space (South Africa)
Naidoo, M
2011-10-01
Full Text Available timescale seeks to provide a spatially comprehensive view of trends while also creating a baseline for comparisons with future projections of air quality through the forcing of air quality models with modelled predicted long term meteorology. Previous...
Rainfall estimation with TFR model using Ensemble Kalman filter
Asyiqotur Rohmah, Nabila; Apriliani, Erna
2018-03-01
Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.
Marginalized approximate filtering of state-space models
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
2018-01-01
Roč. 32, č. 1 (2018), s. 1-12 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GA16-09848S Institutional support: RVO:67985556 Keywords : approximate filtering * marginalized filters * particle filtering Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.708, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/dedecius-0478074.pdf
Directory of Open Access Journals (Sweden)
Marcin Spychała
2016-12-01
Full Text Available The aim of the study was to describe in a mathematical manner the hydraulic capacity of textile filters for wastewater treatment at changeable wastewater levels during a period between consecutive doses, taking into consideration the decisive factors for flow-conditions of filtering media. Highly changeable and slightly changeable flow-conditions tests were performed on reactors equipped with non-woven geo-textile filters. Hydraulic conductivity of filter material coupons was determined. The dry mass covering the surface and contained in internal space of filtering material was then indicated and a mathematical model was elaborated. Flow characteristics during the highly changeable flow-condition test were sensitivity to differentiated values of hydraulic conductivity in horizontal zones of filtering layer. During the slightly changeable flow-conditions experiment the differences in permeability and hydraulic conductivity of different filter (horizontal zones height regions were much smaller. The proposed modelling approach in spite of its simplicity provides a satisfactory agreement with empirical data and therefore enables to simulate the hydraulic capacity of vertically oriented textile filters. The mathematical model reflects the significant impact of the filter characteristics (textile permeability at different filter height and operational conditions (dosing frequency on the textile filters hydraulic capacity.
Magnetic Electron Filtering by Fluid Models for the PEGASES Thruster
Leray, Gary; Chabert, Pascal; Lichtenberg, Allan; Lieberman, Michael
2009-10-01
The PEGASES thruster produces thrust by creating positive and negative ions, which are then accelerated. To accelerate both type of ions, electrons need to be filtered, which is achieved by applying a static magnetic field strong enough to magnetize the electrons but not the ions. A 1D fluid model with three species (electrons, positive and negative ions) and an analytical model are proposed to understand this process for an oxygen plasma with p = 10 mTorr and B0 = 300 G [1]. The resulting ion-ion plasma formation in the transverse direction (perpendicular to the magnetic field) is demonstrated. It is shown that an additional electron/positive ion loss term is required. The solutions are evaluated for two main parameters: the ionizing fraction at the plasma center (x = 0), ne0/ng, and the electronegativity ratio at the center, α0=nn0/ne0. The effect of geometry and magnetic field amplitude are also discussed. [4pt] [1] Leray G, Chabert P, Lichtenberg A J and Lieberman M A, J. Phys. D: Appl. Phys., Plasma Modelling Cluster issue, to appear (2009)
Behavioral model for common mode filter and performance optimization aspects
Roc'h, A.; Bergsma, H.; Bergsma, J.G.; Leferink, Frank Bernardus Johannes
2008-01-01
A well designed common mode filter for motor drive application can significantly improve the level of electromagnetic interference generated by the cable and the motor housing. The subsequent design of this filter is strongly dependent on the actual in situ parameters of the motor drive and often
A Visibility-Aware Model for Pre-Filtering and Rendering Surfaces in Real-Time
Heitz, Eric; Neyret, Fabrice
2011-01-01
We present a multiscale surface appearance representation and a rendering model that accounts for the subpixel visibility distribution. Starting from this model, we propose a method for pre-filtering detailed surfaces and their attributes. Our representation of the filtered attributes takes the correlation with their visibility into account. The masking and shadowing effects lost in geometric filtering of the surface can thus be recovered at rendering. This grants high visual quality of subpi...
Kaijser, Thomas
2013-01-01
A Hidden Markov Model generates two basic stochastic processes, a Markov chain, which is hidden, and an observation sequence. The filtering process of a Hidden Markov Model is, roughly speaking, the sequence of conditional distributions of the hidden Markov chain that is obtained as new observations are received. It is well-known, that the filtering process itself, is also a Markov chain. A classical, theoretical problem is to find conditions which implies that the distributions of the filter...
Characterizing economic trends by Bayesian stochastic model specification search
DEFF Research Database (Denmark)
Grassi, Stefano; Proietti, Tommaso
on whether their parameters are fixed or evolutive. Stochastic model specification is carried out to discriminate two alternative hypotheses concerning the generation of trends: the trend-stationary hypothesis, on the one hand, for which the trend is a deterministic function of time and the short run...... dynamics are represented by a stationary autoregressive process; the difference-stationary hypothesis, on the other, according to which the trend results from the cumulation of the effects of random disturbances. We illustrate the methodology for a set of U.S. macroeconomic time series, which includes...
Reservoir structural model updating using the Ensemble Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Seiler, Alexandra
2010-09-15
In reservoir characterization, a large emphasis is placed on risk management and uncertainty assessment, and the dangers of basing decisions on a single base-case reservoir model are widely recognized. In the last years, statistical methods for assisted history matching have gained popularity for providing integrated models with quantified uncertainty, conditioned on all available data. Structural modeling is the first step in a reservoir modeling work flow and consists in defining the geometrical framework of the reservoir, based on the information from seismic surveys and well data. Large uncertainties are typically associated with the processing and interpretation of seismic data. However, the structural model is often fixed to a single interpretation in history-matching work flows due to the complexity of updating the structural model and related reservoir grid. This thesis present a method that allows to account for the uncertainties in the structural model and continuously update the model and related uncertainties by assimilation of production data using the Ensemble Kalman Filter (EnKF). We consider uncertainties in the depth of the reservoir horizons and in the fault geometry, and assimilate production data, such as oil production rate, gas-oil ratio and water-cut. In the EnKF model-updating work flow, an ensemble of reservoir models, expressing explicitly the model uncertainty, is created. We present a parameterization that allows to generate different realizations of the structural model to account for the uncertainties in faults and horizons and that maintains the consistency throughout the reservoir characterization project, from the structural model to the prediction of production profiles. The uncertainty in the depth of the horizons is parameterized as simulated depth surfaces, the fault position as a displacement vector and the fault throw as a throw-scaling factor. In the EnKF, the model parameters and state variables are updated sequentially in
Neural Model for Left-Handed CPW Bandpass Filter Loaded Split Ring Resonator
Liu, Haiwen; Wang, Shuxin; Tan, Mingtao; Zhang, Qijun
2010-02-01
Compact left-handed coplanar waveguide (CPW) bandpass filter loaded split ring resonator (SRR) is presented in this paper. The proposed filter exhibits a quasi-elliptic function response and its circuit size occupies only 12 × 11.8 mm2 (≈0.21 λg × 0.20 λg). Also, a simple circuit model is given and the parametric study of this filter is discussed. Then, with the aid of NeuroModeler software, a five-layer feed-forward perceptron neural networks model is built up to optimize the proposed filter design fast and accurately. Finally, this newly left-handed CPW bandpass filter was fabricated and measured. A good agreement between simulations and measurement verifies the proposed left-handed filter and the validity of design methodology.
Model for trace metal exposure in filter-feeding flamingos at alkaline Rift Valley Lake, Kenya
Energy Technology Data Exchange (ETDEWEB)
Nelson, Y.M.; DiSante, C.J.; Lion, L.W. [Cornell Univ., Ithaca, NY (United States). School of Civil and Environmental Engineering; Thampy, R.J.; Raini, J.A. [Worldwide Fund for Nature, Nakuru (Kenya). Lake Nakuru Conservation and Development Project; Motelin, G.K. [Egerton Univ., Njoro (Kenya). Dept. of Animal Health
1998-11-01
Toxic trace metals have been implicated as a potential cause of recent flamingo kills at Lake Nakuru, Kenya. Chromium (Cr), copper (Cu), lead (Pb), and zinc (Zn) have accumulated in the lake sediments as a result of unregulated discharges and because this alkaline lake has no natural outlet. Lesser flamingos (Phoeniconaias minor) at Lake Nakuru feed predominantly on the cyanobacterium Spirulina platensis, and because of their filter-feeding mechanism, they are susceptible to exposure to particle-bound metals. Trace metal adsorption isotherms to lake sediments and S. platensis were obtained under simulated lake conditions, and a mathematical model was developed to predict metal exposure via filter feeding based on predicted trace metal phase distribution. Metal adsorption to suspended solids followed the trend Pb {much_gt} Zn > Cr > Cu, and isotherms were linear up to 60 {micro}g/L. Adsorption to S. platensis cells followed the trend Pb {much_gt} Zn > Cu > Cr and fit Langmuir isotherms for Cr, Cu and Zn and a linear isotherm for Pb. Predicted phase distributions indicated that Cr and Pb in Lake Nakuru are predominantly associated with suspended solids, whereas Cu and Zn are distributed more evenly between the dissolved phase and particulate phases of both S. platensis and suspended solids. Based on established flamingo feeding rates and particle size selection, predicted Cr and Pb exposure occurs predominantly through ingestion of suspended solids, whereas Cu and Zn exposure occurs through ingestion of both suspended solids and S. platensis. For the lake conditions at the time of sampling, predicted ingestion rates based on measured metal concentrations in lake suspended solids were 0.71, 6.2, 0.81, and 13 mg/kg-d for Cr, Cu, Pb, and Zn, respectively.
SDG and qualitative trend based model multiple scale validation
Gao, Dong; Xu, Xin; Yin, Jianjin; Zhang, Hongyu; Zhang, Beike
2017-09-01
Verification, Validation and Accreditation (VV&A) is key technology of simulation and modelling. For the traditional model validation methods, the completeness is weak; it is carried out in one scale; it depends on human experience. The SDG (Signed Directed Graph) and qualitative trend based multiple scale validation is proposed. First the SDG model is built and qualitative trends are added to the model. And then complete testing scenarios are produced by positive inference. The multiple scale validation is carried out by comparing the testing scenarios with outputs of simulation model in different scales. Finally, the effectiveness is proved by carrying out validation for a reactor model.
A Practical Core Loss Model for Filter Inductors of Power Electronic Converters
DEFF Research Database (Denmark)
Matsumori, Hiroaki; Shimizu, Toshihisa; Wang, Xiongfei
2018-01-01
This paper proposes a core loss model for filter inductors of power electronic converters. The model allows a computationally efficient analysis on the core loss of the inductor under the square voltage excitation and the premagnetization condition. First, the core loss of the filter inductor und...
Object recognition via MINACE filter trained on synthetic 3D model
Shaulskiy, Dmitry V.; Konstantinov, Maxim V.; Starikov, Rostislav S.
2015-09-01
Paper presents study results of MINACE filter implementation to recognition problem of object subjected to out-of-plane rotation distortion and captured as raster image. Filter training conducted by images acquired from synthetic 3D object model. Dependence of recognition results from 3D model illumination type is shown.
Directory of Open Access Journals (Sweden)
Buddhi Arachchige
2017-11-01
Full Text Available This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery’s State of Charge (SOC and State of Life (SOL by utilizing the battery’s physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge time with cycling has also been included, integrating both EOL (End of Life and EOD prediction models in order to get more accuracy in the estimations.
Modelling of air flows in pleated filters and of their clogging by solid particles
International Nuclear Information System (INIS)
Del Fabbro, L.
2002-01-01
The devices of air cleaning against particles are widely spread in various branches of industry: nuclear, motor, food, electronic,...; among these devices, numerous are constituted by pleated porous media to increase the surface of filtration and thus to reduce the pressure drop, for given air flow. The objective of our work is to compensate a lack evident of knowledge on the evolution of the pressure drop of pleated filter during the clogging and to deduct a modelling from it, on the basis of experiments concerning industrial filters of nuclear and car types. The obtained model is a function of characteristics of the filtering medium and pleats, of the characteristics of solid particles deposited on the filter, of the mass of particles and of the aeraulic conditions of air flow. It also depends on data on the clogging of flat filters of equivalent medium. To elaborate this model of pressure drop, an initial stage was carried out in order to characterize, experimentally and numerically, the pressure drop and the distribution of air flow in clean pleated filters of nuclear (high efficiency particulate air filter, in fiberglasses) and car (mean efficiency filter, in fibers of cellulose) types. The numerical model allowed to understand the fundamental role played by the aeraulic resistance of the filtering medium. From an non-dimensional approach, we established a semi-empirical model of pressure drop for a clean pleated filter valid for both studied types of medium; this model is used of first base for the development of the final model of clogging. The study of the clogging of the filters showed the complexity of the phenomenon dependent mainly on a reduction of the surface of filtration. This observation brings us to propose a clogging of pleated filters in three phases. Both first phases are similar in those observed for flat filters, while last phase corresponds to a reduction of the surface of filtration and leads a strong increase of the filter pressure drop
Directory of Open Access Journals (Sweden)
C. Sunil Kumar
2014-10-01
Full Text Available In this paper, we study the performance of various models for automated evaluation of descriptive answers by using rank based feature selection filters for dimensionality reduction. We quantitatively analyze the best feature selection technique from amongst the five rank based feature selection techniques, namely Chi squared filter, Information gain filter, Gain ratio filter, Relief filter and Symmetrical uncertainty filter. We use Sequential Minimal Optimization with Polynomial kernel to build models and we evaluate the models across various parameters such as Accuracy, Time to build models, Kappa, Mean Absolute Error and Root Mean Squared Error. Except with Relief filter, for all other filters applied models, the accuracies obtained are at least 4% better than accuracies obtained with models with no filters applied. The accuracies recorded are same across Chi squared filter, Information gain filter, Gain ratio filter and Symmetrical Uncertainty filter. Therefore accuracy alone is not the determinant in selecting the best filter. The time taken to build models, Kappa, Mean absolute error and Root Mean Squared Error played a major role in determining the effectiveness of the filters. The overall rank aggregation metric of Symmetrical uncertainty filter is 45 and this is better by 1 rank than the rank aggregation metric of information gain attribute evaluation filter, the nearest contender to Symmetric attribute evaluation filter. Symmetric uncertainty rank aggregation metric is better by 3, 6, 112 ranks respectively when compared to rank aggregation metrics of Chi squared filter, Gain ratio filter and Relief filters. Through these quantitative measurements, we conclude that Symmetrical uncertainty attribute evaluation is the overall best performing rank based feature selection algorithm applicable for auto evaluation of descriptive answers.
International Nuclear Information System (INIS)
Zhao, Yibo; Jiang, Yi; Feng, Jiuchao; Wu, Lifu
2016-01-01
Highlights: • A novel nonlinear Wiener adaptive filters based on the backslash operator are proposed. • The identification approach to the memristor-based chaotic systems using the proposed adaptive filters. • The weight update algorithm and convergence characteristics for the proposed adaptive filters are derived. - Abstract: Memristor-based chaotic systems have complex dynamical behaviors, which are characterized as nonlinear and hysteresis characteristics. Modeling and identification of their nonlinear model is an important premise for analyzing the dynamical behavior of the memristor-based chaotic systems. This paper presents a novel nonlinear Wiener adaptive filtering identification approach to the memristor-based chaotic systems. The linear part of Wiener model consists of the linear transversal adaptive filters, the nonlinear part consists of nonlinear adaptive filters based on the backslash operator for the hysteresis characteristics of the memristor. The weight update algorithms for the linear and nonlinear adaptive filters are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics. Comparing with the adaptive nonlinear polynomial filters, the proposed nonlinear adaptive filters have less identification error.
Study on the Metal Fiber Filter Modeling for Capturing Radioactive Aerosol
International Nuclear Information System (INIS)
Lee, Seunguk; Lee, Chanhyun; Park, Minchan; Lee, Jaekeun
2015-01-01
The components of air cleaning system are demisters to remove entrained moisture, pre-filters to remove the bulk of the particulate matter, high efficiency particulate air (HEPA) filters, iodine absorbers(generally, activated carbon) and HEPA filters after the absorbers for redundancy and collection of carbon fines. The HEPA filters are most important components to prevent radioactive aerosols from being released to control room and adjacent environment. The Conventional HEPA filter has pleated media for low pressure drop. Consequently, the filters must provide high collection efficiency as well as low pressure drop. Unfortunately, conventional HEPA filters are made of glass fiber and polyester, and pose disposal issues since they cannot be recycled. In fact, 31,055 HEPA filters used in nuclear facilities in the U.S are annually disposed. The Analyses at face velocities 1cm/s and 10cm/s are also carried out, and they also show R2 value of 0.995. However, since official HEPA filter standards are established at face velocity of 5cm/s, this value will be used in further analysis. From the comparative studies carried out at different filter thickness and face velocities, a good correlation is found between the model and the experiment
DEFF Research Database (Denmark)
Chen, Yaohui; Xue, Weiqi; Öhman, Filip
2008-01-01
We present a model to interpret enhanced microwave phase shifts based on filter assisted slow and fast light effects in semiconductor optical amplifiers. The model also demonstrates the spectral phase impact of input optical signals.......We present a model to interpret enhanced microwave phase shifts based on filter assisted slow and fast light effects in semiconductor optical amplifiers. The model also demonstrates the spectral phase impact of input optical signals....
Modelling of the modified-LLCL-filter-based single-phase grid-tied Aalborg inverter
DEFF Research Database (Denmark)
Liu, Zifa; Wu, Huiyun; Liu, Yuan
2017-01-01
Owing to less conduction and switching power losses, the recently proposed Aalborg inverter has high efficiency within a wide range of input DC voltage for single-phase DC/AC power conversion. In theory, the conduction power losses can be further decreased, if an LLCL-filter is adopted instead....... In this study, the small signal analysis for the modified-LLCL-filter-based Aalborg inverter is addressed. Through the modelling, it can be proven that compared with the LCL-filter, the modified-LLCL-filter causes no extra control challenge for the Aalborg inverter, and therefore more inductance in the power...... of an LCL-filter for a voltage source inverter, mainly due to the reduced inductance. The Aalborg inverter shows the characteristic of a current source inverter, when working in the `boost' state. Whether the LLCL-filter can meet the control requirement of this type inverter needs to be further explored...
Wutsqa, D. U.; Marwah, M.
2017-06-01
In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.
A model for transient analysis of a multiple-medium confinement filter system
International Nuclear Information System (INIS)
Hyder, M.L.; Ellison, P.G.; Leonard, M.T.; Louie, D.L.Y.; Donbroski, E.L.; Wagner, K.C.
1990-01-01
A computational model is described that calculates the transient behavior of aerosol and vapor (adsorption) filter compartments such as those used in the Savannah River Site (SRS) production reactor confinement system. The principal application of the model is in the analysis of confinement response to hypothetical severe (core melt) accidents. Under these conditions, aerosol and radio-iodine deposition on filter compartments may be substantial. Attendant filter degradation mechanisms are modeled. Sample calculations are included to illustrate model performance. 6 refs., 14 figs., 1 tab
NEW APPROACH TO MODELLING OF SAND FILTER CLOGGING BY SEPTIC TANK EFFLUENT
Directory of Open Access Journals (Sweden)
Jakub Nieć
2016-04-01
Full Text Available The deep bed filtration model elaborated by Iwasaki has many applications, e.g. solids removal from wastewater. Its main parameter, filter coefficient, is directly related to removal efficiency and depends on filter depth and time of operation. In this paper the authors have proposed a new approach to modelling, describing dry organic mass from septic tank effluent and biomass distribution in a sand filter. In this approach the variable filter coefficient value was used as affected by depth and time of operation and the live biomass concentration distribution was approximated by a logistic function. Relatively stable biomass contents in deeper beds compartments were observed in empirical studies. The Iwasaki equations associated with the logistic function can predict volatile suspended solids deposition and biomass content in sand filters. The comparison between the model and empirical data for filtration lasting 10 and 20 days showed a relatively good agreement.
A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting
Directory of Open Access Journals (Sweden)
Youzhu Li
2014-01-01
Full Text Available This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.
Tunable n-path notch filters for blocker suppression: modeling and verification
Ghaffari, A.; Klumperink, Eric A.M.; Nauta, Bram
2013-01-01
N-path switched-RC circuits can realize filters with very high linearity and compression point while they are tunable by a clock frequency. In this paper, both differential and single-ended N-path notch filters are modeled and analyzed. Closed-form equations provide design equations for the main
Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise
Directory of Open Access Journals (Sweden)
Bowen Hou
2017-11-01
Full Text Available As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α -jerk model.
Biomechanics trends in modeling and simulation
Ogden, Ray
2017-01-01
The book presents a state-of-the-art overview of biomechanical and mechanobiological modeling and simulation of soft biological tissues. Seven well-known scientists working in that particular field discuss topics such as biomolecules, networks and cells as well as failure, multi-scale, agent-based, bio-chemo-mechanical and finite element models appropriate for computational analysis. Applications include arteries, the heart, vascular stents and valve implants as well as adipose, brain, collagenous and engineered tissues. The mechanics of the whole cell and sub-cellular components as well as the extracellular matrix structure and mechanotransduction are described. In particular, the formation and remodeling of stress fibers, cytoskeletal contractility, cell adhesion and the mechanical regulation of fibroblast migration in healing myocardial infarcts are discussed. The essential ingredients of continuum mechanics are provided. Constitutive models of fiber-reinforced materials with an emphasis on arterial walls ...
Thrasher, James F; Abad-Vivero, Erika N; Moodie, Crawford; O'Connor, Richard J; Hammond, David; Cummings, K Michael; Yong, Hua-Hie; Salloum, Ramzi G; Czoli, Christine; Reynales-Shigematsu, Luz Myriam
2016-01-01
Objective To describe trends, correlates of use and consumer perceptions related to the product design innovation of flavour capsules in cigarette filters. Methods Quarterly surveys from 2012 to 2014 were analysed from an online consumer panel of adult smokers aged 18–64, living in the USA (n=6865 observations; 4154 individuals); Mexico (n=5723 observations; 3366 individuals); and Australia (n=5864 observations; 2710 individuals). Preferred brand varieties were classified by price (ie, premium; discount) and flavour (ie, regular; flavoured without capsule; flavoured with capsule). Participants reported their preferred brand variety's appeal (ie, satisfaction; stylishness), taste (ie, smoothness, intensity), and harm relative to other brands and varieties. GEE models were used to determine time trends and correlates of flavour capsule use, as well as associations between preferred brand characteristics (ie, price stratum, flavour) and perceptions of relative appeal, taste and harm. Results Preference for flavour capsules increased significantly in Mexico (6% to 14%) and Australia (1% to 3%), but not in the USA (4% to 5%). 18–24 year olds were most likely to prefer capsules in the USA (10%) and Australia (4%), but not Mexico. When compared to smokers who preferred regular brands, smokers who preferred brands with capsules viewed their variety of cigarettes as having more positive appeal (all countries), better taste (all countries), and lesser risk (Mexico, USA) than other brand varieties. Conclusions Results indicate that use of cigarettes with flavour capsules is growing, is associated with misperceptions of relative harm, and differentiates brands in ways that justify regulatory action. PMID:25918129
New Trends, News Values, and New Models.
Higgins, Mary Anne
1996-01-01
Explores implications of the prediction that in the next millennium the public will experience a scarcity of knowledge and a surplus of information. Reviews research suggesting that journalists focus on these news values: emphasizing how/why, devaluing immediacy, specializing/analyzing, representing a constituency. Examines two new models of…
Trend-Stationarity in the I(2) Cointegration Model
DEFF Research Database (Denmark)
Rahbek, Anders; Kongsted, H.C.; Jørgensen, H.C.
1999-01-01
A vector autoregressive model for I(2) processes which allows for trend-stationary components and restricts the deterministic part of the process to be at most linear is defined. A two-step statistical analysis of the model is derived. The joint test of I(1) and I(2) cointegrating ranks is shown ...
A High Order Filter with Galerkin Finite Element Method for the Spherical Local domain Model
Lee, C. H.; Cheong, H. B.; Kang, H. G.
2017-12-01
A High Order Filter with Galerkin Finite Element Method for the Spherical Local domain ModelChung-Hui Lee1 and Hyeong-Bin Cheong and Hyun-Gyu KangDepartment of Environmental Atmospheric Sciences, Pukyong National University, Busan, Korea (1 chlee@pukyong.ac.kr) A high-order filter with Galerkin finite element method is constructed by applying a two dimensional finite element method with quadrilateral basis functions to the spherical limited area domain. The quadrilateral basis function is defined as four shape-functions on separate four grid-boxes which share the same gridpoint. A first-order derivative is represented with an algebraic equation consisting of nine point stencil. Helmholtz equation on a sphere is the basic component of the high order filter and the filtering is performed by solving this equation with two dimensional finite element method. As the theory describes, for spherical Laplacian operator and first-order derivative, the convergence rates of the error were revealed to be second-order and fourth-order, respectively. In addition, since the convergence rate of errors for the filter in this study was the same as the filter with Fourier finite element method, the accuracy of the filter is comparable to the filter based on the Fourier finite element method. The high-order filter was applied to the WRF (Weather Research and Forecasting) as hyper-viscosity and its performance was compared with those of the built-in viscosity scheme of the WRF model. As a result of the tropical cyclone simulation, the forecast error for the high-order filter and the built-in viscosity were similar for the minimum pressure and track prediction. However, for the precipitation and rainfall distribution, the prediction with high-order filter appeared closer to observations than those with built-in viscosity.
Directory of Open Access Journals (Sweden)
Haibo Zou
2018-01-01
Full Text Available After a tropical cyclone (TC making landfall, the numerical model output sea level pressure (SLP presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85% the DDs are small (between −30 km and 30 km, while in a few cases (about 15% the DDs are large (greater than 30 km even 70 km. This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter.
Jan, Shau-Shiun; Kao, Yu-Chun
2013-05-17
The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC) system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM) system that is enabled by global positioning system (GPS) technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF), is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF), is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods.
Directory of Open Access Journals (Sweden)
Shau-Shiun Jan
2013-05-01
Full Text Available The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM system that is enabled by global positioning system (GPS technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF, is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF, is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods.
Directory of Open Access Journals (Sweden)
S. J. Noh
2011-10-01
Full Text Available Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP, is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF and the sequential importance resampling (SIR particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.
Recent trends in specialty pharma business model
Directory of Open Access Journals (Sweden)
Mannching Sherry Ku
2015-12-01
Full Text Available The recent rise of specialty pharma is attributed to its flexible, versatile, and open business model while the traditional big pharma is facing a challenging time with patent cliff, generic threat, and low research and development (R&D productivity. These multinational pharmaceutical companies, facing a difficult time, have been systematically externalizing R&D and some even establish their own corporate venture capital so as to diversify with more shots on goal, with the hope of achieving a higher success rate in their compound pipeline. Biologics and clinical Phase II proof-of-concept (POC compounds are the preferred licensing and collaboration targets. Biologics enjoys a high success rate with a low generic biosimilar threat, while the need is high for clinical Phase II POC compounds, due to its high attrition/low success rate. Repurposing of big pharma leftover compounds is a popular strategy but with limitations. Most old compounds come with baggage either in lackluster clinical performance or short in patent life. Orphan drugs is another area which has gained popularity in recent years. The shorter and less costly regulatory pathway provides incentives, especially for smaller specialty pharma. However, clinical studies on orphan drugs require a large network of clinical operations in many countries in order to recruit enough patients. Big pharma is also working on orphan drugs starting with a small indication, with the hope of expanding the indication into a blockbuster status. Specialty medicine, including orphan drugs, has become the growth engine in the pharmaceutical industry worldwide. Big pharma is also keen on in-licensing technology or projects from specialty pharma to extend product life cycles, in order to protect their blockbuster drug franchises. Ample opportunities exist for smaller players, even in the emerging countries, to collaborate with multinational pharmaceutical companies provided that the technology platforms or
Recent trends in specialty pharma business model.
Ku, Mannching Sherry
2015-12-01
The recent rise of specialty pharma is attributed to its flexible, versatile, and open business model while the traditional big pharma is facing a challenging time with patent cliff, generic threat, and low research and development (R&D) productivity. These multinational pharmaceutical companies, facing a difficult time, have been systematically externalizing R&D and some even establish their own corporate venture capital so as to diversify with more shots on goal, with the hope of achieving a higher success rate in their compound pipeline. Biologics and clinical Phase II proof-of-concept (POC) compounds are the preferred licensing and collaboration targets. Biologics enjoys a high success rate with a low generic biosimilar threat, while the need is high for clinical Phase II POC compounds, due to its high attrition/low success rate. Repurposing of big pharma leftover compounds is a popular strategy but with limitations. Most old compounds come with baggage either in lackluster clinical performance or short in patent life. Orphan drugs is another area which has gained popularity in recent years. The shorter and less costly regulatory pathway provides incentives, especially for smaller specialty pharma. However, clinical studies on orphan drugs require a large network of clinical operations in many countries in order to recruit enough patients. Big pharma is also working on orphan drugs starting with a small indication, with the hope of expanding the indication into a blockbuster status. Specialty medicine, including orphan drugs, has become the growth engine in the pharmaceutical industry worldwide. Big pharma is also keen on in-licensing technology or projects from specialty pharma to extend product life cycles, in order to protect their blockbuster drug franchises. Ample opportunities exist for smaller players, even in the emerging countries, to collaborate with multinational pharmaceutical companies provided that the technology platforms or specialty medicinal
Dissolution Model Development: Formulation Effects and Filter Complications
DEFF Research Database (Denmark)
Berthelsen, Ragna; Holm, Rene; Jacobsen, Jette
2016-01-01
This study describes various complications related to sample preparation (filtration) during development of a dissolution method intended to discriminate among different fenofibrate immediate-release formulations. Several dissolution apparatus and sample preparation techniques were tested. The fl....... With the tested drug–formulation combination, the best in vivo–in vitro correlation was found after filtration of the dissolution samples through 0.45-μm hydrophobic PTFE membrane filters....
3D Microstructure Modeling of Porous Metal Filters
Czech Academy of Sciences Publication Activity Database
Hejtmánek, Vladimír; Čapek, M.
2012-01-01
Roč. 2, č. 3 (2012), s. 344-352 ISSN 2075-4701. [International Conference on Porous Metals and Metallic Foams /7./. Busan, 18.09.2011-21.09.2011] R&D Projects: GA ČR(CZ) GAP204/11/1206; GA ČR GA203/09/1353 Institutional support: RVO:67985858 Keywords : porous metal filter * stochastic reconstruction * microstructural descriptors Subject RIV: CF - Physical ; Theoretical Chemistry
Ramesh, Nisha; Tasdizen, Tolga
2014-10-01
Bayesian frameworks are commonly used in tracking algorithms. An important example is the particle filter, where a stochastic motion model describes the evolution of the state, and the observation model relates the noisy measurements to the state. Particle filters have been used to track the lineage of cells. Propagating the shape model of the cell through the particle filter is beneficial for tracking. We approximate arbitrary shapes of cells with a novel implicit convex function. The importance sampling step of the particle filter is defined using the cost associated with fitting our implicit convex shape model to the observations. Our technique is capable of tracking the lineage of cells for nonmitotic stages. We validate our algorithm by tracking the lineage of retinal and lens cells in zebrafish embryos.
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep; Wei, Junchao; Davis, Lynn
2013-08-08
Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying damage using physics-based models. Life
Energy Technology Data Exchange (ETDEWEB)
Wang, S L; Singer, M A
2009-07-13
The purpose of this report is to evaluate the hemodynamic effects of renal vein inflow and filter position on unoccluded and partially occluded IVC filters using three-dimensional computational fluid dynamics. Three-dimensional models of the TrapEase and Gunther Celect IVC filters, spherical thrombi, and an IVC with renal veins were constructed. Hemodynamics of steady-state flow was examined for unoccluded and partially occluded TrapEase and Gunther Celect IVC filters in varying proximity to the renal veins. Flow past the unoccluded filters demonstrated minimal disruption. Natural regions of stagnant/recirculating flow in the IVC are observed superior to the bilateral renal vein inflows, and high flow velocities and elevated shear stresses are observed in the vicinity of renal inflow. Spherical thrombi induce stagnant and/or recirculating flow downstream of the thrombus. Placement of the TrapEase filter in the suprarenal vein position resulted in a large area of low shear stress/stagnant flow within the filter just downstream of thrombus trapped in the upstream trapping position. Filter position with respect to renal vein inflow influences the hemodynamics of filter trapping. Placement of the TrapEase filter in a suprarenal location may be thrombogenic with redundant areas of stagnant/recirculating flow and low shear stress along the caval wall due to the upstream trapping position and the naturally occurring region of stagnant flow from the renal veins. Infrarenal vein placement of IVC filters in a near juxtarenal position with the downstream cone near the renal vein inflow likely confers increased levels of mechanical lysis of trapped thrombi due to increased shear stress from renal vein inflow.
Filtering Based Recursive Least Squares Algorithm for Multi-Input Multioutput Hammerstein Models
Wang, Ziyun; Wang, Yan; Ji, Zhicheng
2014-01-01
This paper considers the parameter estimation problem for Hammerstein multi-input multioutput finite impulse response (FIR-MA) systems. Filtered by the noise transfer function, the FIR-MA model is transformed into a controlled autoregressive model. The key-term variable separation principle is used to derive a data filtering based recursive least squares algorithm. The numerical examples confirm that the proposed algorithm can estimate parameters more accurately and has a higher computational...
Estimation in continuous-time stochastic| volatility models using nonlinear filters
DEFF Research Database (Denmark)
Nielsen, Jan Nygaard; Vestergaard, M.; Madsen, Henrik
2000-01-01
Presents a correction to the authorship of the article 'Estimation in Continuous-Time Stochastic Volatility Models Using Nonlinear Filters,' published in the periodical 'International Journal of Theoretical and Applied Finance,' Vol. 3, No. 2., pp. 279-308.......Presents a correction to the authorship of the article 'Estimation in Continuous-Time Stochastic Volatility Models Using Nonlinear Filters,' published in the periodical 'International Journal of Theoretical and Applied Finance,' Vol. 3, No. 2., pp. 279-308....
P. Pappas, George; A. Zohdy, Mohamed
2017-01-01
In this paper accurate estimation of parameters, higher order state space prediction methods and Extended Kalman filter (EKF) for modeling shadow power in wireless mobile communications are developed. Path-loss parameter estimation models are compared and evaluated. Shadow power estimation methods in wireless cellular communications are very important for use in power control of mobile device and base station. The methods are validated and compared to existing methods, Kalman Filter (KF) with...
Variable structure control for three-phase LCL-filtered inverters using a reduced converter model
Guzmán Solà, Ramon; García de Vicuña Muñoz de la Nava, José Luis; Castilla Fernández, Miguel; Miret Tomàs, Jaume; Hoz Casas, Jordi de la
2017-01-01
This paper presents a new concept in active damping techniques using a reduced model of a LCL-filtered grid connected inverter. The presence of the LCL filter complicates the design of the inverter control scheme, particularly when uncertainties in the system parameters, especially in the grid inductance, are considered. The proposed control algorithm is addressed to overcome such difficulties using a reduced model of the inverter in a state observer. In this proposal, two of the three state ...
Particle filtering with path sampling and an application to a bimodal ocean current model
International Nuclear Information System (INIS)
Weare, Jonathan
2009-01-01
This paper introduces a recursive particle filtering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particle filters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particle filter but have been inaccessible due to the limitations of standard particle filtering strategies.
A Trend Model for Alzheimer’s Mortality
Directory of Open Access Journals (Sweden)
Örjan Hallberg
2015-09-01
Full Text Available In Sweden, mortality rates from Alzheimer’s disease have increased since early 90’s. In this study, we compared rates reported from 2006-2012 with projected trends determined previously and found a good fit. The objective of this study was to investigate if increased mortality can be modeled as a single exponential function of time lived in a new environment, where the risk of dying from Alzheimer’s disease has been increased. The results demonstrated that the exponential model can be used to predict future mortalities for different scenarios, and that it can also project age-specific trends. We conclude that increasing mortality rates from Alzheimer’s disease seem caused by an environmental change introduced since the 1990’s. Since similar trend breaks also have been reported for different cancers, responsible authorities should seriously address this problem to pinpoint causative factors.
Bachmann-Machnik, Anna; Meyer, Daniel; Waldhoff, Axel; Fuchs, Stephan; Dittmer, Ulrich
2018-04-01
Retention Soil Filters (RSFs), a form of vertical flow constructed wetlands specifically designed for combined sewer overflow (CSO) treatment, have proven to be an effective tool to mitigate negative impacts of CSOs on receiving water bodies. Long-term hydrologic simulations are used to predict the emissions from urban drainage systems during planning of stormwater management measures. So far no universally accepted model for RSF simulation exists. When simulating hydraulics and water quality in RSFs, an appropriate level of detail must be chosen for reasonable balancing between model complexity and model handling, considering the model input's level of uncertainty. The most crucial parameters determining the resultant uncertainties of the integrated sewer system and filter bed model were identified by evaluating a virtual drainage system with a Retention Soil Filter for CSO treatment. To determine reasonable parameter ranges for RSF simulations, data of 207 events from six full-scale RSF plants in Germany were analyzed. Data evaluation shows that even though different plants with varying loading and operation modes were examined, a simple model is sufficient to assess relevant suspended solids (SS), chemical oxygen demand (COD) and NH4 emissions from RSFs. Two conceptual RSF models with different degrees of complexity were assessed. These models were developed based on evaluation of data from full scale RSF plants and column experiments. Incorporated model processes are ammonium adsorption in the filter layer and degradation during subsequent dry weather period, filtration of SS and particulate COD (XCOD) to a constant background concentration and removal of solute COD (SCOD) by a constant removal rate during filter passage as well as sedimentation of SS and XCOD in the filter overflow. XCOD, SS and ammonium loads as well as ammonium concentration peaks are discharged primarily via RSF overflow not passing through the filter bed. Uncertainties of the integrated
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep; Wei, Junchao; Davis, J Lynn
2014-06-24
Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have
Computational Modeling of Blood Flow in the TrapEase Inferior Vena Cava Filter
Energy Technology Data Exchange (ETDEWEB)
Singer, M A; Henshaw, W D; Wang, S L
2008-02-04
To evaluate the flow hemodynamics of the TrapEase vena cava filter using three dimensional computational fluid dynamics, including simulated thrombi of multiple shapes, sizes, and trapping positions. The study was performed to identify potential areas of recirculation and stagnation and areas in which trapped thrombi may influence intrafilter thrombosis. Computer models of the TrapEase filter, thrombi (volumes ranging from 0.25mL to 2mL, 3 different shapes), and a 23mm diameter cava were constructed. The hemodynamics of steady-state flow at Reynolds number 600 was examined for the unoccluded and partially occluded filter. Axial velocity contours and wall shear stresses were computed. Flow in the unoccluded TrapEase filter experienced minimal disruption, except near the superior and inferior tips where low velocity flow was observed. For spherical thrombi in the superior trapping position, stagnant and recirculating flow was observed downstream of the thrombus; the volume of stagnant flow and the peak wall shear stress increased monotonically with thrombus volume. For inferiorly trapped spherical thrombi, marked disruption to the flow was observed along the cava wall ipsilateral to the thrombus and in the interior of the filter. Spherically shaped thrombus produced a lower peak wall shear stress than conically shaped thrombus and a larger peak stress than ellipsoidal thrombus. We have designed and constructed a computer model of the flow hemodynamics of the TrapEase IVC filter with varying shapes, sizes, and positions of thrombi. The computer model offers several advantages over in vitro techniques including: improved resolution, ease of evaluating different thrombus sizes and shapes, and easy adaptation for new filter designs and flow parameters. Results from the model also support a previously reported finding from photochromic experiments that suggest the inferior trapping position of the TrapEase IVC filter leads to an intra-filter region of recirculating
Analysis of the Fiber Bragg Gratings using the Lattice Filter Model
Bae, Jinho; Chun, Joohwan; Lee, Sang Bae
2000-04-01
We propose a new method for analyzing fiber Bragg gratings with an arbitrary index profile and an arbitrary individual grating length. The proposed method is based on the lattice filter model which is widely used in applications ranging from digital filtering to speech synthesis and explosive seismic signal processing. Lattice filter interpretation provides us with an accurate and simple tool for analyzing an arbitrary index profile and arbitrary aperiodic fiber grating structures, and gives us further insight into the understanding of fiber Bragg gratings. To verify the validity of the proposed model experimentally, we have fabricated two grating structures; the short-period (periodic) fiber Bragg grating structure and the chirped fiber Bragg grating structure. We have observed that the calculated transmission spectrum and the calculated reflectivity using our lattice filter model match very closely to the corresponding measured spectrum in the wavelength band of interest.
Modelling of trends in twitter using retweet graph dynamics
Thij, M. ten; Ouboter, T.M.; Worm, D.T.H.; Litvak, N.; Berg, J.L. van den; Bhulai, S.
2014-01-01
In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph
Modeling the hemodynamic response in fMRI using smooth FIR filters
DEFF Research Database (Denmark)
Goutte, Cyril; Nielsen, Finn Årup; Hansen, Lars Kai
2000-01-01
-parameters using the evidence framework, or sampling using a Markov Chain Monte Carlo (MCMC) approach. The authors present a comparison of their model with standard hemodynamic response kernels on simulated data, and perform a full analysis of data acquired during an experiment involving visual stimulation.......-parametric approach based on finite impulse response (FIR) filters. In order to cope with the increase in the number of degrees of freedom, the authors introduce a Gaussian process prior on the filter parameters. They show how to carry on the analysis by incorporating prior knowledge on the filters, optimizing hyper...
Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing
2016-01-01
The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method. Copyright © 2015 Elsevier B.V. All rights reserved.
The ensemble particle filter (EnPF) in rainfall-runoff models
Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.
2009-01-01
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the
Andreh, Angga Muhamad; Subiyanto, Sunardiyo, Said
2017-01-01
Development of non-linear loading in the application of industry and distribution system and also harmonic compensation becomes important. Harmonic pollution is an urgent problem in increasing power quality. The main contribution of the study is the modeling approach used to design a shunt active filter and the application of the cascade multilevel inverter topology to improve the power quality of electrical energy. In this study, shunt active filter was aimed to eliminate dominant harmonic component by injecting opposite currents with the harmonic component system. The active filter was designed by shunt configuration with cascaded multilevel inverter method controlled by PID controller and SPWM. With this shunt active filter, the harmonic current can be reduced so that the current wave pattern of the source is approximately sinusoidal. Design and simulation were conducted by using Power Simulator (PSIM) software. Shunt active filter performance experiment was conducted on the IEEE four bus test system. The result of shunt active filter installation on the system (IEEE four bus) could reduce THD current from 28.68% to 3.09%. With this result, the active filter can be applied as an effective method to reduce harmonics.
A low-complexity interacting multiple model filter for maneuvering target tracking
Khalid, Syed Safwan
2017-01-22
In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.
Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model
Liu, Bo
2016-02-03
An ensemble-based Gaussian mixture (GM) filtering framework is studied in this paper in term of its dependence on the choice of the clustering method to construct the GM. In this approach, a number of particles sampled from the posterior distribution are first integrated forward with the dynamical model for forecasting. A GM representation of the forecast distribution is then constructed from the forecast particles. Once an observation becomes available, the forecast GM is updated according to Bayes’ rule. This leads to (i) a Kalman filter-like update of the particles, and (ii) a Particle filter-like update of their weights, generalizing the ensemble Kalman filter update to non-Gaussian distributions. We focus on investigating the impact of the clustering strategy on the behavior of the filter. Three different clustering methods for constructing the prior GM are considered: (i) a standard kernel density estimation, (ii) clustering with a specified mixture component size, and (iii) adaptive clustering (with a variable GM size). Numerical experiments are performed using a two-dimensional reactive contaminant transport model in which the contaminant concentration and the heterogenous hydraulic conductivity fields are estimated within a confined aquifer using solute concentration data. The experimental results suggest that the performance of the GM filter is sensitive to the choice of the GM model. In particular, increasing the size of the GM does not necessarily result in improved performances. In this respect, the best results are obtained with the proposed adaptive clustering scheme.
A filter-mediated communication model for design collaboration in building construction.
Lee, Jaewook; Jeong, Yongwook; Oh, Minho; Hong, Seung Wan
2014-01-01
Multidisciplinary collaboration is an important aspect of modern engineering activities, arising from the growing complexity of artifacts whose design and construction require knowledge and skills that exceed the capacities of any one professional. However, current collaboration in the architecture, engineering, and construction industries often fails due to lack of shared understanding between different participants and limitations of their supporting tools. To achieve a high level of shared understanding, this study proposes a filter-mediated communication model. In the proposed model, participants retain their own data in the form most appropriate for their needs with domain-specific filters that transform the neutral representations into semantically rich ones, as needed by the participants. Conversely, the filters can translate semantically rich, domain-specific data into a neutral representation that can be accessed by other domain-specific filters. To validate the feasibility of the proposed model, we computationally implement the filter mechanism and apply it to a hypothetical test case. The result acknowledges that the filter mechanism can let the participants know ahead of time what will be the implications of their proposed actions, as seen from other participants' points of view.
Trends of air pollution in Denmark - Normalised by a simple weather index model
International Nuclear Information System (INIS)
Kiilsholm, S.; Rasmussen, A.
2000-01-01
This report is a part of the Traffic Pool projects on 'Traffic and Environments', 1995-99, financed by the Danish Ministry of Transport. The Traffic Pool projects included five different projects on 'Surveillance of the Air Quality', 'Atmospheric Modelling', 'Atmospheric Chemistry Modelling', 'Smog and ozone' and 'Greenhouse effects and Climate', [Rasmussen, 2000]. This work is a part of the project on 'Surveillance of the Air Quality' with the main objectives to make trend analysis of levels of air pollution from traffic in Denmark. Other participants were from the Road Directory mainly focusing on measurement of traffic and trend analysis of the air quality utilising a nordic model for the air pollution in street canyons called BLB (Beregningsmodel for Luftkvalitet i Byluftgader) [Vejdirektoratet 2000], National Environmental Research Institute (HERI) mainly focusing on. measurements of air pollution and trend analysis with the Operational Street Pollution Model (OSPM) [DMU 2000], and the Copenhagen Environmental Protection Agency mainly focusing on measurements. In this study a more simple statistical model has been developed for trend analysis of the air quality. The model is filtering out the influence of the variations from year to year in the meteorological conditions on the air pollution levels. The weather factors found most important are wind speed, wind direction and mixing height. Measurements of CO, NO and NO 2 from three streets in Copenhagen have been used, these streets are Jagtvej, Bredgade and H. C. Andersen's Boulevard (HCAB). The years 1994-1996 were used for evaluation of the method and annual indexes of air pollution index dependent only on meteorological parameters, called WEATHIX, were calculated for the years 1990-1997 and used for normalisation of the observed air pollution trends. Meteorological data were taken from either the background stations at the H.C. Oersted - building situated close to one of the street stations or the synoptic
Collaborative QoS Prediction for Mobile Service with Data Filtering and SlopeOne Model
Directory of Open Access Journals (Sweden)
Yuyu Yin
2017-01-01
Full Text Available The mobile service is a widely used carrier for mobile applications. With the increase of the number of mobile services, for service recommendation and selection, the nonfunctional properties (also known as quality of service, QoS become increasingly important. However, in many cases, the number of mobile services invoked by a user is quite limited, which leads to the large number of missing QoS values. In recent years, many prediction algorithms, such as algorithms extended from collaborative filtering (CF, are proposed to predict QoS values. However, the ideas of most existing algorithms are borrowed from the recommender system community, not specific for mobile service. In this paper, we first propose a data filtering-extended SlopeOne model (filtering-based CF, which is based on the characteristics of a mobile service and considers the relation with location. Also, using the data filtering technique in FB-CF and matrix factorization (MF, this paper proposes another model FB-MF (filtering-based MF. We also build an ensemble model, which combines the prediction results of FB-CF model and FB-MF model. We conduct sufficient experiments, and the experimental results demonstrate that our models outperform all compared methods and achieve good results in high data sparsity scenario.
Vicks, Mary E.
2013-01-01
School districts have implemented filtering and safety policies in response to legislative and social mandates to protect students from the proliferation of objectionable online content. Subject related literature suggests these policies are more restrictive than legal mandates require and are adversely affecting information access and…
Technology trends in econometric energy models: Ignorance or information?
International Nuclear Information System (INIS)
Boyd, G.; Kokkelenberg, E.; State Univ., of New York, Binghamton, NY; Ross, M.; Michigan Univ., Ann Arbor, MI
1991-01-01
Simple time trend variables in factor demand models can be statistically powerful variables, but may tell the researcher very little. Even more complex specification of technical change, e.g. factor biased, are still the economentrician's ''measure of ignorance'' about the shifts that occur in the underlying production process. Furthermore, in periods of rapid technology change the parameters based on time trends may be too large for long run forecasting. When there is clearly identifiable engineering information about new technology adoption that changes the factor input mix, data for the technology adoption may be included in the traditional factor demand model to economically model specific factor biased technical change and econometrically test their contribution. The adoption of thermomechanical pulping (TMP) and electric are furnaces (EAF) are two electricity intensive technology trends in the Paper and Steel industries, respectively. This paper presents the results of including these variables in a tradition econometric factor demand model, which is based on the Generalized Leontief. The coefficients obtained for this ''engineering based'' technical change compares quite favorably to engineering estimates of the impact of TMP and EAF on electricity intensities, improves the estimates of the other price coefficients, and yields a more believable long run electricity forecast. 6 refs., 1 fig
Modeling the Influence of Hemispheric Transport on Trends in ...
We describe the development and application of the hemispheric version of the CMAQ to examine the influence of long-range pollutant transport on trends in surface level O3 distributions. The WRF-CMAQ model is expanded to hemispheric scales and multi-decadal model simulations were recently performed for the period spanning 1990-2010 to examine changes in hemispheric air pollution resulting from changes in emissions over this period. Simulated trends in ozone and precursor species concentrations across the U.S. and the northern hemisphere over the past two decades are compared with those inferred from available measurements during this period. Additionally, the decoupled direct method (DDM) in CMAQ is used to estimate the sensitivity of O3 to emissions from different source regions across the northern hemisphere. The seasonal variations in source region contributions to background O3 is then estimated from these sensitivity calculations and will be discussed. A reduced form model combining these source region sensitivities estimated from DDM with the multi-decadal simulations of O3 distributions and emissions trends, is then developed to characterize the changing contributions of different source regions to background O3 levels across North America. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas
Modeling Flow Rate to Estimate Hydraulic Conductivity in a Parabolic Ceramic Water Filter
Directory of Open Access Journals (Sweden)
Ileana Wald
2012-01-01
Full Text Available In this project we model volumetric flow rate through a parabolic ceramic water filter (CWF to determine how quickly it can process water while still improving its quality. The volumetric flow rate is dependent upon the pore size of the filter, the surface area, and the height of water in the filter (hydraulic head. We derive differential equations governing this flow from the conservation of mass principle and Darcy's Law and find the flow rate with respect to time. We then use methods of calculus to find optimal specifications for the filter. This work is related to the research conducted in Dr. James R. Mihelcic's Civil and Environmental Engineering Lab at USF.
El Gharamti, Mohamad
2012-04-01
Accurate knowledge of the movement of contaminants in porous media is essential to track their trajectory and later extract them from the aquifer. A two-dimensional flow model is implemented and then applied on a linear contaminant transport model in the same porous medium. Because of different sources of uncertainties, this coupled model might not be able to accurately track the contaminant state. Incorporating observations through the process of data assimilation can guide the model toward the true trajectory of the system. The Kalman filter (KF), or its nonlinear invariants, can be used to tackle this problem. To overcome the prohibitive computational cost of the KF, the singular evolutive Kalman filter (SEKF) and the singular fixed Kalman filter (SFKF) are used, which are variants of the KF operating with low-rank covariance matrices. Experimental results suggest that under perfect and imperfect model setups, the low-rank filters can provide estimates as accurate as the full KF but at much lower computational effort. Low-rank filters are demonstrated to significantly reduce the computational effort of the KF to almost 3%. © 2012 American Society of Civil Engineers.
Vila, Natalia; Siblini, Aya; Esposito, Evangelina; Bravo-Filho, Vasco; Zoroquiain, Pablo; Aldrees, Sultan; Logan, Patrick; Arias, Lluis; Burnier, Miguel N
2017-11-02
Light exposure and more specifically the spectrum of blue light contribute to the oxidative stress in Age-related macular degeneration (AMD). The purpose of the study was to establish whether blue light filtering could modify proangiogenic signaling produced by retinal pigmented epithelial (RPE) cells under different conditions simulating risk factors for AMD. Three experiments were carried out in order to expose ARPE-19 cells to white light for 48 h with and without blue light-blocking filters (BLF) in different conditions. In each experiment one group was exposed to light with no BLF protection, a second group was exposed to light with BLF protection, and a control group was not exposed to light. The ARPE-19 cells used in each experiment prior to light exposure were cultured for 24 h as follows: Experiment 1) Normoxia, Experiment 2) Hypoxia, and Experiment 3) Lutein supplemented media in normoxia. The media of all groups was harvested after light exposure for sandwich ELISA-based assays to quantify 10 pro-angiogenic cytokines. A significant decrease in angiogenin secretion levels and a significant increase in bFGF were observed following light exposure, compared to dark conditions, in both normoxia and hypoxia conditions. With the addition of a blue light-blocking filter in normoxia, a significant increase in angiogenin levels was observed. Although statistical significance was not achieved, blue light filters reduce light-induced secretion of bFGF and VEGF to near normal levels. This trend is also observed when ARPE-19 cells are grown under hypoxic conditions and when pre-treated with lutein prior to exposure to experimental conditions. Following light exposure, there is a decrease in angiogenin secretion by ARPE-19 cells, which was abrogated with a blue light - blocking filter. Our findings support the position that blue light filtering affects the secretion of angiogenic factors by retinal pigmented epithelial cells under normoxic, hypoxic, and lutein
The role of model dynamics in ensemble Kalman filter performance for chaotic systems
Ng, G.-H.C.; McLaughlin, D.; Entekhabi, D.; Ahanin, A.
2011-01-01
The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or 'diverging', when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter's update step. We examine how system dynamics affect EnKF performance, and whether the absence of certain dynamic features in the ensemble may lead to divergence. The EnKF is applied to a simple chaotic model, and ensembles are checked against singular vectors of the tangent linear model, corresponding to short-term growth and Lyapunov vectors, corresponding to long-term growth. Results show that the ensemble strongly aligns itself with the subspace spanned by unstable Lyapunov vectors. Furthermore, the filter avoids divergence only if the full linearized long-term unstable subspace is spanned. However, short-term dynamics also become important as non-linearity in the system increases. Non-linear movement prevents errors in the long-term stable subspace from decaying indefinitely. If these errors then undergo linear intermittent growth, a small ensemble may fail to properly represent all important modes, causing filter divergence. A combination of long and short-term growth dynamics are thus critical to EnKF performance. These findings can help in developing practical robust filters based on model dynamics. ?? 2011 The Authors Tellus A ?? 2011 John Wiley & Sons A/S.
CONSISTENT USE OF THE KALMAN FILTER IN CHEMICAL TRANSPORT MODELS (CTMS) FOR DEDUCING EMISSIONS
Past research has shown that emissions can be deduced using observed concentrations of a chemical, a Chemical Transport Model (CTM), and the Kalman filter in an inverse modeling application. An expression was derived for the relationship between the "observable" (i.e., the con...
Non-linear DSGE Models and The Central Difference Kalman Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper introduces a Quasi Maximum Likelihood (QML) approach based on the Cen- tral Difference Kalman Filter (CDKF) to estimate non-linear DSGE models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models...
Identification of electricity spot models by using convolution particle filter
Aihara, ShinIchi; Bagchi, Arunabha; Imreizeeq, E.S.N.
2011-01-01
We consider a slight perturbation of the Schwartz-Smith model for the electricity futures prices and the resulting modied spot model. Using the martingale property of the modied price under the risk neutral measure, we derive the arbitrage free model for the spot and futures prices. As the futures
Energy Technology Data Exchange (ETDEWEB)
Stratakis, G.A.; Pontikakis, G.N.; Stamatelos, A.M. [University of Thessaly, Volos (Greece). Mechanical and Industrial Engineering Dept.
2004-07-01
In this paper, an experimental validation procedure is applied to an improved one-dimensional model of fuel additive assisted regeneration of a diesel particulate filter. Full-scale tests on an engine bench of the regeneration behaviour of a diesel filter fitted to a modern diesel engine run on catalyst-doped fuel are employed for this purpose. The main objectives of the validation procedure concern the ability of the model to predict the effects of exhaust mass flowrate, initial soot loading mass, volatile organic fraction of the soot and additive concentration in the fuel. The results of the validation procedure are intended to demonstrate the scope and extent of applicability of models of this type to real-world design and optimization studies with diesel filters. (author)
Numerical modelling of the erosion and deposition of sand inside a filter layer
DEFF Research Database (Denmark)
Jacobsen, Niels Gjøl; van Gent, Marcel R. A.; Fredsøe, Jørgen
2017-01-01
This paper treats the numerical modelling of the behaviour of a sand core covered by rocks and exposed to waves. The associated displacement of the rock is also studied. A design that allows for erosion and deposition of the sand core beneath a rock layer in a coastal structure requires an accurate...... prediction method to assure that the amount of erosion remains within acceptable limits. This work presents a numerical model that is capable of describing the erosion and deposition patterns inside of an open filter of rock on top of sand. The hydraulic loading is that of incident irregular waves...... additional data sets on the erosion and deposition patterns inside of an open filter. A few cases are defined to study the effect of the sinking of the filter into the erosion hole. The numerical model is also applied to several application cases. The response of the core material (sand) to changes...
The Statistical Modeling of the Trends Concerning the Romanian Population
Directory of Open Access Journals (Sweden)
Gabriela OPAIT
2014-11-01
Full Text Available This paper reflects the statistical modeling concerning the resident population in Romania, respectively the total of the romanian population, through by means of the „Least Squares Method”. Any country it develops by increasing of the population, respectively of the workforce, which is a factor of influence for the growth of the Gross Domestic Product (G.D.P.. The „Least Squares Method” represents a statistical technique for to determine the trend line of the best fit concerning a model.
Effects of thrombosed vena cava filters on blood flow: flow visualization and numerical modeling.
Stewart, Sandy F C; Robinson, Ronald A; Nelson, Robert A; Malinauskas, Richard A
2008-11-01
Inferior vena cava (IVC) filters are used to prevent pulmonary embolism (PE) in patients with deep vein thrombosis for whom anticoagulation is contraindicated. IVC filters have been shown to be effective in trapping embolized clots and preventing PE; however, among the commercially available designs, the optimal balance of clot capture efficiency, clot dissolution, and prevention of to vena cava occlusion is unknown. Clot capture efficiency has been quantified in numerous in vitro studies, in which model clots are released into a mock circulation system, with the relative capture efficiency of various IVC filters analyzed statistically. In general, two-stage filters have been found to be more efficient than one-stage filters. However, other factors may play a role in the ultimate dissolution of clots and in the overall effect of the resulting blood flow on caval vasculature. Clot dissolution has been shown to increase with increasing wall shear stress, while low and oscillating wall shear stresses are known to have a deleterious effect on vessel walls, causing intimal hyperplasia. This paper describes the effect of IVC filters on blood flow, velocity patterns, and wall shear stress by flow visualization and computational fluid dynamics.
RB Particle Filter Time Synchronization Algorithm Based on the DPM Model
Directory of Open Access Journals (Sweden)
Chunsheng Guo
2015-09-01
Full Text Available Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.
RB Particle Filter Time Synchronization Algorithm Based on the DPM Model.
Guo, Chunsheng; Shen, Jia; Sun, Yao; Ying, Na
2015-09-03
Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.
Monte-Carlo modelling to determine optimum filter choices for sub-microsecond optical pyrometry
Ota, Thomas A.; Chapman, David J.; Eakins, Daniel E.
2017-04-01
When designing a spectral-band pyrometer for use at high time resolutions (sub-μs), there is ambiguity regarding the optimum characteristics for a spectral filter(s). In particular, while prior work has discussed uncertainties in spectral-band pyrometry, there has been little discussion of the effects of noise which is an important consideration in time-resolved, high speed experiments. Using a Monte-Carlo process to simulate the effects of noise, a model of collection from a black body has been developed to give insights into the optimum choices for centre wavelength and passband width. The model was validated and then used to explore the effects of centre wavelength and passband width on measurement uncertainty. This reveals a transition centre wavelength below which uncertainties in calculated temperature are high. To further investigate system performance, simultaneous variation of the centre wavelength and bandpass width of a filter is investigated. Using data reduction, the effects of temperature and noise levels are illustrated and an empirical approximation is determined. The results presented show that filter choice can significantly affect instrument performance and, while best practice requires detailed modelling to achieve optimal performance, the expression presented can be used to aid filter selection.
Nonlinear Kalman Filtering in Affine Term Structure Models
DEFF Research Database (Denmark)
Christoffersen, Peter; Dorion, Christian; Jacobs, Kris
When the relationship between security prices and state variables in dynamic term structure models is nonlinear, existing studies usually linearize this relationship because nonlinear fi…ltering is computationally demanding. We conduct an extensive investigation of this linearization and analyze ...... in fi…xed income pricing with nonlinear relationships between the state vector and the observations, such as the estimation of term structure models using coupon bonds and the estimation of quadratic term structure models....
Physical Modeling of the Polyfrequency Filter-Compensating Device Based on the Capacitor-Coil
Butyrin, P. A.; Gusev, G. G.; Mikheev, D. V.; Shakirzianov, F. N.
2017-12-01
The paper presents the results of physical modeling and experimental study of the frequency characteristics of the polyfrequency filter-compensating device (PFCD) based on a capacitor-coil. The amplitude- frequency and phase-frequency characteristics of the physical PFCD model were constructed and its equivalent parameters were identified. The feasibility of a PFCD in the form of a single technical device with high technical and economic characteristics was experimentally proven. In the paper, recommendations for practical applications of the capacitor-coil-based PFCD are made and the advantages of the device over known standard passive filter-compensating devices are evaluated.
Filtering Based Recursive Least Squares Algorithm for Multi-Input Multioutput Hammerstein Models
Directory of Open Access Journals (Sweden)
Ziyun Wang
2014-01-01
Full Text Available This paper considers the parameter estimation problem for Hammerstein multi-input multioutput finite impulse response (FIR-MA systems. Filtered by the noise transfer function, the FIR-MA model is transformed into a controlled autoregressive model. The key-term variable separation principle is used to derive a data filtering based recursive least squares algorithm. The numerical examples confirm that the proposed algorithm can estimate parameters more accurately and has a higher computational efficiency compared with the recursive least squares algorithm.
Estimation of Stochastic Volatility Models by Nonparametric Filtering
DEFF Research Database (Denmark)
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...
Claveau-Mallet, Dominique; Courcelles, Benoît; Comeau, Yves
2014-07-01
This article presents an original numerical model suitable for longevity prediction of alkaline steel slag filters used for phosphorus removal. The model includes kinetic rates for slag dissolution, hydroxyapatite and monetite precipitation and for the transformation of monetite into hydroxyapatite. The model includes equations for slag exhaustion. Short-term batch tests using slag and continuous pH monitoring were conducted. The model parameters were calibrated on these batch tests and experimental results were correctly reproduced. The model was then transposed to long-term continuous flow simulations using the software PHREEQC. Column simulations were run to test the effect of influent P concentration, influent inorganic C concentration and void hydraulic retention time on filter longevity and P retention capacity. High influent concentration of P and inorganic C, and low hydraulic retention time of voids reduced the filter longevity. The model provided realistic P breakthrough at the column outlet. Results were comparable to previous column experiments with the same slag regarding longevity and P retention capacity. A filter design methodology based on a simple batch test and numerical simulations is proposed.
Directory of Open Access Journals (Sweden)
Jinying Kong
2017-01-01
Full Text Available In phrase-based machine translation (PBMT systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28 and Uyghur-Chinese (+0.33 MT.
Small-signal modeling of digitally controlled grid-connected inverters with LCL filters
DEFF Research Database (Denmark)
Zhang, Xiaotian; W. Spencer, Joseph; Guerrero, Josep M.
2013-01-01
When LCL filters are applied to digitally controlled grid-connected inverters, the design of controllers is usually implemented using classic average models. The accuracy of these models in s-domain is only guaranteed in low frequency range. In order to predict the dynamic behaviors, new smallsig......When LCL filters are applied to digitally controlled grid-connected inverters, the design of controllers is usually implemented using classic average models. The accuracy of these models in s-domain is only guaranteed in low frequency range. In order to predict the dynamic behaviors, new...... in predicting instabilities. Experimental results are presented and compared to the average models predictions and z-domain models predictions, which shows the proposed models are capable of predicting the values of control variables at the true sampling instants....
Huang, Lei
2015-01-01
To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409
Directory of Open Access Journals (Sweden)
Bin Liu
2017-03-01
Full Text Available In this study, the authors propose a state space modelling approach for trust evaluation in wireless sensor networks. In their state space trust model (SSTM, each sensor node is associated with a trust metric, which measures to what extent the data transmitted from this node would better be trusted by the server node. Given the SSTM, they translate the trust evaluation problem to be a non-linear state filtering problem. To estimate the state based on the SSTM, a component-wise iterative state inference procedure is proposed to work in tandem with the particle filter (PF, and thus the resulting algorithm is termed as iterative PF (IPF. The computational complexity of the IPF algorithm is theoretically linearly related with the dimension of the state. This property is desirable especially for high-dimensional trust evaluation and state filtering problems. The performance of the proposed algorithm is evaluated by both simulations and real data analysis.
Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue
2016-10-15
With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.
Directory of Open Access Journals (Sweden)
Hao Chen
2016-10-01
Full Text Available With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.
A hand tracking algorithm with particle filter and improved GVF snake model
Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe
2017-07-01
To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.
Model-based Prognostics with Fixed-lag Particle Filters
National Aeronautics and Space Administration — Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a...
Energy Technology Data Exchange (ETDEWEB)
Stewart, Mark L.; Rector, David R.; Muntean, George G.; Maupin, Gary D.
2004-08-01
Cordierite diesel particulate filters (DPFs) offer one of the most promising aftertreatment technologies to meet the quickly approaching EPA 2007 heavy-duty emissions regulations. A critical, yet poorly understood, component of particulate filter modeling is the representation of soot deposition. The structure and distribution of soot deposits upon and within the ceramic substrate directly influence many of the macroscopic phenomenon of interest, including filtration efficiency, back pressure, and filter regeneration. Intrinsic soot cake properties such as packing density and permeability coefficients remain inadequately characterized. The work reported in this paper involves subgrid modeling techniques which may prove useful in resolving these inadequacies. The technique involves the use of a lattice Boltzmann modeling approach. This approach resolves length scales which are orders of magnitude below those typical of a standard computational fluid dynamics (CFD) representation of an aftertreatment device. Individual soot particles are introduced and tracked as they move through the flow field and are deposited on the filter substrate or previously deposited particles. Electron micrographs of actual soot deposits were taken and compared to the model predictions. Descriptions of the modeling technique and the development of the computational domain are provided. Preliminary results are presented, along with some comparisons with experimental observations.
Extended Langmuir model fitting to the filter column adsorption data ...
African Journals Online (AJOL)
Leachate samples collected at different depths of WQD column were analyzed for concentrations of zinc and copper ions using atomic absorption spectrometer. The removal efficiency was around 94% and 92% for zinc and copper respectively using column depth of 1 M at a flow rate of 12 ml/min. The adsorption model ...
Modeling retinal high and low contrast sensitivity filters
Lourens, T; Mira, J; Sandoval, F
1995-01-01
In this paper two types of ganglion cells in the visual system of mammals (monkey) are modeled. A high contrast sensitive type, the so called M-cells, which project to the two magno-cellular layers of the lateral geniculate nucleus (LGN) and a low sensitive type, the P-cells, which project to the
An Object-Oriented Language-Database Integration Model: The Composition-Filters Approach
Aksit, Mehmet; Bergmans, Lodewijk; Vural, S.; Vural, Sinan; Lehrmann Madsen, O.
1992-01-01
This paper introduces a new model, based on so-called object-composition filters, that uniformly integrates database-like features into an object-oriented language. The focus is on providing persistent dynamic data structures, data sharing, transactions, multiple views and associative access,
Model-Based Engine Control Architecture with an Extended Kalman Filter
Csank, Jeffrey T.; Connolly, Joseph W.
2016-01-01
This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.
Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.
Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing
2011-01-01
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Stoustrup, Jakob
1996-01-01
The design problem of filters for robust failure detection and isolation, (FDI) is addressed in this paper. The failure detection problem will be considered with respect to both modeling errors and disturbances. Both an approach based on failure detection observers as well as an approach based on...
Wit, de A.J.W.; Diepen, van C.A.
2007-01-01
Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil
An object-oriented language-database integration model: The composition filters approach
Aksit, Mehmet; Bergmans, Lodewijk; Vural, Sinan; Vural, S.
1991-01-01
This paper introduces a new model, based on so-called object-composition filters, that uniformly integrates database-like features into an object-oriented language. The focus is on providing persistent dynamic data structures, data sharing, transactions, multiple views and associative access,
Development and application of kinetic model on biological anoxic/aerobic filter.
Kim, Youngnoh; Tanaka, Kazuhiro; Lee, Yong-Woo; Chung, Jinwook
2008-01-01
An up-flow biological anoxic filter (BANF) has been developed to achieve high removal performance of suspended solids and BOD removal as well as nitrogen. With a view to understand treatment mechanisms, we developed a filtration model that incorporates filtration, deposit scoring and biological reactions simultaneously. The biological reactions consist of four types of reaction; dissolution of organic particles; utilization of dissolved organic matter; denitrification; and self-degradation of bacteria. Whereas the reactor is generally assumed to be a plug flow reactor in the filtration model, it is assumed a continuous-flow stirred tank reactor (CSTR) in the model of biological reactions. The hydrodynamics is supposed that the filter bottom (the portion sludge settled) is a CSTR and the filter bed (the portion filled with filter media) consists of number of CSTR of equal size arranged in series. The model obtained in this study was verified and simulated using experimental results taken from a pilot-scale plant and predicted the experimental data well, applying to design and operate BANF.
Czech Academy of Sciences Publication Activity Database
Tlustý, J.; Škramlík, Jiří; Švec, J.; Valouch, Viktor
2012-01-01
Roč. 2012, č. 292178 (2012), s. 1-17 ISSN 1024-123X Institutional support: RVO:61388998 Keywords : analytical modeling * four-switch hybrid power filter * sixfold switching symmetry Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.383, year: 2012 http://www.hindawi.com/journals/mpe/2012/292178/
Kalman-filter model for determining block and trickle SNM losses
International Nuclear Information System (INIS)
Barlow, R.E.; Durst, M.J.; Smiriga, N.G.
1982-07-01
This paper describes an integrated decision procedure for deciding whether a diversion of SNM has occurred. Two possible types of diversion are considered: a block loss during a single time period and a cumulative trickle loss over several time periods. The methodology used is based on a compound Kalman filter model. Numerical examples illustrate our approach
Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?
Directory of Open Access Journals (Sweden)
Simionescu Mihaela
2015-06-01
Full Text Available This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component.
Liu, Hua; Wu, Wen
2017-06-13
For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).
Directory of Open Access Journals (Sweden)
Hua Liu
2017-06-01
Full Text Available For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF. The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF, the interacting multiple model cubature Kalman filter (IMMCKF and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF.
Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter
DEFF Research Database (Denmark)
Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro
2009-01-01
A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...... an LCL filter is used. The proposed control strategy allows control of the active and reactive power fed into the grid, reduce the switching frequency within acceptable operational margins and keep balance of the DC-link capacitor voltages while avoiding excitation of the filter resonance frequencies....
Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Hofman, Radek
2012-01-01
Roč. 41, č. 5 (2012), s. 582-589 ISSN 0361-0918 R&D Projects: GA MV VG20102013018; GA ČR GA102/08/0567 Grant - others:ČVUT(CZ) SGS 10/099/OHK3/1T/16 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian methods * Particle filters * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.295, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf
Modeling of HVDC in Dynamic State Estimation Using Unscented Kalman Filter Method
DEFF Research Database (Denmark)
Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth
2016-01-01
HVDC transmission is an integral part of various power system networks. This article presents an Unscented Kalman Filter dynamic state estimator algorithm that considers the presence of HVDC links. The AC - DC power flow analysis, which is implemented as power flow solver for Dynamic State...... Estimation (DSE), creates an updated admittance matrix. First, a hybrid AC/DC network model is developed to combine the AC network and DC links. Then a non-linear state estimator can solve for hybrid AC/DC states by applying the unscented Kalman filter (UKF) algorithm. It is demonstrated that UKF is easy...
The effect of compression on tuning estimates in a simple nonlinear auditory filter model
DEFF Research Database (Denmark)
Marschall, Marton; MacDonald, Ewen; Dau, Torsten
2013-01-01
Behavioral experiments using auditory masking have been used to characterize frequency selectivity, one of the basic properties of the auditory system. However, due to the nonlinear response of the basilar membrane, the interpretation of these experiments may not be straightforward. Specifically...... consists of a compressor between two bandpass filters. The BPNL forms the basis of the dual-resonance nonlinear (DRNL) filter that has been used in a number of modeling studies. The location of the nonlinear element and its effect on estimated tuning in the two measurement paradigms was investigated......, then compression alone may explain a large part of the behaviorally observed differences in tuning between simultaneous and forward-masking conditions....
Energy-Aware Scheduling of FIR Filter Structures using a Timed Automata Model
DEFF Research Database (Denmark)
Wognsen, Erik Ramsgaard; Hansen, Rene Rydhof; Larsen, Kim Guldstrand
2016-01-01
to be severely power hungry. In this work we therefore show how to use tools and techniques developed by the formal methods community to minimize the energy consumption of Finite Impulse Response (FIR) filters which are extensively used in SDR front-ends. We conduct experiments with four different FIR filter...... structures where we initially derive data flow graphs and precedence graphs using the Synchronous Data Flow (SDF) notation. Based on actual measurements on the Altera Cyclone IV FPGA, we derive power and timing estimates for addition and multiplication, including idling power consumption. We next model...
Siciliano, Alessio; De Rosa, Salvatore
2015-01-01
The present work reports the results of a series of experimental tests performed on cylindrically shaped biological aerated filters (BAFs) to define a new model for reactors design. The nitrification performance was analysed by monitoring a laboratory pilot plant over a six-month period; the dependence of the nitrification rate from the biomass surface density, from ammonia nitrogen concentration and dissolved oxygen concentration was determined using kinetic batch tests. The controls performed on the pilot plant exhibited a nitrification efficiency of approximately 98% at loadings up to [Formula: see text]. Over this value, the pilot plant performance decreased without a correlation with the applied loads. In response to the inlet ammonia loading increase, the bacterial surface density showed a logistic growing trend. The results of kinetic tests proved that the nitrification rate was not affected by the ammonia nitrogen concentration; instead, a first-order kinetic with respect to the dissolved oxygen concentration was detected. Moreover, it was observed that a minimum oxygen concentration, which was proportional to the bacterial surface density, was necessary to initiate the nitrification process. The reaction rate related to bacterial surface density exhibited an increasing trend that was followed by a subsequent decreasing behaviour. The results of kinetic tests and the identification of the relationship between bacterial surface density and ammonia loading permitted the formulation of a mathematical model to predict BAFs' nitrification efficiency.
San-Valero, Pau; Dorado, Antonio D; Martínez-Soria, Vicente; Gabaldón, Carmen
2018-01-01
A three-phase dynamic mathematical model based on mass balances describing the main processes in biotrickling filtration: convection, mass transfer, diffusion, and biodegradation was calibrated and validated for the simulation of an industrial styrene-degrading biotrickling filter. The model considered the key features of the industrial operation of biotrickling filters: variable conditions of loading and intermittent irrigation. These features were included in the model switching from the mathematical description of periods with and without irrigation. Model equations were based on the mass balances describing the main processes in biotrickling filtration: convection, mass transfer, diffusion, and biodegradation. The model was calibrated with steady-state data from a laboratory biotrickling filter treating inlet loads at 13-74 g C m -3 h -1 and at empty bed residence time of 30-15 s. The model predicted the dynamic emission in the outlet of the biotrickling filter, simulating the small peaks of concentration occurring during irrigation. The validation of the model was performed using data from a pilot on-site biotrickling filter treating styrene installed in a fiber-reinforced facility. The model predicted the performance of the biotrickling filter working under high-oscillating emissions at an inlet load in a range of 5-23 g C m -3 h -1 and at an empty bed residence time of 31 s for more than 50 days, with a goodness of fit of 0.84. Copyright © 2017 Elsevier Ltd. All rights reserved.
Modelling And Analysis Of Permeability Of Anisotropic Compressed Non-Woven Filters
Prieur du Plessis, J.; Woudberg, Sonia; Le Coq, Laurence
2010-05-01
An existing geometrical pore-scale model for flow through isotropic spongelike media is adapted to predict flow through anisotropic non-woven glass fibre filters. Model predictions are compared to experimental results for the permeability obtained for a filter under different stages of compression to demonstrate the capability of the model to adjust to changes in porosity. The experimental data used are for a glass fibre paper with a uniform fibre diameter. The input parameters of the pore-scale model are the porosity, fibre diameter and some measure of the anisotropy between the in-plane and normal directions to the paper. Correlation between the predictions and the experimental results is satisfactory and provides confidence in the modelling procedure. It is shown that the permeability is very sensitive to changes in the level of anisotropy, i.e. the level of compression of the nonwoven material.
Modeling Adsorption Based Filters (Bio-remediation of Heavy Metal Contaminated Water)
McCarthy, Chris
I will discuss kinetic models of adsorption, as well as models of filters based on those mechanisms. These mathematical models have been developed in support of our interdisciplinary lab group, which is centered at BMCC/CUNY (City University of New York). Our group conducts research into bio-remediation of heavy metal contaminated water via filtration. The filters are constructed out of biomass, such as spent tea leaves. The spent tea leaves are available in large quantities as a result of the industrial production of tea beverages. The heavy metals bond with the surfaces of the tea leaves (adsorption). The models involve differential equations, stochastic methods, and recursive functions. I will compare the models' predictions to data obtained from computer simulations and experimentally by our lab group. Funding: CUNY Collaborative Incentive Research Grant (Round 12); CUNY Research Scholars Program.
Flatness-based control and Kalman filtering for a continuous-time macroeconomic model
Rigatos, G.; Siano, P.; Ghosh, T.; Busawon, K.; Binns, R.
2017-11-01
The article proposes flatness-based control for a nonlinear macro-economic model of the UK economy. The differential flatness properties of the model are proven. This enables to introduce a transformation (diffeomorphism) of the system's state variables and to express the state-space description of the model in the linear canonical (Brunowsky) form in which both the feedback control and the state estimation problem can be solved. For the linearized equivalent model of the macroeconomic system, stabilizing feedback control can be achieved using pole placement methods. Moreover, to implement stabilizing feedback control of the system by measuring only a subset of its state vector elements the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied on the linearized equivalent model of the financial system and of an inverse transformation that is based again on differential flatness theory. The asymptotic stability properties of the control scheme are confirmed.
International Nuclear Information System (INIS)
Bogey, Christophe; Bailly, Christophe
2006-01-01
Large eddy simulations (LES) of round free jets at Mach number M = 0.9 with Reynolds numbers over the range 2.5 x 10 3 ≤ Re D ≤ 4 x 10 5 are performed using explicit selective/high-order filtering with or without dynamic Smagorinsky model (DSM). Features of the flows and of the turbulent kinetic energy budgets in the turbulent jets are reported. The contributions of molecular viscosity, filtering and DSM to energy dissipation are also presented. Using filtering alone, the results are independent of the filtering strength, and the effects of the Reynolds number on jet development are successfully calculated. Using DSM, the effective jet Reynolds number is found to be artificially decreased by the eddy viscosity. The results are also not appreciably modified when subgrid-scale kinetic energy is used. Moreover, unlike filtering which does not significantly affect the larger computed scales, the eddy viscosity is shown to dissipate energy through all the turbulent scales, in the same way as molecular viscosity at lower Reynolds numbers
Energy Technology Data Exchange (ETDEWEB)
Bogey, Christophe [Laboratoire de Mecanique des Fluides et d' Acoustique, UMR CNRS 5509, Ecole Centrale de Lyon, 69134 Ecully Cedex (France)]. E-mail: christophe.bogey@ec-lyon.fr; Bailly, Christophe [Laboratoire de Mecanique des Fluides et d' Acoustique, UMR CNRS 5509, Ecole Centrale de Lyon, 69134 Ecully Cedex (France)]. E-mail: christophe.baily@ec-lyon.fr
2006-08-15
Large eddy simulations (LES) of round free jets at Mach number M = 0.9 with Reynolds numbers over the range 2.5 x 10{sup 3} {<=} Re {sub D} {<=} 4 x 10{sup 5} are performed using explicit selective/high-order filtering with or without dynamic Smagorinsky model (DSM). Features of the flows and of the turbulent kinetic energy budgets in the turbulent jets are reported. The contributions of molecular viscosity, filtering and DSM to energy dissipation are also presented. Using filtering alone, the results are independent of the filtering strength, and the effects of the Reynolds number on jet development are successfully calculated. Using DSM, the effective jet Reynolds number is found to be artificially decreased by the eddy viscosity. The results are also not appreciably modified when subgrid-scale kinetic energy is used. Moreover, unlike filtering which does not significantly affect the larger computed scales, the eddy viscosity is shown to dissipate energy through all the turbulent scales, in the same way as molecular viscosity at lower Reynolds numbers.
Hackett, Timothy M.; Bilen, Sven G.; Ferreira, Paulo Victor R.; Wyglinski, Alexander M.; Reinhart, Richard C.
2016-01-01
In a communications channel, the space environment between a spacecraft and an Earth ground station can potentially cause the loss of a data link or at least degrade its performance due to atmospheric effects, shadowing, multipath, or other impairments. In adaptive and coded modulation, the signal power level at the receiver can be used in order to choose a modulation-coding technique that maximizes throughput while meeting bit error rate (BER) and other performance requirements. It is the goal of this research to implement a generalized interacting multiple model (IMM) filter based on Kalman filters for improved received power estimation on software-dened radio (SDR) technology for satellite communications applications. The IMM filter has been implemented in Verilog consisting of a customizable bank of Kalman filters for choosing between performance and resource utilization. Each Kalman filter can be implemented using either solely a Schur complement module (for high area efficiency) or with Schur complement, matrix multiplication, and matrix addition modules (for high performance). These modules were simulated and synthesized for the Virtex II platform on the JPL Radio Experimenter Development System (EDS) at NASA Glenn Research Center. The results for simulation, synthesis, and hardware testing are presented.
Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java
Rahma Prillantika, Jessica; Apriliani, Erna; Wahyuningsih, Nuri
2018-03-01
Up to now, we often find data which have correlation between time and location. This data also known as spatial data. Inflation rate is one type of spatial data because it is not only related to the events of the previous time, but also has relevance to the other location or elsewhere. In this research, we do comparison between GSTAR model and GSTAR-Kalman Filter to get prediction which have small error rate. Kalman Filter is one estimator that estimates state changes due to noise from white noise. The final result shows that Kalman Filter is able to improve the GSTAR forecast result. This is shown through simulation results in the form of graphs and clarified with smaller RMSE values.
Introduction of a microsurgical in-vivo embolization-model in rats: the aorta-filter model.
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Lucas M Ritschl
Full Text Available Vascular thrombosis with subsequent distal embolization remains a critical event for patients. Prevention of this life-threatening event can be achieved pharmacologically or mechanically with intravascular filter systems. The ability to evaluate the risk of embolization of certain techniques and procedures in vascular and microvascular surgery, such as, tissue glue or fibrin based haemostatic agents lacks convincing models. We performed 64 microvascular anastomoses in 44 rats, including 44 micro-pore polyurethane filter-anastomoses and 20 non-filter anastomoses. The rats were re-anesthetized and the aorta was re-exposed and removed four hours, three, seven, fourteen, thirty-one days, and six months postoperatively. The specimens were examined macro- and microscopically with regard to the appearance of the vessel wall, condition of the filter and the amount of thrombembolic material. Typical postoperative histopathological changes in vessel architecture were observed. Media necrosis was the first significant change three days postoperatively. Localized intimal hyperplasia, media necrosis, increase of media fibromyocytes and adventitial hypercellularity were seen to a significant extent at day seven postoperatively. Significant neovascularization of adventitia adjacent to the filter was seen after 14 days. A significant amount of thrombotic material was seen after four hours, three and 14 days interval. Only three intravascular filters became completely occluded (6.82%. The aorta-filter-anastomosis model appeared to be a valid in-vivo model in situations at risk for thrombembolic events, for microsurgical research and allowed sensitive analysis of surgical procedures and protection of the vascularized tissue. It may be suitable for a wide range of in-vivo microvascular experiments particularly in the rat model.
Fundamental Frequency and Model Order Estimation Using Spatial Filtering
DEFF Research Database (Denmark)
Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll
2014-01-01
In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal...... extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment...... on a trumpet signal show the applicability on real signals....
New Trends in Model Coupling Theory, Numerics and Applications
International Nuclear Information System (INIS)
Coquel, F.; Godlewski, E.; Herard, J. M.; Segre, J.
2010-01-01
This special issue comprises selected papers from the workshop New Trends in Model Coupling, Theory, Numerics and Applications (NTMC'09) which took place in Paris, September 2 - 4, 2009. The research of optimal technological solutions in a large amount of industrial systems requires to perform numerical simulations of complex phenomena which are often characterized by the coupling of models related to various space and/or time scales. Thus, the so-called multi-scale modelling has been a thriving scientific activity which connects applied mathematics and other disciplines such as physics, chemistry, biology or even social sciences. To illustrate the variety of fields concerned by the natural occurrence of model coupling we may quote: meteorology where it is required to take into account several turbulence scales or the interaction between oceans and atmosphere, but also regional models in a global description, solid mechanics where a thorough understanding of complex phenomena such as propagation of cracks needs to couple various models from the atomistic level to the macroscopic level; plasma physics for fusion energy for instance where dense plasmas and collisionless plasma coexist; multiphase fluid dynamics when several types of flow corresponding to several types of models are present simultaneously in complex circuits; social behaviour analysis with interaction between individual actions and collective behaviour. (authors)
Data-driven modeling of surface temperature anomaly and solar activity trends
Friedel, Michael J.
2012-01-01
A novel two-step modeling scheme is used to reconstruct and analyze surface temperature and solar activity data at global, hemispheric, and regional scales. First, the self-organizing map (SOM) technique is used to extend annual modern climate data from the century to millennial scale. The SOM component planes are used to identify and quantify strength of nonlinear relations among modern surface temperature anomalies (<150 years), tropical and extratropical teleconnections, and Palmer Drought Severity Indices (0–2000 years). Cross-validation of global sea and land surface temperature anomalies verifies that the SOM is an unbiased estimator with less uncertainty than the magnitude of anomalies. Second, the quantile modeling of SOM reconstructions reveal trends and periods in surface temperature anomaly and solar activity whose timing agrees with published studies. Temporal features in surface temperature anomalies, such as the Medieval Warm Period, Little Ice Age, and Modern Warming Period, appear at all spatial scales but whose magnitudes increase when moving from ocean to land, from global to regional scales, and from southern to northern regions. Some caveats that apply when interpreting these data are the high-frequency filtering of climate signals based on quantile model selection and increased uncertainty when paleoclimatic data are limited. Even so, all models find the rate and magnitude of Modern Warming Period anomalies to be greater than those during the Medieval Warm Period. Lastly, quantile trends among reconstructed equatorial Pacific temperature profiles support the recent assertion of two primary El Niño Southern Oscillation types. These results demonstrate the efficacy of this alternative modeling approach for reconstructing and interpreting scale-dependent climate variables.
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M. Morzfeld
2012-06-01
Full Text Available Implicit particle filtering is a sequential Monte Carlo method for data assimilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by minimizing, for each particle, a scalar function F of the state variables. Some previous implementations of the implicit filter rely on finding the Hessians of these functions. The calculation of the Hessians can be cumbersome if the state dimension is large or if the underlying physics are such that derivatives of F are difficult to calculate, as happens in many geophysical applications, in particular in models with partial noise, i.e. with a singular state covariance matrix. Examples of models with partial noise include models where uncertain dynamic equations are supplemented by conservation laws with zero uncertainty, or with higher order (in time stochastic partial differential equations (PDE or with PDEs driven by spatially smooth noise processes. We make the implicit particle filter applicable to such situations by combining gradient descent minimization with random maps and show that the filter is efficient, accurate and reliable because it operates in a subspace of the state space. As an example, we consider a system of nonlinear stochastic PDEs that is of importance in geomagnetic data assimilation.
State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter
Mat Daud, Auni Aslah
In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.
An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models
International Nuclear Information System (INIS)
Shin, Hoyoung; Jae, Moosung
2016-01-01
In this study, a probabilistic assessment of the severe accident management strategy through a filtered containment venting system was performed by using decision tree models. In Korea, the filtered containment venting system has been installed for the first time in Wolsong unit 1 as a part of Fukushima follow-up steps, and it is planned to be applied gradually for all the remaining reactors. Filtered containment venting system, one of severe accident countermeasures, prevents a gradual pressurization of the containment building exhausting noncondensable gas and vapor to the outside of the containment building. In this study, a probabilistic assessment of the filtered containment venting strategy, one of the severe accident management strategies, was performed by using decision tree models. Containment failure frequencies of each decision were evaluated by the developed decision tree model. The optimum accident management strategies were evaluated by comparing the results. Various strategies in severe accident management guidelines (SAMG) could be improved by utilizing the methodology in this study and the offsite risk analysis methodology
Stochastic approach to model fouling in membrane filters with complex pore morphology
Sanaei, Pejman; Gu, Binan; Kondic, Lou; Cummings, Linda J.
2017-11-01
Membrane filters are widely used in industrial applications to remove contaminants and undesired impurities (particles) from a solvent. During the filtration process the membrane internal void area becomes fouled with impurities and as a consequence the filter performance deteriorates, a process that depends on filter internal structure, particle concentration and flow dynamics. The complexity of membrane internal morphology and the random nature of the particle dynamics in the flow make the filtration and fouling challenging to predict; nonetheless, mathematical modeling can play a key role in investigating filter fouling, and in suggesting design modifications for more efficient filtration. To date, many models have been proposed to describe the effects of complexity of membrane structure, and the stochasticity of particle dynamics individually but very few studies focus on both together. In this work, we present an idealized mathematical model, in which a membrane consists of a series of bifurcating pores. Pores decrease in size as the membrane is traversed and particles are removed from the feed by adsorption within pores (which shrinks them) and stochastic sieving (pore blocking by large particles). NSF DMS 1615719.
Comparing the data-driven and the model-dependent strategies for improving filtered GRACE signal
Dutt Vishwakarma, Bramha; Sneeuw, Nico
2017-04-01
The noisy level 02 GRACE products from various groups need to be filtered in order to obtain meaningful information about water mass transport within the Earth system. Filtering affects signal, which increases the uncertainty in the filtered GRACE observed total water storage time series. The signal loss is counter acted using a correction strategy that typically makes use of models. The accuracy of model-dependent methods is dependent on the accuracy of the model, which raises doubts on accuracy of corrected GRACE products over poorly modeled regions. This led to the development of data-driven methods. Although research contributions using a model-dependent method or a data-driven method claim that the corrected GRACE products are superior to filtered products, a comparison of model dependent methods and the data-driven methods is essential to choose the best one. In this contribution, we compare the three most popular model-dependent approaches: additive approach, multiplicative approach, scaling approach, and two data-driven methods proposed recently. In order to be comprehensive, we analyze the performance of these correction strategies over 32 catchments of different sizes located in different climate zones. In a realistic closed-loop simulation, we find that the data-driven methods are consistently superior to the model-dependent approaches. At last we analyze the desiccation of Aral Sea and lake Urmia with the GRACE products, and compare the corrected total water storage change with reports and contributions from different groups. We find that the model-dependent approaches have a tendency to overestimate the rate of water mass loss recorded by GRACE satellites.
Models of marine molluscan diseases: Trends and challenges.
Powell, Eric N; Hofmann, Eileen E
2015-10-01
management, manipulation of host abundance, and use of scavengers and filter feeders to limit the concentration of infective particles. (3) The details of host population processes and pathogen transmission dynamics are blended in models that evaluate the effects of natural selection and/or genetic modification in developing disease resistance in the host population. Application of gene-based models to marine diseases is only now beginning and represents a promising approach that may provide a mechanistic basis for managing marine diseases and their host populations. Overall disease models remain both uncommon and underutilized in addressing the needs for managing molluscan diseases and their host populations. Copyright © 2015 Elsevier Inc. All rights reserved.
Claveau-Mallet, Dominique; Courcelles, Benoît; Pasquier, Philippe; Comeau, Yves
2017-12-01
The first version of the P-Hydroslag model for numerical simulations of steel slag filters is presented. This model main original feature is the implementation of slag exhaustion behavior, crystal growth and crystal size effect on crystal solubility, and crystal accumulation effect on slag dissolution. The model includes four mineral phases: calcite, monetite, homogeneous hydroxyapatite (constant size and solubility) and heterogeneous hydroxyapatite (increasing size and decreasing solubility). In the proposed model, slag behavior is represented by CaO dissolution kinetic rate and exhaustion equations; while slag dissolution is limited by a diffusion rate through a crystal layer. An experimental test for measurement of exhaustion equations is provided. The model was calibrated with an experimental program made of three phases. Firstly, batch tests with 300 g slag sample in synthetic solutions were conducted for the determination of exhaustion equation. Secondly, a slag filter column test fed with synthetic solution was run for 623 days, divided into 9 cells and sampled at the end of the experiment. Finally, the column was dismantled, sampled and analyzed with XRD, TEM and SEM. Experimental column curves for pH, oPO 4 , Ca and inorganic carbon were well predicted by the model. Crystal sizes measured by XRD and TEM validated the hypothesis for homogeneous precipitation while SEM observations validated the thin crystal layer hypothesis. A preliminary validation of the model resulted in successful predictions of a steel slag filter longevity fed with real wastewater. Copyright © 2017 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Gershgorin, B.; Majda, A.J.
2011-01-01
A statistically exactly solvable model for passive tracers is introduced as a test model for the authors' Nonlinear Extended Kalman Filter (NEKF) as well as other filtering algorithms. The model involves a Gaussian velocity field and a passive tracer governed by the advection-diffusion equation with an imposed mean gradient. The model has direct relevance to engineering problems such as the spread of pollutants in the air or contaminants in the water as well as climate change problems concerning the transport of greenhouse gases such as carbon dioxide with strongly intermittent probability distributions consistent with the actual observations of the atmosphere. One of the attractive properties of the model is the existence of the exact statistical solution. In particular, this unique feature of the model provides an opportunity to design and test fast and efficient algorithms for real-time data assimilation based on rigorous mathematical theory for a turbulence model problem with many active spatiotemporal scales. Here, we extensively study the performance of the NEKF which uses the exact first and second order nonlinear statistics without any approximations due to linearization. The role of partial and sparse observations, the frequency of observations and the observation noise strength in recovering the true signal, its spectrum, and fat tail probability distribution are the central issues discussed here. The results of our study provide useful guidelines for filtering realistic turbulent systems with passive tracers through partial observations.
System Model Bias Processing Approach for Regional Coordinated States Information Involved Filtering
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Zebo Zhou
2016-01-01
Full Text Available In the Kalman filtering applications, the conventional dynamic model which connects the states information of two consecutive epochs by state transition matrix is usually predefined and assumed to be invariant. Aiming to improve the adaptability and accuracy of dynamic model, we propose multiple historical states involved filtering algorithm. An autoregressive model is used as the dynamic model which is subsequently combined with observation model for deriving the optimal window-recursive filter formulae in the sense of minimum mean square error principle. The corresponding test statistics characteristics of system residuals are discussed in details. The test statistics of regional predicted residuals are then constructed in a time-window for model bias testing with two hypotheses, that is, the null and alternative hypotheses. Based on the innovations test statistics, we develop a model bias processing procedure including bias detection, location identification, and state correction. Finally, the minimum detectable bias and bias-to-noise ratio are both computed for evaluating the internal and external reliability of overall system, respectively.
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction
Li, Zhencai; Wang, Yang; Liu, Zhen
2016-01-01
The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction.
Li, Zhencai; Wang, Yang; Liu, Zhen
2016-01-01
The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state-space NN, and the unscented Kalman filter is used to train NN's weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model.
Enhanced Kalman Filtering for a 2D CFD NS Wind Farm Flow Model
International Nuclear Information System (INIS)
Doekemeijer, B M; Van Wingerden, J W; Boersma, S; Pao, L Y
2016-01-01
Wind turbines are often grouped together for financial reasons, but due to wake development this usually results in decreased turbine lifetimes and power capture, and thereby an increased levelized cost of energy (LCOE). Wind farm control aims to minimize this cost by operating turbines at their optimal control settings. Most state-of-the-art control algorithms are open-loop and rely on low fidelity, static flow models. Closed-loop control relying on a dynamic model and state observer has real potential to further decrease wind's LCOE, but is often too computationally expensive for practical use. In this paper two time-efficient Kalman filter (KF) variants are outlined incorporating the medium fidelity, dynamic flow model “WindFarmSimulator” (WFSim). This model relies on a discretized set of Navier-Stokes equations in two dimensions to predict the flow in wind farms at low computational cost. The filters implemented are an Ensemble KF and an Approximate KF. Simulations in which a high fidelity simulation model represents the true wind farm show that these filters are 10"1 —10"2 times faster than a regular KF with comparable or better performance, correcting for wake dynamics that are not modeled in WFSim (noticeably, wake meandering and turbine hub effects). This is a first big step towards real-time closed-loop control for wind farms. (paper)
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.
Havlicek, Martin; Friston, Karl J; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D
2011-06-15
This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. Copyright © 2011 Elsevier Inc. All rights reserved.
Heave Motion Measurement by Adaptive Filter Based on Longuet-Higgins Wave Model
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Jiazhen Lu
2017-01-01
Full Text Available A method is proposed to obtain heave motion information based on the Longuet-Higgins wave model. The Longuet-Higgins wave model which is closer to the sea wave is introduced. Based on it, random process of the noise is analyzed and the highpass filter is designed to reduce errors. Then it is the key point in this article that an adaptive algorithm is put forward because of the complexity of the waves. The algorithm adjusts the cutoff frequency to reduce the amplitude attenuation of the filter by analyzing the wave. For the same reason the comprehensive parameter of the phase compensation can be also obtained by the algorithm. Simulation measurement results show that under the rough sea situation the maximum value of absolute error is 0.4942 m according to the normal method, the method is 0.1170 m, and the average error ratio of the rough sea test reduces to 3.89% from 12.54%, which demonstrates that the adaptive filter is more effective in measuring heave movement. A variety of simulation cases show that the adaptive filter can also improve the precision of the heave motion under different sea situations.
Recent trends in social systems quantitative theories and quantitative models
Hošková-Mayerová, Šárka; Soitu, Daniela-Tatiana; Kacprzyk, Janusz
2017-01-01
The papers collected in this volume focus on new perspectives on individuals, society, and science, specifically in the field of socio-economic systems. The book is the result of a scientific collaboration among experts from “Alexandru Ioan Cuza” University of Iaşi (Romania), “G. d’Annunzio” University of Chieti-Pescara (Italy), "University of Defence" of Brno (Czech Republic), and "Pablo de Olavide" University of Sevilla (Spain). The heterogeneity of the contributions presented in this volume reflects the variety and complexity of social phenomena. The book is divided in four Sections as follows. The first Section deals with recent trends in social decisions. Specifically, it aims to understand which are the driving forces of social decisions. The second Section focuses on the social and public sphere. Indeed, it is oriented on recent developments in social systems and control. Trends in quantitative theories and models are described in Section 3, where many new formal, mathematical-statistical to...
Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters
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Wen Xu
2016-10-01
Full Text Available Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. Dynamic panel data models have become increasingly popular in macroeconomics to study common relationships across countries or regions. This paper estimates dynamic panel data models with stochastic volatility by maximizing an approximate likelihood obtained via Rao-Blackwellized particle filters. Monte Carlo studies reveal the good and stable performance of our particle filter-based estimator. When the volatility of volatility is high, or when regressors are absent but stochastic volatility exists, our approach can be better than the maximum likelihood estimator which neglects stochastic volatility and generalized method of moments (GMM estimators.
Facial Feature Tracking Using Efficient Particle Filter and Active Appearance Model
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Durkhyun Cho
2014-09-01
Full Text Available For natural human-robot interaction, the location and shape of facial features in a real environment must be identified. One robust method to track facial features is by using a particle filter and the active appearance model. However, the processing speed of this method is too slow for utilization in practice. In order to improve the efficiency of the method, we propose two ideas: (1 changing the number of particles situationally, and (2 switching the prediction model depending upon the degree of the importance of each particle using a combination strategy and a clustering strategy. Experimental results show that the proposed method is about four times faster than the conventional method using a particle filter and the active appearance model, without any loss of performance.
Directory of Open Access Journals (Sweden)
Xuefeng Zhang
2015-01-01
Full Text Available Sequential, adaptive, and gradient diffusion filters are implemented into spatial multiscale three-dimensional variational data assimilation (3DVAR as alternative schemes to model background error covariance matrix for the commonly used correction scale method, recursive filter method, and sequential 3DVAR. The gradient diffusion filter (GDF is verified by a two-dimensional sea surface temperature (SST assimilation experiment. Compared to the existing DF, the new GDF scheme shows a superior performance in the assimilation experiment due to its success in extracting the spatial multiscale information. The GDF can retrieve successfully the longwave information over the whole analysis domain and the shortwave information over data-dense regions. After that, a perfect twin data assimilation experiment framework is designed to study the effect of the GDF on the state estimation based on an intermediate coupled model. In this framework, the assimilation model is subject to “biased” initial fields from the “truth” model. While the GDF reduces the model bias in general, it can enhance the accuracy of the state estimation in the region that the observations are removed, especially in the South Ocean. In addition, the higher forecast skill can be obtained through the better initial state fields produced by the GDF.
Emulation of a Kalman Filter algorithm on a diffusive flood wave propagation model
Pannekoucke, O.; Ricci, S. M.; Ninove, F.; Thual, O.
2011-12-01
River stream flow forecasting is a critical issue for the security of people and infrastructures, the function of power plants, and water resources management. The benefit of data assimilation for free-surface flow simulation and flood forecasting has already been demonstrated as it is applied to optimize model parameters and to improve simulated water level and discharge state [1]. The correction of the hydraulic state with a Kalman Filter algorithm implies the propagation of the background error covariance matrix B by the dynamics of the model. This step requires the formulation and the integration in time of the tangent linear approximation of the model, which is generally fastidious and costly. The aim of this study is to describe the evolution of the background error covariance function with the Kalman Filter algorithm applied to a 1D diffuse flood wave propagation model. For this simplified model, the formulation of the tangent linear model as well as the propagation of B is affordable as opposed as for an operational hydraulics model solving the shallow water equations. Starting from Gaussian background covariance functions, it was first shown that the diffusive flood wave propagation model increases the correlation length and that the propagated covariance function can be approximated by a Gaussian. Working with a steady observation network, it was then demonstrated that the analysis and propagation steps of the Kalman Filter modify the covariance function at the observation point. The resulting covariance function at the observation point is inhomogeneous, with a shorter correlation length downstream of the observation point than upstream. The diagnosed correlation lengths [2] were used to build a parametrized covariance matrix using a diffusion operator with an inhomogenous diffusion coefficient [3]. This approach led to the formulation of a parametrized background error covariance matrix where the evolution of the covariance function with the Kalman
Application of Semi-Automated Filter to Improve Waveform Lidar Sub-Canopy Elevation Model
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Yongwei Sheng
2012-05-01
Full Text Available Modeling sub-canopy elevation is an important step in the processing of waveform lidar data to measure three dimensional forest structure. Here, we present a methodology based on high resolution discrete-return lidar (DRL to correct the ground elevation derived from large-footprint Laser Vegetation Imaging Sensor (LVIS and to improve measurement of forest structure. We use data acquired over Barro Colorado Island, Panama by LVIS large-footprint lidar (LFL in 1998 and DRL in 2009. The study found an average vertical difference of 28.7 cm between 98,040 LVIS last-return points and the discrete-return lidar ground surface across the island. The majority (82.3% of all LVIS points matched discrete return elevations to 2 m or less. Using a multi-step process, the LVIS last-return data is filtered using an iterative approach, expanding window filter to identify outlier points which are not part of the ground surface, as well as applying vertical corrections based on terrain slope within the individual LVIS footprints. The results of the experiment demonstrate that LFL ground surfaces can be effectively filtered using methods adapted from discrete-return lidar point filtering, reducing the average vertical error by 15 cm and reducing the variance in LVIS last-return data by 70 cm. The filters also reduced the largest vertical estimations caused by sensor saturation in the upper reaches of the forest canopy by 14.35 m, which improve forest canopy structure measurement by increasing accuracy in the sub-canopy digital elevation model.
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa
2015-08-13
This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.
Predicting the Hydraulic Conductivity of Metallic Iron Filters: Modeling Gone Astray
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Chicgoua Noubactep
2016-04-01
Full Text Available Since its introduction about 25 years ago, metallic iron (Fe0 has shown its potential as the key component of reactive filtration systems for contaminant removal in polluted waters. Technical applications of such systems can be enhanced by numerical simulation of a filter design to improve, e.g., the service time or the minimum permeability of a prospected system to warrant the required output water quality. This communication discusses the relevant input quantities into such a simulation model, illustrates the possible simplifications and identifies the lack of relevant thermodynamic and kinetic data. As a result, necessary steps are outlined that may improve the numerical simulation and, consequently, the technical design of Fe0 filters. Following a general overview on the key reactions in a Fe0 system, the importance of iron corrosion kinetics is illustrated. Iron corrosion kinetics, expressed as a rate constant kiron, determines both the removal rate of contaminants and the average permeability loss of the filter system. While the relevance of a reasonable estimate of kiron is thus obvious, information is scarce. As a conclusion, systematic experiments for the determination of kiron values are suggested to improve the database of this key input parameter to Fe0 filters.
Modeling and Analysing of Air Filter in Air Intake System in Automobile Engine
Directory of Open Access Journals (Sweden)
R. Manikantan
2013-01-01
Full Text Available As the legislations on the emission and performance of automobiles are being made more stringent, the expected performance of all the subsystems of an internal combustion engine is also becoming crucial. Nowadays the engines are downsized, and their power increased the demand on the air intake system that has increased phenomenally. Hence, an analysis was carried on a typical air filter fitted into the intake system to determine its flow characteristics. In the present investigation, a CAD model of an existing air filter was designed, and CFD analysis was done pertaining to various operating regimes of an internal combustion engine. The numerical results were validated with the experimental data. From the postprocessed result, we can see that there is a deficit in the design of the present filter, as the bottom portion of the filter is preventing the upward movement of air. Hence, the intake passage can be rearranged to provide an upward tangential motion, which can enhance the removal of larger dust and soot particles effectively by the inertial action of air alone.
Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor
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Ye Li
2017-01-01
Full Text Available Recommended system is beneficial to e-commerce sites, which provides customers with product information and recommendations; the recommendation system is currently widely used in many fields. In an era of information explosion, the key challenges of the recommender system is to obtain valid information from the tremendous amount of information and produce high quality recommendations. However, when facing the large mount of information, the traditional collaborative filtering algorithm usually obtains a high degree of sparseness, which ultimately lead to low accuracy recommendations. To tackle this issue, we propose a novel algorithm named Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor, which is based on the trust model and is combined with the user similarity. The novel algorithm takes into account the degree of interest overlap between the two users and results in a superior performance to the recommendation based on Trust Model in criteria of Precision, Recall, Diversity and Coverage. Additionally, the proposed model can effectively improve the efficiency of collaborative filtering algorithm and achieve high performance.
Cai, Rong-Rong; Zhang, Li-Zhi; Yan, Yuying
2017-01-01
Fibrous filters have been proved to be one of the most cost-effective way of particulate matters (specifically PM 2.5) purification. However, due to the complex structure of real fibrous filters, it is difficult to accurately predict the performance of PM2.5 removal. In this study, a new 3D filtration modeling approach is proposed to predict the removal efficiencies of particles by real fibrous filters, by taking the particle rebound effect into consideration. A real filter is considered and ...
Sky-Hook Control and Kalman Filtering in Nonlinear Model of Tracked Vehicle Suspension System
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Jurkiewicz Andrzej
2017-09-01
Full Text Available The essence of the undertaken topic is application of the continuous sky-hook control strategy and the Extended Kalman Filter as the state observer in the 2S1 tracked vehicle suspension system. The half-car model of this suspension system consists of seven logarithmic spiral springs and two magnetorheological dampers which has been described by the Bingham model. The applied continuous sky-hook control strategy considers nonlinear stiffness characteristic of the logarithmic spiral springs. The control is determined on estimates generated by the Extended Kalman Filter. Improve of ride comfort is verified by comparing simulation results, under the same driving conditions, of controlled and passive vehicle suspension systems.
State-Space Dynamic Model for Estimation of Radon Entry Rate, based on Kalman Filtering
Czech Academy of Sciences Publication Activity Database
Brabec, Marek; Jílek, K.
2007-01-01
Roč. 98, - (2007), s. 285-297 ISSN 0265-931X Grant - others:GA SÚJB JC_11/2006 Institutional research plan: CEZ:AV0Z10300504 Keywords : air ventilation rate * radon entry rate * state-space modeling * extended Kalman filter * maximum likelihood estimation * prediction error decomposition Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.963, year: 2007
RB Particle Filter Time Synchronization Algorithm Based on the DPM Model
Guo, Chunsheng; Shen, Jia; Sun, Yao; Ying, Na
2015-01-01
Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are d...
Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing.
Huth, Jacob; Masquelier, Timothée; Arleo, Angelo
2018-01-01
We developed Convis , a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just-in-time optimization and compilation of the model onto CPU or GPU architectures. An alternative implementation based on Theano (Theano Development Team, 2016) is also available, although not fully supported. Through automatic differentiation, any parameter of a specified model can be optimized to approach a desired output which is a significant improvement over e.g., Monte Carlo or particle optimizations without gradients. We show that a number of models including even complex non-linearities such as contrast gain control and spiking mechanisms can be implemented easily. We show in this paper that we can in particular recreate the simulation results of a popular retina simulation software VirtualRetina (Wohrer and Kornprobst, 2009), with the added benefit of providing (1) arbitrary linear filters instead of the product of Gaussian and exponential filters and (2) optimization routines utilizing the gradients of the model. We demonstrate the utility of 3d convolution filters with a simple direction selective filter. Also we show that it is possible to optimize the input for a certain goal, rather than the parameters, which can aid the design of experiments as well as closed-loop online stimulus generation. Yet, Convis is more than a retina simulator. For instance it can also predict the response of V1 orientation selective cells. Convis is open source under the GPL-3.0 license and available from https://github.com/jahuth/convis/ with documentation at https://jahuth.github.io/convis/.
Szczęsna, Agnieszka; Pruszowski, Przemysław
2016-01-01
Inertial orientation tracking is still an area of active research, especially in the context of out-door, real-time, human motion capture. Existing systems either propose loosely coupled tracking approaches where each segment is considered independently, taking the resulting drawbacks into account, or tightly coupled solutions that are limited to a fixed chain with few segments. Such solutions have no flexibility to change the skeleton structure, are dedicated to a specific set of joints, and have high computational complexity. This paper describes the proposal of a new model-based extended quaternion Kalman filter that allows for estimation of orientation based on outputs from the inertial measurements unit sensors. The filter considers interdependencies resulting from the construction of the kinematic chain so that the orientation estimation is more accurate. The proposed solution is a universal filter that does not predetermine the degree of freedom at the connections between segments of the model. To validation the motion of 3-segments single link pendulum captured by optical motion capture system is used. The next step in the research will be to use this method for inertial motion capture with a human skeleton model.
Markov models and the ensemble Kalman filter for estimation of sorption rates.
Energy Technology Data Exchange (ETDEWEB)
Vugrin, Eric D.; McKenna, Sean Andrew (Sandia National Laboratories, Albuquerque, NM); Vugrin, Kay White
2007-09-01
Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.
El Gharamti, Mohamad
2014-02-01
The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.
Directory of Open Access Journals (Sweden)
G. Peláez
2007-01-01
Full Text Available An investigation of the response of a physical pendulum to time delay filtered inputs was conducted. It was shown that the physical pendulum model is more accurate than the simple pendulum for modelling the dynamic response of overhead cranes with loads hanging from hooks. Based on the physical pendulum model a Specified Time Delay filter for an experimental mini overhead crane was synthesized. While somewhat limited in the scope by the hardware conditions placed in the system, the results provide basic insights into the successful application of the Time Delay Filtering method to overhead cranes.
Mathematical modeling of the fibrosis process in the implantation of inferior vena cava filters.
Nicolás, M; Peña, E; Malvè, M; Martínez, M A
2015-12-21
An inferior vena cava filter is a medical device that is implanted in the inferior vena cava and is in charge of capturing blood clots before they reach the lungs, preventing from pulmonary embolism. There are some clinical problems regarding the use of inferior vena cava filters. One of them is the difficulty when retrieving the device due to the remodeling of the vena cava. Huge effort has been made in creating computational models that reproduce tissue remodeling, but no attention has been paid to the fibrosis phenomenon occurring in the inferior vena cava. In this work, a continuum computational model that reproduces the fibrosis in the presence of an antithrombotic filter is presented. Diffusion-reaction equations are used for modeling the mass balance between species in the venous wall. The main species considered to play a key role in the process of fibrosis are smooth muscle cells, endothelial cells, matrix metalloproteinases, vascular growth factors and the extracellular matrix. The developed model has been implemented on an idealized axisymmetric geometric vena cava model. Moreover, a sensitivity analysis has been performed to study the parameters influence on the evolution of the model. Results show that the computational model is able to predict the behavior of the species considered and it captures the key characteristics of lesion growth and the healing process within a vein subjected to non-physiological mechanical forces. Our results suggests that the vessel wall response is mainly caused by the endothelium denudation area and the collagen turnover among other factors. Copyright © 2015 Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Zhang, He
2016-11-20
Angular velocity information is a requisite for a spacecraft guidance, navigation, and control system. In this paper, an approach for angular velocity estimation based merely on star vector measurement with an improved current statistical model Kalman filter is proposed. High-precision angular velocity estimation can be achieved under dynamic conditions. The amount of calculation is also reduced compared to a Kalman filter. Different trajectories are simulated to test this approach, and experiments with real starry sky observation are implemented for further confirmation. The estimation accuracy is proved to be better than 10-4 rad/s under various conditions. Both the simulation and the experiment demonstrate that the described approach is effective and shows an excellent performance under both static and dynamic conditions.
Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy
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Changho Lee
2013-03-01
Full Text Available The International Civil Aviation Organization (ICAO has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS, Automatic Dependent Surveillance-Broadcast (ADS-B, multilateration (MLAT and wide-area multilateration (WAM systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.
Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems
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Man Hong
2013-01-01
Full Text Available To precisely track the reactor temperature in the entire working condition, the constrained Hammerstein-Wiener model describing nonlinear chemical processes such as in the continuous stirred tank reactor (CSTR is proposed. A predictive control algorithm based on the Kalman filter for constrained Hammerstein-Wiener systems is designed. An output feedback control law regarding the linear subsystem is derived by state observation. The size of reaction heat produced and its influence on the output are evaluated by the Kalman filter. The observation and evaluation results are calculated by the multistep predictive approach. Actual control variables are computed while considering the constraints of the optimal control problem in a finite horizon through the receding horizon. The simulation example of the CSTR tester shows the effectiveness and feasibility of the proposed algorithm.
Effect of the time window on the heat-conduction information filtering model
Guo, Qiang; Song, Wen-Jun; Hou, Lei; Zhang, Yi-Lu; Liu, Jian-Guo
2014-05-01
Recommendation systems have been proposed to filter out the potential tastes and preferences of the normal users online, however, the physics of the time window effect on the performance is missing, which is critical for saving the memory and decreasing the computation complexity. In this paper, by gradually expanding the time window, we investigate the impact of the time window on the heat-conduction information filtering model with ten similarity measures. The experimental results on the benchmark dataset Netflix indicate that by only using approximately 11.11% recent rating records, the accuracy could be improved by an average of 33.16% and the diversity could be improved by 30.62%. In addition, the recommendation performance on the dataset MovieLens could be preserved by only considering approximately 10.91% recent records. Under the circumstance of improving the recommendation performance, our discoveries possess significant practical value by largely reducing the computational time and shortening the data storage space.
MODEL-ORIENTED METHOD OF DESIGN IMPLEMENTATION WHEN CREATING DIGITAL FILTERS
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V. Levinskyi
2016-12-01
Full Text Available This article discusses the example of model-oriented method of design and development of digital low-pass filters (LPF for automatic control systems (ACS. Typically, high frequency noise and disturbance attenuation is carried out by analogue LPF. However, technical implementation of analogue filters higher than the second order arouse certain difficulties related with the need of precise passive components ratings selection (resistors, capacitors. If the noise and disturbances spectral composition is known, it is possible to build digital LPF with the Nyquist frequency greater than the maximum frequency in the noise spectrum. Such possibility has appeared because of cheap, energy-efficient, high-speed 32-bit microcontrollers market entry. They have analogue signals sampling rate of 30 kHz and above. The traditional approach using the “manual” method of filter parameters calculation, obtaining their recurrence expressions and further program implementation requires high qualification and a lot of time consumption from the developer. An alternative to this approach is the model-oriented method of design (MOMD in MatLab environment when in the one environment the design of digital LPF, verificaton of its performance as a part of the ACS, generation and compilation of program codes for selected microcontroller family take place. MOMD can also be used in the designs of bandpass and bandstop filters for adaptive control systems or systems of technical diagnostics. If during the commissioning or the operation of ACS there is a need in digital LPF parameters change then this operation can be performed within half an hour. MOMD technology allows to significantly reduce the time for developing a specific product without loss of quality in its design ‘cause of extensive possibilities of MatLab development environment.
Scanlon, Bridget R; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y; Müller Schmied, Hannes; van Beek, Ludovicus P H; Wiese, David N; Wada, Yoshihide; Long, Di; Reedy, Robert C; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F P
2018-02-06
Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002-2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤-0.5 km 3 /y) and increasing (≥0.5 km 3 /y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km 3 /y, whereas most models estimate decreasing trends (-71 to 11 km 3 /y). Land water storage trends, summed over all basins, are positive for GRACE (∼71-82 km 3 /y) but negative for models (-450 to -12 km 3 /y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated. Copyright © 2018 the Author(s). Published by PNAS.
Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.
Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui
2017-01-01
To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.
Directory of Open Access Journals (Sweden)
Nita H. SHAH
2010-07-01
Full Text Available This paper deals with the rigorous photogrammetric solution to model the uncertainty in the orientation parameters of Indian Remote Sensing Satellite IRS-P5 (Cartosat-1. Cartosat-1 is a three axis stabilized spacecraft launched into polar sun-synchronous circular orbit at an altitude of 618 km. The satellite has two panchromatic (PAN cameras with nominal resolution of ~2.5 m. The camera looking ahead is called FORE mounted with +26 deg angle and the other looking near nadir is called AFT mounted with -5 deg, in along track direction. Data Product Generation Software (DPGS system uses the rigorous photogrammetric Collinearity model in order to utilize the full system information, together with payload geometry & control points, for estimating the uncertainty in attitude parameters. The initial orbit, attitude knowledge is obtained from GPS bound orbit measurement, star tracker and gyros. The variations in satellite attitude with time are modelled using simple linear polynomial model. Also, based on this model, Kalman filter approach is studied and applied to improve the uncertainty in the orientation of spacecraft with high quality ground control points (GCPs. The sequential estimator (Kalman filter is used in an iterative process which corrects the parameters at each time of observation rather than at epoch time. Results are presented for three stereo data sets. The accuracy of model depends on the accuracy of the control points.
Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.
Truncation of power law behavior in 'scale-free' network models due to information filtering
International Nuclear Information System (INIS)
Mossa, Stefano; Barthelemy, Marc; Eugene Stanley, H.; Nunes Amaral, Luis A.
2002-01-01
We formulate a general model for the growth of scale-free networks under filtering information conditions--that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network 'accessible' to the node. We test our model with empirical data for the World Wide Web and find agreement
Interaction Admittance Based Modeling of Multi-Paralleled Grid-Connected Inverter with LCL-Filter
DEFF Research Database (Denmark)
Lu, Minghui; Blaabjerg, Frede; Wang, Xiongfei
2016-01-01
This paper investigates the mutual interaction and stability issues of multi-parallel LCL-filtered inverters. The stability and power quality of multiple grid-tied inverters are gaining more and more research attention as the penetration of renewables increases. In this paper, interactions...... and coupling effects among the multi-paralleled inverters and power grid are explicitly revealed. An Interaction Admittance concept is introduced to express and model the interaction through the physical admittances of the network. Compared to the existing modeling methods, the proposed analysis provides...
Online Estimation of Model Parameters of Lithium-Ion Battery Using the Cubature Kalman Filter
Tian, Yong; Yan, Rusheng; Tian, Jindong; Zhou, Shijie; Hu, Chao
2017-11-01
Online estimation of state variables, including state-of-charge (SOC), state-of-energy (SOE) and state-of-health (SOH) is greatly crucial for the operation safety of lithium-ion battery. In order to improve estimation accuracy of these state variables, a precise battery model needs to be established. As the lithium-ion battery is a nonlinear time-varying system, the model parameters significantly vary with many factors, such as ambient temperature, discharge rate and depth of discharge, etc. This paper presents an online estimation method of model parameters for lithium-ion battery based on the cubature Kalman filter. The commonly used first-order resistor-capacitor equivalent circuit model is selected as the battery model, based on which the model parameters are estimated online. Experimental results show that the presented method can accurately track the parameters variation at different scenarios.
International Nuclear Information System (INIS)
Alexander, Lisa
2007-01-01
Full text: Nine global coupled climate models were assessed for their ability to reproduce observed trends in a set of indices representing temperature and precipitation extremes over Australia. Observed trends for 1957-1999 were compared with individual and multi-modelled trends calculated over the same period. When averaged across Australia the magnitude of trends and interannual variability of temperature extremes were well simulated by most models, particularly for the warm nights index. Except for consecutive dry days, the majority of models also reproduced the correct sign of trend for precipitation extremes. A bootstrapping technique was used to show that most models produce plausible trends when averaged over Australia, although only heavy precipitation days simulated from the multi-model ensemble showed significant skill at reproducing the observed spatial pattern of trends. Two of the models with output from different forcings showed that only with anthropogenic forcing included could the models capture the observed areally averaged trend for some of the temperature indices, but the forcing made little difference to the models' ability to reproduce the spatial pattern of trends over Australia. Future projected changes in extremes using three emissions scenarios were also analysed. Australia shows a shift towards significant warming of temperature extremes with much longer dry spells interspersed with periods of increased extreme precipitation irrespective of the scenario used. More work is required to determine whether regional projected changes over Australia are robust
Trickling filter for urea and bio-waste processing - dynamic modelling of nitrogen cycle
Zhukov, Anton; Hauslage, Jens; Tertilt, Gerin; Bornemann, Gerhild
Mankind’s exploration of the solar system requires reliable Life Support Systems (LSS) enabling long duration manned space missions. In the absence of frequent resupply missions, closure of the LSS will play a very important role and its maximisation will to a large extent drive the selection of appropriate LSS architectures. One of the significant issues on the way to full closure is to effectively utilise biological wastes such as urine, inedible biomass etc. A very promising concept of biological waste reprocessing is the use of trickling filters which are currently being developed and investigated by DLR, Cologne, Germany. The concept is called Combined Regenerative Organic-Food Production (C.R.O.P.) and is based on the microbiological treatment of biological wastes and reprocessing them into aqueous fertilizer which can directly be used in a greenhouse for food production. Numerous experiments have been and are being conducted by DLR in order to fully understand and characterize the process. The human space exploration group of the Technical University of Munich (TUM) in cooperation with DLR has started to establish a dynamic model of the trickling filter system to be able to assess its performance on the LSS level. In the first development stage the model covers the nitrogen cycle enabling to simulate urine processing. This paper describes briefly the C.R.O.P. concept and the status of the trickling filter model development. The model is based on enzyme-catalyzed reaction kinetics for the fundamental microbiological reaction chain and is created in MATLAB. Verification and correlation of the developed model with experiment results has been performed. Several predictive studies for batch sequencing behavior have been performed, demonstrating a good capability of C.R.O.P. concept to be used in closed LSS. Achieved results are critically discussed and way forward is presented.
HMM filtering and parameter estimation of an electricity spot price model
International Nuclear Information System (INIS)
Erlwein, Christina; Benth, Fred Espen; Mamon, Rogemar
2010-01-01
In this paper we develop a model for electricity spot price dynamics. The spot price is assumed to follow an exponential Ornstein-Uhlenbeck (OU) process with an added compound Poisson process. In this way, the model allows for mean-reversion and possible jumps. All parameters are modulated by a hidden Markov chain in discrete time. They are able to switch between different economic regimes representing the interaction of various factors. Through the application of reference probability technique, adaptive filters are derived, which in turn, provide optimal estimates for the state of the Markov chain and related quantities of the observation process. The EM algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters. We implement this self-calibrating model on a deseasonalised series of daily spot electricity prices from the Nordic exchange Nord Pool. On the basis of one-step ahead forecasts, we found that the model is able to capture the empirical characteristics of Nord Pool spot prices. (author)
Directory of Open Access Journals (Sweden)
Emrah Altun
2018-01-01
Full Text Available Most of the financial institutions compute the Value-at-Risk (VaR of their trading portfolios using historical simulation-based methods. In this paper, we examine the Filtered Historical Simulation (FHS model introduced by Barone-Adesi et al. (1999 theoretically and empirically. The main goal of this study is to find an answer for the following question: “Does the assumption on innovation process play an important role for the Filtered Historical Simulation model?”. For this goal, we investigate the performance of FHS model with skewed and fat-tailed innovations distributions such as normal, skew normal, Student’s-t, skew-T, generalized error, and skewed generalized error distributions. The performances of FHS models are evaluated by means of unconditional and conditional likelihood ratio tests and loss functions. Based on the empirical results, we conclude that the FHS models with generalized error and skew-T distributions produce more accurate VaR forecasts.
International Nuclear Information System (INIS)
Xu Long; Wang Junping; Chen Quanshi
2012-01-01
Highlights: ► A novel extended Kalman Filtering SOC estimation method based on a stochastic fuzzy neural network (SFNN) battery model is proposed. ► The SFNN which has filtering effect on noisy input can model the battery nonlinear dynamic with high accuracy. ► A robust parameter learning algorithm for SFNN is studied so that the parameters can converge to its true value with noisy data. ► The maximum SOC estimation error based on the proposed method is 0.6%. - Abstract: Extended Kalman filtering is an intelligent and optimal means for estimating the state of a dynamic system. In order to use extended Kalman filtering to estimate the state of charge (SOC), we require a mathematical model that can accurately capture the dynamics of battery pack. In this paper, we propose a stochastic fuzzy neural network (SFNN) instead of the traditional neural network that has filtering effect on noisy input to model the battery nonlinear dynamic. Then, the paper studies the extended Kalman filtering SOC estimation method based on a SFNN model. The modeling test is realized on an 80 Ah Ni/MH battery pack and the Federal Urban Driving Schedule (FUDS) cycle is used to verify the SOC estimation method. The maximum SOC estimation error is 0.6% compared with the real SOC obtained from the discharging test.
A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows
Meldi, M.; Poux, A.
2017-10-01
A Kalman filter based sequential estimator is presented in this work. The estimator is integrated in the structure of segregated solvers for the analysis of incompressible flows. This technique provides an augmented flow state integrating available observation in the CFD model, naturally preserving a zero-divergence condition for the velocity field. Because of the prohibitive costs associated with a complete Kalman Filter application, two model reduction strategies have been proposed and assessed. These strategies dramatically reduce the increase in computational costs of the model, which can be quantified in an augmentation of 10%- 15% with respect to the classical numerical simulation. In addition, an extended analysis of the behavior of the numerical model covariance Q has been performed. Optimized values are strongly linked to the truncation error of the discretization procedure. The estimator has been applied to the analysis of a number of test cases exhibiting increasing complexity, including turbulent flow configurations. The results show that the augmented flow successfully improves the prediction of the physical quantities investigated, even when the observation is provided in a limited region of the physical domain. In addition, the present work suggests that these Data Assimilation techniques, which are at an embryonic stage of development in CFD, may have the potential to be pushed even further using the augmented prediction as a powerful tool for the optimization of the free parameters in the numerical simulation.
A joint matrix completion and filtering model for influenza serological data integration.
Directory of Open Access Journals (Sweden)
Xiao-Tong Yuan
Full Text Available Antigenic characterization based on serological data, such as Hemagglutination Inhibition (HI assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from a number of individual experiments. Measurements from different experiments may be inconsistent due to different experimental conditions. Consequently we will observe a matrix with missing data and possibly inconsistent measurements. In this paper, we develop a new mathematical model, which we refer to as Joint Matrix Completion and Filtering, for HI data integration. In this approach, we simultaneously handle the incompleteness and uncertainty of observations by assuming that the underlying merged HI data matrix has low rank, as well as carefully modeling different levels of noises in each individual table. An efficient blockwise coordinate descent procedure is developed for optimization. The performance of our approach is validated on synthetic and real influenza datasets. The proposed joint matrix completion and filtering model can be adapted as a general model for biological data integration, targeting data noises and missing values within and across experiments.
Continuous updating of a coupled reservoir-seismic model using an ensemble Kalman filter technique
Energy Technology Data Exchange (ETDEWEB)
Skjervheim, Jan-Arild
2007-07-01
This work presents the development of a method based on the ensemble Kalman filter (EnKF) for continuous reservoir model updating with respect to the combination of production data, 3D seismic data and time-lapse seismic data. The reservoir-seismic model system consists of a commercial reservoir simulator coupled to existing rock physics and seismic modelling software. The EnKF provides an ideal-setting for real time updating and prediction in reservoir simulation models, and has been applied to synthetic models and real field cases from the North Sea. In the EnKF method, static parameters as the porosity and permeability, and dynamic variables, as fluid saturations and pressure, are updated in the reservoir model at each step data become available. In addition, we have updated a lithology parameter (clay ratio) which is linked to the rock physics model, and the fracture density in a synthetic fractured reservoir. In the EnKF experiments we have assimilated various types of production and seismic data. Gas oil ratio (GOR), water cut (WCT) and bottom-hole pressure (BHP) are used in the data assimilation. Furthermore, inverted seismic data, such as Poisson's ratio and acoustic impedance, and seismic waveform data have been assimilated. In reservoir applications seismic data may introduce a large amount of data in the assimilation schemes, and the computational time becomes expensive. In this project efficient EnKF schemes are used to handle such large datasets, where challenging aspects such as the inversion of a large covariance matrix and potential loss of rank are considered. Time-lapse seismic data may be difficult to assimilate since they are time difference data, i.e. data which are related to the model variable at two or more time instances. Here we have presented a general sequential Bayesian formulation which incorporates time difference data, and we show that the posterior distribution includes both a filter and a smoother solution. Further, we show
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
DEFF Research Database (Denmark)
Levine, Ari; Heje Pedersen, Lasse
2016-01-01
, that these trend indicators are closely related. In fact, they are equivalent representations in their most general forms. They also capture many other types of filters, such as the Hodrick–Prescott (HP) filter, the Kalman filter, and all other linear filters. We show how these filters can be represented through...
Systematic Wind Farm Measurement Data Filtering Tool for Wake Model Calibration
DEFF Research Database (Denmark)
Rethore, Pierre-Elouan Mikael; Johansen, Nicholas Alan; Frandsen, Sten Tronæs
A set of systematic methods for characterizing the sensors of a wind farm and using these sensors to filter more accurately large volumes of measurement data is proposed. These methods are based on the experience accumulated while processing datasets from two large offshore wind farms in Denmark....... Both wake model developers and wind farm operators seeking to determine how the wind farm operates under specific conditions can find these methods valuable. The methods are general and can be applied successfully to any wind farm by taking into consideration the specific aspects of each wind farm....
Modeling Algal Bloom Dynamics in a River Using the Ensemble Kalman Filter
Kim, K.; Park, M.; Min, J.; Ryu, I.; Kang, M.; Park, L.
2013-12-01
A forecasting framework of algal bloom in a river channel was developed by employing two numerical models coupled in a serial order to simulate a watershed and the main river channel and the ensemble Kalman filter (EnKF) for data assimilation (DA). The HSPF model simulates flow discharge and water quality from the watershed and the EFDC model takes the results as boundary forcing to simulate river hydrodynamics and water quality. The ensemble Kalman filter (EnKF) was applied for DA in the framework, linking uncertainties of model simulations and observations. Stochastic error models to describe HSPF model simulation uncertainty were formed by comparing the simulation and observation values. The ensemble of the simulated HSPF model outputs, generated from the error models, reflect the uncertainties in the HSPF model's initial conditions, model structure and boundary conditions such as meteorological data and water quality data for point pollutant sources. Stochastic forcing terms to consider the model error of the EFDC model and observational error were added during the ensemble simulation of the EFDC model. The framework was applied to a section of the Han River watershed, located in the mid-eastern area of the Korean Peninsula. The HSPF and EFDC models were calibrated before they are used for hindcastings of the first nine months of 2012. DA was conducted with weekly chlorophyll-a (chl-a) data sampled along the river channel by updating chl-a concentrations of the EFDC model grids. The results show that EnKF works efficiently for updating spatial distribution of chl-a concentrations in the downstream part of the river section where flow retention time is relatively long. However, for the upstream part of river section with relatively fast flow, since the ensemble forcing at the tributary confluence points produced by the error models are not updated, the effect of DA is flushed away in just a couple of days by the flow from tributaries. In order to quantify
Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.
Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza
2018-03-01
This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
CFD modeling of catheter-based Chemofilter device for filtering chemotherapy drugs from venous flow
Maani, Nazanin; Yee, Daryl; Nosonovsky, Michael; Greer, Julia; Hetts, Steven; Rayz, Vitaliy
2017-11-01
Purpose: Intra-arterial chemotherapy, a procedure where drugs are injected into arteries supplying a tumor, may cause systemic toxicity. The Chemofilter device, deployed in a vein downstream of the tumor, can chemically filter the excessive drugs from the circulation. In our study, CFD modeling of blood flow through the Chemofilter is used to optimize its hemodynamic performance. Methods:The Chemofilter consists of a porous membrane attached to a stent-like frame of the RX Accunet distal protection filters used for capturing blood clots. The membrane is formed by a lattice of symmetric micro-cells. This design provides a large surface area for the drug binding, and allows blood cells to pass through the lattice. A two-scale modeling approach is used, where the flow through individual micro-cells is simulated to determine the lattice permeability and then the entire device is modeled as a porous membrane. Results: The simulations detected regions of flow stagnation and recirculation caused by the membrane and its supporting frame. The effect of the membrane's leading angle on the velocity and pressure fields was determined. The device optimization will help the efficacy of drug absorption, while the risk of blood clotting reduces. NIH NCI R01CA194533.
A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM
Directory of Open Access Journals (Sweden)
Jianjun Ni
2014-01-01
Full Text Available Robot simultaneous localization and mapping (SLAM problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.
Directory of Open Access Journals (Sweden)
S. L. Heck
2012-02-01
Full Text Available There is a widely recognized need to improve our understanding of biosphere-atmosphere carbon exchanges in areas of complex terrain including the United States Mountain West. CO2 fluxes over mountainous terrain are often difficult to measure due to unusual and complicated influences associated with atmospheric transport. Consequently, deriving regional fluxes in mountain regions with carbon cycle inversion of atmospheric CO2 mole fraction is sensitive to filtering of observations to those that can be represented at the transport model resolution. Using five years of CO2 mole fraction observations from the Regional Atmospheric Continuous CO2 Network in the Rocky Mountains (Rocky RACCOON, five statistical filters are used to investigate a range of approaches for identifying regionally representative CO2 mole fractions. Test results from three filters indicate that subsets based on short-term variance and local CO2 gradients across tower inlet heights retain nine-tenths of the total observations and are able to define representative diel variability and seasonal cycles even for difficult-to-model sites where the influence of local fluxes is much larger than regional mole fraction variations. Test results from two other filters that consider measurements from previous and following days using spline fitting or sliding windows are overly selective. Case study examples showed that these windowing-filters rejected measurements representing synoptic changes in CO2, which suggests that they are not well suited to filtering continental CO2 measurements. We present a novel CO2 lapse rate filter that uses CO2 differences between levels in the model atmosphere to select subsets of site measurements that are representative on model scales. Our new filtering techniques provide guidance for novel approaches to assimilating mountain-top CO2 mole fractions in carbon cycle inverse models.
Correia, Carlos M; Bond, Charlotte Z; Sauvage, Jean-François; Fusco, Thierry; Conan, Rodolphe; Wizinowich, Peter L
2017-10-01
We build on a long-standing tradition in astronomical adaptive optics (AO) of specifying performance metrics and error budgets using linear systems modeling in the spatial-frequency domain. Our goal is to provide a comprehensive tool for the calculation of error budgets in terms of residual temporally filtered phase power spectral densities and variances. In addition, the fast simulation of AO-corrected point spread functions (PSFs) provided by this method can be used as inputs for simulations of science observations with next-generation instruments and telescopes, in particular to predict post-coronagraphic contrast improvements for planet finder systems. We extend the previous results presented in Correia and Teixeira [J. Opt. Soc. Am. A31, 2763 (2014)JOAOD60740-323210.1364/JOSAA.31.002763] to the closed-loop case with predictive controllers and generalize the analytical modeling of Rigaut et al. [Proc. SPIE3353, 1038 (1998)PSISDG0277-786X10.1117/12.321649], Flicker [Technical Report (W. M. Keck Observatory, 2007)], and Jolissaint [J. Eur. Opt. Soc.5, 10055 (2010)1990-257310.2971/jeos.2010.10055]. We follow closely the developments of Ellerbroek [J. Opt. Soc. Am. A22, 310 (2005)JOAOD60740-323210.1364/JOSAA.22.000310] and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors while minimizing the overall residual variance. We discuss applications to (i) analytic AO-corrected PSF modeling in the spatial-frequency domain, (ii) post-coronagraphic contrast enhancement, (iii) filter optimization for real-time wavefront reconstruction, and (iv) PSF reconstruction from system telemetry. Under perfect knowledge of wind velocities, we show that ∼60 nm rms error reduction can be achieved with the distributed Kalman filter embodying antialiasing reconstructors on 10 m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few λ/D separations (∼1-5λ/D) for a
Energy Technology Data Exchange (ETDEWEB)
Ruffio, Jean-Baptiste; Macintosh, Bruce; Nielsen, Eric L.; Czekala, Ian; Bailey, Vanessa P.; Follette, Katherine B. [Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA, 94305 (United States); Wang, Jason J.; Rosa, Robert J. De; Duchêne, Gaspard [Astronomy Department, University of California, Berkeley CA, 94720 (United States); Pueyo, Laurent [Space Telescope Science Institute, Baltimore, MD, 21218 (United States); Marley, Mark S. [NASA Ames Research Center, Mountain View, CA, 94035 (United States); Arriaga, Pauline; Fitzgerald, Michael P. [Department of Physics and Astronomy, University of California, Los Angeles, CA, 90095 (United States); Barman, Travis [Lunar and Planetary Laboratory, University of Arizona, Tucson AZ, 85721 (United States); Bulger, Joanna [Subaru Telescope, NAOJ, 650 North A’ohoku Place, Hilo, HI 96720 (United States); Chilcote, Jeffrey [Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Toronto, ON, M5S 3H4 (Canada); Cotten, Tara [Department of Physics and Astronomy, University of Georgia, Athens, GA, 30602 (United States); Doyon, Rene [Institut de Recherche sur les Exoplanètes, Départment de Physique, Université de Montréal, Montréal QC, H3C 3J7 (Canada); Gerard, Benjamin L. [University of Victoria, 3800 Finnerty Road, Victoria, BC, V8P 5C2 (Canada); Goodsell, Stephen J., E-mail: jruffio@stanford.edu [Gemini Observatory, 670 N. A’ohoku Place, Hilo, HI, 96720 (United States); and others
2017-06-10
We present a new matched-filter algorithm for direct detection of point sources in the immediate vicinity of bright stars. The stellar point-spread function (PSF) is first subtracted using a Karhunen-Loéve image processing (KLIP) algorithm with angular and spectral differential imaging (ADI and SDI). The KLIP-induced distortion of the astrophysical signal is included in the matched-filter template by computing a forward model of the PSF at every position in the image. To optimize the performance of the algorithm, we conduct extensive planet injection and recovery tests and tune the exoplanet spectra template and KLIP reduction aggressiveness to maximize the signal-to-noise ratio (S/N) of the recovered planets. We show that only two spectral templates are necessary to recover any young Jovian exoplanets with minimal S/N loss. We also developed a complete pipeline for the automated detection of point-source candidates, the calculation of receiver operating characteristics (ROC), contrast curves based on false positives, and completeness contours. We process in a uniform manner more than 330 data sets from the Gemini Planet Imager Exoplanet Survey and assess GPI typical sensitivity as a function of the star and the hypothetical companion spectral type. This work allows for the first time a comparison of different detection algorithms at a survey scale accounting for both planet completeness and false-positive rate. We show that the new forward model matched filter allows the detection of 50% fainter objects than a conventional cross-correlation technique with a Gaussian PSF template for the same false-positive rate.
Tsunami Modeling and Prediction Using a Data Assimilation Technique with Kalman Filters
Barnier, G.; Dunham, E. M.
2016-12-01
Earthquake-induced tsunamis cause dramatic damages along densely populated coastlines. It is difficult to predict and anticipate tsunami waves in advance, but if the earthquake occurs far enough from the coast, there may be enough time to evacuate the zones at risk. Therefore, any real-time information on the tsunami wavefield (as it propagates towards the coast) is extremely valuable for early warning systems. After the 2011 Tohoku earthquake, a dense tsunami-monitoring network (S-net) based on cabled ocean-bottom pressure sensors has been deployed along the Pacific coast in Northeastern Japan. Maeda et al. (GRL, 2015) introduced a data assimilation technique to reconstruct the tsunami wavefield in real time by combining numerical solution of the shallow water wave equations with additional terms penalizing the numerical solution for not matching observations. The penalty or gain matrix is determined though optimal interpolation and is independent of time. Here we explore a related data assimilation approach using the Kalman filter method to evolve the gain matrix. While more computationally expensive, the Kalman filter approach potentially provides more accurate reconstructions. We test our method on a 1D tsunami model derived from the Kozdon and Dunham (EPSL, 2014) dynamic rupture simulations of the 2011 Tohoku earthquake. For appropriate choices of model and data covariance matrices, the method reconstructs the tsunami wavefield prior to wave arrival at the coast. We plan to compare the Kalman filter method to the optimal interpolation method developed by Maeda et al. (GRL, 2015) and then to implement the method for 2D.
DEFF Research Database (Denmark)
Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan
2006-01-01
This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state...... are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 2005 Elsevier Ltd. All rights reserved........ The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques...
Interpreting space-based trends in carbon monoxide with multiple models
Directory of Open Access Journals (Sweden)
S. A. Strode
2016-06-01
Full Text Available We use a series of chemical transport model and chemistry climate model simulations to investigate the observed negative trends in MOPITT CO over several regions of the world, and to examine the consistency of time-dependent emission inventories with observations. We find that simulations driven by the MACCity inventory, used for the Chemistry Climate Modeling Initiative (CCMI, reproduce the negative trends in the CO column observed by MOPITT for 2000–2010 over the eastern United States and Europe. However, the simulations have positive trends over eastern China, in contrast to the negative trends observed by MOPITT. The model bias in CO, after applying MOPITT averaging kernels, contributes to the model–observation discrepancy in the trend over eastern China. This demonstrates that biases in a model's average concentrations can influence the interpretation of the temporal trend compared to satellite observations. The total ozone column plays a role in determining the simulated tropospheric CO trends. A large positive anomaly in the simulated total ozone column in 2010 leads to a negative anomaly in OH and hence a positive anomaly in CO, contributing to the positive trend in simulated CO. These results demonstrate that accurately simulating variability in the ozone column is important for simulating and interpreting trends in CO.
2017-01-01
Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate
Prediction of L70 lumen maintenance and chromaticity for LEDs using extended Kalman filter models
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep; Wei, Junchao; Davis, Lynn
2013-09-30
Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying damage using physics-based models. Life
Directory of Open Access Journals (Sweden)
Karl Friston
2010-01-01
Full Text Available We describe a Bayesian filtering scheme for nonlinear state-space models in continuous time. This scheme is called Generalised Filtering and furnishes posterior (conditional densities on hidden states and unknown parameters generating observed data. Crucially, the scheme operates online, assimilating data to optimize the conditional density on time-varying states and time-invariant parameters. In contrast to Kalman and Particle smoothing, Generalised Filtering does not require a backwards pass. In contrast to variational schemes, it does not assume conditional independence between the states and parameters. Generalised Filtering optimises the conditional density with respect to a free-energy bound on the model's log-evidence. This optimisation uses the generalised motion of hidden states and parameters, under the prior assumption that the motion of the parameters is small. We describe the scheme, present comparative evaluations with a fixed-form variational version, and conclude with an illustrative application to a nonlinear state-space model of brain imaging time-series.
Evaluation of nutrient retention in vegetated filter strips using the SWAT model.
Elçi, Alper
2017-11-01
Nutrient fluxes in stream basins need to be controlled to achieve good water quality status. In stream basins with intensive agricultural activities, nutrients predominantly come from diffuse sources. Therefore, best management practices (BMPs) are increasingly implemented to reduce nutrient input to streams. The objective of this study is to evaluate the impact of vegetated filter strip (VFS) application as an agricultural BMP. For this purpose, SWAT is chosen, a semi-distributed water quality assessment model that works at the watershed scale, and applied on the Nif stream basin, a small-sized basin in Western Turkey. The model is calibrated with an automated procedure against measured monthly discharge data. Nutrient loads for each sub-basin are estimated considering basin-wide data on chemical fertilizer and manure usage, population data for septic tank effluents and information about the land cover. Nutrient loads for 19 sub-basins are predicted on an annual basis. Average total nitrogen and total phosphorus loads are estimated as 47.85 t/yr and 13.36 t/yr for the entire basin. Results show that VFS application in one sub-basin offers limited retention of nutrients and that a selection of 20-m filter width is most effective from a cost-benefit perspective.
Trends in hydrodesulfurization catalysis based on realistic surface models
DEFF Research Database (Denmark)
Moses, P.G.; Grabow, L.C.; Fernandez Sanchez, Eva
2014-01-01
Trends in hydrodesulfurization (HDS) activity are investigated on the basis of surface properties calculated by density functional theory for a series of HDS catalysts. It is shown that approximately linear correlations exist between HS group binding energies and activation barriers of key elemen...
Kalman filtering and smoothing for model-based signal extraction that depend on time-varying spectra
Koopman, S.J.; Wong, S.Y.
2011-01-01
We develop a flexible semi-parametric method for the introduction of time-varying parameters in a model-based signal extraction procedure. Dynamic model specifications for the parameters in the model are not required. We show that signal extraction based on Kalman filtering and smoothing can be made
Hadwin, Paul J; Peterson, Sean D
2017-04-01
The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.
Calibration of a Land Subsidence Model Using InSAR Data via the Ensemble Kalman Filter.
Li, Liangping; Zhang, Meijing; Katzenstein, Kurt
2017-11-01
The application of interferometric synthetic aperture radar (InSAR) has been increasingly used to improve capabilities to model land subsidence in hydrogeologic studies. A number of investigations over the last decade show how spatially detailed time-lapse images of ground displacements could be utilized to advance our understanding for better predictions. In this work, we use simulated land subsidences as observed measurements, mimicking InSAR data to inversely infer inelastic specific storage in a stochastic framework. The inelastic specific storage is assumed as a random variable and modeled using a geostatistical method such that the detailed variations in space could be represented and also that the uncertainties of both characterization of specific storage and prediction of land subsidence can be assessed. The ensemble Kalman filter (EnKF), a real-time data assimilation algorithm, is used to inversely calibrate a land subsidence model by matching simulated subsidences with InSAR data. The performance of the EnKF is demonstrated in a synthetic example in which simulated surface deformations using a reference field are assumed as InSAR data for inverse modeling. The results indicate: (1) the EnKF can be used successfully to calibrate a land subsidence model with InSAR data; the estimation of inelastic specific storage is improved, and uncertainty of prediction is reduced, when all the data are accounted for; and (2) if the same ensemble is used to estimate Kalman gain, the analysis errors could cause filter divergence; thus, it is essential to include localization in the EnKF for InSAR data assimilation. © 2017, National Ground Water Association.
Marcin Spychała; Maciej Pawlak; Tadeusz Nawrot
2016-01-01
The aim of the study was to describe in a mathematical manner the hydraulic capacity of textile filters for wastewater treatment at changeable wastewater levels during a period between consecutive doses, taking into consideration the decisive factors for flow-conditions of filtering media. Highly changeable and slightly changeable flow-conditions tests were performed on reactors equipped with non-woven geo-textile filters. Hydraulic conductivity of filter material coupons was determined. The ...
Comparison of Decadal Water Storage Trends from Global Hydrological Models and GRACE Satellite Data
Scanlon, B. R.; Zhang, Z. Z.; Save, H.; Sun, A. Y.; Mueller Schmied, H.; Van Beek, L. P.; Wiese, D. N.; Wada, Y.; Long, D.; Reedy, R. C.; Doll, P. M.; Longuevergne, L.
2017-12-01
Global hydrology is increasingly being evaluated using models; however, the reliability of these global models is not well known. In this study we compared decadal trends (2002-2014) in land water storage from 7 global models (WGHM, PCR-GLOBWB, and GLDAS: NOAH, MOSAIC, VIC, CLM, and CLSM) to storage trends from new GRACE satellite mascon solutions (CSR-M and JPL-M). The analysis was conducted over 186 river basins, representing about 60% of the global land area. Modeled total water storage trends agree with those from GRACE-derived trends that are within ±0.5 km3/yr but greatly underestimate large declining and rising trends outside this range. Large declining trends are found mostly in intensively irrigated basins and in some basins in northern latitudes. Rising trends are found in basins with little or no irrigation and are generally related to increasing trends in precipitation. The largest decline is found in the Ganges (-12 km3/yr) and the largest rise in the Amazon (43 km3/yr). Differences between models and GRACE are greatest in large basins (>0.5x106 km2) mostly in humid regions. There is very little agreement in storage trends between models and GRACE and among the models with values of r2 mostly <0.1. Various factors can contribute to discrepancies in water storage trends between models and GRACE, including uncertainties in precipitation, model calibration, storage capacity, and water use in models and uncertainties in GRACE data related to processing, glacier leakage, and glacial isostatic adjustment. The GRACE data indicate that land has a large capacity to store water over decadal timescales that is underrepresented by the models. The storage capacity in the modeled soil and groundwater compartments may be insufficient to accommodate the range in water storage variations shown by GRACE data. The inability of the models to capture the large storage trends indicates that model projections of climate and human-induced changes in water storage may be
Tropospheric ozone trend over Beijing from 2001-2010: ozonesonde measurements and modeling analysis
Wang, Y.; Konopka, P.; Liu, Y.; Chen, H.; Müller, R.; Plöger, F.; Riese, M.; Cai, Z.; Lü, D.
2012-01-01
Using a combination of ozonesonde data and numerical simulations of the Chemical Lagrangian Model of the Stratosphere (CLaMS), the trend of tropospheric ozone (O_{3}) during 2002–2010 over Beijing was investigated. Tropospheric ozone over Beijing shows a winter minimum and a broad summer maximum with a clear positive trend in the maximum summer ozone concentration over the last decade. The observed significant trend of tropospheric column ozone is mainly caused by photoche...
Tropospheric ozone trend over Beijing from 2002–2010: ozonesonde measurements and modeling analysis
Y. Wang; P. Konopka; Y. Liu; H. Chen; R. Müller; F. Plöger; M. Riese; Z. Cai; D. Lü
2012-01-01
Using a combination of ozonesonde data and numerical simulations of the Chemical Lagrangian Model of the Stratosphere (CLaMS), the trend of tropospheric ozone (O_{3}) during 2002–2010 over Beijing was investigated. Tropospheric ozone over Beijing shows a winter minimum and a broad summer maximum with a clear positive trend in the maximum summer ozone concentration over the last decade. The observed significant trend of tropospheric column ozone for the entire time serie...
Modeling and control of LCL-filtered grid-tied inverters with wide inductance variation
DEFF Research Database (Denmark)
Xie, Chuan; Li, Kai; Zhang, Gang
2017-01-01
with the changing of the inductor current in one cycle of the grid, which challenges the system stability and power quality. In this paper, the current-dependent small-signal model of a three-phase LCL-filtered inverter is derived for designing the corresponding controller. Based on the developed small-signal model......, a capacitor current feedback based active damping loop and a fractional order repetitive control based compound current control loop are designed to stabilize the system and enhance the control accuracy in steady-state, respectively. The controller design procedure is given in detail. Finally, all......-digital simulation has been conducted on a 3.7 kVA inverter system to verify the theoretical expectations....
Directory of Open Access Journals (Sweden)
Yazhe Tang
2015-01-01
Full Text Available This paper presents a novel surveillance system named thermal omnidirectional vision (TOV system which can work in total darkness with a wild field of view. Different to the conventional thermal vision sensor, the proposed vision system exhibits serious nonlinear distortion due to the effect of the quadratic mirror. To effectively model the inherent distortion of omnidirectional vision, an equivalent sphere projection is employed to adaptively calculate parameterized distorted neighborhood of an object in the image plane. With the equivalent projection based adaptive neighborhood calculation, a distortion-invariant gradient coding feature is proposed for thermal catadioptric vision. For robust tracking purpose, a rotational kinematic modeled adaptive particle filter is proposed based on the characteristic of omnidirectional vision, which can handle multiple movements effectively, including the rapid motions. Finally, the experiments are given to verify the performance of the proposed algorithm for human tracking in TOV system.
McLeod, Neil P; Nugent, Philip; Dixon, Douglas; Dennis, Mike; Cornwall, Mark; Mallinson, Gary; Watkins, Nicholas; Thomas, Stephen; Sutton, J Mark
2015-10-01
The P-Capt prion reduction filter (MacoPharma) removes prion infectivity in model systems. This independent evaluation assesses prion removal from endogenously infected animal blood, using CE-marked P-Capt filters, and replicates the proposed use of the filter within the UK Blood Services. Two units of blood, generated from 263K scrapie-infected hamsters, were processed using leukoreduction filters (LXT-quadruple, MacoPharma). Approximately 100 mL of the removed plasma was added back to the red blood cells (RBCs) and the blood was filtered through a P-Capt filter. Samples of unfiltered whole blood, the prion filter input (RBCs plus plasma and SAGM [RBCPS]), and prion-filtered leukoreduced blood (PFB) were injected intracranially into hamsters. Clinical symptoms were monitored for 500 ± 1 day, and brains were assessed for spongiosis and prion protein deposit. In Filtration Run 1, none of the 50 challenged animals were diagnosed with scrapie after inoculation with the RBCPS fraction, while two of 190 hamsters injected with PFB were infected. In Filtration Run 2, one of 49 animals injected with RBCPS and two of 193 hamsters injected with PFB were infected. Run 1 reduced the infectious dose (ID) by 1.467 log (>1.187 log and <0.280 log for leukoreduction and prion filtration, respectively). Run 2 reduced prion infectivity by 1.424 log (1.127 and 0.297 log, respectively). Residual infectivity was estimated at 0.212 ± 0.149 IDs/mL (Run 1) and 0.208 ± 0.147 IDs/mL (Run 2). Leukoreduction removed the majority of infectivity from 263K scrapie hamster blood. The P-Capt filter removed a proportion of the remaining infectivity, but residual infectivity was observed in two independent processes. © 2015 AABB.
Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter
Directory of Open Access Journals (Sweden)
Lei Zhang
2014-05-01
Full Text Available Ultracapacitors (UCs are the focus of increasing attention in electric vehicle and renewable energy system applications due to their excellent performance in terms of power density, efficiency, and lifespan. Modeling and parameterization of UCs play an important role in model-based regulation and management for a reliable and safe operation. In this paper, an equivalent circuit model template composed of a bulk capacitor, a second-order capacitance-resistance network, and a series resistance, is employed to represent the dynamics of UCs. The extended Kalman Filter is then used to recursively estimate the model parameters in the Dynamic Stress Test (DST on a specially established test rig. The DST loading profile is able to emulate the practical power sinking and sourcing of UCs in electric vehicles. In order to examine the accuracy of the identified model, a Hybrid Pulse Power Characterization test is carried out. The validation result demonstrates that the recursively calibrated model can precisely delineate the dynamic voltage behavior of UCs under the discrepant loading condition, and the online identification approach is thus capable of extracting the model parameters in a credible and robust manner.
Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering
El Gharamti, Mohamad
2013-10-01
Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the
Directory of Open Access Journals (Sweden)
Romit Maulik
2017-04-01
Full Text Available Solving two-dimensional compressible turbulence problems up to a resolution of 16, 384^2, this paper investigates the characteristics of two promising computational approaches: (i an implicit or numerical large eddy simulation (ILES framework using an upwind-biased fifth-order weighted essentially non-oscillatory (WENO reconstruction algorithm equipped with several Riemann solvers, and (ii a central sixth-order reconstruction framework combined with various linear and nonlinear explicit low-pass spatial filtering processes. Our primary aim is to quantify the dissipative behavior, resolution characteristics, shock capturing ability and computational expenditure for each approach utilizing a systematic analysis with respect to its modeling parameters or parameterizations. The relative advantages and disadvantages of both approaches are addressed for solving a stratified Kelvin-Helmholtz instability shear layer problem as well as a canonical Riemann problem with the interaction of four shocks. The comparisons are both qualitative and quantitative, using visualizations of the spatial structure of the flow and energy spectra, respectively. We observe that the central scheme, with relaxation filtering, offers a competitive approach to ILES and is much more computationally efficient than WENO-based schemes.
Speech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering
Directory of Open Access Journals (Sweden)
M. H. Savoji
2014-09-01
Full Text Available Gaussian Mixture Models (GMMs of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equations whose solutions lead to the first estimates of speech and noise power spectra. The noise source is also identified and the input SNR estimated in this first step. These first estimates are then refined using approximate but explicit MMSE and MAP estimation formulations. The refined estimates are then used in a Wiener filter to reduce noise and enhance the noisy speech. The proposed schemes show good results. Nevertheless, it is shown that the MAP explicit solution, introduced here for the first time, reduces the computation time to less than one third with a slight higher improvement in SNR and PESQ score and also less distortion in comparison to the MMSE solution.
Subramanian, Aneesh C.
2012-11-01
This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.
Directory of Open Access Journals (Sweden)
Ramon Guzmán
2017-10-01
Full Text Available In this paper, the finite control set model predictive control is combined with the vector operation technique to be applied in the control of a three-phase active power filter. Typically, in the finite control set technique applied to three-phase power converters, eight different vectors are considered in order to obtain the optimum control signal by minimizing a cost function. On the other hand, the vector operation technique is based on dividing the grid voltage period into six different regions. The main advantage of combining both techniques is that for each region the number of possible voltage vectors to be considered can be reduced to a half, thus reducing the computational load employed by the control algorithm. Besides, in each region, only two phase-legs are switching at high frequency while the remaining phase-leg is maintained to a constant dc-voltage value during this interval. Accordingly, a reduction of the switching losses is obtained. Unlike the typical model predictive control methods which make use of the discrete differential equations of the converter, this method considers a Kalman filter in order to improve the behavior of the closed-loop system in noisy environments. Selected experimental results are exposed in order the demonstrate the validity of the control proposal.
Lenartz, F.; Raick, C.; Soetaert, K.E.R.; Grégoire, M.
2007-01-01
The Ensemble Kalman filter (EnKF) has been applied to a 1-D complex ecosystem model coupled with a hydrodynamic model of the Ligurian Sea. In order to improve the performance of the EnKF, an ensemble subsampling strategy has been used to better represent the covariance matrices and a pre-analysis
Aihara, ShinIchi; Bagchi, Arunabha; Saha, S.
We consider the problem of estimating stochastic volatility from stock data. The estimation of the volatility process of the Heston model is not in the usual framework of the filtering theory. Discretizing the continuous Heston model to the discrete-time one, we can derive the exact volatility
Directory of Open Access Journals (Sweden)
H. Zhang
2017-09-01
Full Text Available Land surface models (LSMs use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L and the Community Land Model (CLM using a 5-month calibration (assimilation period (March–July 2012 of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012. As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC or fractions of sand, clay, and organic matter of each layer (CLM are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the
Zhang, Hongjuan; Hendricks Franssen, Harrie-Jan; Han, Xujun; Vrugt, Jasper A.; Vereecken, Harry
2017-09-01
Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil-atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March-July 2012) of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August-December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for
Wang, Qian; Molenaar, Peter; Harsh, Saurabh; Freeman, Kenneth; Xie, Jinyu; Gold, Carol; Rovine, Mike; Ulbrecht, Jan
2014-03-01
An essential component of any artificial pancreas is on the prediction of blood glucose levels as a function of exogenous and endogenous perturbations such as insulin dose, meal intake, and physical activity and emotional tone under natural living conditions. In this article, we present a new data-driven state-space dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of glucose level, insulin dose, and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman filter (EKF) to estimate time-varying coefficients of the patient-specific state-space model. We evaluate our empirical modeling using (1) the FDA-approved UVa/Padova simulator with 30 virtual patients and (2) clinical data of 5 type 1 diabetic patients under natural living conditions. Compared to a forgetting-factor-based recursive ARX model of the same order, the EKF model predictions have higher fit, and significantly better temporal gain and J index and thus are superior in early detection of upward and downward trends in glucose. The EKF based state-space model developed in this article is particularly suitable for model-based state-feedback control designs since the Kalman filter estimates the state variable of the glucose dynamics based on the measured glucose time series. In addition, since the model parameters are estimated in real time, this model is also suitable for adaptive control. © 2014 Diabetes Technology Society.
Controlled laboratory experiments and modeling of vegetative filter strips with shallow water tables
Fox, Garey A.; Muñoz-Carpena, Rafael; Purvis, Rebecca A.
2018-01-01
Natural or planted vegetation at the edge of fields or adjacent to streams, also known as vegetative filter strips (VFS), are commonly used as an environmental mitigation practice for runoff pollution and agrochemical spray drift. The VFS position in lowlands near water bodies often implies the presence of a seasonal shallow water table (WT). In spite of its potential importance, there is limited experimental work that systematically studies the effect of shallow WTs on VFS efficacy. Previous research recently coupled a new physically based algorithm describing infiltration into soils bounded by a water table into the VFS numerical overland flow and transport model, VFSMOD, to simulate VFS dynamics under shallow WT conditions. In this study, we tested the performance of the model against laboratory mesoscale data under controlled conditions. A laboratory soil box (1.0 m wide, 2.0 m long, and 0.7 m deep) was used to simulate a VFS and quantify the influence of shallow WTs on runoff. Experiments included planted Bermuda grass on repacked silt loam and sandy loam soils. A series of experiments were performed including a free drainage case (no WT) and a static shallow water table (0.3-0.4 m below ground surface). For each soil type, this research first calibrated VFSMOD to the observed outflow hydrograph for the free drainage experiments to parameterize the soil hydraulic and vegetation parameters, and then evaluated the model based on outflow hydrographs for the shallow WT experiments. This research used several statistical metrics and a new approach based on hypothesis testing of the Nash-Sutcliffe model efficiency coefficient (NSE) to evaluate model performance. The new VFSMOD routines successfully simulated the outflow hydrographs under both free drainage and shallow WT conditions. Statistical metrics considered the model performance valid with greater than 99.5% probability across all scenarios. This research also simulated the shallow water table experiments with
New efficient optimizing techniques for Kalman filters and numerical weather prediction models
Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis
2016-06-01
The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.
Image segmentation based on random neural network model and Gabor filters.
Lu, Rong; Shen, Yi
2005-01-01
Image segmentation is a fundamental image process technique and plays an essential role in ultrasound image analysis. In this article, we propose an algorithm for image segmentation which is based on the random neural network (RNN) and features extracted by a bank of Gabor filters. With the scientists' work, it is revealed that Gabor functions act as some functions of human vision. And the RNN model proposed by Gelenbe is closer to biophysical reality and mathematically more tractable, in which signals in the form of impulses are transmitted with a certain probability. The segmentation algorithm based on these two techniques provide a good distinguish and classification capability for textures in the image. Furthermore, a strategy which is named as quartered segmentation strategy is also presented here in order to reduce the computation and speed up our approach. The presented algorithm is tested on an image produced by using Brodatz album and an ultrasound image, and the results are promising.
Processing Complex Sounds Passing through the Rostral Brainstem: The New Early Filter Model
Marsh, John E.; Campbell, Tom A.
2016-01-01
The rostral brainstem receives both “bottom-up” input from the ascending auditory system and “top-down” descending corticofugal connections. Speech information passing through the inferior colliculus of elderly listeners reflects the periodicity envelope of a speech syllable. This information arguably also reflects a composite of temporal-fine-structure (TFS) information from the higher frequency vowel harmonics of that repeated syllable. The amplitude of those higher frequency harmonics, bearing even higher frequency TFS information, correlates positively with the word recognition ability of elderly listeners under reverberatory conditions. Also relevant is that working memory capacity (WMC), which is subject to age-related decline, constrains the processing of sounds at the level of the brainstem. Turning to the effects of a visually presented sensory or memory load on auditory processes, there is a load-dependent reduction of that processing, as manifest in the auditory brainstem responses (ABR) evoked by to-be-ignored clicks. Wave V decreases in amplitude with increases in the visually presented memory load. A visually presented sensory load also produces a load-dependent reduction of a slightly different sort: The sensory load of visually presented information limits the disruptive effects of background sound upon working memory performance. A new early filter model is thus advanced whereby systems within the frontal lobe (affected by sensory or memory load) cholinergically influence top-down corticofugal connections. Those corticofugal connections constrain the processing of complex sounds such as speech at the level of the brainstem. Selective attention thereby limits the distracting effects of background sound entering the higher auditory system via the inferior colliculus. Processing TFS in the brainstem relates to perception of speech under adverse conditions. Attentional selectivity is crucial when the signal heard is degraded or masked: e
Evaluation of trends in high temperature extremes in north-western Europe in regional climate models
International Nuclear Information System (INIS)
Min, E; Hazeleger, W; Van Oldenborgh, G J; Sterl, A
2013-01-01
Projections of future changes in weather extremes on the regional and local scale depend on a realistic representation of trends in extremes in regional climate models (RCMs). We have tested this assumption for moderate high temperature extremes (the annual maximum of the daily maximum 2 m temperature, T ann.max ). Linear trends in T ann.max from historical runs of 14 RCMs driven by atmospheric reanalysis data are compared with trends in gridded station data. The ensemble of RCMs significantly underestimates the observed trends over most of the north-western European land surface. Individual models do not fare much better, with even the best performing models underestimating observed trends over large areas. We argue that the inability of RCMs to reproduce observed trends is probably not due to errors in large-scale circulation. There is also no significant correlation between the RCM T ann.max trends and trends in radiation or Bowen ratio. We conclude that care should be taken when using RCM data for adaptation decisions. (letter)
Directory of Open Access Journals (Sweden)
Boronina Lyudmila Vladimirovna
2012-12-01
Full Text Available Improvement of water intake technologies are of great importance. These technologies are required to provide high quality water intake and treatment; they must be sufficiently simple and reliable, and they must be easily adjustable to particular local conditions. A mathematical model of a water supply area near the filtering water intake is proposed. On its basis, a software package designated for the calculation of parameters of the supply area along with its graphical representation is developed. To improve the efficiency of water treatment plants, the authors propose a new method of their integration into the landscape by taking account of velocity distributions in the water supply area within the water reservoir where the plant installation is planned. In the proposed relationship, the filtration rate and the scattering rate at the outlet of the supply area are taken into account, and they assure more precise projections of the inlet velocity. In the present study, assessment of accuracy of the mathematical model involving the scattering of a turbulent flow has been done. The assessment procedure is based on verification of the mean values equality hypothesis and on comparison with the experimental data. The results and conclusions obtained by means of the method developed by the authors have been verified through comparison of deviations of specific values calculated through the employment of similar algorithms in MathCAD, Maple and PLUMBING. The method of the water supply area analysis, with the turbulent scattering area having been taken into account, and the software package enable to numerically estimate the efficiency of the pre-purification process by tailoring a number of parameters of the filtering component of the water intake to the river hydrodynamic properties. Therefore, the method and the software package provide a new tool for better design, installation and operation of water treatment plants with respect to filtration and
Multiscale modeling of a Chemofilter device for filtering chemotherapy toxins from blood
Maani, Nazanin; Beyhaghi, Saman; Yee, Daryl; Nosonovsky, Micheal; Greer, Julia; Hetts, Steven; Rayz, Vitaliy
2016-11-01
Purpose: Chemotherapy drugs injected intra-arterially to treat cancer can cause systemic toxic effects. A catheter-based Chemofilter device, temporarily deployed in a vein during the procedure can filter excessive drug from the blood thus reducing chemotherapy side-effects. CFD modeling is used to design the membrane of the Chemofilter in order to optimize its hemodynamic performance. Methods: Multiscale approach is used to model blood flow through the Chemofilter. The toxins bind to the Chemofilter's membrane formed by a lattice of numerous micro cells deployed in a blood vessel of much larger size. A detailed model of the flow through a 2x2 microcell matrix with periodic boundary conditions is used to determine the permeability of the membrane. The results are used to simulate the flow through the whole device modeled as a uniform porous membrane. The finite-volume solver Fluent is used to obtain the numerical solution. Results: The micro cell matrix has a porosity of 0.92. The pressure drop across the resolved microcells was found to be 630 Pa, resulting in the permeability of 6.21 x10-11 m2 in the normal direction. These values were used to optimize the device geometry in order to increase the contact area of the membrane, while minimizing its obstruction to the flow. NIH NCI R01CA194533.
State-space dynamic model for estimation of radon entry rate, based on Kalman filtering
International Nuclear Information System (INIS)
Brabec, Marek; Jilek, Karel
2007-01-01
To predict the radon concentration in a house environment and to understand the role of all factors affecting its behavior, it is necessary to recognize time variation in both air exchange rate and radon entry rate into a house. This paper describes a new approach to the separation of their effects, which effectively allows continuous estimation of both radon entry rate and air exchange rate from simultaneous tracer gas (carbon monoxide) and radon gas measurement data. It is based on a state-space statistical model which permits quick and efficient calculations. Underlying computations are based on (extended) Kalman filtering, whose practical software implementation is easy. Key property is the model's flexibility, so that it can be easily adjusted to handle various artificial regimens of both radon gas and CO gas level manipulation. After introducing the statistical model formally, its performance will be demonstrated on real data from measurements conducted in our experimental, naturally ventilated and unoccupied room. To verify our method, radon entry rate calculated via proposed statistical model was compared with its known reference value. The results from several days of measurement indicated fairly good agreement (up to 5% between reference value radon entry rate and its value calculated continuously via proposed method, in average). Measured radon concentration moved around the level approximately 600 Bq m -3 , whereas the range of air exchange rate was 0.3-0.8 (h -1 )
New trends in parameter identification for mathematical models
Leitão, Antonio; Zubelli, Jorge
2018-01-01
The Proceedings volume contains 16 contributions to the IMPA conference “New Trends in Parameter Identification for Mathematical Models”, Rio de Janeiro, Oct 30 – Nov 3, 2017, integrating the “Chemnitz Symposium on Inverse Problems on Tour”. This conference is part of the “Thematic Program on Parameter Identification in Mathematical Models” organized at IMPA in October and November 2017. One goal is to foster the scientific collaboration between mathematicians and engineers from the Brazialian, European and Asian communities. Main topics are iterative and variational regularization methods in Hilbert and Banach spaces for the stable approximate solution of ill-posed inverse problems, novel methods for parameter identification in partial differential equations, problems of tomography , solution of coupled conduction-radiation problems at high temperatures, and the statistical solution of inverse problems with applications in physics.
International Nuclear Information System (INIS)
Lee, Seunguk; Park, Minchan; Lee, Jaekeun
2014-01-01
In the U. S., the number of HEPA filters, which are located in the HVAC system of nuclear power plants, generated as wastes is annually 31,055, and tremendous economic/social costs are incurred to deal with them. Thus, it is needed to develop the metal fiber filter that can be reused and has performance equal to the HEPA level to replace the glass fiber HEPA filter. This study, to draw the optimal design factors of the metal fiber filter for removing radioactive aerosol, analyzed the design condition by reflecting the actual temperature and pressure condition that can be generated in the nuclear HVAC system to the particle collection mechanism by single fiber. As a result of performing modeling for the radioactive aerosol particle collection efficiency and the pressure drop of the filter made up with single metal fiber. It was analyzed that when a diameter of the metal fiber is less than 4 μm, thickness more than 1 mm, solidity more than 0.2, and face velocity less than 5 cm, it shows more than 99.97% particle collection efficiency, which is equal to the HEPA level. Because generally as the particle collection efficiency gets higher, the pressure drop gets bigger, it is judged that the filter design factors should be optimized to satisfy the design condition for the HVAC system. It is also judged that, in the future, an additional verification should be conducted through a comparison of the test results of the filter particle collection efficiency and the pressure drop in the condition of actual temperature and pressure, and the modeling results of this study
Briseño, Jessica; Herrera, Graciela S.
2010-05-01
Herrera (1998) proposed a method for the optimal design of groundwater quality monitoring networks that involves space and time in a combined form. The method was applied later by Herrera et al (2001) and by Herrera and Pinder (2005). To get the estimates of the contaminant concentration being analyzed, this method uses a space-time ensemble Kalman filter, based on a stochastic flow and transport model. When the method is applied, it is important that the characteristics of the stochastic model be congruent with field data, but, in general, it is laborious to manually achieve a good match between them. For this reason, the main objective of this work is to extend the space-time ensemble Kalman filter proposed by Herrera, to estimate the hydraulic conductivity, together with hydraulic head and contaminant concentration, and its application in a synthetic example. The method has three steps: 1) Given the mean and the semivariogram of the natural logarithm of hydraulic conductivity (ln K), random realizations of this parameter are obtained through two alternatives: Gaussian simulation (SGSim) and Latin Hypercube Sampling method (LHC). 2) The stochastic model is used to produce hydraulic head (h) and contaminant (C) realizations, for each one of the conductivity realizations. With these realization the mean of ln K, h and C are obtained, for h and C, the mean is calculated in space and time, and also the cross covariance matrix h-ln K-C in space and time. The covariance matrix is obtained averaging products of the ln K, h and C realizations on the estimation points and times, and the positions and times with data of the analyzed variables. The estimation points are the positions at which estimates of ln K, h or C are gathered. In an analogous way, the estimation times are those at which estimates of any of the three variables are gathered. 3) Finally the ln K, h and C estimate are obtained using the space-time ensemble Kalman filter. The realization mean for each one
Temperature trends during the Present and Last Interglacial periods - a multi-model-data comparison
Bakker, P.; Masson-Delmotte, V.; Martrat, B.; Charbit, S.; Renssen, H.; Gröger, M.; Krebs-Kanzow, U.; Lohmann, G.; Lunt, D. J.; Pfeiffer, M.; Phipps, S. J.; Prange, M.; Ritz, S. P.; Schulz, M.; Stenni, B.; Stone, E. J.; Varma, V.
2014-09-01
Though primarily driven by insolation changes associated with well-known variations in Earth's astronomical parameters, the response of the climate system during interglacials includes a diversity of feedbacks involving the atmosphere, ocean, sea ice, vegetation and land ice. A thorough multi-model-data comparison is essential to assess the ability of climate models to resolve interglacial temperature trends and to help in understanding the recorded climatic signal and the underlying climate dynamics. We present the first multi-model-data comparison of transient millennial-scale temperature changes through two intervals of the Present Interglacial (PIG; 8-1.2 ka) and the Last Interglacial (LIG; 123-116.2 ka) periods. We include temperature trends simulated by 9 different climate models, alkenone-based temperature reconstructions from 117 globally distributed locations (about 45% of them within the LIG) and 12 ice-core-based temperature trends from Greenland and Antarctica (50% of them within the LIG). The definitions of these specific interglacial intervals enable a consistent inter-comparison of the two intervals because both are characterised by minor changes in atmospheric greenhouse gas concentrations and more importantly by insolation trends that show clear similarities. Our analysis shows that in general the reconstructed PIG and LIG Northern Hemisphere mid-to-high latitude cooling compares well with multi-model, mean-temperature trends for the warmest months and that these cooling trends reflect a linear response to the warmest-month insolation decrease over the interglacial intervals. The most notable exception is the strong LIG cooling trend reconstructed from Greenland ice cores that is not simulated by any of the models. A striking model-data mismatch is found for both the PIG and the LIG over large parts of the mid-to-high latitudes of the Southern Hemisphere where the data depicts negative temperature trends that are not in agreement with near zero
International Nuclear Information System (INIS)
Li, Yanwen; Wang, Chao; Gong, Jinfeng
2016-01-01
An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. - Highlights: • Recursive least squares method with fuzzy adaptive forgetting factor is presented. • The innovation obeying chi-square distribution is used to identify uncertainty. • A combination Karman filter approach for State of Charge estimation is presented. • The performance of the proposed method is verified by comparison results.
Gharamti, M. E.
2015-05-11
The ensemble Kalman filter (EnKF) is a popular method for state-parameters estimation of subsurface flow and transport models based on field measurements. The common filtering procedure is to directly update the state and parameters as one single vector, which is known as the Joint-EnKF. In this study, we follow the one-step-ahead smoothing formulation of the filtering problem, to derive a new joint-based EnKF which involves a smoothing step of the state between two successive analysis steps. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. This new algorithm bears strong resemblance with the Dual-EnKF, but unlike the latter which first propagates the state with the model then updates it with the new observation, the proposed scheme starts by an update step, followed by a model integration step. We exploit this new formulation of the joint filtering problem and propose an efficient model-integration-free iterative procedure on the update step of the parameters only for further improved performances. Numerical experiments are conducted with a two-dimensional synthetic subsurface transport model simulating the migration of a contaminant plume in a heterogenous aquifer domain. Contaminant concentration data are assimilated to estimate both the contaminant state and the hydraulic conductivity field. Assimilation runs are performed under imperfect modeling conditions and various observational scenarios. Simulation results suggest that the proposed scheme efficiently recovers both the contaminant state and the aquifer conductivity, providing more accurate estimates than the standard Joint and Dual EnKFs in all tested scenarios. Iterating on the update step of the new scheme further enhances the proposed filter’s behavior. In term of computational cost, the new Joint-EnKF is almost equivalent to that of the Dual-EnKF, but requires twice more model
Amanda L. Fox; Dean E. Eisenhauer; Michael G. Dosskey
2005-01-01
Vegetated filters (buffers) are used to intercept overland runoff and reduce sediment and other contaminant loads to streams (Dosskey, 2001). Filters function by reducing runoff velocity and volume, thus enhancing sedimentation and infiltration. lnfiltration is the main mechanism for soluble contaminant removal, but it also plays a role in suspended particle removal....
Evaluation of the retrievability of the OptEase IVC filter in an animal model
Reekers, Jim A.; Hoogeveen, Yvonne L.; Wijnands, Marcel; Bosma, Gjalt; Mulder, Rudy; Oliva, Vincent L.
2004-01-01
PURPOSE: A new inferior vena cava filter was evaluated in vivo to determine the percutaneous retrievability after an implantation period of up to 18 days. MATERIALS AND METHODS: The inferior venae cavae of six goats were percutaneously implanted with three filters, and one animal received two
Filter designs based on coupled transmission line model for double split ring resonators
DEFF Research Database (Denmark)
Yan, Lei; Tang, Meng; Krozer, Viktor
2012-01-01
. According to the filter specifications, the low‐pass prototype parameters are used to calculate the required coupling coefficients between coupled DSRRs. The corresponding coupling coefficients are realized by using asymmetric coupled multi‐conductors networks. The proposed filter synthesis approach...
Tunable High-Q N-Path Band-Pass Filters: Modeling and Verification
Ghaffari, A.; Klumperink, Eric A.M.; Soer, M.C.M.; Nauta, Bram
2011-01-01
Abstract—A differential single-port switched-RC N-path filter with band-pass characteristic is proposed. The switching frequency defines the center frequency, while the RC-time and duty cycle of the clock define the bandwidth. This allows for high-Q highly tunable filters which can for instance be
Development and evaluation of multi-agent models predicting Twitter trends in multiple domains
Attema, T.; Maanen, P.P. van; Meeuwissen, E.
2015-01-01
This paper concerns multi-agent models predicting Twitter trends. We use a step-wise approach to develop a novel agent-based model with the following properties: (1) it uses individual behavior parameters for a set of Twitter users and (2) it uses a retweet graph to model the underlying social
Li, N.; Kinzelbach, W.; Li, H.; Li, W.; Chen, F.; Wang, L.
2017-12-01
Data assimilation techniques are widely used in hydrology to improve the reliability of hydrological models and to reduce model predictive uncertainties. This provides critical information for decision makers in water resources management. This study aims to evaluate a data assimilation system for the Guantao groundwater flow model coupled with a one-dimensional soil column simulation (Hydrus 1D) using an Unbiased Ensemble Square Root Filter (UnEnSRF) originating from the Ensemble Kalman Filter (EnKF) to update parameters and states, separately or simultaneously. To simplify the coupling between unsaturated and saturated zone, a linear relationship obtained from analyzing inputs to and outputs from Hydrus 1D is applied in the data assimilation process. Unlike EnKF, the UnEnSRF updates parameter ensemble mean and ensemble perturbations separately. In order to keep the ensemble filter working well during the data assimilation, two factors are introduced in the study. One is called damping factor to dampen the update amplitude of the posterior ensemble mean to avoid nonrealistic values. The other is called inflation factor to relax the posterior ensemble perturbations close to prior to avoid filter inbreeding problems. The sensitivities of the two factors are studied and their favorable values for the Guantao model are determined. The appropriate observation error and ensemble size were also determined to facilitate the further analysis. This study demonstrated that the data assimilation of both model parameters and states gives a smaller model prediction error but with larger uncertainty while the data assimilation of only model states provides a smaller predictive uncertainty but with a larger model prediction error. Data assimilation in a groundwater flow model will improve model prediction and at the same time make the model converge to the true parameters, which provides a successful base for applications in real time modelling or real time controlling strategies
Modelling surface run-off and trends analysis over India
Indian Academy of Sciences (India)
responsible for run-off generation plays a major role in run-off modelling at region scales. Remote sensing, GIS and advancement of the computer technology based evaluation of land surface prop- erties at spatial and temporal scales are very useful input data for hydrological models. Using remote sensing data is not only ...
Goodsite, M E; Outridge, P M; Christensen, J H; Dastoor, A; Muir, D; Travnikov, O; Wilson, S
2013-05-01
This review compares the reconstruction of atmospheric Hg deposition rates and historical trends over recent decades in the Arctic, inferred from Hg profiles in natural archives such as lake and marine sediments, peat bogs and glacial firn (permanent snowpack), against those predicted by three state-of-the-art atmospheric models based on global Hg emission inventories from 1990 onwards. Model veracity was first tested against atmospheric Hg measurements. Most of the natural archive and atmospheric data came from the Canadian-Greenland sectors of the Arctic, whereas spatial coverage was poor in other regions. In general, for the Canadian-Greenland Arctic, models provided good agreement with atmospheric gaseous elemental Hg (GEM) concentrations and trends measured instrumentally. However, there are few instrumented deposition data with which to test the model estimates of Hg deposition, and these data suggest models over-estimated deposition fluxes under Arctic conditions. Reconstructed GEM data from glacial firn on Greenland Summit showed the best agreement with the known decline in global Hg emissions after about 1980, and were corroborated by archived aerosol filter data from Resolute, Nunavut. The relatively stable or slowly declining firn and model GEM trends after 1990 were also corroborated by real-time instrument measurements at Alert, Nunavut, after 1995. However, Hg fluxes and trends in northern Canadian lake sediments and a southern Greenland peat bog did not exhibit good agreement with model predictions of atmospheric deposition since 1990, the Greenland firn GEM record, direct GEM measurements, or trends in global emissions since 1980. Various explanations are proposed to account for these discrepancies between atmosphere and archives, including problems with the accuracy of archive chronologies, climate-driven changes in Hg transfer rates from air to catchments, waters and subsequently into sediments, and post-depositional diagenesis in peat bogs
Wöhling, T.; Schöniger, A.; Geiges, A.; Nowak, W.; Gayler, S.
2013-12-01
The objective selection of appropriate models for realistic simulations of coupled soil-plant processes is a challenging task since the processes are complex, not fully understood at larger scales, and highly non-linear. Also, comprehensive data sets are scarce, and measurements are uncertain. In the past decades, a variety of different models have been developed that exhibit a wide range of complexity regarding their approximation of processes in the coupled model compartments. We present a method for evaluating experimental design for maximum confidence in the model selection task. The method considers uncertainty in parameters, measurements and model structures. Advancing the ideas behind Bayesian Model Averaging (BMA), we analyze the changes in posterior model weights and posterior model choice uncertainty when more data are made available. This allows assessing the power of different data types, data densities and data locations in identifying the best model structure from among a suite of plausible models. The models considered in this study are the crop models CERES, SUCROS, GECROS and SPASS, which are coupled to identical routines for simulating soil processes within the modelling framework Expert-N. The four models considerably differ in the degree of detail at which crop growth and root water uptake are represented. Monte-Carlo simulations were conducted for each of these models considering their uncertainty in soil hydraulic properties and selected crop model parameters. Using a Bootstrap Filter (BF), the models were then conditioned on field measurements of soil moisture, matric potential, leaf-area index, and evapotranspiration rates (from eddy-covariance measurements) during a vegetation period of winter wheat at a field site at the Swabian Alb in Southwestern Germany. Following our new method, we derived model weights when using all data or different subsets thereof. We discuss to which degree the posterior mean outperforms the prior mean and all
DEFF Research Database (Denmark)
Mu, Xiaobin; Wang, Jiuhe; Wu, Weimin
2018-01-01
The passivity-based control (PBC) has a better control performance using an accurate mathematical model of the control object. It can offer an alternative tracking control scheme for the shunt active power filter (SAPF). However, the conventional PBC-based SAPF cannot achieve zero steady...
Porter, Lon A., Jr.; Chapman, Cole A.; Alaniz, Jacob A.
2017-01-01
In this work, a versatile and user-friendly selection of stereolithography (STL) files and computer-aided design (CAD) models are shared to assist educators and students in the production of simple and inexpensive 3D printed filter fluorometer instruments. These devices are effective resources for supporting active learners in the exploration of…
Multiresolution Source/Filter Model for Low Bitrate Coding of Spot Microphone Signals
Directory of Open Access Journals (Sweden)
Panagiotis Tsakalides
2008-04-01
Full Text Available A multiresolution source/filter model for coding of audio source signals (spot recordings is proposed. Spot recordings are a subset of the multimicrophone recordings of a music performance, before the mixing process is applied for producing the final multichannel audio mix. The technique enables low bitrate coding of spot signals with good audio quality (above 3.0 perceptual grade compared to the original. It is demonstrated that this particular model separates the various microphone recordings of a multimicrophone recording into a part that mainly characterizes a specific microphone signal and a part that is common to all signals of the same recording (and can thus be omitted during transmission. Our interest in low bitrate coding of spot recordings is related to applications such as remote mixing and real-time collaboration of musicians who are geographically distributed. Using the proposed approach, it is shown that it is possible to encode a multimicrophone audio recording using a single audio channel only, with additional information for each spot microphone signal in the order of 5Ã¢Â€Â‰kbps, for good-quality resynthesis. This is verified by employing both objective and subjective measures of performance.
Multiresolution Source/Filter Model for Low Bitrate Coding of Spot Microphone Signals
Directory of Open Access Journals (Sweden)
Mouchtaris Athanasios
2008-01-01
Full Text Available A multiresolution source/filter model for coding of audio source signals (spot recordings is proposed. Spot recordings are a subset of the multimicrophone recordings of a music performance, before the mixing process is applied for producing the final multichannel audio mix. The technique enables low bitrate coding of spot signals with good audio quality (above 3.0 perceptual grade compared to the original. It is demonstrated that this particular model separates the various microphone recordings of a multimicrophone recording into a part that mainly characterizes a specific microphone signal and a part that is common to all signals of the same recording (and can thus be omitted during transmission. Our interest in low bitrate coding of spot recordings is related to applications such as remote mixing and real-time collaboration of musicians who are geographically distributed. Using the proposed approach, it is shown that it is possible to encode a multimicrophone audio recording using a single audio channel only, with additional information for each spot microphone signal in the order of 5 kbps, for good-quality resynthesis. This is verified by employing both objective and subjective measures of performance.
Directory of Open Access Journals (Sweden)
Giovanni Capellari
2015-12-01
Full Text Available Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation, whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated.
Geometric filters for protein–ligand complexes based on phenomenological molecular models
Directory of Open Access Journals (Sweden)
Sudakov O. O.
2013-09-01
Full Text Available Molecular docking is a widely used method of computer-aided drug design capable of accurate prediction of protein-ligand complex conformations. However, scoring functions used to estimate free energy of binding still lack accuracy. Aim. Development of computationally simple and rapid algorithms for ranking ligands based on docking results. Methods. Computational filters utilizing geometry of protein-ligand complex were designed. Efficiency of the filters was verified in a cross-docking study with QXP/Flo software using crystal structures of human serine proteases thrombin (F2 and factor Xa (F10 and two corresponding sets of known selective inhibitors. Results. Evaluation of filtering results in terms of ROC curves with varying filter threshold value has shown their efficiency. However, none of the filters outperformed QXP/Flo built-in scoring function Pi . Nevertheless, usage of the filters with optimized set of thresholds in combination with Pi achieved significant improvement in performance of ligand selection when compared to usage of Pi alone. Conclusions. The proposed geometric filters can be used as a complementary to traditional scoring functions in order to optimize ligand search performance and decrease usage of computational and human resources.
Energy Technology Data Exchange (ETDEWEB)
Page, Ralph H.; Doty, Patrick F.
2017-08-01
The various technologies presented herein relate to a tiled filter array that can be used in connection with performance of spatial sampling of optical signals. The filter array comprises filter tiles, wherein a first plurality of filter tiles are formed from a first material, the first material being configured such that only photons having wavelengths in a first wavelength band pass therethrough. A second plurality of filter tiles is formed from a second material, the second material being configured such that only photons having wavelengths in a second wavelength band pass therethrough. The first plurality of filter tiles and the second plurality of filter tiles can be interspersed to form the filter array comprising an alternating arrangement of first filter tiles and second filter tiles.
Non-existence of Steady State Equilibrium in the Neoclassical Growth Model with a Longevity Trend
DEFF Research Database (Denmark)
Hermansen, Mikkel Nørlem
of steady state equilibrium when considering the empirically observed trend in longevity. We extend a standard continuous time overlapping generations model by a longevity trend and are thereby able to study the properties of mortality-driven population growth. This turns out to be exceedingly complicated...... to handle, and it is shown that in general no steady state equilibrium exists. Consequently analytical results and long run implications cannot be obtained in a setting with a realistic demographic setup....
Directory of Open Access Journals (Sweden)
X. Yang
2009-07-01
Full Text Available A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF, is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated with the latter ensemble and have infinite variance. The assumption of infinite variance corresponds to the limit of "complete lack of knowledge" and differs dramatically from the implicit assumption made in most other ensemble filters, which is that the forecast errors orthogonal to the first guess ensemble have vanishing errors. The DEnF is independent of the detailed covariances assumed in the space orthogonal to the ensemble space, and reduces to conventional ensemble square root filters when the number of ensembles exceeds the model dimension. The DEnF is well defined only in data rich regimes and involves the inversion of relatively large matrices, although this barrier might be circumvented by variational methods. Two algorithms for solving the DEnF, namely the Diffuse Ensemble Kalman Filter (DEnKF and the Diffuse Ensemble Transform Kalman Filter (DETKF, are proposed and found to give comparable results. These filters generally converge to the traditional EnKF and ETKF, respectively, when the ensemble size exceeds the model dimension. Numerical experiments demonstrate that the DEnF eliminates filter collapse, which occurs in ensemble Kalman filters for small ensemble sizes. Also, the use of the DEnF to initialize a conventional square root filter dramatically accelerates the spin-up time for convergence. However, in a perfect model scenario, the DEnF produces larger errors than ensemble square root filters that have covariance localization and inflation. For imperfect forecast models, the DEnF produces smaller errors than the ensemble square root filter with inflation. These experiments suggest that the DEnF has some advantages relative to the ensemble square root filters in the regime of small ensemble size, imperfect model, and copious
Turbulent Combustion Modeling Advances, New Trends and Perspectives
Echekki, Tarek
2011-01-01
Turbulent combustion sits at the interface of two important nonlinear, multiscale phenomena: chemistry and turbulence. Its study is extremely timely in view of the need to develop new combustion technologies in order to address challenges associated with climate change, energy source uncertainty, and air pollution. Despite the fact that modeling of turbulent combustion is a subject that has been researched for a number of years, its complexity implies that key issues are still eluding, and a theoretical description that is accurate enough to make turbulent combustion models rigorous and quantitative for industrial use is still lacking. In this book, prominent experts review most of the available approaches in modeling turbulent combustion, with particular focus on the exploding increase in computational resources that has allowed the simulation of increasingly detailed phenomena. The relevant algorithms are presented, the theoretical methods are explained, and various application examples are given. The book ...
Ettlinger, Andreas; Neuner, Hans; Burgess, Thomas
2018-01-31
The topic of indoor positioning and indoor navigation by using observations from smartphone sensors is very challenging as the determined trajectories can be subject to significant deviations compared to the route travelled in reality. Especially the calculation of the direction of movement is the critical part of pedestrian positioning approaches such as Pedestrian Dead Reckoning ("PDR"). Due to distinct systematic effects in filtered trajectories, it can be assumed that there are systematic deviations present in the observations from smartphone sensors. This article has two aims: one is to enable the estimation of partial redundancies for each observation as well as for observation groups. Partial redundancies are a measure for the reliability indicating how well systematic deviations can be detected in single observations used in PDR. The second aim is to analyze the behavior of partial redundancy by modifying the stochastic and functional model of the Kalman filter. The equations relating the observations to the orientation are condition equations, which do not exhibit the typical structure of the Gauss-Markov model ("GMM"), wherein the observations are linear and can be formulated as functions of the states. To calculate and analyze the partial redundancy of the observations from smartphone-sensors used in PDR, the system equation and the measurement equation of a Kalman filter as well as the redundancy matrix need to be derived in the Gauss-Helmert model ("GHM"). These derivations are introduced in this article and lead to a novel Kalman filter structure based on condition equations, enabling reliability assessment of each observation.
External Influences on Modeled and Observed Cloud Trends
Marvel, Kate; Zelinka, Mark; Klein, Stephen A.; Bonfils, Celine; Caldwell, Peter; Doutriaux, Charles; Santer, Benjamin D.; Taylor, Karl E.
2015-01-01
Understanding the cloud response to external forcing is a major challenge for climate science. This crucial goal is complicated by intermodel differences in simulating present and future cloud cover and by observational uncertainty. This is the first formal detection and attribution study of cloud changes over the satellite era. Presented herein are CMIP5 (Coupled Model Intercomparison Project - Phase 5) model-derived fingerprints of externally forced changes to three cloud properties: the latitudes at which the zonally averaged total cloud fraction (CLT) is maximized or minimized, the zonal average CLT at these latitudes, and the height of high clouds at these latitudes. By considering simultaneous changes in all three properties, the authors define a coherent multivariate fingerprint of cloud response to external forcing and use models from phase 5 of CMIP (CMIP5) to calculate the average time to detect these changes. It is found that given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have already emerged. A search is then made for signals of external forcing in two observational datasets: ISCCP (International Satellite Cloud Climatology Project) and PATMOS-x (Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres - Extended). The datasets are both found to show a poleward migration of the zonal CLT pattern that is incompatible with forced CMIP5 models. Nevertheless, a detectable multivariate signal is predicted by models over the PATMOS-x time period and is indeed present in the dataset. Despite persistent observational uncertainties, these results present a strong case for continued efforts to improve these existing satellite observations, in addition to planning for new missions.
Directory of Open Access Journals (Sweden)
J. Xu
2013-10-01
Full Text Available On the basis of the fifth Coupled Model Intercomparison Project (CMIP5 and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3 simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (radiosonde, reanalysis, CMIP3 and CMIP5, although similarities can be observed. Compared to the CMIP3 model simulations, the simulations in some of the CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the
Camp, Richard J.; Pratt, Thane K.; Gorresen, P. Marcos; Woodworth, Bethany L.; Jeffrey, John J.
2014-01-01
Freed and Cann (2013) criticized our use of linear models to assess trends in the status of Hawaiian forest birds through time (Camp et al. 2009a, 2009b, 2010) by questioning our sampling scheme, whether we met model assumptions, and whether we ignored short-term changes in the population time series. In the present paper, we address these concerns and reiterate that our results do not support the position of Freed and Cann (2013) that the forest birds in the Hakalau Forest National Wildlife Refuge (NWR) are declining, or that the federally listed endangered birds are showing signs of imminent collapse. On the contrary, our data indicate that the 21-year long-term trends for native birds in Hakalau Forest NWR are stable to increasing, especially in areas that have received active management.
Barca, Cristian; Roche, Nicolas; Troesch, Stéphane; Andrès, Yves; Chazarenc, Florent
2018-01-15
Steel slag filters, if well designed and operated, may upgrade phosphorus removal in small wastewater treatment plants such as stabilization ponds and constructed wetlands. The main objective of this study was to develop a systemic modelling approach to describe changes in the hydraulic performances and internal hydrodynamics of steel slag filters under real dynamic operating conditions. The experimental retention time distribution curves (RTD curves) determined from tracer experiments performed at different times during the first year of operation of two field-scale steel slag filters were analyzed through a three stage process. First, a statistical analysis of the RTD curves was performed to determine statistical parameters of the retention time distribution. Second, classical tanks in series (TIS) and plug flow with dispersion (PFD) models were used to obtain a first evaluation of the dispersion and mixing regime. Finally, a multi-flow path TIS model, based on the assumption of several flow paths with different hydraulic properties, is proposed to accurately describe the internal hydrodynamics. Overall, the results of this study indicate that higher CaO content, round shape, and larger grain size distribution of steel slag may promote plug-like flow rather than dispersion. The results of the multi-flow path TIS model suggest that the internal hydrodynamics of steel slag filters can be primarily described by two main flow paths: (i) a faster main flow path showing higher plug flow, followed by (ii) a slower secondary flow path showing higher dispersion. The results also showed that internal hydrodynamics may change over time as a consequence of physical-chemical phenomena occurring in the filter, including accumulation of precipitates, slag hydration and carbonation, and particle segregation. Copyright © 2017 Elsevier Ltd. All rights reserved.
Muñoz-Carpena, Rafael; Ritter, Amy; Fox, Garey A; Perez-Ovilla, Oscar
2015-11-01
Vegetative filter strips (VFS) are a widely adopted practice for limiting pesticide transport from adjacent fields to receiving waterbodies. The efficacy of VFS depends on site-specific input factors. To elucidate the complex and non-linear relationships among these factors requires a process-based modeling framework. Previous research proposed linking existing higher-tier environmental exposure models with a well-tested VFS model (VFSMOD). However, the framework assumed pesticide mass stored in the VFS was not available for transport in subsequent storm events. A new pesticide mass balance component was developed to estimate surface pesticide residue trapped in the VFS and its degradation between consecutive runoff events. The influence and necessity of the updated framework on acute and chronic estimated environmental concentrations (EECs) and percent reductions in EECs were investigated across three, 30-year U.S. EPA scenarios: Illinois corn, California tomato, and Oregon wheat. The updated framework with degradation predicted higher EECs than the existing framework without degradation for scenarios with greater sediment transport, longer VFS lengths, and highly sorbing and persistent pesticides. Global sensitivity analysis (GSA) assessed the relative importance of mass balance and degradation processes in the context of other input factors like VFS length (VL), organic-carbon sorption coefficient (Koc), and soil and water half-lives. Considering VFS pesticide residue and degradation was not important if single, large runoff events controlled transport, as is typical for higher percentiles considered in exposure assessments. Degradation processes become more important when considering percent reductions in acute or chronic EECs, especially under scenarios with lower pesticide losses. Copyright © 2015 Elsevier Ltd. All rights reserved.
Measuring Leaf Area in Soy Plants by HSI Color Model Filtering and Mathematical Morphology
International Nuclear Information System (INIS)
Benalcázar, M; Padín, J; Brun, M; Pastore, J; Ballarin, V; Peirone, L; Pereyra, G
2011-01-01
There has been lately a significant progress in automating tasks for the agricultural sector. One of the advances is the development of robots, based on computer vision, applied to care and management of soy crops. In this task, digital image processing plays an important role, but must solve some important problems, like the ones associated to the variations in lighting conditions during image acquisition. Such variations influence directly on the brightness level of the images to be processed. In this paper we propose an algorithm to segment and measure automatically the leaf area of soy plants. This information is used by the specialists to evaluate and compare the growth of different soy genotypes. This algorithm, based on color filtering using the HSI model, detects green objects from the image background. The segmentation of leaves (foliage) was made applying Mathematical Morphology. The foliage area was estimated counting the pixels that belong to the segmented leaves. From several experiments, consisting in applying the algorithm to measure the foliage of about fifty plants of various genotypes of soy, at different growth stages, we obtained successful results, despite the high brightness variations and shadows in the processed images.
Measuring Leaf Area in Soy Plants by HSI Color Model Filtering and Mathematical Morphology
Benalcázar, M.; Padín, J.; Brun, M.; Pastore, J.; Ballarin, V.; Peirone, L.; Pereyra, G.
2011-12-01
There has been lately a significant progress in automating tasks for the agricultural sector. One of the advances is the development of robots, based on computer vision, applied to care and management of soy crops. In this task, digital image processing plays an important role, but must solve some important problems, like the ones associated to the variations in lighting conditions during image acquisition. Such variations influence directly on the brightness level of the images to be processed. In this paper we propose an algorithm to segment and measure automatically the leaf area of soy plants. This information is used by the specialists to evaluate and compare the growth of different soy genotypes. This algorithm, based on color filtering using the HSI model, detects green objects from the image background. The segmentation of leaves (foliage) was made applying Mathematical Morphology. The foliage area was estimated counting the pixels that belong to the segmented leaves. From several experiments, consisting in applying the algorithm to measure the foliage of about fifty plants of various genotypes of soy, at different growth stages, we obtained successful results, despite the high brightness variations and shadows in the processed images.
Sañudo-Fontaneda, Luis A; Jato-Espino, Daniel; Lashford, Craig; Coupe, Stephen J
2017-05-23
Road drainage is one of the most relevant assets in transport infrastructure due to its inherent influence on traffic management and road safety. Highway filter drains (HFDs), also known as "French Drains", are the main drainage system currently in use in the UK, throughout 7000 km of its strategic road network. Despite being a widespread technique across the whole country, little research has been completed on their design considerations and their subsequent impact on their hydraulic performance, representing a gap in the field. Laboratory experiments have been proven to be a reliable indicator for the simulation of the hydraulic performance of stormwater best management practices (BMPs). In addition to this, stormwater management tools (SMT) have been preferentially chosen as a design tool for BMPs by practitioners from all over the world. In this context, this research aims to investigate the hydraulic performance of HFDs by comparing the results from laboratory simulation and two widely used SMT such as the US EPA's stormwater management model (SWMM) and MicroDrainage®. Statistical analyses were applied to a series of rainfall scenarios simulated, showing a high level of accuracy between the results obtained in laboratory and using SMT as indicated by the high and low values of the Nash-Sutcliffe and R 2 coefficients and root-mean-square error (RMSE) reached, which validated the usefulness of SMT to determine the hydraulic performance of HFDs.
FILTERED VIOLENCE: PROPAGANDA MODEL AND POLITICAL ECONOMY OF THE INDIAN FILM INDUSTRY
Directory of Open Access Journals (Sweden)
Azmat Rasul
2016-01-01
Full Text Available Production, distribution, and consumption of cinematic violence raises several questions of academic import. Despite a plethora of research studies exploring the nature of screen violence and its effects on viewers, a serious debate on the influence of state machinery on the production of sanitized violence in movies is still wanting. Likewise, Bollywood’s role in advancing the Indian government’s agenda in war and peace times has been paid petite attention in academic discourses dealing with media-state interconnection. This article explores the relevance of Herman and Chomsky’s propaganda model as a framework for analysis and analyzes Bollywood’s movies based on stories of violence in war and peace times. The article discusses the connections with the Indian state apparatus that influences production processes in the Indian film industry by providing financial assistance and applying multifarious political, social, economic, and ideological pressures (filters. The findings suggest that the Bollywood movies support diplomatic initiatives of the Indian government through cinematic narratives of sanitized violence.
Nuclear reactor fuel rod behavior modelling and current trends
International Nuclear Information System (INIS)
Colak, Ue.
2001-01-01
Safety assessment of nuclear reactors is carried out by simulating the events to taking place in nuclear reactors by realistic computer codes. Such codes are developed in a way that each event is represented by differential equations derived based on physical laws. Nuclear fuel is an important barrier against radioactive fission gas release. The release of radioactivity to environment is the main concern and this can be avoided by preserving the integrity of fuel rod. Therefore, safety analyses should cover an assessment of fuel rod behavior with certain extent. In this study, common approaches for fuel behavior modeling are discussed. Methods utilized by widely accepted computer codes are reviewed. Shortcomings of these methods are explained. Current research topics to improve code reliability and problems encountered in fuel rod behavior modeling are presented
Bartolomeu, S.; Carvalho, M. J.; Marta-Almeida, M.; Melo-Gonçalves, P.; Rocha, A.
2016-08-01
Spatial and temporal distributions of the trends of extreme precipitation indices were analysed between 1986 and 2005, over the Iberian Peninsula (IP). The knowledge of the patterns of extreme precipitation is important for impacts assessment, development of adaptation and mitigation strategies. As such, there is a growing need for a more detailed knowledge of precipitation climate change. This analysis was performed for Portuguese and Spanish observational datasets and results performed by the Weather Research and Forecast (WRF) model forced by the ERA-Interim reanalysis. Extreme precipitation indices recommended by the Expert Team for Climate Change Detection Monitoring and Indices were computed, by year and season. Then, annual and seasonal trends of the indices were estimated by Theil-Sen method and their significance was tested by the Mann-Kendal test. Additionally, a second simulation forced by the Max Planck Institute Earth System Model (MPI-ESM), was considered. This second modelling configuration was created in order to assess its performance when simulating extremes of precipitation. The annual trends estimated for the 1986-2005, from the observational datasets and from the ERA-driven simulation reveal: 1) negative statistically significant trends of the CWD index in the Galicia and in the centre of the IP; 2) positive statistically significant trends of the CDD index over the south of the IP and negative statistically significant trends in Galicia, north and centre of Portugal; 3) positive statistically significant trends of the R75p index in some regions of the north of the IP; 4) positive statistically significant trends in the R95pTOT index in the Central Mountains Chain, Leon Mountains and in the north of Portugal. Seasonally, negative statistically significant trends of the CWD index were found in Galicia, in winter and in the south of the IP, in summer. Positive statistically significant trends of the CWD index were identified in the Leon Mountains
De Biase, C.
2012-01-01
Vertical flow filters (unplanted) and vertical flow constructed wetlands (planted), simple and inexpensive technologies to treat effectively volatile organic compounds (VOCs) contaminated water, consist of containers filled with granular material which is intermittently fed with contaminated water.
Tropospheric ozone trend over Beijing from 2002–2010: ozonesonde measurements and modeling analysis
Directory of Open Access Journals (Sweden)
Y. Wang
2012-09-01
Full Text Available Using a combination of ozonesonde data and numerical simulations of the Chemical Lagrangian Model of the Stratosphere (CLaMS, the trend of tropospheric ozone (O_{3} during 2002–2010 over Beijing was investigated. Tropospheric ozone over Beijing shows a winter minimum and a broad summer maximum with a clear positive trend in the maximum summer ozone concentration over the last decade. The observed significant trend of tropospheric column ozone is mainly caused by photochemical production (3.1% yr^{−1} for a mean level of 52 DU. This trend is close to the significant trend of partial column ozone in the lower troposphere (0–3 km resulting from the enhanced photochemical production during summer (3.0% yr^{−1} for a mean level of 23 DU. Analysis of the CLaMS simulation shows that transport rather than chemistry drives most of the seasonality of tropospheric ozone. However, dynamical processes alone cannot explain the trend of tropospheric ozone in the observational data. Clearly enhanced ozone values and a negative vertical ozone gradient in the lower troposphere in the observational data emphasize the importance of photochemistry within the troposphere during spring and summer, and suggest that the photochemistry within the troposphere significantly contributes to the tropospheric ozone trend over Beijing during the last decade.
Modeling the Influence of Hemispheric Transport on Trends in O3 Distributions
We describe the development and application of the hemispheric version of the CMAQ to examine the influence of long-range pollutant transport on trends in surface level O3 distributions. The WRF-CMAQ model is expanded to hemispheric scales and multi-decadal model simulations were...
Czech Academy of Sciences Publication Activity Database
Ökzan, E.; Šmídl, Václav; Saha, S.; Lundquist, C.; Gustafsson, F.
2013-01-01
Roč. 49, č. 6 (2013), s. 1566-1575 ISSN 0005-1098 R&D Projects: GA ČR(CZ) GAP102/11/0437 Keywords : Unknown Noise Statistics * Adaptive Filtering * Marginalized Particle Filter * Bayesian Conjugate prior Subject RIV: BC - Control Systems Theory Impact factor: 3.132, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf
Modeling the Air Flow in the 3410 Building Filtered Exhaust Stack System
Energy Technology Data Exchange (ETDEWEB)
Recknagle, Kurtis P.; Barnett, J. Matthew; Suffield, Sarah R.
2013-01-23
Additional ventilation capacity has been designed for the 3410 Building filtered exhaust stack system. The updated system will increase the number of fans from two to three and will include ductwork to incorporate the new fan into the existing stack. Stack operations will involve running various two-fan combinations at any given time. The air monitoring system of the existing two-fan stack was previously found to be in compliance with the ANSI/HPS N13.1-1999 standard, however it is not known if the modified (three-fan) system will comply. Subsequently, a full-scale three-dimensional (3-D) computational fluid dynamics (CFD) model of the modified stack system has been created to examine the sampling location for compliance with the standard. The CFD modeling results show good agreement with testing data collected from the existing 3410 Building stack and suggest that velocity uniformity and flow angles will remain well within acceptance criteria when the third fan and associated ductwork is installed. This includes two-fan flow rates up to 31,840 cfm for any of the two-fan combinations. For simulation cases in which tracer gas and particles are introduced in the main duct, the model predicts that both particle and tracer gas coefficients of variance (COVs) may be larger than the acceptable 20 percent criterion of the ANSI/HPS N13.1-1999 standard for each of the two-fan, 31,840 cfm combinations. Simulations in which the tracers are introduced near the fans result in improved, though marginally acceptable, COV values for the tracers. Due to the remaining uncertainty that the stack will qualify with the addition of the third fan and high flow rates, a stationary air blender from Blender Products, Inc. is considered for inclusion in the stack system. A model of the air blender has been developed and incorporated into the CFD model. Simulation results from the CFD model that includes the air blender show striking improvements in tracer gas mixing and tracer particle
Directory of Open Access Journals (Sweden)
Wang Li
Full Text Available Biomarkers in exhaled breath are useful for respiratory disease diagnosis in human volunteers. Conventional methods that collect non-volatile biomarkers, however, necessitate an extensive dilution and sanitation processes that lowers collection efficiencies and convenience of use. Electret filter emerged in recent decade to collect virus biomarkers in exhaled breath given its simplicity and effectiveness. To investigate the capability of electret filters to collect protein biomarkers, a model that consists of an atomizer that produces protein aerosol and an electret filter that collects albumin and carcinoembryonic antigen-a typical biomarker in lung cancer development- from the atomizer is developed. A device using electret filter as the collecting medium is designed to collect human albumin from exhaled breath of 6 volunteers. Comparison of the collecting ability between the electret filter method and other 2 reported methods is finally performed based on the amounts of albumin collected from human exhaled breath. In conclusion, a decreasing collection efficiency ranging from 17.6% to 2.3% for atomized albumin aerosol and 42% to 12.5% for atomized carcinoembryonic antigen particles is found; moreover, an optimum volume of sampling human exhaled breath ranging from 100 L to 200 L is also observed; finally, the self-designed collecting device shows a significantly better performance in collecting albumin from human exhaled breath than the exhaled breath condensate method (p0.05. In summary, electret filters are potential in collecting non-volatile biomarkers in human exhaled breath not only because it was simpler, cheaper and easier to use than traditional methods but also for its better collecting performance.
Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.
2013-01-01
In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.
Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models
Directory of Open Access Journals (Sweden)
Xiangyun Hu
2015-08-01
Full Text Available Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF. The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second.
Directory of Open Access Journals (Sweden)
V. R. N. Pauwels
2013-09-01
Full Text Available In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.
New trends in interaction, virtual reality and modeling
Penichet, Victor MR; Gallud, José A
2013-01-01
The interaction between a user and a device forms the foundation of today's application design.Covering the following topics: * A suite of five structural principles helping designers to structure their mockups;* An agile method for exploiting desktop eye tracker equipment in combination with mobile devices;* An approach to explore large-scale collections based on classification systems;* A framework based on the use of modeling and components composition techniques to simplify the development of organizational collaborative systems;* A low-cost virtual reality system that provides highly sati
The long-run forecasting of energy prices using the model of shifting trend
International Nuclear Information System (INIS)
Radchenko, Stanislav
2005-01-01
Developing models for accurate long-term energy price forecasting is an important problem because these forecasts should be useful in determining both supply and demand of energy. On the supply side, long-term forecasts determine investment decisions of energy-related companies. On the demand side, investments in physical capital and durable goods depend on price forecasts of a particular energy type. Forecasting long-run rend movements in energy prices is very important on the macroeconomic level for several developing countries because energy prices have large impacts on their real output, the balance of payments, fiscal policy, etc. Pindyck (1999) argues that the dynamics of real energy prices is mean-reverting to trend lines with slopes and levels that are shifting unpredictably over time. The hypothesis of shifting long-term trend lines was statistically tested by Benard et al. (2004). The authors find statistically significant instabilities for coal and natural gas prices. I continue the research of energy prices in the framework of continuously shifting levels and slopes of trend lines started by Pindyck (1999). The examined model offers both parsimonious approach and perspective on the developments in energy markets. Using the model of depletable resource production, Pindyck (1999) argued that the forecast of energy prices in the model is based on the long-run total marginal cost. Because the model of a shifting trend is based on the competitive behavior, one may examine deviations of oil producers from the competitive behavior by studying the difference between actual prices and long-term forecasts. To construct the long-run forecasts (10-year-ahead and 15-year-ahead) of energy prices, I modify the univariate shifting trends model of Pindyck (1999). I relax some assumptions on model parameters, the assumption of white noise error term, and propose a new Bayesian approach utilizing a Gibbs sampling algorithm to estimate the model with autocorrelation. To
International Nuclear Information System (INIS)
Gonçalves, L D; Rocco, E M; De Moraes, R V; Kuga, H K
2015-01-01
This paper aims to simulate part of the orbital trajectory of Lunar Prospector mission to analyze the relevance of using a Kalman filter to estimate the trajectory. For this study it is considered the disturbance due to the lunar gravitational potential using one of the most recent models, the LP100K model, which is based on spherical harmonics, and considers the maximum degree and order up to the value 100. In order to simplify the expression of the gravitational potential and, consequently, to reduce the computational effort required in the simulation, in some cases, lower values for degree and order are used. Following this aim, it is made an analysis of the inserted error in the simulations when using such values of degree and order to propagate the spacecraft trajectory and control. This analysis was done using the standard deviation that characterizes the uncertainty for each one of the values of the degree and order used in LP100K model for the satellite orbit. With knowledge of the uncertainty of the gravity model adopted, lunar orbital trajectory simulations may be accomplished considering these values of uncertainty. Furthermore, it was also used a Kalman filter, where is considered the sensor's uncertainty that defines the satellite position at each step of the simulation and the uncertainty of the model, by means of the characteristic variance of the truncated gravity model. Thus, this procedure represents an effort to approximate the results obtained using lower values for the degree and order of the spherical harmonics, to the results that would be attained if the maximum accuracy of the model LP100K were adopted. Also a comparison is made between the error in the satellite position in the situation in which the Kalman filter is used and the situation in which the filter is not used. The data for the comparison were obtained from the standard deviation in the velocity increment of the space vehicle. (paper)
K. Kannan
2014-01-01
Removing the noise from digital color images plays a vital role in many of the image processing applications. Salt and Pepper noise is one type of the impulse noise which corrupts images during image capture or transmission or storage etc. This paper proposes and implements a new decision based median filter using cloud model to restore the highly corrupted digital color images. The proposed filter is tested on different images and shows better performance than standard median filter, adaptiv...
Ming, Fei; Wang, Dong; Shi, Wei-Nan; Huang, Ai-Jun; Sun, Wen-Yang; Ye, Liu
2018-04-01
The uncertainty principle is recognized as an elementary ingredient of quantum theory and sets up a significant bound to predict outcome of measurement for a couple of incompatible observables. In this work, we develop dynamical features of quantum memory-assisted entropic uncertainty relations (QMA-EUR) in a two-qubit Heisenberg XXZ spin chain with an inhomogeneous magnetic field. We specifically derive the dynamical evolutions of the entropic uncertainty with respect to the measurement in the Heisenberg XXZ model when spin A is initially correlated with quantum memory B. It has been found that the larger coupling strength J of the ferromagnetism ( J 0 ) chains can effectively degrade the measuring uncertainty. Besides, it turns out that the higher temperature can induce the inflation of the uncertainty because the thermal entanglement becomes relatively weak in this scenario, and there exists a distinct dynamical behavior of the uncertainty when an inhomogeneous magnetic field emerges. With the growing magnetic field | B | , the variation of the entropic uncertainty will be non-monotonic. Meanwhile, we compare several different optimized bounds existing with the initial bound proposed by Berta et al. and consequently conclude Adabi et al.'s result is optimal. Moreover, we also investigate the mixedness of the system of interest, dramatically associated with the uncertainty. Remarkably, we put forward a possible physical interpretation to explain the evolutionary phenomenon of the uncertainty. Finally, we take advantage of a local filtering operation to steer the magnitude of the uncertainty. Therefore, our explorations may shed light on the entropic uncertainty under the Heisenberg XXZ model and hence be of importance to quantum precision measurement over solid state-based quantum information processing.
Directory of Open Access Journals (Sweden)
Yun Wang
2016-01-01
Full Text Available Gamma Gaussian inverse Wishart cardinalized probability hypothesis density (GGIW-CPHD algorithm was always used to track group targets in the presence of cluttered measurements and missing detections. A multiple models GGIW-CPHD algorithm based on best-fitting Gaussian approximation method (BFG and strong tracking filter (STF is proposed aiming at the defect that the tracking error of GGIW-CPHD algorithm will increase when the group targets are maneuvering. The best-fitting Gaussian approximation method is proposed to implement the fusion of multiple models using the strong tracking filter to correct the predicted covariance matrix of the GGIW component. The corresponding likelihood functions are deduced to update the probability of multiple tracking models. From the simulation results we can see that the proposed tracking algorithm MM-GGIW-CPHD can effectively deal with the combination/spawning of groups and the tracking error of group targets in the maneuvering stage is decreased.
Shanechi, Maryam M; Orsborn, Amy L; Carmena, Jose M
2016-04-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter
Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model
Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P. O.
2013-06-01
This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-07-12
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.
A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade
Directory of Open Access Journals (Sweden)
Rodolfo Metulini
2018-02-01
Full Text Available Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each other due to spatial and network autocorrelation, these methods may lead to biased parameter estimates. To overcome this problem, eigenvector spatial filtering (ESF variants of the Poisson/negative binomial specifications have been proposed in the literature on gravity modelling of trade. However, no specific treatment has been developed for cases in which many zero flows are present. This paper contributes to the literature in two ways. First, by employing a stepwise selection criterion for spatial filters that is based on robust (sandwich p-values and does not require likelihood-based indicators. In this respect, we develop an ad hoc backward stepwise function in R. Second, using this function, we select a reduced set of spatial filters that properly accounts for importer-side and exporter-side specific spatial effects, as well as network effects, both at the count and the logit processes of zero-inflated methods. Applying this estimation strategy to a cross-section of bilateral trade flows between a set of 64 countries for the year 2000, we find that our specification outperforms the benchmark models in terms of model fitting, both considering the AIC and in predicting zero (and small flows.
Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns
Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto
2017-09-01
Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.
Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model.
Liao, Ruizhi; Ning, Lipeng; Chen, Zhenrui; Rigolo, Laura; Gong, Shun; Pasternak, Ofer; Golby, Alexandra J; Rathi, Yogesh; O'Donnell, Lauren J
2017-01-01
Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography. We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography. We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in
Trends in Ocean Irradiance using a Radiative Model Forced with Terra Aerosols and Clouds
Gregg, Watson; Casey, Nancy; Romanou, Anastasia
2010-01-01
Aerosol and cloud information from MODIS on Terra provide enhanced capability to understand surface irradiance over the oceans and its variability. These relationships can be important for ocean biology and carbon cycles. An established radiative transfer model, the Ocean-Atmosphere Spectral Irradiance Model (OASIM) is used to describe ocean irradiance variability on seasonal to decadal time scales. The model is forced with information on aerosols and clouds from the MODIS sensor on Terra and Aqua. A 7-year record (2000-2006) showed no trends in global ocean surface irradiance or photosynthetic available irradiance (PAR). There were significant (P20 W/sq m. The trends using MODIS data contrast with results from OASIM using liquid water path estimates from the International Satellite Cloud Climatology Project (ISCCP). Here, a global trend of -2 W/sq m was observed, largely dues to a large negative trend in the Antarctic -12 W/sq m. These results suggest the importance of the choice of liquid water path data sets in assessments of medium-length trends in ocean surface irradiance. The choices also impact the evaluation of changes in ocean biogeochemistry.
Examining secular trend and seasonality in count data using dynamic generalized linear modelling
DEFF Research Database (Denmark)
Lundbye-Christensen, Søren; Dethlefsen, Claus; Gorst-Rasmussen, Anders
Aims Time series of incidence counts often show secular trends and seasonal patterns. We present a model for incidence counts capable of handling a possible gradual change in growth rates and seasonal patterns, serial correlation and overdispersion. Methods The model resembles an ordinary time...... series regression model for Poisson counts. It differs in allowing the regression coefficients to vary gradually over time in a random fashion. Data In the period January 1980 to 1999, 17,989 incidents of acute myocardial infarction were recorded in the county of Northern Jutland, Denmark. Records were...... updated daily. Results The model with a seasonal pattern and an approximately linear trend was fitted to the data, and diagnostic plots indicate a good model fit. The analysis with the dynamic model revealed peaks coinciding with influenza epidemics. On average the peak-to-trough ratio is estimated...
Purich, Ariaan; Cai, Wenju; England, Matthew H; Cowan, Tim
2016-02-04
Despite global warming, total Antarctic sea ice coverage increased over 1979-2013. However, the majority of Coupled Model Intercomparison Project phase 5 models simulate a decline. Mechanisms causing this discrepancy have so far remained elusive. Here we show that weaker trends in the intensification of the Southern Hemisphere westerly wind jet simulated by the models may contribute to this disparity. During austral summer, a strengthened jet leads to increased upwelling of cooler subsurface water and strengthened equatorward transport, conducive to increased sea ice. As the majority of models underestimate summer jet trends, this cooling process is underestimated compared with observations and is insufficient to offset warming in the models. Through the sea ice-albedo feedback, models produce a high-latitude surface ocean warming and sea ice decline, contrasting the observed net cooling and sea ice increase. A realistic simulation of observed wind changes may be crucial for reproducing the recent observed sea ice increase.
Technological development of multispectral filter assemblies for micro bolometer
Le Goff, Roland; Tanguy, François; Fuss, Philippe; Etcheto, Pierre
2017-11-01
Since 2007 Sodern has successfully developed visible and near infrared multispectral filter assemblies for Earth remote sensing imagers. Filter assembly is manufactured by assembling several sliced filter elements (so-called strips), each corresponding to one spectral band. These strips are cut from wafers using a two dimensional accuracy precision process. In the frame of a 2011 R&T preparatory initiative undertaken by the French agency CNES, the filter assembly concept was adapted by Sodern to the long wave infrared spectral band taken into account the germanium substrate, the multilayer bandpass filters and the F-number of the optics. Indeed the current trend in space instrumentation toward more compact uncooled infrared radiometer leads to replace the filter wheel with a multispectral filter assembly mounted directly above the micro bolometer window. The filter assembly was customized to fit the bolometer size. For this development activity we consider a ULIS VGA LWIR micro bolometer with 640 by 480 pixels and 25 microns pixel pitch. The feasibility of the concept and the ability to withstand space environment were investigated and demonstrated by bread boarding activities. The presentation will contain a detailed description of the bolometer and filter assembly design, the stray light modeling analysis assessing the crosstalk between adjacent spectral bands and the results of the manufacturing and environmental tests (damp heat and thermal vacuum cycling).
Directory of Open Access Journals (Sweden)
SZOPOS, E.
2012-05-01
Full Text Available This paper presents an iterative method for designing FIR filters that implement arbitrary magnitude characteristics, defined by the user through a set of frequency-magnitude points (frequency samples. The proposed method is based on the non-uniform frequency sampling algorithm. For each iteration a new set of frequency samples is generated, by processing the set used in the previous run; this implies changing the samples location around the previous frequency values and adjusting their magnitude through interpolation. If necessary, additional samples can be introduced, as well. After each iteration the magnitude characteristic of the resulting filter is determined by using the non-uniform DFT and compared with the required one; if the errors are larger than the acceptable levels (set by the user a new iteration is run; the length of the resulting filter and the values of its coefficients are also taken into consideration when deciding a re-run. To demonstrate the efficiency of the proposed method a tool for designing FIR filters that match human audiograms was implemented in LabVIEW. It was shown that the resulting filters have smaller coefficients than the standard one, and can also have lower order, while the errors remain relatively small.
Herzing, Andrew A; Ro, Hyun Wook; Soles, Christopher L; DeLongchamp, Dean M
2013-09-24
The morphology of the active layer in an organic photovoltaic bulk-heterojunction device is controlled by the extent and nature of phase separation during processing. We have studied the effects of fullerene crystallinity during heat treatment in model structures consisting of a layer of poly(3-hexylthiophene) (P3HT) sandwiched between two layers of [6,6]-phenyl-C61-butyric acid methyl ester (PCBM). Utilizing a combination of focused ion-beam milling and energy-filtered transmission electron microscopy, we monitored the local changes in phase distribution as a function of annealing time at 140 °C. In both cases, dissolution of PCBM within the surrounding P3HT was directly visualized and quantitatively described. In the absence of crystalline PCBM, the overall phase distribution remained stable after intermediate annealing times up to 60 s, whereas microscale PCBM aggregates were observed after annealing for 300 s. Aggregate growth proceeded vertically from the substrate interface via uptake of PCBM from the surrounding region, resulting in a large PCBM-depleted region in their vicinity. When precrystallized PCBM was present, amorphous PCBM was observed to segregate from the intermediate P3HT layer and ripen the crystalline PCBM underneath, owing to the far lower solubility of crystalline PCBM within P3HT. This process occurred rapidly, with segregation already evident after annealing for 10 s and with uptake of nearly all of the amorphous PCBM by the crystalline layer after 60 s. No microscale aggregates were observed in the precrystallized system, even after annealing for 300 s.
A generalized adaptive mathematical morphological filter for LIDAR data
Cui, Zheng
Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in
Tang, Malcolm S. Y.; Chenoli, Sheeba Nettukandy; Samah, Azizan Abu; Hai, Ooi See
2018-03-01
The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 oS to 60 oS) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts "Interim" reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979-2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year-1. For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution.
Lubineau, Gilles
2009-05-16
The post-processing of experiments with nonuniform fields is still a challenge: the information is often much richer, but its interpretation for identification purposes is not straightforward. However, this is a very promising field of development because it would pave the way for the robust identification of multiple material parameters using only a small number of experiments. This paper presents a goal-oriented filtering technique in which data are combined into new output fields which are strongly correlated with specific quantities of interest (the material parameters to be identified). Thus, this combination, which is nonuniform in space, constitutes a filter of the experimental outputs, whose relevance is quantified by a quality function based on global variance analysis. Then, this filter is optimized using genetic algorithms. © 2009 Springer-Verlag.
Modeling of a narrow band pass filter for Bathymetry light detection and ranging (LIDAR) system
Butt, M. A.; Fomchenkov, S. A.; Khonina, S. N.
2017-11-01
In this work, a narrow band pass Fabry-Perot filter is designed which can be used in an airborne light detection (ALB) and ranging bathymetry. LIDAR is done by reflecting a pulse laser beam from a target and detecting the round-trip propagation time between the source and the target. ALB systems consist of Nd: YAG laser that emits the pulses at two different wavelengths such as 1064 nm and 532 nm. Infrared pulses at 1064 nm are reflected from the water surface and the green pulses at 532 nm which penetrates the water surface and are reflected from the ground. Filters are desirable to suppress the ambient light that is reflected by the surface of the water or an atmosphere which always enter the detector as a noise. The designed filter shows a high quality with an average transmission of more than 95 % at 532 nm which is considered as practically ideal for water penetration in typical coastal waters.
Mook, H.W.
1999-01-01
The invention relates to a Wien filter provided with electrodes for generating an electric field, and magnetic poles for generating a magnetic field, said electrodes and magnetic poles being positioned around and having a finite length along a filter axis, and being positioned around the filter axis
Directory of Open Access Journals (Sweden)
Y. A. Bladyko
2010-01-01
Full Text Available The paper contains definition of a smoothing factor which is suitable for any rectifier filter. The formulae of complex smoothing factors have been developed for simple and complex passive filters. The paper shows conditions for application of calculation formulae and filters.
Sutton, J.B.; Torrey, J.V.P.
1958-08-26
A process is described for reconditioning fused alumina filters which have become clogged by the accretion of bismuth phosphate in the filter pores, The method consists in contacting such filters with faming sulfuric acid, and maintaining such contact for a substantial period of time.
Population trends for North American winter birds based on hierarchical models
Soykan, Candan U.; Sauer, John; Schuetz, Justin G.; LeBaron, Geoffrey S.; Dale, Kathy; Langham, Gary M.
2016-01-01
Managing widespread and persistent threats to birds requires knowledge of population dynamics at large spatial and temporal scales. For over 100 yrs, the Audubon Christmas Bird Count (CBC) has enlisted volunteers in bird monitoring efforts that span the Americas, especially southern Canada and the United States. We employed a Bayesian hierarchical model to control for variation in survey effort among CBC circles and, using CBC data from 1966 to 2013, generated early-winter population trend estimates for 551 species of birds. Selecting a subset of species that do not frequent bird feeders and have ≥25% range overlap with the distribution of CBC circles (228 species) we further estimated aggregate (i.e., across species) trends for the entire study region and at the level of states/provinces, Bird Conservation Regions, and Landscape Conservation Cooperatives. Moreover, we examined the relationship between ten biological traits—range size, population size, migratory strategy, habitat affiliation, body size, diet, number of eggs per clutch, age at sexual maturity, lifespan, and tolerance of urban/suburban settings—and CBC trend estimates. Our results indicate that 68% of the 551 species had increasing trends within the study area over the interval 1966–2013. When trends were examined across the subset of 228 species, the median population trend for the group was 0.9% per year at the continental level. At the regional level, aggregate trends were positive in all but a few areas. Negative population trends were evident in lower latitudes, whereas the largest increases were at higher latitudes, a pattern consistent with range shifts due to climate change. Nine of 10 biological traits were significantly associated with median population trend; however, none of the traits explained >34% of the deviance in the data, reflecting the indirect relationships between population trend estimates and species traits. Trend estimates based on the CBC are broadly congruent with
GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors.
Shang, Jianga; Hu, Xuke; Cheng, Wen; Fan, Hongchao
2016-12-15
Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity.
GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors
Directory of Open Access Journals (Sweden)
Jianga Shang
2016-12-01
Full Text Available Although map filtering-aided Pedestrian Dead Reckoning (PDR is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity.
Directory of Open Access Journals (Sweden)
S. N. Naikwad
2009-01-01
Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.
A Framework for 3D Model-Based Visual Tracking Using a GPU-Accelerated Particle Filter.
Brown, J A; Capson, D W
2012-01-01
A novel framework for acceleration of particle filtering approaches to 3D model-based, markerless visual tracking in monocular video is described. Specifically, we present a methodology for partitioning and mapping the computationally expensive weight-update stage of a particle filter to a graphics processing unit (GPU) to achieve particle- and pixel-level parallelism. Nvidia CUDA and Direct3D are employed to harness the massively parallel computational power of modern GPUs for simulation (3D model rendering) and evaluation (segmentation, feature extraction, and weight calculation) of hundreds of particles at high speeds. The proposed framework addresses the computational intensity that is intrinsic to all particle filter approaches, including those that have been modified to minimize the number of particles required for a particular task. Performance and tracking quality results for rigid object and articulated hand tracking experiments demonstrate markerless, model-based visual tracking on consumer-grade graphics hardware with pixel-level accuracy up to 95 percent at 60+ frames per second. The framework accelerates particle evaluation up to 49 times over a comparable CPU-only implementation, providing an increased particle count while maintaining real-time frame rates.
Belkhatir, Zehor
2015-11-23
This paper discusses the estimation of distributed Cerebral Blood Flow (CBF) using spatiotemporal traveling wave model. We consider a damped wave partial differential equation that describes a physiological relationship between the blood mass density and the CBF. The spatiotemporal model is reduced to a finite dimensional system using a cubic b-spline continuous Galerkin method. A Kalman Filter with Unknown Inputs without Direct Feedthrough (KF-UI-WDF) is applied on the obtained reduced differential model to estimate the source term which is the CBF scaled by a factor. Numerical results showing the performances of the adopted estimator are provided.
Robust option replication for a Black-Scholes model extended with nondeterministic trends
Schoenmakers, J.G.M.; Kloeden, P.E.
1999-01-01
Statistical analysis on various stocks reveals long range dependence behavior of the stock prices that is not consistent with the classical Black and Scholes model. This memory or nondeterministic trend behavior is often seen as a reflection of market sentiments and causes that the historical
Age Differences within Secular IQ Trends: An Individual Growth Modeling Approach
Kanaya, Tomoe; Ceci, Stephen J.; Scullin, Matthew H.
2005-01-01
Age differences within the yo-yo trend in IQ, caused when aging norms that produce inflated scores are replaced with new norms, were examined using longitudinal WISC, WISC-R and WISC-III records of students tested for special education services from 10 school districts. Descriptive and individual growth modeling analyses revealed that while the…
Directory of Open Access Journals (Sweden)
Andreas Ettlinger
2018-01-01
Full Text Available The topic of indoor positioning and indoor navigation by using observations from smartphone sensors is very challenging as the determined trajectories can be subject to significant deviations compared to the route travelled in reality. Especially the calculation of the direction of movement is the critical part of pedestrian positioning approaches such as Pedestrian Dead Reckoning (“PDR”. Due to distinct systematic effects in filtered trajectories, it can be assumed that there are systematic deviations present in the observations from smartphone sensors. This article has two aims: one is to enable the estimation of partial redundancies for each observation as well as for observation groups. Partial redundancies are a measure for the reliability indicating how well systematic deviations can be detected in single observations used in PDR. The second aim is to analyze the behavior of partial redundancy by modifying the stochastic and functional model of the Kalman filter. The equations relating the observations to the orientation are condition equations, which do not exhibit the typical structure of the Gauss-Markov model (“GMM”, wherein the observations are linear and can be formulated as functions of the states. To calculate and analyze the partial redundancy of the observations from smartphone-sensors used in PDR, the system equation and the measurement equation of a Kalman filter as well as the redundancy matrix need to be derived in the Gauss-Helmert model (“GHM”. These derivations are introduced in this article and lead to a novel Kalman filter structure based on condition equations, enabling reliability assessment of each observation.
Development, Validation and ECM Embedment of a Physics-Based SCR on Filter Model
Ramesh, S.; Nieuwenhof, R.J.C.; Seykens, X.; Srinivasan, S.; Sharma, G.
2016-01-01
SCR on Filter (SCRoF) is an efficient and compact NOX and PM reduction technology already used in series production for light-duty applications. The technology is now finding its way into the medium duty and heavy duty market. One of the key challenges for successful application is the robustness to
Why are models unable to reproduce multi-decadal trends in lower tropospheric baseline ozone levels?
Hu, L.; Liu, J.; Mickley, L. J.; Strahan, S. E.; Steenrod, S.
2017-12-01
Assessments of tropospheric ozone radiative forcing rely on accurate model simulations. Parrish et al (2014) found that three chemistry-climate models (CCMs) overestimate present-day O3 mixing ratios and capture only 50% of the observed O3 increase over the last five decades at 12 baseline sites in the northern mid-latitudes, indicating large uncertainties in our understanding of the ozone trends and their implications for radiative forcing. Here we present comparisons of outputs from two chemical transport models (CTMs) - GEOS-Chem and the Global Modeling Initiative model - with O3 observations from the same sites and from the global ozonesonde network. Both CTMs are driven by reanalysis meteorological data (MERRA or MERRA2) and thus are expected to be different in atmospheric transport processes relative to those freely running CCMs. We test whether recent model developments leading to more active ozone chemistry affect the computed ozone sensitivity to perturbations in emissions. Preliminary results suggest these CTMs can reproduce present-day ozone levels but fail to capture the multi-decadal trend since 1980. Both models yield widespread overpredictions of free tropospheric ozone in the 1980s. Sensitivity studies in GEOS-Chem suggest that the model estimate of natural background ozone is too high. We discuss factors that contribute to the variability and trends of tropospheric ozone over the last 30 years, with a focus on intermodel differences in spatial resolution and in the representation of stratospheric chemistry, stratosphere-troposphere exchange, halogen chemistry, and biogenic VOC emissions and chemistry. We also discuss uncertainty in the historical emission inventories used in models, and how these affect the simulated ozone trends.
International Nuclear Information System (INIS)
Shiba, Kazuo; Nagao, Koji; Akiyama, Toshio; Tanaka, Fumikazu; Osumi, Akira; Hirao, Yasuhiro.
1997-01-01
The filter unit is used by attaching to a dustproof mask, and used in a radiation controlled area such as in a nuclear power plant. The filter unit comprises sheet-like front and back filtering members disposed vertically in parallel, a spacer for keeping the filtering members to a predetermined distance and front and back covering members for covering the two filtering members respectively. An electrostatic filter prepared by applying resin-fabrication to a base sheet comprising 100% by weight of organic fibers as fiber components, for example, wool felt, synthetic fiber non-woven fabric, wool and synthetic fiber blend non-woven fabric and then electrifying the resin is used for the filtering members. Then, residue of ashes can be eliminated substantially or completely after burning them. (I.N.)
Handling ability of gaseous microemboli of two pediatric arterial filters in a simulated CPB model.
Strother, A; Wang, S; Kunselman, A R; Ündar, A
2013-05-01
The purpose of this experiment was to compare the Sorin KIDS D131 and the Terumo Capiox AF02 pediatric arterial filters in a simulated CPB procedure to determine which filter is the better for clinical use. The experimental circuit was primed with an 800 ml combination of lactated Ringer's solution and human blood (hematocrit (Hct) 30%). The two filters were tested under flow rates of 500, 1000, and 1500 ml/min at room temperature and their purge lines opened and closed as 5cc of air was injected into the circuit. As the flow rates increased, the number of gaseous microemboli (GME) being returned to the pseudo patient increased for both of the pediatric arterial filters. Having an open purge line increased the number of GME removed from the CPB circuit, caused less of a pressure drop than when closed and increased the total hemodynamic energy loss than when closed. Both of the filters performed and reacted similarly in decreasing GME, hemodynamic energy loss and pressure drop. The only minor difference was that the Capiox AF02 had slightly less stolen blood flow (109.5 ± 1.7 ml/min at 500 ml/min, 114.7 ± 1.1 ml/min at 1000 ml/min and 105.8 ± 4.2 ml/min at 1500ml/min) from the open purge line than the KIDS D131 (119.5 ± 2.5 ml/min at 500 ml/min, 128.3 ± 1.0 ml/min at 1000 ml/min and 126.3 ± 3.1 ml/min at 1500 ml/min). Our study confirmed that both the Sorin KIDS D131 and the Terumo Capiox AF02 were equivalent in their ability to remove significant numbers of GME, the amount of pressure drop and the total hemodynamic energy loss across the arterial filters at the various flow rates. An arterial filter is not an option, but a necessity for removing microemboli delivered to the patient.
Li, Jiahao; Klee Barillas, Joaquin; Guenther, Clemens; Danzer, Michael A.
2014-02-01
Battery state monitoring is one of the key techniques in battery management systems e.g. in electric vehicles. An accurate estimation can help to improve the system performance and to prolong the battery remaining useful life. Main challenges for the state estimation for LiFePO4 batteries are the flat characteristic of open-circuit-voltage over battery state of charge (SOC) and the existence of hysteresis phenomena. Classical estimation approaches like Kalman filtering show limitations to handle nonlinear and non-Gaussian error distribution problems. In addition, uncertainties in the battery model parameters must be taken into account to describe the battery degradation. In this paper, a novel model-based method combining a Sequential Monte Carlo filter with adaptive control to determine the cell SOC and its electric impedance is presented. The applicability of this dual estimator is verified using measurement data acquired from a commercial LiFePO4 cell. Due to a better handling of the hysteresis problem, results show the benefits of the proposed method against the estimation with an Extended Kalman filter.
Directory of Open Access Journals (Sweden)
L. Li
2012-02-01
Full Text Available The normal-score ensemble Kalman filter (NS-EnKF is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i the localized NS-EnKF can characterize the non-multiGaussian underlying hydraulic distribution even when an erroneous prior random function model is used, (ii localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii the NS-EnKF works equally well under very different flow configurations.
Energy Technology Data Exchange (ETDEWEB)
Vazquez Martinez, V.; Bosch Roig, I.; Sanz Requena, R.
2016-07-01
In Dynamic Contrast-Enhanced Magnetic Resonance (DCEMR) studies with high temporal resolution, images are quite noisy due to the complicate balance between temporal and spatial resolution. For this reason, the temporal curves extracted from the images present remarkable noise levels and, because of that, the pharmacokinetic parameters calculated by least squares fitting from the curves and the arterial phase (a useful marker in tumour diagnosis which appears in curves with high arterial contribution) are affected. In order to solve these limitations, an automatic filtering method was developed by our group. In this work, an advanced automatic filtering methodology is presented to further improve noise reduction of the temporal curves in order to obtain more accurate kinetic parameters and a proper modelling of the arterial phase. (Author)
Generic Kalman Filter Software
Lisano, Michael E., II; Crues, Edwin Z.
2005-01-01
the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.
Directory of Open Access Journals (Sweden)
Matthew Brolly
2014-07-01
Full Text Available This study describes the novel use of a macroecological plant and forest structure model in conjunction with a Radiative Transfer (RT model to better understand interactions between microwaves and forest canopies. Trends predicted by the RT model, resulting from interactions with mixed age, mono and multi species forests, are analysed in comparison to those predicted using a simplistic structure based scattering model. This model relates backscatter to scatterer cross sectional or volume specifications, dependent on the size. The Spatially Explicit Reiterative Algorithm (SERA model is used to provide a widely varied tree size distribution while maintaining allometric consistency to produce a natural-like forest representation. The RT model is parameterised using structural information from SERA and microwave backscatter simulations are used to analyse the impact of changes to the forest stand. Results show that the slope of the saturation curve observed in the Synthetic Aperture Radar (SAR backscatter-biomass relationship is sensitive to thinning and therefore forest basal area. Due to similarities displayed between the results of the RT and simplistic model, it is determined that forest SAR backscatter behaviour at long microwave wavelengths may be described generally using equations related to total stem volume and basal area. The nature of these equations is such that they describe saturating behaviour of forests in the absence of attenuation in comparable fashion to the trends exhibited using the RT model. Both modelled backscatter trends predict a relationship to forest basal area from an early age when forest volume is increasing. When this is not the case, it is assumed to be a result of attenuation of the dominant stem-ground interaction due to the presence of excessive numbers of stems. This work shows how forest growth models can be successfully incorporated into existing independent scattering models and reveals, through the RT
Directory of Open Access Journals (Sweden)
I. Hoteit
2003-01-01
Full Text Available A singular evolutive extended Kalman (SEEK filter is used to assimilate real in situ data in a water column marine ecosystem model. The biogeochemistry of the ecosystem is described by the European Regional Sea Ecosystem Model (ERSEM, while the physical forcing is described by the Princeton Ocean Model (POM. In the SEEK filter, the error statistics are parameterized by means of a suitable basis of empirical orthogonal functions (EOFs. The purpose of this contribution is to track the possibility of using data assimilation techniques for state estimation in marine ecosystem models. In the experiments, real oxygen and nitrate data are used and the results evaluated against independent chlorophyll data. These data were collected from an offshore station at three different depths for the needs of the MFSPP project. The assimilation results show a continuous decrease in the estimation error and a clear improvement in the model behavior. Key words. Oceanography: general (ocean prediction; numerical modelling – Oceanography: biological and chemical (ecosystems and ecology
El Gharamti, Mohamad
2015-11-26
The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.
Lastre, Arlys; Torriente, Ives; Méndez, Erik F.; Cordovés, Alexis
2017-06-01
In the present investigation, the authors propose a conceptual model for the analysis and the decision making of the corrective models to use in the mitigation of the harmonic distortion. The authors considered the setting of conventional models, and such adaptive models like the filters incorporation to networks neuronal artificial (RNA's) for the mitigating effect. In addition to the present work is a showing of the experimental model that learns by means of a flowchart denoting the need to use artificial intelligence skills for the exposition of the proposed model. The other aspect considered and analyzed are the adaptability and usage of the same, considering a local reference of the laws and lineaments of energy quality that demands the Department of Electricity and Energy Renewable (MEER) of Equator.
Using regression heteroscedasticity to model trends in the mean and variance of floods
Hecht, Jory; Vogel, Richard
2015-04-01
Changes in the frequency of extreme floods have been observed and anticipated in many hydrological settings in response to numerous drivers of environmental change, including climate, land cover, and infrastructure. To help decision-makers design flood control infrastructure in settings with non-stationary hydrological regimes, a parsimonious approach for detecting and modeling trends in extreme floods is needed. An approach using ordinary least squares (OLS) to fit a heteroscedastic regression model can accommodate nonstationarity in both the mean and variance of flood series while simultaneously offering a means of (i) analytically evaluating type I and type II trend detection errors, (ii) analytically generating expressions of uncertainty, such as confidence and prediction intervals, (iii) providing updated estimates of the frequency of floods exceeding the flood of record, (iv) accommodating a wide range of non-linear functions through ladder of powers transformations, and (v) communicating hydrological changes in a single graphical image. Previous research has shown that the two-parameter lognormal distribution can adequately model the annual maximum flood distribution of both stationary and non-stationary hydrological regimes in many regions of the United States. A simple logarithmic transformation of annual maximum flood series enables an OLS heteroscedastic regression modeling approach to be especially suitable for creating a non-stationary flood frequency distribution with parameters that are conditional upon time or physically meaningful covariates. While heteroscedasticity is often viewed as an impediment, we document how detecting and modeling heteroscedasticity presents an opportunity for characterizing both the conditional mean and variance of annual maximum floods. We introduce an approach through which variance trend models can be analytically derived from the behavior of residuals of the conditional mean flood model. Through case studies of
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks
Ghosh, Saurav; Chakraborty, Prithwish; Nsoesie, Elaine O.; Cohn, Emily; Mekaru, Sumiko R.; Brownstein, John S.; Ramakrishnan, Naren
2017-01-01
In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.
Following a trend with an exponential moving average: Analytical results for a Gaussian model
Grebenkov, Denis S.; Serror, Jeremy
2014-01-01
We investigate how price variations of a stock are transformed into profits and losses (P&Ls) of a trend following strategy. In the frame of a Gaussian model, we derive the probability distribution of P&Ls and analyze its moments (mean, variance, skewness and kurtosis) and asymptotic behavior (quantiles). We show that the asymmetry of the distribution (with often small losses and less frequent but significant profits) is reminiscent to trend following strategies and less dependent on peculiarities of price variations. At short times, trend following strategies admit larger losses than one may anticipate from standard Gaussian estimates, while smaller losses are ensured at longer times. Simple explicit formulas characterizing the distribution of P&Ls illustrate the basic mechanisms of momentum trading, while general matrix representations can be applied to arbitrary Gaussian models. We also compute explicitly annualized risk adjusted P&L and strategy turnover to account for transaction costs. We deduce the trend following optimal timescale and its dependence on both auto-correlation level and transaction costs. Theoretical results are illustrated on the Dow Jones index.
Mook, H.W.
1999-01-01
The invention relates to a Wien filter provided with electrodes for generating an electric field, and magnetic poles for generating a magnetic field, said electrodes and magnetic poles being positioned around and having a finite length along a filter axis, and being positioned around the filter axis such that electric and magnetic forces induced by the respective fields and exerted on an electrically charged particle moving substantially along the fileter axis at a certain velocity
Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid
2017-08-01
In this study, 60-year monthly rainfall data of Bangladesh were analysed to detect trends. Modified Mann-Kendall, Spearman's rho tests and Sen's slope estimators were applied to find the long-term annual, dry season and monthly trends. Sequential Mann-Kendall analysis was applied to detect the potential trend turning points. Spatial variations of the trends were examined using inverse distance weighting (IDW) interpolation. AutoRegressive integrated moving average (ARIMA) model was used for the country mean rainfall and for other two stations data which depicted the highest and the lowest trend in the Mann-Kendall and Spearman's rho tests. Results showed that there is no significant trend in annual rainfall pattern except increasing trends for Cox's Bazar, Khulna, Satkhira and decreasing trend for Srimagal areas. For the dry season, only Bogra area represented significant decreasing trend. Long-term monthly trends demonstrated a mixed pattern; both negative and positive changes were found from February to September. Comilla area showed a significant decreasing trend for consecutive 3 months while Rangpur and Khulna stations confirmed the significant rising trends for three different months in month-wise trends analysis. Rangpur station data gave a maximum increasing trend in April whereas a maximum decreasing trend was found in August for Comilla station. ARIMA models predict +3.26, +8.6 and -2.30 mm rainfall per year for the country, Cox's Bazar and Srimangal areas, respectively. However, all the test results and predictions revealed a good agreement among them in the study.
Razgulin, A. V.; Sazonova, S. V.
2017-09-01
A novel statement of the Fourier filtering problem based on the use of matrix Fourier filters instead of conventional multiplier filters is considered. The basic properties of the matrix Fourier filtering for the filters in the Hilbert-Schmidt class are established. It is proved that the solutions with a finite energy to the periodic initial boundary value problem for the quasi-linear functional differential diffusion equation with the matrix Fourier filtering Lipschitz continuously depend on the filter. The problem of optimal matrix Fourier filtering is formulated, and its solvability for various classes of matrix Fourier filters is proved. It is proved that the objective functional is differentiable with respect to the matrix Fourier filter, and the convergence of a version of the gradient projection method is also proved.
Research of combination model for prediction of the trend of outbreak of hepatitis B
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Yin-ping CHEN
2014-03-01
Full Text Available Objective To establish a combination model of autoregressive integrated moving average model and the grey dynamics (ARIMA-GM of hepatitis B incidence rate (1/100 000 to predict the trend of outbreak of hepatitis B, as to provide a scientific basis for the early discovery of the infectious diseases for the performance of countermeasures of controlling its spread. Methods The monthly incidence of hepatitis B in Qian'an city, Hebei province, was collected from Jan 2004 to Dec 2012, and a model (ARIMA was reproduced with SPSS software. The GM (1,1 model was used to correct the residual sequence with a threshold value, and a combined forecasting model was reproduced. This combination model was used to predict the monthly incidence rate in this city in 2013. Results The model ARIMA(0,1,1(0,1,112 was established successfully and the residual sequence was a white noise sequence. Then the GM (1,1 model with a threshold of 3 was used to correct its residuals and obtain its nonlinear feature extraction of information. The forecasting model met required precision standards (C=0.673, P=0.877, the fitting accuracy of which was basically qualified. The results showed that the MAE, MAPE of the ARIMA-GM combined model were smaller than that of a single model, and the combined model could improve the prediction accuracy. Using the combined model to forecast the incidence of hepatitis B during Jan 2013 to Dec 2013, the overall trend was relatively consistent with the condition of previous years. Conclusion The ARIMA-GM combined model can better fit the incidence rate of hepatitis B with a greater accuracy than the seasonal ARIMA model. The prediction results can provide the reference for the early warning system of HBV. DOI: 10.11855/j.issn.0577-7402.2014.01.12
Keppenne, Christian L.
2013-01-01
A two-step ensemble recentering Kalman filter (ERKF) analysis scheme is introduced. The algorithm consists of a recentering step followed by an ensemble Kalman filter (EnKF) analysis step. The recentering step is formulated such as to adjust the prior distribution of an ensemble of model states so that the deviations of individual samples from the sample mean are unchanged but the original sample mean is shifted to the prior position of the most likely particle, where the likelihood of each particle is measured in terms of closeness to a chosen subset of the observations. The computational cost of the ERKF is essentially the same as that of a same size EnKF. The ERKF is applied to the assimilation of Argo temperature profiles into the OGCM component of an ensemble of NASA GEOS-5 coupled models. Unassimilated Argo salt data are used for validation. A surprisingly small number (16) of model trajectories is sufficient to significantly improve model estimates of salinity over estimates from an ensemble run without assimilation. The two-step algorithm also performs better than the EnKF although its performance is degraded in poorly observed regions.
Energy Technology Data Exchange (ETDEWEB)
Juxiu Tong; Bill X. Hu; Hai Huang; Luanjin Guo; Jinzhong Yang
2014-03-01
With growing importance of water resources in the world, remediations of anthropogenic contaminations due to reactive solute transport become even more important. A good understanding of reactive rate parameters such as kinetic parameters is the key to accurately predicting reactive solute transport processes and designing corresponding remediation schemes. For modeling reactive solute transport, it is very difficult to estimate chemical reaction rate parameters due to complex processes of chemical reactions and limited available data. To find a method to get the reactive rate parameters for the reactive urea hydrolysis transport modeling and obtain more accurate prediction for the chemical concentrations, we developed a data assimilation method based on an ensemble Kalman filter (EnKF) method to calibrate reactive rate parameters for modeling urea hydrolysis transport in a synthetic one-dimensional column at laboratory scale and to update modeling prediction. We applied a constrained EnKF method to pose constraints to the updated reactive rate parameters and the predicted solute concentrations based on their physical meanings after the data assimilation calibration. From the study results we concluded that we could efficiently improve the chemical reactive rate parameters with the data assimilation method via the EnKF, and at the same time we could improve solute concentration prediction. The more data we assimilated, the more accurate the reactive rate parameters and concentration prediction. The filter divergence problem was also solved in this study.
Long-term surface pCO2 trends from observations and models
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Jerry F. Tjiputra
2014-05-01
Full Text Available We estimate regional long-term surface ocean pCO2 growth rates using all available underway and bottled biogeochemistry data collected over the past four decades. These observed regional trends are compared with those simulated by five state-of-the-art Earth system models over the historical period. Oceanic pCO2 growth rates faster than the atmospheric growth rates indicate decreasing atmospheric CO2 uptake, while ocean pCO2 growth rates slower than the atmospheric growth rates indicate increasing atmospheric CO2 uptake. Aside from the western subpolar North Pacific and the subtropical North Atlantic, our analysis indicates that the current observation-based basin-scale trends may be underestimated, indicating that more observations are needed to determine the trends in these regions. Encouragingly, good agreement between the simulated and observed pCO2 trends is found when the simulated fields are subsampled with the observational coverage. In agreement with observations, we see that the simulated pCO2 trends are primarily associated with the increase in surface dissolved inorganic carbon (DIC associated with atmospheric carbon uptake, and in part by warming of the sea surface. Under the RCP8.5 future scenario, DIC continues to be the dominant driver of pCO2 trends, with little change in the relative contribution of SST. However, the changes in the hydrological cycle play an increasingly important role. For the contemporary (1970–2011 period, the simulated regional pCO2 trends are lower than the atmospheric growth rate over 90% of the ocean. However, by year 2100 more than 40% of the surface ocean area has a higher oceanic pCO2 trend than the atmosphere, implying a reduction in the atmospheric CO2 uptake rate. The fastest pCO2 growth rates are projected for the subpolar North Atlantic, while the high-latitude Southern Ocean and eastern equatorial Pacific have the weakest growth rates, remaining below the atmospheric pCO2 growth rate. Our work
Sea Ice Trends in Climate Models Only Accurate in Runs with Biased Global Warming
Rosenblum, Erica; Eisenman, Ian
2017-08-01
Observations indicate that the Arctic sea ice cover is rapidly retreating while the Antarctic sea ice cover is steadily expanding. State-of-the-art climate models, by contrast, typically simulate a moderate decrease in both the Arctic and Antarctic sea ice covers. However, in each hemisphere there is a small subset of model simulations that have sea ice trends similar to the observations. Based on this, a number of recent studies have suggested that the models are consistent with the observations in each hemisphere when simulated internal climate variability is taken into account. Here we examine sea ice changes during 1979-2013 in simulations from the most recent Coupled Model Intercomparison Project (CMIP5) as well as the Community Earth System Model Large Ensemble (CESM-LE), drawing on previous work that found a close relationship in climate models between global-mean surface temperature and sea ice extent. We find that all of the simulations with 1979-2013 Arctic sea ice retreat as fast as observed have considerably more global warming than observations during this time period. Using two separate methods to estimate the sea ice retreat that would occur under the observed level of global warming in each simulation in both ensembles, we find that simulated Arctic sea ice retreat as fast as observed would occur less than 1% of the time. This implies that the models are not consistent with the observations. In the Antarctic, we find that simulated sea ice expansion as fast as observed typically corresponds with too little global warming, although these results are more equivocal. We show that because of this, the simulations do not capture the observed asymmetry between Arctic and Antarctic sea ice trends. This suggests that the models may be getting the right sea ice trends for the wrong reasons in both polar regions.
Trend modelling of wave parameters and application in onboard prediction of ship responses
DEFF Research Database (Denmark)
Montazeri, Najmeh; Nielsen, Ulrik Dam; Jensen, J. Juncher
2015-01-01
This paper presents a trend analysis for prediction of sea state parameters onboard shipsduring voyages. Given those parameters, a JONSWAP model and also the transfer functions, prediction of wave induced ship responses are thus made. The procedure is tested with full-scale data of an in-service...... container ship. Comparison between predictions and the actual measurements, implies a good agreementin general. This method can be an efficient way to improve decision support on board ships....
Prediction Model of Machining Failure Trend Based on Large Data Analysis
Li, Jirong
2017-12-01
The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.
Data assimilation using Bayesian filters and B-spline geological models
Duan, Lian
2011-04-01
This paper proposes a new approach to problems of data assimilation, also known as history matching, of oilfield production data by adjustment of the location and sharpness of patterns of geological facies. Traditionally, this problem has been addressed using gradient based approaches with a level set parameterization of the geology. Gradient-based methods are robust, but computationally demanding with real-world reservoir problems and insufficient for reservoir management uncertainty assessment. Recently, the ensemble filter approach has been used to tackle this problem because of its high efficiency from the standpoint of implementation, computational cost, and performance. Incorporation of level set parameterization in this approach could further deal with the lack of differentiability with respect to facies type, but its practical implementation is based on some assumptions that are not easily satisfied in real problems. In this work, we propose to describe the geometry of the permeability field using B-spline curves. This transforms history matching of the discrete facies type to the estimation of continuous B-spline control points. As filtering scheme, we use the ensemble square-root filter (EnSRF). The efficacy of the EnSRF with the B-spline parameterization is investigated through three numerical experiments, in which the reservoir contains a curved channel, a disconnected channel or a 2-dimensional closed feature. It is found that the application of the proposed method to the problem of adjusting facies edges to match production data is relatively straightforward and provides statistical estimates of the distribution of geological facies and of the state of the reservoir.
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Chunmei Wang
2016-06-01
Full Text Available In this paper, the way topographic spatial information changes with resolution was investigated using semi-variograms and an Independent Structures Model (ISM to identify the mechanisms involved in changes of topographic parameters as resolution becomes coarser or finer. A typical Loess Hilly area in the Loess Plateau of China was taken as the study area. DEMs with resolutions of 2.5 m and 25 m were derived from topographic maps with map scales of 1:10,000 using ANUDEM software. The ISM, in which the semi-variogram was modeled as the sum of component semi-variograms, was used to model the measured semi-variogram of the elevation surface. Components were modeled using an analytic ISM model and corresponding landscape components identified using Kriging and filter bank analyses. The change in the spatial components as resolution became coarser was investigated by modeling upscaling as a low pass linear filter and applying a general result to obtain an analytic model for the scaling process in terms of semi-variance. This investigation demonstrated how topographic structures could be effectively characterised over varying scales using the ISM model for the semi-variogram. The loss of information in the short range components with resolution is a major driver for the observed change in derived topographic parameters such as slope. This paper has helped to quantify how information is distributed among scale components and how it is lost in natural terrain surfaces as resolution becomes coarser. It is a basis for further applications in the field of geomorphometry.
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Peek Andrew S
2007-06-01
Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid
Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories.
Chen, Xiaojian; Cui, Tingting; Fu, Jianhong; Peng, Jianwei; Shan, Jie
2016-12-01
Low-cost GPS (receiver) has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a) it is able to model trajectories and detect outliers adaptively; (b) only one critical value for outlier scores needs to be set; (c) it is able to robustly detect unapparent outliers; and (d) it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques.
Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories
Directory of Open Access Journals (Sweden)
Xiaojian Chen
2016-12-01
Full Text Available Low-cost GPS (receiver has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a it is able to model trajectories and detect outliers adaptively; (b only one critical value for outlier scores needs to be set; (c it is able to robustly detect unapparent outliers; and (d it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques.
Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters
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Gonzalez Juan G
2002-01-01
Full Text Available Linear filtering theory has been largely motivated by the characteristics of Gaussian signals. In the same manner, the proposed Myriad Filtering methods are motivated by the need for a flexible filter class with high statistical efficiency in non-Gaussian impulsive environments that can appear in practice. Myriad filters have a solid theoretical basis, are inherently more powerful than median filters, and are very general, subsuming traditional linear FIR filters. The foundation of the proposed filtering algorithms lies in the definition of the myriad as a tunable estimator of location derived from the theory of robust statistics. We prove several fundamental properties of this estimator and show its optimality in practical impulsive models such as the -stable and generalized- . We then extend the myriad estimation framework to allow the use of weights. In the same way as linear FIR filters become a powerful generalization of the mean filter, filters based on running myriads reach all of their potential when a weighting scheme is utilized. We derive the "normal" equations for the optimal myriad filter, and introduce a suboptimal methodology for filter tuning and design. The strong potential of myriad filtering and estimation in impulsive environments is illustrated with several examples.
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Jatin Narula
2010-05-01
Full Text Available Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad-a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models.
Caiazzo, A; Caforio, Federica; Montecinos, Gino; Muller, Lucas O; Blanco, Pablo J; Toro, Eluterio F
2016-10-25
This work presents a detailed investigation of a parameter estimation approach on the basis of the reduced-order unscented Kalman filter (ROUKF) in the context of 1-dimensional blood flow models. In particular, the main aims of this study are (1) to investigate the effects of using real measurements versus synthetic data for the estimation procedure (i.e., numerical results of the same in silico model, perturbed with noise) and (2) to identify potential difficulties and limitations of the approach in clinically realistic applications to assess the applicability of the filter to such setups. For these purposes, the present numerical study is based on a recently published in vitro model of the arterial network, for which experimental flow and pressure measurements are available at few selected locations. To mimic clinically relevant situations, we focus on the estimation of terminal resistances and arterial wall parameters related to vessel mechanics (Young's modulus and wall thickness) using few experimental observations (at most a single pressure or flow measurement per vessel). In all cases, we first perform a theoretical identifiability analysis on the basis of the generalized sensitivity function, comparing then the results owith the ROUKF, using either synthetic or experimental data, to results obtained using reference parameters and to available measurements. Copyright © 2016 John Wiley & Sons, Ltd.
Mondal, Sourav; Mondal, Raka; de, Sirshendu; Griffiths, Ian
2017-11-01
Purification of contaminated water following the safe water guidelines while generating sufficiently large throughput is a crucial requirement for the steady supply of safe water to large populations. Adsorption-based filtration processes using a multilayer soil bed has been posed as a viable method to achieve this goal. This work describes the theory of operation and prediction of the long-term behaviour of such a system. The fixed-bed column has a single input of contaminated water from the top and an output from the bottom. As the contaminant passes through the column, it is adsorbed by the medium. Like any other adsorption medium, the filter has a certain lifespan, beyond which the filtrate does not meet the safe limit of drinking water, which is defined as `breakthrough'. A mathematical model is developed that couples the fluid flow through the porous medium to the convective, diffusive and adsorptive transport of the contaminant. The results are validated with experimental observations and the model is then used to predict the breakthrough and lifetime of the filter. The key advantage of this model is that it can predict the long-term behaviour of any adsorption column system for any set of physical characteristics of the system. This worked was supported by the EPSRC Global Challenge Research Fund Institutional Sponsorship 2016.
Energy Technology Data Exchange (ETDEWEB)
Moreno, David Leonardo
2009-04-15
In the present work a coupling methodology between level set methods and the ensemble Kalman filter (EnKF) for modeling and conditioning geological facies with respect to production and, well data is presented. The modeling of the facies is based on the concept of implicit interfaces where level set methods are used to add dynamics to the implicit interfaces. The conditioning of the facies models is done through the application of the ensemble Kalman filter (EnKF), a sequential Bayesian inversion technique completely automatic that does not require the calculation of gradients or uses the information of previous states. The EnKF has been presented as an evolution of the extended Kalman filter (EKF) that solves the problem of the unbounded error growth of the covariance of a non-linear dynamical system by extending the traditional Kalman filter(KF) to a Monte Carlo ensemble type filter where the covariance representation is centered on the first moment of an ensemble distribution instead of the unknown true model, however, for the problem to be well defined, the parameters to be estimated should still be Gaussian or approximately Gaussian. For updating facies models defined as highly non-Gaussian systems the EnKF fails at first, since if facies types with different petrophysical properties are mixed, the generated petrophysical model will have some average properties that do not behave like any of the original facies types. The methodology presented in this work is designed to avoid the problem of the non-Gaussianity provided by facies models by applying a transformation of the facies into implicit interfaces and uses a more Gaussian variable to perturb and move the implicit representations of the facies. The result is a methodology that conditions Gaussian random fields (GRFs) to production and well data with the EnKF, later used as velocity fields in the level set equations for moving boundaries between facies systems with the purpose of obtaining good topological
Yan, Gang; Xu, Xia; Yao, Lirong; Lu, Liqiao; Zhao, Tingting; Zhang, Wenyi
2011-04-01
As one of the plug-flow reactors, biological aerated filter (BAF) reactor was divided into four sampling sectors to understand the characteristics of elemental nitrogen transformation during the reaction process, and then the different characteristics of elemental nitrogen transformation caused by different NH(3)-N loadings, biological quantities and activities in each section were obtained. The results showed that the total transformation ratio in the nitrifying reactor was more than 90% in the absence of any organic carbon resource, at the same time, more than 65% NH(3)-N in the influent were nitrified at the filter height of 70 cm below under the conditions of the influent runoff 9-19 L/h, the gas-water ratio 4-5:1, the dissolved oxygen 3.0-5.8 mg/L and the NH(3)-N load 0.28-0.48 kg NH(3)-N/m(3) d. On the base of the Eckenfelder mode, the kinetics equation of the NH(3)-N transformation along the reactor was S(e)=S(0) exp(-0.0134D/L(1.2612)). Copyright © 2011 Elsevier Ltd. All rights reserved.
Amezcua, Javier
This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion
Patrón, Verónica A.; Álvarez Borrego, Josué; Coronel Beltrán, Ángel
2015-09-01
Eye tracking has many useful applications that range from biometrics to face recognition and human-computer interaction. The analysis of the characteristics of the eyes has become one of the methods to accomplish the location of the eyes and the tracking of the point of gaze. Characteristics such as the contrast between the iris and the sclera, the shape, and distribution of colors and dark/light zones in the area are the starting point for these analyses. In this work, the focus will be on the contrast between the iris and the sclera, performing a correlation in the frequency domain. The images are acquired with an ordinary camera, which with were taken images of thirty-one volunteers. The reference image is an image of the subjects looking to a point in front of them at 0° angle. Then sequences of images are taken with the subject looking at different angles. These images are processed in MATLAB, obtaining the maximum correlation peak for each image, using two different filters. Each filter were analyzed and then one was selected, which is the filter that gives the best performance in terms of the utility of the data, which is displayed in graphs that shows the decay of the correlation peak as the eye moves progressively at different angle. This data will be used to obtain a mathematical model or function that establishes a relationship between the angle of vision (AOV) and the maximum correlation peak (MCP). This model will be tested using different input images from other subject not contained in the initial database, being able to predict angle of vision using the maximum correlation peak data.
International Nuclear Information System (INIS)
Truong, Dinh Quang; Ahn, Kyoung Kwan
2014-01-01
An ion polymer metal composite (IPMC) is an electroactive polymer that bends in response to a small applied electric field as a result of mobility of cations in the polymer network and vice versa. This paper presents an innovative and accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The model is constructed via a general multilayer perceptron neural network (GMLPNN) integrated with a smart learning mechanism (SLM) that is based on an extended Kalman filter with self-decoupling ability (SDEKF). Here the GMLPNN is built with an ability to autoadjust its structure based on its characteristic vector. Furthermore, by using the SLM based on the SDEKF, the GMLPNN parameters are optimized with small computational effort, and the modeling accuracy is improved. An apparatus employing an IPMC actuator is first set up to investigate the IPMC characteristics and to generate the data for training and validating the model. The advanced NBBM model for the IPMC system is then created with the proper inputs to estimate IPMC tip displacement. Next, the model is optimized using the SLM mechanism with the training data. Finally, the optimized NBBM model is verified with the validating data. A comparison between this model and the previously developed model is also carried out to prove the effectiveness of the proposed modeling technique. (paper)
Trends and Challenges of Electronic Auctions as a New Business Model
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Margarita Janeska
2014-11-01
Full Text Available The virtual internet based market allowed different forms of trading. Electronic business opens up new markets and market segments. Auctions are a very reliable and competitive trading model which allows to achieve fair prices and to choose the optimal business partners. The purpose of this model is that the system can extend the duration of the auction until they met various commercial applications.The basic motivation for research in this paper is the increasing trend of the use of electronic auctions in the process of selling and buying products and services.
Energy Technology Data Exchange (ETDEWEB)
Zargar, G.
2005-10-15
In this thesis, we present a new approach, which consists in directly up-scaling the geostatistical permeability distribution rather than the individual realizations. Practically, filtering techniques based on. the FFT (Fast Fourier Transform), allows us to generate geostatistical images, which sample the up-scaled distributions. In the log normal case, an equivalence hydraulic criterion is proposed, allowing to re-estimate the geometric mean of the permeabilities. In the anisotropic case, the effective geometric mean becomes a tensor which depends on the level of filtering used and it can be calculated by a method of renormalisation. Then, the method was generalized for the categorial model. Numerical tests of the method were set up for isotropic, anisotropic and categorial models, which shows good agreement with theory. (author)
A Novel Ship Detection Method Using Model-Based Decomposition as a Polarimetric Band-Stop Filter
Sugimoto, Mitsunobu; Marino, Armando; Ouchi, Kazuo; Nakamura, Yasuhiro
2013-08-01
In this study, a novel ship detection method using model-based decomposition is suggested. The model-based decomposition is one of the popular analytical methods of POLSAR (polarimetric SAR) data. Since most of the scattering on the sea is surface scattering, the model-based decomposition can be used as a band-stop filter, to block out surface scattering component. As a result, ships, which generally have more complex scattering process, can be detected. Advanced Land Observation Satellite-Phased Array L-band SAR (ALOS-PALSAR) polarimetric SAR data and available reference data for validation are used in the study. The result was processed using adaptive-CFAR (constant false alarm rate) technique and compared with the reference data.
Model-simulated trend of surface carbon monoxide for the 2001–2010 decade
Yoon, Jongmin; Pozzer, Andrea
2015-04-01
We present decadal trend estimates of surface carbon monoxide (CO) simulated using the atmospheric chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) based on the emission scenarios Representative Concentration Pathways (RCP) 8.5 for anthropogenic activity and Global Fire Emissions Database (GFED) v3.1 for biomass burning from 2001 through 2010. The spatial distribution of the modeled surface CO is evaluated with monthly data from the Measurements Of Pollution In The Troposphere (MOPITT) thermal infrared product. The global means of correlation coefficient and relative bias for the decade 2001-2010 are 0.95 and -4.29 %, respectively. We also find a reasonable correlation (R =0.78) between the trends of EMAC surface CO and full 10-year monthly records from ground-based observation (World Data Centre for Greenhouse Gases, WDCGG). Over western Europe, eastern USA, and northern Australia, the significant decreases in EMAC surface CO are estimated at -35.5±5.8, -59.6±9.1, and -13.7±9.5 ppbv per decade, respectively. In contrast, the surface CO increases by +8.9±4.8 ppbv per decade over southern Asia. A high correlation (R =0.92) between the changes in EMAC-simulated surface CO and total emission flux shows that the significant regional trends are attributed to the changes in primary and direct emissions from both anthropogenic activity and biomass burning.
Du, Tien Duc; Ngo-Duc, Thanh; Kieu, Chanh
2017-07-01
This study presents an approach to assimilate tropical cyclone (TC) real-time reports and the University of Wisconsin-Cooperative Institute for Meteorological Satellite Studies (CIMSS) Atmospheric Motion Vectors (AMV) data into the Weather Research and Forecasting (WRF) model for TC forecast applications. Unlike current methods in which TC real-time reports are used to either generate a bogus vortex or spin up a model initial vortex, the proposed approach ingests the TC real-time reports through blending a dynamically consistent synthetic vortex structure with the CIMSS-AMV data. The blended dataset is then assimilated into the WRF initial condition, using the local ensemble transform Kalman filter (LETKF) algorithm. Retrospective experiments for a number of TC cases in the northwestern Pacific basin during 2013-2014 demonstrate that this approach could effectively increase both the TC circulation and enhance the large-scale environment that the TCs are embedded in. Further evaluation of track and intensity forecast errors shows that track forecasts benefit more from improvement in the large-scale flow at 4-5-day lead times, whereas the intensity improvement is minimal. While the difference between the track and intensity improvement could be due to a specific model configuration, this result appears to be consistent with the recent reports of insignificant impacts of inner core data assimilation in operational TC models at the long range of 4-5 days. The new approach will be most beneficial for future regional TC models that are directly initialized from very high-resolution global models whose storm initial locations are sufficiently accurate at the initial analysis that there is no need to carry out any artificial vortex removal or filtering steps.
Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter
Massonnet, F.; Goosse, H.; Fichefet, T.; Counillon, F.
2014-07-01
The choice of parameter values is crucial in the course of sea ice model development, since parameters largely affect the modeled mean sea ice state. Manual tuning of parameters will soon become impractical, as sea ice models will likely include more parameters to calibrate, leading to an exponential increase of the number of possible combinations to test. Objective and automatic methods for parameter calibration are thus progressively called on to replace the traditional heuristic, "trial-and-error" recipes. Here a method for calibration of parameters based on the ensemble Kalman filter is implemented, tested and validated in the ocean-sea ice model NEMO-LIM3. Three dynamic parameters are calibrated: the ice strength parameter P*, the ocean-sea ice drag parameter Cw, and the atmosphere-sea ice drag parameter Ca. In twin, perfect-model experiments, the default parameter values are retrieved within 1 year of simulation. Using 2007-2012 real sea ice drift data, the calibration of the ice strength parameter P* and the oceanic drag parameter Cw improves clearly the Arctic sea ice drift properties. It is found that the estimation of the atmospheric drag Ca is not necessary if P* and Cw are already estimated. The large reduction in the sea ice speed bias with calibrated parameters comes with a slight overestimation of the winter sea ice areal export through Fram Strait and a slight improvement in the sea ice thickness distribution. Overall, the estimation of parameters with the ensemble Kalman filter represents an encouraging alternative to manual tuning for ocean-sea ice models.
State-space modeling of population sizes and trends in Nihoa Finch and Millerbird
Gorresen, P. Marcos; Brinck, Kevin W.; Camp, Richard J.; Farmer, Chris; Plentovich, Sheldon M.; Banko, Paul C.
2016-01-01
Both of the 2 passerines endemic to Nihoa Island, Hawai‘i, USA—the Nihoa Millerbird (Acrocephalus familiaris kingi) and Nihoa Finch (Telespiza ultima)—are listed as endangered by federal and state agencies. Their abundances have been estimated by irregularly implemented fixed-width strip-transect sampling from 1967 to 2012, from which area-based extrapolation of the raw counts produced highly variable abundance estimates for both species. To evaluate an alternative survey method and improve abundance estimates, we conducted variable-distance point-transect sampling between 2010 and 2014. We compared our results to those obtained from strip-transect samples. In addition, we applied state-space models to derive improved estimates of population size and trends from the legacy time series of strip-transect counts. Both species were fairly evenly distributed across Nihoa and occurred in all or nearly all available habitat. Population trends for Nihoa Millerbird were inconclusive because of high within-year variance. Trends for Nihoa Finch were positive, particularly since the early 1990s. Distance-based analysis of point-transect counts produced mean estimates of abundance similar to those from strip-transects but was generally more precise. However, both survey methods produced biologically unrealistic variability between years. State-space modeling of the long-term time series of abundances obtained from strip-transect counts effectively reduced uncertainty in both within- and between-year estimates of population size, and allowed short-term changes in abundance trajectories to be smoothed into a long-term trend.
Time reversal mirror and perfect inverse filter in a microscopic model for sound propagation
Calvo, Hernán L.; Danieli, Ernesto P.; Pastawski, Horacio M.
2007-09-01
Time reversal of quantum dynamics can be achieved by a global change of the Hamiltonian sign (a hasty Loschmidt daemon), as in the Loschmidt Echo experiments in NMR, or by a local but persistent procedure (a stubborn daemon) as in the time reversal mirror (TRM) used in ultrasound acoustics. While the first is limited by chaos and disorder, the last procedure seems to benefit from it. As a first step to quantify such stability we develop a procedure, the perfect inverse filter (PIF), that accounts for memory effects, and we apply it to a system of coupled oscillators. In order to ensure a numerical many-body dynamics intrinsically reversible, we develop an algorithm, the pair partitioning, based on the Trotter strategy used for quantum dynamics. We analyze situations where the PIF gives substantial improvements over the TRM.
The Rao-Blackwellized Particle Filter: A Filter Bank Implementation
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Karlsson Rickard
2010-01-01
Full Text Available For computational efficiency, it is important to utilize model structure in particle filtering. One of the most important cases occurs when there exists a linear Gaussian substructure, which can be efficiently handled by Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF. This contribution suggests an alternative formulation of this well-known result that facilitates reuse of standard filtering components and which is also suitable for object-oriented programming. Our RBPF formulation can be seen as a Kalman filter bank with stochastic branching and pruning.
Modelling impacts of temperature, and acidifying and eutrophying deposition on DOC trends
Sawicka, Kasia; Rowe, Ed; Evans, Chris; Monteith, Don; Vanguelova, Elena; Wade, Andrew; Clark, Joanna
2017-04-01
Surface water dissolved organic carbon (DOC) concentrations in large parts of the northern hemisphere have risen over the past three decades, raising concern about enhanced contributions of carbon to the atmosphere and seas and oceans. The effect of declining acid deposition has been identified as a key control on DOC trends in soil and surface waters, since pH and ionic strength affect sorption and desorption of DOC. However, since DOC is derived mainly from recently-fixed carbon, and organic matter decomposition rates are considered sensitive to temperature, uncertainty persists regarding the extent to the relative importance of different drivers that affect these upward trends. We ran the dynamic model MADOC (Model of Acidity and Soil Organic Carbon) for a range of UK soils (podzols, gleysols and peatland), for which the time-series were available, to consider the likely relative importance of decreased deposition of sulphate and chloride, accumulation of reactive N, and higher temperatures, on DOC production in different soils. Modelled patterns of DOC change generally agreed favourably with measurements collated over 10-20 years, but differed markedly between sites. While the acidifying effect of sulphur deposition appeared to be the predominant control on the observed soil water DOC trends in all the soils considered other than a blanket peat, the model suggested that over the long term, the effects of nitrogen deposition on N-limited soils may have been sufficient to elevate the DOC recovery trajectory significantly. The second most influential cause of rising DOC in the model simulations was N deposition in ecosystems that are N-limited and respond with stimulated plant growth. Although non-marine chloride deposition made some contribution to acidification and recovery, it was not amongst the main drivers of DOC change. Warming had almost no effect on modelled historic DOC trends, but may prove to be a significant driver of DOC in future via its influence
Directory of Open Access Journals (Sweden)
Ines Baccouche
2017-05-01
Full Text Available Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV and the state of charge (SOC is required for adaptive SOC estimation during the lithium-ion (Li-ion battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.
International Nuclear Information System (INIS)
Vanin, V.R.
1990-01-01
The multidetector systems for high resolution gamma spectroscopy are presented. The observable parameters for identifying nuclides produced simultaneously in the reaction are analysed discussing the efficiency of filter systems. (M.C.K.)
A computational model to monitor and predict trends in bacterial resistance.
Alawieh, Ali; Sabra, Zahraa; Bizri, Abdul Rahman; Davies, Christopher; White, Roger; Zaraket, Fadi A
2015-09-01
Current concern over the emergence of multidrug-resistant superbugs has renewed interest in approaches that can monitor existing trends in bacterial resistance and make predictions of future trends. Recent advances in bacterial surveillance and the development of online repositories of susceptibility tests across wide geographical areas provide an important new resource, yet there are only limited computational tools for its exploitation. Here we propose a hybrid computational model called BARDmaps for automated analysis of antibacterial susceptibility tests from surveillance records and for performing future predictions. BARDmaps was designed to include a structural computational model that can detect patterns among bacterial resistance changes as well as a behavioural computational model that can use the detected patterns to predict future changes in bacterial resistance. Data from the European Antimicrobial Resistance Surveillance Network (EARS-Net) were used to validate and apply the model. BARDmaps was compared with standard curve-fitting approaches used in epidemiological research. Here we show that BARDmaps can reliably predict future trends in bacterial resistance across Europe. BARDmaps performed better than other curve-fitting approaches for predicting future resistance levels. In addition, BARDmaps was also able to detect abrupt changes in bacterial resistance in response to outbreaks and interventions as well as to compare bacterial behaviour across countries and drugs. In conclusion, BARDmaps is a reliable tool to automatically predict and analyse changes in bacterial resistance across Europe. We anticipate that BARDmaps will become an invaluable tool both for clinical providers and governmental agencies to help combat the threat posed by antibiotic-resistant bacteria.
Numerical study of canister filters with alternatives filter cap configurations
Mohammed, A. N.; Daud, A. R.; Abdullah, K.; Seri, S. M.; Razali, M. A.; Hushim, M. F.; Khalid, A.
2017-09-01
Air filtration system and filter play an important role in getting a good quality air into turbo machinery such as gas turbine. The filtration system and filter has improved the quality of air and protect the gas turbine part from contaminants which could bring damage. During separation of contaminants from the air, pressure drop cannot be avoided but it can be minimized thus helps to reduce the intake losses of the engine [1]. This study is focused on the configuration of the filter in order to obtain the minimal pressure drop along the filter. The configuration used is the basic filter geometry provided by Salutary Avenue Manufacturing Sdn Bhd. and two modified canister filter cap which is designed based on the basic filter model. The geometries of the filter are generated by using SOLIDWORKS software and Computational Fluid Dynamics (CFD) software is used to analyse and simulates the flow through the filter. In this study, the parameters of the inlet velocity are 0.032 m/s, 0.063 m/s, 0.094 m/s and 0.126 m/s. The total pressure drop produce by basic, modified filter 1 and 2 is 292.3 Pa, 251.11 Pa and 274.7 Pa. The pressure drop reduction for the modified filter 1 is 41.19 Pa and 14.1% lower compared to basic filter and the pressure drop reduction for modified filter 2 is 17.6 Pa and 6.02% lower compared to the basic filter. The pressure drops for the basic filter are slightly different with the Salutary Avenue filter due to limited data and experiment details. CFD software are very reliable in running a simulation rather than produces the prototypes and conduct the experiment thus reducing overall time and cost in this study.
Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.
Groth, Detlef
2017-04-01
Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later
A Comparison of Filter-based Approaches for Model-based Prognostics
National Aeronautics and Space Administration — Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is...
Geospatial Modeling for Investigating Spatial Pattern and Change Trend of Temperature and Rainfall
Directory of Open Access Journals (Sweden)
Md. Abu Syed
2016-04-01
Full Text Available Bangladesh has been experiencing increased temperature and change in precipitation regime, which might adversely affect the important ecosystems in the country differentially. The river flows and groundwater recharge over space and time are determined by changes in temperature, evaporation and crucially precipitation. These again have a spatio-temporal dimension. This geospatial modeling research aimed at investigating spatial patterns and changing trends of temperature and rainfall within the geographical boundary of Bangladesh. This would facilitate better understanding the change pattern and their probable impacts on the ecosystem. The southeastern region, which is one of the most important forest ecosystem zones in the country, is experiencing early onset and withdrawal of rain but increasing trends in total rainfall except in the Monsoon season. This means that the region is experiencing a lower number of rainy days. However, total rainfall has not changed significantly. The differential between maximum and minimum showed an increasing trend. This changing pattern in average max and min temperature along with precipitation might cause a situation in which the species that are growing now may shift to suitable habitats elsewhere in the future. Consequently, the biodiversity, watersheds and fisheries, productivity of land, agriculture and food security in the region will be affected by these observed changes in climate.
Silva, Mónica A; Jonsen, Ian; Russell, Deborah J F; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F
2014-01-01
Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.
Energy Technology Data Exchange (ETDEWEB)
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory
2009-01-01
Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.
Directory of Open Access Journals (Sweden)
Devidas G. Jadhav
2014-01-01
Full Text Available The Swine Influenza Model Based Optimization (SIMBO family is a newly introduced speedy optimization technique having the adaptive features in its mechanism. In this paper, the authors modified the SIMBO to make the algorithm further quicker. As the SIMBO family is faster, it is a better option for searching the basin. Thus, it is utilized in local searches in developing the proposed memetic algorithms (MAs. The MA has a faster speed compared to SIMBO with the balance in exploration and exploitation. So, MAs have small tradeoffs in convergence velocity for comprehensively optimizing the numerical standard benchmark test bed having functions with different properties. The utilization of SIMBO in the local searching is inherently the exploitation of better characteristics of the algorithms employed for the hybridization. The developed MA is applied to eliminate the power line interference (PLI from the biomedical signal ECG with the use of adaptive filter whose weights are optimized by the MA. The inference signal required for adaptive filter is obtained using the selective reconstruction of ECG from the intrinsic mode functions (IMFs of empirical mode decomposition (EMD.
Directory of Open Access Journals (Sweden)
Niancheng Zhou
2014-08-01
Full Text Available The influence of electric vehicle charging stations on power grid harmonics is becoming increasingly significant as their presence continues to grow. This paper studies the operational principles of the charging current in the continuous and discontinuous modes for a three-phase uncontrolled rectification charger with a passive power factor correction link, which is affected by the charging power. A parameter estimation method is proposed for the equivalent circuit of the charger by using the measured characteristic AC (Alternating Current voltage and current data combined with the charging circuit constraints in the conduction process, and this method is verified using an experimental platform. The sensitivity of the current harmonics to the changes in the parameters is analyzed. An analytical harmonic model of the charging station is created by separating the chargers into groups by type. Then, the harmonic current amplification caused by the shunt active power filter is researched, and the analytical formula for the overload factor is derived to further correct the capacity of the shunt active power filter. Finally, this method is validated through a field test of a charging station.
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...
Rodhouse, Thomas J.; Ormsbee, Patricia C.; Irvine, Kathryn M.; Vierling, Lee A.; Szewczak, Joseph M.; Vierling, Kerri T.
2012-01-01
Bats face unprecedented threats from habitat loss, climate change, disease, and wind power development, and populations of many species are in decline. A better ability to quantify bat population status and trend is urgently needed in order to develop effective conservation strategies. We used a Bayesian autoregressive approach to develop dynamic distribution models for Myotis lucifugus, the little brown bat, across a large portion of northwestern USA, using a four-year detection history matrix obtained from a regional monitoring program. This widespread and abundant species has experienced precipitous local population declines in northeastern USA resulting from the novel disease white-nose syndrome, and is facing likely range-wide declines. Our models were temporally dynamic and accounted for imperfect detection. Drawing on species–energy theory, we included measures of net primary productivity (NPP) and forest cover in models, predicting that M. lucifugus occurrence probabilities would covary positively along those gradients.
Connolly, Joseph W.; Csank, Jeffrey Thomas; Chicatelli, Amy; Kilver, Jacob
2013-01-01
This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.
Directory of Open Access Journals (Sweden)
Zeyu Shi
2017-01-01
Full Text Available Active power filter (APF is the most popular device in regulating power quality issues. Currently, most literatures ignored the impact of grid impedance and assumed the load voltage is ideal, which had not described the system accurately. In addition, the controllers applied PI control; thus it is hard to improve the compensation quality. This paper establishes a precise model which consists of APF, load, and grid impedance. The Bode diagram of traditional simplified model is obviously different with complete model, which means the descriptions of the system based on the traditional simplified model are inaccurate and incomplete. And then design exact feedback linearization and quasi-sliding mode control (FBL-QSMC is based on precise model in inner current loop. The system performances in different parameters are analyzed and dynamic performance of proposed algorithm is compared with traditional PI control algorithm. At last, simulations are taken in three cases to verify the performance of proposed control algorithm. The results proved that the proposed feedback linearization and quasi-sliding mode control algorithm has fast response and robustness; the compensation performance is superior to PI control obviously, which also means the complete modeling and proposed control algorithm are correct.
Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E
2016-11-22
Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.
Nonlinear Trend and Purchasing Power Parity
luo, yinghao
2016-01-01
Abstract. After the collapse of the Bretton Woods system, the evidence on the purchasing power parity (PPP) in the long run is still a matter of debate. The difficulties of the problem are the possible nonstationarity of relative price indices and nominal exchange rates. The traditional ways to deal with nonstationarity such as unit root model and cointegration have some problems. In this paper, to deal with nonstationarity, we apply the Hodrick－Prescott (HP) trend-cycle filter in real busine...
Hurwitz, M. M.; Newman, P. A.
2010-01-01
This study examines trends in Antarctic temperature and APSC, a temperature proxy for the area of polar stratospheric clouds, in an ensemble of Goddard Earth Observing System (GEOS) chemistry-climate model (CCM) simulations of the 21st century. A selection of greenhouse gas, ozone-depleting substance, and sea surface temperature scenarios is used to test the trend sensitivity to these parameters. One scenario is used to compare temperature trends in two versions of the GEOS CCM. An extended austral winter season is examined in detail. In May, June, and July, the expected future increase in CO2-related radiative cooling drives temperature trends in the Antarctic lower stratosphere. At 50 hPa, a 1.3 K cooling is expected between 2000 and 2100. Ozone levels increase, despite this robust cooling signal and the consequent increase in APSC, suggesting the enhancement of stratospheric transport in future. In the lower stratosphere, the choice of climate change scenarios does not affect the magnitude of the early winter cooling. Midwinter temperature trends are generally small. In October, APSC trends have the same sign as the prescribed halogen trends. That is, there are negative APSC trends in "grealistic future" simulations, where halogen loading decreases in accordance with the Montreal Protocol and CO2 continues to increase. In these simulations, the speed of ozone recovery is not influenced by either the choice of sea surface temperature and greenhouse gas scenarios or by the model version.
Perspectives on Nonlinear Filtering
Law, Kody
2015-01-07
The solution to the problem of nonlinear filtering may be given either as an estimate of the signal (and ideally some measure of concentration), or as a full posterior distribution. Similarly, one may evaluate the fidelity of the filter either by its ability to track the signal or its proximity to the posterior filtering distribution. Hence, the field enjoys a lively symbiosis between probability and control theory, and there are plenty of applications which benefit from algorithmic advances, from signal processing, to econometrics, to large-scale ocean, atmosphere, and climate modeling. This talk will survey some recent theoretical results involving accurate signal tracking with noise-free (degenerate) dynamics in high-dimensions (infinite, in principle, but say d between 103 and 108 , depending on the size of your application and your computer), and high-fidelity approximations of the filtering distribution in low dimensions (say d between 1 and several 10s).
Unscented Kalman filtering for articulated human tracking
DEFF Research Database (Denmark)
Boesen Lindbo Larsen, Anders; Hauberg, Søren; Pedersen, Kim Steenstrup
2011-01-01
-of-the-art trackers utilize particle filters, our unimodal likelihood model allows us to use an unscented Kalman filter. This robust and efficient filter allows us to improve the quality of the tracker while using substantially fewer likelihood evaluations. The system is compared to one based on a particle filter...
Data from modern soil water contents probes can be used for data assimilation in soil water flow modeling, i.e. continual correction of the flow model performance based on observations. The ensemble Kalman filter appears to be an appropriate method for that. The method requires estimates of the unce...
Application of Wavelet Filters in an Evaluation of Photochemical Model Performance
Air quality model evaluation can be enhanced with time-scale specific comparisons of outputs and observations. For example, high-frequency (hours to one day) time scale information in observed ozone is not well captured by deterministic models and its incorporation into model pe...
Spaaks, J.H.; Bouten, W.
2013-01-01
In hydrological modeling, model structures are developed in an iterative cycle as more and different types of measurements become available and our understanding of the hillslope or watershed improves. However, with increasing complexity of the model, it becomes more and more difficult to detect
Gharamti, M. E.
2014-03-01
Isothermal compositional flow models require coupling transient compressible flows and advective transport systems of various chemical species in subsurface porous media. Building such numerical models is quite challenging and may be subject to many sources of uncertainties because of possible incomplete representation of some geological parameters that characterize the system\\'s processes. Advanced data assimilation methods, such as the ensemble Kalman filter (EnKF), can be used to calibrate these models by incorporating available data. In this work, we consider the problem of estimating reservoir permeability using information about phase pressure as well as the chemical properties of fluid components. We carry out state-parameter estimation experiments using joint and dual updating schemes in the context of the EnKF with a two-dimensional single-phase compositional flow model (CFM). Quantitative and statistical analyses are performed to evaluate and compare the performance of the assimilation schemes. Our results indicate that including chemical composition data significantly enhances the accuracy of the permeability estimates. In addition, composition data provide more information to estimate system states and parameters than do standard pressure data. The dual state-parameter estimation scheme provides about 10% more accurate permeability estimates on average than the joint scheme when implemented with the same ensemble members, at the cost of twice more forward model integrations. At similar computational cost, the dual approach becomes only beneficial after using large enough ensembles.
Directory of Open Access Journals (Sweden)
Frank Winde
2011-03-01
Full Text Available As the second part of a series of four, this paper reviews a number of case studies of natural uranium attenuation in peat, as well as underlying chemical mechanisms reported in literature. Based on this review, a generic, conceptual, model for peat to act as filter for dissolved uranium (U is developed for guiding subsequent field investigations. The model consists of a chemical and an hydraulic component which is derived largely from data reported in literature as well as from limited field observations. For the chemical model component 10 different processes, each controlled by factors relating to water chemistry, have been identified to govern the attenuation of U in peat via a net balance of immobilization and remobilization. For the hydraulic aspect of the filter model, five different principal modes of U polluted water coming in contact with peat are discussed, focusing on the associated peat-water contact time as a crucial parameter controlling chemical U attenuation. Moreover, links between the two model components are discussed and, based on the integrated conceptual model, possible effects of natural and anthropogenic events on U attenuation in peatlands are outlined. Guided by the model, various site-specific field and laboratory investigations are finally designed to verify how far the identified generic factors and processes are indeed applicable to the Gerhard Minnebron Peatland.
A Multiple-Model Particle Filter Fusion Algorithm for GNSS/DR Slide Error Detection and Compensation
Directory of Open Access Journals (Sweden)
Rafael Toledo-Moreo
2018-03-01
Full Text Available Continuous accurate positioning is a key element for the deployment of many advanced driver assistance systems (ADAS and autonomous vehicle navigation. To achieve the necessary performance, global navigation satellite systems (GNSS must be combined with other technologies. A common onboard sensor-set that allows keeping the cost low, features the GNSS unit, odometry, and inertial sensors, such as a gyro. Odometry and inertial sensors compensate for GNSS flaws in many situations and, in normal conditions, their errors can be easily characterized, thus making the whole solution not only more accurate but also with more integrity. However, odometers do not behave properly when friction conditions make the tires slide. If not properly considered, the positioning perception will not be sound. This article introduces a hybridization approach that takes into consideration the sliding situations by means of a multiple model particle filter (MMPF. Tests with real datasets show the goodness of the proposal.
DEFF Research Database (Denmark)
Behera, Chitta Ranjan; Santoro, Domenico; Gernaey, Krist V.
2018-01-01
In this study, we perform a systematic plant-wide assessment of the organic carbon recovery concept on wastewater treatment plants by an advanced cellulose recovery enabling technology called rotating belt filter (RBF). To this end, first, an empirical model is developed to describe organic carbon...... not increase the greenhouse gas (N2ON2O) emission. Further sensitivity analysis indicates that the impact of the organic carbon recovery concept depends on the wastewater characteristics, especially the cellulose content and its biodegradability. Overall, the organic carbon recovery technology can be used...... to provide plant specific improvements achieved by maximizing organic carbon recovery in the form of methane gas or enhancing nitrogen removal depending on the treatment plant operation objectives and priorities....
Bereznoy, A V; Saygitov, R T
Digital revolution is one of the major global trends resulting in the unprecedented scale and depth of penetration of information and communication technologies into all sectors of national economy, including healthcare. The development of this trend brought about high expectations related to the improvement of quality of medical assistance, accessibility and economic efficiency of healthcare services. However, euphoria of the first steps of digital revolution is passing now, opening doors to more realistic analysis of opportunities and conditions of realization of the true potential hidden in the digital transformation of healthcare. More balanced perception of the peculiarities of innovation processes in the sector is coming together with understanding of the serious barriers, hampering implementation of the new ideas and practices due to complicated interweaving of social, economic, ethical and psychological factors. When taking into account the industry specifics it becomes evident that digital revolution cannot be a quick turnaround but rather would pass a number of phases and is likely to last more than one decade. In this context the article focuses on the prospects of the new business models, capable of making significant changes in today’s economic landscape of the sector (including uber-medicine, retail clinics, retainer medicine, network models of medical services). The authors also provide assessment of the current situation and perspectives of digital healthcare development in Russia.
Nelson, Matthew P.; Tazik, Shawna K.; Bangalore, Arjun S.; Treado, Patrick J.; Klem, Ethan; Temple, Dorota
2017-05-01
Hyperspectral imaging (HSI) systems can provide detection and identification of a variety of targets in the presence of complex backgrounds. However, current generation sensors are typically large, costly to field, do not usually operate in real time and have limited sensitivity and specificity. Despite these shortcomings, HSI-based intelligence has proven to be a valuable tool, thus resulting in increased demand for this type of technology. By moving the next generation of HSI technology into a more adaptive configuration, and a smaller and more cost effective form factor, HSI technologies can help maintain a competitive advantage for the U.S. armed forces as well as local, state and federal law enforcement agencies. Operating near the physical limits of HSI system capability is often necessary and very challenging, but is often enabled by rigorous modeling of detection performance. Specific performance envelopes we consistently strive to improve include: operating under low signal to background conditions; at higher and higher frame rates; and under less than ideal motion control scenarios. An adaptable, low cost, low footprint, standoff sensor architecture we have been maturing includes the use of conformal liquid crystal tunable filters (LCTFs). These Conformal Filters (CFs) are electro-optically tunable, multivariate HSI spectrometers that, when combined with Dual Polarization (DP) optics, produce optimized spectral passbands on demand, which can readily be reconfigured, to discriminate targets from complex backgrounds in real-time. With DARPA support, ChemImage Sensor Systems (CISS™) in collaboration with Research Triangle Institute (RTI) International are developing a novel, real-time, adaptable, compressive sensing short-wave infrared (SWIR) hyperspectral imaging technology called the Reconfigurable Conformal Imaging Sensor (RCIS) based on DP-CF technology. RCIS will address many shortcomings of current generation systems and offer improvements in
DEFF Research Database (Denmark)
Wu, Jian; Jansson, P.E.; van der Linden, Leon
2013-01-01
Temperate forests are globally important carbon sinks and stocks. Trends in net ecosystem exchange have been observed in a Danish beech forest and this trend cannot be entirely attributed to changing climatic drivers. This study sought to clarify the mechanisms responsible for the observed trend......, using a dynamic ecosystem model (CoupModel) and model data fusion with multiple constraints and model experiments. Experiments with different validation datasets showed that a multiple constraints model data fusion approach that included the annual tree growth, the seasonal canopy development......, the latent and sensible heat fluxes and the CO2 fluxes decreased the parameter uncertainty considerably compared to using CO2 fluxes as validation data alone. The fitted model was able to simulate the observed carbon fluxes well (R2=0.8, mean error=0.1gCm−2d−1) but did not reproduce the decadal (1997...
Riisgård, H. U.; Lassen, J.; Kortegaard, M.; Møller, L. F.; Friedrichs, M.; Jensen, M. H.; Larsen, P. S.
2007-11-01
The shallow Odense Fjord (Denmark) is characterized by a large biomass of filter-feeding polychaetes ( Nereis diversicolor), clams ( Mya arenaria), cockles ( Cerastoderma glaucum), and amphipods ( Corophium volutator). The present paper summarizes studies on zoobenthic filter-feeding in Odense Fjord from the last 10 years. The general principles discovered are extracted and compared to available tools for modelling of the primary characteristics of interplay between benthic filter-feeders and hydrodynamics. Earlier works have been supplemented with data from a recent field study conducted in the shallow inner part of the fjord. Based on data from this study site, the reduction in phytoplankton for fully mixed and incompletely mixed flows has been modelled. It was found that fully mixed flow results in a potential half-life for phytoplankton of only 1.3 h, whereas for the incompletely mixed water the half-life is 2.7 times longer. The field measurements clearly demonstrate the presence of a strong interplay between filter-feeders and hydrodynamics, but although a certain grazing impact is evident from vertical chlorophyll a profiles with often strongly reduced near-bottom concentrations it is not straightforward to identify and model even the main bio-physical processes that prevent the dense populations of filter-feeders to completely control the phytoplankton biomass in Odense Fjord.
TR146 cells grown on filters as a model of human buccal epithelium
DEFF Research Database (Denmark)
Mørck Nielsen, H; Rømer Rassing, M; Nielsen, Hanne Mørck
2000-01-01
The objective of the present study was to characterise the TR146 cell culture model as an in vitro model of human buccal mucosa with respect to the enzyme activity in the tissues. For this purpose, the contents of aminopeptidase, carboxypeptidase and esterase in homogenate supernatants of the TR146...... cell culture model, and human and porcine buccal epithelium were compared. The esterase activity in the intact cell culture model and in the porcine buccal mucosa was compared. Further, the TR146 cell culture model was used to study the permeability rate and metabolism of leu-enkephalin. The activity...... (3.73+/-0.53 nmol/min per mg protein), whereas the level of esterase activity was significantly higher (223.39+/-69.82 nmol/min per mg protein). In the TR146 cell culture model, the apical esterase activity was found significantly higher than the basal activity, and found comparable to the porcine...
Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter
Zeng, N; Wang, Z; Zhang, H
2016-01-01
This paper is concerned with the problem of learning structure of the lateral flow immunoassay (LFIA) devices via short but available time series of the experiment measurement. The model for the LFIA is considered as a nonlinear state-space model that includes equations describing both the biochemical reaction process of LFIA system and the observation output. Especially, the time-delays occurring among the biochemical reactions are considered in the established model. Furthermore, we utilize...
Couto, Luis D.; Kinnaert, Michel
2017-01-01
Accurate state estimation of large-scale lithium-ion battery packs is necessary for the advanced control of batteries, which could potentially increase their lifetime through e.g. reconfiguration. To tackle this problem, an enhanced reduced-order electrochemical model is used here. This model allows considering a wider operating range and thermal coupling between cells, the latter turning out to be significant. The resulting nonlinear model is exploited for state estimation through unscented ...
Directory of Open Access Journals (Sweden)
Martin KERNAN
2004-02-01
Full Text Available The dynamic model MAGIC is used to predict the future response of surface waters to reductions in S deposition as stipulated by the recently agreed emission protocol (the 1999 Gothenburg Protocol. MAGIC was calibrated to 30 sites in the Scottish mountains with the best available soil and deposition data derived from large scale spatial datasets, and surface water chemistry from a regional loch survey conducted in October 2000. A comparison of input parameters and model responses are made at Lochnagar, a site for which detailed, high resolution spatial/temporal data exist. The model is capable of reproducing observed trends in non-marine SO4 2-, however simulated NO3 - from 1990 to 2000 is lower than the observed trends at Lochnagar due to possible hydrological controls and in-lake processes, rather than terrestrial processes. The Scottish Highlands are remote from emission sources and consequently peak deposition inputs of S in the 1980s are relatively low (33 kg S ha-1 y-1 compared to other regions in Europe. Nonetheless the amount of deposition appears sufficient to cause environmental damage in this acid sensitive region. During the 1980s, simulated Acid Neutralising Capacity (ANC of 13% of the modelled lakes was <20 μeq l-1, a chemical condition that potentially can cause damage to freshwater ecology. Regional and site simulations captured the recovery to 2000 in response to the existing emission reductions. Predictions to 2016 indicates the potential for biological recovery and a return to 'good status' as required by the EU Water Framework Directive, although the hydrochemistry of some sites remain some way from simulated pre-acidification conditions.
Liu, Min; He, Yue; Qian, Weili; Wei, Yangliu; Liu, Xiaoyan
2017-10-06
Developing algorithms for plant cell population tracking is very critical for the modeling of plant cell growth pattern and gene expression dynamics. The tracking of plant cells in microscopic image stacks is very challenging for several reasons: (1) plant cells are densely packed in a specific honeycomb structure; (2) they are frequently dividing; (3) they are imaged in different layers within 3D image stacks. Based on an existing 2D local graph matching algorithm, this paper focuses on building a 3D plant cell matching model, by exploiting the cells' 3D spatiotemporal context. Furthermore, the Interacting Multi-Model filter (IMM) is combined with the 3D local graph matching model to track the plant cell population simultaneously. Because our tracking algorithm does not require the identification of "tracking seeds", the tracking stability and efficiency are greatly enhanced. Last, the plant cell lineages are achieved by associating the cell tracklets, using a maximum-a-posteriori (MAP) method. Compared with the 2D matching method, the experimental results on multiple datasets show that our proposed approach does not only greatly improve the tracking accuracy by 18%, but also successfully tracks the plant cells located at the high curvature primordial region, which is not addressed in previous work.
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MEHDI AMIAN
2013-10-01
Full Text Available Functional near infrared spectroscopy (fNIRS is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2 and deoxyhemoglobin (HHb concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR is about 2 dB higher for ARMA based method.
Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing
2012-01-01
COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load. PMID:23012564
Directory of Open Access Journals (Sweden)
Bing Luo
2012-07-01
Full Text Available COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System. Since the ultra-tight GPS/INS (Inertial Navigation System integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF, and INS’s accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load.
Directory of Open Access Journals (Sweden)
J. H. Spaaks
2013-09-01
Full Text Available In hydrological modeling, model structures are developed in an iterative cycle as more and different types of measurements become available and our understanding of the hillslope or watershed improves. However, with increasing complexity of the model, it becomes more and more difficult to detect which parts of the model are deficient, or which processes should also be incorporated into the model during the next development step. In this study, we first compare two methods (the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA and the Simultaneous parameter Optimization and Data Assimilation algorithm (SODA to calibrate a purposely deficient 3-D hillslope-scale model to error-free, artificially generated measurements. We use a multi-objective approach based on distributed pressure head at the soil–bedrock interface and hillslope-scale discharge and water balance. For these idealized circumstances, SODA's usefulness as a diagnostic methodology is demonstrated by its ability to identify the timing and location of processes that are missing in the model. We show that SODA's state updates provide information that could readily be incorporated into an improved model structure, and that this type of information cannot be gained from parameter estimation methods such as SCEM-UA. We then expand on the SODA result by performing yet another calibration, in which we investigate whether SODA's state updating patterns are still capable of providing insight into model structure deficiencies when there are fewer measurements, which are moreover subject to measurement noise. We conclude that SODA can help guide the discussion between experimentalists and modelers by providing accurate and detailed information on how to improve spatially distributed hydrologic models.
Filtering and identification of stochastic volatility for parabolic type factor models
Aihara, ShinIchi; Bagchi, Arunabha
2006-01-01
We consider the dynamics of forward rate process which is modeled by a parabolic type infinite-dimensional factor model with stochastic volatility. The parameters included in the stochastic volatility dynamics are estimated from the factor process as the observation data. Based on the maximum
An Object-Oriented Model for Extensible Concurrent Systems: the Composition-Filters Approach
Bergmans, Lodewijk; Aksit, Mehmet; Wakita, K.; Wakita, Ken; Yonezawa, Akinori
1992-01-01
Applying the object-oriented paradigm for the development of large and complex software systems offers several advantages, of which increased extensibility and reusability are the most prominent ones. The object-oriented model is also quite suitable for modeling concurrent systems. However, it
Azizian, Morvarid; Grant, Stanley B; Kessler, Adam J; Cook, Perran L M; Rippy, Megan A; Stewardson, Michael J
2015-09-15
Bedforms are a focal point of carbon and nitrogen cycling in streams and coastal marine ecosystems. In this paper, we develop and test a mechanistic model, the "pumping and streamline segregation" or PASS model, for nitrate removal in bedforms. The PASS model dramatically reduces computational overhead associated with modeling nitrogen transformations in bedforms and reproduces (within a factor of 2 or better) previously published measurements and models of biogeochemical reaction rates, benthic fluxes, and in-sediment nutrient and oxygen concentrations. Application of the PASS model to a diverse set of marine and freshwater environments indicates that (1) physical controls on nitrate removal in a bedform include the pore water flushing rate, residence time distribution, and relative rates of respiration and transport (as represented by the Damkohler number); (2) the biogeochemical pathway for nitrate removal is an environment-specific combination of direct denitrification of stream nitrate and coupled nitrification-denitrification of stream and/or sediment ammonium; and (3) permeable sediments are almost always a net source of dissolved inorganic nitrogen. The PASS model also provides a mechanistic explanation for previously published empirical correlations showing denitrification velocity (N2 flux divided by nitrate concentration) declines as a power law of nitrate concentration in a stream (Mulholland et al. Nature, 2008, 452, 202-205).
Mathematical modeling of a biogenous filter cake and identification of oilseed material parameters
Directory of Open Access Journals (Sweden)
Očenášek J.
2009-12-01
Full Text Available Mathematical modeling of the filtration and extrusion process inside a linear compression chamber has gained a lot of attention during several past decades. This subject was originally related to mechanical and hydraulic properties of soils (in particular work of Terzaghi and later was this approach adopted for the modeling of various technological processes in the chemical industry (work of Shirato. Developed mathematical models of continuum mechanics of porous materials with interstitial fluid were then applied also to the problem of an oilseed expression. In this case, various simplifications and partial linearizations are introduced in models for the reason of an analytical or numerical solubility; or it is not possible to generalize the model formulation into the fully 3D problem of an oil expression extrusion with a complex geometry such as it has a screw press extruder.We proposed a modified model for the oil seeds expression process in a linear compression chamber. The model accounts for the rheological properties of the deformable solid matrix of compressed seed, where the permeability of the porous solid is described by the Darcy's law. A methodology of the experimental work necessary for a material parameters identification is presented together with numerical simulation examples.
Directory of Open Access Journals (Sweden)
Cheng Gong
2014-01-01
Full Text Available This paper investigates the H∞ filtering problem of discrete singular Markov jump systems (SMJSs with mode-dependent time delay based on T-S fuzzy model. First, by Lyapunov-Krasovskii functional approach, a delay-dependent sufficient condition on H∞-disturbance attenuation is presented, in which both stability and prescribed H∞ performance are required to be achieved for the filtering-error systems. Then, based on the condition, the delay-dependent H∞ filter design scheme for SMJSs with mode-dependent time delay based on T-S fuzzy model is developed in term of linear matrix inequality (LMI. Finally, an example is given to illustrate the effectiveness of the result.
National Aeronautics and Space Administration — This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman...
Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework
National Aeronautics and Space Administration — This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the...
Interaction and aggregated modeling of multiple paralleled inverters with LCL filter
DEFF Research Database (Denmark)
Lu, Minghui; Wang, Xiongfei; Loh, Poh Chiang
2015-01-01
This paper discusses the dynamic interaction of multi-paralleled inverters within a weak grid. Interactive current and common current models are proposed to explain the interaction among these inverters, which are studied with both open loop and closed loop analysis. An aggregated model is propos...... to describe the totality of multi-inverters. Additionally, system stability is explicitly studied and classified as interactively and commonly stable. The study is validated by simulations and experiments....
Menlyadiev, Marlen R; Tadjimukhamedov, Fatkhulla Kh; Tarassov, Alexander; Wollnik, Hermann; Eiceman, Gary A
2014-01-15
Mixtures of ions produced in sources at atmospheric pressure, including chemical ionization (APCI) and electrospray ionization (ESI) can be simplified at or near ambient pressure using ion mobility based filters. A low-mobility-pass filter (LMPF) based on a simple mechanical design and simple electronic control was designed, modeled and tested with vapors of 2-hexadecanone in an APCI source and with spray of peptide solutions in an ESI source. The LMPF geometry was planar and small (4 mm wide × 13 mm long) and electric control was through a symmetric waveform in low kHz with amplitude between 0 and 10 V. Computational models established idealized performance for transmission efficiency of ions of several reduced mobility coefficients over the range of amplitudes and were matched by computed values from ion abundances in mass spectra. The filter exhibited a broad response function, equivalent to a Bode Plot in electronic filters, suggesting that ion filtering could be done in blocks ~50 m/z units wide. The benefit of this concept is that discrimination against ions of high mobility is controlled by only a single parameter: waveform amplitude at fixed frequency. The effective removal of high mobility ions, those of low mass-to-charge, can be beneficial for applications with ion-trap-based mass spectrometers to remove excessive levels of solvent or matrix ions. Copyright © 2013 John Wiley & Sons, Ltd.
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J. I. Allen
2003-01-01
Full Text Available The purpose of this paper is to examine the use of a complex ecosystem model along with near real-time in situ data and a sequential data assimilation method for state estimation. The ecosystem model used is the European Regional Seas Ecosystem Model (ERSEM; Baretta et al., 1995 and the assimilation method chosen is the Ensemble Kalman Filer (EnKF. Previously, it has been shown that this method captures the nonlinear error evolution in time and is capable of both tracking the observations and providing realistic error estimates for the estimated state. This system has been used to assimilate long time series of in situ chlorophyll taken from a data buoy in the Cretan Sea. The assimilation of this data using the EnKF method results in a marked improvement in the ability of ERSEM to hindcast chlorophyll. The sensitivity of this system to the type of data used for assimilation, the frequency of assimilation, ensemble size and model errors is discussed. The predictability window of the EnKF appears to be at least 2 days. This is an indication that the methodology might be suitable for future operational data assimilation systems using more complex three-dimensional models. Key words. Oceanography: general (numerical modelling; ocean prediction – Oceanography: biological and chemical (plankton
Directory of Open Access Journals (Sweden)
J. I. Allen
Full Text Available The purpose of this paper is to examine the use of a complex ecosystem model along with near real-time in situ data and a sequential data assimilation method for state estimation. The ecosystem model used is the European Regional Seas Ecosystem Model (ERSEM; Baretta et al., 1995 and the assimilation method chosen is the Ensemble Kalman Filer (EnKF. Previously, it has been shown that this method captures the nonlinear error evolution in time and is capable of both tracking the observations and providing realistic error estimates for the estimated state. This system has been used to assimilate long time series of in situ chlorophyll taken from a data buoy in the Cretan Sea. The assimilation of this data using the EnKF method results in a marked improvement in the ability of ERSEM to hindcast chlorophyll. The sensitivity of this system to the type of data used for assimilation, the frequency of assimilation, ensemble size and model errors is discussed. The predictability window of the EnKF appears to be at least 2 days. This is an indication that the methodology might be suitable for future operational data assimilation systems using more complex three-dimensional models.
Key words. Oceanography: general (numerical modelling; ocean prediction – Oceanography: biological and chemical (plankton
Leroux, Romain; Chatellier, Ludovic; David, Laurent
2018-01-01
This article is devoted to the estimation of time-resolved particle image velocimetry (TR-PIV) flow fields using a time-resolved point measurements of a voltage signal obtained by hot-film anemometry. A multiple linear regression model is first defined to map the TR-PIV flow fields onto the voltage signal. Due to the high temporal resolution of the signal acquired by the hot-film sensor, the estimates of the TR-PIV flow fields are obtained with a multiple linear regression method called orthonormalized partial least squares regression (OPLSR). Subsequently, this model is incorporated as the observation equation in an ensemble Kalman filter (EnKF) applied on a proper orthogonal decomposition reduced-order model to stabilize it while reducing the effects of the hot-film sensor noise. This method is assessed for the reconstruction of the flow around a NACA0012 airfoil at a Reynolds number of 1000 and an angle of attack of {20}°. Comparisons with multi-time delay-modified linear stochastic estimation show that both the OPLSR and EnKF combined with OPLSR are more accurate as they produce a much lower relative estimation error, and provide a faithful reconstruction of the time evolution of the velocity flow fields.
Keppenne, Christian L.; Rienecker, Michele M.; Koblinsky, Chester (Technical Monitor)
2001-01-01
A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been implemented for the Poseidon ocean circulation model and tested with a Pacific Basin model configuration. There are about two million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF configuration are discussed. It is shown that the regionalization of the background covariances; has a negligible impact on the quality of the analyses. The parallel algorithm is very efficient for large numbers of observations but does not scale well beyond 100 PEs at the current model resolution. On a platform with distributed memory, memory rather than speed is the limiting factor.
Keppenne, Christian L.; Rienecker, Michele; Borovikov, Anna Y.; Suarez, Max
1999-01-01
A massively parallel ensemble Kalman filter (EnKF)is used to assimilate temperature data from the TOGA/TAO array and altimetry from TOPEX/POSEIDON into a Pacific basin version of the NASA Seasonal to Interannual Prediction Project (NSIPP)ls quasi-isopycnal ocean general circulation model. The EnKF is an approximate Kalman filter in which the error-covariance propagation step is modeled by the integration of multiple instances of a numerical model. An estimate of the true error covariances is then inferred from the distribution of the ensemble of model state vectors. This inplementation of the filter takes advantage of the inherent parallelism in the EnKF algorithm by running all the model instances concurrently. The Kalman filter update step also occurs in parallel by having each processor process the observations that occur in the region of physical space for which it is responsible. The massively parallel data assimilation system is validated by withholding some of the data and then quantifying the extent to which the withheld information can be inferred from the assimilation of the remaining data. The distributions of the forecast and analysis error covariances predicted by the ENKF are also examined.
Momentum Strategies with L1 Filter
Dao, Tung-Lam
2014-01-01
In this article, we discuss various implementation of L1 filtering in order to detect some properties of noisy signals. This filter consists of using a L1 penalty condition in order to obtain the filtered signal composed by a set of straight trends or steps. This penalty condition, which determines the number of breaks, is implemented in a constrained least square problem and is represented by a regularization parameter ? which is estimated by a cross-validation procedure. Financial time seri...
Random matrix theory filters and currency portfolio optimisation
Daly, J.; Crane, M.; Ruskin, H. J.
2010-04-01
Random matrix theory (RMT) filters have recently been shown to improve the optimisation of financial portfolios. This paper studies the effect of three RMT filters on realised portfolio risk, using bootstrap analysis and out-of-sample testing. We considered the case of a foreign exchange and commodity portfolio, weighted towards foreign exchange, and consisting of 39 assets. This was intended to test the limits of RMT filtering, which is more obviously applicable to portfolios with larger numbers of assets. We considered both equally and exponentially weighted covariance matrices, and observed that, despite the small number of assets involved, RMT filters reduced risk in a way that was consistent with a much larger S&P 500 portfolio. The exponential weightings indicated showed good consistency with the value suggested by Riskmetrics, in contrast to previous results involving stocks. This decay factor, along with the low number of past moves preferred in the filtered, equally weighted case, displayed a trend towards models which were reactive to recent market changes. On testing portfolios with fewer assets, RMT filtering provided less or no overall risk reduction. In particular, no long term out-of-sample risk reduction was observed for a portfolio consisting of 15 major currencies and commodities.
Fisher, R.; Hoffmann, W. A.; Muszala, S.
2014-12-01
The introduction of second-generation dynamic vegetation models - which simulate the distribution of light resources between plant types along the vertical canopy profile, and therefore facilitate the representation of plant competition explicitly - is a large increase in the complexity and fidelity with which the terrestrial biosphere is abstracted into Earth System Models. In this new class of model, biome boundaries are predicted as the emergent properties of plant physiology, and are therefore sensitive to the high-dimensional parameterizations of plant functional traits. These new approaches offer the facility to quantitatively test ecophysiological hypotheses of plant distribution at large scales, a field which remains surprisingly under-developed. Here we describe experiments conducted with the Community Land Model Ecosystem Demography component, CLM(ED), in which we reduce the complexity of the problem by testing how individual plant functional trait changes to control the location of biome boundaries between functional types. Specifically, we investigate which physiological trade-offs determine the boundary between frequently burned savanna and forest biomes, and attempt to distinguish how each strategic life-history trade-off (carbon storage, bark investment, re-sprouting strategy) contributes towards the maintenance of sharp geographical gradients between fire adapted and typically inflammable closed canopy ecosystems. This study forms part of the planning for a model-inspired fire manipulation experiment at the cerrado-forest boundary in South-Eastern Brazil, and the results will be used to guide future data-collection and analysis strategies.
Jongeward, A.; Li, Z.
2017-12-01
Aerosols from natural and anthropogenic sources can influence atmospheric variability and alter Earth's radiative balance through direct and indirect processes. Recently, policies targeting anthropogenic species (e.g. the Clean Air Act) have seen success in improving air quality. The anthropogenic contributions to the total aerosol loading and its spatiotemporal pattern/trend are anticipated to be altered. In this work the aerosol loading and trend over the North Atlantic Ocean since 2002 are examined, a period of significant change due to anthropogenic emissions control measures within the U.S. Monthly mean data from satellite (MODIS), ground (AERONET, IMPROVE), and model (GOCART, MERRA) sources are employed. Two annual trends in aerosol optical depth (AOD) observed by MODIS are present: a -0.020 decade-1 trend in the mid-latitudes and a 0.015 decade-1 trend in the sub-tropics. Trends in GOCART species AOD reveal anthropogenic (natural) species as the likely driver of the mid-latitude (sub-tropical) trend. AERONET AOD trends confirm negative AOD trends at three upwind sites in the Eastern U.S. and IMPROVE particulate matter (PM) observations identifies the role of decreasing ammonium sulfate in the overall PM decrease. Meanwhile, an increasing AOD trend seen during summertime in the eastern sub-tropics is associated with dust aerosol from North Africa. A dust parameterization from Kaufman et al. (2005) allows for changes in the flux transport across the sub-tropics to be calculated and analyzed. Using MERRA reanalysis fields, it is hypothesized that amplified warming and increases in baroclinic instability over the Saharan desert may lead to increased dust mobilization and export from North Africa to the sub-tropical Atlantic. This study provides updated analysis through 2016.
TR146 cells grown on filters as a model of human buccal epithelium
DEFF Research Database (Denmark)
Nielsen, H M; Rassing, M R; Nielsen, Hanne Mørck
2000-01-01
and porcine buccal mucosa. Further, the permeability rates of ten beta-adrenoceptor antagonists (acebutolol, alprenolol, atenolol, labetalol, metoprolol, oxprenolol, pindolol, propranolol, timolol and tertatolol) across the TR146 cell culture model and porcine buccal mucosa were related to their lipophilicity...... x 10(-6) cm/s (metoprolol). For propranolol the cellular permeability value (P(c)) was lower than expected, probably due to accumulation in the TR146 cell layers. Limited correlation of permeability with k' was observed both for the TR146 cell culture model and the porcine buccal mucosa, although...
DEFF Research Database (Denmark)
Baadsgaard, Mikkel; Nielsen, Jan Nygaard; Madsen, Henrik
2000-01-01
An econometric analysis of continuous-timemodels of the term structure of interest rates is presented. A panel of coupon bond prices with different maturities is used to estimate the embedded parameters of a continuous-discrete state space model of unobserved state variables: the spot interest rate......, the central tendency and stochastic volatility. Emphasis is placed on the particular class of exponential-affine term structure models that permits solving the bond pricing PDE in terms of a system of ODEs. It is assumed that coupon bond prices are contaminated by additive white noise, where the stochastic...... and empirical results based on the Danish bond market are presented....
Directory of Open Access Journals (Sweden)
Gabere MN
2016-06-01
Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1
Ballabrera-Poy, Joaquim; Busalacchi, Antonio J.; Murtugudde, Ragu
2000-01-01
A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N. In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions I up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.
Ismail, A.; Hassan, Noor I.
2013-09-01
Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.
Moonen, Dominicus Johannes Guilielmus; Buesink, Frederik Johannes Karel; Leferink, Frank Bernardus Johannes
2016-01-01
To reduce common mode(CM) and differential mode(DM) interference, a DM/CM integrated filter is often required to reduce the level of interference. This paper will show coupling from a Common Mode Choke(CMC) into Cx capacitors can decrease the DM-filtering performance significantly at high
Economic trends of tokamak power plants independent of physics scaling models
International Nuclear Information System (INIS)
Reid, R.L.; Steiner, D.
1978-01-01
This study examines the effects of plasma radius, field on axis, plasma impurity level, and aspect ratio on power level and unit capital cost, $/kW/sub e/, of tokamak power plants sized independent of plasma physics scaling models. It is noted that tokamaks sized in this manner are thermally unstable based on trapped particle scaling relationships. It is observed that there is an economic advantage for larger power level tokamaks achieved by physics independent sizing; however, the incentive for increased power levels is less than that for fission reactors. It is further observed that the economic advantage of these larger power level tokamaks is decreased when plasma thermal stability measures are incorporated, such as by increasing the plasma impurity concentration. This trend of economy with size obtained by physics independent sizing is opposite to that observed when the tokamak designs are constrained to obey the trapped particle and empirical scaling relationships
Modeling phosphorus removal in wet ponds with filter zones containing sand or crushed concrete
DEFF Research Database (Denmark)
Sønderup, Melanie J.; Egemose, Sara; Hoffmann, Carl Christian
2014-01-01
demonstrated that crushed concrete has high affinity for dissolved phosphorus (TDP), and potentially could be an effective new measure to reduce discharge of phosphorus (P) to downstream located P-limited lakes and estuaries. To verify this potential we have developed a dynamic model for a combined...
Filtering and smoothing of stae vector for diffuse state space models
Koopman, S.J.; Durbin, J.
2003-01-01
This paper presents exact recursions for calculating the mean and mean square error matrix of the state vector given the observations for the multi-variate linear Gaussian state-space model in the case where the initial state vector is (partially) diffuse.
Impact of brain tissue filtering on neurostimulation fields: a modeling study
Wagner, Tim; Eden, Uri; Rushmore, Jarrett; Russo, Christopher J.; Dipietro, Laura; Fregni, Felipe; Simon, Stephen; Rotman, Stephen; Pitskel, Naomi B.; Ramos-Estebanez, Ciro; Pascual-Leone, Alvaro; Grodzinsky, Alan J.; Zahn, Markus; Valero-Cabre, Antoni
2013-01-01
Electrical neurostimulation techniques, such as deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS), are increasingly used in the neurosciences, e.g., for studying brain function, and for neurotherapeutics, e.g., for treating depression, epilepsy, and Parkinson’s disease. The characterization of electrical properties of brain tissue has guided our fundamental understanding and application of these methods, from electrophysiologic theory to clinical dosing-metrics. Nonetheless, prior computational models have primarily relied on ex-vivo impedance measurements. We recorded the in-vivo impedances of brain tissues during neurosurgical procedures and used these results to construct MRI guided computational models of TMS and DBS neurostimulatory fields and conductance-based models of neurons exposed to stimulation. We demonstrated that tissues carry neurostimulation currents through frequency dependent resistive and capacitive properties not typically accounted for by past neurostimulation modeling work. We show that these fundamental brain tissue properties can have significant effects on the neurostimulatory-fields (capacitive and resistive current composition and spatial/temporal dynamics) and neural responses (stimulation threshold, ionic currents, and membrane dynamics). These findings highlight the importance of tissue impedance properties on neurostimulation and impact our understanding of the biological mechanisms and technological potential of neurostimulatory methods. PMID:23850466
Bove, Patricia; Claveau-Mallet, Dominique; Boutet, Étienne; Lida, Félix; Comeau, Yves
2018-02-01
The main objective of this project was to develop a steel slag filter effluent neutralization process by acidification with CO 2 -enriched air coming from a bioprocess. Sub-objectives were to evaluate the neutralization capacity of different configurations of neutralization units in lab-scale conditions and to propose a design model of steel slag effluent neutralization. Two lab-scale column neutralization units fed with two different types of influent were operated at hydraulic retention time of 10 h. Tested variables were mode of flow (saturated or percolating), type of media (none, gravel, Bionest and AnoxKaldnes K3), type of air (ambient or CO 2 -enriched) and airflow rate. One neutralization field test (saturated and no media, 2000-5000 ppm CO 2 , sequential feeding, hydraulic retention time of 7.8 h) was conducted for 7 days. Lab-scale and field-scale tests resulted in effluent pH of 7.5-9.5 when the aeration rate was sufficiently high. A model was implemented in the PHREEQC software and was based on the carbonate system, CO 2 transfer and calcite precipitation; and was calibrated on ambient air lab tests. The model was validated with CO 2 -enriched air lab and field tests, providing satisfactory validation results over a wide range of CO 2 concentrations. The flow mode had a major impact on CO 2 transfer and hydraulic efficiency, while the type of media had little influence. The flow mode also had a major impact on the calcite surface concentration in the reactor: it was constant in saturated mode and was increasing in percolating mode. Predictions could be made for different steel slag effluent pH and different operation conditions (hydraulic retention time, CO 2 concentration, media and mode of flow). The pH of the steel slag filter effluent and the CO 2 concentration of the enriched air were factors that influenced most the effluent pH of the neutralization process. An increased concentration in CO 2 in the enriched air reduced calcite precipitation
DEFF Research Database (Denmark)
Lee, Carson; Albrechtsen, Hans-Jørgen; Smets, Barth F.
activated carbon and are often used following ozonation to remove additional biodegradable organics created during ozonation. In Europe, biological filters are also used to remove ammonium and reduced forms of iron and manganese. These compounds can cause biological instability in the distribution system...... for chlorine addition following treatment. Under the normal conditions found in many water treatment plants, reduced iron can be oxidized through aeration and the precipitates can be captured by the filter media. Ammonium and manganese can be removed biologically. This research uses both pilot and full scale...... studies to determine how operating conditions affect the performance of the filters. Substrate concentrations, particle/precipitate accumulation, and biomass kinetics are monitored throughout the depth of the filter and over the operational cycle of the filter. Tracer tests, using a conservative salt...
Lin, Chih-Hsien Michelle; Lyubchich, Vyacheslav; Glibert, Patricia M
2018-03-01
The harmful dinoflagellate, Karlodnium veneficum, has been implicated in fish-kill and other toxic, harmful algal bloom (HAB) events in waters worldwide. Blooms of K. veneficum are known to be related to coastal nutrient enrichment but the relationship is complex because this HAB taxon relies not only on dissolved nutrients but also particulate prey, both of which have also changed over time. Here, applying cross-correlations of climate-related physical factors, nutrients and prey, with abundance of K. veneficum over a 10-year (2002-2011) period, a synthesis of the interactive effects of multiple factors on this species was developed for Chesapeake Bay, where blooms of the HAB have been increasing. Significant upward trends in the time series of K. veneficum were observed in the mesohaline stations of the Bay, but not in oligohaline tributary stations. For the mesohaline regions, riverine sources of nutrients with seasonal lags, together with particulate prey with zero lag, explained 15%-46% of the variation in the K. veneficum time series. For the oligohaline regions, nutrients and particulate prey generally showed significant decreasing trends with time, likely a reflection of nutrient reduction efforts. A conceptual model of mid-Bay blooms is presented, in which K. veneficum, derived from the oceanic end member of the Bay, may experience enhanced growth if it encounters prey originating from the tributaries with different patterns of nutrient loading and which are enriched in nitrogen. For all correlation models developed herein, prey abundance was a primary factor in predicting K. veneficum abundance. Copyright © 2018 Elsevier B.V. All rights reserved.
Modeling the status, trends, and impacts of wild bee abundance in the United States
Koh, Insu; Lonsdorf, Eric V.; Williams, Neal M.; Brittain, Claire; Isaacs, Rufus; Gibbs, Jason; Ricketts, Taylor H.
2016-01-01
Wild bees are highly valuable pollinators. Along with managed honey bees, they provide a critical ecosystem service by ensuring stable pollination to agriculture and wild plant communities. Increasing concern about the welfare of both wild and managed pollinators, however, has prompted recent calls for national evaluation and action. Here, for the first time to our knowledge, we assess the status and trends of wild bees and their potential impacts on pollination services across the coterminous United States. We use a spatial habitat model, national land-cover data, and carefully quantified expert knowledge to estimate wild bee abundance and associated uncertainty. Between 2008 and 2013, modeled bee abundance declined across 23% of US land area. This decline was generally associated with conversion of natural habitats to row crops. We identify 139 counties where low bee abundances correspond to large areas of pollinator-dependent crops. These areas of mismatch between supply (wild bee abundance) and demand (cultivated area) for pollination comprise 39% of the pollinator-dependent crop area in the United States. Further, we find that the crops most highly dependent on pollinators tend to experience more severe mismatches between declining supply and increasing demand. These trends, should they continue, may increase costs for US farmers and may even destabilize crop production over time. National assessments such as this can help focus both scientific and political efforts to understand and sustain wild bees. As new information becomes available, repeated assessments can update findings, revise priorities, and track progress toward sustainable management of our nation’s pollinators. PMID:26699460
Particle Filtering for Model-Based Anomaly Detection in Sensor Networks
Solano, Wanda; Banerjee, Bikramjit; Kraemer, Landon
2012-01-01
A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The
2015-01-01
state estimation and forecast in real applica- tions using general circulation models (GCMs). In addition, other spatial multiscale variational analysis...Journal of Geophysical Research C: Oceans, vol. 102, no. 3, pp. 5655–5667, 1997. [15] P. C. Chu, W. Guihua, and Y. Chen, “Japan Sea thermohaline ...structure and circulation . Part III: autocorrelation functions,” Journal of Physical Oceanography, vol. 32, no. 12, pp. 3596–3615, 2002. [16] K.-A. Park and J
Unit Root Properties of Seasonal Adjustment and Related Filters: Special Cases
Directory of Open Access Journals (Sweden)
Bell William.R.
2017-03-01
Full Text Available Bell (2012 catalogued unit root factors contained in linear filters used in seasonal adjustment (model-based or from the X-11 method but noted that, for model-based seasonal adjustment, special cases could arise where filters could contain more unit root factors than was indicated by the general results. This article reviews some special cases that occur with canonical ARIMA model based adjustment in which, with some commonly used ARIMA models, the symmetric seasonal filters contain two extra nonseasonal differences (i.e., they include an extra (1 - B(1 - F. This increases by two the degree of polynomials in time that are annihilated by the seasonal filter and reproduced by the seasonal adjustment filter. Other results for canonical ARIMA adjustment that are reported in Bell (2012, including properties of the trend and irregular filters, and properties of the asymmetric and finite filters, are unaltered in these special cases. Special cases for seasonal adjustment with structural ARIMA component models are also briefly discussed.
Directory of Open Access Journals (Sweden)
V. M. Khade
2013-03-01
Full Text Available The ensemble adjustment Kalman filter (EAKF is used to estimate the erodibility fraction parameter field in a coupled meteorology and dust aerosol model (Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS over the Sahara desert. Erodibility is often employed as the key parameter to map dust source. It is used along with surface winds (or surface wind stress to calculate dust emissions. Using the Saharan desert as a test bed, a perfect model Observation System Simulation Experiments (OSSEs with 40 ensemble members, and observations of aerosol optical depth (AOD, the EAKF is shown to recover correct values of erodibility at about 80% of the points in the domain. It is found that dust advected from upstream grid points acts as noise and complicates erodibility estimation. It is also found that the rate of convergence is significantly impacted by the structure of the initial distribution of erodibility estimates; isotropic initial distributions exhibit slow convergence, while initial distributions with geographically localized structure converge more quickly. Experiments using observations of Deep Blue AOD retrievals from the MODIS satellite sensor result in erodibility estimates that are considerably lower than the values used operationally. Verification shows that the use of the tuned erodibility field results in better predictions of AOD over the west Sahara and the Arabian Peninsula.
Directory of Open Access Journals (Sweden)
J. Liu
2008-08-01
Full Text Available This paper compares the performance of the Local Ensemble Transform Kalman Filter (LETKF with the Physical-Space Statistical Analysis System (PSAS under a perfect model scenario. PSAS is a 3D-Var assimilation system used operationally in the Goddard Earth Observing System Data Assimilation System (GEOS-4 DAS. The comparison is carried out using simulated winds and geopotential height observations and the finite volume Global Circulation Model with 72 grid points zonally, 46 grid points meridionally and 55 vertical levels. With forty ensemble members, the LETKF obtains analyses and forecasts with significantly lower RMS errors than those from PSAS, especially over the Southern Hemisphere and oceans. This observed advantage of the LETKF over PSAS is due to the ability of the 40-member ensemble LETKF to capture flow-dependent errors and thus create a good estimate of the evolving background uncertainty. An initial decrease of the forecast errors in the Northern Hemisphere observed in the PSAS but not in the LETKF suggests that the LETKF analysis is more balanced.
Parametric modeling of energy filtering by energy barriers in thermoelectric nanocomposites
Energy Technology Data Exchange (ETDEWEB)
Zianni, Xanthippi, E-mail: xzianni@teiste.gr, E-mail: xzianni@gmail.com [Department of Aircraft Technology, Technological Educational Institution of Sterea Ellada, 34400 Psachna (Greece); Department of Microelectronics, INN, NCSR “Demokritos,” 15310 Athens (Greece); Narducci, Dario [Department of Materials Science, University of Milano Bicocca, 20125 Milano (Italy)
2015-01-21
We present a parametric modeling of the thermoelectric transport coefficients based on a model previously used to interpret experimental measurements on the conductivity, σ, and Seebeck coefficient, S, in highly Boron-doped polycrystalline Si, where a very significant thermoelectric power factor (TPF) enhancement was observed. We have derived analytical formalism for the transport coefficients in the presence of an energy barrier assuming thermionic emission over the barrier for (i) non-degenerate and (ii) degenerate one-band semiconductor. Simple generic parametric equations are found that are in agreement with the exact Boltzmann transport formalism in a wide range of parameters. Moreover, we explore the effect of energy barriers in 1-d composite semiconductors in the presence of two phases: (a) the bulk-like phase and (b) the barrier phase. It is pointed out that significant TPF enhancement can be achieved in the composite structure of two phases with different thermal conductivities. The TPF enhancement is estimated as a function of temperature, the Fermi energy position, the type of scattering, and the barrier height. The derived modeling provides guidance for experiments and device design.
1987-01-01
Biomedical Optical Company of America's suntiger lenses eliminate more than 99% of harmful light wavelengths. NASA derived lenses make scenes more vivid in color and also increase the wearer's visual acuity. Distant objects, even on hazy days, appear crisp and clear; mountains seem closer, glare is greatly reduced, clouds stand out. Daytime use protects the retina from bleaching in bright light, thus improving night vision. Filtering helps prevent a variety of eye disorders, in particular cataracts and age related macular degeneration.
Forkel, M.; Carvalhais, N.; Reichstein, M.; Thonicke, K.
2012-04-01
Satellite observations of Normalized Difference Vegetation Index (NDVI) showed increasing trends in the arctic tundra and the boreal forests since the 1980s. This greening is related to an increase in photosynthetic activity and is driven by increasing temperatures and a prolongation of the growing season. However, NDVI experienced a decrease in large regions of the boreal forests since the mid-1990s. This browning is related to fire disturbances, temperature-induced summer drought and potentially to insect infestations and diseases. Terrestrial biosphere models (TBM) can be used to assess the impacts of these changes in vegetation productivity on the carbon and water cycles and on the climate system. In general, these models provide descriptions of ecosystem processes and states that are forced by and feedback to the climate system such as photosynthesis and transpiration, ecosystem respiration, soil carbon and water stocks and vegetation composition. The evaluation of TBMs against observations is a necessary step to assess their suitability to simulate such processes and dynamics. The increasing availability of long-term observations of vegetation activity enables us to evaluate the model ability to diagnose these vegetation greening and browning trends in arctic and boreal regions. The first aim of this study is to evaluate trends in vegetation activity in high-latitude regions as simulated by TBMs against observed trends in vegetation activity. The second aim is to identify potential drivers of these observed and simulated trends to evaluate the ability of models to reproduce the observed functional relations between climatic and environmental drivers and the vegetation trends. The trends in vegetation activity were estimated for a set of satellite-based remote sensing products: NDVI from AVHRR (Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectrometer), as well as FAPAR observations (Fraction of Observed Photosynthetically
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B.-M. Sinnhuber
2009-04-01
Full Text Available Bromine compounds play an important role in the depletion of stratospheric ozone. We have calculated the changes in stratospheric ozone in response to changes in the halogen loading over the past decades, using a two-dimensional (latitude/height model constrained by source gas mixing ratios at the surface. Model calculations of the decrease of total column ozone since 1980 agree reasonably well with observed ozone trends, in particular when the contribution from very short-lived bromine compounds is included. Model calculations with bromine source gas mixing ratios fixed at 1959 levels, corresponding approximately to a situation with no anthropogenic bromine emissions, show an ozone column reduction between 1980 and 2005 at Northern Hemisphere mid-latitudes of only ≈55% compared to a model run including all halogen source gases. In this sense anthropogenic bromine emissions are responsible for ≈45% of the model estimated column ozone loss at Northern Hemisphere mid-latitudes. However, since a large fraction of the bromine induced ozone loss is due to the combined BrO/ClO catalytic cycle, the effect of bromine would have been smaller in the absence of anthropogenic chlorine emissions. The chemical efficiency of bromine relative to chlorine for global total ozone depletion from our model calculations, expressed by the so called α-factor, is 64 on an annual average. This value is much higher than previously published results. Updates in reaction rate constants can explain only part of the differences in α. The inclusion of bromine from very short-lived source gases has only a minor effect on the global mean α-factor.
Borgese, L; Salmistraro, M; Gianoncelli, A; Zacco, A; Lucchini, R; Zimmerman, N; Pisani, L; Siviero, G; Depero, L E; Bontempi, E
2012-01-30
This work is presented as an improvement of a recently introduced method for airborne particulate matter (PM) filter analysis [1]. X-ray standing wave (XSW) and total reflection X-ray fluorescence (TXRF) were performed with a new dedicated laboratory instrumentation. The main advantage of performing both XSW and TXRF, is the possibility to distinguish the nature of the sample: if it is a small droplet dry residue, a thin film like or a bulk sample. Another advantage is related to the possibility to select the angle of total reflection to make TXRF measurements. Finally, the possibility to switch the X-ray source allows to measure with more accuracy lighter and heavier elements (with a change in X-ray anode, for example from Mo to Cu). The aim of the present study is to lay the theoretical foundation of the new proposed method for airborne PM filters quantitative analysis improving the accuracy and efficiency of quantification by means of an external standard. The theoretical model presented and discussed demonstrated that airborne PM filters can be considered as thin layers. A set of reference samples is prepared in laboratory and used to obtain a calibration curve. Our results demonstrate that the proposed method for quantitative analysis of air PM filters is affordable and reliable without the necessity to digest filters to obtain quantitative chemical analysis, and that the use of XSW improve the accuracy of TXRF analysis. Copyright © 2011 Elsevier B.V. All rights reserved.
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Maryam Salari
2017-09-01
Full Text Available Objectives: The physical injuries and financial implications as a result of road accidents have serious economic, cultural, and social effects. We conducted this study to determine any changes in the trend of road-accident-related deaths in Asian and North African countries from 1990 to 2010. Methods: The current study was carried out using data from the Global Burden of Disease database. First, the process was assessed using the growth curve divided into six regions. Moreover, the classification was done based on the death rate using growth mixed modeling. Results: The road injury death trend for men had more variations than women. Classification of these countries based on mortality using the latent growth mixture model resulted in more homogeneous classes according to trend in road fatalities. Disregarding gender and sex, there were four optimal classes. The first three classes had a decreasing trend with the third class having the greatest decreasing trend. South Korea and Taiwan were in this group. Afghanistan, Indonesia, Thailand, Iran, the UAE, Saudi Arabia, and Oman lay in group 4 and had an increasing trend in road injury deaths. Conclusions: Successful interventions that developed countries have used to avoid casualties of road injuries could be used in developing countries. These include passing laws making the use of seatbelts and child seats compulsory and determining appropriate speed limits.
Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series
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H. Sadeghi
2016-02-01
costs of water subscribers between 1388 and 1390. In structural time series model, the model was generated by entering the invisibility part of the process and development of a state-space model, as well as using maximum likelihood method and the Kalman-Filter algorithm. Results and Discussion: Given the value of the test statistic ADF, with the exception of changing water use variables with a time difference of the steady rest. Superpopulation different modes of behavior were assessed based on the demand for water. Due to the likelihood ratio statistic is most suitable for the parameters, was diagnosed the steady-state level of randomness and the slope. Price and income elastic