Visser, H.; Molenaar, J.
1995-05-01
The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of
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...
Tracking the business cycle of the Euro area: A multivariate model-based band-pass filter
Azevedo, J.M.; Koopman, S.J.; Rua, A.
2006-01-01
This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties.
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
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...
Separating yolk from white: A filter based on economic properties of trend and cycle
Zhou, Peng
2017-01-01
This paper proposes a new filter technique to separate trend and cycle based on stylised economic properties of trend and cycle, rather than relying on ad hoc statistical proper-ties such as frequency. Given the theoretical separation between economic growth and business cycle literature, it is necessary to make the measures of trend and cycle match what the respective theories intend to explain. The proposed filter is applied to the long macroeconomic data collected by the Bank of England (1...
A Distributional Representation Model For Collaborative Filtering
Junlin, Zhang; Heng, Cai; Tongwen, Huang; Huiping, Xue
2015-01-01
In this paper, we propose a very concise deep learning approach for collaborative filtering that jointly models distributional representation for users and items. The proposed framework obtains better performance when compared against current state-of-art algorithms and that made the distributional representation model a promising direction for further research in the collaborative filtering.
Relevance Models for Collaborative Filtering
J. Wang (Jun)
2008-01-01
htmlabstractCollaborative filtering is the common technique of predicting the interests of a user by collecting preference information from many users. Although it is generally regarded as a key information retrieval technique, its relation to the existing information retrieval theory is unclear.
Multiple model cardinalized probability hypothesis density filter
Georgescu, Ramona; Willett, Peter
2011-09-01
The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.
Kalman filter-based gap conductance modeling
International Nuclear Information System (INIS)
Tylee, J.L.
1983-01-01
Geometric and thermal property uncertainties contribute greatly to the problem of determining conductance within the fuel-clad gas gap of a nuclear fuel pin. Accurate conductance values are needed for power plant licensing transient analysis and for test analyses at research facilities. Recent work by Meek, Doerner, and Adams has shown that use of Kalman filters to estimate gap conductance is a promising approach. A Kalman filter is simply a mathematical algorithm that employs available system measurements and assumed dynamic models to generate optimal system state vector estimates. This summary addresses another Kalman filter approach to gap conductance estimation and subsequent identification of an empirical conductance model
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...
Geometric Models for Collaborative Search and Filtering
Bitton, Ephrat
2011-01-01
This dissertation explores the use of geometric and graphical models for a variety of information search and filtering applications. These models serve to provide an intuitive understanding of the problem domains and as well as computational efficiencies to our solution approaches. We begin by considering a search and rescue scenario where both…
Charalel, Resmi A; Durack, Jeremy C; Mao, Jialin; Ross, Joseph S; Meltzer, Andrew J; Sedrakyan, Art
2018-03-01
Public awareness of inferior vena cava (IVC) filter-related controversies has been elevated by the Food and Drug Administration (FDA) safety communication in 2010. To examine population level trends in IVC filter utilization, complications, retrieval rates, and subsequent pulmonary embolism (PE) risk. A retrospective cohort study. Patients receiving IVC filters during 2005-2014 in New York State. IVC filter-specific complications, new PE occurrences and IVC filter retrievals were evaluated as time-to-event data using Kaplan-Meier analysis. Estimated cumulative risks were obtained at various timepoints during follow-up. There were 91,873 patients receiving IVC filters between 2005 and 2014 in New York State included in the study. The average patient age was 67 years and 46.6% were male. Age-adjusted rates of IVC filter placement increased from 48 cases/100,000 in 2005 to 52 cases/100,000 in 2009, and decreased afterwards to 36 cases/100,000 in 2014. The estimated risks of having an IVC filter-related complication and filter retrieval within 1 year was 1.5% [95% confidence interval (CI), 1.4%-1.6%] and 3.5% (95% CI, 3.4%-3.6%). One-year retrieval rate was higher post-2010 when compared with pre-2010 years (hazard ratio, 2.70; 95% CI, 2.50-2.91). Among the 58,176 patients who did not have PE events before or at the time of IVC filter placement, the estimated risk of developing subsequent PE at 1 year was 2.0% (95% CI, 1.9%-2.1%). Our findings suggest that FDA communications may be effective in modifying statewide clinical practices. Given the 2% observed PE rate following prophylactic IVC filter placement, large scale pragmatic studies are needed to determine contemporary safety and effectiveness of IVC filters.
Improved Collaborative Filtering Algorithm using Topic Model
Directory of Open Access Journals (Sweden)
Liu Na
2016-01-01
Full Text Available Collaborative filtering algorithms make use of interactions rates between users and items for generating recommendations. Similarity among users or items is calculated based on rating mostly, without considering explicit properties of users or items involved. In this paper, we proposed collaborative filtering algorithm using topic model. We describe user-item matrix as document-word matrix and user are represented as random mixtures over item, each item is characterized by a distribution over users. The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on Movie Lens data sets.
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.
Changing Trends in Modeling Mobility
Directory of Open Access Journals (Sweden)
Aarti Munjal
2012-01-01
Full Text Available A phenomenal increase in the number of wireless devices has led to the evolution of several interesting and challenging research problems in opportunistic networks. For example, the random waypoint mobility model, an early, popular effort to model mobility, involves generating random movement patterns. Previous research efforts, however, validate that movement patterns are not random; instead, human mobility is predictable to some extent. Since the performance of a routing protocol in an opportunistic network is greatly improved if the movement patterns of mobile users can be somewhat predicted in advance, several research attempts have been made to understand human mobility. The solutions developed use our understanding of movement patterns to predict the future contact probability for mobile nodes. In this work, we summarize the changing trends in modeling human mobility as random movements to the current research efforts that model human walks in a more predictable manner. Mobility patterns significantly affect the performance of a routing protocol. Thus, the changing trend in modeling mobility has led to several changes in developing routing protocols for opportunistic networks. For example, the simplest opportunistic routing protocol forwards a received packet to a randomly selected neighbor. With predictable mobility, however, routing protocols can use the expected contact information between a pair of mobile nodes in making forwarding decisions. In this work, we also describe the previous and current research efforts in developing routing protocols for opportunistic networks.
Modelling modulation perception : modulation low-pass filter or modulation filter bank?
Dau, T.; Kollmeier, B.; Kohlrausch, A.G.
1995-01-01
In current models of modulation perception, the stimuli are first filtered and nonlinearly transformed (mostly half-wave rectified). In order to model the low-pass characteristic of measured modulation transfer functions, the next stage in the models is a first-order low-pass filter with a typical
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.
Dreano, Denis; Mallick, Bani; Hoteit, Ibrahim
2015-01-01
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.
Application of the Trend Filtering Algorithm for Photometric Time Series Data
Gopalan, Giri; Plavchan, Peter; van Eyken, Julian; Ciardi, David; von Braun, Kaspar; Kane, Stephen R.
2016-08-01
Detecting transient light curves (e.g., transiting planets) requires high-precision data, and thus it is important to effectively filter systematic trends affecting ground-based wide-field surveys. We apply an implementation of the Trend Filtering Algorithm (TFA) to the 2MASS calibration catalog and select Palomar Transient Factory (PTF) photometric time series data. TFA is successful at reducing the overall dispersion of light curves, however, it may over-filter intrinsic variables and increase “instantaneous” dispersion when a template set is not judiciously chosen. In an attempt to rectify these issues we modify the original TFA from the literature by including measurement uncertainties in its computation, including ancillary data correlated with noise, and algorithmically selecting a template set using clustering algorithms as suggested by various authors. This approach may be particularly useful for appropriately accounting for variable photometric precision surveys and/or combined data sets. In summary, our contributions are to provide a MATLAB software implementation of TFA and a number of modifications tested on synthetics and real data, summarize the performance of TFA and various modifications on real ground-based data sets (2MASS and PTF), and assess the efficacy of TFA and modifications using synthetic light curve tests consisting of transiting and sinusoidal variables. While the transiting variables test indicates that these modifications confer no advantage to transit detection, the sinusoidal variables test indicates potential improvements in detection accuracy.
Kalman Filter for Generalized 2-D Roesser Models
Institute of Scientific and Technical Information of China (English)
SHENG Mei; ZOU Yun
2007-01-01
The design problem of the state filter for the generalized stochastic 2-D Roesser models, which appears when both the state and measurement are simultaneously subjected to the interference from white noise, is discussed. The wellknown Kalman filter design is extended to the generalized 2-D Roesser models. Based on the method of "scanning line by line", the filtering problem of generalized 2-D Roesser models with mode-energy reconstruction is solved. The formula of the optimal filtering, which minimizes the variance of the estimation error of the state vectors, is derived. The validity of the designed filter is verified by the calculation steps and the examples are introduced.
Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second....... 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...
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.
Infrared image background modeling based on improved Susan filtering
Yuehua, Xia
2018-02-01
When SUSAN filter is used to model the infrared image, the Gaussian filter lacks the ability of direction filtering. After filtering, the edge information of the image cannot be preserved well, so that there are a lot of edge singular points in the difference graph, increase the difficulties of target detection. To solve the above problems, the anisotropy algorithm is introduced in this paper, and the anisotropic Gauss filter is used instead of the Gauss filter in the SUSAN filter operator. Firstly, using anisotropic gradient operator to calculate a point of image's horizontal and vertical gradient, to determine the long axis direction of the filter; Secondly, use the local area of the point and the neighborhood smoothness to calculate the filter length and short axis variance; And then calculate the first-order norm of the difference between the local area of the point's gray-scale and mean, to determine the threshold of the SUSAN filter; Finally, the built SUSAN filter is used to convolution the image to obtain the background image, at the same time, the difference between the background image and the original image is obtained. The experimental results show that the background modeling effect of infrared image is evaluated by Mean Squared Error (MSE), Structural Similarity (SSIM) and local Signal-to-noise Ratio Gain (GSNR). Compared with the traditional filtering algorithm, the improved SUSAN filter has achieved better background modeling effect, which can effectively preserve the edge information in the image, and the dim small target is effectively enhanced in the difference graph, which greatly reduces the false alarm rate of the image.
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.
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
. The quantity of wear particles in gear oil is analysed with respect to system running conditions. It is shown that the model fits the data in terms of startup “particle burst” phenomenon, quasi-stationary conditions during operation, and clean-up filtration when placed out of operation. In order to establish...... 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...... particle generation is made possible by model parameter estimation and identification of an unintended lack of filter change. The model may also be used to optimise system and filtration performance, and to enable continuous condition monitoring....
Energy models: methods and trends
Energy Technology Data Exchange (ETDEWEB)
Reuter, A [Division of Energy Management and Planning, Verbundplan, Klagenfurt (Austria); Kuehner, R [IER Institute for Energy Economics and the Rational Use of Energy, University of Stuttgart, Stuttgart (Germany); Wohlgemuth, N [Department of Economy, University of Klagenfurt, Klagenfurt (Austria)
1997-12-31
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. 2 figs., 19 refs.
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 for optimising the execution of anti-spam filters
Directory of Open Access Journals (Sweden)
David Ruano-Ordás
2016-12-01
Full Text Available During last years, the combination of several filtering techniques for the development of anti-spam systems has gained a enormous popularity. However, although the accuracy achieved by these models has increased considerably, its use has entailed the emergence of new challenges such as the need to reduce the excessive use of computational resources, the increase of filtering speed and the adjustment of the weights used for the combination of several filtering techniques. In order to achieve this goal we have been refined several aspects including: (i the design and development of small technical improvements to increase the overall performance of the filter, (ii application of genetic algorithms to increase filtering accuracy and (iii the use of scheduling algorithms to improve filtering throughput.
The Trend Odds Model for Ordinal Data‡
Capuano, Ana W.; Dawson, Jeffrey D.
2013-01-01
Ordinal data appear in a wide variety of scientific fields. These data are often analyzed using ordinal logistic regression models that assume proportional odds. When this assumption is not met, it may be possible to capture the lack of proportionality using a constrained structural relationship between the odds and the cut-points of the ordinal values (Peterson and Harrell, 1990). We consider a trend odds version of this constrained model, where the odds parameter increases or decreases in a monotonic manner across the cut-points. We demonstrate algebraically and graphically how this model is related to latent logistic, normal, and exponential distributions. In particular, we find that scale changes in these potential latent distributions are consistent with the trend odds assumption, with the logistic and exponential distributions having odds that increase in a linear or nearly linear fashion. We show how to fit this model using SAS Proc Nlmixed, and perform simulations under proportional odds and trend odds processes. We find that the added complexity of the trend odds model gives improved power over the proportional odds model when there are moderate to severe departures from proportionality. A hypothetical dataset is used to illustrate the interpretation of the trend odds model, and we apply this model to a Swine Influenza example where the proportional odds assumption appears to be violated. PMID:23225520
The trend odds model for ordinal data.
Capuano, Ana W; Dawson, Jeffrey D
2013-06-15
Ordinal data appear in a wide variety of scientific fields. These data are often analyzed using ordinal logistic regression models that assume proportional odds. When this assumption is not met, it may be possible to capture the lack of proportionality using a constrained structural relationship between the odds and the cut-points of the ordinal values. We consider a trend odds version of this constrained model, wherein the odds parameter increases or decreases in a monotonic manner across the cut-points. We demonstrate algebraically and graphically how this model is related to latent logistic, normal, and exponential distributions. In particular, we find that scale changes in these potential latent distributions are consistent with the trend odds assumption, with the logistic and exponential distributions having odds that increase in a linear or nearly linear fashion. We show how to fit this model using SAS Proc NLMIXED and perform simulations under proportional odds and trend odds processes. We find that the added complexity of the trend odds model gives improved power over the proportional odds model when there are moderate to severe departures from proportionality. A hypothetical data set is used to illustrate the interpretation of the trend odds model, and we apply this model to a swine influenza example wherein the proportional odds assumption appears to be violated. Copyright © 2012 John Wiley & Sons, Ltd.
Methodology for modeling the microbial contamination of air filters.
Joe, Yun Haeng; Yoon, Ki Young; Hwang, Jungho
2014-01-01
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.
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.
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...
Hybrid Models of Alternative Current Filter for Hvdc
Directory of Open Access Journals (Sweden)
Ufa Ruslan A.
2017-01-01
Full Text Available Based on a hybrid simulation concept of HVDC, the developed hybrid AC filter models, providing the sufficiently full and adequate modeling of all single continuous spectrum of quasi-steady-state and transient processes in the filter, are presented. The obtained results suggest that usage of the hybrid simulation approach is carried out a methodically accurate with guaranteed instrumental error solution of differential equation systems of mathematical models of HVDC.
Virtual Organizations: Trends and Models
Nami, Mohammad Reza; Malekpour, Abbaas
The Use of ICT in business has changed views about traditional business. With VO, organizations with out physical, geographical, or structural constraint can collaborate with together in order to fulfill customer requests in a networked environment. This idea improves resource utilization, reduces development process and costs, and saves time. Virtual Organization (VO) is always a form of partnership and managing partners and handling partnerships are crucial. Virtual organizations are defined as a temporary collection of enterprises that cooperate and share resources, knowledge, and competencies to better respond to business opportunities. This paper presents an overview of virtual organizations and main issues in collaboration such as security and management. It also presents a number of different model approaches according to their purpose and applications.
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.
Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models
El Gharamti, Mohamad
2010-01-01
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.
Virtual Universities: Current Models and Future Trends.
Guri-Rosenblit, Sarah
2001-01-01
Describes current models of distance education (single-mode distance teaching universities, dual- and mixed-mode universities, extension services, consortia-type ventures, and new technology-based universities), including their merits and problems. Discusses future trends in potential student constituencies, faculty roles, forms of knowledge…
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.
Samuel, Manoj P; Senthilvel, S; Tamilmani, D; Mathew, A C
2012-09-01
A horizontal flow multimedia stormwater filter was developed and tested for hydraulic efficiency and pollutant removal efficiency. Gravel, coconut (Cocos nucifera) fibre and sand were selected as the media and filled in 1:1:1 proportion. A fabric screen made up of woven sisal hemp was used to separate the media. The adsorption behaviour of coir fibre was determined in a series of column and batch studies and the corresponding isotherms were developed. The hydraulic efficiency of the filter showed a diminishing trend as the sediment level in inflow increased. The filter exhibited 100% sediment removal at lower sediment concentrations in inflow water (>6 g L(-1)). The filter could remove NO3(-), SO4(2-) and total solids (TS) effectively. Removal percentages of Mg(2+) and Na(+) were also found to be good. Similar results were obtained from a field evaluation study. Studies were also conducted to determine the pattern of silt and sediment deposition inside the filter body. The effects of residence time and rate of flow on removal percentages of NO3(-) and TS were also investigated out. In addition, a multiple regression equation that mathematically represents the filtration process was developed. Based on estimated annual costs and returns, all financial viability criteria (internal rate of return, net present value and benefit-cost ratio) were found favourable and affordable to farmers for investment in the developed filtration system. The model MUSIC was calibrated and validated for field conditions with respect to the developed stormwater filter.
Modeling, simulation, and design of SAW grating filters
Schwelb, Otto; Adler, E. L.; Slaboszewicz, J. K.
1990-05-01
A systematic procedure for modeling, simulating, and designing SAW (surface acoustic wave) grating filters, taking losses into account, is described. Grating structures and IDTs (interdigital transducers) coupling to SAWs are defined by cascadable transmission-matrix building blocks. Driving point and transfer characteristics (immittances) of complex architectures consisting of gratings, transducers, and coupling networks are obtained by chain-multiplying building-block matrices. This modular approach to resonator filter analysis and design combines the elements of lossy filter synthesis with the transmission-matrix description of SAW components. A multipole filter design procedure based on a lumped-element-model approximation of one-pole two-port resonator building blocks is given and the range of validity of this model examined. The software for simulating the performance of SAW grating devices based on this matrix approach is described, and its performance, when linked to the design procedure to form a CAD/CAA (computer-aided design and analysis) multiple-filter design package, is illustrated with a resonator filter design example.
Estimating the Competitive Storage Model with Trending Commodity Prices
Gouel , Christophe; LEGRAND , Nicolas
2017-01-01
We present a method to estimate jointly the parameters of a standard commodity storage model and the parameters characterizing the trend in commodity prices. This procedure allows the influence of a possible trend to be removed without restricting the model specification, and allows model and trend selection based on statistical criteria. The trend is modeled deterministically using linear or cubic spline functions of time. The results show that storage models with trend are always preferred ...
Test models for improving filtering with model errors through stochastic parameter estimation
International Nuclear Information System (INIS)
Gershgorin, B.; Harlim, J.; Majda, A.J.
2010-01-01
The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.
Linear theory for filtering nonlinear multiscale systems with model error.
Berry, Tyrus; Harlim, John
2014-07-08
In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online , as part of a filtering
A numerical storm surge forecast model with Kalman filter
Institute of Scientific and Technical Information of China (English)
Yu Fujiang; Zhang Zhanhai; Lin Yihua
2001-01-01
Kalman filter data assimilation technique is incorporated into a standard two-dimensional linear storm surge model. Imperfect model equation and imperfect meteorological forcimg are accounted for by adding noise terms to the momentum equations. The deterministic model output is corrected by using the available tidal gauge station data. The stationary Kalman filter algorithm for the model domain is calculated by an iterative procedure using specified information on the inaccuracies in the momentum equations and specified error information for the observations. An application to a real storm surge that occurred in the summer of 1956 in the East China Sea is performed by means of this data assimilation technique. The result shows that Kalman filter is useful for storm surge forecast and hindcast.
New Trends in European Companies’ Business Models
Directory of Open Access Journals (Sweden)
Georgeta ILIE
2014-01-01
Full Text Available Companies constantly reconsider and reconfigure their business models in order to create value and generate growth. They also reassess the price-performance correlation and new levels of capital efficiency. The new business models are frequently needed to provide goods at affordable prices through the adaptation of packaging strategies, pricing strategies, the product itself, and by helping to sustain financially the demand. In the context of current financial and economic difficulties, it reveals the inclusive business models that provide goods and services to poor people and also create employment. The paper tries to emphasize ways in which business models are evolving, and how to determine the right model for companies. In the same time, it also seeks to highlight trends in the development of new business models in the European countries which creates basic economic activities, giving people facing social and economic problems access to products and services that meet their needs.
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
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 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.
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.
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...... the potential of the unscented Kalman …filter to properly capture nonlinearities. To illustrate the advantages of the unscented Kalman …filter, we analyze the cross section of swap rates, which are relatively simple non-linear instruments, and cap prices, which are highly nonlinear in the states. An extensive...
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...... and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data...
Cake filtration modeling: Analytical cake filtration model and filter medium characterization
Energy Technology Data Exchange (ETDEWEB)
Koch, Michael
2008-05-15
Cake filtration is a unit operation to separate solids from fluids in industrial processes. The build up of a filter cake is usually accompanied with a decrease in overall permeability over the filter leading to an increased pressure drop over the filter. For an incompressible filter cake that builds up on a homogeneous filter cloth, a linear pressure drop profile over time is expected for a constant fluid volume flow. However, experiments show curved pressure drop profiles, which are also attributed to inhomogeneities of the filter (filter medium and/or residual filter cake). In this work, a mathematical filter model is developed to describe the relationship between time and overall permeability. The model considers a filter with an inhomogeneous permeability and accounts for fluid mechanics by a one-dimensional formulation of Darcy's law and for the cake build up by solid continuity. The model can be solved analytically in the time domain. The analytic solution allows for the unambiguous inversion of the model to determine the inhomogeneous permeability from the time resolved overall permeability, e.g. pressure drop measurements. An error estimation of the method is provided by rewriting the model as convolution transformation. This method is applied to simulated and experimental pressure drop data of gas filters with textile filter cloths and various situations with non-uniform flow situations in practical problems are explored. A routine is developed to generate characteristic filter cycles from semi-continuous filter plant operation. The model is modified to investigate the impact of non-uniform dust concentrations. (author). 34 refs., 40 figs., 1 tab
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.
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.
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
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.
SOME TRENDS IN MATHEMATICAL MODELING FOR BIOTECHNOLOGY
Directory of Open Access Journals (Sweden)
O. M. Klyuchko
2018-02-01
Full Text Available The purpose of present research is to demonstrate some trends of development of modeling methods for biotechnology according to contemporary achievements in science and technique. At the beginning the general approaches are outlined, some types of classifications of modeling methods are observed. The role of mathematic methods modeling for biotechnology in present époque of information computer technologies intensive development is studied and appropriate scheme of interrelation of all these spheres is proposed. Further case studies are suggested: some mathematic models in three different spaces (1D, 2D, 3D models are described for processes in living objects of different levels of hierarchic organization. In course of this the main attention was paid to some processes modeling in neurons as well as in their aggregates of different forms, including glioma cell masses (1D, 2D, 3D brain processes models. Starting from the models that have only theoretical importance for today, we describe at the end a model which application may be important for the practice. The work was done after the analysis of approximately 250 current publications in fields of biotechnology, including the authors’ original works.
Modeling the filtration ability of stockpiled filtering facepiece
Rottach, Dana R.
2016-03-01
Filtering facepiece respirators (FFR) are often stockpiled for use during public health emergencies such as an infectious disease outbreak or pandemic. While many stockpile administrators are aware of shelf life limitations, environmental conditions can lead to premature degradation. Filtration performance of a set of FFR retrieved from a storage room with failed environmental controls was measured. Though within the expected shelf life, the filtration ability of several respirators was degraded, allowing twice the penetration of fresh samples. The traditional picture of small particle capture by fibrous filter media qualitatively separates the effect of inertial impaction, interception from the streamline, diffusion, settling, and electrostatic attraction. Most of these mechanisms depend upon stable conformational properties. However, common FFR rely on electrets to achieve their high performance, and over time heat and humidity can cause the electrostatic media to degrade. An extension of the Langevin model with correlations to classical filtration concepts will be presented. The new computational model will be used to predict the change in filter effectiveness as the filter media changes with time.
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.
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.
Collaborative filtering recommendation model based on fuzzy clustering algorithm
Yang, Ye; Zhang, Yunhua
2018-05-01
As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.
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.
Directory of Open Access Journals (Sweden)
Jingyi Zhang
2018-06-01
Full Text Available This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.
Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng
2018-06-11
This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.
TUNNEL POINT CLOUD FILTERING METHOD BASED ON ELLIPTIC CYLINDRICAL MODEL
Directory of Open Access Journals (Sweden)
N. Zhu
2016-06-01
Full Text Available The large number of bolts and screws that attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, make the laser point cloud data include lots of non-tunnel section points (hereinafter referred to as non-points, therefore affecting the accuracy for modeling and deformation monitoring. This paper proposed a filtering method for the point cloud based on the elliptic cylindrical model. The original laser point cloud data was firstly projected onto a horizontal plane, and a searching algorithm was given to extract the edging points of both sides, which were used further to fit the tunnel central axis. Along the axis the point cloud was segmented regionally, and then fitted as smooth elliptic cylindrical surface by means of iteration. This processing enabled the automatic filtering of those inner wall non-points. Experiments of two groups showed coincident results, that the elliptic cylindrical model based method could effectively filter out the non-points, and meet the accuracy requirements for subway deformation monitoring. The method provides a new mode for the periodic monitoring of tunnel sections all-around deformation in subways routine operation and maintenance.
Mark W. French
2005-01-01
The cycle in output and hours worked is not symmetric: it behaves differently around recessions than in expansions. Similarly, the trend in multifactor productivity (MFP) seems to pass through different regimes; there was an extended period of slow MFP growth from about 1973 through 1995, and faster growth thereafter. Typical linear models and linear filters such as the Kalman filter deal poorly with asymmetry and regime changes. This paper attempts to determine more accurately and quickly an...
Modeling international trends in energy efficiency
International Nuclear Information System (INIS)
Stern, David I.
2012-01-01
I use a stochastic production frontier to model energy efficiency trends in 85 countries over a 37-year period. Differences in energy efficiency across countries are modeled as a stochastic function of explanatory variables and I estimate the model using the cross-section of time-averaged data, so that no structure is imposed on technological change over time. Energy efficiency is measured using a new energy distance function approach. The country using the least energy per unit output, given its mix of outputs and inputs, defines the global production frontier. A country's relative energy efficiency is given by its distance from the frontier—the ratio of its actual energy use to the minimum required energy use, ceteris paribus. Energy efficiency is higher in countries with, inter alia, higher total factor productivity, undervalued currencies, and smaller fossil fuel reserves and it converges over time across countries. Globally, technological change was the most important factor counteracting the energy-use and carbon-emissions increasing effects of economic growth.
The effect of bathymetric filtering on nearshore process model results
Plant, N.G.; Edwards, K.L.; Kaihatu, J.M.; Veeramony, J.; Hsu, L.; Holland, K.T.
2009-01-01
Nearshore wave and flow model results are shown to exhibit a strong sensitivity to the resolution of the input bathymetry. In this analysis, bathymetric resolution was varied by applying smoothing filters to high-resolution survey data to produce a number of bathymetric grid surfaces. We demonstrate that the sensitivity of model-predicted wave height and flow to variations in bathymetric resolution had different characteristics. Wave height predictions were most sensitive to resolution of cross-shore variability associated with the structure of nearshore sandbars. Flow predictions were most sensitive to the resolution of intermediate scale alongshore variability associated with the prominent sandbar rhythmicity. Flow sensitivity increased in cases where a sandbar was closer to shore and shallower. Perhaps the most surprising implication of these results is that the interpolation and smoothing of bathymetric data could be optimized differently for the wave and flow models. We show that errors between observed and modeled flow and wave heights are well predicted by comparing model simulation results using progressively filtered bathymetry to results from the highest resolution simulation. The damage done by over smoothing or inadequate sampling can therefore be estimated using model simulations. We conclude that the ability to quantify prediction errors will be useful for supporting future data assimilation efforts that require this information.
Model Calibration of Exciter and PSS Using Extended Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Kalsi, Karanjit; Du, Pengwei; Huang, Zhenyu
2012-07-26
Power system modeling and controls continue to become more complex with the advent of smart grid technologies and large-scale deployment of renewable energy resources. As demonstrated in recent studies, inaccurate system models could lead to large-scale blackouts, thereby motivating the need for model calibration. Current methods of model calibration rely on manual tuning based on engineering experience, are time consuming and could yield inaccurate parameter estimates. In this paper, the Extended Kalman Filter (EKF) is used as a tool to calibrate exciter and Power System Stabilizer (PSS) models of a particular type of machine in the Western Electricity Coordinating Council (WECC). The EKF-based parameter estimation is a recursive prediction-correction process which uses the mismatch between simulation and measurement to adjust the model parameters at every time step. Numerical simulations using actual field test data demonstrate the effectiveness of the proposed approach in calibrating the parameters.
Adaptive filters and internal models: multilevel description of cerebellar function.
Porrill, John; Dean, Paul; Anderson, Sean R
2013-11-01
Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles. Copyright © 2012 Elsevier Ltd. All rights reserved.
Mathematical modelling of tissue formation in chondrocyte filter cultures.
Catt, C J; Schuurman, W; Sengers, B G; van Weeren, P R; Dhert, W J A; Please, C P; Malda, J
2011-12-17
In the field of cartilage tissue engineering, filter cultures are a frequently used three-dimensional differentiation model. However, understanding of the governing processes of in vitro growth and development of tissue in these models is limited. Therefore, this study aimed to further characterise these processes by means of an approach combining both experimental and applied mathematical methods. A mathematical model was constructed, consisting of partial differential equations predicting the distribution of cells and glycosaminoglycans (GAGs), as well as the overall thickness of the tissue. Experimental data was collected to allow comparison with the predictions of the simulation and refinement of the initial models. Healthy mature equine chondrocytes were expanded and subsequently seeded on collagen-coated filters and cultured for up to 7 weeks. Resulting samples were characterised biochemically, as well as histologically. The simulations showed a good representation of the experimentally obtained cell and matrix distribution within the cultures. The mathematical results indicate that the experimental GAG and cell distribution is critically dependent on the rate at which the cell differentiation process takes place, which has important implications for interpreting experimental results. This study demonstrates that large regions of the tissue are inactive in terms of proliferation and growth of the layer. In particular, this would imply that higher seeding densities will not significantly affect the growth rate. A simple mathematical model was developed to predict the observed experimental data and enable interpretation of the principal underlying mechanisms controlling growth-related changes in tissue composition.
Characterizing economic trends by Bayesian stochastic model specifi cation search
Grassi, Stefano; Proietti, Tommaso
2010-01-01
We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and deterministic trends. In particular, we formulate autoregressive models with stochastic trends components and decide on whether a specific feature of the series, i.e. the underlying level and/or the rate...
Development, characterization, and modeling of a tunable filter camera
Sartor, Mark Alan
1999-10-01
This paper describes the development, characterization, and modeling of a Tunable Filter Camera (TFC). The TFC is a new multispectral instrument with electronically tuned spectral filtering and low-light-level sensitivity. It represents a hybrid between hyperspectral and multispectral imaging spectrometers that incorporates advantages from each, addressing issues such as complexity, cost, lack of sensitivity, and adaptability. These capabilities allow the TFC to be applied to low- altitude video surveillance for real-time spectral and spatial target detection and image exploitation. Described herein are the theory and principles of operation for the TFC, which includes a liquid crystal tunable filter, an intensified CCD, and a custom apochromatic lens. The results of proof-of-concept testing, and characterization of two prototype cameras are included, along with a summary of the design analyses for the development of a multiple-channel system. A significant result of this effort was the creation of a system-level model, which was used to facilitate development and predict performance. It includes models for the liquid crystal tunable filter and intensified CCD. Such modeling was necessary in the design of the system and is useful for evaluation of the system in remote-sensing applications. Also presented are characterization data from component testing, which included quantitative results for linearity, signal to noise ratio (SNR), linearity, and radiometric response. These data were used to help refine and validate the model. For a pre-defined source, the spatial and spectral response, and the noise of the camera, system can now be predicted. The innovation that sets this development apart is the fact that this instrument has been designed for integrated, multi-channel operation for the express purpose of real-time detection/identification in low- light-level conditions. Many of the requirements for the TFC were derived from this mission. In order to provide
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
Hydraulic modeling of clay ceramic water filters for point-of-use water treatment.
Schweitzer, Ryan W; Cunningham, Jeffrey A; Mihelcic, James R
2013-01-02
The acceptability of ceramic filters for point-of-use water treatment depends not only on the quality of the filtered water, but also on the quantity of water the filters can produce. This paper presents two mathematical models for the hydraulic performance of ceramic water filters under typical usage. A model is developed for two common filter geometries: paraboloid- and frustum-shaped. Both models are calibrated and evaluated by comparison to experimental data. The hydraulic models are able to predict the following parameters as functions of time: water level in the filter (h), instantaneous volumetric flow rate of filtrate (Q), and cumulative volume of water produced (V). The models' utility is demonstrated by applying them to estimate how the volume of water produced depends on factors such as the filter shape and the frequency of filling. Both models predict that the volume of water produced can be increased by about 45% if users refill the filter three times per day versus only once per day. Also, the models predict that filter geometry affects the volume of water produced: for two filters with equal volume, equal wall thickness, and equal hydraulic conductivity, a filter that is tall and thin will produce as much as 25% more water than one which is shallow and wide. We suggest that the models can be used as tools to help optimize filter performance.
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...... with that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in the states, and cap prices, which are highly nonlinear. When caps are used to filter the states, the unscented Kalman filter significantly outperforms its extended counterpart. The unscented Kalman filter also...... 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....
Trend time-series modeling and forecasting with neural networks.
Qi, Min; Zhang, G Peter
2008-05-01
Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.
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
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
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......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...... parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we...
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...
Topographic filtering simulation model for sediment source apportionment
Cho, Se Jong; Wilcock, Peter; Hobbs, Benjamin
2018-05-01
We propose a Topographic Filtering simulation model (Topofilter) that can be used to identify those locations that are likely to contribute most of the sediment load delivered from a watershed. The reduced complexity model links spatially distributed estimates of annual soil erosion, high-resolution topography, and observed sediment loading to determine the distribution of sediment delivery ratio across a watershed. The model uses two simple two-parameter topographic transfer functions based on the distance and change in elevation from upland sources to the nearest stream channel and then down the stream network. The approach does not attempt to find a single best-calibrated solution of sediment delivery, but uses a model conditioning approach to develop a large number of possible solutions. For each model run, locations that contribute to 90% of the sediment loading are identified and those locations that appear in this set in most of the 10,000 model runs are identified as the sources that are most likely to contribute to most of the sediment delivered to the watershed outlet. Because the underlying model is quite simple and strongly anchored by reliable information on soil erosion, topography, and sediment load, we believe that the ensemble of simulation outputs provides a useful basis for identifying the dominant sediment sources in the watershed.
Scheme of adaptive polarization filtering based on Kalman model
Institute of Scientific and Technical Information of China (English)
Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande
2006-01-01
A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.
Robins, J Eli; Ragai, Ihab; Yamaguchi, Dean J
2018-05-01
Inferior vena cava (IVC) filters are used in patients at risk for pulmonary embolism who cannot be anticoagulated. Unfortunately, these filters are not without risk, and complications include perforation, migration, and filter fracture. The most prevalent complication is filter perforation of the IVC, with incidence varying among filter models. To our knowledge, the mechanical properties of IVC filters have not been evaluated and are not readily available through the manufacturer. This study sought to determine whether differences in mechanical properties are similar to differences in documented perforation rates. The radial expansion forces of Greenfield (Boston Scientific, Marlborough, Mass), Cook Celect (Cook Medical, Bloomington, Ind), and Cook Platinum filters were analyzed with three replicates per group. The intrinsic force exerted by the filter on the measuring device was collected in real time during controlled expansion. Replicates were averaged and significance was determined by calculating analysis of covariance using SAS software (SAS Institute, Cary, NC). Each filter model generated a significantly different radial expansion force (P filter, followed by the Cook Celect and Greenfield filters. Radial force dispersion during expansion was greatest in the Cook Celect, followed by the Cook Platinum and Greenfield filters. Differences in radial expansion forces among IVC filter models are consistent with documented perforation rates. Cook Celect IVC filters have a higher incidence of perforation compared with Greenfield filters when they are left in place for >90 days. Evaluation of Cook Celect filters yielded a significantly higher radial expansion force at minimum caval diameter, with greater force dispersion during expansion. Copyright © 2018 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.
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).
Model for predicting fabric filter and ESP costs
International Nuclear Information System (INIS)
Hoskins, W.; Terrill, J.K.
1992-01-01
United Engineers and Constructors (UE and C) has developed a personal computer (PC) based program (Model) for estimating capital and operating costs of fabric filters (FFs) and electrostatic precipitators (ESPs). The program contains proprietary sizing routines for both types of particulate control devices. For the FF, it determines the number of compartments, number of bags, physical dimensions and other important information. For the ESP, it determines specific collection area (SCA), number of cells, and number of TR sets. This paper reports that the program has the capability of handling a wide range of gas flows. It adjusts construction costs for the labor productivity factors in different locations. The capital costs are considered conceptual in nature with an absolute accuracy range of ±25%. The capital and operating costs are used along with economic factors to determine present worth costs. This allows site specific side-by-side comparisons of FFs and ESPs
Raitoharju, Matti; Nurminen, Henri; Piché, Robert
2015-12-01
Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.
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.
Empirical intrinsic geometry for nonlinear modeling and time series filtering.
Talmon, Ronen; Coifman, Ronald R
2013-07-30
In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.
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)
Theoretical model for a background noise limited laser-excited optical filter for doubled Nd lasers
Shay, Thomas M.; Garcia, Daniel F.
1990-01-01
A simple theoretical model for the calculation of the dependence of filter quantum efficiency versus laser pump power in an atomic Rb vapor laser-excited optical filter is reported. Calculations for Rb filter transitions that can be used to detect the practical and important frequency-doubled Nd lasers are presented. The results of these calculations show the filter's quantum efficiency versus the laser pump power. The required laser pump powers required range from 2.4 to 60 mW/sq cm of filter aperture.
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.
Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study
Directory of Open Access Journals (Sweden)
Siavash Hosseinyalamdary
2018-04-01
Full Text Available Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU, have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.
Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study.
Hosseinyalamdary, Siavash
2018-04-24
Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.
Experimental study of filter cake formation on different filter media
International Nuclear Information System (INIS)
Saleem, M.
2009-01-01
Removal of particulate matter from gases generated in the process industry is important for product recovery as well as emission control. Dynamics of filtration plant depend on operating conditions. The models, that predict filter plant behaviour, involve empirical resistance parameters which are usually derived from limited experimental data and are characteristics of the filter media and filter cake (dust deposited on filter medium). Filter cake characteristics are affected by the nature of filter media, process parameters and mode of filter regeneration. Removal of dust particles from air is studied in a pilot scale jet pulsed bag filter facility resembling closely to the industrial filters. Limestone dust and ambient air are used in this study with two widely different filter media. All important parameters like pressure drop, gas flow rate, dust settling, are recorded continuously at 1s interval. The data is processed for estimation of the resistance parameters. The pressure drop rise on test filter media is compared. Results reveal that the surface of filter media has an influence on pressure drop rise (concave pressure drop rise). Similar effect is produced by partially jet pulsed filter surface. Filter behaviour is also simulated using estimated parameters and a simplified model and compared with the experimental results. Distribution of cake area load is therefore an important aspect of jet pulse cleaned bag filter modeling. Mean specific cake resistance remains nearly constant on thoroughly jet pulse cleaned membrane coated filter bags. However, the trend can not be confirmed without independent cake height and density measurements. Thus the results reveal the importance of independent measurements of cake resistance. (author)
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.
A comparison of nonlinear filtering approaches in the context of an HIV model.
Banks, H Thomas; Hu, Shuhua; Kenz, Zackary R; Tran, Hien T
2010-04-01
In this paper three different filtering methods, the Extended Kalman Filter (EKF), the Gauss-Hermite Filter (GHF), and the Unscented Kalman Filter (UKF), are compared for state-only and coupled state and parameter estimation when used with log state variables of a model of the immunologic response to the human immunodeficiency virus (HIV) in individuals. The filters are implemented to estimate model states as well as model parameters from simulated noisy data, and are compared in terms of estimation accuracy and computational time. Numerical experiments reveal that the GHF is the most computationally expensive algorithm, while the EKF is the least expensive one. In addition, computational experiments suggest that there is little difference in the estimation accuracy between the UKF and GHF. When measurements are taken as frequently as every week to two weeks, the EKF is the superior filter. When measurements are further apart, the UKF is the best choice in the problem under investigation.
Characterizing economic trends by Bayesian stochastic model specification search
DEFF Research Database (Denmark)
Grassi, Stefano; Proietti, Tommaso
We extend a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. In particular, we focus on autoregressive models with possibly time-varying intercept and slope and decide on ...
Streamflow data assimilation in SWAT model using Extended Kalman Filter
Sun, Leqiang; Nistor, Ioan; Seidou, Ousmane
2015-12-01
The Extended Kalman Filter (EKF) is coupled with the Soil and Water Assessment Tools (SWAT) model in the streamflow assimilation of the upstream Senegal River in West Africa. Given the large number of distributed variables in SWAT, only the average watershed scale variables are included in the state vector and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori/a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Both the soil moisture and CN2 are significantly updated in the wet season, yet they have opposite update patterns. A case study for a large flood forecast shows that for up to seven days, the streamflow forecast is moderately improved using the EKF-subsequent open loop scheme but significantly improved with a newly designed quasi-error update scheme. The former has better performances in the flood rising period while the latter has better performances in the recession period. For both schemes, the streamflow forecast is improved more significantly when the lead time is shorter.
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
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 ...
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…
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
2016-05-01
To describe trends, correlates of use and consumer perceptions related to the product design innovation of flavour capsules in cigarette filters. 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. 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. 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
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
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.
Assessment of damage localization based on spatial filters using numerical crack propagation models
International Nuclear Information System (INIS)
Deraemaeker, Arnaud
2011-01-01
This paper is concerned with vibration based structural health monitoring with a focus on non-model based damage localization. The type of damage investigated is cracking of concrete structures due to the loss of prestress. In previous works, an automated method based on spatial filtering techniques applied to large dynamic strain sensor networks has been proposed and tested using data from numerical simulations. In the simulations, simplified representations of cracks (such as a reduced Young's modulus) have been used. While this gives the general trend for global properties such as eigen frequencies, the change of more local features, such as strains, is not adequately represented. Instead, crack propagation models should be used. In this study, a first attempt is made in this direction for concrete structures (quasi brittle material with softening laws) using crack-band models implemented in the commercial software DIANA. The strategy consists in performing a non-linear computation which leads to cracking of the concrete, followed by a dynamic analysis. The dynamic response is then used as the input to the previously designed damage localization system in order to assess its performances. The approach is illustrated on a simply supported beam modeled with 2D plane stress elements.
Luterbacher, Jeremy S; Moran-Mirabal, Jose M; Burkholder, Eric W; Walker, Larry P
2015-01-01
Enzymatic hydrolysis is one of the critical steps in depolymerizing lignocellulosic biomass into fermentable sugars for further upgrading into fuels and/or chemicals. However, many studies still rely on empirical trends to optimize enzymatic reactions. An improved understanding of enzymatic hydrolysis could allow research efforts to follow a rational design guided by an appropriate theoretical framework. In this study, we present a method to image cellulosic substrates with complex three-dimensional structure, such as filter paper, undergoing hydrolysis under conditions relevant to industrial saccharification processes (i.e., temperature of 50°C, using commercial cellulolytic cocktails). Fluorescence intensities resulting from confocal images were used to estimate parameters for a diffusion and reaction model. Furthermore, the observation of a relatively constant bound enzyme fluorescence signal throughout hydrolysis supported our modeling assumption regarding the structure of biomass during hydrolysis. The observed behavior suggests that pore evolution can be modeled as widening of infinitely long slits. The resulting model accurately predicts the concentrations of soluble carbohydrates obtained from independent saccharification experiments conducted in bulk, demonstrating its relevance to biomass conversion work. © 2014 Wiley Periodicals, Inc.
Study on the Metal Fiber Filter Modeling for Capturing Radioactive Aerosol
Energy Technology Data Exchange (ETDEWEB)
Lee, Seunguk; Lee, Chanhyun; Park, Minchan; Lee, Jaekeun [EcoEnergy Research Institute, Busan (Korea, Republic of)
2015-05-15
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.
Modelling firm heterogeneity with spatial 'trends'
Energy Technology Data Exchange (ETDEWEB)
Sarmiento, C. [North Dakota State University, Fargo, ND (United States). Dept. of Agricultural Business & Applied Economics
2004-04-15
The hypothesis underlying this article is that firm heterogeneity can be captured by spatial characteristics of the firm (similar to the inclusion of a time trend in time series models). The hypothesis is examined in the context of modelling electric generation by coal powered plants in the presence of firm heterogeneity.
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...... 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....... 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...
Accounting for model error due to unresolved scales within ensemble Kalman filtering
Mitchell, Lewis; Carrassi, Alberto
2014-01-01
We propose a method to account for model error due to unresolved scales in the context of the ensemble transform Kalman filter (ETKF). The approach extends to this class of algorithms the deterministic model error formulation recently explored for variational schemes and extended Kalman filter. The model error statistic required in the analysis update is estimated using historical reanalysis increments and a suitable model error evolution law. Two different versions of the method are describe...
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....
Comparison of several Kalman filter models for establishing MUF
International Nuclear Information System (INIS)
Pike, D.H.; Morrison, G.W.; Holland, C.W.
1976-01-01
Detection of MUF in a material balance area is a problem in nuclear material control. It has been shown that the Kalman filter can detect a MUF in situations which could not be detected by the traditional control chart approach using LEMUF. The Kalman filter is extended in this paper to cover two additional scenarios: (1) the case where a random quantity with a mean of M(t) is removed per period, and (2) the case where MUF is a fraction of the on-hand inventory each period. The Kalman filter is robust, sensitive, produces estimates of the error covariance matrix, and is an iterative technique which is suited for on-line-direct-input information systems
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
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.
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.
Statistical analysis of strait time index and a simple model for trend and trend reversal
Chen, Kan; Jayaprakash, C.
2003-06-01
We analyze the daily closing prices of the Strait Time Index (STI) as well as the individual stocks traded in Singapore's stock market from 1988 to 2001. We find that the Hurst exponent is approximately 0.6 for both the STI and individual stocks, while the normal correlation functions show the random walk exponent of 0.5. We also investigate the conditional average of the price change in an interval of length T given the price change in the previous interval. We find strong correlations for price changes larger than a threshold value proportional to T; this indicates that there is no uniform crossover to Gaussian behavior. A simple model based on short-time trend and trend reversal is constructed. We show that the model exhibits statistical properties and market swings similar to those of the real market.
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.
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
International Nuclear Information System (INIS)
Chen, Lin; Fan, Xiangtao; Du, Xiaoping
2014-01-01
Point cloud filtering is the basic and key step in LiDAR data processing. Adaptive Triangle Irregular Network Modelling (ATINM) algorithm and Threshold Segmentation on Elevation Statistics (TSES) algorithm are among the mature algorithms. However, few researches concentrate on the parameter selections of ATINM and the iteration condition of TSES, which can greatly affect the filtering results. First the paper presents these two key problems under two different terrain environments. For a flat area, small height parameter and angle parameter perform well and for areas with complex feature changes, large height parameter and angle parameter perform well. One-time segmentation is enough for flat areas, and repeated segmentations are essential for complex areas. Then the paper makes comparisons and analyses of the results by these two methods. ATINM has a larger I error in both two data sets as it sometimes removes excessive points. TSES has a larger II error in both two data sets as it ignores topological relations between points. ATINM performs well even with a large region and a dramatic topology while TSES is more suitable for small region with flat topology. Different parameters and iterations can cause relative large filtering differences
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.
Su, Gui-yang; Li, Jian-hua; Ma, Ying-hua; Li, Sheng-hong
2004-09-01
With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applications which can block or filter such information are used. Approaches in those systems can be roughly classified into two kinds: metadata based and content based. With the development of distributed technologies, content based filtering technologies will play a more and more important role in filtering systems. Keyword matching is a content based method used widely in harmful text filtering. Experiments to evaluate the recall and precision of the method showed that the precision of the method is not satisfactory, though the recall of the method is rather high. According to the results, a new pornographic text filtering model based on reconfirming is put forward. Experiments showed that the model is practical, has less loss of recall than the single keyword matching method, and has higher precision.
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)
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.
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
Modeling and Simulation of the Visual Effects of Colored Filters
2015-02-01
chromaticity coordinates on the MCC under illuminant C. Measurements were taken with and without filters in front of the colorimeter . Note, for the actual...to measure the chromaticity and luminance values of the different components displayed on the calibrated monitor using a spot colorimeter (Minolta CS...of Illuminant C and the chromaticity values for each of the colored squares were measured using a spot colorimeter (Minolta CS-100). Three
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....
Language Modelling for Collaborative Filtering: Application to Job Applicant Matching
Schmitt , Thomas; Gonard , François; Caillou , Philippe; Sebag , Michèle
2017-01-01
International audience; This paper addresses a collaborative retrieval problem , the recommendation of job ads to applicants. Specifically, two proprietary databases are considered. The first one focuses on the context of unskilled low-paid jobs/applicants; the second one focuses on highly qualified jobs/applicants. Each database includes the job ads and applicant resumes together with the collaborative filtering data recording the applicant clicks on job ads. The proposed approach, called LA...
Cubature/ Unscented/ Sigma Point Kalman Filtering with Angular Measurement Models
2015-07-06
similarly transformed to work with the Laplace distribution. Cubature formulae for w(x) = 1 over regions of various shapes could be used for evaluating...measurement and process non- linearities, such as the cubature Kalman filter, can perform ex- tremely poorly in many applications involving angular...in the form of the “unscented transform ”) consider just converting such measurements into Cartesian coordinates and feeding the converted measurements
Quantum model for a periodically driven selectivity filter in a K+ ion channel
International Nuclear Information System (INIS)
Cifuentes, A A; Semião, F L
2014-01-01
In this work, we present a quantum transport model for the selectivity filter in the KcsA potassium ion channel. This model is fully consistent with the fact that two conduction pathways are involved in the translocation of ions through the filter, and we show that the presence of a second path may actually bring advantages for the filter as a result of quantum interference. To highlight interferences and resonances in the model, we consider the selectivity filter to be driven by a controlled time-dependent external field, which changes the free-energy scenario and consequently the conduction of the ions. In particular, we demonstrate that the two-pathway conduction mechanism is more advantageous for the filter when dephasing in the transient configurations is lower than in the main configurations. As a matter of fact, K + ions in the main configurations are highly coordinated by oxygen atoms of the filter backbone, and this increases noise. Moreover, we also show that for a wide range of dephasing rates and driving frequencies, the two-pathway conduction used by the filter leads to higher ionic currents than the single–path model. (paper)
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
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
elementary reactions in HDS of thiophene. These linear correlations are used to develop a simple kinetic model, which qualitatively describes experimental trends in activity. The kinetic model identifies the HS-binding energy as a descriptor of HDS activity. This insight contributes to understanding...... the effect of promotion and structure-activity relationships. Graphical Abstract: [Figure not available: see fulltext.] © 2014 Springer Science+Business Media New York....
Kalman filtering and smoothing for linear wave equations with model error
International Nuclear Information System (INIS)
Lee, Wonjung; McDougall, D; Stuart, A M
2011-01-01
Filtering is a widely used methodology for the incorporation of observed data into time-evolving systems. It provides an online approach to state estimation inverse problems when data are acquired sequentially. The Kalman filter plays a central role in many applications because it is exact for linear systems subject to Gaussian noise, and because it forms the basis for many approximate filters which are used in high-dimensional systems. The aim of this paper is to study the effect of model error on the Kalman filter, in the context of linear wave propagation problems. A consistency result is proved when no model error is present, showing recovery of the true signal in the large data limit. This result, however, is not robust: it is also proved that arbitrarily small model error can lead to inconsistent recovery of the signal in the large data limit. If the model error is in the form of a constant shift to the velocity, the filtering and smoothing distributions only recover a partial Fourier expansion, a phenomenon related to aliasing. On the other hand, for a class of wave velocity model errors which are time dependent, it is possible to recover the filtering distribution exactly, but not the smoothing distribution. Numerical results are presented which corroborate the theory, and also propose a computational approach which overcomes the inconsistency in the presence of model error, by relaxing the model
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.
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.
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
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
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
Modeling the system dynamics for nutrient removal in an innovative septic tank media filter.
Xuan, Zhemin; Chang, Ni-Bin; Wanielista, Martin
2012-05-01
A next generation septic tank media filter to replace or enhance the current on-site wastewater treatment drainfields was proposed in this study. Unit operation with known treatment efficiencies, flow pattern identification, and system dynamics modeling was cohesively concatenated in order to prove the concept of a newly developed media filter. A multicompartmental model addressing system dynamics and feedbacks based on our assumed microbiological processes accounting for aerobic, anoxic, and anaerobic conditions in the media filter was constructed and calibrated with the aid of in situ measurements and the understanding of the flow patterns. Such a calibrated system dynamics model was then applied for a sensitivity analysis under changing inflow conditions based on the rates of nitrification and denitrification characterized through the field-scale testing. This advancement may contribute to design such a drainfield media filter in household septic tank systems in the future.
3D Microstructure Modeling of Porous Metal Filters
Directory of Open Access Journals (Sweden)
Vladimír Hejtmánek
2012-09-01
Full Text Available The contribution presents a modified method of stochastic reconstruction of two porous stainless-steel filters. The description of their microstructures was based on a combination of the two-point probability function for the void phase and the lineal-path functions for the void and solid phases. The method of stochastic reconstruction based on simulated annealing was capable of reproducing good connectivity of both phases, which was confirmed by calculating descriptors of the local porosity theory. Theoretical values of permeability were compared with their experimental counterparts measured by means of quasi-stationary permeation of four inert gases.
Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia
2012-01-01
Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.
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
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
Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The alignment accuracy of the strap-down inertial navigation system (SINS) of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.
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.
A low-complexity interacting multiple model filter for maneuvering target tracking
Khalid, Syed Safwan; Abrar, Shafayat
2017-01-01
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.
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.
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.
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.
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.
The Environmental Technology Verification report discusses the technology and performance of the Excel Filter, Model SBG24242898 air filter for dust and bioaerosol filtration manufactured by Glasfloss Industries, Inc. The pressure drop across the filter was 82 Pa clean and 348 Pa...
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.
Optimum filter selection for Dual Energy X-ray Applications through Analytical Modeling
International Nuclear Information System (INIS)
Koukou, V; Martini, N; Sotiropoulou, P; Nikiforidis, G; Michail, C; Kalyvas, N; Kandarakis, I; Fountos, G
2015-01-01
In this simulation study, an analytical model was used in order to determine the optimal acquisition parameters for a dual energy breast imaging system. The modeled detector system, consisted of a 33.91mg/cm 2 Gd 2 O 2 S:Tb scintillator screen, placed in direct contact with a high resolution CMOS sensor. Tungsten anode X-ray spectra, filtered with various filter materials and filter thicknesses were examined for both the low- and high-energy beams, resulting in 3375 combinations. The selection of these filters was based on their K absorption edge (K-edge filtering). The calcification signal-to-noise ratio (SNR tc ) and the mean glandular dose (MGD) were calculated. The total mean glandular dose was constrained to be within acceptable levels. Optimization was based on the maximization of the SNR tc /MGD ratio. The results showed that the optimum spectral combination was 40kVp with added beam filtration of 100 μm Ag and 70kVp Cu filtered spectrum of 1000 μm for the low- and high-energy, respectively. The minimum detectable calcification size was 150 μm. Simulations demonstrate that this dual energy X-ray technique could enhance breast calcification detection. (paper)
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
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 under...... buck chopper excitation is evaluated with the proposed model and compared with the conventional methods. The comparison shows that the proposed method results in a better core loss prediction under the premagnetized condition than that of conventional alternatives. Then, the core loss of the filter...... inductor with the pulsewidth modulated inverter excitation is evaluated, which shows that the proposed model not only accurately predicts the core loss but also identifies the hysteresis loss part. These results demonstrate that the approach can further be used for the development of magnetic materials...
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.
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...
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...
Sixdenier, Fabien; Yade, Ousseynou; Martin, Christian; Bréard, Arnaud; Vollaire, Christian
2018-05-01
Electromagnetic interference (EMI) filters design is a rather difficult task where engineers have to choose adequate magnetic materials, design the magnetic circuit and choose the size and number of turns. The final design must achieve the attenuation requirements (constraints) and has to be as compact as possible (goal). Alternating current (AC) analysis is a powerful tool to predict global impedance or attenuation of any filter. However, AC analysis are generally performed without taking into account the frequency-dependent complex permeability behaviour of soft magnetic materials. That's why, we developed two frequency-dependent complex permeability models able to be included into SPICE models. After an identification process, the performances of each model are compared to measurements made on a realistic EMI filter prototype in common mode (CM) and differential mode (DM) to see the benefit of the approach. Simulation results are in good agreement with the measured ones especially in the middle frequency range.
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...... and the open filters are surface piercing. Due to the few experimental data sets on sediment transport inside of rock layers, a sediment transport formulation has been proposed based on a matching between the numerical model and experimental data on the profile deformation inside an open filter. The rock layer...
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.
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.
Scalable learning of probabilistic latent models for collaborative filtering
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre
2015-01-01
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...
National Research Council Canada - National Science Library
Hatch, Andrew G; Smith, Ralph C; De, Tathagata; Salapaka, Murti V
2005-01-01
.... In this paper, we illustrate the construction of inverse filters, based on homogenized energy models, which can be used to approximately linearize the piezoceramic transducer behavior for linear...
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
Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue
2016-01-01
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. PMID:27754456
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.
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.
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.
Multiple Model Particle Filtering For Multi-Target Tracking
National Research Council Canada - National Science Library
Hero, Alfred; Kreucher, Chris; Kastella, Keith
2004-01-01
.... The details of this method have been presented elsewhere 1. One feature of real targets is that they are poorly described by a single kinematic model Target behavior may change dramatically i.e...
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...
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)
International Nuclear Information System (INIS)
Ling, Tsz Yan; Wang Jing; Pui, David Y. H.
2011-01-01
Membrane filtration has been demonstrated to be effective for the removal of liquid-borne nanoparticles (NPs). Such technique can be applied to purify and disinfect drinking water as well as remove NPs in highly pure chemicals used in the industries. This study aims to study the filtration process of a model membrane filter, the Nuclepore filter. Experiments were carried out using standard filtration tools and the nanoparticle tracking analysis (NTA) technique was used to measure particle (50–500 nm) concentration upstream and downstream of the filter to determine the filtration efficiency. The NTA technique has been calibrated using 150-nm polystyrene latex particles to determine its accuracy of particle concentration measurement. Measurements were found reliable within a certain concentration limit (about 10 8 –10 10 particles/cm 3 ), which is dependent on the camera settings during the measurement. Experimental results are comparable with previously published data obtained using the aerosolization method, validating the capability of the NTA technique. The capillary tube model modified from that developed for aerosol filtration was found to be useful to represent the experimental results, when a sticking coefficient of 0.15 is incorporated. This suggests that only 15% of the particle collisions with the filter results in successful attachment. The small sticking coefficient found can be explained by the unfavorable surface interactions between the particles and the filter medium.
Cheung, Shao-Yong; Lee, Chieh-Han; Yu, Hwa-Lung
2017-04-01
Due to the limited hydrogeological observation data and high levels of uncertainty within, parameter estimation of the groundwater model has been an important issue. There are many methods of parameter estimation, for example, Kalman filter provides a real-time calibration of parameters through measurement of groundwater monitoring wells, related methods such as Extended Kalman Filter and Ensemble Kalman Filter are widely applied in groundwater research. However, Kalman Filter method is limited to linearity. This study propose a novel method, Bayesian Maximum Entropy Filtering, which provides a method that can considers the uncertainty of data in parameter estimation. With this two methods, we can estimate parameter by given hard data (certain) and soft data (uncertain) in the same time. In this study, we use Python and QGIS in groundwater model (MODFLOW) and development of Extended Kalman Filter and Bayesian Maximum Entropy Filtering in Python in parameter estimation. This method may provide a conventional filtering method and also consider the uncertainty of data. This study was conducted through numerical model experiment to explore, combine Bayesian maximum entropy filter and a hypothesis for the architecture of MODFLOW groundwater model numerical estimation. Through the virtual observation wells to simulate and observe the groundwater model periodically. The result showed that considering the uncertainty of data, the Bayesian maximum entropy filter will provide an ideal result of real-time parameters estimation.
Offline estimation of decay time for an optical cavity with a low pass filter cavity model.
Kallapur, Abhijit G; Boyson, Toby K; Petersen, Ian R; Harb, Charles C
2012-08-01
This Letter presents offline estimation results for the decay-time constant for an experimental Fabry-Perot optical cavity for cavity ring-down spectroscopy (CRDS). The cavity dynamics are modeled in terms of a low pass filter (LPF) with unity DC gain. This model is used by an extended Kalman filter (EKF) along with the recorded light intensity at the output of the cavity in order to estimate the decay-time constant. The estimation results using the LPF cavity model are compared to those obtained using the quadrature model for the cavity presented in previous work by Kallapur et al. The estimation process derived using the LPF model comprises two states as opposed to three states in the quadrature model. When considering the EKF, this means propagating two states and a (2×2) covariance matrix using the LPF model, as opposed to propagating three states and a (3×3) covariance matrix using the quadrature model. This gives the former model a computational advantage over the latter and leads to faster execution times for the corresponding EKF. It is shown in this Letter that the LPF model for the cavity with two filter states is computationally more efficient, converges faster, and is hence a more suitable method than the three-state quadrature model presented in previous work for real-time estimation of the decay-time constant for the cavity.
Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel
2015-10-01
A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).
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...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Solution to the spectral filter problem of residual terrain modelling (RTM)
Rexer, Moritz; Hirt, Christian; Bucha, Blažej; Holmes, Simon
2018-06-01
In physical geodesy, the residual terrain modelling (RTM) technique is frequently used for high-frequency gravity forward modelling. In the RTM technique, a detailed elevation model is high-pass-filtered in the topography domain, which is not equivalent to filtering in the gravity domain. This in-equivalence, denoted as spectral filter problem of the RTM technique, gives rise to two imperfections (errors). The first imperfection is unwanted low-frequency (LF) gravity signals, and the second imperfection is missing high-frequency (HF) signals in the forward-modelled RTM gravity signal. This paper presents new solutions to the RTM spectral filter problem. Our solutions are based on explicit modelling of the two imperfections via corrections. The HF correction is computed using spectral domain gravity forward modelling that delivers the HF gravity signal generated by the long-wavelength RTM reference topography. The LF correction is obtained from pre-computed global RTM gravity grids that are low-pass-filtered using surface or solid spherical harmonics. A numerical case study reveals maximum absolute signal strengths of ˜ 44 mGal (0.5 mGal RMS) for the HF correction and ˜ 33 mGal (0.6 mGal RMS) for the LF correction w.r.t. a degree-2160 reference topography within the data coverage of the SRTM topography model (56°S ≤ φ ≤ 60°N). Application of the LF and HF corrections to pre-computed global gravity models (here the GGMplus gravity maps) demonstrates the efficiency of the new corrections over topographically rugged terrain. Over Switzerland, consideration of the HF and LF corrections reduced the RMS of the residuals between GGMplus and ground-truth gravity from 4.41 to 3.27 mGal, which translates into ˜ 26% improvement. Over a second test area (Canada), our corrections reduced the RMS of the residuals between GGMplus and ground-truth gravity from 5.65 to 5.30 mGal (˜ 6% improvement). Particularly over Switzerland, geophysical signals (associated, e.g. with
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...... tracer, are performed during an operational cycle of a filter to examine how the filter flow changes with time. The data is used to validate a mathematical model that can both predict process performance and to gain an understanding of how dynamic conditions can influence filter performance....... The mathematical model developed is intended to assist in the design of new filters, set up of pilot plant studies, and as a tool to troubleshoot existing problems in full scale filters. Unlike previous models, the model developed accounts for the effects of particle/precipitate accumulation and its effects...
Virtual microphone sensing through vibro-acoustic modelling and Kalman filtering
van de Walle, A.; Naets, F.; Desmet, W.
2018-05-01
This work proposes a virtual microphone methodology which enables full field acoustic measurements for vibro-acoustic systems. The methodology employs a Kalman filtering framework in order to combine a reduced high-fidelity vibro-acoustic model with a structural excitation measurement and small set of real microphone measurements on the system under investigation. By employing model order reduction techniques, a high order finite element model can be converted in a much smaller model which preserves the desired accuracy and maintains the main physical properties of the original model. Due to the low order of the reduced-order model, it can be effectively employed in a Kalman filter. The proposed methodology is validated experimentally on a strongly coupled vibro-acoustic system. The virtual sensor vastly improves the accuracy with respect to regular forward simulation. The virtual sensor also allows to recreate the full sound field of the system, which is very difficult/impossible to do through classical measurements.
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.
Transfer Learning for Collaborative Filtering Using a Psychometrics Model
Directory of Open Access Journals (Sweden)
Haijun Zhang
2016-01-01
Full Text Available In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain to help improve the recommendation performance of another website (target domain is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance.
Ma, Hongchao; Cai, Zhan; Zhang, Liang
2018-01-01
This paper discusses airborne light detection and ranging (LiDAR) point cloud filtering (a binary classification problem) from the machine learning point of view. We compared three supervised classifiers for point cloud filtering, namely, Adaptive Boosting, support vector machine, and random forest (RF). Nineteen features were generated from raw LiDAR point cloud based on height and other geometric information within a given neighborhood. The test datasets issued by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to evaluate the performance of the three filtering algorithms; RF showed the best results with an average total error of 5.50%. The paper also makes tentative exploration in the application of transfer learning theory to point cloud filtering, which has not been introduced into the LiDAR field to the authors' knowledge. We performed filtering of three datasets from real projects carried out in China with RF models constructed by learning from the 15 ISPRS datasets and then transferred with little to no change of the parameters. Reliable results were achieved, especially in rural area (overall accuracy achieved 95.64%), indicating the feasibility of model transfer in the context of point cloud filtering for both easy automation and acceptable accuracy.
Bergner, Yoav; Droschler, Stefan; Kortemeyer, Gerd; Rayyan, Saif; Seaton, Daniel; Pritchard, David E.
2012-01-01
We apply collaborative filtering (CF) to dichotomously scored student response data (right, wrong, or no interaction), finding optimal parameters for each student and item based on cross-validated prediction accuracy. The approach is naturally suited to comparing different models, both unidimensional and multidimensional in ability, including a…
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.
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,
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...
Filter Design for Failure Detection and Isolation in the Presence of Modeling Erros and Disturbances
DEFF Research Database (Denmark)
Stoustrup, Jakob; Niemann, Hans Henrik
1996-01-01
The design problem of filters for robust Failure Detectionand Isolation, (FDI) is addressed in this paper. The failure detectionproblem will be considered with respect to both modeling errors anddisturbances. Both an approach based on failure detection observes aswell as an approach based...
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
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,
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.
Speed Estimation of Induction Motor Using Model Reference Adaptive System with Kalman Filter
Directory of Open Access Journals (Sweden)
Pavel Brandstetter
2013-01-01
Full Text Available The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control.
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
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.
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...
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
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.
Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin
Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.
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.
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.
The Hierarchical Trend Model for property valuation and local price indices
Francke, M.K.; Vos, G.A.
2002-01-01
This paper presents a hierarchical trend model (HTM) for selling prices of houses, addressing three main problems: the spatial and temporal dependence of selling prices and the dependency of price index changes on housing quality. In this model the general price trend, cluster-level price trends,
Directory of Open Access Journals (Sweden)
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.
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
An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models
Energy Technology Data Exchange (ETDEWEB)
Shin, Hoyoung; Jae, Moosung [Hanyang University, Seoul (Korea, Republic of)
2016-10-15
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.
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.
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
Scargle, Jeffrey D.
1990-01-01
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
Pulse cleaning flow models and numerical computation of candle ceramic filters.
Tian, Gui-shan; Ma, Zhen-ji; Zhang, Xin-yi; Xu, Ting-xiang
2002-04-01
Analytical and numerical computed models are developed for reverse pulse cleaning system of candle ceramic filters. A standard turbulent model is demonstrated suitably to the designing computation of reverse pulse cleaning system from the experimental and one-dimensional computational result. The computed results can be used to guide the designing of reverse pulse cleaning system, which is optimum Venturi geometry. From the computed results, the general conclusions and the designing methods are obtained.
Modeling pitting growth data and predicting degradation trend
International Nuclear Information System (INIS)
Viglasky, T.; Awad, R.; Zeng, Z.; Riznic, J.
2007-01-01
A non-statistical modeling approach to predict material degradation is presented in this paper. In this approach, the original data series is processed using Accumulated Generating Operation (AGO). With the aid of the AGO which weakens the random fluctuation embedded in the data series, an approximately exponential curve is established. The generated data series described by the exponential curve is then modeled by a differential equation. The coefficients of the differential equation can be deduced by approximate difference formula based on least-squares algorithm. By solving the differential equation and processing an inverse AGO, a predictive model can be obtained. As this approach is not established on the basis of statistics, the prediction can be performed with a limited amount of data. Implementation of this approach is demonstrated by predicting the pitting growth rate in specimens and wear trend in steam generator tubes. The analysis results indicate that this approach provides a powerful tool with reasonable precision to predict material degradation. (author)
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 and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors whilst 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 $\\sim$60 nm rms error reduction can be achieved with the distributed Kalman filter embodying anti- aliasing reconstructors on 10 m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few ${\\lambda}/D$ separations ($\\sim1-5{\\lambda}/D$) for a 0 magnitude star and reaching close to one order of magnitude for a 12 magnitude star.
Utility of silicone filtering for diffusive model CO2 sensors in field experiments
Directory of Open Access Journals (Sweden)
Shinjiro Ohkubo
2013-05-01
Full Text Available Installing a diffusive model CO2 sensor in the soil is a direct and useful method to observe the time variation of gas CO2 concentration in soil. Furthermore, it requires no bulky measurement system. A hydrophobic silicone filter prevents water infiltration. Therefore, a sensor whose detection element is covered with a silicone filter can be durable in the field even when experiencing inundation (e.g. farmland with snow melting, wetland with varying water level. The utility of a diffusive model of CO2 sensor covered with silicone filter was examined in laboratory and field experiments. Applying the silicone filter delays the response to change in ambient CO2 concentration, which results from lower gas permeability than those of other conventionally used filters made of materials, such as polytetrafluoroethylene. Theoretically, apart from the precision of the sensor itself, diurnal variation of soil gas CO2 concentration is calculable from obtained series of data with a silicone-covered sensor with negligible error. The error is estimated at approximately 1% of the diurnal amplitude in most cases of a 10-min logging interval. Drastic changes that occur, such as those of a rainfall event, cause a larger gap separating calculated and real values. However, the proportion of this gap to the extent of the drastic increase was extremely small (0.43% for a 10-min logging interval. For accurate estimation, a smoothly varied data series must be prepared as input data. Using a moving average or applying a fitting curve can be useful when using a sensor or data logger with low resolution. Estimating the gas permeability coefficient is crucial for calculation. The gas permeability coefficient can be estimated through laboratory experiments. This study revealed the possibility of evaluating the time variation of soil gas CO2 concentration by installing a diffusive model of silicone-covered sensor in an inundated field.
Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters
Directory of Open Access Journals (Sweden)
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.
Institute of Scientific and Technical Information of China (English)
FENG Bo; MA Hong-Bin; FU Meng-Yin; WANG Shun-Ting
2013-01-01
Kalman filtering techniques have been widely used in many applications,however,standard Kalman filters for linear Gaussian systems usually cannot work well or even diverge in the presence of large model uncertainty.In practical applications,it is expensive to have large number of high-cost experiments or even impossible to obtain an exact system model.Motivated by our previous pioneering work on finite-model adaptive control,a framework of finite-model Kalman filtering is introduced in this paper.This framework presumes that large model uncertainty may be restricted by a finite set of known models which can be very different from each other.Moreover,the number of known models in the set can be flexibly chosen so that the uncertain model may always be approximated by one of the known models,in other words,the large model uncertainty is "covered" by the "convex hull" of the known models.Within the presented framework according to the idea of adaptive switching via the minimizing vector distance principle,a simple finite-model Kalman filter,MVDP-FMKF,is mathematically formulated and illustrated by extensive simulations.An experiment of MEMS gyroscope drift has verified the effectiveness of the proposed algorithm,indicating that the mechanism of finite-model Kalman filter is useful and efficient in practical applications of Kalman filters,especially in inertial navigation systems.
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.
Fukumori, Ichiro; Malanotte-Rizzoli, Paola
1995-04-01
A practical method of data assimilation for use with large, nonlinear, ocean general circulation models is explored. A Kaiman filter based on approximations of the state error covariance matrix is presented, employing a reduction of the effective model dimension, the error's asymptotic steady state limit, and a time-invariant linearization of the dynamic model for the error integration. The approximations lead to dramatic computational savings in applying estimation theory to large complex systems. We examine the utility of the approximate filter in assimilating different measurement types using a twin experiment of an idealized Gulf Stream. A nonlinear primitive equation model of an unstable east-west jet is studied with a state dimension exceeding 170,000 elements. Assimilation of various pseudomeasurements are examined, including velocity, density, and volume transport at localized arrays and realistic distributions of satellite altimetry and acoustic tomography observations. Results are compared in terms of their effects on the accuracies of the estimation. The approximate filter is shown to outperform an empirical nudging scheme used in a previous study. The examples demonstrate that useful approximate estimation errors can be computed in a practical manner for general circulation models.
Forecasting oil price trends using wavelets and hidden Markov models
International Nuclear Information System (INIS)
Souza e Silva, Edmundo G. de; Souza e Silva, Edmundo A. de; Legey, Luiz F.L.
2010-01-01
The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market. (author)
Analyzing research trends on drug safety using topic modeling.
Zou, Chen
2018-04-06
Published drug safety data has evolved in the past decade due to scientific and technological advances in the relevant research fields. Considering that a vast amount of scientific literature has been published in this area, it is not easy to identify the key information. Topic modeling has emerged as a powerful tool to extract meaningful information from a large volume of unstructured texts. Areas covered: We analyzed the titles and abstracts of 4347 articles in four journals dedicated to drug safety from 2007 to 2016. We applied Latent Dirichlet allocation (LDA) model to extract 50 main topics, and conducted trend analysis to explore the temporal popularity of these topics over years. Expert Opinion/Commentary: We found that 'benefit-risk assessment and communication', 'diabetes' and 'biologic therapy for autoimmune diseases' are the top 3 most published topics. The topics relevant to the use of electronic health records/observational data for safety surveillance are becoming increasingly popular over time. Meanwhile, there is a slight decrease in research on signal detection based on spontaneous reporting, although spontaneous reporting still plays an important role in benefit-risk assessment. The topics related to medical conditions and treatment showed highly dynamic patterns over time.
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...... noise term should account for model errors. A nonlinear filtering method is used to compute estimates of the state variables, and the model parameters are estimated by a quasimaximum likelihood method provided that some assumptions are imposed on the model residuals. Both Monte Carlo simulation results...
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2015-01-01
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
Directory of Open Access Journals (Sweden)
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.
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.
Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor
Directory of Open Access Journals (Sweden)
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.
Sky-Hook Control and Kalman Filtering in Nonlinear Model of Tracked Vehicle Suspension System
Directory of Open Access Journals (Sweden)
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.
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......Because of the low power losses and moderate cost, the magnetic powder cores are popular in producing the filtering inductors for the high efficient and cost-effective power converters. However, the soft magnetic property of the powder cores leads to the wide variation of inductance along......, 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...
Huang, Guanghui; Wan, Jianping; Chen, Hui
2013-02-01
Nonlinear stochastic differential equation models with unobservable state variables are now widely used in analysis of PK/PD data. Unobservable state variables are usually estimated with extended Kalman filter (EKF), and the unknown pharmacokinetic parameters are usually estimated by maximum likelihood estimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE is known to be biased downwards. A density-based Monte Carlo filter (DMF) is proposed to estimate the unobservable state variables, and a simulation-based M estimator is proposed to estimate the unknown parameters in this paper, where a genetic algorithm is designed to search the optimal values of pharmacokinetic parameters. The performances of EKF and DMF are compared through simulations for discrete time and continuous time systems respectively, and it is found that the results based on DMF are more accurate than those given by EKF with respect to mean absolute error. Copyright © 2012 Elsevier Ltd. All rights reserved.
Hao Chen; Xiaoyun Xie; Wanneng Shu; Naixue Xiong
2016-01-01
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 livin...
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
Probability-based collaborative filtering model for predicting gene–disease associations
Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan
2017-01-01
Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. Methods We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our mo...
Scanlon, Bridget R.; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y.; van Beek, Ludovicus P. H.; Wiese, David N.; Reedy, Robert C.; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F. P.
2018-01-01
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 km3/y) and increasing (≥0.5 km3/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 km3/y, whereas most models estimate decreasing trends (−71 to 11 km3/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km3/y) but negative for models (−450 to −12 km3/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. PMID:29358394
High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.
Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong
2018-08-01
This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.
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.
Modelling and simulation of lamp-pumped thallium atomic line filters
International Nuclear Information System (INIS)
Molisch, A.F.
1994-06-01
Atomic Line Filters (ALFs) are ultra-narrow-band, wide-field-of-view optical filters for the detection of weak optical signals embedded in broadband background noise. The central component is a quartz cell filled with atomic vapor where signal photons are absorbed and subsequently re-emitted at a different wavelength. At the 'Institut fuer Nachrichtentechnik und Hochfrequenztechnik', an ALF based on Thallium (Tl) vapor, which is pumped by a Tl spectral lamp, has been under development. The aim of this thesis is to model the physical processes in this filter (especially in the vapor cell) and to make simulations in order to find the optimum design. For this purpose, a theoretical 'toolbox' is to be created, which should be capable of describing quantitatively the various physical effects. The accuracy of the simulation should be about ±10 %, i.e. about the accuracy of the available atomic data. In part I, the physics that form the basis of ALFs are briefly explained. In chapter 1, the principle of an ALF is explained, and the parameters that describe such filters are defined. In the next two chapters, atomic energy levels and atomic line shapes are described. We then summarize the data of the UV and green resonance lines of Thallium. After giving an overview over the methods of description for trapping problems, (Holstein equation, equation-of-radiative-transfer plus rate-equation, Monte Carlo simulation), we describe the (generalized) Milne theory, an approximate method which allows a description of trapping by a differential equation. In part II, we then make use of these formalisms to describe the Tl ALF mathematically. After giving a description of the whole filter system, we show the various influences on the lifetime of the metastable Tl atoms. Then the pump phase of the filter is described. In that phase, we have non-linear trapping in a 3-level system. This problem is solved by a combination of finite-difference solution of the equation of radiative
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
Selection vector filter framework
Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.
2003-10-01
We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.
International Nuclear Information System (INIS)
Gershgorin, B.; Harlim, J.; Majda, A.J.
2010-01-01
The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates
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 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 mostly underestimated. Future GRACE and model studies should try to reduce the various sources of uncertainty in water storage trends and should consider expanding the modeled storage capacity of the soil profiles and their interaction with groundwater.
El Gharamti, Mohamad; Hoteit, Ibrahim
2014-01-01
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.
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.
Li, Mingming; Li, Lin; Li, Qiang; Zou, Zongshu
2018-05-01
A filter-based Euler-Lagrange multiphase flow model is used to study the mixing behavior in a combined blowing steelmaking converter. The Euler-based volume of fluid approach is employed to simulate the top blowing, while the Lagrange-based discrete phase model that embeds the local volume change of rising bubbles for the bottom blowing. A filter-based turbulence method based on the local meshing resolution is proposed aiming to improve the modeling of turbulent eddy viscosities. The model validity is verified through comparison with physical experiments in terms of mixing curves and mixing times. The effects of the bottom gas flow rate on bath flow and mixing behavior are investigated and the inherent reasons for the mixing result are clarified in terms of the characteristics of bottom-blowing plumes, the interaction between plumes and top-blowing jets, and the change of bath flow structure.
Trending Topic Extraction using Topic Models and Biterm Discrimination
Minor Eduardo Quesada Grosso; Edgar Casasola; Jorge Antonio Leoni de León
2017-01-01
Mining and exploitation of data in social networks has been the focus of many efforts, but despite the resources and energy invested, still remains a lot for doing given its complexity, which requires the adoption of a multidisciplinary approach. Specifically, on what concerns to this research, the content of the texts published regularly, and at a very rapid pace, at sites of microblogs (eg Twitter.com) can be used to analyze global and local trends. These trends are marked by micr...
Multi-sensor fusion with interacting multiple model filter for improved aircraft position accuracy.
Cho, Taehwan; Lee, Changho; Choi, Sangbang
2013-03-27
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.
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.
An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model.
Fan, Qigao; Zhang, Hai; Sun, Yan; Zhu, Yixin; Zhuang, Xiangpeng; Jia, Jie; Zhang, Pengsong
2018-05-02
Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.
Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems
Directory of Open Access Journals (Sweden)
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.
MODEL-ORIENTED METHOD OF DESIGN IMPLEMENTATION WHEN CREATING DIGITAL FILTERS
Directory of Open Access Journals (Sweden)
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.
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.
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.
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...
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
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.
Building a good initial model for full-waveform inversion using frequency shift filter
Wang, Guanchao; Wang, Shangxu; Yuan, Sanyi; Lian, Shijie
2018-05-01
Accurate initial model or available low-frequency data is an important factor in the success of full waveform inversion (FWI). The low-frequency helps determine the kinematical relevant components, low-wavenumber of the velocity model, which are in turn needed to avoid FWI trap in local minima or cycle-skipping. However, in the field, acquiring data that common point of low- and high-frequency signal, then utilize the high-frequency data to obtain the low-wavenumber velocity model. It is well known that the instantaneous amplitude envelope of a wavelet is invariant under frequency shift. This means that resolution is constant for a given frequency bandwidth, and independent of the actual values of the frequencies. Based on this property, we develop a frequency shift filter (FSF) to build the relationship between low- and high-frequency information with a constant frequency bandwidth. After that, we can use the high-frequency information to get a plausible recovery of the low-wavenumber velocity model. Numerical results using synthetic data from the Marmousi and layer model demonstrate that our proposed envelope misfit function based on the frequency shift filter can build an initial model with more accurate long-wavelength components, when low-frequency signals are absent in recorded data.
El Gharamti, Mohamad; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2015-01-01
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
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)
International Nuclear Information System (INIS)
Harlim, John; Mahdi, Adam; Majda, Andrew J.
2014-01-01
A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model
Aihara, ShinIchi; Bagchi, Arunabha; Saha, S.
Despite the success of particle filter, there are two factors which cause difficulties in its implementation. The first one is the choice of importance functions commonly used in the literature which are far from being optimal. The second one is the combined state and parameter estimation problem.
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.
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.
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.
Extended Kalman filtering for model-based sensor fusion in robotics
International Nuclear Information System (INIS)
Fujii, Yuji; Wehe, D.K.; Lee, J.C.
1990-01-01
Remote surveillance and maintenance in advanced nuclear power plants will benefit from the increased utilization of mobile robotic systems. For these robotic systems to function most effectively in hazardous environments, they should be able to make decisions and take necessary actions with minimal human supervision. To accomplish this, the robot must be able to construct an accurate model of the power plant environment from diverse sensory data and a priori maps. In this paper, the authors demonstrate how a recursive parameter estimation technique known as Kalman filtering can integrate noisy data from various sensors to construct a consistent representation of the sensed environment
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 is mainly caused by photoche...
Application of a baseflow filter for evaluating model structure suitability of the IHACRES CMD
Kim, H. S.
2015-02-01
The main objective of this study was to assess the predictive uncertainty from the rainfall-runoff model structure coupling a conceptual module (non-linear module) with a metric transfer function module (linear module). The methodology was primarily based on the comparison between the outputs of the rainfall-runoff model and those from an alternative model approach. An alternative model approach was used to minimise uncertainties arising from data and the model structure. A baseflow filter was adopted to better understand deficiencies in the forms of the rainfall-runoff model by avoiding the uncertainties related to data and the model structure. The predictive uncertainty from the model structure was investigated for representative groups of catchments having similar hydrological response characteristics in the upper Murrumbidgee Catchment. In the assessment of model structure suitability, the consistency (or variability) of catchment response over time and space in model performance and parameter values has been investigated to detect problems related to the temporal and spatial variability of the model accuracy. The predictive error caused by model uncertainty was evaluated through analysis of the variability of the model performance and parameters. A graphical comparison of model residuals, effective rainfall estimates and hydrographs was used to determine a model's ability related to systematic model deviation between simulated and observed behaviours and general behavioural differences in the timing and magnitude of peak flows. The model's predictability was very sensitive to catchment response characteristics. The linear module performs reasonably well in the wetter catchments but has considerable difficulties when applied to the drier catchments where a hydrologic response is dominated by quick flow. The non-linear module has a potential limitation in its capacity to capture non-linear processes for converting observed rainfall into effective rainfall in
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.
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.
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....... 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...... 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....
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.
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.
Madi, Mahmoud K; Karameh, Fadi N
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
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.
International Nuclear Information System (INIS)
Wu, Xuedong; Zhu, Zhiyu; Su, Xunliang; Fan, Shaosheng; Du, Zhaoping; Chang, Yanchao; Zeng, Qingjun
2015-01-01
Wind speed prediction is one important methods to guarantee the wind energy integrated into the whole power system smoothly. However, wind power has a non–schedulable nature due to the strong stochastic nature and dynamic uncertainty nature of wind speed. Therefore, wind speed prediction is an indispensable requirement for power system operators. Two new approaches for hourly wind speed prediction are developed in this study by integrating the single multiplicative neuron model and the iterated nonlinear filters for updating the wind speed sequence accurately. In the presented methods, a nonlinear state–space model is first formed based on the single multiplicative neuron model and then the iterated nonlinear filters are employed to perform dynamic state estimation on wind speed sequence with stochastic uncertainty. The suggested approaches are demonstrated using three cases wind speed data and are compared with autoregressive moving average, artificial neural network, kernel ridge regression based residual active learning and single multiplicative neuron model methods. Three types of prediction errors, mean absolute error improvement ratio and running time are employed for different models’ performance comparison. Comparison results from Tables 1–3 indicate that the presented strategies have much better performance for hourly wind speed prediction than other technologies. - Highlights: • Developed two novel hybrid modeling methods for hourly wind speed prediction. • Uncertainty and fluctuations of wind speed can be better explained by novel methods. • Proposed strategies have online adaptive learning ability. • Proposed approaches have shown better performance compared with existed approaches. • Comparison and analysis of two proposed novel models for three cases are provided
da Silva, Claudia Pereira; Emídio, Elissandro Soares; de Marchi, Mary Rosa Rodrigues
2015-01-01
This paper describes the validation of a method consisting of solid-phase extraction followed by gas chromatography-tandem mass spectrometry for the analysis of the ultraviolet (UV) filters benzophenone-3, ethylhexyl salicylate, ethylhexyl methoxycinnamate and octocrylene. The method validation criteria included evaluation of selectivity, analytical curve, trueness, precision, limits of detection and limits of quantification. The non-weighted linear regression model has traditionally been used for calibration, but it is not necessarily the optimal model in all cases. Because the assumption of homoscedasticity was not met for the analytical data in this work, a weighted least squares linear regression was used for the calibration method. The evaluated analytical parameters were satisfactory for the analytes and showed recoveries at four fortification levels between 62% and 107%, with relative standard deviations less than 14%. The detection limits ranged from 7.6 to 24.1 ng L(-1). The proposed method was used to determine the amount of UV filters in water samples from water treatment plants in Araraquara and Jau in São Paulo, Brazil. Copyright © 2014 Elsevier B.V. All rights reserved.
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.
Akbarnejad, Shahin; Jonsson, Lage Tord Ingemar; Kennedy, Mark William; Aune, Ragnhild Elizabeth; Jönsson, Pӓr Göran
2016-08-01
This paper presents experimental results of pressure drop measurements on 30, 50, and 80 pores per inch (PPI) commercial alumina ceramic foam filters (CFF) and compares the obtained pressure drop profiles to numerically modeled values. In addition, it is aimed at investigating the adequacy of the mathematical correlations used in the analytical and the computational fluid dynamics (CFD) simulations. It is shown that the widely used correlations for predicting pressure drop in porous media continuously under-predict the experimentally obtained pressure drop profiles. For analytical predictions, the negative deviations from the experimentally obtained pressure drop using the unmodified Ergun and Dietrich equations could be as high as 95 and 74 pct, respectively. For the CFD predictions, the deviation to experimental results is in the range of 84.3 to 88.5 pct depending on filter PPI. Better results can be achieved by applying the Forchheimer second-order drag term instead of the Brinkman-Forchheimer drag term. Thus, the final deviation of the CFD model estimates lie in the range of 0.3 to 5.5 pct compared to the measured values.
Directory of Open Access Journals (Sweden)
Tao Li
2016-03-01
Full Text Available The derivation of a conventional error model for the miniature gyroscope-based measurement while drilling (MGWD system is based on the assumption that the errors of attitude are small enough so that the direction cosine matrix (DCM can be approximated or simplified by the errors of small-angle attitude. However, the simplification of the DCM would introduce errors to the navigation solutions of the MGWD system if the initial alignment cannot provide precise attitude, especially for the low-cost microelectromechanical system (MEMS sensors operated in harsh multilateral horizontal downhole drilling environments. This paper proposes a novel nonlinear error model (NNEM by the introduction of the error of DCM, and the NNEM can reduce the propagated errors under large-angle attitude error conditions. The zero velocity and zero position are the reference points and the innovations in the states estimation of particle filter (PF and Kalman filter (KF. The experimental results illustrate that the performance of PF is better than KF and the PF with NNEM can effectively restrain the errors of system states, especially for the azimuth, velocity, and height in the quasi-stationary condition.
Li, Tao; Yuan, Gannan; Li, Wang
2016-03-15
The derivation of a conventional error model for the miniature gyroscope-based measurement while drilling (MGWD) system is based on the assumption that the errors of attitude are small enough so that the direction cosine matrix (DCM) can be approximated or simplified by the errors of small-angle attitude. However, the simplification of the DCM would introduce errors to the navigation solutions of the MGWD system if the initial alignment cannot provide precise attitude, especially for the low-cost microelectromechanical system (MEMS) sensors operated in harsh multilateral horizontal downhole drilling environments. This paper proposes a novel nonlinear error model (NNEM) by the introduction of the error of DCM, and the NNEM can reduce the propagated errors under large-angle attitude error conditions. The zero velocity and zero position are the reference points and the innovations in the states estimation of particle filter (PF) and Kalman filter (KF). The experimental results illustrate that the performance of PF is better than KF and the PF with NNEM can effectively restrain the errors of system states, especially for the azimuth, velocity, and height in the quasi-stationary condition.
A coupling method for a cardiovascular simulation model which includes the Kalman filter.
Hasegawa, Yuki; Shimayoshi, Takao; Amano, Akira; Matsuda, Tetsuya
2012-01-01
Multi-scale models of the cardiovascular system provide new insight that was unavailable with in vivo and in vitro experiments. For the cardiovascular system, multi-scale simulations provide a valuable perspective in analyzing the interaction of three phenomenons occurring at different spatial scales: circulatory hemodynamics, ventricular structural dynamics, and myocardial excitation-contraction. In order to simulate these interactions, multiscale cardiovascular simulation systems couple models that simulate different phenomena. However, coupling methods require a significant amount of calculation, since a system of non-linear equations must be solved for each timestep. Therefore, we proposed a coupling method which decreases the amount of calculation by using the Kalman filter. In our method, the Kalman filter calculates approximations for the solution to the system of non-linear equations at each timestep. The approximations are then used as initial values for solving the system of non-linear equations. The proposed method decreases the number of iterations required by 94.0% compared to the conventional strong coupling method. When compared with a smoothing spline predictor, the proposed method required 49.4% fewer iterations.
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...
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.
Probability-based collaborative filtering model for predicting gene-disease associations.
Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan
2017-12-28
Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.
Energy Technology Data Exchange (ETDEWEB)
Paluch, W.
1987-07-01
Filters used for mine draining in brown coal surface mines are tested by the Mine Draining Department of Poltegor. Laboratory tests of new types of filters developed by Poltegor are analyzed. Two types of tests are used: tests of scale filter models and tests of experimental units of new filters. Design and operation of the test stands used for testing mechanical properties and hydraulic properties of filters for coal mines are described: dimensions, pressure fluctuations, hydraulic equipment. Examples of testing large-diameter filters for brown coal mines are discussed.
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.
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)
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
Modeling the hyperfine state selectivity of a short lamb-shift spin-filter polarimeter
International Nuclear Information System (INIS)
Mendez, A.J.; Roper, C.D.; Clegg, T.B.
1995-01-01
An rf cavity, previously used as a spin filter in a Lamb-shift polarized ion source, is being adapted for use as a polarimeter in an atomic beam polarized hydrogen and deuterium ion source. Paramount among the design criteria is maintaining the current source performance while providing on-line beam polarization monitoring. This requires minimizing both the polarimeter system length and the coupling with the magnetic fields of the other ion source systems. Detailed computer calculations have modeled the four-level interaction involving the 2S 1/2 -2P 1/2 states of the atomic beam. These indicate that a significantly shorter spin-filter cavity and uniform axial magnetic field than used in the Lamb-shift source do not compromise the spin-state selectivity. The calculations also predict the axial magnetic field uniformity needed as well as the gains achieved from proper shaping of the cavity rf and dc fields. copyright 1995 American Institute of Physics
Analytical model and figures of merit for filtered Microwave Photonic Links.
Gasulla, Ivana; Capmany, José
2011-09-26
The concept of filtered Microwave Photonic Links is proposed in order to provide the most general and versatile description of complex analog photonic systems. We develop a field propagation model where a global optical filter, characterized by its optical transfer function, embraces all the intermediate optical components in a linear link. We assume a non-monochromatic light source characterized by an arbitrary spectral distribution which has a finite linewidth spectrum and consider both intensity modulation and phase modulation with balanced and single detection. Expressions leading to the computation of the main figures of merit concerning the link gain, noise and intermodulation distortion are provided which, to our knowledge, are not available in the literature. The usefulness of this derivation resides in the capability to directly provide performance criteria results for complex links just by substituting in the overall closed-form formulas the numerical or measured optical transfer function characterizing the link. This theory is presented thus as a potential tool for a wide range of relevant microwave photonic application cases which is extendable to multiport radio over fiber systems. © 2011 Optical Society of America
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.
Estimation of the Diesel Particulate Filter Soot Load Based on an Equivalent Circuit Model
Directory of Open Access Journals (Sweden)
Yanting Du
2018-02-01
Full Text Available In order to estimate the diesel particulate filter (DPF soot load and improve the accuracy of regeneration timing, a novel method based on an equivalent circuit model is proposed based on the electric-fluid analogy. This proposed method can reduce the impact of the engine transient operation on the soot load, accurately calculate the flow resistance, and improve the estimation accuracy of the soot load. Firstly, the least square method is used to identify the flow resistance based on the World Harmonized Transient Cycle (WHTC test data, and the relationship between flow resistance, exhaust temperature and soot load is established. Secondly, the online estimation of the soot load is achieved by using the dual extended Kalman filter (DEKF. The results show that this method has good convergence and robustness with the maximal absolute error of 0.2 g/L at regeneration timing, which can meet engineering requirements. Additionally, this method can estimate the soot load under engine transient operating conditions and avoids a large number of experimental tests, extensive calibration and the analysis of complex chemical reactions required in traditional methods.
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.
Bartlett correction in the stable AR(1) model with intercept and trend
van Giersbergen, N.P.A.
2004-01-01
The Bartlett correction is derived for testing hypotheses about the autoregressive parameter ρ in the stable: (i) AR(1) model; (ii) AR(1) model with intercept; (iii) AR(1) model with intercept and linear trend. The correction is found explicitly as a function of ρ. In the models with deterministic
Wang, W.; Hashimoto, H.; Ganguly, S.; Votava, P.; Nemani, R. R.; Myneni, R. B.
2010-12-01
Large uncertainties exist in our understanding of the trends and variability in global net primary production (NPP) and its controls. This study attempts to address this question through a multi-model ensemble experiment. In particular, we drive ecosystem models including CASA, LPJ, Biome-BGC, TOPS-BGC, and BEAMS with a long-term climate dataset (i.e., CRU-NCEP) to estimate global NPP from 1901 to 2009 at a spatial resolution of 0.5 x 0.5 degree. We calculate the trends of simulated NPP during different time periods and test their sensitivities to climate variables of solar radiation, air temperature, precipitation, vapor pressure deficit (VPD), and atmospheric CO2 levels. The results indicate a large diversity among the simulated NPP trends over the past 50 years, ranging from nearly no trend to an increasing trend of ~0.1 PgC/yr. Spatial patterns of the NPP generally show positive trends in boreal forests, induced mainly by increasing temperatures in these regions; they also show negative trends in the tropics, although the spatial patterns are more diverse. These diverse trends result from different climatic sensitivities of NPP among the tested models. Depending the ecological processes (e.g., photosynthesis or respiration) a model emphasizes, it can be more or less responsive to changes in solar radiation, temperatures, water, or atmospheric CO2 levels. Overall, these results highlight the limit of current ecosystem models in simulating NPP, which cannot be easily observed. They suggest that the traditional single-model approach is not ideal for characterizing trends and variability in global carbon cycling.
Interactive Gaussian Graphical Models for Discovering Depth Trends in ChemCam Data
Oyen, D. A.; Komurlu, C.; Lanza, N. L.
2018-04-01
Interactive Gaussian graphical models discover surface compositional features on rocks in ChemCam targets. Our approach visualizes shot-to-shot relationships among LIBS observations, and identifies the wavelengths involved in the trend.
The changing model of big pharma: impact of key trends.
Gautam, Ajay; Pan, Xiaogang
2016-03-01
Recent years have seen exciting breakthroughs in biomedical sciences that are producing truly novel therapeutics for unmet patient needs. However, the pharmaceutical industry is also facing significant barriers in the form of pricing and reimbursement, continued patent expirations and challenging market dynamics. In this article, we have analyzed data from the 1995-2015 period, on key aspects such as revenue distribution, research units, portfolio mix and emerging markets to identify four key trends that help to understand the change in strategic focus, realignment of R&D footprint, the shift from primary care toward specialty drugs and biologics and the growth of emerging markets as major revenue drivers for big pharma. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
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.
Nere, Nandkishor K; Allen, Kimberley C; Marek, James C; Bordawekar, Shailendra V
2012-10-01
Drying an early stage active pharmaceutical ingredient candidate required excessively long cycle times in a pilot plant agitated filter dryer. The key to faster drying is to ensure sufficient heat transfer and minimize mass transfer limitations. Designing the right mixing protocol is of utmost importance to achieve efficient heat transfer. To this order, a composite model was developed for the removal of bound solvent that incorporates models for heat transfer and desolvation kinetics. The proposed heat transfer model differs from previously reported models in two respects: it accounts for the effects of a gas gap between the vessel wall and solids on the overall heat transfer coefficient, and headspace pressure on the mean free path length of the inert gas and thereby on the heat transfer between the vessel wall and the first layer of solids. A computational methodology was developed incorporating the effects of mixing and headspace pressure to simulate the drying profile using a modified model framework within the Dynochem software. A dryer operational protocol was designed based on the desolvation kinetics, thermal stability studies of wet and dry cake, and the understanding gained through model simulations, resulting in a multifold reduction in drying time. Copyright © 2012 Wiley-Liss, Inc.
Gong, Jian; Viswanathan, Sandeep; Rothamer, David A; Foster, David E; Rutland, Christopher J
2017-10-03
Motivated by high filtration efficiency (mass- and number-based) and low pressure drop requirements for gasoline particulate filters (GPFs), a previously developed heterogeneous multiscale filtration (HMF) model is extended to simulate dynamic filtration characteristics of GPFs. This dynamic HMF model is based on a probability density function (PDF) description of the pore size distribution and classical filtration theory. The microstructure of the porous substrate in a GPF is resolved and included in the model. Fundamental particulate filtration experiments were conducted using an exhaust filtration analysis (EFA) system for model validation. The particulate in the filtration experiments was sampled from a spark-ignition direct-injection (SIDI) gasoline engine. With the dynamic HMF model, evolution of the microscopic characteristics of the substrate (pore size distribution, porosity, permeability, and deposited particulate inside the porous substrate) during filtration can be probed. Also, predicted macroscopic filtration characteristics including particle number concentration and normalized pressure drop show good agreement with the experimental data. The resulting dynamic HMF model can be used to study the dynamic particulate filtration process in GPFs with distinct microstructures, serving as a powerful tool for GPF design and optimization.
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.
Subramanian, Aneesh C.; Hoteit, Ibrahim; Cornuelle, Bruce; Miller, Arthur J.; Song, Hajoon
2012-01-01
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)
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
Robust estimation of event-related potentials via particle filter.
Fukami, Tadanori; Watanabe, Jun; Ishikawa, Fumito
2016-03-01
In clinical examinations and brain-computer interface (BCI) research, a short electroencephalogram (EEG) measurement time is ideal. The use of event-related potentials (ERPs) relies on both estimation accuracy and processing time. We tested a particle filter that uses a large number of particles to construct a probability distribution. We constructed a simple model for recording EEG comprising three components: ERPs approximated via a trend model, background waves constructed via an autoregressive model, and noise. We evaluated the performance of the particle filter based on mean squared error (MSE), P300 peak amplitude, and latency. We then compared our filter with the Kalman filter and a conventional simple averaging method. To confirm the efficacy of the filter, we used it to estimate ERP elicited by a P300 BCI speller. A 400-particle filter produced the best MSE. We found that the merit of the filter increased when the original waveform already had a low signal-to-noise ratio (SNR) (i.e., the power ratio between ERP and background EEG). We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging, and determined that the particle filter yielded a maximum 42.8% reduction in measurement time. The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency. For EEG data produced by the P300 speller, we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method, irrespective of the amount of averaging. We confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR. Additionally, particle filters can perform robust ERP estimation for EEG data produced via a P300 speller. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
A. Al-Zoubi
2013-01-01
Full Text Available Microgravity investigations are now recognized as a powerful tool for subsurface imaging and especially for the localization of underground karsts. However numerous natural (geological, technical, and environmental factors interfere with microgravity survey processing and interpretation. One of natural factors that causes the most disturbance in complex geological environments is the influence of regional trends. In the Dead Sea coastal areas the influence of regional trends can exceed residual gravity effects by some tenfold. Many widely applied methods are unable to remove regional trends with sufficient accuracy. We tested number of transformation methods (including computing gravity field derivatives, self-adjusting and adaptive filtering, Fourier series, wavelet, and other procedures on a 3D model (complicated by randomly distributed noise, and field investigations were carried out in Ghor Al-Haditha (the eastern side of the Dead Sea in Jordan. We show that the most effective methods for regional trend removal (at least for the theoretical and field cases here are the bilinear saddle and local polynomial regressions. Application of these methods made it possible to detect the anomalous gravity effect from buried targets in the theoretical model and to extract the local gravity anomaly at the Ghor Al-Haditha site. The local anomaly was utilized for 3D gravity modeling to construct a physical-geological model (PGM.
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.
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
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
Development and validation of a Kalman filter-based model for vehicle slip angle estimation
Gadola, M.; Chindamo, D.; Romano, M.; Padula, F.
2014-01-01
It is well known that vehicle slip angle is one of the most difficult parameters to measure on a vehicle during testing or racing activities. Moreover, the appropriate sensor is very expensive and it is often difficult to fit to a car, especially on race cars. We propose here a strategy to eliminate the need for this sensor by using a mathematical tool which gives a good estimation of the vehicle slip angle. A single-track car model, coupled with an extended Kalman filter, was used in order to achieve the result. Moreover, a tuning procedure is proposed that takes into consideration both nonlinear and saturation characteristics typical of vehicle lateral dynamics. The effectiveness of the proposed algorithm has been proven by both simulation results and real-world data.
International Nuclear Information System (INIS)
Ciftcioglu, O.
1991-03-01
Detection of failure in the operational status of a NPP is described. The method uses lattice form of the signal modelling established by means of Kalman filtering methodology. In this approach each lattice parameter is considered to be a state and the minimum variance estimate of the states is performed adaptively by optimal parameter estimation together with fast convergence and favourable statistical properties. In particular, the state covariance is also the covariance of the error committed by that estimate of the state value and the Mahalanobis distance formed for pattern comparison takes x 2 distribution for normally distributed signals. The failure detection is performed after a decision making process by probabilistic assessments based on the statistical information provided. The failure detection system is implemented in multi-channel signal environment of Borssele NPP and its favourable features are demonstrated. (author). 29 refs.; 7 figs
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
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
Modern Notation of business models: а visual Trend
Tatiana, Gavrilova; Artem, Alsufyev; Anna-sophia, Yanson
2014-01-01
Information overf low and dynamic market changes encourage managers to search for a relevant and eloquent model to describe their business. This paper provides a new framework for visualizing business models, guided by wellshaped visualization based on a mind mapping technique. Due to the simplicity of perception, this approach has a positive impact on managers and employees’ understanding of companies’ business models and promotes a productive exchange of ideas and knowledge. The mindmapping...
City Logistics Modeling Efforts : Trends and Gaps - A Review
Anand, N.R.; Quak, H.J.; Van Duin, J.H.R.; Tavasszy, L.A.
2012-01-01
In this paper, we present a review of city logistics modeling efforts reported in the literature for urban freight analysis. The review framework takes into account the diversity and complexity found in the present-day city logistics practice. Next, it covers the different aspects in the modeling
Activities and trends in physical protection modeling with microcomputers
International Nuclear Information System (INIS)
Chapman, L.D.; Harlan, C.P.
1985-01-01
Sandia National Laboratories developed several models in the mid to late 1970's including the Safeguards Automated Facility Evaluation (SAFE) method. The Estimate of Adversary Sequence Interruption (EASI), the Safeguards Network Analysis Procedure (SNAP), the Brief Adversary Threat Loss Estimator (BATLE), and others. These models were implemented on large computers such as the VAX 11/780 and the CDC machines. With the recent development and widespread use of the IBM PC and other microcomputers, it has become evident that several physical protection models should be made available for use on these microcomputers. Currently, there are programs under way to convert the EASI, SNAP and BATLE models to the IBM PC. The input and analysis using the EASI model has been designed to be very user friendly through the utilization of menu driven options. The SNAP modeling technique will be converted to an IBM PC/AT with many enhancements to user friendliness. Graphical assistance for entering the model and reviewing traces of the simulated output are planned. The BATLE model is being converted to the IBM PC while preserving its interactive nature. The current status of the these developments is reported in this paper
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
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 )
Modelling of a sand bed filter in the cell exhaust air pathway
International Nuclear Information System (INIS)
Schmid, M.
1983-01-01
Sandbed filters are appropriate incident filters for zircaloy fires, dissolver fires, and explosions. The alternative treatment of these incidents with and without SBF can thus also quantify the safety gain if an SBF is used. The SBF is considered to be a pure incident filter and according to a planning the SBF is by-passed during normal operation. In case of a temperature rise in the cell the by-pass is blocked by a fire protection valve. (orig./DG) [de
Approximate bandpass and frequency response models of the difference of Gaussian filter
Birch, Philip; Mitra, Bhargav; Bangalore, Nagachetan M.; Rehman, Saad; Young, Rupert; Chatwin, Chris
2010-12-01
The Difference of Gaussian (DOG) filter is widely used in optics and image processing as, among other things, an edge detection and correlation filter. It has important biological applications and appears to be part of the mammalian vision system. In this paper we analyse the filter and provide details of the full width half maximum, bandwidth and frequency response in order to aid the full characterisation of its performance.
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
Modelled long term trends of surface ozone over South Africa
CSIR Research Space (South Africa)
Naidoo, M
2011-09-01
Full Text Available focused on SA Highveld, 2006 ? Keeping all CAMx inputs ?standardized?, leaving only meteorology as a variable ? CSIR 2010 Slide 11 CAMx data flow CAMx Met model USGS surface data Emissions Initial & boundary Haze & albedo Photolysis rates...
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.
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 ...
Large urban fire environment: trends and model city predictions
International Nuclear Information System (INIS)
Larson, D.A.; Small, R.D.
1983-01-01
The urban fire environment that would result from a megaton-yield nuclear weapon burst is considered. The dependence of temperatures and velocities on fire size, burning intensity, turbulence, and radiation is explored, and specific calculations for three model urban areas are presented. In all cases, high velocity fire winds are predicted. The model-city results show the influence of building density and urban sprawl on the fire environment. Additional calculations consider large-area fires with the burning intensity reduced in a blast-damaged urban center
Model-based and memory-based collaborative filtering algorithms for complex knowledge models
Lozano, E.; Gracia, J.; Collarana, D.; Corcho, O.; Gómez-Pérez, A.; Villazón, B.; Latour, S.; Liem, J.
2011-01-01
In DynaLearn, learners, teachers and domain experts create Qualitative Reasoning (QR) conceptual models that may store in a common repository. These models represent a valuable source of knowledge that could be used to assist new users in the creation of models with related topics. However, finding
A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality.
Yousefzadeh-Chabok, Shahrokh; Ranjbar-Taklimie, Fatemeh; Malekpouri, Reza; Razzaghi, Alireza
2016-09-01
Road traffic accident (RTA) is one of the main causes of trauma and known as a growing public health concern worldwide, especially in developing countries. Assessing the trend of fatalities in the past years and forecasting it enables us to make the appropriate planning for prevention and control. This study aimed to assess the trend of RTAs and forecast it in the next years by using time series modeling. In this historical analytical study, the RTA mortalities in Zanjan Province, Iran, were evaluated during 2007 - 2013. The time series analyses including Box-Jenkins models were used to assess the trend of accident fatalities in previous years and forecast it for the next 4 years. The mean age of the victims was 37.22 years (SD = 20.01). From a total of 2571 deaths, 77.5% (n = 1992) were males and 22.5% (n = 579) were females. The study models showed a descending trend of fatalities in the study years. The SARIMA (1, 1, 3) (0, 1, 0) 12 model was recognized as a best fit model in forecasting the trend of fatalities. Forecasting model also showed a descending trend of traffic accident mortalities in the next 4 years. There was a decreasing trend in the study and the future years. It seems that implementation of some interventions in the recent decade has had a positive effect on the decline of RTA fatalities. Nevertheless, there is still a need to pay more attention in order to prevent the occurrence and the mortalities related to traffic accidents.
A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality
Yousefzadeh-Chabok, Shahrokh; Ranjbar-Taklimie, Fatemeh; Malekpouri, Reza; Razzaghi, Alireza
2016-01-01
Background Road traffic accident (RTA) is one of the main causes of trauma and known as a growing public health concern worldwide, especially in developing countries. Assessing the trend of fatalities in the past years and forecasting it enables us to make the appropriate planning for prevention and control. Objectives This study aimed to assess the trend of RTAs and forecast it in the next years by using time series modeling. Materials and Methods In this historical analytical study, the RTA mortalities in Zanjan Province, Iran, were evaluated during 2007 - 2013. The time series analyses including Box-Jenkins models were used to assess the trend of accident fatalities in previous years and forecast it for the next 4 years. Results The mean age of the victims was 37.22 years (SD = 20.01). From a total of 2571 deaths, 77.5% (n = 1992) were males and 22.5% (n = 579) were females. The study models showed a descending trend of fatalities in the study years. The SARIMA (1, 1, 3) (0, 1, 0) 12 model was recognized as a best fit model in forecasting the trend of fatalities. Forecasting model also showed a descending trend of traffic accident mortalities in the next 4 years. Conclusions There was a decreasing trend in the study and the future years. It seems that implementation of some interventions in the recent decade has had a positive effect on the decline of RTA fatalities. Nevertheless, there is still a need to pay more attention in order to prevent the occurrence and the mortalities related to traffic accidents. PMID:27800467
Modelling surface run-off and trends analysis over India
Indian Academy of Sciences (India)
exponential model was developed between the rainfall and the run-off that predicted the run-off with an R2 of ... precipitation and other climate parameters is well documented ...... Sen P K 1968 Estimates of the regression coefficient based.
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....
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
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.
Hui, Zhenyang; Wu, Beiping; Hu, Youjian; Ziggah, Yao Yevenyo
2017-12-01
Obtaining high-precision filtering results from airborne lidar point clouds in complex environments has always been a hot topic. Mathematical morphology was widely used for filtering, owing to its simplicity and high efficiency. However, the morphology-based algorithms are deficient in preserving terrain details. In order to obtain a better filtering effect, this paper proposed an improved progressive morphological filter based on hierarchical radial basis function interpolation (PMHR) to refine the classical progressive morphological filter. PMHR involved two main improvements, namely, automatic setting of self-adaptive thresholds and terrain details preservation, respectively. The performance of PMHR was evaluated using datasets provided by the International Society for Photogrammetry and Remote Sensing. Experimental results show that PMHR achieved good performance under variant terrain features with an average total error of 4.27% and average Kappa coefficient of 84.57%.
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
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
Sediment measurement and transport modeling: impact of riparian and filter strip buffers.
Moriasi, Daniel N; Steiner, Jean L; Arnold, Jeffrey G
2011-01-01
Well-calibrated models are cost-effective tools to quantify environmental benefits of conservation practices, but lack of data for parameterization and evaluation remains a weakness to modeling. Research was conducted in southwestern Oklahoma within the Cobb Creek subwatershed (CCSW) to develop cost-effective methods to collect stream channel parameterization and evaluation data for modeling in watersheds with sparse data. Specifically, (i) simple stream channel observations obtained by rapid geomorphic assessment (RGA) were used to parameterize the Soil and Water Assessment Tool (SWAT) model stream channel variables before calibrating SWAT for streamflow and sediment, and (ii) average annual reservoir sedimentation rate, measured at the Crowder Lake using the acoustic profiling system (APS), was used to cross-check Crowder Lake sediment accumulation rate simulated by SWAT. Additionally, the calibrated and cross-checked SWAT model was used to simulate impacts of riparian forest buffer (RF) and bermudagrass [ (L.) Pers.] filter strip buffer (BFS) on sediment yield and concentration in the CCSW. The measured average annual sedimentation rate was between 1.7 and 3.5 t ha yr compared with simulated sediment rate of 2.4 t ha yr Application of BFS across cropped fields resulted in a 72% reduction of sediment delivery to the stream, while the RF and the combined RF and BFS reduced the suspended sediment concentration at the CCSW outlet by 68 and 73%, respectively. Effective riparian practices have potential to increase reservoir life. These results indicate promise for using the RGA and APS methods to obtain data to improve water quality simulations in ungauged watersheds. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.
Predicting soil acidification trends at Plynlimon using the SAFE model
Directory of Open Access Journals (Sweden)
B. Reynolds
1997-01-01
Full Text Available The SAFE model has been applied to an acid grassland site, located on base-poor stagnopodzol soils derived from Lower Palaeozoic greywackes. The model predicts that acidification of the soil has occurred in response to increased acid deposition following the industrial revolution. Limited recovery is predicted following the decline in sulphur deposition during the mid to late 1970s. Reducing excess sulphur and NOx deposition in 1998 to 40% and 70% of 1980 levels results in further recovery but soil chemical conditions (base saturation, soil water pH and ANC do not return to values predicted in pre-industrial times. The SAFE model predicts that critical loads (expressed in terms of the (Ca+Mg+K:Alcrit ratio for six vegetation species found in acid grassland communities are not exceeded despite the increase in deposited acidity following the industrial revolution. The relative growth response of selected vegetation species characteristic of acid grassland swards has been predicted using a damage function linking growth to soil solution base cation to aluminium ratio. The results show that very small growth reductions can be expected for 'acid tolerant' plants growing in acid upland soils. For more sensitive species such as Holcus lanatus, SAFE predicts that growth would have been reduced by about 20% between 1951 and 1983, when acid inputs were greatest. Recovery to c. 90% of normal growth (under laboratory conditions is predicted as acidic inputs decline.
Parr, D.; Wang, G.; Fu, C.
2015-12-01
As shown by climate models, increasing global temperatures and enhanced greenhouse gas concentration such as CO2 have had major effects on the dynamics of the hydrologic cycle and the surface energy budget, in particular, on evapotranspiration (ET). ET has significant decadal variations whether it be regionally or globally and variations of ET have major environmental and socioeconomic impacts. A number of recent studies have found a global increase in annual mean ET around 7mm per year per decade from about 1982 to the late 1990s. These results correspond with what is expected from an intensification of the hydrological cycle. However, the increasing ET trend did not continue after 1998 and from 1998-2008 this global trend was replaced with a decreasing trend of similar magnitude. This study uses numerical modeling to investigate if similar changing ET trends emerge in the continental U.S and part of northern Mexico. After validating model simulated evaporative fluxes and comparing spatial patterns to the aforementioned studies, various changing trends of different signs are identified across the U.S., and specific regions with strong signals of change are chosen for further examination with the purpose of identifying the root causes of these changing trends and which variables are most influential towards change. Experimental simulations conducted to isolate the most influential factors towards ET reveal that precipitation amount as well as its characteristics have the greatest impact on the ET trends discovered, with other factors like wind and air temperatures displaying less influence over inter-annual trends. This study helps better understand terrestrial ET and it's interactions which will help facilitate better predictions of change in surface climate such as heatwaves and droughts as well as impacts on water resources.
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.
Preliminary Analytical Reviews on the Performance of Fibrous Filter
International Nuclear Information System (INIS)
Choi, Yu Jung; Hong, Tae Hyub; Kim, Hyeong-Taek
2015-01-01
The wet type Containment Filtered Vent System (CFVS) is composed of a tank including nozzles in a liquid pool, moisture separators, and a few dry filters such as a metal fiber filter and a molecular sieve. After injecting gases from the containment into the CFVS under severe accident conditions, the CFVS will release decontaminated radioactive materials to the environment. To protect against the release of uncontrolled fission products to the environment, we need to confirm the performance of the CFVS in terms of not only the integral capability but also the capabilities of the individual components. It is crucial to confirm the performance of the metal fiber filter in both analytical and experimental ways. Pressure drop across a filter and collection efficiency are ways to explain the performance of a fibrous filter. Based on data from the literature survey, pressure drop and collection efficiency for a single filter were calculated. The trends of pressure drop and collection efficiencies due to various deposition mechanisms of particles onto the fiber of the filters were roughly confirmed. Therefore, to obtain better quantitative predictions of the performance of the metal fiber filter, a new model able to evaluate the performance of fibrous filters under severe conditions should be developed
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...
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
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.
Doutsi, Effrosyni; Fillatre, Lionel; Antonini, Marc; Gaulmin, Julien
2018-07-01
This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper. This filter is connected to the dynamic behavior of the retina, which enables the retina to increase the sharpness of the visual stimulus during filtering before its transmission to the brain. We establish that this retina-inspired transform forms a group of spatio-temporal Weighted Difference of Gaussian (WDoG) filters when it is applied to a still image visible for a given time. We analyze the spatial frequency bandwidth of the retina-inspired filter with respect to time. It is shown that the WDoG spectrum varies from a lowpass filter to a bandpass filter. Therefore, while time increases, the retina-inspired filter enables to extract different kinds of information from the input image. Finally, we discuss the benefits of using the retina-inspired filter in image processing applications such as edge detection and compression.
Rings, J.; Vrugt, J.A.; Schoups, G.; Huisman, J.A.; Vereecken, H.
2012-01-01
Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive
Li, F.
2018-01-01
This paper examines how digital technologies facilitate business model innovations in the creative industries. Through a systematic literature review, a holistic business model framework is developed, which is then used to analyse the empirical evidence from the creative industries. The research found that digital technologies have facilitated pervasive changes in business models, and some significant trends have emerged. However, the reconfigured business models are often not ‘new’ in the un...
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
Oppewal, H.; Louviere, J.J.; Timmermans, H.J.P.
2000-01-01
This article proposes and demonstrates how conjoint methods can be adapted to allow the modeling of managerial reactions to various changes in economic and competitive environments and their effects on observed sales levels. Because in general micro-level data on strategic decision making over time
Secular trends and climate drift in coupled ocean-atmosphere general circulation models
Covey, Curt; Gleckler, Peter J.; Phillips, Thomas J.; Bader, David C.
2006-02-01
Coupled ocean-atmosphere general circulation models (coupled GCMs) with interactive sea ice are the primary tool for investigating possible future global warming and numerous other issues in climate science. A long-standing problem with such models is that when different components of the physical climate system are linked together, the simulated climate can drift away from observation unless constrained by ad hoc adjustments to interface fluxes. However, 11 modern coupled GCMs, including three that do not employ flux adjustments, behave much better in this respect than the older generation of models. Surface temperature trends in control run simulations (with external climate forcing such as solar brightness and atmospheric carbon dioxide held constant) are small compared with observed trends, which include 20th century climate change due to both anthropogenic and natural factors. Sea ice changes in the models are dominated by interannual variations. Deep ocean temperature and salinity trends are small enough for model control runs to extend over 1000 simulated years or more, but trends in some regions, most notably the Arctic, differ substantially among the models and may be problematic. Methods used to initialize coupled GCMs can mitigate climate drift but cannot eliminate it. Lengthy "spin-ups" of models, made possible by increasing computer power, are one reason for the improvements this paper documents.
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.
International Nuclear Information System (INIS)
Kim, S; Alaei, P
2015-01-01
Purpose: To implement full/half bowtie filter models in a commercial treatment planning system (TPS) to calculate kilovoltage (kV) x-ray imaging dose of Varian On-Board Imager (OBI) cone beam CT (CBCT) system. Methods: Full/half bowtie filters of Varian OBI were created as compensator models in Pinnacle TPS (version 9.6) using Matlab software (version 2011a). The profiles of both bowtie filters were acquired from the manufacturer, imported into the Matlab system and hard coded in binary file format. A Pinnacle script was written to import each bowtie filter data into a Pinnacle treatment plan as a compensator. A kV x-ray beam model without including the compensator model was commissioned per each bowtie filter setting based on percent depth dose and lateral profile data acquired from Monte Carlo simulations. To validate the bowtie filter models, a rectangular water phantom was generated in the planning system and an anterior/posterior beam with each bowtie filter was created. Using the Pinnacle script, each bowtie filter compensator was added to the treatment plan. Lateral profile at the depth of 3cm and percent depth dose were measured using an ion chamber and compared with the data extracted from the treatment plans. Results: The kV x-ray beams for both full and half bowtie filter have been modeled in a commercial TPS. The difference of lateral and depth dose profiles between dose calculations and ion chamber measurements were within 6%. Conclusion: Both full/half bowtie filter models provide reasonable results in kV x-ray dose calculations in the water phantom. This study demonstrates the possibility of using a model-based treatment planning system to calculate the kV imaging dose for both full and half bowtie filter modes. Further study is to be performed to evaluate the models in clinical situations
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.
Li, Peizhi; Wang, Yong; Dong, Qingli
2017-04-01
Cities in China suffer from severe smog and haze, and a forecasting system with high accuracy is of great importance to foresee the concentrations of the airborne particles. Compared with chemical transport models, the growing artificial intelligence models can simulate nonlinearities and interactive relationships and getting more accurate results. In this paper, the Kolmogorov-Zurbenko (KZ) filter is modified and firstly applied to construct the model using an artificial intelligence method. The concentration of inhalable particles and fine particulate matter in Dalian are used to analyze the filtered components and test the forecasting accuracy. Besides, an extended experiment is made by implementing a comprehensive comparison and a stability test using data in three other cities in China. Results testify the excellent performance of the developed hybrid models, which can be utilized to better understand the temporal features of pollutants and to perform a better air pollution control and management. Copyright © 2017 Elsevier B.V. All rights reserved.
Real-time modelling and simulation of an active power filter
Energy Technology Data Exchange (ETDEWEB)
Beaulieu, S.; Ouhrouche, M. [Quebec Univ., Chicoutimi, PQ (Canada); Dufour, C.; Allaire, P.F. [Opal RT Technologies Inc., Montreal, PQ (Canada)
2007-07-01
Power electronics converters generate harmonics and cause electromagnetic compatibility problems. Active power filter (APF) technology has advanced to the point that it can compensate for harmonics in electrical networks and provide reactive power and neutral current in AC networks. This paper presented a contribution in the design of a shunt APF for harmonics compensation in real-time simulation using the RT-LAB software package running on a simple personal computer. Real-time simulations were performed to validate the effectiveness of the proposed model. Several high-tech industries have adopted this tool for rapid control prototyping and for Hardware-in-the-Loop applications. The switching signals of the APF are determined by the hysteresis band current controller. The suitable current reference signals were determined by the algorithm based on synchronous reference frame. Real-time simulation runs showed good performance in harmonics compensation, thus satisfying the requirements of IEEE Standard 519-1992. The rate of total harmonic distortion for the source current decreased from 30 to 5 per cent. 12 refs., 1 tab., 9 figs.
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.
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.
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.
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.
Potocki, J K; Tharp, H S
1993-01-01
The success of treating cancerous tissue with heat depends on the temperature elevation, the amount of tissue elevated to that temperature, and the length of time that the tissue temperature is elevated. In clinical situations the temperature of most of the treated tissue volume is unknown, because only a small number of temperature sensors can be inserted into the tissue. A state space model based on a finite difference approximation of the bioheat transfer equation (BHTE) is developed for identification purposes. A full-order extended Kalman filter (EKF) is designed to estimate both the unknown blood perfusion parameters and the temperature at unmeasured locations. Two reduced-order estimators are designed as computationally less intensive alternatives to the full-order EKF. Simulation results show that the success of the estimation scheme depends strongly on the number and location of the temperature sensors. Superior results occur when a temperature sensor exists in each unknown blood perfusion zone, and the number of sensors is at least as large as the number of unknown perfusion zones. Unacceptable results occur when there are more unknown perfusion parameters than temperature sensors, or when the sensors are placed in locations that do not sample the unknown perfusion information.
Petersen, Øyvind Wiig
2014-01-01
Force identification in structural dynamics is an inverse problem concerned with finding loads from measured structural response. The main objective of this thesis is to perform and study state (displacement and velocity) and force estimation by Kalman filtering. Theory on optimal control and state-space models are presented, adapted to linear structural dynamics. Accommodation for measurement noise and model inaccuracies are attained by stochastic-deterministic coupling. Explicit requirem...
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
Institute of Scientific and Technical Information of China (English)
L(U) Wei-cai; XU Shao-quan
2004-01-01
Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.
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
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......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...... 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.
International Nuclear Information System (INIS)
Laheij, G.M.H.; Eggink, G.J.
1996-08-01
In accordance with the revised Euratom 'basic safety standards', the Member States have to fill in the details of their own radiation protection policy on radionuclides from natural sources. In this framework the radiological consequences of zinc-rich filter cake at a storage facility of the steelplant Hoogovens Staal, in IJmuiden, Netherlands, were investigated in a model study. Also investigated were the consequences of possible transport of the filter cake to a storage facility in the country. The radiation dose was calculated for Hoogovens workers and for members of the population. For workers, the relevant exposure pathways are inhalation of resuspended filter cake, direct ingestion of filter cake and external radiation. Relevant exposure pathways for members of the population are inhalation of resuspended filter cake, ingestion of green vegetables on which resuspended filter cake is deposited and external radiation, which for workers at the storage facility the radiation dose is 7 mSv/a. The radiation dose for drivers during transport and for workers at a C3-storage facility depends strongly on whether the material is immobilized or not. The maximum radiation dose for both the transport and storage is expected to be almost equal to the radiation dose for workers at the storage facility at Hoogovens. For members of the population living around the storage facility at Hoogovens, the radiation dose is 3.6 mSv/a. Here too, the radiation dose at the storage facility depends strongly on whether the material is immobilized or not. During transport no radiation dose above the secondary level of 0.4 mSv/a is expected due to the short exposure times. 23 refs
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.
A two-dimensional model study of past trends in global ozone
International Nuclear Information System (INIS)
Wuebbles, D.J.; Kinnison, D.E.
1988-08-01
Emissions and atmospheric concentrations of several trace gases important to atmospheric chemistry are known to have increased substantially over recent decades. Solar flux variations and the atmospheric nuclear test series are also likely to have affected stratospheric ozone. In this study, the LLNL two-dimensional chemical-radiative-transport model of the troposphere and stratosphere has been applied to an analysis of the effects that these natural and anthropogenic influences may have had on global ozone concentrations over the last three decades. In general, model determined species distributions and the derived ozone trends agree well with published analyses of land-based and satellite-based observations. Also, the total ozone and ozone distribution trends derived from CFC and other trace gas effects have a different response with latitude than the derived trends from solar flux variations, thus providing a ''signature'' for anthropogenic effects on ozone. 24 refs., 5 figs
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)
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.
Energy Technology Data Exchange (ETDEWEB)
Qamar, I.; Bayles, G.A.; Tierney, J.W.; Chiang, S.-H.; Klinzing, G.E.
1987-01-01
A three dimensional bond-flow correlated network model has been successfully used to calculate equilibrium desaturation curves for coal filter cakes. A simple cubic lattice with the pore sizes correlated in the direction of macroscopic flow is used as the network. A new method of pore volume assignment is presented in which the pore volume occupied by the large pores (which give rise to capillary pressures less than a calculated critical value) is assigned to the nodes and the rest is distributed to the bonds according to an experimentally determined micrographic pore size distribution. Equilibrium desaturation curves for -32 mesh, -200 mesh and -100 + 200 mesh coal cakes (Pittsburgh Seam coal), formed with distilled water have been calculated. A bond flow correlation factor, F/sub c/ is introduced to account for channeling of the displacing fluid through high volume, low resistance flow paths - a phenomenon which is displayed by many real systems. It is determined that a single value of 0.6 for F/sub c/ is required for -32 mesh and -200 mesh coals. However, for -100 + 200 mesh coal, where all small as well as large particles have been removed, a value of 1.0 is required. The results of six -32 mesh cakes formed with surfactants show that the effect of surfactants can be accounted for by modifying one of the model parameters, the entry diameter correction. A correlation is presented to estimate the modified correction using experimentally determined surface tension and contact angle values. Further, the predicted final saturations agree with the experimental values within an average absolute error of 5%. 16 refs., 11 figs., 2 tabs.
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.
AOD trends during 2001-2010 from observations and model simulations
Pozzer, Andrea; de Meij, Alexander; Yoon, Jongmin; Astitha, Marina
2016-04-01
The trend of aerosol optical depth (AOD) between 2001 and 2010 is estimated globally and regionally from remote sensed observations by the MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer) and SeaWIFS (Sea-viewing Wide Field-of-view Sensor) satellite sensor. The resulting trends have been compared to model results from the EMAC (ECHAM5/MESSy Atmospheric Chemistry {[1]}), model. Although interannual variability is applied only to anthropogenic and biomass-burning emissions, the model is able to quantitatively reproduce the AOD trends as observed by MODIS, while some discrepancies are found when compared to MISR and SeaWIFS. An additional numerical simulation with the same model was performed, neglecting any temporal change in the emissions, i.e. with no interannual variability for any emission source. It is shown that decreasing AOD trends over the US and Europe are due to the decrease in the (anthropogenic) emissions. On contrary over the Sahara Desert and the Middle East region, the meteorological/dynamical changes in the last decade play a major role in driving the AOD trends. Further, over Southeast Asia, both meteorology and emissions changes are equally important in defining AOD trends {[2]}. Finally, decomposing the regional AOD trends into individual aerosol components reveals that the soluble components are the most dominant contributors to the total AOD, as their influence on the total AOD is enhanced by the aerosol water content. {[1]}: Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717-752, doi:10.5194/gmd-3-717-2010, 2010. {[2]}: Pozzer, A., de Meij, A., Yoon, J., Tost, H., Georgoulias, A. K., and Astitha, M.: AOD trends during 2001-2010 from observations and model simulations, Atmos. Chem. Phys., 15, 5521-5535, doi:10.5194/acp-15-5521-2015, 2015.
Historical Trends in Mean and Extreme Runoff and Streamflow Based on Observations and Climate Models
Directory of Open Access Journals (Sweden)
Behzad Asadieh
2016-05-01
Full Text Available To understand changes in global mean and extreme streamflow volumes over recent decades, we statistically analyzed runoff and streamflow simulated by the WBM-plus hydrological model using either observational-based meteorological inputs from WATCH Forcing Data (WFD, or bias-corrected inputs from five global climate models (GCMs provided by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP. Results show that the bias-corrected GCM inputs yield very good agreement with the observation-based inputs in average magnitude of runoff and streamflow. On global average, the observation-based simulated mean runoff and streamflow both decreased about 1.3% from 1971 to 2001. However, GCM-based simulations yield increasing trends over that period, with an inter-model global average of 1% for mean runoff and 0.9% for mean streamflow. In the GCM-based simulations, relative changes in extreme runoff and extreme streamflow (annual maximum daily values and annual-maximum seven-day streamflow are slightly greater than those of mean runoff and streamflow, in terms of global and continental averages. Observation-based simulations show increasing trend in mean runoff and streamflow for about one-half of the land areas and decreasing trend for the other half. However, mean and extreme runoff and streamflow based on the GCMs show increasing trend for approximately two-thirds of the global land area and decreasing trend for the other one-third. Further work is needed to understand why GCM simulations appear to indicate trends in streamflow that are more positive than those suggested by climate observations, even where, as in ISI-MIP, bias correction has been applied so that their streamflow climatology is realistic.
Random forest meteorological normalisation models for Swiss PM10 trend analysis
Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph
2018-05-01
Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.
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.
Guzel Isayevna Gumerova; Elmira Shamilevna Shaimieva
2015-01-01
Objective to define the notion of ldquohightech businessrdquo to elaborate classification of hightech businesses to elaborate the business model for hightech business management. Methods general scientific methods of theoretical and empirical cognition. Results the research presents a business model of hightech businesses management basing on the trends of explicit and explicit knowledge market with the dominating implicit knowledge market classification of hightech business...
Global long-term ozone trends derived from different observed and modelled data sets
Coldewey-Egbers, M.; Loyola, D.; Zimmer, W.; van Roozendael, M.; Lerot, C.; Dameris, M.; Garny, H.; Braesicke, P.; Koukouli, M.; Balis, D.
2012-04-01
The long-term behaviour of stratospheric ozone amounts during the past three decades is investigated on a global scale using different observed and modelled data sets. Three European satellite sensors GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/METOP are combined and a merged global monthly mean total ozone product has been prepared using an inter-satellite calibration approach. The data set covers the 16-years period from June 1995 to June 2011 and it exhibits an excellent long-term stability, which is required for such trend studies. A multiple linear least-squares regression algorithm using different explanatory variables is applied to the time series and statistically significant positive trends are detected in the northern mid latitudes and subtropics. Global trends are also estimated using a second satellite-based Merged Ozone Data set (MOD) provided by NASA. For few selected geographical regions ozone trends are additionally calculated using well-maintained measurements of individual Dobson/Brewer ground-based instruments. A reasonable agreement in the spatial patterns of the trends is found amongst the European satellite, the NASA satellite, and the ground-based observations. Furthermore, two long-term simulations obtained with the Chemistry-Climate Models E39C-A provided by German Aerospace Center and UMUKCA-UCAM provided by University of Cambridge are analysed.
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.
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.
Modelling BSE trend over time in Europe, a risk assessment perspective
Ducrot, C.; Sala, C.; Ru, G.; Koeijer, de A.A.; Sheridan, H.; Saegerman, C.; Selhorst, T.; Arnold, M.; Polak, M.P.; Calavas, D.
2010-01-01
BSE is a zoonotic disease that caused the emergence of variant Creuzfeldt-Jakob disease in the mid 1990s. The trend of the BSE epidemic in seven European countries was assessed and compared, using Age-Period-Cohort and Reproduction Ratio modelling applied to surveillance data 2001-2007. A strong
Report on Spending Trends Highlights Inequities in Model for Financing Colleges
Blumenstyk, Goldie
2009-01-01
An analysis of spending trends that is designed to discourage policy makers' focus on finding new revenue rather than reining in spending suggests that the model for financing colleges has reinforced educational inequities and failed to increase the rate at which students graduate. According to the analysis, "serious fault lines" in the current…
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
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.
Application of wavelet-based multi-model Kalman filters to real-time flood forecasting
Chou, Chien-Ming; Wang, Ru-Yih
2004-04-01
This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.
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.
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
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.
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.
Directory of Open Access Journals (Sweden)
Yanhui Li
2014-01-01
Full Text Available This paper investigates the Hankel norm filter design problem for stochastic time-delay systems, which are represented by Takagi-Sugeno (T-S fuzzy model. Motivated by the parallel distributed compensation (PDC technique, a novel filtering error system is established. The objective is to design a suitable filter that guarantees the corresponding filtering error system to be mean-square asymptotically stable and to have a specified Hankel norm performance level γ. Based on the Lyapunov stability theory and the Itô differential rule, the Hankel norm criterion is first established by adopting the integral inequality method, which can make some useful efforts in reducing conservativeness. The Hankel norm filtering problem is casted into a convex optimization problem with a convex linearization approach, which expresses all the conditions for the existence of admissible Hankel norm filter as standard linear matrix inequalities (LMIs. The effectiveness of the proposed method is demonstrated via a numerical example.
State and force observers based on multibody models and the indirect Kalman filter
Sanjurjo, Emilio; Dopico, Daniel; Luaces, Alberto; Naya, Miguel Ángel
2018-06-01
The aim of this work is to present two new methods to provide state observers by combining multibody simulations with indirect extended Kalman filters. One of the methods presented provides also input force estimation. The observers have been applied to two mechanism with four different sensor configurations, and compared to other multibody-based observers found in the literature to evaluate their behavior, namely, the unscented Kalman filter (UKF), and the indirect extended Kalman filter with simplified Jacobians (errorEKF). The new methods have some more computational cost than the errorEKF, but still much less than the UKF. Regarding their accuracy, both are better than the errorEKF. The method with input force estimation outperforms also the UKF, while the method without force estimation achieves results almost identical to those of the UKF. All the methods have been implemented as a reusable MATLAB® toolkit which has been released as Open Source in https://github.com/MBDS/mbde-matlab.
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.
Queirós, David; Afonso, Paulo; Vieira, Filipa Dionísio
2013-01-01
This research project focuses on the discussion of trends and new business models for the textile and clothing industry in general and for the fashion industry, in particular, using tools and an approach supported on strategic innovation. A documentary analysis was performed and they were conducted a series of semi-structured interviews. It was noted the importance of the strategic analysis and particularly strategic innovation for the design of new successful business models. Main findings a...
Modeling the neutron spin-flip process in a time-of-flight spin-resonance energy filter
Parizzi, A A; Klose, F
2002-01-01
A computer program for modeling the neutron spin-flip process in a novel time-of-flight (TOF) spin-resonance energy filter has been developed. The software allows studying the applicability of the device in various areas of spallation neutron scattering instrumentation, for example as a dynamic TOF monochromator. The program uses a quantum-mechanical approach to calculate the local spin-dependent spectra and is essential for optimizing the magnetic field profiles along the resonator axis. (orig.)
Naikwad, S. N.; Dudul, S. V.
2009-01-01
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 tempora...
Rastetter, Edward B; Williams, Mathew; Griffin, Kevin L; Kwiatkowski, Bonnie L; Tomasky, Gabrielle; Potosnak, Mark J; Stoy, Paul C; Shaver, Gaius R; Stieglitz, Marc; Hobbie, John E; Kling, George W
2010-07-01
Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter
International Nuclear Information System (INIS)
Rodriguez Miranda, J. P.
2010-01-01
This paper describes the correlation of a mathematical model that considers the pressure drop (energy) in the mixing zone of beds in operation filters as drinking water treatment, filters applied in conventional pilot operated and mounted on a water treatment plant of a municipally in Colombia. (Author) 20 refs.
Directory of Open Access Journals (Sweden)
Tara L Crewe
Full Text Available The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling to reduce the probability that individuals will
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
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.
Model-Based Attribution of High-Resolution Streamflow Trends in Two Alpine Basins of Western Austria
Directory of Open Access Journals (Sweden)
Christoph Kormann
2016-02-01
Full Text Available Several trend studies have shown that hydrological conditions are changing considerably in the Alpine region. However, the reasons for these changes are only partially understood and trend analyses alone are not able to shed much light. Hydrological modelling is one possible way to identify the trend drivers, i.e., to attribute the detected streamflow trends, given that the model captures all important processes causing the trends. We modelled the hydrological conditions for two alpine catchments in western Austria (a large, mostly lower-altitude catchment with wide valley plains and a nested high-altitude, glaciated headwater catchment with the distributed, physically-oriented WaSiM-ETH model, which includes a dynamical glacier module. The model was calibrated in a transient mode, i.e., not only on several standard goodness measures and glacier extents, but also in such a way that the simulated streamflow trends fit with the observed ones during the investigation period 1980 to 2007. With this approach, it was possible to separate streamflow components, identify the trends of flow components, and study their relation to trends in atmospheric variables. In addition to trends in annual averages, highly resolved trends for each Julian day were derived, since they proved powerful in an earlier, data-based attribution study. We were able to show that annual and highly resolved trends can be modelled sufficiently well. The results provide a holistic, year-round picture of the drivers of alpine streamflow changes: Higher-altitude catchments are strongly affected by earlier firn melt and snowmelt in spring and increased ice melt throughout the ablation season. Changes in lower-altitude areas are mostly caused by earlier and lower snowmelt volumes. All highly resolved trends in streamflow and its components show an explicit similarity to the local temperature trends. Finally, results indicate that evapotranspiration has been increasing in the lower
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.
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
problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...
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...
Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model
Liu, Bo; El Gharamti, Mohamad; Hoteit, Ibrahim
2016-01-01
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
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.
Velazco, Julio G.; Rodríguez-Álvarez, María Xosé; Boer, Martin P.; Jordan, David R.; Eilers, Paul H.C.; Malosetti, Marcos; Eeuwijk, van Fred A.
2017-01-01
Key message: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Abstract: Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and
J.G. Velazco (Julio G.); M.X. Rodríguez-Álvarez (María Xosé); M.P. Boer (Martin); D.R. Jordan (David R.); P.H.C. Eilers (Paul); M. Malosetti (Marcos); F. van Eeuwijk (Fred)
2017-01-01
markdownabstract_Key message: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials._ __Abstract:__ Adjustment for spatial trends in plant breeding field trials is essential for
Elias, E.; Rango, A.; James, D.; Maxwell, C.; Anderson, J.; Abatzoglou, J. T.
2016-12-01
Researchers evaluating climate projections across southwestern North America observed a decreasing precipitation trend. Aridification was most pronounced in the cold (non-monsoonal) season, whereas downward trends in precipitation were smaller in the warm (monsoonal) season. In this region, based upon a multimodel mean of 20 Coupled Model Intercomparison Project 5 models using a business-as-usual (Representative Concentration Pathway 8.5) trajectory, midcentury precipitation is projected to increase slightly during the monsoonal time period (July-September; 6%) and decrease slightly during the remainder of the year (October-June; -4%). We use observed long-term (1915-2015) monthly precipitation records from 16 weather stations to investigate how well measured trends corroborate climate model predictions during the monsoonal and non-monsoonal timeframe. Running trend analysis using the Mann-Kendall test for 15 to 101 year moving windows reveals that half the stations showed significant (p≤0.1), albeit small, increasing trends based on the longest term record. Trends based on shorter-term records reveal a period of significant precipitation decline at all stations representing the 1950s drought. Trends from 1930 to 2015 reveal significant annual, monsoonal and non-monsoonal increases in precipitation (Fig 1). The 1960 to 2015 time window shows no significant precipitation trends. The more recent time window (1980 to 2015) shows a slight, but not significant, increase in monsoonal precipitation and a larger, significant decline in non-monsoonal precipitation. GCM precipitation projections are consistent with more recent trends for the region. Running trends from the most recent time window (mid-1990s to 2015) at all stations show increasing monsoonal precipitation and decreasing Oct-Jun precipitation, with significant trends at 6 of 16 stations. Running trend analysis revealed that the long-term trends were not persistent throughout the series length, but depended
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.
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)
International Nuclear Information System (INIS)
Rochette, D; Clain, S; Andre, P; Bussiere, W; Gentils, F
2007-01-01
Medium voltage (MV) cells have to respect standards (for example IEC ones (IEC TC 17C 2003 IEC 62271-200 High Voltage Switchgear and Controlgear-Part 200 1st edn)) that define security levels against internal arc faults such as an accidental electrical arc occurring in the apparatus. New protection filters based on porous materials are developed to provide better energy absorption properties and a higher protection level for people. To study the filter behaviour during a major electrical accident, a two-dimensional model is proposed. The main point is the use of a dedicated numerical scheme for a non-conservative hyperbolic problem. We present a numerical simulation of the process during the first 0.2 s when the safety valve bursts and we compare the numerical results with tests carried out in a high power test laboratory on real electrical apparatus
Rochette, D.; Clain, S.; André, P.; Bussière, W.; Gentils, F.
2007-05-01
Medium voltage (MV) cells have to respect standards (for example IEC ones (IEC TC 17C 2003 IEC 62271-200 High Voltage Switchgear and Controlgear—Part 200 1st edn)) that define security levels against internal arc faults such as an accidental electrical arc occurring in the apparatus. New protection filters based on porous materials are developed to provide better energy absorption properties and a higher protection level for people. To study the filter behaviour during a major electrical accident, a two-dimensional model is proposed. The main point is the use of a dedicated numerical scheme for a non-conservative hyperbolic problem. We present a numerical simulation of the process during the first 0.2 s when the safety valve bursts and we compare the numerical results with tests carried out in a high power test laboratory on real electrical apparatus.
Energy Technology Data Exchange (ETDEWEB)
Rochette, D [Laboratoire Arc Electrique et Plasmas Thermiques, CNRS UMR 6069, Universite Blaise Pascal, IUT de Montlucon, Avenue Aristide Briand, BP 2235, 03101 Montlucon Cedex (France); Clain, S [Laboratoire de Mathematiques pour l' Industrie et la Physique, CNRS UMR 5640, Universite Paul Sabatier Toulouse 3, 118 route de Narbonne, 31062 Toulouse Cedex 4 (France); Andre, P [Laboratoire Arc Electrique et Plasmas Thermiques, CNRS UMR 6069, Universite Blaise Pascal, IUT de Montlucon, Avenue Aristide Briand, BP 2235, 03101 Montlucon Cedex (France); Bussiere, W [Laboratoire Arc Electrique et Plasmas Thermiques, CNRS UMR 6069, Universite Blaise Pascal, IUT de Montlucon, Avenue Aristide Briand, BP 2235, 03101 Montlucon Cedex (France); Gentils, F [Schneider Electric-Science and Technology Division-Research Center A2, 38050 Grenoble Cedex 9 (France)
2007-05-21
Medium voltage (MV) cells have to respect standards (for example IEC ones (IEC TC 17C 2003 IEC 62271-200 High Voltage Switchgear and Controlgear-Part 200 1st edn)) that define security levels against internal arc faults such as an accidental electrical arc occurring in the apparatus. New protection filters based on porous materials are developed to provide better energy absorption properties and a higher protection level for people. To study the filter behaviour during a major electrical accident, a two-dimensional model is proposed. The main point is the use of a dedicated numerical scheme for a non-conservative hyperbolic problem. We present a numerical simulation of the process during the first 0.2 s when the safety valve bursts and we compare the numerical results with tests carried out in a high power test laboratory on real electrical apparatus.
DEFF Research Database (Denmark)
Levine, Ari; Heje Pedersen, Lasse
2016-01-01
Managed futures funds and commodity trading advisers (CTAs) use heuristics or statistical measures often called “filters” to trade on price trends. Two key statistical measures of trends are “time-series momentum” and “moving-average crossovers.” We show, empirically and theoretically, that these......Managed futures funds and commodity trading advisers (CTAs) use heuristics or statistical measures often called “filters” to trade on price trends. Two key statistical measures of trends are “time-series momentum” and “moving-average crossovers.” We show, empirically and theoretically......, 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...
Research of combination model for prediction of the trend of outbreak of hepatitis B
Directory of Open Access Journals (Sweden)
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
Mubamba, C; Ramsay, G; Abolnik, C; Dautu, G; Gummow, B
2016-10-01
Newcastle Disease (ND) is a highly infectious disease of poultry that seriously impacts on food security and livelihoods of livestock farmers and communities in tropical regions of the world. ND is a constant problem in the eastern province of Zambia which has more than 740 000 rural poultry. Very few studies give a situational analysis of the disease that can be used for disease control planning in the region. With this background in mind, a retrospective epidemiological study was conducted using Newcastle Disease data submitted to the eastern province headquarters for the period from 1989 to 2014. The study found that Newcastle Disease cases in eastern Zambia followed a seasonal and cyclic pattern with peaks in the hot dry season (Overall Seasonal Index 1.1) as well as cycles every three years with an estimated provincial incidence range of 0.16 to 1.7% per year. Annual trends were compared with major intervention policies implemented by the Zambian government, which often received donor support from the international community during the study period. Aid delivered through government programmes appeared to have no major impact on ND trends between 1989 and 2014 and reasons for this are discussed. There were apparent spatial shifts in districts with outbreaks over time which could be as a result of veterinary interventions chasing outbreaks rather than implementing uniform control. Data was also fitted to a predictive time series model for ND which could be used to plan for future ND control. Time series modelling showed an increasing trend in ND annual incidence over 25 years if existing interventions continue. A different approach to controlling the disease is needed if this trend is to be halted. Conversely, the positive trend may be a function of improved reporting by farmers as a result of more awareness of the disease. Copyright © 2016 Elsevier B.V. All rights reserved.
Long-term surface pCO2 trends from observations and models
International Nuclear Information System (INIS)
Tjiputra, Jerry F.; Olsen, Are; Heinze, Christoph; Bopp, Laurent; Roy, Tilla
2014-01-01
We estimate regional long-term surface ocean pCO 2 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 pCO 2 growth rates faster than the atmospheric growth rates indicate decreasing atmospheric CO 2 uptake, while ocean pCO 2 growth rates slower than the atmospheric growth rates indicate increasing atmospheric CO 2 uptake. Aside from the western sub-polar 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 pCO 2 trends is found when the simulated fields are sub sampled with the observational coverage. In agreement with observations, we see that the simulated pCO 2 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 pCO 2 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 pCO 2 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 pCO 2 trend than the atmosphere, implying a reduction in the atmospheric CO 2 uptake rate. The fastest pCO 2 growth rates are projected for the sub-polar North Atlantic, while the high-latitude Southern Ocean and eastern equatorial Pacific have the weakest growth rates, remaining below the atmospheric pCO 2 growth rate. Our work
Long-term surface pCO2 trends from observations and models
Directory of Open Access Journals (Sweden)
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
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.
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
Directory of Open Access Journals (Sweden)
Hongjie Wu
2013-01-01
Full Text Available State of charge (SOC is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.
Koopman, S.J.; Creal, D.D.
2010-01-01
We develop a flexible business cycle indicator that accounts for potential time variation in macroeconomic variables. The coincident economic indicator is based on a multivariate trend cycle decomposition model and is constructed from a moderate set of US macroeconomic time series. In particular, we
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.
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.
Actuator Fault Diagnosis in a Boeing 747 Model via Adaptive Modified Two-Stage Kalman Filter
Directory of Open Access Journals (Sweden)
Fikret Caliskan
2014-01-01
Full Text Available An adaptive modified two-stage linear Kalman filtering algorithm is utilized to identify the loss of control effectiveness and the magnitude of low degree of stuck faults in a closed-loop nonlinear B747 aircraft. Control effectiveness factors and stuck magnitudes are used to quantify faults entering control systems through actuators. Pseudorandom excitation inputs are used to help distinguish partial loss and stuck faults. The partial loss and stuck faults in the stabilizer are isolated and identified successfully.
Huang, Steven Y; Odisio, Bruno C; Sabir, Sharjeel H; Ensor, Joe E; Niekamp, Andrew S; Huynh, Tam T; Kroll, Michael; Gupta, Sanjay
2017-07-01
Our purpose was to develop a predictive model for short-term survival (i.e. filter placement in patients with venous thromboembolism (VTE) and solid malignancy. Clinical and laboratory parameters were retrospectively reviewed for patients with solid malignancy who received a filter between January 2009 and December 2011 at a tertiary care cancer center. Multivariate Cox proportional hazards modeling was used to assess variables associated with 6 month survival following filter placement in patients with VTE and solid malignancy. Significant variables were used to generate a predictive model. 397 patients with solid malignancy received a filter during the study period. Three variables were associated with 6 month survival: (1) serum albumin [hazard ratio (HR) 0.496, P filter placement can be predicted from three patient variables. Our predictive model could be used to help physicians decide whether a permanent or retrievable filter may be more appropriate as well as to assess the risks and benefits for filter retrieval within the context of survival longevity in patients with cancer.
International Nuclear Information System (INIS)
Li, Qiang; Zhang, Wenjuan; Li, Huiquan; He, Peng
2017-01-01
China announced its promise on CO_2 emission peak. When and what level of CO_2 emission peak China's primary aluminum industry will reach is in suspense. In this paper, a system dynamic model is established, with five subsystems of economy development, primary aluminum production, secondary aluminum production, CO_2 emission intensity and policies making involved. The model is applied to examine potential CO_2 emission trends of China's primary aluminum industry in next fifteen years with three scenarios of “no new policies”, “13th five-year plan” and “additional policies”. Simulation results imply that: merely relying on rapid expansion of domestic scarps recycling and reuse could not mitigate CO_2 emission continuously. Combination of energy-saving technology application and electrolytic technology innovation, as well as promoting hydropower utilization in primary aluminum industry are necessary for long term low-carbon development. From a global prospective, enhancing international cooperation on new primary aluminum capacity construction in other countries, especially with rich low-carbon energy, could bring about essential CO_2 emission for both China's and global primary aluminum industry. - Highlights: • A system dynamic model is established for future CO_2 emission trend of China's primary aluminum industry. • Three potential policy scenarios are simulated. • The impacts of potential policies implication on the CO_2 emission trend are discussed.
He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Dong, Hongxing
2014-12-01
Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.
Failure of CMIP5 climate models in simulating post-1950 decreasing trend of Indian monsoon
Saha, Anamitra; Ghosh, Subimal; Sahana, A. S.; Rao, E. P.
2014-10-01
Impacts of climate change on Indian Summer Monsoon Rainfall (ISMR) and the growing population pose a major threat to water and food security in India. Adapting to such changes needs reliable projections of ISMR by general circulation models. Here we find that, majority of new generation climate models from Coupled Model Intercomparison Project phase5 (CMIP5) fail to simulate the post-1950 decreasing trend of ISMR. The weakening of monsoon is associated with the warming of Southern Indian Ocean and strengthening of cyclonic formation in the tropical western Pacific Ocean. We also find that these large-scale changes are not captured by CMIP5 models, with few exceptions, which is the reason of this failure. Proper representation of these highlighted geophysical processes in next generation models may improve the reliability of ISMR projections. Our results also alert the water resource planners to evaluate the CMIP5 models before using them for adaptation strategies.
Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica
2009-01-01
In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.
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.
International Nuclear Information System (INIS)
Tylee, J.L.
1980-01-01
A low-order, nonlinear model of the Loss-of-Fluid Test (LOFT) reactor plant, for use in Kalman filter estimators, is developed, described, and evaluated. This model consists of 31 differential equations and represents all major subsystems of both the primary and secondary sides of the LOFT plant. Comparisons between model calculations and available LOFT power range testing transients demonstrate the accuracy of the low-order model. The nonlinear model is numerically linearized for future implementation in Kalman filter and optimal control algorithms. The linearized model is shown to be an adequate representation of the nonlinear plant dynamics
International Nuclear Information System (INIS)
Butterworth, D.J.
1980-01-01
This invention relates to liquid filters, precoated by replaceable powders, which are used in the production of ultra pure water required for steam generation of electricity. The filter elements are capable of being installed and removed by remote control so that they can be used in nuclear power reactors. (UK)
Directory of Open Access Journals (Sweden)
Sergio Solinas
2010-05-01
Full Text Available The way the cerebellar granular layer transforms incoming mossy fiber signals into new spike patterns to be related to Purkinje cells is not yet clear. Here, a realistic computational model of the granular layer was developed and used to address four main functional hypotheses: center-surround organization, time-windowing, high-pass filtering in responses to spike bursts and coherent oscillations in response to diffuse random activity. The model network was activated using patterns inspired by those recorded in vivo. Burst stimulation of a small mossy fiber bundle resulted in granule cell bursts delimited in time (time windowing and space (center-surround by network inhibition. This burst-burst transmission showed marked frequency-dependence configuring a high-pass filter with cut-off frequency around 100 Hz. The contrast between center and surround properties was regulated by the excitatory-inhibitory balance. The stronger excitation made the center more responsive to 10-50 Hz input frequencies and enhanced the granule cell output (with spike occurring earlier and with higher frequency and number compared to the surround. Finally, over a certain level of mossy fiber background activity, the circuit generated coherent oscillations in the theta-frequency band. All these processes were fine-tuned by NMDA and GABA-A receptor activation and neurotransmitter vesicle cycling in the cerebellar glomeruli. This model shows that available knowledge on cellular mechanisms is sufficient to unify the main functional hypotheses on the cerebellum granular layer and suggests that this network can behave as an adaptable spatio-temporal filter coordinated by theta-frequency oscillations.
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.
Data assimilation using Bayesian filters and B-spline geological models
International Nuclear Information System (INIS)
Duan Lian; Farmer, Chris; Hoteit, Ibrahim; Luo Xiaodong; Moroz, Irene
2011-01-01
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.
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
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
Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters
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Juan G. Gonzalez
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 ÃŽÂ±-stable and generalized-t. 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 Ã¢Â€ÂœnormalÃ¢Â€Â 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.
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.
Directory of Open Access Journals (Sweden)
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.
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
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
Modeled distribution and abundance of a pelagic seabird reveal trends in relation to fisheries
Renner, Martin; Parrish, Julia K.; Piatt, John F.; Kuletz, Kathy J.; Edwards, Ann E.; Hunt, George L.
2013-01-01
The northern fulmar Fulmarus glacialis is one of the most visible and widespread seabirds in the eastern Bering Sea and Aleutian Islands. However, relatively little is known about its abundance, trends, or the factors that shape its distribution. We used a long-term pelagic dataset to model changes in fulmar at-sea distribution and abundance since the mid-1970s. We used an ensemble model, based on a weighted average of generalized additive model (GAM), multivariate adaptive regression splines (MARS), and random forest models to estimate the pelagic distribution and density of fulmars in the waters of the Aleutian Archipelago and Bering Sea. The most important predictor variables were colony effect, sea surface temperature, distribution of fisheries, location, and primary productivity. We calculated a time series from the ratio of observed to predicted values and found that fulmar at-sea abundance declined from the 1970s to the 2000s at a rate of 0.83% (± 0.39% SE) per annum. Interpolating fulmar densities on a spatial grid through time, we found that the center of fulmar distribution in the Bering Sea has shifted north, coinciding with a northward shift in fish catches and a warming ocean. Our study shows that fisheries are an important, but not the only factor, shaping fulmar distribution and abundance trends in the eastern Bering Sea and Aleutian Islands.
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
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)
Phan, Anh Tuan; Ho, Duc Du; Hermann, Gilles; Wira, Patrice
2015-12-01
For power quality issues like reducing harmonic pollution, reactive power and load unbalance, the estimation of the fundamental frequency of a power lines in a fast and precise way is essential. This paper introduces a new state-space model to be used with an extended Kalman filter (EKF) for estimating the frequency of distorted power system signals in real-time. The proposed model takes into account all the characteristics of a general three-phase power system and mainly the unbalance. Therefore, the symmetrical components of the power system, i.e., their amplitude and phase angle values, can also be deduced at each iteration from the proposed state-space model. The effectiveness of the method has been evaluated. Results and comparisons of online frequency estimation and symmetrical components identification show the efficiency of the proposed method for disturbed and time-varying signals.
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)
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)
Trend in Air Quality of Kathmandu Valley: A Satellite, Observation and Modelling Perspective
Mahapatra, P. S.; Praveen, P. S.; Adhikary, B.; Panday, A. K.; Putero, D.; Bonasoni, P.
2016-12-01
Kathmandu (floor area of 340 km2) in Nepal is considered to be a `hot spot' of urban air pollution in South Asia. Its structure as a flat basin surrounded by tall mountains provides a unique case study for analyzing pollution trapped by topography. Only a very small number of cities with similar features have been studied extensively including Mexico and Santiago-de-Chile. This study presents the trend in satellite derived Aerosol Optical Depth (AOD) from MODIS AQUA and TERRA (3x3km, Level 2) over Kathmandu from 2000 to 2015. Trend analysis of AOD shows 35% increase during the study period. Determination of the background pollution would reveal the contribution of only Kathmandu Valley for the observation period. For this, AOD at 1340m altitude outside Kathmandu, but nearby areas were considered as background. This analysis was further supported by investigating AOD at different heights around Kathmandu as well as determining AOD from CALIPSO vertical profiles. These analysis suggest that background AOD contributed 30% in winter and 60% in summer to Kathmandu Valley's observed AOD. Thereafter the background AOD was subtracted from total Kathmandu AOD to determine contribution of only Kathmandu Valley's AOD. Trend analysis of only Kathmandu Valley AOD (subtracting background AOD) suggested an increase of 50% during the study period. Further analysis of Kathmandu's visibility and AOD suggest profound role of background AOD on decreasing visibility. In-situ Black Carbon (BC) mass concentration measurements (BC being used as a proxy for surface observations) at two sites within Kathmandu valley have been analyzed. Kathmandu valley lacks long term trends of ambient air quality measurement data. Therefore, surface observations would be coupled with satellite measurements for understanding the urban air pollution scenario. Modelling studies to estimate the contribution of background pollution to Kathmandu's own pollution as well as the weekend effect on air quality will
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.
A transport-based model of material trends in nonproportionality of scintillators
International Nuclear Information System (INIS)
Li Qi; Grim, Joel Q.; Williams, R. T.; Bizarri, G. A.; Moses, W. W.
2011-01-01
Electron-hole pairs created by the passage of a high-energy electron in a scintillator radiation detector find themselves in a very high radial concentration gradient of the primary electron track. Since nonlinear quenching that is generally regarded to be at the root of nonproportional response depends on the fourth or sixth power of the track radius in a cylindrical track model, radial diffusion of charge carriers and excitons on the ∼10 picosecond duration typical of nonlinear quenching can compete with and thereby modify that quenching. We use a numerical model of transport and nonlinear quenching to examine trends affecting local light yield versus excitation density as a function of charge carrier and exciton diffusion coefficients. Four trends are found: (1) nonlinear quenching associated with the universal 'roll-off' of local light yield versus dE/dx is a function of the lesser of mobilities μ e and μ h or of D EXC as appropriate, spanning a broad range of scintillators and semiconductor detectors; (2) when μ e ≅μ h , excitons dominate free carriers in transport, the corresponding reduction of scattering by charged defects and optical phonons increases diffusion out of the track in competition with nonlinear quenching, and a rise in proportionality is expected; (3) when μ h e as in halide scintillators with hole self-trapping, the branching between free carriers and excitons varies strongly along the track, leading to a 'hump' in local light yield versus dE/dx; (4) anisotropic mobility can promote charge separation along orthogonal axes and leads to a characteristic shift of the 'hump' in halide local light yield. Trends 1 and 2 have been combined in a quantitative model of nonlinear local light yield which is predictive of empirical nonproportionality for a wide range of oxide and semiconductor radiation detector materials where band mass or mobility data are the determinative material parameters.
Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production
Directory of Open Access Journals (Sweden)
Zaichun Zhu
2013-03-01
Full Text Available Projected changes in the frequency and severity of droughts as a result of increase in greenhouse gases have a significant impact on the role of vegetation in regulating the global carbon cycle. Drought effect on vegetation Gross Primary Production (GPP is usually modeled as a function of Vapor Pressure Deficit (VPD and/or soil moisture. Climate projections suggest a strong likelihood of increasing trend in VPD, while regional changes in precipitation are less certain. This difference in projections between VPD and precipitation can cause considerable discrepancies in the predictions of vegetation behavior depending on how ecosystem models represent the drought effect. In this study, we scrutinized the model responses to drought using the 30-year record of Global Inventory Modeling and Mapping Studies (GIMMS 3g Normalized Difference Vegetation Index (NDVI dataset. A diagnostic ecosystem model, Terrestrial Observation and Prediction System (TOPS, was used to estimate global GPP from 1982 to 2009 under nine different experimental simulations. The control run of global GPP increased until 2000, but stayed constant after 2000. Among the simulations with single climate constraint (temperature, VPD, rainfall and solar radiation, only the VPD-driven simulation showed a decrease in 2000s, while the other scenarios simulated an increase in GPP. The diverging responses in 2000s can be attributed to the difference in the representation of the impact of water stress on vegetation in models, i.e., using VPD and/or precipitation. Spatial map of trend in simulated GPP using GIMMS 3g data is consistent with the GPP driven by soil moisture than the GPP driven by VPD, confirming the need for a soil moisture constraint in modeling global GPP.
Identifying trends in climate: an application to the cenozoic
Richards, Gordon R.
1998-05-01
The recent literature on trending in climate has raised several issues, whether trends should be modeled as deterministic or stochastic, whether trends are nonlinear, and the relative merits of statistical models versus models based on physics. This article models trending since the late Cretaceous. This 68 million-year interval is selected because the reliability of tests for trending is critically dependent on the length of time spanned by the data. Two main hypotheses are tested, that the trend has been caused primarily by CO2 forcing, and that it reflects a variety of forcing factors which can be approximated by statistical methods. The CO2 data is obtained from model simulations. Several widely-used statistical models are found to be inadequate. ARIMA methods parameterize too much of the short-term variation, and do not identify low frequency movements. Further, the unit root in the ARIMA process does not predict the long-term path of temperature. Spectral methods also have little ability to predict temperature at long horizons. Instead, the statistical trend is estimated using a nonlinear smoothing filter. Both of these paradigms make it possible to model climate as a cointegrated process, in which temperature can wander quite far from the trend path in the intermediate term, but converges back over longer horizons. Comparing the forecasting properties of the two trend models demonstrates that the optimal forecasting model includes CO2 forcing and a parametric representation of the nonlinear variability in climate.
Trends in domestic violence service and leadership: implications for an integrated shelter model.
Panzer, P G; Philip, M B; Hayward, R A
2000-05-01
Domestic violence is a dangerous and prevalent social problem affecting up to 4 million women and countless children annually. Shelters offer safety and an opportunity for change during the crisis of family violence. These individuals also have the potential for retraumatization if leadership within the program recapitulates the abuse and coercion felt at home. This article reviews three related trends through the lens of power and control--domestic violence policy and service, models of leadership, and the study of traumatic stress disorders and recovery--and describes their implications for modern shelter service delivery.
Time reversal mirror and perfect inverse filter in a microscopic model for sound propagation
International Nuclear Information System (INIS)
Calvo, Hernan L.; Danieli, Ernesto P.; Pastawski, Horacio M.
2007-01-01
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
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.)
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.
Directory of Open Access Journals (Sweden)
Mónica A Silva
Full Text Available 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 <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6 ± 5.6 km was nearly half that of LS estimates (11.6 ± 8.4 km. Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw 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.
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.
Raick, C.; Alvera-Azcarate, A.; Barth, A.; Brankart, J.-M.; Soetaert, K.E.R.; Grégoire, M.
2007-01-01
The Singular Evolutive Extended Kalman (SEEK) filter has been implemented to assimilate in-situ data in a 1D coupled physical-ecosystem model of the Ligurian Sea. The biogeochemical model describes the partly decoupled nitrogen and carbon cycles of the pelagic food web. The GHER hydrodynamic model
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.
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.
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...
Liu, Ruiling; Dix-Cooper, Linda; Hammond, S Katharine
2015-01-01
Flight attendants were exposed to elevated levels of secondhand smoke (SHS) in commercial aircraft when smoking was allowed on planes. During flight attendants' working years, their occupational SHS exposure was influenced by various factors, including the prevalence of active smokers on planes, fliers' smoking behaviors, airplane flight load factors, and ventilation systems. These factors have likely changed over the past six decades and would affect SHS concentrations in commercial aircraft. However, changes in flight attendants' exposure to SHS have not been examined in the literature. This study estimates the magnitude of the changes and the historic trends of flight attendants' SHS exposure in U.S. domestic commercial aircraft by integrating historical changes of contributing factors. Mass balance models were developed and evaluated to estimate flight attendants' exposure to SHS in passenger cabins, as indicated by two commonly used tracers (airborne nicotine and particulate matter (PM)). Monte Carlo simulations integrating historical trends and distributions of influence factors were used to simulate 10,000 flight attendants' exposure to SHS on commercial flights from 1955 to 1989. These models indicate that annual mean SHS PM concentrations to which flight attendants were exposed in passenger cabins steadily decreased from approximately 265 μg/m(3) in 1955 and 1960 to 93 μg/m(3) by 1989, and airborne nicotine exposure among flight attendants also decreased from 11.1 μg/m(3) in 1955 to 6.5 μg/m(3) in 1989. Using duration of employment as an indicator of flight attendants' cumulative occupational exposure to SHS in epidemiological studies would inaccurately assess their lifetime exposures and thus bias the relationship between the exposure and health effects. This historical trend should be considered in future epidemiological studies.
Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering
El Gharamti, Mohamad; Hoteit, Ibrahim; Valstar, Johan R.
2013-01-01
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
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.
Schneider, David P.; Deser, Clara
2017-09-01
Recent work suggests that natural variability has played a significant role in the increase of Antarctic sea ice extent during 1979-2013. The ice extent has responded strongly to atmospheric circulation changes, including a deepened Amundsen Sea Low (ASL), which in part has been driven by tropical variability. Nonetheless, this increase has occurred in the context of externally forced climate change, and it has been difficult to reconcile observed and modeled Antarctic sea ice trends. To understand observed-model disparities, this work defines the internally driven and radiatively forced patterns of Antarctic sea ice change and exposes potential model biases using results from two sets of historical experiments of a coupled climate model compared with observations. One ensemble is constrained only by external factors such as greenhouse gases and stratospheric ozone, while the other explicitly accounts for the influence of tropical variability by specifying observed SST anomalies in the eastern tropical Pacific. The latter experiment reproduces the deepening of the ASL, which drives an increase in regional ice extent due to enhanced ice motion and sea surface cooling. However, the overall sea ice trend in every ensemble member of both experiments is characterized by ice loss and is dominated by the forced pattern, as given by the ensemble-mean of the first experiment. This pervasive ice loss is associated with a strong warming of the ocean mixed layer, suggesting that the ocean model does not locally store or export anomalous heat efficiently enough to maintain a surface environment conducive to sea ice expansion. The pervasive upper-ocean warming, not seen in observations, likely reflects ocean mean-state biases.
Schneider, David P.; Deser, Clara
2018-06-01
Recent work suggests that natural variability has played a significant role in the increase of Antarctic sea ice extent during 1979-2013. The ice extent has responded strongly to atmospheric circulation changes, including a deepened Amundsen Sea Low (ASL), which in part has been driven by tropical variability. Nonetheless, this increase has occurred in the context of externally forced climate change, and it has been difficult to reconcile observed and modeled Antarctic sea ice trends. To understand observed-model disparities, this work defines the internally driven and radiatively forced patterns of Antarctic sea ice change and exposes potential model biases using results from two sets of historical experiments of a coupled climate model compared with observations. One ensemble is constrained only by external factors such as greenhouse gases and stratospheric ozone, while the other explicitly accounts for the influence of tropical variability by specifying observed SST anomalies in the eastern tropical Pacific. The latter experiment reproduces the deepening of the ASL, which drives an increase in regional ice extent due to enhanced ice motion and sea surface cooling. However, the overall sea ice trend in every ensemble member of both experiments is characterized by ice loss and is dominated by the forced pattern, as given by the ensemble-mean of the first experiment. This pervasive ice loss is associated with a strong warming of the ocean mixed layer, suggesting that the ocean model does not locally store or export anomalous heat efficiently enough to maintain a surface environment conducive to sea ice expansion. The pervasive upper-ocean warming, not seen in observations, likely reflects ocean mean-state biases.
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
2016-01-05
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.
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.
International Nuclear Information System (INIS)
Moura, Fernando S; Aya, Julio C C; Lima, Raul G; Fleury, Agenor T
2008-01-01
One of the electrical impedance tomography objectives is to estimate the electrical resistivity distribution in a domain based only on contour electrical potential measurements caused by an imposed electrical current distribution into the boundary. In biomedical applications, the random walk model is frequently used as evolution model and, under this conditions, it is observed poor tracking ability of the Extended Kalman Filter (EKF). An analytically developed evolution model is not feasible at this moment. The present work investigates the possibility of identifying the evolution model in parallel to the EKF and updating the evolution model with certain periodicity. The evolution model is identified using the history of resistivity distribution obtained by a sensitivity matrix based algorithm. To numerically identify the linear evolution model, it is used the Ibrahim Time Domain Method, normally used to identify the transition matrix on structural dynamics. The investigation was performed by numerical simulations of a time varying domain with the addition of noise. Numerical dificulties to compute the transition matrix were solved using a Tikhonov regularization. The EKF numerical simulations suggest that the tracking ability is significantly improved.
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.
A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.
Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd
2017-08-01
The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.
Modeling trends of health and health related indicators in Ethiopia (1995-2008: a time-series study
Directory of Open Access Journals (Sweden)
Nigatu Tilahun H
2009-12-01
Full Text Available Abstract Background The Federal Ministry of Health of Ethiopia has been publishing Health and Health related indicators of the country annually since 1987 E.C. These indicators have been of high importance in indicating the status of health in the country in those years. However, the trends/patterns of these indicators and the factors related to the trends have not yet been investigated in a systematic manner. In addition, there were minimal efforts to develop a model for predicting future values of Health and Health related indicators based on the current trend. Objectives The overall aim of this study was to analyze trends of and develop model for prediction of Health and Health related indicators. More specifically, it described the trends of Health and Health related indicators, identified determinants of mortality and morbidity indicators and developed model for predicting future values of MDG indicators. Methods This study was conducted on Health and Health related indicators of Ethiopia from the year 1987 E.C to 2000 E.C. Key indicators of Mortality and Morbidity, Health service coverage, Health systems resources, Demographic and socio-economic, and Risk factor indicators were extracted and analyzed. The trends in these indicators were established using trend analysis techniques. The determinants of the established trends were identified using ARIMA models in STATA. The trend-line equations were then used to predict future values of the indicators. Results Among the mortality indicators considered in this study, it was only Maternal Mortality Ratio that showed statistically significant decrement within the study period. The trends of Total Fertility Rate, physician per 100,000 population, skilled birth attendance and postnatal care coverage were found to have significant association with Maternal Mortality Ratio trend. There was a reversal of malaria parasite prevalence in 1999 E.C from Plasmodium Falciparum to Plasmodium Vivax. Based on
International Nuclear Information System (INIS)
Bills, K.C.; Kress, R.L.; Kwon, D.S.; Baker, C.P.
1994-01-01
This paper describes ORNL's development of an environment for the simulation of robotic manipulators. Simulation includes the modeling of kinematics, dynamics, sensors, actuators, control systems, operators, and environments. Models will be used for manipulator design, proposal evaluation, control system design and analysis, graphical preview of proposed motions, safety system development, and training. Of particular interest is the development of models for robotic manipulators having at least one flexible link. As a first application, models have been developed for the Pacific Northwest Laboratory's Flexible Beam Test Bed (PNL FBTB), which is a 1-Degree-of-Freedom, flexible arm with a hydraulic base actuator. ORNL transferred control algorithms developed for the PNL FBTB to controlling IGRIP models. A robust notch filter is running in IGRIP controlling a full dynamics model of the PNL test bed. Model results provide a reasonable match to the experimental results (quantitative results are being determined) and can run on ORNL's Onyx machine in approximately realtime. The flexible beam is modeled as six rigid sections with torsional springs between each segment. The spring constants were adjusted to match the physical response of the flexible beam model to the experimental results. The controller is able to improve performance on the model similar to the improvement seen on the experimental system. Some differences are apparent, most notably because the IGRIP model presently uses a different trajectory planner than the one used by ORNL on the PNL test bed. In the future, the trajectory planner will be modified so that the experiments and models are the same. The successful completion of this work provides the ability to link C code with IGRIP, thus allowing controllers to be developed, tested, and tuned in simulation and then ported directly to hardware systems using the C language
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.
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)
Hongxiao Yu
2015-05-01
Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.