Filtering Spam: Current Trends and Techniques
Directory of Open Access Journals (Sweden)
Geerthik S.
2013-07-01
Full Text Available This article gives an overview about latest trend and techniques in spam filtering. We analyzed the problems which is introduced by spam ,what spam actually do and how to measure the spam .This article mainly focuses on automated, non-interactive filters, with a broad review ranging from commercial implementations to ideas confined to current research papers. The solutions using both machine and non –machine learning approaches are reviewed and taxonomy of different approaches is presented. While a range of different techniques have and continue to be evaluated in academic research, heuristic and Bayesian filtering, along with its variants provide the greatest potential for future spam prevention.
Gradient based filtering of digital elevation models
DEFF Research Database (Denmark)
Knudsen, Thomas; Andersen, Rune Carbuhn
We present a filtering method for digital terrain models (DTMs). The method is based on mathematical morphological filtering within gradient (slope) defined domains. The intention with the filtering procedure is to improbé the cartographic quality of height contours generated from a DTM based on ...... in the landscape are washed out and misrepresented....
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...
Beams Propagation Modelled by Bi-filters
Lacaze, Bernard
2010-01-01
In acoustic, ultrasonic or electromagnetic propagation, crossed media are often modelled by linear filters with complex gains in accordance with the Beer-Lambert law. This paper addresses the problem of propagation in media where polarization has to be taken into account. Because waves are now bi-dimensional, an unique filter is not sufficient to represent the effects of the medium. We propose a model which uses four linear invariant filters, which allows to take into account exchanges betwee...
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....... Following this line of research, we propose a probabilistic collaborative filtering model that explicitly represents all items and users simultaneously in the model. Experimental results show that the proposed system obtains significantly better results than other collaborative filtering systems (evaluated...
Model based optimization of EMC input filters
Energy Technology Data Exchange (ETDEWEB)
Raggl, K; Kolar, J. W. [Swiss Federal Institute of Technology, Power Electronic Systems Laboratory, Zuerich (Switzerland); Nussbaumer, T. [Levitronix GmbH, Zuerich (Switzerland)
2008-07-01
Input filters of power converters for compliance with regulatory electromagnetic compatibility (EMC) standards are often over-dimensioned in practice due to a non-optimal selection of number of filter stages and/or the lack of solid volumetric models of the inductor cores. This paper presents a systematic filter design approach based on a specific filter attenuation requirement and volumetric component parameters. It is shown that a minimal volume can be found for a certain optimal number of filter stages for both the differential mode (DM) and common mode (CM) filter. The considerations are carried out exemplarily for an EMC input filter of a single phase power converter for the power levels of 100 W, 300 W, and 500 W. (author)
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...
Parameter Estimation, Model Reduction and Quantum Filtering
Chase, Bradley A
2009-01-01
This dissertation explores the topics of parameter estimation and model reduction in the context of quantum filtering. Chapters 2 and 3 provide a review of classical and quantum probability theory, stochastic calculus and filtering. Chapter 4 studies the problem of quantum parameter estimation and introduces the quantum particle filter as a practical computational method for parameter estimation via continuous measurement. Chapter 5 applies these techniques in magnetometry and studies the estimator's uncertainty scalings in a double-pass atomic magnetometer. Chapter 6 presents an efficient feedback controller for continuous-time quantum error correction. Chapter 7 presents an exact model of symmetric processes of collective qubit systems.
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...
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.
Application of the Trend Filtering Algorithm for Photometric Time Series Data
Gopalan, Giri; van Eyken, Julian; Ciardi, David; von Braun, Kaspar; Kane, Stephen R
2016-01-01
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) (Kovacs et al. 2005) 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 literature TFA 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. ...
Kalman filter estimation model in flood forecasting
Husain, Tahir
Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.
Theoretical model for a Stark anomalous dispersion optical filter
Yin, B.; Shay, T. M.
1993-01-01
A theoretical model for the first atomic Stark anomalous dispersion optical filter is reported. The results show the filter may serve as a widely tunable narrow bandwidth and high throughput optical filter for freespace laser communications and remote sensing.
A latent model for collaborative filtering
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre
Recommender systems based on collaborative filtering have received a great deal of interest over the last decade. Typically, these types of systems either take a user-centered or an item-centered approach when making recommendations, but by employing only one of these two perspectives we may...... unintentionally leave out important information that could otherwise have improved the recommendations. In this paper, we propose a collaborative filtering model that contains an explicit representation of all items and users. Experimental results show that the proposed system obtains significantly better results...... than other collaborative filtering systems (evaluated on the MovieLens data set). Furthermore, the explicit representation of all users and items allows the model to e.g. make group-based recommendations balancing the preferences of the individual users....
Evaluation of trends in wheat yield models
Ferguson, M. C.
1982-01-01
Trend terms in models for wheat yield in the U.S. Great Plains for the years 1932 to 1976 are evaluated. The subset of meteorological variables yielding the largest adjusted R(2) is selected using the method of leaps and bounds. Latent root regression is used to eliminate multicollinearities, and generalized ridge regression is used to introduce bias to provide stability in the data matrix. The regression model used provides for two trends in each of two models: a dependent model in which the trend line is piece-wise continuous, and an independent model in which the trend line is discontinuous at the year of the slope change. It was found that the trend lines best describing the wheat yields consisted of combinations of increasing, decreasing, and constant trend: four combinations for the dependent model and seven for the independent model.
Parameter estimation, model reduction and quantum filtering
Chase, Bradley A.
This thesis explores the topics of parameter estimation and model reduction in the context of quantum filtering. The last is a mathematically rigorous formulation of continuous quantum measurement, in which a stream of auxiliary quantum systems is used to infer the state of a target quantum system. Fundamental quantum uncertainties appear as noise which corrupts the probe observations and therefore must be filtered in order to extract information about the target system. This is analogous to the classical filtering problem in which techniques of inference are used to process noisy observations of a system in order to estimate its state. Given the clear similarities between the two filtering problems, I devote the beginning of this thesis to a review of classical and quantum probability theory, stochastic calculus and filtering. This allows for a mathematically rigorous and technically adroit presentation of the quantum filtering problem and solution. Given this foundation, I next consider the related problem of quantum parameter estimation, in which one seeks to infer the strength of a parameter that drives the evolution of a probe quantum system. By embedding this problem in the state estimation problem solved by the quantum filter, I present the optimal Bayesian estimator for a parameter when given continuous measurements of the probe system to which it couples. For cases when the probe takes on a finite number of values, I review a set of sufficient conditions for asymptotic convergence of the estimator. For a continuous-valued parameter, I present a computational method called quantum particle filtering for practical estimation of the parameter. Using these methods, I then study the particular problem of atomic magnetometry and review an experimental method for potentially reducing the uncertainty in the estimate of the magnetic field beyond the standard quantum limit. The technique involves double-passing a probe laser field through the atomic system, giving
Modeling branching pore structures in membrane filters
Sanaei, Pejman; Cummings, Linda J.
2016-11-01
Membrane filters are in widespread industrial use, and mathematical models to predict their efficacy are potentially very useful, as such models can suggest design modifications to improve filter performance and lifetime. Many models have been proposed to describe particle capture by membrane filters and the associated fluid dynamics, but most such models are based on a very simple structure in which the pores of the membrane are assumed to be simple circularly-cylindrical tubes spanning the depth of the membrane. Real membranes used in applications usually have much more complex geometry, with interconnected pores which may branch and bifurcate. Pores are also typically larger on the upstream side of the membrane than on the downstream side. We present an idealized mathematical model, in which a membrane consists of a series of bifurcating pores, which decrease in size as the membrane is traversed. Feed solution is forced through the membrane by applied pressure, and particles are removed from the feed either by sieving, or by particle adsorption within pores (which shrinks them). Thus the membrane's permeability decreases as the filtration progresses, ultimately falling to zero. We discuss how filtration efficiency depends on the characteristics of the branching structure. Partial support from NSF DMS 1261596 is gratefully acknowledged.
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.
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.
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.
Identification of hydrological model parameter variation using ensemble Kalman filter
Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao
2016-12-01
Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.
Noncausal spatial prediction filtering based on an ARMA model
Institute of Scientific and Technical Information of China (English)
Liu Zhipeng; Chen Xiaohong; Li Jingye
2009-01-01
Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.
Parsimonious modeling with information filtering networks
Barfuss, Wolfram; Massara, Guido Previde; Di Matteo, T.; Aste, Tomaso
2016-12-01
We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided.
Theoretical model for a Faraday anomalous dispersion optical filter
Yin, B.; Shay, T. M.
1991-01-01
A model for the Faraday anomalous dispersion optical filter is presented. The model predicts a bandwidth of 0.6 GHz and a transmission peak of 0.98 for a filter operating on the Cs (D2) line. The model includes hyperfine effects and is valid for arbitrary magnetic fields.
Directory of Open Access Journals (Sweden)
R. PURUSHOTHAMAN NAIR
2011-02-01
Full Text Available In this paper a set of normalized weighted averages which may be called as bi-average, tri-average, quadric-average or in general kth poly average, k=2,3,4,… is introduced. The weights can be easily assigned using the integer k. The linear combination of the weights with the samples is biased to latest samples of a given discrete data set when the samples are considered chronologically or sequentially. Hence these averages can generate moving and realistic trends of data without being a moving average. Computations of these averages are not explicitly depending on the size of the data set and can be done in a progressive way. The advantage is that it is not necessary to store the data samples or its size for computing these averages. An inferring mechanism is derived based on which one can easily decide whether current sample is continuous or not with previous samples based on the computed average. Illustrative examples are presented to establish the effectiveness of this inferring mechanism in testing continuous trends and filtering of discontinuous samples of flight telemetry data of a typical launch vehicle and that of sample data sets of standard continuous signals. Mathematical properties ofthese averages are discussed.
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL
Directory of Open Access Journals (Sweden)
X. Yang
2016-06-01
Full Text Available GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy.
A filter based encoding model for mouse retinal ganglion cells.
Zhong, Q; Roychowdhury, V; Boykin, P; Jacobs, A; Nirenberg, S
2005-01-01
We adopt a system theoretic approach and explore the model of retinal ganglion cells as linear filters followed by a maximum-likelihood Bayesian predictor. We evaluate the model by using cross-validation, i.e., first the model parameters are estimated using a training set, and then the prediction error is computed (by comparing the stochastic rate predicted by the model with the rate code of the response) for a test set. As in system identification theory, we present spatially uniform stimuli to the retina, whose temporal intensity is drawn independently from a Gaussian distribution, and we simultaneously record the spike trains from multiple neurons. The optimal linear filter for each cell is obtained by maximizing the mutual information between the filtered stimulus values and the output of the cell (as measured in terms of a stochastic rate code). Our results show that the model presented in this paper performs well on the test set, and it outperforms the identity Bayesian model and the traditional linear model. Moreover, in order to reduce the number of optimal filters needed for prediction, we cluster the cells based on the filters' shapes, and use the cluster consensus filters to predict the firing rates of all neurons in the same class. We obtain almost the same performance with these cluster filters. These results provide hope that filter-based retinal prosthetics might be an effective and feasible idea.
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.
Evaluation of the Crux IVC Filter in an animal model.
Murphy, Erin H; White, Rodney A; Rosenthal, David; Johnson, Eric D; Zarins, Christopher K; Fogarty, Thomas J; Arko, Frank R
2008-06-01
To determine the safety and performance of a new inferior vena cava (IVC) filter in an ovine model and evaluate the retrievability at 5 weeks. The Crux Vena Cava Filter (VCF) is composed of 2 nitinol spiral supports with a polymeric filter suspended between them. Retrieval tails on each end facilitate retrieval. Twelve filters were placed in the infrarenal IVCs of 12 sheep. The vessels were imaged pre and post deployment to assess acute device performance. At 5 weeks, the vessels were re-imaged to evaluate continued device performance and vessel integrity. Nine of 12 filters were retrieved, and the animals were returned to their housing. The other 3 animals were sacrificed, and the filters and vessels were processed for gross and histological examination. At 9 weeks, 4 weeks after filter retrieval, vessel integrity of the remaining 9 animals was again assessed under fluoroscopy. The animals were sacrificed, and the IVCs were explanted for study. All 12 filters were implanted without complications at the intended deployment site and remained fixed over the implantation period. At 5 weeks, the filters intended for recovery were successfully retrieved, with a mean capture time of 9.6+/-13.7 minutes. There were no complications during the 4-week follow-up after filter retrieval. Post-retrieval imaging at 5 and 9 weeks showed no visible signs of vessel wall damage. Histological study of 3 explanted vessels and filters revealed slight neointima encapsulation of the filter elements and minimal incorporation. Gross examination of the post-retrieval vessel walls after the 4-week healing period showed minimal superficial vessel damage; histology showed minimal residual signs of hemorrhage, with little to no inflammatory reaction. The Crux VCF was deployed and safely retrieved without incident at 5 weeks in an animal model. There was no significant damage seen to the IVCs 1 month after filter retrieval.
Non-linear DSGE Models and The Optimized Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes...... the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and this filter is therefore much more efficient than the standard particle filter....
Image Denoising Using Total Variation Model Guided by Steerable Filter
Directory of Open Access Journals (Sweden)
Wenxue Zhang
2014-01-01
Full Text Available We propose an adaptive total variation (TV model by introducing the steerable filter into the TV-based diffusion process for image filtering. The local energy measured by the steerable filter can effectively characterize the object edges and ramp regions and guide the TV-based diffusion process so that the new model behaves like the TV model at edges and leads to linear diffusion in flat and ramp regions. This way, the proposed model can provide a better image processing tool which enables noise removal, edge-preserving, and staircase suppression.
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.
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...
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...
Scalable learning of probabilistic latent models for collaborative filtering
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre
2015-01-01
Collaborative filtering has emerged as a popular way of making user recommendations, but with the increasing sizes of the underlying databases scalability is becoming a crucial issue. In this paper we focus on a recently proposed probabilistic collaborative filtering model that explicitly...
Trends in Substitution Models of Molecular Evolution
Directory of Open Access Journals (Sweden)
Miguel eArenas
2015-10-01
Full Text Available Substitution models of evolution describe the process of genetic variation through fixed mutations and constitute the basis of the evolutionary analysis at the molecular level. Almost forty years after the development of first substitution models, highly sophisticated and data-specific substitution models continue emerging with the aim of better mimicking real evolutionary processes. Here I describe current trends in substitution models of DNA, codon and amino acid sequence evolution, including advantages and pitfalls of the most popular models. The perspective concludes that despite the large number of currently available substitution models, further research is required for more realistic modeling, especially for DNA coding and amino acid data. Additionally, the development of more accurate complex models should be coupled with new implementations and improvements of methods and frameworks for substitution model selection and downstream evolutionary analysis.
The Effect of Bathymetric Filtering on Nearshore Process Model Results
2009-01-01
Filtering on Nearshore Process Model Results 6. AUTHOR(S) Nathaniel Plant, Kacey L. Edwards, James M. Kaihatu, Jayaram Veeramony, Yuan-Huang L. Hsu...filtering on nearshore process model results Nathaniel G. Plant **, Kacey L Edwardsb, James M. Kaihatuc, Jayaram Veeramony b, Larry Hsu’’, K. Todd Holland...assimilation efforts that require this information. Published by Elsevier B.V. 1. Introduction Nearshore process models are capable of predicting
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.
Toy models for macroevolutionary patterns and trends.
Alicea, Bradly; Gordon, Richard
2014-09-01
Many models have been used to simplify and operationalize the subtle but complex mechanisms of biological evolution. Toy models are gross simplifications that nevertheless attempt to retain major essential features of evolution, bridging the gap between empirical reality and formal theoretical understanding. In this paper, we examine thirteen models which describe evolution that also qualify as such toy models, including the tree of life, branching processes, adaptive ratchets, fitness landscapes, and the role of nonlinear avalanches in evolutionary dynamics. Such toy models are intended to capture features such as evolutionary trends, coupled evolutionary dynamics of phenotype and genotype, adaptive change, branching, and evolutionary transience. The models discussed herein are applied to specific evolutionary contexts in various ways that simplify the complexity inherent in evolving populations. While toy models are overly simplistic, they also provide sufficient dynamics for capturing the fundamental mechanism(s) of evolution. Toy models might also be used to aid in high-throughput data analysis and the understanding of cultural evolutionary trends. This paper should serve as an introductory guide to the toy modeling of evolutionary complexity.
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....
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.
A FUZZY FILTERING MODEL FOR CONTOUR DETECTION
Directory of Open Access Journals (Sweden)
T.C. Rajakumar
2011-04-01
Full Text Available Contour detection is the basic property of image processing. Fuzzy Filtering technique is proposed to generate thick edges in two dimensional gray images. Fuzzy logic is applied to extract value for an image and is used for object contour detection. Fuzzy based pixel selection can reduce the drawbacks of conventional methods(Prewitt, Robert. In the traditional methods, filter mask is used for all kinds of images. It may succeed in one kind of image but fail in another one. In this frame work the threshold parameter values are obtained from the fuzzy histogram of the input image. The Fuzzy inference method selects the complete information about the border of the object and the resultant image has less impulse noise and the contrast of the edge is increased. The extracted object contour is thicker than the existing methods. The performance of the algorithm is tested with Peak Signal Noise Ratio(PSNR and Complex Wavelet Structural Similarity Metrics(CWSSIM.
Filtering nonlinear dynamical systems with linear stochastic models
Harlim, J.; Majda, A. J.
2008-06-01
An important emerging scientific issue is the real time filtering through observations of noisy signals for nonlinear dynamical systems as well as the statistical accuracy of spatio-temporal discretizations for filtering such systems. From the practical standpoint, the demand for operationally practical filtering methods escalates as the model resolution is significantly increased. For example, in numerical weather forecasting the current generation of global circulation models with resolution of 35 km has a total of billions of state variables. Numerous ensemble based Kalman filters (Evensen 2003 Ocean Dyn. 53 343-67 Bishop et al 2001 Mon. Weather Rev. 129 420-36 Anderson 2001 Mon. Weather Rev. 129 2884-903 Szunyogh et al 2005 Tellus A 57 528-45 Hunt et al 2007 Physica D 230 112-26) show promising results in addressing this issue; however, all these methods are very sensitive to model resolution, observation frequency, and the nature of the turbulent signals when a practical limited ensemble size (typically less than 100) is used. In this paper, we implement a radical filtering approach to a relatively low (40) dimensional toy model, the L-96 model (Lorenz 1996 Proc. on Predictability (ECMWF, 4-8 September 1995) pp 1-18) in various chaotic regimes in order to address the 'curse of ensemble size' for complex nonlinear systems. Practically, our approach has several desirable features such as extremely high computational efficiency, filter robustness towards variations of ensemble size (we found that the filter is reasonably stable even with a single realization) which makes it feasible for high dimensional problems, and it is independent of any tunable parameters such as the variance inflation coefficient in an ensemble Kalman filter. This radical filtering strategy decouples the problem of filtering a spatially extended nonlinear deterministic system to filtering a Fourier diagonal system of parametrized linear stochastic differential equations (Majda and Grote
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.
Physics-based prognostic modelling of filter clogging phenomena
Eker, Omer F.; Camci, Fatih; Jennions, Ian K.
2016-06-01
In industry, contaminant filtration is a common process to achieve a desired level of purification, since contaminants in liquids such as fuel may lead to performance drop and rapid wear propagation. Generally, clogging of filter phenomena is the primary failure mode leading to the replacement or cleansing of filter. Cascading failures and weak performance of the system are the unfortunate outcomes due to a clogged filter. Even though filtration and clogging phenomena and their effects of several observable parameters have been studied for quite some time in the literature, progression of clogging and its use for prognostics purposes have not been addressed yet. In this work, a physics based clogging progression model is presented. The proposed model that bases on a well-known pressure drop equation is able to model three phases of the clogging phenomena, last of which has not been modelled in the literature yet. In addition, the presented model is integrated with particle filters to predict the future clogging levels and to estimate the remaining useful life of fuel filters. The presented model has been implemented on the data collected from an experimental rig in the lab environment. In the rig, pressure drop across the filter, flow rate, and filter mesh images are recorded throughout the accelerated degradation experiments. The presented physics based model has been applied to the data obtained from the rig. The remaining useful lives of the filters used in the experimental rig have been reported in the paper. The results show that the presented methodology provides significantly accurate and precise prognostic results.
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. T...
Crowdsourcing Big Trace Data Filtering: a Partition-And Model
Yang, X.; Tang, L.
2016-06-01
GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy.
Trends in modeling of porous media combustion
Energy Technology Data Exchange (ETDEWEB)
Mujeebu, M. Abdul; Abdullah, M. Zulkifly [Porous Media Combustion Laboratory, School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang (Malaysia); Mohamad, A.A. [College of Engineering, Alfaisal University, Riyadh 11533, P.O. Box 50927 (Saudi Arabia); Bakar, M.Z. Abu [School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang (Malaysia)
2010-12-15
Porous media combustion (PMC) has interesting advantages compared with free flame combustion due to higher burning rates, increased power dynamic range, extension of the lean flammability limits, and low emissions of pollutants. Extensive experimental and numerical works were carried out and are still underway, to explore the feasibility of this interesting technology for practical applications. For this purpose, numerical modeling plays a crucial role in the design and development of promising PMC systems. This article provides an exhaustive review of the fundamental aspects and emerging trends in numerical modeling of gas combustion in porous media. The modeling works published to date are reviewed, classified according to their objectives and presented with general conclusions. Numerical modeling of liquid fuel combustion in porous media is excluded. (author)
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.
Quantum Model for the Selectivity Filter in K$^{+}$ Ion Channel
Cifuentes, A A
2013-01-01
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 thorough 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 driving frequencie...
Experimental assessment of presumed filtered density function models
Stetsyuk, V.; Soulopoulos, N.; Hardalupas, Y.; Taylor, A. M. K. P.
2015-06-01
Measured filtered density functions (FDFs) as well as assumed beta distribution model of mixture fraction and "subgrid" scale (SGS) scalar variance z '' 2 ¯ , used typically in large eddy simulations, were studied by analysing experimental data, obtained from two-dimensional planar, laser induced fluorescence measurements in isothermal swirling turbulent flows at a constant Reynolds number of 29 000 for different swirl numbers (0.3, 0.58, and 1.07). Two-dimensional spatial filtering, by using a box filter, was performed in order to obtain the filtered variables, namely, resolved mean and "subgrid" scale scalar variance. These were used as inputs for assumed beta distribution of mixture fraction and top-hat FDF shape estimates. The presumed beta distribution model, top-hat FDF, and the measured filtered density functions were used to integrate a laminar flamelet solution in order to calculate the corresponding resolved temperature. The experimentally measured FDFs varied with the flow swirl number and both axial and radial positions in the flow. The FDFs were unimodal at flow regions with low SGS scalar variance, z '' 2 ¯ 0.02. Bimodal FDF could be observed for a filter size of approximately 1.5-2 times the Batchelor scale. Unimodal FDF could be observed for a filter size as large as four times the Batchelor scale under well-mixed conditions. In addition, two common computational models (a gradient assumption and a scale similarity model) for the SGS scalar variance were used with the aim to evaluate their validity through comparison with the experimental data. It was found that the gradient assumption model performed generally better than the scale similarity one.
Recursive three-dimensional model reconstruction based on Kalman filtering.
Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen
2005-06-01
A recursive two-step method to recover structure and motion from image sequences based on Kalman filtering is described in this paper. The algorithm consists of two major steps. The first step is an extended Kalman filter (EKF) for the estimation of the object's pose. The second step is a set of EKFs, one for each model point, for the refinement of the positions of the model features in the three-dimensional (3-D) space. These two steps alternate from frame to frame. The initial model converges to the final structure as the image sequence is scanned sequentially. The performance of the algorithm is demonstrated with both synthetic data and real-world objects. Analytical and empirical comparisons are made among our approach, the interleaved bundle adjustment method, and the Kalman filtering-based recursive algorithm by Azarbayejani and Pentland. Our approach outperformed the other two algorithms in terms of computation speed without loss in the quality of model reconstruction.
Experimental analysis and modeling of a stormwater perlite filter.
Gironás, Jorge; Adriasola, José M; Fernández, Bonifacio
2008-06-01
This paper presents the study of a mixed porous media composed of expanded perlite and a nonwoven needle-punched geotextile used to reduce the suspended solids load and concentration in urban runoff. Laboratory procedures were designed to quantify the suspended solids removal efficiency and variation in time of filtration rate. Different grain-size distributions of expanded perlite, diverse suspended solids concentrations, and different hydraulic and geometric conditions were tested to determine the most effective filter media. A dimensionless parameter, termed Global Performance Index (GPI), was developed to reach this objective. Measured data were also used to build a dimensional and a regression model to represent the performance of the filter media mathematically. The theory, derivation, and performance of both models are presented and compared with an existent empirical model. The dimensional model better reproduces the observations, becoming a useful tool for the design, operation, and evaluation of commercial porous media filters.
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.
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.
A GARCH option pricing model with filtered historical simulation
Mancini, Loriano; Barone-Adesi, Giovanni; Engle, Robert
2008-01-01
We propose a new method for pricing options based on GARCH models with filtered historical innovaions. In an incomplete market framework, we allow for different distributions of historical and pricing return dynamics, which enhances the model’s flexibility to fit market option prices. An extensive empirical analysis based on S&P 500 Index options shows that our model outperforms other competing GARCH pricing models and ad hoc Black–Scholes models. We show that the flexible change of me...
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.
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
Mathematical Modeling of Flow Field in Ceramic Candle Filter
Institute of Scientific and Technical Information of China (English)
TaewonSeo; Joo－HongChoi; 等
1998-01-01
Integrated gasification combined cycle(IGCC)is one of the candidates to achieve stringent environmental regulation among the clean coal technologies.Advancing the technology of the hot gas cleanup systems is the most critical component in the development of the IGCC.Thus the aim of this study is to understand the flow field in the ceramic filter and the influence of ceramic filter in removal of the particles contained in the hot gas flow.The numerical model based on the Reynolds stress turbulence model with the Darycy's law in the porous region is adopted.It is found that the effect of the porosity in the flowfield is negligibly small while the effect of the filter length is significant.It is also found as the permeability decreases,the reattachment point due to the flow separation moves upstream,This is because the fluid is sucked into the filter region due to the pressure drop before the flow separation occurs.The particle follows well with the fluid stream and the particle is directly sucked into the filter due to the pressure drop even in the flow separation region.
Streamflow Data Assimilation in SWAT Model Using Extended Kalman Filter
Sun, L.; Nistor, I.; Seidou, O.
2014-12-01
Although Extended Kalman Filter (EKF) is regarded as the de facto method for the application of Kalman Filter in non-linear system, it's application to complex distributed hydrological models faces a lot of challenges. Ensemble Kalman Filter (EnKF) is often preferred because it avoids the calculation of the linearization Jacobian Matrix and the propagation of estimation error covariance. EnKF is however difficult to apply to large models because of the huge computation demand needed for parallel propagation of ensemble members. This paper deals with the application of EKF in stream flow prediction using the SWAT model in the watershed of Senegal River, West Africa. In the Jacobian Matrix calculation, SWAT is regarded as a black box model and the derivatives are calculated in the form of differential equations. The state vector is the combination of runoff, soil, shallow aquifer and deep aquifer water contents. As an initial attempt, only stream flow observations are assimilated. Despite the fact that EKF is a sub-optimal filter, the coupling of EKF significantly improves the estimation of daily streamflow. The results of SWAT+EKF are also compared to those of a simpler quasi linear streamflow prediction model where both state and parameters are updated with the EKF.
Directory of Open Access Journals (Sweden)
Wei Zhu
2016-06-01
Full Text Available In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF, interacting multiple models unscented Kalman filter (IMMUKF, 5CKF and the optimal mode transition matrix IMM (OMTM-IMM.
Zhu, Wei; Wang, Wei; Yuan, Gannan
2016-06-01
In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM).
Filtering multiscale dynamical systems in the presence of model error
Harlim, John
2013-01-01
In this review article, we report two important competing data assimilation schemes that were developed in the past 20 years, discuss the current methods that are operationally used in weather forecasting applications, and point out one major challenge in data assimilation community: "utilize these existing schemes in the presence of model error". The aim of this paper is to provide theoretical guidelines to mitigate model error in practical applications of filtering multiscale dynamical systems with reduced models. This is a prototypical situation in many applications due to limited ability to resolve the smaller scale processes as well as the difficulty to model the interaction across scales. We present simple examples to point out the importance of accounting for model error when the separation of scales are not apparent. These examples also elucidate the necessity of treating model error as a stochastic process in a nontrivial fashion for optimal filtering, in the sense that the mean and covariance estima...
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
Fast Filtering and Smoothing for Multivariate State Space Models
Koopman, S.J.M.; Durbin, J.
1998-01-01
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually. This strategy leads to computationally efficient methods f
Potential Worst-case System for Testing EMI Filters Tested on Simple Filter Models
Directory of Open Access Journals (Sweden)
Z. Raida
2008-09-01
Full Text Available This paper deals with the approximate worst-case test method for testing the insertion loss of the EMI filters. The systems with 0.1 ÃŽÂ© and 100 ÃŽÂ© impedances are usually used for this testing. These systems are required by the international CISPR 17 standard. The main disadvantage of this system is the use of two impedance transformers. Especially the impedance transformer with 0.1 ÃŽÂ© output impedance is not easy to be produced. These transformers have usually narrow bandwidth. This paper discusses the alternative system with 1 ÃŽÂ© and 100 ÃŽÂ© impedances. The performance of these systems was tested on several filtersÃ¢Â€Â™ models and the obtained data are depicted, too. The performance comparison of several filters in several systems is also included. The performance of alternate worst-case system is discussed in the conclusion.
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.
Fuel Efficient Diesel Particulate Filter (DPF) Modeling and Development
Energy Technology Data Exchange (ETDEWEB)
Stewart, Mark L.; Gallant, Thomas R.; Kim, Do Heui; Maupin, Gary D.; Zelenyuk, Alla
2010-08-01
The project described in this report seeks to promote effective diesel particulate filter technology with minimum fuel penalty by enhancing fundamental understanding of filtration mechanisms through targeted experiments and computer simulations. The overall backpressure of a filtration system depends upon complex interactions of particulate matter and ash with the microscopic pores in filter media. Better characterization of these phenomena is essential for exhaust system optimization. The acicular mullite (ACM) diesel particulate filter substrate is under continuing development by Dow Automotive. ACM is made up of long mullite crystals which intersect to form filter wall framework and protrude from the wall surface into the DPF channels. ACM filters have been demonstrated to effectively remove diesel exhaust particles while maintaining relatively low backpressure. Modeling approaches developed for more conventional ceramic filter materials, such as silicon carbide and cordierite, have been difficult to apply to ACM because of properties arising from its unique microstructure. Penetration of soot into the high-porosity region of projecting crystal structures leads to a somewhat extended depth filtration mode, but with less dramatic increases in pressure drop than are normally observed during depth filtration in cordierite or silicon carbide filters. Another consequence is greater contact between the soot and solid surfaces, which may enhance the action of some catalyst coatings in filter regeneration. The projecting crystals appear to provide a two-fold benefit for maintaining low backpressures during filter loading: they help prevent soot from being forced into the throats of pores in the lower porosity region of the filter wall, and they also tend to support the forming filter cake, resulting in lower average cake density and higher permeability. Other simulations suggest that soot deposits may also tend to form at the tips of projecting crystals due to the axial
Information Filtering via Collaborative User Clustering Modeling
Zhang, Chu-Xu; Yu, Lu; Liu, Chuang; Liu, Hao; Yan, Xiao-Yong
2013-01-01
The past few years have witnessed the great success of recommender systems, which can significantly help users find out personalized items for them from the information era. One of the most widely applied recommendation methods is the Matrix Factorization (MF). However, most of researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information, but also takes into account the user interest. We compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on a real-world dataset, MovieLens, show that our method performs much better than other three methods in the accuracy of recommendation.
Information filtering via collaborative user clustering modeling
Zhang, Chu-Xu; Zhang, Zi-Ke; Yu, Lu; Liu, Chuang; Liu, Hao; Yan, Xiao-Yong
2014-02-01
The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information but also the user information. In addition, we compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on two real-world datasets, MovieLens 1M and MovieLens 100k, show that our method performs better than other three methods in the accuracy of recommendation.
IIR Filter Modeling Using an Algorithm Inspired on Electromagnetism
Directory of Open Access Journals (Sweden)
Cuevas-Jiménez E.
2013-01-01
Full Text Available Infinite-impulse-response (IIR filtering provides a powerful approach for solving a variety of problems. However, its design represents a very complicated task, since the error surface of IIR filters is generally multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a new method based on the Electromagnetism-Like Optimization Algorithm (EMO is proposed for IIR filter modeling. EMO originates from the electro-magnetism theory of physics by assuming potential solutions as electrically charged particles which spread around the solution space. The charge of each particle depends on its objective function value. This algorithm employs a collective attraction-repulsion mechanism to move the particles towards optimality. The experimental results confirm the high performance of the proposed method in solving various benchmark identification problems.
Simplified model for fouling of a pleated membrane filter
Sanaei, Pejman; Cummings, Linda
2014-11-01
Pleated filter cartridge are widely used to remove undesired impurities from a fluid. A filter membrane is sandwiched between porous support layers, then pleated and packed in to an annular cylindrical cartridge. Although this arrangement offers a high ratio of surface filtration area to volume, the filter performance (measured, e.g., by graph of total flux versus throughput for a given pressure drop), is not as good as a flat filter membrane. The reasons for this difference in performance are currently unclear, but likely factors include the additional resistance of the porous support layers upstream and downstream of the membrane, the pleat packing density (PPD) and possible damage to the membrane during the pleating process. To investigate this, we propose a simplified mathematical model of the filtration within a single pleat. We consider the fluid dynamics through the membrane and support layers, and propose a model by which the pores of the membrane become fouled (i) by particles smaller than the membrane pore size; and (ii) by particles larger than the pores.We present some simulations of our model, investigating how flow and fouling differ between not only flat and pleated membranes, but also for support layers of different permeability profiles. NSF DMS-1261596.
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 whether their parameters are fixed or evolutive. Stochastic model specification is carried out to discriminate two alternative hypotheses concerning the generation of trends: the trend-stationary hypothesis, on the one hand, for which the trend is a deterministic function of time and the short run......, estimated by a suitable Gibbs sampling scheme, provides useful insight on quasi-integrated nature of the specifications selected....
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.
X-ray fluoroscopy noise modeling for filter design.
Cesarelli, M; Bifulco, P; Cerciello, T; Romano, M; Paura, L
2013-03-01
Fluoroscopy is an invaluable tool in various medical practices such as catheterization or image-guided surgery. Patient's screen for prolonged time requires substantial reduction in X-ray exposure: The limited number of photons generates relevant quantum noise. Denoising is essential to enhance fluoroscopic image quality and can be considerably improved by considering the peculiar noise characteristics. This study presents analytical models of fluoroscopic noise to express the variance of noise as a function of gray level, a practical method to estimate the parameters of the models and a possible application to improve the performance of noise filtering. Quantum noise is modeled as a Poisson distribution and results strongly signal-dependent. However, fluoroscopic devices generally apply gray-level transformations (i.e., logarithmic-mapping, gamma-correction) for image enhancement. The resulting statistical transformations of the noise were analytically derived. In addition, a characterization of the statistics of noise for fluoroscopic image differences was offered by resorting to Skellam distribution. Real fluoroscopic sequences of a simple step-phantom were acquired by a conventional fluoroscopic device and were utilized as actual noise measurements to compare with. An adaptive spatio-temporal filter based on the local conditional average of similar pixels has been proposed. The gray-level differences between the local pixel and the neighboring pixels have been assumed as measure of similarity. Filter performance was evaluated by using real fluoroscopic images of a step phantom and acquired during a pacemaker implantation. The comparison between experimental data and the analytical derivation of the relationship between noise variance and mean pixel intensity (noise-parameter models) were presented relatively to raw-images, after applying logarithmic-mapping or gamma-correction and for difference images. Results have confirmed a great agreement (adjusted R
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.
Filter length scale for continuum modeling of subgrid physics
Simeonov, Julian; Calantoni, Joseph
2014-11-01
Modeling the wide range of scales of geophysical processes with direct numerical simulations (DNS) is currently not feasible. It is therefore typical to explicitly resolve only the large energy-containing scales and to parameterize the unresolved small scales. One approach to separate the scales is by means of spatial filters and here we discuss practical considerations regarding the choice of a volume averaging scale L. We use a macroscopically homogeneous scalar field and quantify the smoothness of the filtered field using a noise metric, ν, defined by the standard deviation of the filtered field normalized by the domain-averaged value of the field. For illustration, we consider the continuum modeling of the particle phase in discrete element method (DEM) simulations and the salt fingers in DNS of double-diffusive convection. We find that ν2 follows an inverse power law dependence on L with an exponent and coefficients proportional to the domain-averaged field value. The empirical power law relation can aid in the development of continuum models from fully resolved simulations while also providing uncertainty estimates of the modeled continuum fields.
Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
. The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported...
Tunnel Point Cloud Filtering Method Based on Elliptic Cylindrical Model
Zhua, Ningning; Jiaa, Yonghong; Luo, Lun
2016-06-01
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.
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.
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...
Underwater 3d Modeling: Image Enhancement and Point Cloud Filtering
Sarakinou, I.; Papadimitriou, K.; Georgoula, O.; Patias, P.
2016-06-01
This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images' radiometry (captured at shallow depths) and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software). Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck) captured at three different depths (3.5m, 10m and 14m respectively). Four models have been created from the first dataset (seafloor) in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a) the definition of parameters for the point cloud filtering and the creation of a reference model, b) the radiometric editing of images, followed by the creation of three improved models and c) the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m) and different objects (part of a wreck and a small boat's wreck) in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.
UNDERWATER 3D MODELING: IMAGE ENHANCEMENT AND POINT CLOUD FILTERING
Directory of Open Access Journals (Sweden)
I. Sarakinou
2016-06-01
Full Text Available This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images’ radiometry (captured at shallow depths and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software. Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck captured at three different depths (3.5m, 10m and 14m respectively. Four models have been created from the first dataset (seafloor in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a the definition of parameters for the point cloud filtering and the creation of a reference model, b the radiometric editing of images, followed by the creation of three improved models and c the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m and different objects (part of a wreck and a small boat's wreck in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.
A personalized web page content filtering model based on segmentation
Kuppusamy, K S; 10.5121/ijist.2012.2104
2012-01-01
In the view of massive content explosion in World Wide Web through diverse sources, it has become mandatory to have content filtering tools. The filtering of contents of the web pages holds greater significance in cases of access by minor-age people. The traditional web page blocking systems goes by the Boolean methodology of either displaying the full page or blocking it completely. With the increased dynamism in the web pages, it has become a common phenomenon that different portions of the web page holds different types of content at different time instances. This paper proposes a model to block the contents at a fine-grained level i.e. instead of completely blocking the page it would be efficient to block only those segments which holds the contents to be blocked. The advantages of this method over the traditional methods are fine-graining level of blocking and automatic identification of portions of the page to be blocked. The experiments conducted on the proposed model indicate 88% of accuracy in filter...
When do we need a trend model in kriging
Energy Technology Data Exchange (ETDEWEB)
Journel, A.G.; Rossi, M.E. (Stanford Univ., CA (USA))
1989-10-01
Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option.
Application of Kalman Filter on modelling interest rates
Directory of Open Access Journals (Sweden)
Long H. Vo
2014-03-01
Full Text Available This study aims to test the feasibility of using a data set of 90-day bank bill forward rates from the Australian market to predict spot interest rates. To achieve this goal I utilized the application of Kalman Filter in a state space model with time-varying state variable. It is documented that in the case of short-term interest rates,the state space model yields robust predictive power. In addition, this predictive power of implied forward rate is heavily impacted by the existence of a time-varying risk premium in the term structure.
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.
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...
Research on Time-series Modeling and Filtering Methods for MEMS Gyroscope Random Drift Error
Wang, Xiao Yi; Meng, Xiu Yun
2017-03-01
The precision of MEMS gyroscope is reduced by random drift error. This paper applied time series analysis to model random drift error of MEMS gyroscope. Based on the model established, Kalman filter was employed to compensate for the error. To overcome the disadvantages of conventional Kalman filter, Sage-Husa adaptive filtering algorithm was utilized to improve the accuracy of filtering results and the orthogonal property of innovation in the process of filtering was utilized to deal with outliers. The results showed that, compared with conventional Kalman filter, the modified filter can not only enhance filter accuracy, but also resist to outliers and this assured the stability of filtering thus improving the performance of gyroscopes.
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 app
Statistical Process Control of a Kalman Filter Model
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A.
2014-01-01
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations. PMID:25264959
Statistical Process Control of a Kalman Filter Model
Directory of Open Access Journals (Sweden)
Sonja Gamse
2014-09-01
Full Text Available For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.
Statistical process control of a Kalman filter model.
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A
2014-09-26
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Shanghai Mode Lingerie continually strives to underline its status as a veritable reference on the fashion scene: an opportunity to explore trends, interpret key directions and gain an in-depth overview of lines to follow.
Hidden Markov Model Based Visual Perception Filtering in Robotic Soccer
Directory of Open Access Journals (Sweden)
Can Kavaklioglu
2009-02-01
Full Text Available Autonomous robots can initiate their mission plans only after gathering sufficient information about the environment. Therefore reliable perception information plays a major role in the overall success of an autonomous robot. The Hidden Markov Model based post-perception filtering module proposed in this paper aims to identify and remove spurious perception information in a given perception sequence using the generic metapose definition. This method allows representing uncertainty in more abstract terms compared to the common physical representations. Our experiments with the four legged AIBO robot indicated that the proposed module improved perception and localization performance significantly.
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.
Altitude dependence of atmospheric temperature trends: Climate models versus observation
Douglass, D H; Singer, F
2004-01-01
As a consequence of greenhouse forcing, all state of the art general circulation models predict a positive temperature trend that is greater for the troposphere than the surface. This predicted positive trend increases in value with altitude until it reaches a maximum ratio with respect to the surface of as much as 1.5 to 2.0 at about 200 to 400 hPa. However, the temperature trends from several independent observational data sets show decreasing as well as mostly negative values. This disparity indicates that the three models examined here fail to account for the effects of greenhouse forcings.
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......A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered...... 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...
Modeling Distortion Effects in Class-D Amplifier Filter Inductors
DEFF Research Database (Denmark)
Knott, Arnold; Stegenborg-Andersen, Tore; Thomsen, Ole Cornelius
2010-01-01
Distortion is generally accepted as a quantifier to judge the quality of audio power amplifiers. In switchmode power amplifiers various mechanisms influence this performance measure. After giving an overview of those, this paper focuses on the particular effect of the nonlinearity of the output...... filter components on the audio performance. While the physical reasons for both, the capacitor and the inductor induced distortion are given, the practical in depth demonstration is done for the inductor only. This includes measuring the inductors performance, modeling through fitting and resulting...... into simulation models. The fitted models achieve distortion values between 0.03 % and 0.2 % as a basis to enable the design of a 200 W amplifier....
Application of SIR epidemiological model: new trends
Rodrigues, Helena Sofia
2016-01-01
The simplest epidemiologic model composed by mutually exclusive compartments SIR (susceptible-infected-susceptible) is presented to describe a reality. From health concerns to situations related with marketing, informatics or even sociology, several are the fields that are using this epidemiological model as a first approach to better understand a situation. In this paper, the basic transmission model is analyzed, as well as simple tools that allows us to extract a great deal of information about possible solutions. A set of applications - traditional and new ones - is described to show the importance of this model.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-02-06
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively.
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...
Modeling the variation trends of glacier systems
Directory of Open Access Journals (Sweden)
Z. Xie
2013-01-01
Full Text Available The basic principles and methods for a functional glacier systems model are introduced and applied for glaciers of Northwest China. When running the model we assume that a glacier system is under steady state conditions in the initial year. The median size of a glacier system is used as representative for the system. The curve of glacier area distribution against elevation is used to compute the increase in equilibrium line altitude (ELA, and the annual glacier ablation is calculated using a global formula a = 1.33(9.66 + ts².⁸⁵ [4, p. 96]. The net mass balance near the ELA under steady state conditions represents the net mass balance of the whole glacier system, and the time required for glacier runoff to return to the initial year level is calculated according to the law of glacier runoff variation, and used to calculate the variation of glacier area. The variation of glacier runoff is modeled according to ablation at the ELA, and the variation of glacier volume is modeled according to the absolute value of the mass balance. The observed changes in surveyed glaciers in China over recent decades were broadly consistent with predictions of the glacier system model. The model therefore offers a reliable method for the prediction of changes in glacier systems in response to changing climate.
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.
Stock Market Trend Analysis Using Hidden Markov Models
Kavitha, G.; Udhayakumar, A.; D. Nagarajan
2013-01-01
Price movements of stock market are not totally random. In fact, what drives the financial market and what pattern financial time series follows have long been the interest that attracts economists, mathematicians and most recently computer scientists [17]. This paper gives an idea about the trend analysis of stock market behaviour using Hidden Markov Model (HMM). The trend once followed over a particular period will sure repeat in future. The one day difference in close value of stocks for a...
Modelling of a recirculating granular medium filter's processes.
Boutin, C; Parouty, R; Ménoret, C; Liénard, A; Brissaud, F
2002-01-01
The effluents of French small farm factories will soon be submitted to regulation. Only a few treatment techniques are available to deal with these kind of effluent (high concentration and small daily volumes). To allow the treatment, in the particular economic context of small food processing industries, Cemagref is trying to adapt a treatment based on attached growth cultures on fine media, a system known to be easy to operate and relatively inexpensive. A model, based on four sub-models (hydrodynamic characteristics, oxygen transport, solute transport in the mobile and immobile phases and bacterial evolution) describes this process. Based on wastewater concentration, hydraulic load, applied organic loads, feeding/rest cycles and recycling phases number, this model predicts: eliminated organic loads and the discharge concentration as a function of time, oxygen and biomass contents as a function of time and depth. The determination of the model's parameters is based on a comparison between simulations and performances achieved on experimental columns. This model would be helpful in sizing full-scale filters treating different types of agro-food wastewater. The aim of this article is to present the model's structure, to give all parameter values and to compare the simulations with the results obtained on pilot and full scale plants.
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 ...
Trends in modeling Biomedical Complex Systems
Directory of Open Access Journals (Sweden)
Remondini Daniel
2009-10-01
Full Text Available Abstract In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.
Modeling variability and trends in pesticide concentrations in streams
Vecchia, A.V.; Martin, J.D.; Gilliom, R.J.
2008-01-01
A parametric regression model was developed for assessing the variability and long-term trends in pesticide concentrations in streams. The dependent variable is the logarithm of pesticide concentration and the explanatory variables are a seasonal wave, which represents the seasonal variability of concentration in response to seasonal application rates; a streamflow anomaly, which is the deviation of concurrent daily streamflow from average conditions for the previous 30 days; and a trend, which represents long-term (inter-annual) changes in concentration. Application of the model to selected herbicides and insecticides in four diverse streams indicated the model is robust with respect to pesticide type, stream location, and the degree of censoring (proportion of nondetections). An automatic model fitting and selection procedure for the seasonal wave and trend components was found to perform well for the datasets analyzed. Artificial censoring scenarios were used in a Monte Carlo simulation analysis to show that the fitted trends were unbiased and the approximate p-values were accurate for as few as 10 uncensored concentrations during a three-year period, assuming a sampling frequency of 15 samples per year. Trend estimates for the full model were compared with a model without the streamflow anomaly and a model in which the seasonality was modeled using standard trigonometric functions, rather than seasonal application rates. Exclusion of the streamflow anomaly resulted in substantial increases in the mean-squared error and decreases in power for detecting trends. Incorrectly modeling the seasonal structure of the concentration data resulted in substantial estimation bias and moderate increases in mean-squared error and decreases in power. ?? 2008 American Water Resources Association.
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
Entropy Based Modelling for Estimating Demographic Trends.
Directory of Open Access Journals (Sweden)
Guoqi Li
Full Text Available In this paper, an entropy-based method is proposed to forecast the demographical changes of countries. We formulate the estimation of future demographical profiles as a constrained optimization problem, anchored on the empirically validated assumption that the entropy of age distribution is increasing in time. The procedure of the proposed method involves three stages, namely: 1 Prediction of the age distribution of a country's population based on an "age-structured population model"; 2 Estimation the age distribution of each individual household size with an entropy-based formulation based on an "individual household size model"; and 3 Estimation the number of each household size based on a "total household size model". The last stage is achieved by projecting the age distribution of the country's population (obtained in stage 1 onto the age distributions of individual household sizes (obtained in stage 2. The effectiveness of the proposed method is demonstrated by feeding real world data, and it is general and versatile enough to be extended to other time dependent demographic variables.
Trend stationarity in the I(2) cointegration model
DEFF Research Database (Denmark)
Rahbek, Anders; Kongsted, Hans Christian; Jørgensen, Clara
1999-01-01
A vector autoregressive model for I(2) processes which allows for trend-stationary components and restricts the deterministic part of the process to be at most linear is defined. A two-step statistical analysis of the model is derived. The joint test of I(1) and I(2) cointegrating ranks is shown...
Groot, S.; Harmanny, R.; Driessen, H.; Yarovoy, A.
2013-01-01
In this article, a novel motion model-based particle filter implementation is proposed to classify human motion and to estimate key state variables, such as motion type, i.e. running or walking, and the subject’s height. Micro-Doppler spectrum is used as the observable information. The system and
Cointegration between trends and their estimators in state space models and CVAR models
DEFF Research Database (Denmark)
Johansen, Søren; Tabor, Morten Nyboe
2017-01-01
In a linear state space model Y(t)=BT(t) e(t), we investigate if the unobserved trend, T(t), cointegrates with the predicted trend, E(t), and with the estimated predicted trend, in the sense that the spreads are stationary. We find that this result holds for the spread B......(T(t)-E(t)) and the estimated spread. For the spread between the trend and the estimated trend, T(t)-E(t), however, cointegration depends on the identification of B. The same results are found, if the observations Y(t), from the state space model are analysed using a cointegrated vector autoregressive model, where the trend...... is defined as the common trend. Finally, we investigate cointegration between the spread between trends and their estimators based on the two models, and find the same results. We illustrate with two examples and confirm the results by a small simulation study....
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.
Symmetric Collaborative Filtering Using the Noisy Sensor Model
Sharma, Rita; Poole, David L.
2013-01-01
Collaborative filtering is the process of making recommendations regarding the potential preference of a user, for example shopping on the Internet, based on the preference ratings of the user and a number of other users for various items. This paper considers collaborative filtering based on explicitmulti-valued ratings. To evaluate the algorithms, weconsider only {em pure} collaborative filtering, using ratings exclusively, and no other information about the people or items.Our approach is ...
Unit root modeling for trending stock market series
Directory of Open Access Journals (Sweden)
Afees A. Salisu
2016-06-01
Full Text Available In this paper, we examine how the unit root for stock market series should be modeled. We employ the Narayan and Liu (2015 trend GARCH-based unit root and its variants in order to more carefully capture the inherent statistical behavior of the series. We utilize daily, weekly and monthly data covering nineteen countries across the regions of America, Asia and Europe. We find that the nature of data frequency matters for unit root testing when dealing with stock market data. Our evidence also suggests that stock market data is better modeled in the presence of structural breaks, conditional heteroscedasticity and time trend.
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
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.
Link performance model for filter bank based multicarrier systems
Petrov, Dmitry; Oborina, Alexandra; Giupponi, Lorenza; Stitz, Tobias Hidalgo
2014-12-01
This paper presents a complete link level abstraction model for link quality estimation on the system level of filter bank multicarrier (FBMC)-based networks. The application of mean mutual information per coded bit (MMIB) approach is validated for the FBMC systems. The considered quality measure of the resource element for the FBMC transmission is the received signal-to-noise-plus-distortion ratio (SNDR). Simulation results of the proposed link abstraction model show that the proposed approach is capable of estimating the block error rate (BLER) accurately, even when the signal is propagated through the channels with deep and frequent fades, as it is the case for the 3GPP Hilly Terrain (3GPP-HT) and Enhanced Typical Urban (ETU) models. The FBMC-related results of link level simulations are compared with cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) analogs. Simulation results are also validated through the comparison to reference publicly available results. Finally, the steps of link level abstraction algorithm for FBMC are formulated and its application for system level simulation of a professional mobile radio (PMR) network is discussed.
On Wiener filtering and the physics behind statistical modeling.
Marbach, Ralf
2002-01-01
The closed-form solution of the so-called statistical multivariate calibration model is given in terms of the pure component spectral signal, the spectral noise, and the signal and noise of the reference method. The "statistical" calibration model is shown to be as much grounded on the physics of the pure component spectra as any of the "physical" models. There are no fundamental differences between the two approaches since both are merely different attempts to realize the same basic idea, viz., the spectrometric Wiener filter. The concept of the application-specific signal-to-noise ratio (SNR) is introduced, which is a combination of the two SNRs from the reference and the spectral data. Both are defined and the central importance of the latter for the assessment and development of spectroscopic instruments and methods is explained. Other statistics like the correlation coefficient, prediction error, slope deficiency, etc., are functions of the SNR. Spurious correlations and other practically important issues are discussed in quantitative terms. Most important, it is shown how to use a priori information about the pure component spectra and the spectral noise in an optimal way, thereby making the distinction between statistical and physical calibrations obsolete and combining the best of both worlds. Companies and research groups can use this article to realize significant savings in cost and time for development efforts.
METHODOLOGICAL APPROACH AND MODEL ANALYSIS FOR IDENTIFICATION OF TOURIST TRENDS
Directory of Open Access Journals (Sweden)
Neven Šerić
2015-06-01
Full Text Available The draw and diversity of the destination’s offer is an antecedent of the tourism visits growth. The destination supply differentiation is carried through new, specialised tourism products. The usual approach consists of forming specialised tourism products in accordance with the existing tourism destination image. Another approach, prevalent in practice of developed tourism destinations is based on innovating the destination supply through accordance with the global tourism trends. For this particular purpose, it is advisable to choose a monitoring and analysis method of tourism trends. The goal is to determine actual trends governing target markets, differentiating whims from trends during the tourism preseason. When considering the return on investment, modifying the destination’s tourism offer on the basis of a tourism whim is a risky endeavour, indeed. Adapting the destination’s supply to tourism whims can result in a shifted image, one that is unable to ensure a long term interest and tourist vacation growth. With regard to tourism trend research and based on the research conducted, an advisable model for evaluating tourism phenomena is proposed, one that determines whether tourism phenomena is a tourism trend or a tourism whim.
Filter-matrix lattice Boltzmann model for microchannel gas flows.
Zhuo, Congshan; Zhong, Chengwen
2013-11-01
The lattice Boltzmann method has been shown to be successful for microscale gas flows, and it has attracted significant research interest. In this paper, the recently proposed filter-matrix lattice Boltzmann (FMLB) model is first applied to study the microchannel gas flows, in which a Bosanquet-type effective viscosity is used to capture the flow behaviors in the transition regime. A kinetic boundary condition, the combined bounce-back and specular-reflection scheme with the second-order slip scheme, is also designed for the FMLB model. By analyzing a unidirectional flow, the slip velocity and the discrete effects related to the boundary condition are derived within the FMLB model, and a revised scheme is presented to overcome such effects, which have also been validated through numerical simulations. To gain an accurate simulation in a wide range of Knudsen numbers, covering the slip and the entire transition flow regimes, a set of slip coefficients with an introduced fitting function is adopted in the revised second-order slip boundary condition. The periodic and pressure-driven microchannel flows have been investigated by the present model in this study. The numerical results, including the velocity profile and the mass flow rate, as well as the nonlinear pressure distribution along the channel, agree fairly well with the solutions of the linearized Boltzmann equation, the direct simulation Monte Carlo results, the experimental data, and the previous results of the multiple effective relaxation lattice Boltzmann model. Also, the present results of the velocity profile and the mass flow rate show that the present model with the fitting function can yield improved predictions for the microchannel gas flow with higher Knudsen numbers in the transition flow regime.
Simulating migrated seismic data by filtering an earth model: A MATLAB® implementation
Toxopeus, G.; Thorbecke, J.; Petersen, S.; Wapenaar, K.; Slob, E.
2010-02-01
An earth model is used in a collaborative environment in which some members provide information for its construction and others utilize the result. Validating an earth model by simulating a migration image is an important step. However, the high computational cost of generating 3D synthetic data, followed by the process of migration, limits the number of scenarios that can be validated. To overcome this computational cost, a novel strategy is used where a migration image is simulated by filtering a model with a spatial resolution filter. One of the key properties of this approach is that the model that describes a target-zone is decoupled from the macro-velocity model that is used to compute the spatial resolution filters. Consequently, different models can be filtered with the same resolution filter. For a horizontally layered medium, the Gazdag phase-shift operators are used to construct a common-offset spatial resolution filter to simulate the phase of 2D primary reflection data. To approximate a spatial resolution filter in a laterally variant medium, ray trace information is used as an illumination constraint. Additionally, the influence of seismic uncertainties on the shape of a spatial resolution filter and the resulting migration image are simulated. These filters enhance an iterative earth modeling approach.
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
Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In airborne tracking, the blind Doppler makes the target undetectable, resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types: targets' intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model (IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity.
Behavioral model for common mode filter and performance optimization aspects
Roc'h, A.; Bergsma, H.; Bergsma, J.G.; Leferink, Frank Bernardus Johannes
2008-01-01
A well designed common mode filter for motor drive application can significantly improve the level of electromagnetic interference generated by the cable and the motor housing. The subsequent design of this filter is strongly dependent on the actual in situ parameters of the motor drive and often
Model for trace metal exposure in filter-feeding flamingos at alkaline Rift Valley Lake, Kenya
Energy Technology Data Exchange (ETDEWEB)
Nelson, Y.M.; DiSante, C.J.; Lion, L.W. [Cornell Univ., Ithaca, NY (United States). School of Civil and Environmental Engineering; Thampy, R.J.; Raini, J.A. [Worldwide Fund for Nature, Nakuru (Kenya). Lake Nakuru Conservation and Development Project; Motelin, G.K. [Egerton Univ., Njoro (Kenya). Dept. of Animal Health
1998-11-01
Toxic trace metals have been implicated as a potential cause of recent flamingo kills at Lake Nakuru, Kenya. Chromium (Cr), copper (Cu), lead (Pb), and zinc (Zn) have accumulated in the lake sediments as a result of unregulated discharges and because this alkaline lake has no natural outlet. Lesser flamingos (Phoeniconaias minor) at Lake Nakuru feed predominantly on the cyanobacterium Spirulina platensis, and because of their filter-feeding mechanism, they are susceptible to exposure to particle-bound metals. Trace metal adsorption isotherms to lake sediments and S. platensis were obtained under simulated lake conditions, and a mathematical model was developed to predict metal exposure via filter feeding based on predicted trace metal phase distribution. Metal adsorption to suspended solids followed the trend Pb {much_gt} Zn > Cr > Cu, and isotherms were linear up to 60 {micro}g/L. Adsorption to S. platensis cells followed the trend Pb {much_gt} Zn > Cu > Cr and fit Langmuir isotherms for Cr, Cu and Zn and a linear isotherm for Pb. Predicted phase distributions indicated that Cr and Pb in Lake Nakuru are predominantly associated with suspended solids, whereas Cu and Zn are distributed more evenly between the dissolved phase and particulate phases of both S. platensis and suspended solids. Based on established flamingo feeding rates and particle size selection, predicted Cr and Pb exposure occurs predominantly through ingestion of suspended solids, whereas Cu and Zn exposure occurs through ingestion of both suspended solids and S. platensis. For the lake conditions at the time of sampling, predicted ingestion rates based on measured metal concentrations in lake suspended solids were 0.71, 6.2, 0.81, and 13 mg/kg-d for Cr, Cu, Pb, and Zn, respectively.
Modelling of trends in Twitter using retweet graph dynamics
Ten Thij, Marijn; Ouboter, Tanneke; Worm, Daniël; Litvak, Nelly; Berg, van den Hans; Bhulai, Sandjai; Bonata, Anthony; Chung, Fan Chung; Pralat, Pawel
2014-01-01
In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph
Coupled continuum and molecular model of flow through fibrous filter
Zhao, Shunliu; Povitsky, Alex
2013-11-01
A coupled approach combining the continuum boundary singularity method (BSM) and the molecular direct simulation Monte Carlo (DSMC) is developed and validated using Taylor-Couette flow and the flow about a single fiber confined between two parallel walls. In the proposed approach, the DSMC is applied to an annular region enclosing the fiber and the BSM is employed in the entire flow domain. The parameters used in the DSMC and the coupling procedure, such as the number of simulated particles, the cell size, and the size of the coupling zone are determined by inspecting the accuracy of pressure drop obtained for the range of Knudsen numbers between zero and unity. The developed approach is used to study flowfield of fibrous filtration flows. It is observed that in the partial-slip flow regime, Kn ⩽ 0.25, the results obtained by the proposed coupled BSM-DSMC method match the solution by BSM combined with the heuristic partial-slip boundary conditions. For transition molecular-to-continuum Knudsen numbers, 0.25 pressure drop and velocity between these two approaches is significant. This difference increases with the Knudsen number that confirms the usefulness of coupled continuum and molecular methods in numerical modeling of transition low Reynolds number flows in fibrous filters.
Modelling surface run-off and trends analysis over India
Gupta, P. K.; Chauhan, S.; Oza, M. P.
2016-08-01
The present study is mainly concerned with detecting the trend of run-off over the mainland of India, during a time period of 35 years, from 1971-2005 (May-October). Rainfall, soil texture, land cover types, slope, etc., were processed and run-off modelling was done using the Natural Resources Conservation Service (NRCS) model with modifications and cell size of 5×5 km. The slope and antecedent moisture corrections were incorporated in the existing model. Trend analysis of estimated run-off was done by taking into account different analysis windows such as cell, medium and major river basins, meteorological sub-divisions and elevation zones across India. It was estimated that out of the average 1012.5 mm of rainfall over India (considering the study period of 35 years), 33.8% got converted to surface run-off. An exponential model was developed between the rainfall and the run-off that predicted the run-off with an R 2 of 0.97 and RMSE of 8.31 mm. The run-off trend analysed using the Mann-Kendall test revealed that a significant pattern exists in 22 medium, two major river basins and three meteorological sub-divisions, while there was no evidence of a statistically significant trend in the elevation zones. Among the medium river basins, the highest positive rate of change in the run-off was observed in the Kameng basin (13.6 mm/yr), while the highest negative trend was observed in the Tista upstream basin (-21.4 mm/yr). Changes in run-off provide valuable information for understanding the region's sensitivity to climatic variability.
Modelling surface run-off and trends analysis over India
Indian Academy of Sciences (India)
P K Gupta; S Chauhan; M P Oza
2016-08-01
The present study is mainly concerned with detecting the trend of run-off over the mainland of India, during a time period of 35 years, from 1971–2005 May–October). Rainfall, soil texture, land cover types, slope, etc., were processed and run-off modelling was done using the Natural Resources ConservationService (NRCS) model with modifications and cell size of 5×5 km. The slope and antecedent moisture corrections were incorporated in the existing model. Trend analysis of estimated run-off was done by taking into account different analysis windows such as cell, medium and major river basins, meteorologicalsub-divisions and elevation zones across India. It was estimated that out of the average 1012.5 mm of rainfall over India (considering the study period of 35 years), 33.8% got converted to surface run-off. An exponential model was developed between the rainfall and the run-off that predicted the run-off with an $R^2$ of 0.97 and RMSE of 8.31 mm. The run-off trend analysed using the Mann–Kendall test revealed that a significant pattern exists in 22 medium, two major river basins and three meteorological subdivisions, while there was no evidence of a statistically significant trend in the elevation zones. Among the medium river basins, the highest positive rate of change in the run-off was observed in the Kameng basin (13.6 mm/yr), while the highest negative trend was observed in the Tista upstream basin (−21.4 mm/yr). Changes in run-off provide valuable information for understanding the region’s sensitivity to climatic variability.
Assessment of damage localization based on spatial filters using numerical crack propagation models
Energy Technology Data Exchange (ETDEWEB)
Deraemaeker, Arnaud, E-mail: aderaema@ulb.ac.be [Universite Libre de Bruxelles, Civil Engineering Department (BATir), 50 av. Franklin Roosevelt, CP 194/02, B-1050 Brussels (Belgium)
2011-07-19
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.
A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting
Directory of Open Access Journals (Sweden)
Youzhu Li
2014-01-01
Full Text Available This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.
MATHEMATICAL MODELING AND INTENSIFICATION OF CONDENSATION GRAVITY FINE AIR FILTER OPERATION
Directory of Open Access Journals (Sweden)
V. V. Shitov
2011-04-01
Full Text Available Problem statement. A problem of mathematical modeling and intensification of operation of the flow thermal diffusion chamber of the condensation gravity filter as one of the most efficient air filter is solved.Results and conclusions. This paper presents an example of the practical application of the model of heat and mass exchange in the thermodiffusion chamber as the main operating element of condensation gravity filter for high-performance air purification due to the generation of supersaturation fields with controlled properties. A criterion for quantitative assessment of purification efficiency in the form of breakthrough function is developed. The typical results of numerical modeling of the operation of the condensation gravity-type filter are presented for the most common case in practice. The possibility of intensification of the filter operation either at the stage of use or at the stage of design is shown based on the proposed approach, obtained models, and calculations.
Zhang, Hongjuan; Hendricks-Franssen, Harrie-Jan; Han, Xujun; Vrugt, Jasper A.; Vereecken, Harry
2016-04-01
Land surface models (LSMs) resolve the water and energy balance with different parameters and state variables. Many of the parameters of these models cannot be measured directly in the field, and require calibration against flux and soil moisture data. Two LSMs are used in our work: Variable Infiltration Capacity Hydrologic Model (VIC) and the Community Land Model (CLM). Temporal variations in soil moisture content at 5, 20 and 50 cm depth in the Rollesbroich experimental watershed in Germany are simulated in both LSMs. Data assimilation (DA) provides a good way to jointly estimate soil moisture content and soil properties of the resolved soil domain. Four DA methods combined with the two LSMs are used in our work: the Ensemble Kalman Filter (EnKF) using state augmentation or dual estimation, the Residual Resampling Particle Filter (RRPF) and Markov chain Monte Carlo Particle Filter (MCMCPF). These four DA methods are tuned and calibrated for a five month period, and subsequently evaluated for another five month period. Performances of the two LSMs and the four DA methods are compared. Our results show that all DA methods improve the estimation of soil moisture content of the VIC and CLM models, especially if the soil hydraulic properties (VIC), the maximum baseflow velocity (VIC) and/or soil texture (CLM) are jointly estimated with soil moisture content. The augmentation and dual estimation methods performed slightly better than RRPF and MCMCPF in the evaluation period. The differences in simulated soil moisture content between CLM and VIC were larger than variations among the DA methods. The CLM performed better than the VIC model. The strong underestimation of soil moisture content in the third layer of the VIC model is likely related to an inadequate parameterization of groundwater drainage.
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.
Dynamic Model Predicting Overweight, Obesity, and Extreme Obesity Prevalence Trends
Thomas, Diana M.; Weedermann, Marion; Fuemmeler, Bernard F.; Martin, Corby K.; Dhurandhar, Nikhil V.; Bredlau, Carl; Heymsfield, Steven B.; Ravussin, Eric; Bouchard, Claude
2013-01-01
Objective Obesity prevalence in the United States (US) appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. Design and Methods A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and non-social influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. Results The dynamic model predicts that: obesity prevalence is a function of birth rate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of obesity, overweight, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9%, respectively. Conclusions The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. PMID:23804487
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
Control model for compressible cake filtration of green liquor in cassette filter
Bornefelt, Kajsa
2006-01-01
In the closed chemical recovery cycle in the sulphate pulp mill it is important to remove non-process elements. This is done by clarification of the green liquor, either in clarifiers or in filters. This project focuses on a cassette filter developed by Kvaerner Pulping AB. The cassette filter is semi-continuous and the aim of the project was to model the filter in order to be able to control cycle time and feed towards optimization of the capacity. The green liquor sludge forms a compressibl...
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.
Low Pass Filter Model for Chemical Sensors in Response to Gases and Odors
Directory of Open Access Journals (Sweden)
Mahmoud Z. Iskandarani
2012-01-01
Full Text Available Design and Modeling multi-gap sensing odor system for the objectives of odor recognition, classification and correlation are carried out. The model illustrates the low pass functionality of the multi-gap sensor acting as a filter for odors. Problem statement: Odor filtering is an important issue in today's world. In addition knowing the original material that an odor belongs to even after being mixed with others is also of vital importance. In addition measuring quality of mixed odors in terms of their affinity and belonging to a specific category or is critical. Approach: Mathematical modeling using low pass filter is carried out. Results: Clear evidence of ability to filter components of an odor mixture as the multi-gap sensor is acting as a filter. Conclusion: The ability to custom design chemical sensors to indicate the presence of various odors.
Design of generalised orthogonal filters: application to the modelling of dynamical systems
Nikolić, Saša S.; Antić, Dragan S.; Perić, Staniša Lj.; Danković, Nikola B.; Milojković, Marko T.
2016-02-01
In this article, we define a new class of orthogonal filters with complex poles and zeroes inside their transfer function. This further improvement of classical orthogonal filters allows the possibility to model a wider range of real systems, that is, the systems whose mathematical models have complex zeroes besides real ones. These filters can be applied in the following areas: circuit theory, telecommunications, signal processing, bond graphs, theory approximations and control system theory. First, we describe the rational functions with complex poles and zeroes, and prove their orthogonality. Based on these functions, we designed the block diagram of orthogonal Legendre-type filter with complex poles and zeroes. After that an appropriate analogue scheme of this filter for practical realisation is derived. To validate theoretical results, we performed an experiment with a cascade-connected system designed and practically realised in our laboratories. The experiments proved the quality of the designed orthogonal model in terms of accuracy and simplicity.
Evaluation of a Filter-Based Model for Computations of Cavitating Flows
Institute of Scientific and Technical Information of China (English)
HUANG Biao; WANG Guo-Yu
2011-01-01
To identify ways to improve the predictive capability of the current RANS-based cavitating turbulent closure,a filter-based model (FBM) is introduced by considering sub-filter stresses. The sub-filter stress is constructed directly by using the filter size and the conventional turbulence closure. The model is evaluated in steady cavitating flow over a blunt body revolution and unsteady cavitating flow around a Clark-Y hydrofoil respectively.Compared with the experimental data, those results indicate that FBM can be used to improve the predictive capability considerably.%@@ To identify ways to improve the predictive capability of the current RANS-based cavitating turbulent closure, a filter-based model (FBM) is introduced by considering sub-filter stresses.The sub-filter stress is constructed directly by using the filter size and the conventional turbulence closure.The model is evaluated in steady cavitating flow over a blunt body revolution and unsteady cavitating flow around a Clark-Y hydrofoil respectively.Compared with the experimental data, those results indicate that FBM can be used to improve the predictive capability considerably.
Tunable n-path notch filters for blocker suppression: modeling and verification
Ghaffari, A.; Klumperink, Eric A.M.; Nauta, Bram
2013-01-01
N-path switched-RC circuits can realize filters with very high linearity and compression point while they are tunable by a clock frequency. In this paper, both differential and single-ended N-path notch filters are modeled and analyzed. Closed-form equations provide design equations for the main
Zhao, Fu; Landis, Heather R; Skerlos, Steven J
2005-01-01
A methodology for producing a pore-scale, 3D computational model of porous filter permeability is developed that is based on the analysis of 2D images of the filter matrix and first principles. The computationally reconstructed porous filter model retains statistical details of porosity and the spatial correlations of porosity within the filter and can be used to calculate permeability for either isotropic or 1D anisotropic porous filters. In the isotropic case, validation of the methodology was conducted using 0.2 and 0.8 microm ceramic membrane filters,forwhich it is shown that the image-based computational models provide a viable statistical reproduction of actual porosity characteristics. It is also shown that these models can predict water flux directly from first principles with deviations from experimental measurements in the range of experimental error. In the anisotropic case, validation of the methodology was conducted using a natural river sand filter. For this case, it is shown that the methodology yields predictions of filtration velocity that are similar or better than predictions offered by existing filtration models. It was found for the sand filter that the deviation between observation and prediction was mostly due to swelling during the preparation of the sand filter for imaging and can be reduced significantly using alternative methods reported in the literature. On the basis of these results, it is concluded that the computational reconstruction methodology is valid for porous filter modeling, and given that it captures pore-scale details, it has potential application to the investigation of permeability decline underthe influence of pore-scale fouling mechanisms.
Emerging Trends and Statistical Analysis in Computational Modeling in Agriculture
Directory of Open Access Journals (Sweden)
Sunil Kumar
2015-03-01
Full Text Available In this paper the authors have tried to describe emerging trend in computational modelling used in the sphere of agriculture. Agricultural computational modelling with the use of intelligence techniques for computing the agricultural output by providing minimum input data to lessen the time through cutting down the multi locational field trials and also the labours and other inputs is getting momentum. Development of locally suitable integrated farming systems (IFS is the utmost need of the day, particularly in India where about 95% farms are under small and marginal holding size. Optimization of the size and number of the various enterprises to the desired IFS model for a particular set of agro-climate is essential components of the research to sustain the agricultural productivity for not only filling the stomach of the bourgeoning population of the country, but also to enhance the nutritional security and farms return for quality life. Review of literature pertaining to emerging trends in computational modelling applied in field of agriculture is done and described below for the purpose of understanding its trends mechanism behavior and its applications. Computational modelling is increasingly effective for designing and analysis of the system. Computa-tional modelling is an important tool to analyses the effect of different scenarios of climate and management options on the farming systems and its interaction among themselves. Further, authors have also highlighted the applications of computational modeling in integrated farming system, crops, weather, soil, climate, horticulture and statistical used in agriculture which can show the path to the agriculture researcher and rural farming community to replace some of the traditional techniques.
Directory of Open Access Journals (Sweden)
S. J. Noh
2011-04-01
Full Text Available Applications of data assimilation techniques have been widely used to improve hydrologic prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC methods, known as "particle filters", provide the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response time of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on Markov chain Monte Carlo (MCMC is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, WEP is implemented for the sequential data assimilation through the updating of state variables. Particle filtering is parallelized and implemented in the multi-core computing environment via open message passing interface (MPI. We compare performance results of particle filters in terms of model efficiency, predictive QQ plots and particle diversity. The improvement of model efficiency and the preservation of particle diversity are found in the lagged regularized particle filter.
Modeling and Simulation of the Visual Effects of Colored Filters
2015-02-01
Air Force Research Laboratory This report is published in the interest of scientific and technical information exchange, and its publication does not... Visualisation of the effects of laser eye protection filters on colour perception. TNO Report TM-01-A059, TNO, Soesterberg, The Netherlands. 9. McCamy, C. S
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.
Energy Technology Data Exchange (ETDEWEB)
Goodarz Ahmadi
2002-07-01
In this project, a computational modeling approach for analyzing flow and ash transport and deposition in filter vessels was developed. An Eulerian-Lagrangian formulation for studying hot-gas filtration process was established. The approach uses an Eulerian analysis of gas flows in the filter vessel, and makes use of the Lagrangian trajectory analysis for the particle transport and deposition. Particular attention was given to the Siemens-Westinghouse filter vessel at Power System Development Facility in Wilsonville in Alabama. Details of hot-gas flow in this tangential flow filter vessel are evaluated. The simulation results show that the rapidly rotation flow in the spacing between the shroud and the vessel refractory acts as cyclone that leads to the removal of a large fraction of the larger particles from the gas stream. Several alternate designs for the filter vessel are considered. These include a vessel with a short shroud, a filter vessel with no shroud and a vessel with a deflector plate. The hot-gas flow and particle transport and deposition in various vessels are evaluated. The deposition patterns in various vessels are compared. It is shown that certain filter vessel designs allow for the large particles to remain suspended in the gas stream and to deposit on the filters. The presence of the larger particles in the filter cake leads to lower mechanical strength thus allowing for the back-pulse process to more easily remove the filter cake. A laboratory-scale filter vessel for testing the cold flow condition was designed and fabricated. A laser-based flow visualization technique is used and the gas flow condition in the laboratory-scale vessel was experimental studied. A computer model for the experimental vessel was also developed and the gas flow and particle transport patterns are evaluated.
Directory of Open Access Journals (Sweden)
S. J. Noh
2011-10-01
Full Text Available Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP, is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF and the sequential importance resampling (SIR particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.
Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development
National Research Council Canada - National Science Library
Pendergrass, Sarah A; Frase, Alex; Wallace, John; Wolfe, Daniel; Katiyar, Neerja; Moore, Carrie; Ritchie, Marylyn D
2013-01-01
.... We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based...
Ramesh, Nisha; Tasdizen, Tolga
2016-01-01
Bayesian frameworks are commonly used in tracking algorithms. An important example is the particle filter, where a stochastic motion model describes the evolution of the state, and the observation model relates the noisy measurements to the state. Particle filters have been used to track the lineage of cells. Propagating the shape model of the cell through the particle filter is beneficial for tracking. We approximate arbitrary shapes of cells with a novel implicit convex function. The importance sampling step of the particle filter is defined using the cost associated with fitting our implicit convex shape model to the observations. Our technique is capable of tracking the lineage of cells for nonmitotic stages. We validate our algorithm by tracking the lineage of retinal and lens cells in zebrafish embryos. PMID:27403085
Apply a hydrological model to estimate local temperature trends
Igarashi, Masao; Shinozawa, Tatsuya
2014-03-01
Continuous times series {f(x)} such as a depth of water is written f(x) = T(x)+P(x)+S(x)+C(x) in hydrological science where T(x),P(x),S(x) and C(x) are called the trend, periodic, stochastic and catastrophic components respectively. We simplify this model and apply it to the local temperature data such as given E. Halley (1693), the UK (1853-2010), Germany (1880-2010), Japan (1876-2010). We also apply the model to CO2 data. The model coefficients are evaluated by a symbolic computation by using a standard personal computer. The accuracy of obtained nonlinear curve is evaluated by the arithmetic mean of relative errors between the data and estimations. E. Halley estimated the temperature of Gresham College from 11/1692 to 11/1693. The simplified model shows that the temperature at the time rather cold compared with the recent of London. The UK and Germany data sets show that the maximum and minimum temperatures increased slowly from the 1890s to 1940s, increased rapidly from the 1940s to 1980s and have been decreasing since the 1980s with the exception of a few local stations. The trend of Japan is similar to these results.
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 flow......-through cell apparatus (USP 4) was found unfit for dissolution testing of fenofibrate MeltDose formulations due to clogging of filters and varying flow rates. A mini paddle dissolution setup produced dissolution profiles of the tested formulations that correlated well with clinical data. The work towards...... the mini paddle dissolution method demonstrates that sample preparation influenced the results. The investigations show that excipients from the formulations directly affected the drug–filter interaction, thereby affecting the dissolution profiles and the ability to predict the in vivo data...
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.
Estimation in continuous-time stochastic| volatility models using nonlinear filters
DEFF Research Database (Denmark)
Nielsen, Jan Nygaard; Vestergaard, M.; Madsen, Henrik
2000-01-01
Presents a correction to the authorship of the article 'Estimation in Continuous-Time Stochastic Volatility Models Using Nonlinear Filters,' published in the periodical 'International Journal of Theoretical and Applied Finance,' Vol. 3, No. 2., pp. 279-308.......Presents a correction to the authorship of the article 'Estimation in Continuous-Time Stochastic Volatility Models Using Nonlinear Filters,' published in the periodical 'International Journal of Theoretical and Applied Finance,' Vol. 3, No. 2., pp. 279-308....
2011-01-01
Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows ...
Accounting for model error due to unresolved scales within ensemble Kalman filtering
Mitchell, Lewis
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 described; a time-constant model error treatment where the same model error statistical description is time-invariant, and a time-varying treatment where the assumed model error statistics is randomly sampled at each analysis step. We compare both methods with the standard method of dealing with model error through inflation and localization, and illustrate our results with numerical simulations on a low order nonlinear system exhibiting chaotic dynamics. The results show that the filter skill is significantly improved through th...
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep; Wei, Junchao; Davis, J Lynn
2014-06-24
Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have
Computational Modeling of Blood Flow in the TrapEase Inferior Vena Cava Filter
Energy Technology Data Exchange (ETDEWEB)
Singer, M A; Henshaw, W D; Wang, S L
2008-02-04
To evaluate the flow hemodynamics of the TrapEase vena cava filter using three dimensional computational fluid dynamics, including simulated thrombi of multiple shapes, sizes, and trapping positions. The study was performed to identify potential areas of recirculation and stagnation and areas in which trapped thrombi may influence intrafilter thrombosis. Computer models of the TrapEase filter, thrombi (volumes ranging from 0.25mL to 2mL, 3 different shapes), and a 23mm diameter cava were constructed. The hemodynamics of steady-state flow at Reynolds number 600 was examined for the unoccluded and partially occluded filter. Axial velocity contours and wall shear stresses were computed. Flow in the unoccluded TrapEase filter experienced minimal disruption, except near the superior and inferior tips where low velocity flow was observed. For spherical thrombi in the superior trapping position, stagnant and recirculating flow was observed downstream of the thrombus; the volume of stagnant flow and the peak wall shear stress increased monotonically with thrombus volume. For inferiorly trapped spherical thrombi, marked disruption to the flow was observed along the cava wall ipsilateral to the thrombus and in the interior of the filter. Spherically shaped thrombus produced a lower peak wall shear stress than conically shaped thrombus and a larger peak stress than ellipsoidal thrombus. We have designed and constructed a computer model of the flow hemodynamics of the TrapEase IVC filter with varying shapes, sizes, and positions of thrombi. The computer model offers several advantages over in vitro techniques including: improved resolution, ease of evaluating different thrombus sizes and shapes, and easy adaptation for new filter designs and flow parameters. Results from the model also support a previously reported finding from photochromic experiments that suggest the inferior trapping position of the TrapEase IVC filter leads to an intra-filter region of recirculating
Stratospheric age-of-air trends: Reanalysis v. climate models
Monge-Sanz, Beatriz; Dee, Dick; Hersbach, Hans; Simmons, Adrian; Parodi, Jose A.; Haenel, Florian; Stiller, Gabriele; Chipperfield, Martyn; Feng, Wuhu
2017-04-01
Knowing how the Brewer-Dobson circulation (BDC) has evolved in the recent past and will continue to evolve is crucial for atmospheric composition in the UTLS and stratosphere, as well as for feedbacks with climate. Most climate models have predicted an intensification of the stratospheric circulation with the increase in greenhouse gases concentrations, which translates into younger age-of-air (AoA) values modelled in the stratosphere. Nevertheless, balloon and satellite observations do not agree with the widespread modelled trend towards younger age-of-air for the recent past (Engel et al., 2009; Stiller et al., 2012; Haenel et al. 2015). Furthermore, a few recent studies with chemistry transport models (CTMs) driven by ERA-Interim reanalysis (Dee et al., 2011) have also shown agreement with the observed trends and not with those from climate models (e.g. Monge-Sanz et al., 2012; Diallo et al., 2012; Ploeger et al., 2015). To increase our confidence in climate-chemistry projections, the causes for the apparent disagreement in trends of age-of-air between observations and most climate models need to be identified. In this study we have carried out simulations with a CTM to assess the stratospheric circulation with the ERA-Interim dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as with data produced from an equivalent climate system. AoA trends from our model results with ERA-Interim fields are in good agreement with the recent age-of-air studies based on observations and differ from the results we obtain with the corresponding climate data. We will show that biases in the mean AoA values are also different for these datasets compared to observations. In addition we have used recent experimental datasets from the ECMWF system to identify potential causes for the differences in AoA distribution and trends. The validation of our model results has been performed against the new revised AoA dataset based on MIPAS SF6
Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model
Directory of Open Access Journals (Sweden)
Wang Jing
2008-01-01
Full Text Available Abstract We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM, a high-dimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will then be used to classify object trajectories, predict the next motion state, and provide Gaussian process dynamical samples for the particle filter. In addition, Histogram-Bhattacharyya, GMM Kullback-Leibler, and the rotation invariant appearance models are employed, respectively, and compared in the particle filter as complimentary features to coordinate data used in GPDM. The simulation results demonstrate that the approach can track more than four targets with reasonable runtime overhead and performance. In addition, it can successfully deal with occasional missing frames and temporary occlusion.
A Nonlinear Entropic Variational Model for Image Filtering
Directory of Open Access Journals (Sweden)
Krim Hamid
2004-01-01
Full Text Available We propose an information-theoretic variational filter for image denoising. It is a result of minimizing a functional subject to some noise constraints, and takes a hybrid form of a negentropy variational integral for small gradient magnitudes and a total variational integral for large gradient magnitudes. The core idea behind this approach is to use geometric insight in helping to construct regularizing functionals and avoiding a subjective choice of a prior in maximum a posteriori estimation. Illustrative experimental results demonstrate a much improved performance of the approach in the presence of Gaussian and heavy-tailed noise.
Recent trends in the modeling of cellulose hydrolysis
Directory of Open Access Journals (Sweden)
R. Sousa Jr.
2011-12-01
Full Text Available This work reviews recent trends in the modeling of cellulose hydrolysis, within the perspective of application of kinetic models in a bioreactor engineering framework, including scale-up, design and process optimization. From this point of view, despite the phenomenological insight that mechanistic models can provide, the expectation that more detailed approaches could be a basis for extrapolations to different substrates and/or enzymatic pools is still not fulfilled. The complexity of the lignocellulosic matrix, the different mechanisms of catalytic action, the role of mass transfer limitations and the deviations from ideal mixing are important difficulties for the modeler, which will continue to impose more conservative approaches for scale-up. Nevertheless, the search for more robust models is a very important task, provided that the engineer is aware of their limitations. Data-driven, non-mechanistic models such as artificial neural networks, perhaps in combination with other approaches in the so-called hybrid models, is also a promising alternative.
Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation
Kollmann, Robert
2013-01-01
This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a state-space representation of the second-order solution based on the ‘pruning’ scheme of Kim, Kim, Schaumburg and Sims (2008). By contrast to particle filters, no stochastic simulations are needed for the filter here--the present method is thus much faster. In Monte Carlo e...
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter
Zhen Zhang; Yaopeng Ma
2016-01-01
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) al...
Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing
2016-01-01
The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method.
Cointegration between trends and their estimators in state space models and CVAR models
DEFF Research Database (Denmark)
Johansen, Søren; Tabor, Morten Nyboe
In a linear state space model, y_{t+1}=BT_{t}+eps_{t+1}, we investigate if the unobserved trend, T_{t}, cointegrates with the extracted trend E_{t}T_{t}, and with the estimated trend E^_{t}T_{t}, in the sense that the spreads T_{t}-E_{t}T_{t} and E_{t}T_{t}-E^_{t}T_{t} are stationary. We find...... that this result holds for BT_{t}-BE_{t}T_{t} and BE_{t}T_{t}-B^E^_{t}T_{t}. For the trends T_{t} and E^_{t}T_{t}, however, this type cointegration depends on the identification of B and T_{t}. The same results are found, if the observations, y_{t}, from the state space model are analysed using a cointegrated...... vector autoregressive model, where the trend is defined as the common trend. Finally we investigate cointegration between trends and their estimators based on the two models, and find the same results. We illustrate with two examples and confirm the results by a small simulation study....
Modeling acoustic attenuation of soft tissue with a minimum-phase filter.
Kuc, R
1984-01-01
Soft biological tissue has been observed to exhibit an acoustic attenuation log-magnitude characteristic which increases as an approximately linear function of frequency. This paper describes the implementation of a finite-impulse-response (FIR) digital filter model for simulating this behavior on a digital computer. To insure that the filter is causal, the minimum-phase constraint is imposed. For minimum-phase filters, the log-magnitude and phase characteristics form a Hilbert Transform pair. The discrete-time Hilbert Transform of the linear log-magnitude characteristic was evaluated to determine the phase of the filter. The inverse Fourier Transform of the resulting real and imaginary components of the frequency transform produces the finite-duration unit-sample response of the digital filter model. Experimental results using plexiglas material, which has a linear-with-frequency loss characteristic, indicate that the minimum-phase model is more accurate than the linear-phase model, resulting in a rms error between predicted and observed time waveforms that is 3 times smaller. The effects of varying the sampling period and the size of the FIR filter are discussed. A FORTRAN program to calculate the minimum-phase unit-sample response from the slope of the log-magnitude characteristic is included in the Appendix.
Boz, Utku; Basdogan, Ipek
2015-12-01
Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.
Bedard, C; Destexhe, A; Bédard, Claude; Kroeger, Helmut; Destexhe, Alain
2003-01-01
Extracellular local field potentials (LFP) are usually modeled as arising from a set of current sources embedded in a homogeneous extracellular medium. Although this formalism can successfully model several properties of LFPs, it does not account for their frequency-dependent attenuation with distance, a property essential to correctly model extracellular spikes. Here we derive expressions for the extracellular potential that include this frequency-dependent attenuation. We first show that, if the extracellular conductivity is non-homogeneous, there is induction of non-homogeneous charge densities which may result in a low-pass filter. We next derive a simplified model consisting of a punctual (or spherical) current source with spherically-symmetric conductivity/permittivity gradients around the source. We analyze the effect of different radial profiles of conductivity and permittivity on the frequency-filtering behavior of this model. We show that this simple model generally displays low-pass filtering behav...
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.
Nonlinear Inverse Problem for an Ion-Exchange Filter Model: Numerical Recovery of Parameters
Directory of Open Access Journals (Sweden)
Balgaisha Mukanova
2015-01-01
Full Text Available This paper considers the problem of identifying unknown parameters for a mathematical model of an ion-exchange filter via measurement at the outlet of the filter. The proposed mathematical model consists of a material balance equation, an equation describing the kinetics of ion-exchange for the nonequilibrium case, and an equation for the ion-exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion-exchange and several parameters. First, a numerical solution of the direct problem, the calculation of the impurities concentration at the outlet of the filter, is provided. Then, the inverse problem, finding the parameters of the ion-exchange process in nonequilibrium conditions, is formulated. A method for determining the approximate values of these parameters from the impurities concentration measured at the outlet of the filter is proposed.
IMPACT OF SPATIAL FILTER ON LAND-USE CHANGES MODELLING USING URBAN CELLULAR AUTOMATA
Directory of Open Access Journals (Sweden)
M. Omidipoor
2017-09-01
Full Text Available Urban cellular automata is used vastly in simulating of urban evolutions and dynamics. Finding an appropriate neighbourhood size in urban cellular automata modelling is important because the outputs are strongly influenced by input parameters. This paper investigates the impact of spatial filters on behaviour and outcome of urban cellular automata models. In this study different spatial filters in various sizes including 3*3, 5*5, 7*7, 9*9, 11*11, 13*13, 15*15 and 17*17 cells are used in a scenario of land-use changes. The proposed method is examined changes in size and shape of spatial filter whereas the resolution was kept fixed. The implementation results in Ahvaz city demonstrated that KAPPA index is changed in different shapes and types at the time when different spatial filters are used. However, circular shape with size of 5*5 offers better accuracy.
DEFF Research Database (Denmark)
Sørensen, Jacob Viborg Tornfeldt; Madsen, Henrik; Madsen, H.
2006-01-01
sensitivity study of three well known Kalman filter approaches for the assimilation of water levels in a three dimensional hydrodynamic modelling system. The filters considered are the ensemble Kalman filter (EnKF), the reduced rank square root Kalman filter (RRSQRT) and the steady Kalman filter...... is to be encouraged in this perspective. However, the predicted uncertainty of the assimilation results are sensitive to the parameters and hence must be applied with care. The sensitivity study further demonstrates the effectiveness of the steady Kalman filter in the given system as well as the great impact...
Institute of Scientific and Technical Information of China (English)
张秋实; 朱锋杰; 周浩淼
2015-01-01
A lumped-equivalent circuit model of a novel magnetoelectric tunable bandpass filter, which is realized in the form of multi-stage cascading between a plurality of magnetoelectric laminates, is established in this paper for convenient analysis. The multi-stage cascaded filter is degraded to the coupling microstrip filter with only one magnetoelectric laminate and then compared with the existing experiment results. The comparison reveals that the insertion loss curves predicted by the degraded circuit model are in good agreement with the experiment results and the predicted results of the electromagnetic field simulation, thus the validity of the model is verified. The model is then degraded to the two-stage cascaded magneto-electric filter with two magnetoelectric laminates. It is revealed that if the applied external bias magnetic or electric fields on the two magnetoelectric laminates are identical, then the passband of the filter will drift under the changed external field; that is to say, the filter has the characteristics of external magnetic field tunability and electric field tunability. If the applied external bias magnetic or electric fields on two magnetoelectric laminates are different, then the passband will disappear so that the switching characteristic is achieved. When the same magnetic fields are applied to the laminates, the passband bandwidth of the two-stage cascaded magnetoelectric filter with two magnetoelectric laminates becomes nearly doubled in comparison with the passband filter which contains only one magnetoelectric laminate. The bandpass effect is also improved obviously. This research will provide a theoretical basis for the design, preparation, and application of a new high performance magnetoelectric tunable microwave device.
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.
The ensemble particle filter (EnPF) in rainfall-runoff models
Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.
2009-01-01
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the
Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models
Mandel, Jan; Beezley, Jonathan D.; Coen, Janice L.; Kim, Minjeong
2007-01-01
Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on semi-empirical fire spread by the level let method. The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.
Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models
Mandel, Jan; Coen, Janice L; Kim, Minjeong
2007-01-01
Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on empirical fire spread by the level let method. The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.
The ensemble particle filter (EnPF) in rainfall-runoff models
Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.
2009-01-01
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the
Recursive parameter identification for infinite-dimensional factor model by using particle filter
Bagchi, Arunabha; Kamajima, K; Aihara, ShinIchi
2007-01-01
We consider the dynamics of forward rate process which is modeled by the parabolic type infinite-dimensional factor model. The parameters included in this parabolic model are estimated by using the yield curve as the observation data. In this paper, we propose the filtering and identification method
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.
A low-complexity interacting multiple model filter for maneuvering target tracking
Khalid, Syed Safwan
2017-01-22
In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.
Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model
Liu, Bo
2016-02-03
An ensemble-based Gaussian mixture (GM) filtering framework is studied in this paper in term of its dependence on the choice of the clustering method to construct the GM. In this approach, a number of particles sampled from the posterior distribution are first integrated forward with the dynamical model for forecasting. A GM representation of the forecast distribution is then constructed from the forecast particles. Once an observation becomes available, the forecast GM is updated according to Bayes’ rule. This leads to (i) a Kalman filter-like update of the particles, and (ii) a Particle filter-like update of their weights, generalizing the ensemble Kalman filter update to non-Gaussian distributions. We focus on investigating the impact of the clustering strategy on the behavior of the filter. Three different clustering methods for constructing the prior GM are considered: (i) a standard kernel density estimation, (ii) clustering with a specified mixture component size, and (iii) adaptive clustering (with a variable GM size). Numerical experiments are performed using a two-dimensional reactive contaminant transport model in which the contaminant concentration and the heterogenous hydraulic conductivity fields are estimated within a confined aquifer using solute concentration data. The experimental results suggest that the performance of the GM filter is sensitive to the choice of the GM model. In particular, increasing the size of the GM does not necessarily result in improved performances. In this respect, the best results are obtained with the proposed adaptive clustering scheme.
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...
Real-Time Flood Forecasting System Using Channel Flow Routing Model with Updating by Particle Filter
Kudo, R.; Chikamori, H.; Nagai, A.
2008-12-01
A real-time flood forecasting system using channel flow routing model was developed for runoff forecasting at water gauged and ungaged points along river channels. The system is based on a flood runoff model composed of upstream part models, tributary part models and downstream part models. The upstream part models and tributary part models are lumped rainfall-runoff models, and the downstream part models consist of a lumped rainfall-runoff model for hillslopes adjacent to a river channel and a kinematic flow routing model for a river channel. The flow forecast of this model is updated by Particle filtering of the downstream part model as well as by the extended Kalman filtering of the upstream part model and the tributary part models. The Particle filtering is a simple and powerful updating algorithm for non-linear and non-gaussian system, so that it can be easily applied to the downstream part model without complicated linearization. The presented flood runoff model has an advantage in simlecity of updating procedure to the grid-based distributed models, which is because of less number of state variables. This system was applied to the Gono-kawa River Basin in Japan, and flood forecasting accuracy of the system with both Particle filtering and extended Kalman filtering and that of the system with only extended Kalman filtering were compared. In this study, water gauging stations in the objective basin were divided into two types of stations, that is, reference stations and verification stations. Reference stations ware regarded as ordinary water gauging stations and observed data at these stations are used for calibration and updating of the model. Verification stations ware considered as ungaged or arbitrary points and observed data at these stations are used not for calibration nor updating but for only evaluation of forecasting accuracy. The result confirms that Particle filtering of the downstream part model improves forecasting accuracy of runoff at
The Hierarchical Trend Model for property valuation and local price indices
M.K. Francke; G.A. Vos
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, an
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...
Flora, Joseph R V; Hargis, Richard A; O'Dowd, William J; Karash, Andrew; Pennline, Henry W; Vidic, Radisav D
2006-03-01
A mathematical model based on simple cake filtration theory was coupled to a previously developed two-stage mathematical model for mercury (Hg) removal using powdered activated carbon injection upstream of a baghouse filter. Values of the average permeability of the filter cake and the filter resistance extracted from the model were 4.4 x 10(-13) m2 and 2.5 x 10(-4) m(-1), respectively. The flow is redistributed during partial cleaning of the filter, with flows higher across the newly cleaned filter section. The calculated average Hg removal efficiency from the baghouse is lower because of the high mass flux of Hg exiting the filter in the newly cleaned section. The model shows that calculated average Hg removal is affected by permeability, filter resistance, fraction of the baghouse cleaned, and cleaning interval.
Stochastic modeling of Lake Van water level time series with jumps and multiple trends
Directory of Open Access Journals (Sweden)
H. Aksoy
2013-06-01
Full Text Available In the 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey, has risen up about 2 m. Analysis of the hydrometeorological data shows that change in the water level is related to the water budget of the lake. In this study, stochastic models are proposed for simulating monthly water level data. Two models considering mono- and multiple-trend time series are developed. The models are derived after removal of trend and periodicity in the dataset. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. In the so-called mono-trend model, the time series is treated as a whole under the hypothesis that the lake water level has an increasing trend. In the second model (so-called multiple-trend, the time series is divided into a number of segments to each a linear trend can be fitted separately. Application on the lake water level data shows that four segments, each fitted with a trend line, are meaningful. Both the mono- and multiple-trend models are used for simulation of synthetic lake water level time series under the hypothesis that the observed mono- and multiple-trend structure of the lake water level persist during the simulation period. The multiple-trend model is found better for planning the future infrastructural projects in surrounding areas of the lake as it generates higher maxima for the simulated lake water level.
NUMERICAL EXPERIMENTS AND ANALYSIS OF DIGITAL FILTER INITIALIZATION FOR WRF MODEL
Institute of Scientific and Technical Information of China (English)
WANG Shu-chang; HUANG Si-xun; ZHANG Wei-min; ZHU Xiao-qian; CAO Xiao-qun; LI Yi
2008-01-01
Initialization and initial imbalance problem were discussed in the context of a three-dimensional variational data assimilation system of the new generation "Weather Research and Forecasting Model". Severaloptions of digital filter initialization have been tested with a rain storm case. It is shown that digital filter initialization, especially diabatic digital filter initialization and twice digital filter initialization, have effectively removed spurious high frequency noise from initial data for numerical weather prediction and produced balanced initial conditions. For six consecutive intermittent data assimilation cycles covering a 3-day period, mean initialization increments and impact on forecast variables are studied. DFI has been demonstrated to provide better adjustment of the hydrometeors and vertical velocity, reduced spin-up time, and improved forecast variables quantity.
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.
Cooperative Trends in a Modified Image Scoring Model
Institute of Scientific and Technical Information of China (English)
ANDREASEN Jonathan; 欧阳颀
2002-01-01
The evolution of modern cooperative trends now seen in society have not yet been easily explained. After extensive computational studies and theoretical analysis, Nowak and Sigmund proposed that cooperation was established largely due to the emergence of indirect reciprocity. Our previous studies show that a high information flow rate stimulates cooperation in a society. In this study we find that the decrease of cooperation cost will make a society more cooperative, and the inheritance of wealth will induce cooperation in the society even when the exchange rate is comparatively low. We also study the distribution of knowledge according to wealth. Wefind that, for this model, cooperation is slightly less likely to occur if the exchange rate is low.
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.
Model of phosphorus precipitation and crystal formation in electric arc furnace steel slag filters.
Claveau-Mallet, Dominique; Wallace, Scott; Comeau, Yves
2012-02-07
The objective of this study was to develop a phosphorus retention mechanisms model based on precipitation and crystallization in electric arc furnace slag filters. Three slag columns were fed during 30 to 630 days with a reconstituted mining effluent at different void hydraulic retention times. Precipitates formed in columns were characterized by X-ray diffraction and transmission electronic microscopy. The proposed model is expressed in the following steps: (1) the rate limiting dissolution of slag is represented by the dissolution of CaO, (2) a high pH in the slag filter results in phosphorus precipitation and crystal growth, (3) crystal retention takes place by filtration, settling and growth densification, (4) the decrease in available reaction volume is caused by crystal and other particulate matter accumulation (and decrease in available reaction time), and (5) the pH decreases in the filter over time if the reaction time is too low (which results in a reduced removal efficiency). Crystal organization in a slag filter determines its phosphorus retention capacity. Supersaturation and water velocity affect crystal organization. A compact crystal organization enhances the phosphorus retention capacity of the filter. A new approach to define filter performance is proposed: saturation retention capacity is expressed in units of mg P/mL voids.
Directory of Open Access Journals (Sweden)
Jingjing Wu
2015-01-01
Full Text Available A robust particle filter (PF and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images.
Astroza, Rodrigo; Ebrahimian, Hamed; Conte, Joel P.
2015-03-01
This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and stochastic filtering techniques to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. Using input-output data recorded during earthquake events, the proposed framework updates the nonlinear FE model of the structure. The updated FE model can be directly used for damage identification and further used for damage prognosis. To update the unknown time-invariant parameters of the FE model, two alternative stochastic filtering methods are used: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). A three-dimensional, 5-story, 2-by-1 bay reinforced concrete (RC) frame is used to verify the proposed framework. The RC frame is modeled using fiber-section displacement-based beam-column elements with distributed plasticity and is subjected to the ground motion recorded at the Sylmar station during the 1994 Northridge earthquake. The results indicate that the proposed framework accurately estimate the unknown material parameters of the nonlinear FE model. The UKF outperforms the EKF when the relative root-mean-square error of the recorded responses are compared. In addition, the results suggest that the convergence of the estimate of modeling parameters is smoother and faster when the UKF is utilized.
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.
An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models
Chow, Sy-Miin; Ferrer, Emilio; Nesselroade, John R.
2007-01-01
In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways:…
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...
Aguirre, Luis Antonio; Billings, S. A.
This paper investigates the identification of global models from chaotic data corrupted by additive noise. It is verified that noise has a strong influence on the identification of chaotic systems. In particular, there seems to be a critical noise level beyond which the accurate estimation of polynomial models from chaotic data becomes very difficult. Similarities with the estimation of the largest Lyapunov exponent from noisy data suggest that part of the problem might be related to the limited ability of predicting the data records when these are chaotic. A nonlinear filtering scheme is suggested in order to reduce the noise in the data and thereby enable the estimation of good models. This prediction-based filtering incorporates a resetting mechanism which enables the filtering of chaotic data and which is also applicable to non-chaotic data.
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
prediction method to assure that the amount of erosion remains within acceptable limits. This work presents a numerical model that is capable of describing the erosion and deposition patterns inside of an open filter of rock on top of sand. The hydraulic loading is that of incident irregular waves...... additional data sets on the erosion and deposition patterns inside of an open filter. A few cases are defined to study the effect of the sinking of the filter into the erosion hole. The numerical model is also applied to several application cases. The response of the core material (sand) to changes......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...
Morzfeld, Matthias
2011-01-01
Implicit particle filtering is a sequential Monte Carlo method for data assim- ilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by min- imizing, 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. This is the case in many geophysical applica- tions, in particular for models with partial noise, i.e. with a singular state covariance matrix. Examples of models with partial noise include stochastic partial differential equations driven by spatially smooth noise processes and models for which uncertain dynamic equations are supplemented by con- servation laws with zero uncertainty. We make the implicit particle filter applicable to such situation...
Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.
Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun
2016-10-01
This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.
Farahani, Hassan H.; Ditmar, Pavel; Inácio, Pedro; Didova, Olga; Gunter, Brian; Klees, Roland; Guo, Xiang; Guo, Jing; Sun, Yu; Liu, Xianglin; Zhao, Qile; Riva, Riccardo
2017-01-01
We present a high resolution model of the linear trend in the Earth's mass variations based on DMT-2 (Delft Mass Transport model, release 2). DMT-2 was produced primarily from K-Band Ranging (KBR) data of the Gravity Recovery And Climate Experiment (GRACE). It comprises a time series of monthly solutions complete to spherical harmonic degree 120. A novel feature in its production was the accurate computation and incorporation of stochastic properties of coloured noise when processing KBR data. The unconstrained DMT-2 monthly solutions are used to estimate the linear trend together with a bias, as well as annual and semi-annual sinusoidal terms. The linear term is further processed with an anisotropic Wiener filter, which uses full noise and signal covariance matrices. Given the fact that noise in an unconstrained model of the trend is reduced substantially as compared to monthly solutions, the Wiener filter associated with the trend is much less aggressive compared to a Wiener filter applied to monthly solutions. Consequently, the trend estimate shows an enhanced spatial resolution. It allows signals in relatively small water bodies, such as Aral sea and Ladoga lake, to be detected. Over the ice sheets, it allows for a clear identification of signals associated with some outlet glaciers or their groups. We compare the obtained trend estimate with the ones from the CSR-RL05 model using (i) the same approach based on monthly noise covariance matrices and (ii) a commonly-used approach based on the DDK-filtered monthly solutions. We use satellite altimetry data as independent control data. The comparison demonstrates a high spatial resolution of the DMT-2 linear trend. We link this to the usage of high-accuracy monthly noise covariance matrices, which is due to an accurate computation and incorporation of coloured noise when processing KBR data. A preliminary comparison of the linear trend based on DMT-2 with that computed from GSFC_global_mascons_v01 reveals, among
Modelling and Design of a Microstrip Band-Pass Filter Using Space Mapping Techniques
Tavakoli, Saeed; Mohanna, Shahram
2010-01-01
Determination of design parameters based on electromagnetic simulations of microwave circuits is an iterative and often time-consuming procedure. Space mapping is a powerful technique to optimize such complex models by efficiently substituting accurate but expensive electromagnetic models, fine models, with fast and approximate models, coarse models. In this paper, we apply two space mapping, an explicit space mapping as well as an implicit and response residual space mapping, techniques to a case study application, a microstrip band-pass filter. First, we model the case study application and optimize its design parameters, using explicit space mapping modelling approach. Then, we use implicit and response residual space mapping approach to optimize the filter's design parameters. Finally, the performance of each design methods is evaluated. It is shown that the use of above-mentioned techniques leads to achieving satisfactory design solutions with a minimum number of computationally expensive fine model eval...
RB Particle Filter Time Synchronization Algorithm Based on the DPM Model
Directory of Open Access Journals (Sweden)
Chunsheng Guo
2015-09-01
Full Text Available Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.
Michaels, Patrick J; Christy, John R; Herman, Chad S; Liljegren, Lucia M; Annan, James D
2013-01-01
Assessing the consistency between short-term global temperature trends in observations and climate model projections is a challenging problem. While climate models capture many processes governing short-term climate fluctuations, they are not expected to simulate the specific timing of these somewhat random phenomena - the occurrence of which may impact the realized trend. Therefore, to assess model performance, we develop distributions of projected temperature trends from a collection of climate models running the IPCC A1B emissions scenario. We evaluate where observed trends of length 5 to 15 years fall within the distribution of model trends of the same length. We find that current trends lie near the lower limits of the model distributions, with cumulative probability-of-occurrence values typically between 5 percent and 20 percent, and probabilities below 5 percent not uncommon. Our results indicate cause for concern regarding the consistency between climate model projections and observed climate behavior...
Grey Box Non-Linearities Modeling and Characterization of a BandPass BAW Filter
Directory of Open Access Journals (Sweden)
M. Mabrouk
2016-06-01
Full Text Available In this work, the non-linearities of a 3G/UMTS geared BandPass Bulk Acoustic Wave ladder filter composed of five resonators were modeled using non-linear modified Butterworth-Van Dyke model. The non-linear characteristics were measured and simulated, and they were compared and found to be fairly identical. The filter's central frequency is 2.12 GHz, the corresponding bandwidth is 61.55 MHz, and the quality factor is 34.55.
Stochastic modeling of Lake Van water level time series with jumps and multiple trends
Directory of Open Access Journals (Sweden)
H. Aksoy
2013-02-01
Full Text Available In 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey has risen up about 2 m. Analysis of the hydrometeorological shows that change in the water level is related to the water budget of the lake. In this study, a stochastic model is generated using the measured monthly water level data of the lake. The model is derived after removal of trend and periodicity in the data set. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. For the multiple-trend, the time series is first divided into homogeneous segments by means of SEGMENTER, segmentation software. Four segments are found meaningful practically each fitted with a trend line. Two models considering mono- and multiple-trend time series are developed. The multiple-trend model is found better for planning future development in surrounding areas of the lake.
Claveau-Mallet, Dominique; Courcelles, Benoît; Comeau, Yves
2014-07-01
This article presents an original numerical model suitable for longevity prediction of alkaline steel slag filters used for phosphorus removal. The model includes kinetic rates for slag dissolution, hydroxyapatite and monetite precipitation and for the transformation of monetite into hydroxyapatite. The model includes equations for slag exhaustion. Short-term batch tests using slag and continuous pH monitoring were conducted. The model parameters were calibrated on these batch tests and experimental results were correctly reproduced. The model was then transposed to long-term continuous flow simulations using the software PHREEQC. Column simulations were run to test the effect of influent P concentration, influent inorganic C concentration and void hydraulic retention time on filter longevity and P retention capacity. High influent concentration of P and inorganic C, and low hydraulic retention time of voids reduced the filter longevity. The model provided realistic P breakthrough at the column outlet. Results were comparable to previous column experiments with the same slag regarding longevity and P retention capacity. A filter design methodology based on a simple batch test and numerical simulations is proposed.
Experimental modeling of Wiener filters estimated on an operating diesel engine
Drouet, Julie; Leclère, Quentin; Parizet, Etienne
2015-01-01
Sound source separation in diesel engines can be implemented using a Wiener filter, or spectrofilter, that can extract the combustion contribution in the overall noise. In this study this filter characterizes the transfer function between a cylinder pressure and a measurement point. An engine is characterized by several filters (one for each cylinder) which are estimated for many operating conditions (engine speed and load). The purpose of this work is to obtain an averaged spectrofilter allowing the synthesis of combustion noise in all operating conditions. This synthesis should be accurate enough to be used in perceptive studies. In order to refine the spectrofilter estimation in the medium frequency band, this paper consists in taking advantage of the multitude of information given by the estimations from different operating conditions. To do this, an experimental model is adopted so modal parameters are extracted from a great number of measured filters. Different procedures such as the ESPRIT method or the LSCE method (modal analysis) are used to decompose the impulse responses on a complex exponential basis. The spectrofilters estimated from different operating conditions are analyzed and compared in this reduced basis, in order to identify the underlying structural parameters. These parameters are compared to the results of an experimental characterization of the stopped engine. The accuracy of the synthesis (number of components of the filter) is an important issue because these filters will be used in perceptive applications, extracting combustion noises. This paper is an extended version of the work initially presented at the conference Surveillance 6 in November 2011 in Compiègne, France [1] (J. Drouet, Quentin Leclere, Etienne Parizet. Experimental modeling of Wiener filters estimated on an operating diesel engine, in: Proceedings of the Surveillance, vol. 6, Compi'egne, France, 2011.).
Huang, Lei
2015-09-30
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.
Modeling the hemodynamic response in fMRI using smooth FIR filters
DEFF Research Database (Denmark)
Goutte, Cyril; Nielsen, Finn Årup; Hansen, Lars Kai
2000-01-01
-parametric approach based on finite impulse response (FIR) filters. In order to cope with the increase in the number of degrees of freedom, the authors introduce a Gaussian process prior on the filter parameters. They show how to carry on the analysis by incorporating prior knowledge on the filters, optimizing hyper......Modeling the hemodynamic response in functional magnetic resonance (fMRI) experiments is an important aspect of the analysis of functional neuroimages. This has been done in the past using parametric response function, from a limited family. In this contribution, the authors adopt a semi......-parameters using the evidence framework, or sampling using a Markov Chain Monte Carlo (MCMC) approach. The authors present a comparison of their model with standard hemodynamic response kernels on simulated data, and perform a full analysis of data acquired during an experiment involving visual stimulation....
Directory of Open Access Journals (Sweden)
Hao Chen
2016-10-01
Full Text Available With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.
A hand tracking algorithm with particle filter and improved GVF snake model
Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe
2017-07-01
To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.
Model-based Prognostics with Fixed-lag Particle Filters
National Aeronautics and Space Administration — Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a...
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).
Canfield, Stephen
1999-01-01
This work will demonstrate the integration of sensor and system dynamic data and their appropriate models using an optimal filter to create a robust, adaptable, easily reconfigurable state (motion) estimation system. This state estimation system will clearly show the application of fundamental modeling and filtering techniques. These techniques are presented at a general, first principles level, that can easily be adapted to specific applications. An example of such an application is demonstrated through the development of an integrated GPS/INS navigation system. This system acquires both global position data and inertial body data, to provide optimal estimates of current position and attitude states. The optimal states are estimated using a Kalman filter. The state estimation system will include appropriate error models for the measurement hardware. The results of this work will lead to the development of a "black-box" state estimation system that supplies current motion information (position and attitude states) that can be used to carry out guidance and control strategies. This black-box state estimation system is developed independent of the vehicle dynamics and therefore is directly applicable to a variety of vehicles. Issues in system modeling and application of Kalman filtering techniques are investigated and presented. These issues include linearized models of equations of state, models of the measurement sensors, and appropriate application and parameter setting (tuning) of the Kalman filter. The general model and subsequent algorithm is developed in Matlab for numerical testing. The results of this system are demonstrated through application to data from the X-33 Michael's 9A8 mission and are presented in plots and simple animations.
Dynamically constrained uncertainty for the Kalman filter covariance in the presence of model error
Grudzien, Colin; Carrassi, Alberto; Bocquet, Marc
2017-04-01
The forecasting community has long understood the impact of dynamic instability on the uncertainty of predictions in physical systems and this has led to innovative filtering design to take advantage of the knowledge of process models. The advantages of this combined approach to filtering, including both a dynamic and statistical understanding, have included dimensional reductions and robust feature selection in the observational design of filters. In the context of a perfect models we have shown that the uncertainty in prediction is damped along the directions of stability and the support of the uncertainty conforms to the dominant system instabilities. Our current work likewise demonstrates this constraint on the uncertainty for systems with model error, specifically, - we produce analytical upper bounds on the uncertainty in the stable, backwards orthogonal Lyapunov vectors in terms of the local Lyapunov exponents and the scale of the additive noise. - we demonstrate that for systems with model noise, the least upper bound on the uncertainty depends on the inverse relationship of the leading Lyapunov exponent and the observational certainty. - we numerically compute the invariant scaling factor of the model error which determines the asymptotic uncertainty. This dynamic scaling of model error is identifiable independently of the noise and is computable directly in terms of the system's dynamic invariants -- in this way the physical process itself may mollify the growth of modelling errors. For systems with strongly dissipative behaviour, we demonstrate that the growth of the uncertainty can be confined to the unstable-neutral modes independently of the filtering process, and we connect the observational design to take advantage of a dynamic characteristic of the filtering error.
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...... Monte Carlo experiment demonstrates that the unscented Kalman fi…lter is much more accurate than its extended counterpart in fi…ltering the states and forecasting swap rates and caps. Our fi…ndings suggest that the unscented Kalman fi…lter may prove to be a good approach for a number of other problems...... in fi…xed income pricing with nonlinear relationships between the state vector and the observations, such as the estimation of term structure models using coupon bonds and the estimation of quadratic term structure models....
Modeling of the subgrid-scale term of the filtered magnetic field transport equation
Balarac, Guillaume; Kosovichev, Alexander; Brugière, Olivier; Wray, Alan; Mansour, Nagi
2010-01-01
Accurate subgrid-scale turbulence models are needed to perform realistic numerical magnetohydrodynamic (MHD) simulations of the subsurface flows of the Sun. To perform large-eddy simulations (LES) of turbulent MHD flows, three unknown terms have to be modeled. As a first step, this work proposes to use a priori tests to measure the accuracy of various models proposed to predict the SGS term appearing in the transport equation of the filtered magnetic field. It is proposed to evaluate the SGS ...
Erdogan, Eren; Onur Karslioglu, Mahmut; Durmaz, Murat; Aghakarimi, Armin
2014-05-01
In this study, particle filter (PF) which is mainly based on the Monte Carlo simulation technique has been carried out for polynomial modeling of the local ionospheric conditions above the selected ground based stations. Less sensitivity to the errors caused by linearization of models and the effect of unknown or unmodeled components in the system model is one of the advantages of the particle filter as compared to the Kalman filter which is commonly used as a recursive filtering method in VTEC modeling. Besides, probability distribution of the system models is not necessarily required to be Gaussian. In this work third order polynomial function has been incorporated into the particle filter implementation to represent the local VTEC distribution. Coefficients of the polynomial model presenting the ionospheric parameters and the receiver inter frequency biases are the unknowns forming the state vector which has been estimated epoch-wise for each ground station. To consider the time varying characteristics of the regional VTEC distribution, dynamics of the state vector parameters changing permanently have been modeled using the first order Gauss-Markov process. In the processing of the particle filtering, multi-variety probability distribution of the state vector through the time has been approximated by means of randomly selected samples and their associated weights. A known drawback of the particle filtering is that the increasing number of the state vector parameters results in an inefficient filter performance and requires more samples to represent the probability distribution of the state vector. Considering the total number of unknown parameters for all ground stations, estimation of these parameters which were inserted into a single state vector has caused the particle filter to produce inefficient results. To solve this problem, the PF implementation has been carried out separately for each ground station at current time epochs. After estimation of unknown
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...
Dynamic Mathematical Modelling of the Removal of Hydrophilic VOCs by Biotrickling Filters
Directory of Open Access Journals (Sweden)
Pau San-Valero
2015-01-01
Full Text Available A mathematical model for the simulation of the removal of hydrophilic compounds using biotrickling filtration was developed. The model takes into account that biotrickling filters operate by using an intermittent spraying pattern. During spraying periods, a mobile liquid phase was considered, while during non-spraying periods, a stagnant liquid phase was considered. The model was calibrated and validated with data from laboratory- and industrial-scale biotrickling filters. The laboratory experiments exhibited peaks of pollutants in the outlet of the biotrickling filter during spraying periods, while during non-spraying periods, near complete removal of the pollutant was achieved. The gaseous outlet emissions in the industrial biotrickling filter showed a buffered pattern; no peaks associated with spraying or with instantaneous variations of the flow rate or inlet emissions were observed. The model, which includes the prediction of the dissolved carbon in the water tank, has been proven as a very useful tool in identifying the governing processes of biotrickling filtration.
A Stabilized Scale-Similarity Model for Explicitly-Filtered LES
Edoh, Ayaboe; Karagozian, Ann; Sankaran, Venkateswaran
2016-11-01
Accurate simulation of the filtered-scales in LES is affected by the competing presence of modeling and discretization errors. In order to properly assess modeling techniques, it is imperative to minimize the influence of the numerical scheme. The current investigation considers the inclusion of resolved and un-resolved sub-filter stress ([U]RSFS) components in the governing equations, which is suggestive of a mixed-model approach. Taylor-series expansions of discrete filter stencils are used to inform proper scaling of a Scale-Similarity model representation of the RSFS term, and accompanying stabilization is provided by tunable and scale-discriminant filter-based artificial dissipation techniques that represent the URSFS term implicitly. Effective removal of numerical error from the LES solution is studied with respect to the 1D Burgers equation with synthetic turbulence, and extension to 3D Navier-Stokes system computations is motivated. Distribution A: Approved for public release, distribution unlimited. Supported by AFOSR (PMs: Drs. Chiping Li and Michael Kendra).
Modelling and simulation of wastewater treatment plants based on bio filters
Energy Technology Data Exchange (ETDEWEB)
Babary, J.P.; Bourrel, S. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France). Lab. d`Automatique et d`Analyse des Systemes; Le Lann, J.M.; Jacob, J.; Koehret, B. [ENSIGC, 31 - Toulouse (France); Capdeville, B.; N`Guyen, K.M. [Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1993-10-01
In this paper, a general mathematical model describing the dynamic behaviour of a submerged granular bed bio-filter is presented as well as simulation results obtained by two numerical approaches (a global approach using finite difference method connected with a Gear`s integration method for DAE systems and an orthogonal collocation approximation approach) with comparison with plant data. (author). 8 refs.
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.
Wit, de A.J.W.; Diepen, van C.A.
2007-01-01
Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil
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.
Souza, André L. G.; Ishihara, João Y.; Ferreira, Henrique C.; Borges, Renato A.; Borges, Geovany A.
2016-12-01
The present work proposes a new approach for an antenna pointing system for satellite tracking. Such a system uses the received signal to estimate the beam pointing deviation and then adjusts the antenna pointing. The present work has two contributions. First, the estimation is performed by a Kalman filter based conical scan technique. This technique uses the Kalman filter avoiding the batch estimator and applies a mathematical manipulation avoiding the linearization approximations. Secondly, a control technique based on the model predictive control together with an explicit state feedback solution are obtained in order to reduce the computational burden. Numerical examples illustrate the results.
Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter
DEFF Research Database (Denmark)
Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro
2009-01-01
A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...... an LCL filter is used. The proposed control strategy allows control of the active and reactive power fed into the grid, reduce the switching frequency within acceptable operational margins and keep balance of the DC-link capacitor voltages while avoiding excitation of the filter resonance frequencies....
Modelling And Analysis Of Permeability Of Anisotropic Compressed Non-Woven Filters
Prieur du Plessis, J.; Woudberg, Sonia; Le Coq, Laurence
2010-05-01
An existing geometrical pore-scale model for flow through isotropic spongelike media is adapted to predict flow through anisotropic non-woven glass fibre filters. Model predictions are compared to experimental results for the permeability obtained for a filter under different stages of compression to demonstrate the capability of the model to adjust to changes in porosity. The experimental data used are for a glass fibre paper with a uniform fibre diameter. The input parameters of the pore-scale model are the porosity, fibre diameter and some measure of the anisotropy between the in-plane and normal directions to the paper. Correlation between the predictions and the experimental results is satisfactory and provides confidence in the modelling procedure. It is shown that the permeability is very sensitive to changes in the level of anisotropy, i.e. the level of compression of the nonwoven material.
The valley filter efficiency of monolayer graphene and bilayer graphene line defect model
Cheng, Shu-guang; Zhou, Jiaojiao; Jiang, Hua; Sun, Qing-Feng
2016-10-01
In addition to electron charge and spin, novel materials host another degree of freedom, the valley. For a junction composed of valley filter sandwiched by two normal terminals, we focus on the valley efficiency under disorder with two valley filter models based on monolayer and bilayer graphene. Applying the transfer matrix method, valley resolved transmission coefficients are obtained. We find that: (i) under weak disorder, when the line defect length is over about 15 {nm}, it functions as a perfect channel (quantized conductance) and valley filter (totally polarized); (ii) in the diffusive regime, combination effects of backscattering and bulk states assisted intervalley transmission enhance the conductance and suppress the valley polarization; (iii) for very long line defect, though the conductance is small, polarization is indifferent to length. Under perpendicular magnetics field, the characters of charge and valley transport are only slightly affected. Finally we discuss the efficiency of transport valley polarized current in a hybrid system.
Gaussian Sum PHD Filtering Algorithm for Nonlinear Non-Gaussian Models
Institute of Scientific and Technical Information of China (English)
Yin Jianjun; Zhang Jianqiu; Zhuang Zesen
2008-01-01
A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density (GSPHD) filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussiaa sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaassian mixture probability hypothesis density (GMPHD) filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special ease of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.
Preference transfer model in collaborative filtering for implicit data
Institute of Scientific and Technical Information of China (English)
Bin JU; Yun-tao QIAN; Min-chao YE
2016-01-01
Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative fi ltering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative fi ltering, became an important task in collaborative fi ltering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative fi ltering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.
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.
Bootstrapping Nonlinear Least Squares Estimates in the Kalman Filter Model.
1986-01-01
Bias Bootstrapa 3.933 x 103 0.651 x 103 -0.166 x 10-- b b Newton - Rapshon 1.380 x 10- 0.479 x 10- 10_c 0_ c , e -.., Emperical 3.605 x 10 -0.026 x 10...most cases, parameter estimation for the KF model has been accomplished by maximum likelihood techniques involving the use of scoring or Newton ...is well behaved, the Newton -Raphson and scoring procedures enjoy quadratic convergence in the neighborhood of the maximum and one has a ready-made
State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter
Mat Daud, Auni Aslah
In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.
A new model to predict weak-lensing peak counts III. Filtering technique comparisons
Lin, Chieh-An; Pires, Sandrine
2016-01-01
This is the third in a series of papers that develop a new and flexible model to predict weak-lensing (WL) peak counts, which have been shown to be a very valuable non-Gaussian probe of cosmology. In this paper, we compare the cosmological information extracted from WL peak counts using different filtering techniques of the galaxy shear data, including linear filtering with a Gaussian and two compensated filters (the starlet wavelet and the aperture mass), and the nonlinear filtering method MRLens. We present improvements to our model that account for realistic survey conditions, which are masks, shear-to-convergence transformations, and non-constant noise. We create simulated peak counts from our stochastic model, from which we obtain constraints on the matter density $\\Omega_\\mathrm{m}$, the power spectrum normalization $\\sigma_8$, and the dark-energy parameter $w_0^\\mathrm{de}$. We use two methods for parameter inference, a copula likelihood, and approximate Bayesian computation (ABC). We measure the conto...
Mathematical modelling of scanner-specific bowtie filters for Monte Carlo CT dosimetry
Kramer, R.; Cassola, V. F.; Andrade, M. E. A.; de Araújo, M. W. C.; Brenner, D. J.; Khoury, H. J.
2017-02-01
The purpose of bowtie filters in CT scanners is to homogenize the x-ray intensity measured by the detectors in order to improve the image quality and at the same time to reduce the dose to the patient because of the preferential filtering near the periphery of the fan beam. For CT dosimetry, especially for Monte Carlo calculations of organ and tissue absorbed doses to patients, it is important to take the effect of bowtie filters into account. However, material composition and dimensions of these filters are proprietary. Consequently, a method for bowtie filter simulation independent of access to proprietary data and/or to a specific scanner would be of interest to many researchers involved in CT dosimetry. This study presents such a method based on the weighted computer tomography dose index, CTDIw, defined in two cylindrical PMMA phantoms of 16 cm and 32 cm diameter. With an EGSnrc-based Monte Carlo (MC) code, ratios CTDIw/CTDI100,a were calculated for a specific CT scanner using PMMA bowtie filter models based on sigmoid Boltzmann functions combined with a scanner filter factor (SFF) which is modified during calculations until the calculated MC CTDIw/CTDI100,a matches ratios CTDIw/CTDI100,a, determined by measurements or found in publications for that specific scanner. Once the scanner-specific value for an SFF has been found, the bowtie filter algorithm can be used in any MC code to perform CT dosimetry for that specific scanner. The bowtie filter model proposed here was validated for CTDIw/CTDI100,a considering 11 different CT scanners and for CTDI100,c, CTDI100,p and their ratio considering 4 different CT scanners. Additionally, comparisons were made for lateral dose profiles free in air and using computational anthropomorphic phantoms. CTDIw/CTDI100,a determined with this new method agreed on average within 0.89% (max. 3.4%) and 1.64% (max. 4.5%) with corresponding data published by CTDosimetry (www.impactscan.org) for the CTDI HEAD and BODY phantoms
System Model Bias Processing Approach for Regional Coordinated States Information Involved Filtering
Directory of Open Access Journals (Sweden)
Zebo Zhou
2016-01-01
Full Text Available In the Kalman filtering applications, the conventional dynamic model which connects the states information of two consecutive epochs by state transition matrix is usually predefined and assumed to be invariant. Aiming to improve the adaptability and accuracy of dynamic model, we propose multiple historical states involved filtering algorithm. An autoregressive model is used as the dynamic model which is subsequently combined with observation model for deriving the optimal window-recursive filter formulae in the sense of minimum mean square error principle. The corresponding test statistics characteristics of system residuals are discussed in details. The test statistics of regional predicted residuals are then constructed in a time-window for model bias testing with two hypotheses, that is, the null and alternative hypotheses. Based on the innovations test statistics, we develop a model bias processing procedure including bias detection, location identification, and state correction. Finally, the minimum detectable bias and bias-to-noise ratio are both computed for evaluating the internal and external reliability of overall system, respectively.
Model calibration for pressure drop in a pulse-jet cleaned fabric filter
Koehler, John L.; David, Leith
A model based on Darcy's law allows prediction of pressure drop in a pulse-jet cleaned fabric filter. The model considers the effects of filtration velocity, dust areal density added during one filtration cycle, and pulse pressure. Data used to calibrate the model were collected in experiments with three fabric surface treatments and three dusts conducted at three filtration velocities, for a total of 27 different experimental conditions. The fabric used was polyester felt with untreated, singed, or PTFE-laminated surface. The dusts used were granite, limestone and fly ash. Filtration velocities were 50,75 and 100 mm s -1. Dust areal density added during one filtration cycle was constant, as was pulse pressure. Under these conditions, fabric surface treatment alone largely determined the values for two of the three constants in the model; the third constant depends on pressure drop characteristics of the venturi at the top of each filter bag.
Directory of Open Access Journals (Sweden)
Lianhui Li
2015-12-01
Full Text Available Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.
Auxiliary particle filter-model predictive control of the vacuum arc remelting process
Lopez, F.; Beaman, J.; Williamson, R.
2016-07-01
Solidification control is required for the suppression of segregation defects in vacuum arc remelting of superalloys. In recent years, process controllers for the VAR process have been proposed based on linear models, which are known to be inaccurate in highly-dynamic conditions, e.g. start-up, hot-top and melt rate perturbations. A novel controller is proposed using auxiliary particle filter-model predictive control based on a nonlinear stochastic model. The auxiliary particle filter approximates the probability of the state, which is fed to a model predictive controller that returns an optimal control signal. For simplicity, the estimation and control problems are solved using Sequential Monte Carlo (SMC) methods. The validity of this approach is verified for a 430 mm (17 in) diameter Alloy 718 electrode melted into a 510 mm (20 in) diameter ingot. Simulation shows a more accurate and smoother performance than the one obtained with an earlier version of the controller.
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.
Interpreting Space-Based Trends in Carbon Monoxide with Multiple Models
Strode, Sarah A.; Worden, Helen M.; Damon, Megan; Douglass, Anne R.; Duncan, Bryan N.; Emmons, Louisa K.; Lamarque, Jean-Francois; Manyin, Michael; Oman, Luke D.; Rodriguez, Jose M.; Strahan, Susan E.; Tilmes, Simone
2016-01-01
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 timedependent 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.
Pulse cleaning flow models and numerical computation of candle ceramic filters
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
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 onedimensional 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.
Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model
Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P.O.
2013-01-01
This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically wit...
Econometric Models, Methodology and Trends regarding public debt and external debt
Directory of Open Access Journals (Sweden)
Gheorghe Săvoiu
2013-10-01
Full Text Available Statistically-mathematically describing few econometric models as variables, this article approaches the impact and trends regarding public debt and external debt. Conceptually and practically analyzing the evolution of indicators, there are identified specific trends in the economy of Romania, some characteristic models are being parametrised and tested.
Testing for integration using evolving trend and seasonal models: A Bayesian approach
G. Koop (Gary); H.K. van Dijk (Herman)
1999-01-01
textabstractIn this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are
Enhanced Kalman Filtering for a 2D CFD NS Wind Farm Flow Model
Doekemeijer, B. M.; van Wingerden, J. W.; Boersma, S.; Pao, L. Y.
2016-09-01
Wind turbines are often grouped together for financial reasons, but due to wake development this usually results in decreased turbine lifetimes and power capture, and thereby an increased levelized cost of energy (LCOE). Wind farm control aims to minimize this cost by operating turbines at their optimal control settings. Most state-of-the-art control algorithms are open-loop and rely on low fidelity, static flow models. Closed-loop control relying on a dynamic model and state observer has real potential to further decrease wind's LCOE, but is often too computationally expensive for practical use. In this paper two time-efficient Kalman filter (KF) variants are outlined incorporating the medium fidelity, dynamic flow model “WindFarmSimulator” (WFSim). This model relies on a discretized set of Navier-Stokes equations in two dimensions to predict the flow in wind farms at low computational cost. The filters implemented are an Ensemble KF and an Approximate KF. Simulations in which a high fidelity simulation model represents the true wind farm show that these filters are 101 —102 times faster than a regular KF with comparable or better performance, correcting for wake dynamics that are not modeled in WFSim (noticeably, wake meandering and turbine hub effects). This is a first big step towards real-time closed-loop control for wind farms.
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.
Can a global model reproduce observed trends in summertime surface ozone levels?
Directory of Open Access Journals (Sweden)
S. Koumoutsaris
2012-01-01
Full Text Available Quantifying trends in surface ozone concentrations are critical for assessing pollution control strategies. Here we use observations and results from a global chemical transport model to examine the trends (1991–2005 in daily maximum 8-hour average concentrations in summertime surface ozone at rural sites in Europe and the United States. We find a decrease in observed ozone concentrations at the high end of the probability distribution at many of the sites in both regions. The model attributes these trends to a decrease in local anthropogenic ozone precursors, although simulated decreasing trends are overestimated in comparison with observed ones. The low end of observed distribution show small upward trends over Europe and the western US and downward trends in Eastern US. The model cannot reproduce these observed trends, especially over Europe and the western US. In particular, simulated changes between the low and high end of the distributions in these two regions are not significant. Sensitivity simulations indicate that emissions from far away source regions do not affect significantly ozone trends at both ends of the distribution. This is in contrast with previously available results, which indicated that increasing ozone trends at the low percentiles may reflect an increase in ozone background associated with increasing remote sources of ozone precursors. Possible reasons for discrepancies between observed and simulated trends are discussed.
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.
DEFF Research Database (Denmark)
Levine, Ari; Heje Pedersen, Lasse
2016-01-01
, that these trend indicators are closely related. In fact, they are equivalent representations in their most general forms. They also capture many other types of filters, such as the Hodrick–Prescott (HP) filter, the Kalman filter, and all other linear filters. We show how these filters can be represented through...
A Trend-Switching Financial Time Series Model with Level-Duration Dependence
Directory of Open Access Journals (Sweden)
Qingsheng Wang
2012-01-01
overcome the difficult problem that motivates our researches in this paper. An asymmetric and nonlinear model with the change of local trend depending on local high-low turning point process is first proposed in this paper. As the point process can be decomposed into the two different processes, a high-low level process and an up-down duration process, we then establish the so-called trend-switching model which depends on both level and duration (Trend-LD. The proposed model can predict efficiently the direction and magnitude of the local trend of a time series by incorporating the local high-low turning point information. The numerical results on six indices in world stock markets show that the proposed Trend-LD model is suitable for fitting the market data and able to outperform the traditional random walk model.
KALMAN FILTERING CORRECTION IN REAL-TIME FORECASTING WITH HYDRODYNAMIC MODEL
Institute of Scientific and Technical Information of China (English)
WU Xiao-ling; WANG Chuan-hai; CHEN Xi; XIANG Xiao-hua; ZHOU Quan
2008-01-01
Accurate and reliable flood forecast is crucial for efficient real-time river management, including flood control, flood warning, reservoir operation and river regulation. In order to improve the estimate of the initial state of the forecasting system and to reduce the errors in the forecast period a data assimilation procedure was often need. The Kalman filter was proven to be an efficient method to adjust real-time flood series and improve the initial conditions before the forecast. A new model integrating the hydraulic model with the Kalman filter for real-time correction of flood forecast was developed and applied in the Three Gorges interzone of the Yangtze River. The method was calibrated and verified against the observed flood stage and discharge during Three Gorges Dam construction periods (2004). The results demonstrate that the new model incorporates an accurate and fast updating technique can improve the reliability of the flood forecast.
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.
Identification of parameters in nonlinear geotechnical models using extenden Kalman filter
Directory of Open Access Journals (Sweden)
Nestorović Tamara
2014-01-01
Full Text Available Direct measurement of relevant system parameters often represents a problem due to different limitations. In geomechanics, measurement of geotechnical material constants which constitute a material model is usually a very diffcult task even with modern test equipment. Back-analysis has proved to be a more effcient and more economic method for identifying material constants because it needs measurement data such as settlements, pore pressures, etc., which are directly measurable, as inputs. Among many model parameter identification methods, the Kalman filter method has been applied very effectively in recent years. In this paper, the extended Kalman filter – local iteration procedure incorporated with finite element analysis (FEA software has been implemented. In order to prove the effciency of the method, parameter identification has been performed for a nonlinear geotechnical model.
Pyramidal Edge Detection Method Based on AWFM Filtering and Fuzzy Linking Model
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to fine edge detection. The validation experiment results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector andprevious multiresolution approach, especially in impulse noise environment.
Lagrangian filtered density function for LES-based stochastic modelling of turbulent dispersed flows
Innocenti, A; Chibbaro, S
2016-01-01
The Eulerian-Lagrangian approach based on Large-Eddy Simulation (LES) is one of the most promising and viable numerical tools to study turbulent dispersed flows when the computational cost of Direct Numerical Simulation (DNS) becomes too expensive. The applicability of this approach is however limited if the effects of the Sub-Grid Scales (SGS) of the flow on particle dynamics are neglected. In this paper, we propose to take these effects into account by means of a Lagrangian stochastic SGS model for the equations of particle motion. The model extends to particle-laden flows the velocity-filtered density function method originally developed for reactive flows. The underlying filtered density function is simulated through a Lagrangian Monte Carlo procedure that solves for a set of Stochastic Differential Equations (SDEs) along individual particle trajectories. The resulting model is tested for the reference case of turbulent channel flow, using a hybrid algorithm in which the fluid velocity field is provided b...
Modelling and measurement of wear particle flow in a dual oil filter system for condition monitoring
DEFF Research Database (Denmark)
Henneberg, Morten; Eriksen, René Lynge; Fich, Jens
2016-01-01
Wear debris is an indicator of the health of machinery, and the availability of accurate methods for characterising debris is important. In this work, a dual filter model for a gear oil system is used in conjunction with operational data to indicate three different system operating states. The qu...... 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.......Wear debris is an indicator of the health of machinery, and the availability of accurate methods for characterising debris is important. In this work, a dual filter model for a gear oil system is used in conjunction with operational data to indicate three different system operating states....... 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...
Fukumori, Ichiro; Malanotte-Rizzoli, Paola
1995-01-01
A practical method of data assimilation for use with large, nonlinear, ocean general circulation models is explored. A Kalman filter based on approximation 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.
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.
Trends in hydrodesulfurization catalysis based on realistic surface models
DEFF Research Database (Denmark)
Moses, P.G.; Grabow, L.C.; Fernandez Sanchez, Eva
2014-01-01
Trends in hydrodesulfurization (HDS) activity are investigated on the basis of surface properties calculated by density functional theory for a series of HDS catalysts. It is shown that approximately linear correlations exist between HS group binding energies and activation barriers of key elemen...
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... 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...
Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling
Hammond, Matthew L.; Beaulieu, Claudie; Sahu, Sujit K.; Henson, Stephanie A.
2017-07-01
The presence, magnitude, and even direction of long-term trends in phytoplankton abundance over the past few decades are still debated in the literature, primarily due to differences in the data sets and methodologies used. Recent work has suggested that the satellite chlorophyll record is not yet long enough to distinguish climate change trends from natural variability, despite the high density of coverage in both space and time. Previous work has typically focused on using linear models to determine the presence of trends, where each grid cell is considered independently from its neighbors. However, trends can be more thoroughly evaluated using a spatially resolved approach. Here a Bayesian hierarchical spatiotemporal model is fitted to quantify trends in ocean chlorophyll from September 1997 to December 2013. The approach used in this study explicitly accounts for the dependence between neighboring grid cells, which allows us to estimate trend by "borrowing strength" from the spatial correlation. By way of comparison, a model without spatial correlation is also fitted. This results in a notable loss of accuracy in model fit. Additionally, we find an order of magnitude smaller global trend, and larger uncertainty, when using the spatiotemporal model: -0.023 ± 0.12% yr-1 as opposed to -0.38 ± 0.045% yr-1 when the spatial correlation is not taken into account. The improvement in accuracy of trend estimates and the more complete account of their uncertainty emphasize the solution that space-time modeling offers for studying global long-term change.
A Monte Carlo Uncertainty Analysis of Ozone Trend Predictions in a Two Dimensional Model. Revision
Considine, D. B.; Stolarski, R. S.; Hollandsworth, S. M.; Jackman, C. H.; Fleming, E. L.
1998-01-01
We use Monte Carlo analysis to estimate the uncertainty in predictions of total O3 trends between 1979 and 1995 made by the Goddard Space Flight Center (GSFC) two-dimensional (2D) model of stratospheric photochemistry and dynamics. The uncertainty is caused by gas-phase chemical reaction rates, photolysis coefficients, and heterogeneous reaction parameters which are model inputs. The uncertainty represents a lower bound to the total model uncertainty assuming the input parameter uncertainties are characterized correctly. Each of the Monte Carlo runs was initialized in 1970 and integrated for 26 model years through the end of 1995. This was repeated 419 times using input parameter sets generated by Latin Hypercube Sampling. The standard deviation (a) of the Monte Carlo ensemble of total 03 trend predictions is used to quantify the model uncertainty. The 34% difference between the model trend in globally and annually averaged total O3 using nominal inputs and atmospheric trends calculated from Nimbus 7 and Meteor 3 total ozone mapping spectrometer (TOMS) version 7 data is less than the 46% calculated 1 (sigma), model uncertainty, so there is no significant difference between the modeled and observed trends. In the northern hemisphere midlatitude spring the modeled and observed total 03 trends differ by more than 1(sigma) but less than 2(sigma), which we refer to as marginal significance. We perform a multiple linear regression analysis of the runs which suggests that only a few of the model reactions contribute significantly to the variance in the model predictions. The lack of significance in these comparisons suggests that they are of questionable use as guides for continuing model development. Large model/measurement differences which are many multiples of the input parameter uncertainty are seen in the meridional gradients of the trend and the peak-to-peak variations in the trends over an annual cycle. These discrepancies unambiguously indicate model formulation
Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development.
Pendergrass, Sarah A; Frase, Alex; Wallace, John; Wolfe, Daniel; Katiyar, Neerja; Moore, Carrie; Ritchie, Marylyn D
2013-12-30
The ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information. Biofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories.Via Biofilter 2.0 researchers can:• Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation• Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest• Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing. Biofilter is a software
Noise assisted excitation energy transfer in a linear model of a selectivity filter backbone strand
Bassereh, Hassan; Salari, Vahid; Shahbazi, Farhad
2015-07-01
In this paper, we investigate the effect of noise and disorder on the efficiency of excitation energy transfer (EET) in a N=5 sites linear chain with ‘static’ dipole-dipole couplings. In fact, here, the disordered chain is a toy model for one strand of the selectivity filter backbone in ion channels. It has recently been discussed that the presence of quantum coherence in the selectivity filter is possible and can play a role in mediating ion-conduction and ion-selectivity in the selectivity filter. The question is ‘how a quantum coherence can be effective in such structures while the environment of the channel is dephasing (i.e. noisy)?’ Basically, we expect that the presence of the noise should have a destructive effect in the quantum transport. In fact, we show that such expectation is valid for ordered chains. However, our results indicate that introducing the dephasing in the disordered chains leads to the weakening of the localization effects, arising from the multiple back-scatterings due to the randomness, and then increases the efficiency of quantum energy transfer. Thus, the presence of noise is crucial for the enhancement of EET efficiency in disordered chains. We also show that the contribution of both classical and quantum mechanical effects are required to improve the speed of energy transfer along the chain. Our analysis may help for better understanding of fast and efficient functioning of the selectivity filters in ion channels.
Noise assisted excitation energy transfer in a linear model of a selectivity filter backbone strand.
Bassereh, Hassan; Salari, Vahid; Shahbazi, Farhad
2015-07-15
In this paper, we investigate the effect of noise and disorder on the efficiency of excitation energy transfer (EET) in a N = 5 sites linear chain with 'static' dipole-dipole couplings. In fact, here, the disordered chain is a toy model for one strand of the selectivity filter backbone in ion channels. It has recently been discussed that the presence of quantum coherence in the selectivity filter is possible and can play a role in mediating ion-conduction and ion-selectivity in the selectivity filter. The question is 'how a quantum coherence can be effective in such structures while the environment of the channel is dephasing (i.e. noisy)?' Basically, we expect that the presence of the noise should have a destructive effect in the quantum transport. In fact, we show that such expectation is valid for ordered chains. However, our results indicate that introducing the dephasing in the disordered chains leads to the weakening of the localization effects, arising from the multiple back-scatterings due to the randomness, and then increases the efficiency of quantum energy transfer. Thus, the presence of noise is crucial for the enhancement of EET efficiency in disordered chains. We also show that the contribution of both classical and quantum mechanical effects are required to improve the speed of energy transfer along the chain. Our analysis may help for better understanding of fast and efficient functioning of the selectivity filters in ion channels.
Systematic Design and Modeling of a OTA-C Filter for Portable ECG Detection.
Shuenn-Yuh Lee; Chih-Jen Cheng
2009-02-01
This study presents a systematic design of the fully differential operational transconductance amplifier-C (OTA-C) filter for a heart activities detection apparatus. Since the linearity and noise of the filter is dependent on the building cell, a precise behavioral model for the real OTA circuit is created. To reduce the influence of coefficient sensitivity and maintain an undistorted biosignal, a fifth-order ladder-type lowpass Butterworth is employed. Based on this topology, a chip fabricated in a 0.18- mum CMOS process is simulated and measured to validate the system estimation. Since the battery life and the integration with the low-voltage digital processor are the most critical requirement for the portable diagnosis device, the OTA-based circuit is operated in the subthreshold region to save power under the supply voltage of 1V. Measurement results show that this low-voltage and low-power filter possesses the HD3 of -48.9 dB, dynamic range (DR) of 50 dB, and power consumption of 453 nW. Therefore, the OTA-C filter can be adopted to eliminate the out-of-band interference of the electrocardiogram (ECG) whose signal bandwidth is located within 250 Hz.
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.
DEFF Research Database (Denmark)
Sørensen, Jacob Viborg Tornfeldt; Madsen, Henrik; Madsen, H.
2006-01-01
sensitivity study of three well known Kalman filter approaches for the assimilation of water levels in a three dimensional hydrodynamic modelling system. The filters considered are the ensemble Kalman filter (EnKF), the reduced rank square root Kalman filter (RRSQRT) and the steady Kalman filter....... A sensitivity analysis of key parameters in the schemes is undertaken for a setup in an idealised bay. The sensitivity of the resulting root mean square error (RMSE) is shown to be low to moderate. Hence the schemes are robust within an acceptable range and their application even with misspecified parameters...... is to be encouraged in this perspective. However, the predicted uncertainty of the assimilation results are sensitive to the parameters and hence must be applied with care. The sensitivity study further demonstrates the effectiveness of the steady Kalman filter in the given system as well as the great impact...
Tropospheric ozone trend over Beijing from 2002–2010: ozonesonde measurements and modeling analysis
Y. Wang; Konopka, P.; Liu, Y.; Chen, H; Müller, R.; F. Plöger; M. Riese; Cai, Z.; D. Lü
2012-01-01
Using a combination of ozonesonde data and numerical simulations of the Chemical Lagrangian Model of the Stratosphere (CLaMS), the trend of tropospheric ozone (O_{3}) during 2002–2010 over Beijing was investigated. Tropospheric ozone over Beijing shows a winter minimum and a broad summer maximum with a clear positive trend in the maximum summer ozone concentration over the last decade. The observed significant trend of tropospheric column ozone for the entire time serie...
Tropospheric ozone trend over Beijing from 2002–2010: ozonesonde measurements and modeling analysis
Wang, Y.; Konopka, P.; Liu, Y.; Chen, H; Müller, R.; F. Plöger; M. Riese; Cai, Z.; 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...
Simulation of Change Trend of Drought in Shaanxi Province in Future Based on PRECIS Model
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
[Objective] The aim was to predict the change trend of drought in Shaanxi Province in future. [Method] Based on the regional climate model PRECIS from Hadley Climate Center, British Meteorological Bureau, taking precipitation anomaly percentage as assessment index, the change trend of drought in Shaanxi Province in reference years (1971-1990) was simulated, and the change trend of drought in Shaanxi Province from 2071 to 2100 was predicted. [Result] The simulated value of drought frequency in reference year...
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.
Automated sleep spindle detection using IIR filters and a Gaussian Mixture Model.
Patti, Chanakya Reddy; Penzel, Thomas; Cvetkovic, Dean
2015-08-01
Sleep spindle detection using modern signal processing techniques such as the Short-Time Fourier Transform and Wavelet Analysis are common research methods. These methods are computationally intensive, especially when analysing data from overnight sleep recordings. The authors of this paper propose an alternative using pre-designed IIR filters and a multivariate Gaussian Mixture Model. Features extracted with IIR filters are clustered using a Gaussian Mixture Model without the use of any subject independent thresholds. The Algorithm was tested on a database consisting of overnight sleep PSG of 5 subjects and an online public spindles database consisting of six 30 minute sleep excerpts. An overall sensitivity of 57% and a specificity of 98.24% was achieved in the overnight database group and a sensitivity of 65.19% at a 16.9% False Positive proportion for the 6 sleep excerpts.
Energy Technology Data Exchange (ETDEWEB)
Sarkar, Avik [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sun, Xin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sundaresan, Sankaran [Princeton Univ., NJ (United States)
2014-04-23
The accuracy of coarse-grid multiphase CFD simulations of fluidized beds may be improved via the inclusion of filtered constitutive models. In our previous study (Sarkar et al., Chem. Eng. Sci., 104, 399-412), we developed such a set of filtered drag relationships for beds with immersed arrays of cooling tubes. Verification of these filtered drag models is addressed in this work. Predictions from coarse-grid simulations with the sub-grid filtered corrections are compared against accurate, highly-resolved simulations of full-scale turbulent and bubbling fluidized beds. The filtered drag models offer a computationally efficient yet accurate alternative for obtaining macroscopic predictions, but the spatial resolution of meso-scale clustering heterogeneities is sacrificed.
The effect of compression on tuning estimates in a simple nonlinear auditory filter model
DEFF Research Database (Denmark)
Marschall, Marton; MacDonald, Ewen; Dau, Torsten
2013-01-01
, there is evidence that human frequency-selectivity estimates depend on whether an iso-input or an iso-response measurement paradigm is used (Eustaquio-Martin et al., 2011). This study presents simulated tuning estimates using a simple compressive auditory filter model, the bandpass nonlinearity (BPNL), which......, then compression alone may explain a large part of the behaviorally observed differences in tuning between simultaneous and forward-masking conditions....
Bartosova, Alena; Arheimer, Berit; Capell, Rene; Donnelly, Chantal; Strömqvist, Johan
2016-04-01
Nutrient transport models are important tools for large scale assessments of macro-nutrient fluxes (nitrogen, phosphorus) and thus can serve as support tool for environmental assessment and management. Results from model applications over large areas, i.e. from major river basin to continental scales can fill a gap where monitoring data is not available. Here, we present results from the pan-European rainfall-runoff and nutrient transfer model E-HYPE, which is based on open data sources. We investigate the ability of the E-HYPE model to replicate the spatial and temporal variations found in observed time-series of riverine N and P concentrations, and illustrate the model usefulness for nutrient source detection, trend analyses, and scenario modelling. The results show spatial patterns in N concentration in rivers across Europe which can be used to further our understanding of nutrient issues across the European continent. E-HYPE results show hot spots with highest concentrations of total nitrogen in Western Europe along the North Sea coast. Source apportionment was performed to rank sources of nutrient inflow from land to sea along the European coast. An integrated dynamic model as E-HYPE also allows us to investigate impacts of climate change and measure programs, which was illustrated in a couple of scenarios for the Baltic Sea. Comparing model results with observations shows large uncertainty in many of the data sets and the assumptions used in the model set-up, e.g. point source release estimates. However, evaluation of model performance at a number of measurement sites in Europe shows that mean N concentration levels are generally well simulated. P levels are less well predicted which is expected as the variability of P concentrations in both time and space is higher. Comparing model performance with model set-ups using local data for the Weaver River (UK) did not result in systematically better model performance which highlights the complexity of model
METHODOLOGICAL APPROACH AND MODEL ANALYSIS FOR IDENTIFICATION OF TOURIST TRENDS
Neven Šerić; Marijana Jurišić
2015-01-01
The draw and diversity of the destination’s offer is an antecedent of the tourism visits growth. The destination supply differentiation is carried through new, specialised tourism products. The usual approach consists of forming specialised tourism products in accordance with the existing tourism destination image. Another approach, prevalent in practice of developed tourism destinations is based on innovating the destination supply through accordance with the global tourism trends. For this ...
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.
Non-linear DSGE Models and The Central Difference Kalman Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
solved up to third order. A Monte Carlo study shows that this QML estimator is basically unbiased and normally distributed infi…nite samples for DSGE models solved using a second order or a third order approximation. These results hold even when structural shocks are Gaussian, Laplace distributed......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...
Trend Validation of a Musculoskeletal Model with a Workstation Design Parameter
Pontonnier, Charles; Samani, Afshin; Dumont, Georges; Madeleine, Pascal
2012-01-01
The aim of this article is to present the application of a trend validation to validate a simulation model. The workstation parameter used to define the trend is the table height of simulated meat cutting tasks (well known to be related to MSD).
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.
Relationships between observer and Kalman Filter models for human dynamic spatial orientation.
Selva, Pierre; Oman, Charles M
2012-01-01
How does the central nervous system (CNS) combine sensory information from semicircular canal, otolith, and visual systems into perceptions of rotation, translation and tilt? Over the past four decades, a variety of input-output ("black box") mathematical models have been proposed to predict human dynamic spatial orientation perception and eye movements. The models have proved useful in vestibular diagnosis, aircraft accident investigation, and flight simulator design. Experimental refinement continues. This paper briefly reviews the history of two widely known model families, the linear "Kalman Filter" and the nonlinear "Observer". Recent physiologic data supports the internal model assumptions common to both. We derive simple 1-D and 3-D examples of each model for vestibular inputs, and show why - despite apparently different structure and assumptions - the linearized model predictions are dynamically equivalent when the four free model parameters are adjusted to fit the same empirical data, and perceived head orientation remains near upright. We argue that the motion disturbance and sensor noise spectra employed in the Kalman Filter formulation may reflect normal movements in daily life and perceptual thresholds, and thus justify the interpretation that the CNS cue blending scheme may well minimize least squares angular velocity perceptual errors.
Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea
Ponsar, Stéphanie; Luyten, Patrick; Dulière, Valérie
2016-08-01
Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters' values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification.
Strahl, Stefan; Mertins, Alfred
2008-07-18
Evidence that neurosensory systems use sparse signal representations as well as improved performance of signal processing algorithms using sparse signal models raised interest in sparse signal coding in the last years. For natural audio signals like speech and environmental sounds, gammatone atoms have been derived as expansion functions that generate a nearly optimal sparse signal model (Smith, E., Lewicki, M., 2006. Efficient auditory coding. Nature 439, 978-982). Furthermore, gammatone functions are established models for the human auditory filters. Thus far, a practical application of a sparse gammatone signal model has been prevented by the fact that deriving the sparsest representation is, in general, computationally intractable. In this paper, we applied an accelerated version of the matching pursuit algorithm for gammatone dictionaries allowing real-time and large data set applications. We show that a sparse signal model in general has advantages in audio coding and that a sparse gammatone signal model encodes speech more efficiently in terms of sparseness than a sparse modified discrete cosine transform (MDCT) signal model. We also show that the optimal gammatone parameters derived for English speech do not match the human auditory filters, suggesting for signal processing applications to derive the parameters individually for each applied signal class instead of using psychometrically derived parameters. For brain research, it means that care should be taken with directly transferring findings of optimality for technical to biological systems.
Using Multi-Scale Filtering to Initialize a Background Extraction Model
Directory of Open Access Journals (Sweden)
N. A. Lili
2012-01-01
Full Text Available Problem statement: Probability-based methods which usually work based on the saved history of each pixel are utilized severally in extracting a background image for moving detection systems. Probability-based methods suffer from a lack of information when the system first begins to work. The model should be initialized using an alternative accurate method. Approach: The use of a nonparametric filtering to calculate the most probable value for each pixel in the initialization phase can be useful. In this study a complete system to extract an adaptable gray scale background image is presented. It is a probability-based system and especially suitable for outdoor applications. The proposed method is initialized using a multi scale filtering method. Results: The results of the experiments certify that not only the quality of the final extracted background is about 10% more accurate in comparison to four recent re-implemented methods, but also the time consumption of the extraction are acceptable. Conclusion: Using multi-scale filtering to initialize the background model and to extract the background using a probability-based method proposes an accurate and adaptable background extraction method which is able to handle sudden and large illumination changes.
Energy Technology Data Exchange (ETDEWEB)
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Design method of dichroic filter using color appearance model in LCD projection systems
Kim, Jee-Hong
1998-11-01
A new design method using a color appearance model is proposed for the dichroic filters in LCD projection systems. The dichroic filters used for color separation/composition play a dominant role in the performance of color reproduction so that its spectral transmittance should be designed to have optimized color performance. In the proposed method, a reproducible color gamut in the 3D color space is used as a performance index, and we find the optimal half-power wavelengths of dichroic filters, which are applicable to the development of LCD projection TV systems. Considering diverse viewing conditions with moderate ambient light, the design parameters are optimized by maximizing the volume of the color gamut in an apparent color space for each condition. Here, the RLAB color space developed by Fairchild is used as a color appearance model and the white balancing method is applied to restore the designate color temperature of peak white. The optimal wavelengths are compared with the design based on the CIELAB color space without considering nonstandard viewing conditions, and both results do not show as much difference as expected due to the white balance.
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.
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.
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.
Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter
Directory of Open Access Journals (Sweden)
Haitong Wang
2017-01-01
Full Text Available This paper presents a modeling methodology for the thermal error of machine tool. The temperatures predicted by modified lumped-mass method and the temperatures measured by sensors are fused by the data fusion method of Kalman filter. The fused temperatures, instead of the measured temperatures used in traditional methods, are applied to predict the thermal error. The genetic algorithm is implemented to optimize the parameters in modified lumped-mass method and the covariances in Kalman filter. The simulations indicate that the proposed method performs much better compared with the traditional method of MRA, in terms of prediction accuracy and robustness under a variety of operating conditions. A compensation system is developed based on the controlling system of Siemens 840D. Validated by the compensation experiment, the thermal error after compensation has been reduced dramatically.
Particle capture in axial magnetic filters with power law flow model
Abbasov, T; Koksal, M
1999-01-01
A theory of capture of magnetic particle carried by laminar flow of viscous non-Newtonian (power law) fluid in axially ordered filters is presented. The velocity profile of the fluid flow is determined by the Kuwabara-Happel cell model. For the trajectory of the particle, the capture area and the filter performance simple analytical expressions are obtained. These expressions are valid for particle capture processes from both Newtonian and non-Newtonian fluids. For this reason the obtained theoretical results make it possible to widen the application of high-gradient magnetic filtration (HGMF) to other industrial areas. For Newtonian fluids the theoretical results are shown to be in good agreement with the experimental ones reported in the literature. (author)
Particle capture in axial magnetic filters with power law flow model
Abbasov, T.; Herdem, S.; Köksal, M.
1999-05-01
A theory of capture of magnetic particle carried by laminar flow of viscous non-Newtonian (power law) fluid in axially ordered filters is presented. The velocity profile of the fluid flow is determined by the Kuwabara-Happel cell model. For the trajectory of the particle, the capture area and the filter performance simple analytical expressions are obtained. These expressions are valid for particle capture processes from both Newtonian and non-Newtonian fluids. For this reason the obtained theoretical results make it possible to widen the application of high-gradient magnetic filtration (HGMF) to other industrial areas. For Newtonian fluids the theoretical results are shown to be in good agreement with the experimental ones reported in the literature.
Particle capture in axial magnetic filters with power law flow model
Energy Technology Data Exchange (ETDEWEB)
Abbasov, T.; Herdem, S.; Koksal, M. [Inonu University, Engineering Faculty, Department of Electrical and Electronics, Malatya (Turkey)
1999-05-21
A theory of capture of magnetic particle carried by laminar flow of viscous non-Newtonian (power law) fluid in axially ordered filters is presented. The velocity profile of the fluid flow is determined by the Kuwabara-Happel cell model. For the trajectory of the particle, the capture area and the filter performance simple analytical expressions are obtained. These expressions are valid for particle capture processes from both Newtonian and non-Newtonian fluids. For this reason the obtained theoretical results make it possible to widen the application of high-gradient magnetic filtration (HGMF) to other industrial areas. For Newtonian fluids the theoretical results are shown to be in good agreement with the experimental ones reported in the literature. (author)
Neuville, Amélie; Schmittbuhl, Jean; 10.1111/j.1365-246X.2011.05126.x
2011-01-01
Natural open joints in rocks commonly present multi-scale self-affine apertures. This geometrical complexity affects fluid transport and heat exchange between the flow- ing fluid and the surrounding rock. In particular, long range correlations of self-affine apertures induce strong channeling of the flow which influences both mass and heat advection. A key question is to find a geometrical model of the complex aperture that describes at best the macroscopic properties (hydraulic conductivity, heat exchange) with the smallest number of parameters. Solving numerically the Stokes and heat equa- tions with a lubrication approximation, we show that a low pass filtering of the aperture geometry provides efficient estimates of the effective hydraulic and thermal properties (apertures). A detailed study of the influence of the bandwidth of the lowpass filtering on these transport properties is also performed. For instance, keeping the information of amplitude only of the largest Fourier length scales allows us to rea...
Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy
Directory of Open Access Journals (Sweden)
Changho Lee
2013-03-01
Full Text Available The International Civil Aviation Organization (ICAO has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS, Automatic Dependent Surveillance-Broadcast (ADS-B, multilateration (MLAT and wide-area multilateration (WAM systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.
Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Zhang, He
2016-11-20
Angular velocity information is a requisite for a spacecraft guidance, navigation, and control system. In this paper, an approach for angular velocity estimation based merely on star vector measurement with an improved current statistical model Kalman filter is proposed. High-precision angular velocity estimation can be achieved under dynamic conditions. The amount of calculation is also reduced compared to a Kalman filter. Different trajectories are simulated to test this approach, and experiments with real starry sky observation are implemented for further confirmation. The estimation accuracy is proved to be better than 10-4 rad/s under various conditions. Both the simulation and the experiment demonstrate that the described approach is effective and shows an excellent performance under both static and dynamic conditions.
A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling
Bekaert, D. P.; Segall, P.; Wright, T. J.; Hooper, A. J.
2016-12-01
Time-dependent slip modeling can be a powerful tool to improve our understanding of the interaction of earthquake cycle processes such as interseismic, coseismic, postseismic, and aseismic slip. Interferometric Synthetic Aperture Radar (InSAR) observations allow us to model slip at depth with a higher spatial resolution than when using GNSS alone. Typically the temporal resolution of InSAR has been limited. However, the recent generation of SAR satellites including Sentinel-1, COSMO-SkyMED, and RADARSAT-2 permits the use of InSAR for time-dependent slip modeling, at intervals of a few days when combined. The increasing amount of SAR data makes a simultaneous data inversion of all epochs challenging. Here, we expanded the original Network Inversion Filter (Segall and Matthews, 1997) to include InSAR observations of surface displacements in addition to GNSS. In the NIF framework, geodetic observations are limited to those of a given epoch, where a physical model describes the slip evolution over time. The combination of the Kalman forward filtering and backward smoothing allows all geodetic observations to constrain the complete observation period. Combining GNSS and InSAR allows us to model time-dependent slip at an unprecedented spatial resolution. We validate the approach with a simulation of the 2006 Guerrero slow slip event. In our study, we emphasize the importance of including the InSAR covariance information, and demonstrate that InSAR provides an additional constraint on the spatial extent of the slow slip. References: Segall, P., and M. Matthews (1997), Time dependent inversion of geodetic data, J. Geophys. Res., 102 (B10), 22,391 - 22,409, doi:10.1029/97JB01795. Bekaert, D., P. Segall, T.J. Wright, and A. Hooper (2016), A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling, JGR, doi:10.1002/2015JB012638 (open access).
A Novel Magnetic Linear Encoder Designed by Using the Slant Multi-Phase Filtering Model
Institute of Scientific and Technical Information of China (English)
SHI Yu; XING Huai-Zhong; ZHANG Huai-Wu; LIU Ying-Li; JING Yu-Lan; ZHONG Zhi-Yong
2004-01-01
@@ A novel design model based on the slant multi-phase filtering model is presented. A magnetic linear encoder with sinusoidal output voltage waveform has been investigated, and the improved sinusoidal output waveform can be easily acquired. A minimum 6% of distortion factor, when the difference of slant phase is 2π/3, is observed. It is found that the Wheatstone bridge type sensor, made of NiFe(450A)/NiO(300A) bilayers deposited on Si (001)substrate, can enhance both output signal and thermal stability, and then can be widely used in the field of magneto-resistive sensor.
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.
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.
A storage model approach to the assessment of snow depth trends
Woody, Jonathan; Lund, Robert; Grundstein, Andrew J.; Mote, Thomas L.
2009-10-01
This paper introduces a stochastic storage model capable of assessing trends in daily snow depth series. The model allows for seasonal features, which permits the analysis of daily data. Breakpoint times, which occur when the observing station changes location or instrumentation, are shown to greatly influence estimated trend margins and are accounted for in this analysis. The model is fitted by numerically minimizing a sum of squares of daily prediction errors. Standard errors for the model parameters, useful in making trend inferences, are presented. The methods are illustrated in the analysis of a century of daily snow depth observations from Napoleon, North Dakota. The results here show that snow depths are significantly declining at Napoleon, with spring ablation occurring earlier, and that breakpoint features are very influential in deriving realistic trend estimates.
Trickling filter for urea and bio-waste processing - dynamic modelling of nitrogen cycle
Zhukov, Anton; Hauslage, Jens; Tertilt, Gerin; Bornemann, Gerhild
Mankind’s exploration of the solar system requires reliable Life Support Systems (LSS) enabling long duration manned space missions. In the absence of frequent resupply missions, closure of the LSS will play a very important role and its maximisation will to a large extent drive the selection of appropriate LSS architectures. One of the significant issues on the way to full closure is to effectively utilise biological wastes such as urine, inedible biomass etc. A very promising concept of biological waste reprocessing is the use of trickling filters which are currently being developed and investigated by DLR, Cologne, Germany. The concept is called Combined Regenerative Organic-Food Production (C.R.O.P.) and is based on the microbiological treatment of biological wastes and reprocessing them into aqueous fertilizer which can directly be used in a greenhouse for food production. Numerous experiments have been and are being conducted by DLR in order to fully understand and characterize the process. The human space exploration group of the Technical University of Munich (TUM) in cooperation with DLR has started to establish a dynamic model of the trickling filter system to be able to assess its performance on the LSS level. In the first development stage the model covers the nitrogen cycle enabling to simulate urine processing. This paper describes briefly the C.R.O.P. concept and the status of the trickling filter model development. The model is based on enzyme-catalyzed reaction kinetics for the fundamental microbiological reaction chain and is created in MATLAB. Verification and correlation of the developed model with experiment results has been performed. Several predictive studies for batch sequencing behavior have been performed, demonstrating a good capability of C.R.O.P. concept to be used in closed LSS. Achieved results are critically discussed and way forward is presented.
Lumped modeling with circuit elements for nonreciprocal magnetoelectric tunable band-pass filter
Li, Xiao-Hong; Zhou, Hao-Miao; Zhang, Qiu-shi; Hu, Wen-Wen
2016-11-01
This paper presents a lumped equivalent circuit model of the nonreciprocal magnetoelectric tunable microwave band-pass filter. The reciprocal coupled-line circuit is based on the converse magnetoelectric effect of magnetoelectric composites, includes the electrical tunable equivalent factor of the piezoelectric layer, and is established by the introduced lumped elements, such as radiation capacitance, radiation inductance, and coupling inductance, according to the transmission characteristics of the electromagnetic wave and magnetostatic wave in an inverted-L-shaped microstrip line and ferrite slab. The nonreciprocal transmission property of the filter is described by the introduced T-shaped circuit containing controlled sources. Finally, the lumped equivalent circuit of a nonreciprocal magnetoelectric tunable microwave band-pass filter is given and the lumped parameters are also expressed. When the deviation angles of the ferrite slab are respectively 0° and 45°, the corresponding magnetoelectric devices are respectively a reciprocal device and a nonreciprocal device. The curves of S parameter obtained by the lumped equivalent circuit model and electromagnetic simulation are in good agreement with the experimental results. When the deviation angle is between 0° and 45°, the maximum value of the S parameter predicted by the lumped equivalent circuit model is in good agreement with the experimental result. The comparison results of the paper show that the lumped equivalent circuit model is valid. Further, the effect of some key material parameters on the performance of devices is predicted by the lumped equivalent circuit model. The research can provide the theoretical basis for the design and application of nonreciprocal magnetoelectric tunable devices. Project supported by the National Natural Science Foundation of China (Grant Nos. 11172285, 11472259, and 11302217) and the Natural Science Foundation of Zhejiang Province, China (Grant No. LR13A020002).
A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling
Bekaert, D. P. S.; Segall, P.; Wright, T. J.; Hooper, A. J.
2016-03-01
Studies of the earthquake cycle benefit from long-term time-dependent slip modeling, as it can be a powerful means to improve our understanding on the interaction of earthquake cycle processes such as interseismic, coseismic, post seismic, and aseismic slip. Observations from Interferometric Synthetic Aperture Radar (InSAR) allow us to model slip at depth with a higher spatial resolution than when using Global Navigation Satellite Systems (GNSS) alone. While the temporal resolution of InSAR has typically been limited, the recent fleet of SAR satellites including Sentinel-1, COSMO-SkyMED, and RADARSAT-2 permits the use of InSAR for time-dependent slip modeling at intervals of a few days when combined. With the vast amount of SAR data available, simultaneous data inversion of all epochs becomes challenging. Here we expanded the original network inversion filter to include InSAR observations of surface displacements in addition to GNSS. In the Network Inversion Filter (NIF) framework, geodetic observations are limited to those of a given epoch, with a stochastic model describing slip evolution over time. The combination of the Kalman forward filtering and backward smoothing allows all geodetic observations to constrain the complete observation period. Combining GNSS and InSAR allows modeling of time-dependent slip at unprecedented spatial resolution. We validate the approach with a simulation of the 2006 Guerrero slow slip event. We highlight the importance of including InSAR covariance information and demonstrate that InSAR provides an additional constraint on the spatial extent of the slow slip.
Dong, Guangzhong; Chen, Zonghai; Wei, Jingwen; Zhang, Chenbin; Wang, Peng
2016-01-01
The state-of-energy of lithium-ion batteries is an important evaluation index for energy storage systems in electric vehicles and smart grids. To improve the battery state-of-energy estimation accuracy and reliability, an online model-based estimation approach is proposed against uncertain dynamic load currents and environment temperatures. Firstly, a three-dimensional response surface open-circuit-voltage model is built up to improve the battery state-of-energy estimation accuracy, taking various temperatures into account. Secondly, a total-available-energy-capacity model that involves temperatures and discharge rates is reconstructed to improve the accuracy of the battery model. An extended-Kalman-filter and particle-filter based dual filters algorithm is then developed to establish an online model-based estimator for the battery state-of-energy. The extended-Kalman-filter is employed to update parameters of the battery model using real-time battery current and voltage at each sampling interval, while the particle-filter is applied to estimate the battery state-of-energy. Finally, the proposed approach is verified by experiments conducted on a LiFePO4 lithium-ion battery under different operating currents and temperatures. Experimental results indicate that the battery model simulates battery dynamics robustly with high accuracy, and the estimates of the dual filters converge to the real state-of-energy within an error of ±4%.
An I(2) Cointegration Model with Piecewise Linear Trends: Likelihood Analysis and Application
DEFF Research Database (Denmark)
Kurita, Takamitsu; Nielsen, Heino Bohn; Rahbæk, Anders
for the cointegration ranks, extending the result for I(2) models with a linear trend in Nielsen and Rahbek (2007) and for I(1) models with piecewise linear trends in Johansen, Mosconi, and Nielsen (2000). The provided asymptotic theory extends also the results in Johansen, Juselius, Frydman, and Goldberg (2009) where...... asymptotic inference is discussed in detail for one of the cointegration parameters. To illustrate, an empirical analysis of US consumption, income and wealth, 1965 - 2008, is performed, emphasizing the importance of a change in nominal price trends after 1980....
Can a global model reproduce observed trends in summertime surface ozone levels?
S. Koumoutsaris; I. Bey
2012-01-01
Quantifying trends in surface ozone concentrations are critical for assessing pollution control strategies. Here we use observations and results from a global chemical transport model to examine the trends (1991–2005) in daily maximum 8-hour average concentrations in summertime surface ozone at rural sites in Europe and the United States. We find a decrease in observed ozone concentrations at the high end of the probability distribution at many of the sites in both regions. The model attribut...
Directory of Open Access Journals (Sweden)
Milan Kalas
2013-11-01
Full Text Available Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases.
Kalman filter-based microphone array signal processing using the equivalent source model
Bai, Mingsian R.; Chen, Ching-Cheng
2012-10-01
This paper demonstrates that microphone array signal processing can be implemented by using adaptive model-based filtering approaches. Nearfield and farfield sound propagation models are formulated into state-space forms in light of the Equivalent Source Method (ESM). In the model, the unknown source amplitudes of the virtual sources are adaptively estimated by using Kalman filters (KFs). The nearfield array aimed at noise source identification is based on a Multiple-Input-Multiple-Output (MIMO) state-space model with minimal realization, whereas the farfield array technique aimed at speech quality enhancement is based on a Single-Input-Multiple-Output (SIMO) state-space model. Performance of the nearfield array is evaluated in terms of relative error of the velocity reconstructed on the actual source surface. Numerical simulations for the nearfield array were conducted with a baffled planar piston source. From the error metric, the proposed KF algorithm proved effective in identifying noise sources. Objective simulations and subjective experiments are undertaken to validate the proposed farfield arrays in comparison with two conventional methods. The results of objective tests indicated that the farfield arrays significantly enhanced the speech quality and word recognition rate. The results of subjective tests post-processed with the analysis of variance (ANOVA) and a post-hoc Fisher's least significant difference (LSD) test have shown great promise in the KF-based microphone array signal processing techniques.
Energy Technology Data Exchange (ETDEWEB)
Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States); Mahdi, Adam, E-mail: amahdi@ncsu.edu [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Majda, Andrew J., E-mail: jonjon@cims.nyu.edu [Department of Mathematics and Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 (United States)
2014-01-15
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.
Mikelonis, Anne M; Lawler, Desmond F; Passalacqua, Paola
2016-10-01
This research examined how variations in synthesis methods of silver nanoparticles affect both the release of silver from ceramic water filters (CWFs) and disinfection efficacy. The silver nanoparticles used were stabilized by four different molecules: citrate, polyvinylpyrrolidone, branched polyethylenimine, and casein. A multilevel statistical model was built to quantify if there was a significant difference in: a) extent of silver lost, b) initial amount of silver lost, c) silver lost for water of different quality, and d) total coliform removal. Experiments were performed on location at Pure Home Water, a CWF factory in Tamale, Ghana using stored rainwater and dugout water (a local surface water). The results indicated that using dugout vs. rainwater significantly affects the initial (p-value 0.0015) and sustained (p-value 0.0124) loss of silver, but that silver type does not have a significant effect. On average, dugout water removed 37.5μg/L more initial silver and had 1.1μg/L more silver in the filtrate than rainwater. Initially, filters achieved 1.9 log reduction values (LRVs) on average, but among different silver and water types this varied by as much as 2.5 LRV units. Overall, bacterial removal effectiveness was more challenging to evaluate, but some data suggest that the branched polyethylenimine silver nanoparticles provided improved initial bacterial removal over filters which were not painted with silver nanoparticles (p-value 0.038).
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.
A localized particle filter for data assimilation in high-dimensional geophysical models.
Poterjoy, Jonathan; Anderon, Jeffrey
2016-04-01
This talk introduces an ensemble data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. PFs make no assumptions regarding prior and posterior error distributions, allowing them to perform well for most applications provided with a sufficiently large number of particles. The proposed method is similar to the PF in that ensemble realizations of the model state are weighted based on the likelihood of observations to approximate posterior probabilities of the system state. The new approach, denoted the local PF, reduces the influence of distant observations on the weight calculations via a localization function. Unlike standard PFs, the local PF provides accurate results using ensemble sizes small enough to be affordable for large models. Comparisons of the local PF and ensemble Kalman filters using a simplified atmospheric general circulation model (with 25 particles) demonstrate that the new method is a viable data assimilation technique for large geophysical systems. The local PF also shows substantial benefits over the EnKF when observation networks consist of measurements that relate nonlinearly to the model state - analogous to remotely sensed data used frequently in atmospheric analyses.
Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter
Institute of Scientific and Technical Information of China (English)
Sheng JIN; Dian-hai WANG; Cheng XU; Dong-fang MA
2013-01-01
In this paper; a prediction model is developed that combines a Gaussian mixture model (GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision (TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts (PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.
A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows
Meldi, M.; Poux, A.
2017-10-01
A Kalman filter based sequential estimator is presented in this work. The estimator is integrated in the structure of segregated solvers for the analysis of incompressible flows. This technique provides an augmented flow state integrating available observation in the CFD model, naturally preserving a zero-divergence condition for the velocity field. Because of the prohibitive costs associated with a complete Kalman Filter application, two model reduction strategies have been proposed and assessed. These strategies dramatically reduce the increase in computational costs of the model, which can be quantified in an augmentation of 10%- 15% with respect to the classical numerical simulation. In addition, an extended analysis of the behavior of the numerical model covariance Q has been performed. Optimized values are strongly linked to the truncation error of the discretization procedure. The estimator has been applied to the analysis of a number of test cases exhibiting increasing complexity, including turbulent flow configurations. The results show that the augmented flow successfully improves the prediction of the physical quantities investigated, even when the observation is provided in a limited region of the physical domain. In addition, the present work suggests that these Data Assimilation techniques, which are at an embryonic stage of development in CFD, may have the potential to be pushed even further using the augmented prediction as a powerful tool for the optimization of the free parameters in the numerical simulation.
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.
Institute of Scientific and Technical Information of China (English)
JIN Zhi-jian; WANG Feng-hua; ZHU Zi-shu
2007-01-01
Electric arc furnaces (EAFs) represent one of the most disturbing loads in the subtransmission or transmission electric power systems. Therefore, it is necessary to build a practical model to descript the behavior of EAF in the simulation of power system for power quality issues. This paper deals with the modeling of EAF based on the combination of extended Kalman filter to identify the parameter of arc current and the power balance equation to obtain the dynamic, multi-valued u-i characteristics of EAF load. The whole EAF systems are simulated by means of power system blockset in Matlab to validate the proposed EAF model. This model can also be used to assess the impact of the new plant or highly varying nonlinear loads that exhibit chaos in power systems.
A SAS/IML program using the Kalman filter for estimating state space models.
Gu, Fei; Yung, Yiu-Fai
2013-03-01
To help disseminate the knowledge and software implementation of a state space model (SSM), this article provides a SAS/IML (SAS Institute, 2010) program for estimating the parameters of general linear Gaussian SSMs using the Kalman filter algorithm. In order to use this program, the user should have SAS installed on a computer and have a valid license for SAS/IML. Since the code is completely open, it is expected that this program can be used not only by applied researchers, but also by quantitative methodologists who are interested in improving their methods and promoting SSM as a research instrument.
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
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 se
“Modeling Trends in Air Pollutant Concentrations over the ...
Regional model calculations over annual cycles have pointed to the need for accurately representing impacts of long-range transport. Linking regional and global scale models have met with mixed success as biases in the global model can propagate and influence regional calculations and often confound interpretation of model results. Since transport is efficient in the free-troposphere and since simulations over Continental scales and annual cycles provide sufficient opportunity for “atmospheric turn-over”, i.e., exchange between the free-troposphere and the boundary-layer, a conceptual framework is needed wherein interactions between processes occurring at various spatial and temporal scales can be consistently examined. The coupled WRF-CMAQ model is expanded to hemispheric scales and model simulations over period spanning 1990-current are analyzed to examine changes in hemispheric air pollution resulting from changes in emissions over this period. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for pr
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.
Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.
Wilson, Emma D; Assaf, Tareq; Pearson, Martin J; Rossiter, Jonathan M; Dean, Paul; Anderson, Sean R; Porrill, John
2015-01-01
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.
Biohybrid control of general linear systems using the adaptive filter model of cerebellum
Directory of Open Access Journals (Sweden)
Emma D. Wilson
2015-07-01
Full Text Available The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems such as the vestibulo-ocular reflex (VOR and to sensory processing problems such as the adaptive cancellation of reafferent noise. It has also been successfully applied to problems in robotics such as adaptive camera stabilisation and sensor noise cancellation. In previous applications to inverse control problems the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity control of this plant results in unstable learning and control. To be more generally useful in engineering problems it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC scheme, which stabilises the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.
An efficient implementation of a high-order filter for a cubed-sphere spectral element model
Kang, Hyun-Gyu; Cheong, Hyeong-Bin
2017-03-01
A parallel-scalable, isotropic, scale-selective spatial filter was developed for the cubed-sphere spectral element model on the sphere. The filter equation is a high-order elliptic (Helmholtz) equation based on the spherical Laplacian operator, which is transformed into cubed-sphere local coordinates. The Laplacian operator is discretized on the computational domain, i.e., on each cell, by the spectral element method with Gauss-Lobatto Lagrange interpolating polynomials (GLLIPs) as the orthogonal basis functions. On the global domain, the discrete filter equation yielded a linear system represented by a highly sparse matrix. The density of this matrix increases quadratically (linearly) with the order of GLLIP (order of the filter), and the linear system is solved in only O (Ng) operations, where Ng is the total number of grid points. The solution, obtained by a row reduction method, demonstrated the typical accuracy and convergence rate of the cubed-sphere spectral element method. To achieve computational efficiency on parallel computers, the linear system was treated by an inverse matrix method (a sparse matrix-vector multiplication). The density of the inverse matrix was lowered to only a few times of the original sparse matrix without degrading the accuracy of the solution. For better computational efficiency, a local-domain high-order filter was introduced: The filter equation is applied to multiple cells, and then the central cell was only used to reconstruct the filtered field. The parallel efficiency of applying the inverse matrix method to the global- and local-domain filter was evaluated by the scalability on a distributed-memory parallel computer. The scale-selective performance of the filter was demonstrated on Earth topography. The usefulness of the filter as a hyper-viscosity for the vorticity equation was also demonstrated.
Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries.
Dupont, Emmanuelle; Commandeur, Jacques J F; Lassarre, Sylvain; Bijleveld, Frits; Martensen, Heike; Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George; Hermans, Elke; Pérez, Katherine; Santamariña-Rubio, Elena; Usami, Davide Shingo; Giustiniani, Gabriele
2014-10-01
In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020. Copyright © 2014 Elsevier Ltd. All rights reserved.
Past and present of analogue modelling, and its future trend
Koyi, Hemin
2015-04-01
Since Hull (1815) published his article on modelling, analogue modelling has expanded to simulate both a wider range of tectonic regimes and target more challenging set-ups, and has become an integrated part of the fields of tectonics and structural geology. Establishment of new laboratories testifies for the increased attention the technique receives. The ties between modellers and field geoscientists have become stronger with the focus being on understanding the parameters that govern the evolution of a tectonic regime and the processes that dominate it. Since the first sand castle was built with damp sand on a beach, sand has proven to be an appropriate material analogue. Even though granular materials is the most widely used analogue material, new materials are also (re)introduced as rock analogues. Emphasis has been on more precise measurements of the mechanical properties of the materials and on minimizing the preparation effects, which have a great impact on scaling, interpretations and benchmarking. The analytical technique used to quantify model results has also seen a great deal of improvement. In addition to X-ray tomography used to visualise internal structures of models, new techniques (e.g. PIV, high-resolution laser scanning, and interferometry) have enabled monitoring kinematics with a higher precision. Benchmarking exercises have given modelling an additional checking tool by outlining, in addition to the rheology of the modelling materials, the impact of different preparation approaches, the effect of boundary conditions, and the human factor on model results. However, despite the different approaches and deformation rigs, results of models of different tectonic laboratories have shown a great deal of similarities. Even with the introduction of more sophisticated numerical codes and usage of more powerful computers which enable the simulation of more challenging material properties and combinations of those, and 3D model set-up, analogue modelling
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.
Ruffio, Jean-Baptiste; Macintosh, Bruce; Wang, Jason J.; Pueyo, Laurent; Nielsen, Eric L.; De Rosa, Robert J.; Czekala, Ian; Marley, Mark S.; Arriaga, Pauline; Bailey, Vanessa P.; Barman, Travis; Bulger, Joanna; Chilcote, Jeffrey; Cotten, Tara; Doyon, Rene; Duchêne, Gaspard; Fitzgerald, Michael P.; Follette, Katherine B.; Gerard, Benjamin L.; Goodsell, Stephen J.; Graham, James R.; Greenbaum, Alexandra Z.; Hibon, Pascale; Hung, Li-Wei; Ingraham, Patrick; Kalas, Paul; Konopacky, Quinn; Larkin, James E.; Maire, Jérôme; Marchis, Franck; Marois, Christian; Metchev, Stanimir; Millar-Blanchaer, Maxwell A.; Morzinski, Katie M.; Oppenheimer, Rebecca; Palmer, David; Patience, Jennifer; Perrin, Marshall; Poyneer, Lisa; Rajan, Abhijith; Rameau, Julien; Rantakyrö, Fredrik T.; Savransky, Dmitry; Schneider, Adam C.; Sivaramakrishnan, Anand; Song, Inseok; Soummer, Remi; Thomas, Sandrine; Wallace, J. Kent; Ward-Duong, Kimberly; Wiktorowicz, Sloane; Wolff, Schuyler
2017-06-01
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.
Goodsite, M E; Outridge, P M; Christensen, J H; Dastoor, A; Muir, D; Travnikov, O; Wilson, S
2013-05-01
This review compares the reconstruction of atmospheric Hg deposition rates and historical trends over recent decades in the Arctic, inferred from Hg profiles in natural archives such as lake and marine sediments, peat bogs and glacial firn (permanent snowpack), against those predicted by three state-of-the-art atmospheric models based on global Hg emission inventories from 1990 onwards. Model veracity was first tested against atmospheric Hg measurements. Most of the natural archive and atmospheric data came from the Canadian-Greenland sectors of the Arctic, whereas spatial coverage was poor in other regions. In general, for the Canadian-Greenland Arctic, models provided good agreement with atmospheric gaseous elemental Hg (GEM) concentrations and trends measured instrumentally. However, there are few instrumented deposition data with which to test the model estimates of Hg deposition, and these data suggest models over-estimated deposition fluxes under Arctic conditions. Reconstructed GEM data from glacial firn on Greenland Summit showed the best agreement with the known decline in global Hg emissions after about 1980, and were corroborated by archived aerosol filter data from Resolute, Nunavut. The relatively stable or slowly declining firn and model GEM trends after 1990 were also corroborated by real-time instrument measurements at Alert, Nunavut, after 1995. However, Hg fluxes and trends in northern Canadian lake sediments and a southern Greenland peat bog did not exhibit good agreement with model predictions of atmospheric deposition since 1990, the Greenland firn GEM record, direct GEM measurements, or trends in global emissions since 1980. Various explanations are proposed to account for these discrepancies between atmosphere and archives, including problems with the accuracy of archive chronologies, climate-driven changes in Hg transfer rates from air to catchments, waters and subsequently into sediments, and post-depositional diagenesis in peat bogs
Naive Bayesian for Email Filtering
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The paper presents a method of email filter based on Naive Bayesian theory that can effectively filter junk mail and illegal mail. Furthermore, the keys of implementation are discussed in detail. The filtering model is obtained from training set of email. The filtering can be done without the users specification of filtering rules.
REAL-TIME FLOOD FORECASTING MODELING OF 1D UNSTEADY CHANNEL FLOW AND KALMAN FILTER
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The model of 1D unsteady channel flow combined with the Kalmanfilter for real-time channel flood forecasting was attempted in this study. The suitable upstream and downstream boundary conditions were suggested. The system equation was given by the linearization of the finitedifference equations of the mass conservation and momentum equations as well as the boundary conditions. In the Kalman filter updating model, because the number of measurement variable is less then that of state-space variables, the measurement error covariance matrix could be estimated in real time through the innovation sequence, and the system error covariance matrix needs to be estimated preliminarily. A real example of flood forecasting in the Huaihe River was given to explain how the method works. The results show that the model is reasonable and effective.
The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere
Shin, Seoleun; Kang, Ji-Sun; Jo, Youngsoon
2016-07-01
We develop an ensemble data assimilation system using the four-dimensional local ensemble transform kalman filter (LEKTF) for a global hydrostatic numerical weather prediction (NWP) model formulated on the cubed sphere. Forecast-analysis cycles run stably and thus provide newly updated initial states for the model to produce ensemble forecasts every 6 h. Performance of LETKF implemented to the global NWP model is verified using the ECMWF reanalysis data and conventional observations. Global mean values of bias and root mean square difference are significantly reduced by the data assimilation. Besides, statistics of forecast and analysis converge well as the forecast-analysis cycles are repeated. These results suggest that the combined system of LETKF and the global NWP formulated on the cubed sphere shows a promising performance for operational uses.
Adaptive update using visual models for lifting-based motion-compensated temporal filtering
Li, Song; Xiong, H. K.; Wu, Feng; Chen, Hong
2005-03-01
Motion compensated temporal filtering is a useful framework for fully scalable video compression schemes. However, when supposed motion models cannot represent a real motion perfectly, both the temporal high and the temporal low frequency sub-bands may contain artificial edges, which possibly lead to a decreased coding efficiency, and ghost artifacts appear in the reconstructed video sequence at lower bit rates or in case of temporal scaling. We propose a new technique that is based on utilizing visual models to mitigate ghosting artifacts in the temporal low frequency sub-bands. Specifically, we propose content adaptive update schemes where visual models are used to determine image dependent upper bounds on information to be updated. Experimental results show that the proposed algorithm can significantly improve subjective visual quality of the low-pass temporal frames and at the same time, coding performance can catch or exceed the classical update steps.
Model-Based Hand Tracking by Chamfer Distance and Adaptive Color Learning Using Particle Filter
Directory of Open Access Journals (Sweden)
Kerdvibulvech Chutisant
2009-01-01
Full Text Available We propose a new model-based hand tracking method for recovering of three-dimensional hand motion from an image sequence. We first build a three-dimensional hand model using truncated quadrics. The degrees of freedom (DOF for each joint correspond to the DOF of a real hand. This feature extraction is performed by using the Chamfer Distance function for the edge likelihood. The silhouette likelihood is performed by using a Bayesian classifier and the online adaptation of skin color probabilities. Therefore, it is to effectively deal with any illumination changes. Particle filtering is used to track the hand by predicting the next state of three-dimensional hand model. By using these techniques, this method adds the useful ability of automatic recovery from tracking failures. This method can also be used to track the guitarist's hand.
Directory of Open Access Journals (Sweden)
J. Rasmussen
2015-02-01
Full Text Available 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, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common local analysis localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1 more ensemble members are needed when fewer groundwater head observations are assimilated, and (2 assimilating discharge observations 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 local analysis localization.
Modelling trends in tropical column ozone with the UKCA chemistry-climate model
Keeble, James; Bednarz, Ewa; Banerjee, Antara; Abraham, Luke; Harris, Neil; Maycock, Amanda; Pyle, John
2016-04-01
Trends in tropical column ozone under a number of different emissions scenarios are explored with the UM-UKCA coupled chemistry climate model. A transient 1960-2100 simulation was run following the RCP6 scenario. Tropical averaged (10S-10N) total column ozone values decrease from the 1970s, reaching a minimum around 2000, and return to their 1980 values around 2040, consistent with the use and emission of ozone depleting substances, and their later controls under the Montreal Protocol. However, when the total column is subdivided into three partial columns, extending from the surface to the tropopause, the tropopause to 30km, and 30km to 50km, significant differences to the total column trend are seen. Modelled tropospheric column values increase from 1960-2000 before remaining steady throughout the 21st Century. Lower stratospheric column values decrease rapidly from 1960-2000, remain steady until 2050 before slowly decreasing to 2100, never recovering to their 1980s values. Upper stratospheric values decrease from 1960-2000, before rapidly increasing throughout the 21st Century, recovering to 1980s values by ~2020 and are significantly increased above the 1980s values by 2100. Using a series of idealised model simulations with varying concentrations of greenhouse gases and ozone depleting substances, we assess the physical processes driving the partial column response in the troposphere, lower stratosphere and upper stratosphere, and assess how these processes change under different emissions scenarios. Finally, we present a simple, linearised model for predicting tropical column ozone values based on greenhouse gas and ozone depleting substance scenarios.
Innocenti, Alessio; Marchioli, Cristian; Chibbaro, Sergio
2016-11-01
The Eulerian-Lagrangian approach based on Large-Eddy Simulation (LES) is one of the most promising and viable numerical tools to study particle-laden turbulent flows, when the computational cost of Direct Numerical Simulation (DNS) becomes too expensive. The applicability of this approach is however limited if the effects of the Sub-Grid Scales (SGSs) of the flow on particle dynamics are neglected. In this paper, we propose to take these effects into account by means of a Lagrangian stochastic SGS model for the equations of particle motion. The model extends to particle-laden flows the velocity-filtered density function method originally developed for reactive flows. The underlying filtered density function is simulated through a Lagrangian Monte Carlo procedure that solves a set of Stochastic Differential Equations (SDEs) along individual particle trajectories. The resulting model is tested for the reference case of turbulent channel flow, using a hybrid algorithm in which the fluid velocity field is provided by LES and then used to advance the SDEs in time. The model consistency is assessed in the limit of particles with zero inertia, when "duplicate fields" are available from both the Eulerian LES and the Lagrangian tracking. Tests with inertial particles were performed to examine the capability of the model to capture the particle preferential concentration and near-wall segregation. Upon comparison with DNS-based statistics, our results show improved accuracy and considerably reduced errors with respect to the case in which no SGS model is used in the equations of particle motion.
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.
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 ...
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.
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.
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)
J. Xu
2013-10-01
Full Text Available On the basis of the fifth Coupled Model Intercomparison Project (CMIP5 and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3 simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (radiosonde, reanalysis, CMIP3 and CMIP5, although similarities can be observed. Compared to the CMIP3 model simulations, the simulations in some of the CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the
Saito, Naritatsu; Shimamoto, Takeshi; Takeda, Takahide; Marui, Akira; Kimura, Takeshi; Ikeda, Tadashi; Sakata, Ryuzo
2010-05-01
Although most Günther Tulip filters (GTFs) can be safely retrieved within a few months after implantation, their recommended safe retrieval period is within a few weeks. This study aims to assess the feasibility of excimer laser-assisted retrieval of GTFs incorporated into the inferior vena cava (IVC) wall in a canine model. Six GTFs were implanted in six mongrel dogs and retrieved after four weeks. The retrieval system consisted of a 14-F excimer laser sheath, an 8-F guide catheter, and a 15-mm Goose Neck snare. All filters were tightly fixed to the IVC wall. After ablation of the adhesions by excimer laser emission, all filters were successfully retrieved. Final cavography after retrieval revealed no caval damage except for minor extravasation in three dogs. Examination of the caval specimen taken from a dog immediately after filter retrieval revealed partial absence of the intima and media. In the remaining five dogs, cavography performed 2 days after filter retrieval revealed complete hemostasis and almost indistinguishable intimal indentations. On follow-up cavography 28 days after filter retrieval, caval stenosis with 38% +/- 11% diameter narrowing was noted. The caval specimen obtained from a dog at 28 days showed neointima formation at the level where the filter struts were in contact with the caval wall. The other four dogs have survived for more than 3 months without any adverse events. Laser-assisted retrieval of a GTF incorporated into the IVC wall is feasible in dogs.
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.
Statistical modeling and trend detection of extreme sea level records in the Pearl River Estuary
Wang, Weiwen; Zhou, Wen
2017-03-01
Sea level rise has become an important issue in global climate change studies. This study investigates trends in sea level records, particularly extreme records, in the Pearl River Estuary, using measurements from two tide gauge stations in Macau and Hong Kong. Extremes in the original sea level records (daily higher high water heights) and in tidal residuals with and without the 18.6-year nodal modulation are investigated separately. Thresholds for defining extreme sea levels are calibrated based on extreme value theory. Extreme events are then modeled by peaks-over-threshold models. The model applied to extremes in original sea level records does not include modeling of their durations, while a geometric distribution is added to model the duration of extremes in tidal residuals. Realistic modeling results are recommended in all stationary models. Parametric trends of extreme sea level records are then introduced to nonstationary models through a generalized linear model framework. The result shows that, in recent decades, since the 1960s, no significant trends can be found in any type of extreme at any station, which may be related to a reduction in the influence of tropical cyclones in the region. For the longer-term record since the 1920s at Macau, a regime shift of tidal amplitudes around the 1970s may partially explain the diverse trend of extremes in original sea level records and tidal residuals.
Hiremath, K.R.; Lohmeyer, Manfred
2007-01-01
Microresonator filters, realized by evanescent coupling of circular cavities with two parallel bus waveguides, are promising candidates for applications in dense wavelength division multiplexing. Tunability of these filters is an essential feature for their successful deployment. In this paper we
Bartolomeu, S.; Carvalho, M. J.; Marta-Almeida, M.; Melo-Gonçalves, P.; Rocha, A.
2016-08-01
Spatial and temporal distributions of the trends of extreme precipitation indices were analysed between 1986 and 2005, over the Iberian Peninsula (IP). The knowledge of the patterns of extreme precipitation is important for impacts assessment, development of adaptation and mitigation strategies. As such, there is a growing need for a more detailed knowledge of precipitation climate change. This analysis was performed for Portuguese and Spanish observational datasets and results performed by the Weather Research and Forecast (WRF) model forced by the ERA-Interim reanalysis. Extreme precipitation indices recommended by the Expert Team for Climate Change Detection Monitoring and Indices were computed, by year and season. Then, annual and seasonal trends of the indices were estimated by Theil-Sen method and their significance was tested by the Mann-Kendal test. Additionally, a second simulation forced by the Max Planck Institute Earth System Model (MPI-ESM), was considered. This second modelling configuration was created in order to assess its performance when simulating extremes of precipitation. The annual trends estimated for the 1986-2005, from the observational datasets and from the ERA-driven simulation reveal: 1) negative statistically significant trends of the CWD index in the Galicia and in the centre of the IP; 2) positive statistically significant trends of the CDD index over the south of the IP and negative statistically significant trends in Galicia, north and centre of Portugal; 3) positive statistically significant trends of the R75p index in some regions of the north of the IP; 4) positive statistically significant trends in the R95pTOT index in the Central Mountains Chain, Leon Mountains and in the north of Portugal. Seasonally, negative statistically significant trends of the CWD index were found in Galicia, in winter and in the south of the IP, in summer. Positive statistically significant trends of the CWD index were identified in the Leon Mountains
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
2012-05-01
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 for the entire time series is 4.6% 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 during summer (3.4% 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 contributed to the tropospheric ozone trend over Beijing during the last decade.
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.
Energy Technology Data Exchange (ETDEWEB)
Sarkar, Avik; Milioli, Fernando E.; Ozarkar, Shailesh; Li, Tingwen; Sun, Xin; Sundaresan, Sankaran
2016-10-01
The accuracy of fluidized-bed CFD predictions using the two-fluid model can be improved significantly, even when using coarse grids, by replacing the microscopic kinetic-theory-based closures with coarse-grained constitutive models. These coarse-grained constitutive relationships, called filtered models, account for the unresolved gas-particle structures (clusters and bubbles) via sub-grid corrections. Following the previous 2-D approaches of Igci et al. [AIChE J., 54(6), 1431-1448, 2008] and Milioli et al. [AIChE J., 59(9), 3265-3275, 2013], new filtered models are constructed from highly-resolved 3-D simulations of gas-particle flows. Although qualitatively similar to the older 2-D models, the new 3-D relationships exhibit noticeable quantitative and functional differences. In particular, the filtered stresses are strongly dependent on the gas-particle slip velocity. Closures for the filtered inter-phase drag, gas- and solids-phase pressures and viscosities are reported. A new model for solids stress anisotropy is also presented. These new filtered 3-D constitutive relationships are better suited to practical coarse-grid 3-D simulations of large, commercial-scale devices.
In vitro identification of four-element windkessel models based on iterated unscented Kalman filter.
Huang, Huan; Yang, Ming; Zang, Wangfu; Wu, Shunjie; Pang, Yafei
2011-09-01
Mock circulatory loops (MCLs) have been widely used to test left ventricular assist devices. The hydraulic properties of the mock systemic arterial system are usually described by two alternative four-element windkessel (W4) models. Compared with three-element windkessel model, their parameters, especially the inertial term, are much more difficult to estimate. In this paper, an estimator based on the iterated unscented Kalman filter (IUKF) algorithm is proposed to identify model parameters. Identifiability of these parameters for different measurements is described. Performance of the estimator for different model structures is first evaluated using numerical simulation data contaminated with artificial noise. An MCL is developed to test the proposed algorithm. Parameter estimates for different models are compared with the calculated values derived from the mechanical and hydraulic properties of the MCL to validate model structures. In conclusion, the W4 model with an inertance and an aortic characteristic resistance arranged in series is proposed to represent the mock systemic arterial system. Once model structure is appropriately selected, IUKF can provide reasonable estimation accuracy in a limited time and may be helpful for future clinical applications.
Modelled long term trends of surface ozone over South Africa
CSIR Research Space (South Africa)
Naidoo, M
2011-09-01
Full Text Available /CAMx CAMx in NRE MM5 Past Future ? Retrospective Air quality ? CSIR 2010 Slide 5 New framework for air quality forecast ? CCAM/CAMx CAMx in NRE CCAM Past Future Future Air quality ? CSIR 2010 Slide 6 New research - air quality forecast... Current research focus ? The response of air quality to changes in climate ? Simulations on longer time scales ? Drive air quality models with long term forecasted meteorology ? Need a baseline (1989 ? 2009) ? To date: Initial testing and 2 years...
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.
The Environmental Technology Verification report discusses the technology and performance of the Predator II, Model 8VADTP123C23CC000 air filter for dust and bioaerosol filtration manufactured by Tri-Dim Filter Corporation. The pressure drop across the filter was 138 Pa clean and...
Causal Modeling--Path Analysis a New Trend in Research in Applied Linguistics
Rastegar, Mina
2006-01-01
This article aims at discussing a new statistical trend in research in applied linguistics. This rather new statistical procedure is causal modeling--path analysis. The article demonstrates that causal modeling--path analysis is the best statistical option to use when the effects of a multitude of L2 learners' variables on language achievement are…
Godinez, Humberto C; Fierro, Alexandre O; Guimond, Stephen R; Kao, Jim
2011-01-01
In this work we present the assimilation of dual-Doppler radar observations for rapidly intensifying hurricane Guillermo (1997) using the Ensemble Kalman Filter (EnKF) to determine key model parameters. A unique aspect of Guillermo was that during the period of radar observations strong convective bursts, attributable to wind shear, formed primarily within the eastern semicircle of the eyewall. To reproduce this observed structure within a hurricane model, background wind shear of some magnitude must be specified; as well as turbulence and surface parameters appropriately specified so that the impact of the shear on the simulated hurricane vortex can be realized. To first illustrate the complex nonlinear interactions induced by changes in these parameters, an ensemble of 120 simulations have been conducted in which individual members were formulated by sampling the parameters within a certain range via a Latin hypercube approach. Next, data from the 120 simulations and two distinct derived fields of observati...
Analysis of Process Mining Model Using Frequentgroup Based Noise Filtering Algorithm
Directory of Open Access Journals (Sweden)
V. Priyadharshini
2014-02-01
Full Text Available Process mining is a process management system used to analyze business processes based on event logs. The knowledge is extracted from event logs by using knowledge retrieval techniques. The process mining algorithms are capable of automatically discover models to give details of all the events registered in some log traces provided as input. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets from a set of traces. The main objective of this paper is to propose new concept Frequentgroup based noise filtering algorithm. The experiment is done based on standard bench mark dataset HELIX and RALIC datasets. The performance of the proposed system is better than existing method. Keywords:
Two-mode model for metal-dielectric guided-mode resonance filters.
Tuambilangana, Christelle; Pardo, Fabrice; Sakat, Emilie; Bouchon, Patrick; Pelouard, Jean-Luc; Haïdar, Riad
2015-12-14
Symmetric metal-dielectric guided-mode resonators (GMR) can operate as infrared band-pass filters, thanks to high-transmission resonant peaks and good rejection ratio. Starting from matrix formalism, we show that the behavior of the system can be described by a two-mode model. This model reduces to a scalar formula and the GMR is described as the combination of two independent Fabry-Perot resonators. The formalism has then been applied to the case of asymmetric GMR, in order to restore the properties of the symmetric system. This result allows designing GMR-on-substrate as efficient as free-standing systems, the same high transmission maximum value and high quality factor being conserved.
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
A comparison of ozone trends from SME and SBUV satellite observations and model calculations
Rusch, D. W.; Clancy, R. T.
1988-01-01
Data on monthly ozone abundance trends near the stratopause, observed by the Ultraviolet Spectrometer (UVS) on the SME and by the Solar Backscatter Ultraviolet Instrument (SBUV) on NIMBUS-7 are presented for June, September, and January of the years 1982-1986. Globally averaged trends determined from the SME data (-0.5 + or - 1.3 percent/yr) were found to fall within model calculations by Rusch and Clancy (1988); the SBUV trends, on the other hand, were found to exceed maximum predicted ozone decreases by a factor of 3 or more. Detailed comparison of the two data sets indicated that an absolute offset of 3 percent/yr accounts for much of the difference between the two trends; the offset is considered to be due to incomplete characterization of the SBUV calibration drift. Both the UVS and SBUV data exhibited similar seasonal and latitudinal variations in ozone trends, which were reproduced by photochemical model calculations that included latitude-dependent NMC temperature trends over the 1982-1986 period.
Application of multivariate storage model to quantify trends in seasonally frozen soil
Directory of Open Access Journals (Sweden)
Woody Jonathan
2016-06-01
Full Text Available This article presents a study of the ground thermal regime recorded at 11 stations in the North Dakota Agricultural Network. Particular focus is placed on detecting trends in the annual ground freeze process portion of the ground thermal regime’s daily temperature signature. A multivariate storage model from queuing theory is fit to a quantity of estimated daily depths of frozen soil. Statistical inference on a trend parameter is obtained by minimizing a weighted sum of squares of a sequence of daily one-step-ahead predictions. Standard errors for the trend estimates are presented. It is shown that the daily quantity of frozen ground experienced at these 11 sites exhibited a negative trend over the observation period.
Application of multivariate storage model to quantify trends in seasonally frozen soil
Woody, Jonathan; Wang, Yan; Dyer, Jamie
2016-06-01
This article presents a study of the ground thermal regime recorded at 11 stations in the North Dakota Agricultural Network. Particular focus is placed on detecting trends in the annual ground freeze process portion of the ground thermal regime's daily temperature signature. A multivariate storage model from queuing theory is fit to a quantity of estimated daily depths of frozen soil. Statistical inference on a trend parameter is obtained by minimizing a weighted sum of squares of a sequence of daily one-step-ahead predictions. Standard errors for the trend estimates are presented. It is shown that the daily quantity of frozen ground experienced at these 11 sites exhibited a negative trend over the observation period.
Directory of Open Access Journals (Sweden)
Hong-jun BAO
2011-03-01
Full Text Available A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning’s roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning’s roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning’s roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.
Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering
El Gharamti, Mohamad
2013-10-01
Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the
Directory of Open Access Journals (Sweden)
Romit Maulik
2017-04-01
Full Text Available Solving two-dimensional compressible turbulence problems up to a resolution of 16, 384^2, this paper investigates the characteristics of two promising computational approaches: (i an implicit or numerical large eddy simulation (ILES framework using an upwind-biased fifth-order weighted essentially non-oscillatory (WENO reconstruction algorithm equipped with several Riemann solvers, and (ii a central sixth-order reconstruction framework combined with various linear and nonlinear explicit low-pass spatial filtering processes. Our primary aim is to quantify the dissipative behavior, resolution characteristics, shock capturing ability and computational expenditure for each approach utilizing a systematic analysis with respect to its modeling parameters or parameterizations. The relative advantages and disadvantages of both approaches are addressed for solving a stratified Kelvin-Helmholtz instability shear layer problem as well as a canonical Riemann problem with the interaction of four shocks. The comparisons are both qualitative and quantitative, using visualizations of the spatial structure of the flow and energy spectra, respectively. We observe that the central scheme, with relaxation filtering, offers a competitive approach to ILES and is much more computationally efficient than WENO-based schemes.
A scaffold-filter model for studying the chondrogenic differentiation of stem cells in vitro.
Zhang, Ling; Zheng, Li; Fan, Hong S; Zhang, Xing D
2017-01-01
This study was undertaken to explore the synergistic effect of scaffold materials and a cartilage-like environment on the chondrogenic differentiation of stem cells. Because stem cells encapsulated in a cartilage scaffold will be induced by scaffold molecules as well as permeable molecules from the surroundings, it is impossible to optimize a chondro-inducible scaffold without considering environmental sensitivity. How do we know if a designed scaffold will be sufficient prior to implantation? In this study, bone marrow mesenchymal stem cells (bMSCs) were seeded in various scaffolds, including collagen hydrogel, collage/sodium alginate hydrogel, collagen sponge and silk fibroin sponge. The cell-scaffold complex was encapsulated in a filter pocket to avoid direct contact with co-cultured chondrocytes. Scaffolds differed in the ability to adsorb inducible molecules expressed by chondrocytes, as evidenced by various expressions of cartilage specific proteins and genes. Collagen hydrogel unexpectedly supported chondrogenic differentiation in an environment filled with chondrocytes secretion better than other reinforced scaffolds, which is consistent with the previous experiment in vivo. This result indicated that the environmental sensitivity of a scaffold is important for in vivo chondro-induction. This in vitro scaffold-filter model may be useful as a precursor to investigate the chondro-inducing potential of various scaffolds for cartilage repair.
Variational B-spline level-set: a linear filtering approach for fast deformable model evolution.
Bernard, Olivier; Friboulet, Denis; Thévenaz, Philippe; Unser, Michael
2009-06-01
In the field of image segmentation, most level-set-based active-contour approaches take advantage of a discrete representation of the associated implicit function. We present in this paper a different formulation where the implicit function is modeled as a continuous parametric function expressed on a B-spline basis. Starting from the active-contour energy functional, we show that this formulation allows us to compute the solution as a restriction of the variational problem on the space spanned by the B-splines. As a consequence, the minimization of the functional is directly obtained in terms of the B-spline coefficients. We also show that each step of this minimization may be expressed through a convolution operation. Because the B-spline functions are separable, this convolution may in turn be performed as a sequence of simple 1-D convolutions, which yields an efficient algorithm. As a further consequence, each step of the level-set evolution may be interpreted as a filtering operation with a B-spline kernel. Such filtering induces an intrinsic smoothing in the algorithm, which can be controlled explicitly via the degree and the scale of the chosen B-spline kernel. We illustrate the behavior of this approach on simulated as well as experimental images from various fields.
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.
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.
Non-intrusive Ensemble Kalman filtering for large scale geophysical models
Amour, Idrissa; Kauranne, Tuomo
2016-04-01
Advanced data assimilation techniques, such as variational assimilation methods, present often challenging implementation issues for large-scale models, both because of computational complexity and because of complexity of implementation. We present a non-intrusive wrapper library that addresses this problem by isolating the direct model and the linear algebra employed in data assimilation from each other completely. In this approach we have adopted a hybrid Variational Ensemble Kalman filter that combines Ensemble propagation with a 3DVAR analysis stage. The inverse problem of state and covariance propagation from prior to posterior estimates is thereby turned into a time-independent problem. This feature allows the linear algebra and minimization steps required in the variational step to be conducted outside the direct model and no tangent linear or adjoint codes are required. Communication between the model and the assimilation module is conducted exclusively via standard input and output files of the model. This non-intrusive approach is tested with the comprehensive 3D lake and shallow sea model COHERENS that is used to forecast and assimilate turbidity in lake Säkylän Pyhäjärvi in Finland, using both sparse satellite images and continuous real-time point measurements as observations.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
There are two kinds of methods in researching the crust deformation: geophysical method and geometrical (or observational) method. Considerable differences usually exist between the two kinds of results, because of the datum differences, geophysical model errors, observational model errors, and so on. Thus, it is reasonable to combine the two kinds of information to collect the crust deformation information. To use the reliable geometrical and geophysical information, we have to control the observational and geophysical model error influences on the estimated deformation parameters, and to balance their contributions to the evaluated parameters. A hybrid estimation strategy is proposed here for evaluating the deformation parameters employing an adaptively robust filtering. The effects of measurement outliers on the estimated parameters are controlled by robust equivalent weights. Adaptive factors are introduced to balance the contribution of the geophysical model information and the geometrical measurements to the model parameters. The datum for the local deformation analysis is mainly determined by the highly accurate IGS station velocities. The hybrid estimation strategy is applied in an actual GPS monitoring network. It is shown that the hybrid technique employs locally repeated geometrical displacements to reduce the displacement errors caused by the mis-modeling of geophysical technique, and thus improves the precision of the estimated crust deformation parameters.
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.
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.
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.
Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter.
Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei
2016-11-02
Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system's error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the
Trend modelling of wave parameters and application in onboard prediction of ship responses
DEFF Research Database (Denmark)
Montazeri, Najmeh; Nielsen, Ulrik Dam; Jensen, J. Juncher
2015-01-01
This paper presents a trend analysis for prediction of sea state parameters onboard shipsduring voyages. Given those parameters, a JONSWAP model and also the transfer functions, prediction of wave induced ship responses are thus made. The procedure is tested with full-scale data of an in-service ......This paper presents a trend analysis for prediction of sea state parameters onboard shipsduring voyages. Given those parameters, a JONSWAP model and also the transfer functions, prediction of wave induced ship responses are thus made. The procedure is tested with full-scale data of an in...
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......Longevity has been increasing in the developed countries for almost two centuries and further increases are expected in the future. In the neoclassical growth models the case of population growth driven by fertility is well-known, whereas the properties of population growth caused by persistently...
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 st
Purich, Ariaan; Cai, Wenju; England, Matthew H.; Cowan, Tim
2016-02-01
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.
Sun, Xiaodian; Jin, Li; Xiong, Momiao
2008-01-01
It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.
Global epidemic trend of tuberculosis during 1990-2010: using segmented regression model.
Kazemnejad, Anoushiravan; Arsang Jang, Shahram; Amani, Firouz; Omidi, Alireza
2014-01-01
Tuberculosis (TB) is a pandemic disease. It is the second leading cause of death from infectious diseases after human immunodeficiency virus (HIV) in the world.The main objective of this paper was to determine and compare the epidemiology of TB incidence rate and its trend changes during 1990-2010 in six WHO regions regarding age, gender and income levels. The Average Annual Percent Change (AAPC) and Annual Percent Change (APC) of TB incidence, mortality, treatment-successes, case detection rates, as well as change points of trend was estimated using segmented regression model. The number of change points was selected by the permutation procedure based on likelihood ratio test. Two change points for global TB incidence rate trend with AAPC5years equaling -1.4 % was estimated, the maximum AAPC5years of six regions was attributed to the American region (-3.5%). AACP of TB treatment-successes rate for Eastern Mediterranean (+2.2), the Americas (+1.6), south East Asia (+.8) and Global (+1.1) were significant (P<0.05). Moreover AACP5years of TB case detection rate for South East Asia (+7.5), Eastern Mediterranean (+4.9), Africa (+2.8) and the Americas (+1.7) were significant (P<0.05). Globally, all of income categories had descending trend of TB incidence and mortality rate, except the upper-middle income level that had ascending incidence trend (AAPC=+0.7%). Globally, TB incidence and mortality rates have downturn trend and TB treatment successes and detection rates have upward trend, but their changes rate are insufficient to reach the goal of TB stop strategy. The economic levels have effect on trend, with no clear pattern, so it seems necessary that evaluation TB control programs based on characteristics of countries for reach TB control goals.
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
Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study
Directory of Open Access Journals (Sweden)
Forster Alan J
2011-07-01
Full Text Available Abstract Background Clinicians informally assess changes in patients' status over time to prognosticate their outcomes. The incorporation of trends in patient status into regression models could improve their ability to predict outcomes. In this study, we used a unique approach to measure trends in patient hospital death risk and determined whether the incorporation of these trend measures into a survival model improved the accuracy of its risk predictions. Methods We included all adult inpatient hospitalizations between 1 April 2004 and 31 March 2009 at our institution. We used the daily mortality risk scores from an existing time-dependent survival model to create five trend indicators: absolute and relative percent change in the risk score from the previous day; absolute and relative percent change in the risk score from the start of the trend; and number of days with a trend in the risk score. In the derivation set, we determined which trend indicators were associated with time to death in hospital, independent of the existing covariates. In the validation set, we compared the predictive performance of the existing model with and without the trend indicators. Results Three trend indicators were independently associated with time to hospital mortality: the absolute change in the risk score from the previous day; the absolute change in the risk score from the start of the trend; and the number of consecutive days with a trend in the risk score. However, adding these trend indicators to the existing model resulted in only small improvements in model discrimination and calibration. Conclusions We produced several indicators of trend in patient risk that were significantly associated with time to hospital death independent of the model used to create them. In other survival models, our approach of incorporating risk trends could be explored to improve their performance without the collection of additional data.
Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model.
Shuaiqi, Liu; Jie, Zhao; Mingzhu, Shi
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF) and spiking cortical model (SCM). Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.
Directory of Open Access Journals (Sweden)
Deguang Wang
2011-02-01
Full Text Available Intrusion detection is a computer network system that collects information on several key points. and it gets these information from the security audit, monitoring, attack recognition and response aspects, check if there are some the behavior and signs against the network security policy. The classification of data acquisition is a key part of intrusion detection. In this article, we use the data cloud model to classify the invasion, effectively maintaining a continuous data on the qualitative ambiguity of the concept and evaluation phase of the invasion against the use of the coordination level filtering recommendation algorithm greatly improves the intrusion detection system in the face of massive data processing efficiency suspicious intrusion.
New efficient optimizing techniques for Kalman filters and numerical weather prediction models
Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis
2016-06-01
The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.
Modeling and Testing of a PV/T hybrid system with Water based Optical Filter
Directory of Open Access Journals (Sweden)
Sachin Gupta
2015-12-01
Full Text Available A theoretical model has been developed for the Non-imaging V-trough hybrid PV/T concentrator systems along with optical filter and validated with the designed and fabricated system to assess over all thermal efficiency of the PV/T system. A V-trough concentrator system has been developed for two axes tracking. Commercially available solar modules were evaluated for their usability under 2-sun concentration. V-trough concentrator with geometric concentration ratio of 2 (2-sun, we are getting an average overall efficiency of the PV/T system increased by 23.54 % extra overall thermal efficiency of the PV/T system as compared to the solar module efficiency at standard test conditions.
First stage of LISA data processing. II. Alternative filtering dynamic models for LISA
Wang, Yan; Heinzel, Gerhard; Danzmann, Karsten
2015-08-01
Space-borne gravitational wave detectors, such as (e)LISA, are designed to operate in the low-frequency band (mHz to Hz), where there is a variety of gravitational wave sources of great scientific value [arXiv:1305.5720 and S. Babak et al., Classical Quantum Gravity 28, 114001 (2011)]. To achieve the extraordinary sensitivity of these detectors, the precise synchronization of the clocks on the separate spacecraft and the accurate determination of the interspacecraft distances are important ingredients. In our previous paper [Y. Wang et al., Phys. Rev. D 90, 064016 (2014)], we have described a hybrid-extend Kalman filter with a full state vector to do this job. In this paper, we explore several different state vectors and their corresponding (phenomenological) dynamic models to reduce the redundancy in the full state vector, to accelerate the algorithm, and to make the algorithm easily extendable to more complicated scenarios.
First stage of LISA data processing II: Alternative filtering dynamic models for LISA
Wang, Yan; Danzmann, Karsten
2015-01-01
Space-borne gravitational wave detectors, such as (e)LISA, are designed to operate in the low-frequency band (mHz to Hz), where there is a variety of gravitational wave sources of great scientific value. To achieve the extraordinary sensitivity of these detector, the precise synchronization of the clocks on the separate spacecraft and the accurate determination of the interspacecraft distances are important ingredients. In our previous paper (Phys. Rev. D 90, 064016 [2014]), we have described a hybrid-extend Kalman filter with a full state vector to do this job. In this paper, we explore several different state vectors and their corresponding (phenomenological) dynamic models, to reduce the redundancy in the full state vector, to accelerate the algorithm, and to make the algorithm easily extendable to more complicated scenarios.
Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model
Directory of Open Access Journals (Sweden)
Liu Shuaiqi
2015-01-01
Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF and spiking cortical model (SCM. Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.
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.g., speech in
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
Population trends for North American winter birds based on hierarchical models
Soykan, Candan U.; Sauer, John; Schuetz, Justin G.; LeBaron, Geoffrey S.; Dale, Kathy; Langham, Gary M.
2016-01-01
Managing widespread and persistent threats to birds requires knowledge of population dynamics at large spatial and temporal scales. For over 100 yrs, the Audubon Christmas Bird Count (CBC) has enlisted volunteers in bird monitoring efforts that span the Americas, especially southern Canada and the United States. We employed a Bayesian hierarchical model to control for variation in survey effort among CBC circles and, using CBC data from 1966 to 2013, generated early-winter population trend estimates for 551 species of birds. Selecting a subset of species that do not frequent bird feeders and have ≥25% range overlap with the distribution of CBC circles (228 species) we further estimated aggregate (i.e., across species) trends for the entire study region and at the level of states/provinces, Bird Conservation Regions, and Landscape Conservation Cooperatives. Moreover, we examined the relationship between ten biological traits—range size, population size, migratory strategy, habitat affiliation, body size, diet, number of eggs per clutch, age at sexual maturity, lifespan, and tolerance of urban/suburban settings—and CBC trend estimates. Our results indicate that 68% of the 551 species had increasing trends within the study area over the interval 1966–2013. When trends were examined across the subset of 228 species, the median population trend for the group was 0.9% per year at the continental level. At the regional level, aggregate trends were positive in all but a few areas. Negative population trends were evident in lower latitudes, whereas the largest increases were at higher latitudes, a pattern consistent with range shifts due to climate change. Nine of 10 biological traits were significantly associated with median population trend; however, none of the traits explained >34% of the deviance in the data, reflecting the indirect relationships between population trend estimates and species traits. Trend estimates based on the CBC are broadly congruent with
Aguirre, Luis Antonio; Teixeira, Bruno Otávio S.; Tôrres, Leonardo Antônio B.
2005-08-01
This paper addresses the problem of state estimation for nonlinear systems by means of the unscented Kalman filter (UKF). Compared to the traditional extended Kalman filter, the UKF does not require the local linearization of the system equations used in the propagation stage. Important results using the UKF have been reported recently but in every case the system equations used by the filter were considered known. Not only that, such models are usually considered to be differential equations, which requires that numerical integration be performed during the propagation phase of the filter. In this paper the dynamical equations of the system are taken to be difference equations—thus avoiding numerical integration—and are built from data without prior knowledge. The identified models are subsequently implemented in the filter in order to accomplish state estimation. The paper discusses the impact of not knowing the exact equations and using data-driven models in the context of state and joint state-and-parameter estimation. The procedure is illustrated by means of examples that use simulated and measured data.
Rigatos, G; Rigatou, E; Djida, J D
2015-01-01
The derivative-free nonlinear Kalman filter is proposed for state estimation and fault diagnosis in distributed parameter systems of the wave-type and particularly in the Peyrard-Bishop-Dauxois model of DNA dynamics. At a first stage, a nonlinear filtering approach is introduced for estimating the dynamics of the Peyrard-Bishop-Dauxois 1D nonlinear wave equation, through the processing of a small number of measurements. It is shown that the numerical solution of the associated partial differential equation results in a set of nonlinear ordinary differential equations. With the application of a diffeomorphism that is based on differential flatness theory it is shown that an equivalent description of the system is obtained in the linear canonical (Brunovsky) form. This transformation enables to obtain local estimates about the state vector of the DNA model through the application us of the standard Kalman filter recursion. At a second stage, the local statistical approach to fault diagnosis is used to perform fault diagnosis for this distributed parameter system by processing with statistical tools the differences (residuals) between the output of the Kalman filter and the measurements obtained from the distributed parameter system. Optimal selection of the fault threshold is succeeded by using the local statistical approach to fault diagnosis. The efficiency of the proposed filtering approach in the problem of fault diagnosis for parametric change detection, in nonlinear wave-type models of DNA dynamics, is confirmed through simulation experiments.
What Do Observational Datasets Say about Modeled Tropospheric Temperature Trends since 1979?
Directory of Open Access Journals (Sweden)
David Douglass
2010-09-01
Full Text Available Updated tropical lower tropospheric temperature datasets covering the period 1979–2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (TLT trend over these 31 years is +0.09 ± 0.03 °C decade−1. Given that the surface temperature (Tsfc trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade−1 this indicates that the “scaling ratio” (SR, or ratio of atmospheric trend to surface trend: TLT/Tsfc of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods.
Multiscale modeling of a Chemofilter device for filtering chemotherapy toxins from blood
Maani, Nazanin; Beyhaghi, Saman; Yee, Daryl; Nosonovsky, Micheal; Greer, Julia; Hetts, Steven; Rayz, Vitaliy
2016-11-01
Purpose: Chemotherapy drugs injected intra-arterially to treat cancer can cause systemic toxic effects. A catheter-based Chemofilter device, temporarily deployed in a vein during the procedure can filter excessive drug from the blood thus reducing chemotherapy side-effects. CFD modeling is used to design the membrane of the Chemofilter in order to optimize its hemodynamic performance. Methods: Multiscale approach is used to model blood flow through the Chemofilter. The toxins bind to the Chemofilter's membrane formed by a lattice of numerous micro cells deployed in a blood vessel of much larger size. A detailed model of the flow through a 2x2 microcell matrix with periodic boundary conditions is used to determine the permeability of the membrane. The results are used to simulate the flow through the whole device modeled as a uniform porous membrane. The finite-volume solver Fluent is used to obtain the numerical solution. Results: The micro cell matrix has a porosity of 0.92. The pressure drop across the resolved microcells was found to be 630 Pa, resulting in the permeability of 6.21 x10-11 m2 in the normal direction. These values were used to optimize the device geometry in order to increase the contact area of the membrane, while minimizing its obstruction to the flow. NIH NCI R01CA194533.
Simpson, Elizabeth C.
1989-01-01
Motion estimation is a field of great interest because of its many applications in areas such as robotics and image coding. The optic flow method is one such scheme which, although fairly accurate, is prone to error in the presence of noise. This thesis describes the use of the reduced order model Kalman filter (ROMKF) in reducing errors in displacement estimation due to degradation of the sequence. The implementation of filtering and motion estimation algorithms on the SUN workstation is also discussed. Results from preliminary testing were used to determine the degrees of freedom available for the ROMKF in the SUN software. The tests indicated that increasing the state to the left leads to slight improvement over the minimum state case. Therefore, the software uses the minimum model, with the option of adding states to the left only. The ROMKF was then used in conjunction with a hierarchical pel recursive motion estimation algorithm. Applying the ROMKF to the degraded displacements themselves generally yielded slight improvements in cases with noise degradation and noise plus blur. Filtering the images of the degraded sequence prior to motion estimation was less effective in these cases. Both methods performed badly in the case of blur alone, resulting in increased displacement errors. This is thought to be due in part to filter artifacts. Some improvements were obtained by varying the filter parameters when filtering the displacements directly. This result suggests that further study in varying filter parameters may lead to better results. The results of this thesis indicate that the ROMKF can play a part in reducing motion estimation errors from degraded sequences. However, more work needs to be done before the use of the ROMKF can be a practical solution.
Institute of Scientific and Technical Information of China (English)
LI JunWei; LIN BoLiang; SUN ZhiHui; GENG XueFei
2009-01-01
On the basis of measurable time series of mainline and ramp flows from traffic counts and the assumption of travel time distributions, this research presents a dynamic system model and its on-line estimation algorithm for recursive estimation of Ume-varying origin-destination (OD) matrices in expressway corridors. The proposed model employs a macro-traffic flow model to estimate travel times of OD flows and uses parameters of the traffic model as state variables, which are added to the constrained function of the system. To improve the model efficiency, we revise the travel time distribution based on the feature of normal distribution. The research employs a newly developed filtering technique, called unscented Kalman filter. The proposed model is evaluated with simulation experiments.Numerical analyses with respect to the sensitivity of the selection of initial parameters on the estimation results indicate that the proposed model is sufficiently reasonable and stable for real-world applications.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
On the basis of measurable time series of mainline and ramp flows from traffic counts and the assumption of travel time distributions, this research presents a dynamic system model and its on-line estimation algorithm for recursive estimation of time-varying origin-destination (OD) matrices in expressway corridors. The proposed model employs a macro-traffic flow model to estimate travel times of OD flows and uses parameters of the traffic model as state variables, which are added to the constrained function of the system. To improve the model efficiency, we revise the travel time distribution based on the feature of normal distribution. The research employs a newly developed filtering technique, called unscented Kalman filter. The proposed model is evaluated with simulation experiments. Numerical analyses with respect to the sensitivity of the selection of initial parameters on the estimation results indicate that the proposed model is sufficiently reasonable and stable for real-world appli-cations.
Varouchakis, Emmanouil
2017-04-01
Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic data driven approach of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable model (ARX model). The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA). KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. Three new modified versions of the original form of the ARX model are proposed and investigated: the first considers a larger time scale, the second a larger time delay in terms of the groundwater level input and the third considers the groundwater level difference between the last two hydrological years, which is incorporated in the model as a third input variable. We apply KFAA to time series of groundwater level values from Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to successfully predict the mean annual and biannual groundwater level for recent years (2007-2010).
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 dec
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The soil moisture in Shaanxi Province,a region with complex topography,is simulated using the distributed hydrological model Soil Water Assessment Tool(SWAT).Comparison and contrast of modeled and observed soil moisture show that the SWAT model can reasonably simulate the long-term trend in soil moisture and the spatiotemporal variability of soil moisture in the region.Comparisons to NCEP/NCAR and ERA40 reanalysis of soil moisture show that the trend of variability in soil moisture simulated by SWAT is more consistent with the observed.SWAT model results suggested that high soil moisture in surface soil layers appears in the southern Shaanxi with high vegetation cover,and the Qinling mountainous region with frequent orographic precipitation.In deeper soil layers,high soil moisture appears in the river basins and plains.The regional soil moisture showed a generally decreasing trend on all soil layers from 1951 to 2004,with a stronger and significant decreasing trend in deeper soil layers,especially in the northern parts of the province.
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
Directory of Open Access Journals (Sweden)
M. Manimozhi
2014-05-01
Full Text Available Fault Detection and Isolation (FDI using Linear Kalman Filter (LKF is not sufficient for effective monitoring of nonlinear processes. Most of the chemical plants are nonlinear in nature while operating the plant in a wide range of process variables. In this study we present an approach for designing of Multi Model Adaptive Linear Kalman Filter (MMALKF for Fault Detection and Isolation (FDI of a nonlinear system. The uses a bank of adaptive Kalman filter, with each model based on different fault hypothesis. In this study the effectiveness of the MMALKF has been demonstrated on a spherical tank system. The proposed method is detecting and isolating the sensor and actuator soft faults which occur sequentially or simultaneously.
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
Ensemble Kalman Filter Data Assimilation with the ParFlow Hydrologic Model
Williams, J. L., III
2015-12-01
Hydrometeorological research has shown that simulations of atmospheric processes benefit from sophisticated land surface formulations. Moisture and energy fluxes between the land surface and lower atmosphere are influenced strongly not only by atmospheric conditions, but by terrestrial hydrologic processes, soil moisture distribution in particular. By improving the representation of hydrologic processes, better predictive skill can be achieved in a fully-coupled weather forcasting model. Further improvements in the model can be realized by incorporating observed data values into the hydrologic model. This work applies the Ensemble Kalman Filter functionality included in the Data Assimilation Assimilation Research Testbed (DART), a collection of data assimilation tools maintained at the National Center for Atmospheric Research, to the ParFlow hydrologic model—the hydrologic component of the TerrSysMP fully coupled hydrologic - land surface - atmospheric model system. This generalized data assimilation tool allows observations of variables in the hydrologic component of the system to be incorporated into the overall error covariance matrix thus guiding the development of quantities that define the model state. Single dimension column tests, and a three-dimensional idealized catchment drainage and dry-out test were performed with the ParFlow-DART system to evaluate the effects of assimilating pressure head, soil moisture, and outflow observations on the development of the model through time. The data assimilation system was then applied to the hydrologic portion a fully-coupled (subsurface, land surface, and atmosphere) simulation over the North Rhine-Westphalia region in western Germany to demonstrate the utility of this system in a non-idealized and realistic forecasting situation. The success of these tests will allow the ParFlow-DART system to be developed into a complete data assimilation package for the TerrSysMP fully-coupled modeling system.
Data Assimilation for Vadose Zone Flow Modeling Using the Ensemble Kalman Filter
Zhang, Y.; Schaap, M. G.; Zha, Y.; Xue, L.
2015-12-01
The natural system is open and complex and the hydraulic parameters needed for describing flow and transport in the vadose zone are often poorly known, making it prone to multiple interpretations, mathematical descriptions and uncertainty. Quite often a reasonable "handle" on a sites flow characteristics can be gained only through direct observation of the flow processes itself, determination of the spatial- and probability distributions of material properties combined with computationally expensive inversions of the Richards equation. In groundwater systems, the ensemble Kalman filter (EnKF) has proven to be an effective alternative to model inversions by assimilating observations directly into an ensemble of groundwater models from which time and/or space-variable variable probabilistic quantities of the flow process can be derived. Application of EnKF to Richards equation-type unsaturated flow problems, however, is more challenging than in groundwater systems because the relation of state and model parameters is strongly nonlinear. In addition, the type of functional dependence of moisture content and hydraulic conductivity on matric potential leads to high-dimensional (in the parameter space) problems even under conditions where closed-form expressions of these models such as van Genuchten-Mualem formulations are used. In this study, we updated soil water retention parameters and hydraulic conductivity together and used Restart EnKF, which rerun the nonlinear model from the initial time to obtain the updated state variables, in synthetic cases to explore the factors that may influence estimation results, including the initial estimate, the ensemble size, the observation error, and the assimilation interval. We embedded the EnKF into the Bayesian model averaging framework to enhance the model reliability and reduce predictive uncertainties. This approach is evaluated from a 15 m deep semi-arid highly heterogeneous and anisotropic vadose zone site at the
Divelbiss, Daniel William; Boccelli, Dominic Louis; Succop, Paul Allan; Oerther, Daniel Barton
2013-02-05
In rural health development practice, engineers and scientists must recognize the complex interactions that influence individuals' contact with disease-causing pathogens and understand how household habits may impact the adoption and long-term sustainability of new technology. The goal of this study was to measure the effect of various environmental health factors and household demographics on the operation and maintenance of the Biosand filter (Centre for Affordable Water and Sanitation Technology, Calgary, Alberta, Canada) and diarrhea health burden in the region. In July and August 2010, randomized household surveys (n = 286) were completed in rural Guatemala detailing water access, sanitation availability, hygiene practice, socio-economic status, education level, filter operation and maintenance, and diarrhea health burden of the home. A hypothesized structural equation model was developed based on a review of published research and tested using the surveyed data. Model-derived parameter estimates indicated that: (a) proper personal hygiene practices significantly promote proper filter operation and maintenance; and (b) higher household education level, proper filter operation and maintenance, and improved water supply significantly reduce diarrhea health burden. Additionally, a high level of unexplained variance in diarrhea indicated the filter, though protective of health, is not the only factor influencing diarrhea.
Kim, Du Yong; Jeon, Moongu
2013-02-01
In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-known difficulties in visual tracking. The proposed algorithm is based on a type of auxiliary particle filtering that uses a spatio-temporal sliding window. Compared to conventional particle filtering algorithms, spatio-temporal auxiliary particle filtering is computationally efficient and successfully implemented in visual tracking. In addition, a real-time robust principal component pursuit (RRPCP) equipped with l(1)-norm optimization has been utilized to obtain a new appearance model learning block for reliable visual tracking especially for occlusions in object appearance. The overall tracking framework based on the dual ideas is robust against occlusions and out-of-plane motions because of the proposed spatio-temporal filtering and recursive form of RRPCP. The designed tracker has been evaluated using challenging video sequences, and the results confirm the advantage of using this tracker.
Low-cost adaptive square-root cubature Kalman filter for systems with process model uncer tainty
Institute of Scientific and Technical Information of China (English)
An Zhang; Shuida Bao; Wenhao Bi; Yuan Yuan
2016-01-01
A novel low-cost adaptive square-root cubature Kalman filter (LCASCKF) is proposed to enhance the robustness of pro-cess models while only increasing the computational load slightly. It is wel-known that the Kalman filter cannot handle uncertainties in a process model, such as initial state estimation errors, parameter mismatch and abrupt state changes. These uncertainties severely affect filter performance and may even provoke divergence. A strong tracking filter (STF), which utilizes a suboptimal fading fac-tor, is an adaptive approach that is commonly adopted to solve this problem. However, if the strong tracking SCKF (STSCKF) uses the same method as the extended Kalman filter (EKF) to introduce the suboptimal fading factor, it greatly increases the computational load. To avoid this problem, a low-cost introductory method is proposed and a hypothesis testing theory is applied to detect uncertainties. The computational load analysis is performed by counting the total number of floating-point operations and it is found that the computational load of LCASCKF is close to that of SCKF. Experimental results prove that the LCASCKF performs as wel as STSCKF, while the increase in computational load is much lower than STSCKF.
Sediment transport modelling based on grain size trend analysis in Augusta Harbour (Sicily)
Barbera, Giuseppe; Feo, Roberto; Freni, Gabriele
2015-12-01
To support marine civil engineer in pollutant studies, sediment management or dredging operations, is useful to know how the sediments move in accumulation basin. This paper investigates the dynamic of the sediment path using a two-dimensional numeric model: the Grain Size Trend Analysis (GSTA). The GSTA was applied using GiSedTrend plugin, under GIS software. The case study is the Augusta Harbour, which is one of the most polluted Italian harbours. It is the marine part of the Site of National Interest (SNI) of Priolo Gargallo (Siracusa, Italy) and it can be hydrodynamically considered as a lagoon. Two scenarios were obtained by using different geostatistical criteria.
Velazco, Julio G; Rodríguez-Álvarez, María Xosé; Boer, Martin P; Jordan, David R; Eilers, Paul H C; Malosetti, Marcos; van Eeuwijk, Fred A
2017-07-01
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. Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.
Directory of Open Access Journals (Sweden)
Baker Syed
2011-01-01
Full Text Available Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF, rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Baker, Syed Murtuza; Poskar, C Hart; Junker, Björn H
2011-10-11
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Nie, Suping; Zhu, Jiang; Luo, Yong
2010-05-01
The purpose of this study is to explore the performances of different model error scheme in soil moisture data assimilation. Based on the ensemble Kalman filter (EnKF) and the atmosphere-vegetation interaction model (AVIM), point-scale analysis results for three schemes, 1) covariance inflation (CI), 2) direct random disturbance (DRD), and 3) error source random disturbance (ESRD), are combined under conditions of different observational error estimations, different observation layers, and different observation intervals using a series of idealized experiments. The results shows that all these schemes obtain good assimilation results when the assumed observational error is an accurate statistical representation of the actual error used to perturb the original truth value, and the ESRD scheme has the least root mean square error (RMSE). Overestimation or underestimation of the observational errors can affect the assimilation results of CI and DRD schemes sensitively. The performances of these two schemes deteriorate obviously while the ESRD scheme keeps its capability well. When the observation layers or observation interval increase, the performances of both CI and DRD schemes decline evidently. But for the ESRD scheme, as it can assimilate multi-layer observations coordinately, the increased observations improve the assimilation results further. Moreover, as the ESRD scheme contains a certain amount of model error estimation functions in its assimilation process, it also has a good performance in assimilating sparse-time observations.
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
Gotelli, Nicholas J.; Dorazio, Robert M.; Ellison, Aaron M.; Grossman, Gary D.
2010-01-01
Quantifying patterns of temporal trends in species assemblages is an important analytical challenge in community ecology. We describe methods of analysis that can be applied to a matrix of counts of individuals that is organized by species (rows) and time-ordered sampling periods (columns). We first developed a bootstrapping procedure to test the null hypothesis of random sampling from a stationary species abundance distribution with temporally varying sampling probabilities. This procedure can be modified to account for undetected species. We next developed a hierarchical model to estimate species-specific trends in abundance while accounting for species-specific probabilities of detection. We analysed two long-term datasets on stream fishes and grassland insects to demonstrate these methods. For both assemblages, the bootstrap test indicated that temporal trends in abundance were more heterogeneous than expected under the null model. We used the hierarchical model to estimate trends in abundance and identified sets of species in each assemblage that were steadily increasing, decreasing or remaining constant in abundance over more than a decade of standardized annual surveys. Our methods of analysis are broadly applicable to other ecological datasets, and they represent an advance over most existing procedures, which do not incorporate effects of incomplete sampling and imperfect detection.
Trend estimates of AERONET-observed and model-simulated AOT percentiles between 1993 and 2013
Yoon, Jongmin; Pozzer, Andrea; Chang, Dong Yeong; Lelieveld, Jos
2016-04-01
Recent Aerosol Optical thickness (AOT) trend studies used monthly or annual arithmetic means that discard details of the generally right-skewed AOT distributions. Potentially, such results can be biased by extreme values (including outliers). This study additionally uses percentiles (i.e., the lowest 5%, 25%, 50%, 75% and 95% of the monthly cumulative distributions fitted to Aerosol Robotic Network (AERONET)-observed and ECHAM/MESSy Atmospheric Chemistry (EMAC)-model simulated AOTs) that are less affected by outliers caused by measurement error, cloud contamination and occasional extreme aerosol events. Since the limited statistical representativeness of monthly percentiles and means can lead to bias, this study adopts the number of observations as a weighting factor, which improves the statistical robustness of trend estimates. By analyzing the aerosol composition of AERONET-observed and EMAC-simulated AOTs in selected regions of interest, we distinguish the dominant aerosol types and investigate the causes of regional AOT trends. The simulated and observed trends are generally consistent with a high correlation coefficient (R = 0.89) and small bias (slope±2σ = 0.75 ± 0.19). A significant decrease in EMAC-decomposed AOTs by water-soluble compounds and black carbon is found over the USA and the EU due to environmental regulation. In particular, a clear reversal in the AERONET AOT trend percentiles is found over the USA, probably related to the AOT diurnal cycle and the frequency of wildfires.
Trend Estimates of AERONET-Observed and Model-Simulated AOTs Between 1993 and 2013
Yoon, J.; Pozzer, A.; Chang, D. Y.; Lelieveld, J.; Kim, J.; Kim, M.; Lee, Y. G.; Koo, J.-H.; Lee, J.; Moon, K. J.
2015-01-01
Recently, temporal changes in Aerosol Optical Thickness (AOT) have been investigated based on model simulations, satellite and ground-based observations. Most AOT trend studies used monthly or annual arithmetic means that discard details of the generally right-skewed AOT distributions. Potentially, such results can be biased by extreme values (including outliers). This study additionally uses percentiles (i.e., the lowest 5%, 25%, 50%, 75% and 95% of the monthly cumulative distributions fitted to Aerosol Robotic Network (AERONET)-observed and ECHAM/MESSy Atmospheric Chemistry (EMAC)-model simulated AOTs) that are less affected by outliers caused by measurement error, cloud contamination and occasional extreme aerosol events. Since the limited statistical representativeness of monthly percentiles and means can lead to bias, this study adopts the number of observations as a weighting factor, which improves the statistical robustness of trend estimates. By analyzing the aerosol composition of AERONET-observed and EMAC-simulated AOTs in selected regions of interest, we distinguish the dominant aerosol types and investigate the causes of regional AOT trends. The simulated and observed trends are generally consistent with a high correlation coefficient (R = 0.89) and small bias (slope+/-2(sigma) = 0.75 +/- 0.19). A significant decrease in EMAC-decomposed AOTs by water-soluble compounds and black carbon is found over the USA and the EU due to environmental regulation. In particular, a clear reversal in the AERONET AOT trend percentiles is found over the USA, probably related to the AOT diurnal cycle and the frequency of wildfires. In most of the selected regions of interest, EMAC-simulated trends are mainly attributed to the significant changes of the dominant aerosols; e.g., significant decrease in sea salt and water soluble compounds over Central America, increase in dust over Northern Africa and Middle East, and decrease in black carbon and organic carbon over
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
Trend estimates of AERONET-observed and model-simulated AOTs between 1993 and 2013
Yoon, J.; Pozzer, A.; Chang, D. Y.; Lelieveld, J.; Kim, J.; Kim, M.; Lee, Y. G.; Koo, J.-H.; Lee, J.; Moon, K. J.
2016-01-01
Recently, temporal changes in Aerosol Optical Thickness (AOT) have been investigated based on model simulations, satellite and ground-based observations. Most AOT trend studies used monthly or annual arithmetic means that discard details of the generally right-skewed AOT distributions. Potentially, such results can be biased by extreme values (including outliers). This study additionally uses percentiles (i.e., the lowest 5%, 25%, 50%, 75% and 95% of the monthly cumulative distributions fitted to Aerosol Robotic Network (AERONET)-observed and ECHAM/MESSy Atmospheric Chemistry (EMAC)-model simulated AOTs) that are less affected by outliers caused by measurement error, cloud contamination and occasional extreme aerosol events. Since the limited statistical representativeness of monthly percentiles and means can lead to bias, this study adopts the number of observations as a weighting factor, which improves the statistical robustness of trend estimates. By analyzing the aerosol composition of AERONET-observed and EMAC-simulated AOTs in selected regions of interest, we distinguish the dominant aerosol types and investigate the causes of regional AOT trends. The simulated and observed trends are generally consistent with a high correlation coefficient (R = 0.89) and small bias (slope±2σ = 0.75 ± 0.19). A significant decrease in EMAC-decomposed AOTs by water-soluble compounds and black carbon is found over the USA and the EU due to environmental regulation. In particular, a clear reversal in the AERONET AOT trend percentiles is found over the USA, probably related to the AOT diurnal cycle and the frequency of wildfires. In most of the selected regions of interest, EMAC-simulated trends are mainly attributed to the significant changes of the dominant aerosols; e.g., significant decrease in sea salt and water soluble compounds over Central America, increase in dust over Northern Africa and Middle East, and decrease in black carbon and organic carbon over Australia.
DEFF Research Database (Denmark)
Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan
2006-01-01
. 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....
Model-based x-ray energy spectrum estimation algorithm from CT scanning data with spectrum filter
Li, Lei; Wang, Lin-Yuan; Yan, Bin
2016-10-01
With the development of technology, the traditional X-ray CT can't meet the modern medical and industry needs for component distinguish and identification. This is due to the inconsistency of X-ray imaging system and reconstruction algorithm. In the current CT systems, X-ray spectrum produced by X-ray source is continuous in energy range determined by tube voltage and energy filter, and the attenuation coefficient of object is varied with the X-ray energy. So the distribution of X-ray energy spectrum plays an important role for beam-hardening correction, dual energy CT image reconstruction or dose calculation. However, due to high ill-condition and ill-posed feature of system equations of transmission measurement data, statistical fluctuations of X ray quantum and noise pollution, it is very hard to get stable and accurate spectrum estimation using existing methods. In this paper, a model-based X-ray energy spectrum estimation method from CT scanning data with energy spectrum filter is proposed. First, transmission measurement data were accurately acquired by CT scan and measurement using phantoms with different energy spectrum filter. Second, a physical meaningful X-ray tube spectrum model was established with weighted gaussian functions and priori information such as continuity of bremsstrahlung and specificity of characteristic emission and estimation information of average attenuation coefficient. The parameter in model was optimized to get the best estimation result for filtered spectrum. Finally, the original energy spectrum was reconstructed from filtered spectrum estimation with filter priori information. Experimental results demonstrate that the stability and accuracy of X ray energy spectrum estimation using the proposed method are improved significantly.
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
A flexible additive inflation scheme for treating model error in ensemble Kalman Filters
Sommer, Matthias; Janjic, Tijana
2017-04-01
Data assimilation algorithms require an accurate estimate of the uncertainty of the prior, background, field. However, the background error covariance derived from the ensemble of numerical model simulations does not adequately represent the uncertainty of it. This is partially due to the sampling error that arises from the use of a small number of ensemble members to represent the background error covariance. It is also partially a consequence of the fact that the model does not represent its own error. Several mechanisms have been introduced so far aiming at alleviating the detrimental e ffects of misrepresented ensemble covariances, allowing for the successful implementation of ensemble data assimilation techniques for atmospheric dynamics. One of the established approaches in ensemble data assimilation is additive inflation which perturbs each ensemble member with a sample from a given distribution. This results in a fixed rank of the model error covariance matrix. Here, a more flexible approach is suggested where the model error samples are treated as additional synthetic ensemble members which are used in the update step of data assimilation but are not forecast. In this way, the rank of the model error covariance matrix can be chosen independently of the ensemble. The eff ect of this altered additive inflation method on the performance of the filter is analyzed here in an idealised experiment. It is shown that the additional synthetic ensemble members can make it feasible to achieve convergence in an otherwise divergent setting of data assimilation. The use of this method also allows for a less stringent localization radius.
He, Kaiming; Sun, Jian; Tang, Xiaoou
2013-06-01
In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.
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...... 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...
Directory of Open Access Journals (Sweden)
Giovanni Capellari
2015-12-01
Full Text Available Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation, whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated.
Biomass accumulation modelling in a highly loaded biotrickling filter for hydrogen sulphide removal.
Mannucci, Alberto; Munz, Giulio; Mori, Gualtiero; Lubello, Claudio
2012-07-01
A pilot scale test on a biotrickling filter packed with polyurethane foam cubes was carried out for 110 d at high volumetric mass load (up to 280 g m(bed)(-3) h(-1)) with the aim of studying the accumulation of solids in the treatment of H(2)S. Removal rate up to 245 g m(bed)(-3) h(-1) was obtained; however, an accumulation of gypsum, elemental sulphur and, above all, inert biomass was identified as the cause of an increased pressure drop over the long term. A mathematical model was applied and calibrated with the experimental results to describe the accumulation of biomass. The model was capable of describing the accumulation of solids and, corresponding to a solids retention time of 50 d, the observed yield resulted in 0.07 g of solids produced g(-1) H(2)S removed. Respirometric tests showed that heterotrophic activity is inhibited at low pH (pH < 2.3), and the contribution to biomass removal through decay was negligible.
Capellari, Giovanni; Azam, Saeed Eftekhar; Mariani, Stefano
2015-12-22
Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated.
A local ensemble transform Kalman filter data assimilation system for the NCEP global model
Szunyogh, Istvan; Kostelich, Eric J.; Gyarmati, Gyorgyi; Kalnay, Eugenia; Hunt, Brian R.; Ott, Edward; Satterfield, Elizabeth; Yorke, James A.
2008-01-01
The accuracy and computational efficiency of a parallel computer implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme on the model component of the 2004 version of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) is investigated. Numerical experiments are carried out at model resolution T62L28. All atmospheric observations that were operationally assimilated by NCEP in 2004, except for satellite radiances, are assimilated with the LETKF. The accuracy of the LETKF analyses is evaluated by comparing it to that of the Spectral Statistical Interpolation (SSI), which was the operational global data assimilation scheme of NCEP in 2004. For the selected set of observations, the LETKF analyses are more accurate than the SSI analyses in the Southern Hemisphere extratropics and are comparably accurate in the Northern Hemisphere extratropics and in the Tropics. The computational wall-clock times achieved on a Beowulf cluster of 3.6 GHz Xeon processors make our implementation of the LETKF on the NCEP GFS a widely applicable analysis-forecast system, especially for research purposes. For instance, the generation of four daily analyses at the resolution of the NCAR-NCEP reanalysis (T62L28) for a full season (90 d), using 40 processors, takes less than 4 d of wall-clock time.
Directory of Open Access Journals (Sweden)
Junichi Susaki
2012-06-01
Full Text Available A filtering algorithm is proposed that accurately extracts ground data from airborne light detection and ranging (LiDAR measurements and generates an estimated digital terrain model (DTM. The proposed algorithm utilizes planar surface features and connectivity with locally lowest points to improve the extraction of ground points (GPs. A slope parameter used in the proposed algorithm is updated after an initial estimation of the DTM, and thus local terrain information can be included. As a result, the proposed algorithm can extract GPs from areas where different degrees of slope variation are interspersed. Specifically, along roads and streets, GPs were extracted from urban areas, from hilly areas such as forests, and from flat area such as riverbanks. Validation using reference data showed that, compared with commercial filtering software, the proposed algorithm extracts GPs with higher accuracy. Therefore, the proposed filtering algorithm effectively generates DTMs, even for dense urban areas, from airborne LiDAR data.
Statistical modelling and power analysis for detecting trends in total suspended sediment loads
Wang, You-Gan; Wang, Shen S. J.; Dunlop, Jason
2015-01-01
The export of sediments from coastal catchments can have detrimental impacts on estuaries and near shore reef ecosystems such as the Great Barrier Reef. Catchment management approaches aimed at reducing sediment loads require monitoring to evaluate their effectiveness in reducing loads over time. However, load estimation is not a trivial task due to the complex behaviour of constituents in natural streams, the variability of water flows and often a limited amount of data. Regression is commonly used for load estimation and provides a fundamental tool for trend estimation by standardising the other time specific covariates such as flow. This study investigates whether load estimates and resultant power to detect trends can be enhanced by (i) modelling the error structure so that temporal correlation can be better quantified, (ii) making use of predictive variables, and (iii) by identifying an efficient and feasible sampling strategy that may be used to reduce sampling error. To achieve this, we propose a new regression model that includes an innovative compounding errors model structure and uses two additional predictive variables (average discounted flow and turbidity). By combining this modelling approach with a new, regularly optimised, sampling strategy, which adds uniformity to the event sampling strategy, the predictive power was increased to 90%. Using the enhanced regression model proposed here, it was possible to detect a trend of 20% over 20 years. This result is in stark contrast to previous conclusions presented in the literature.
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks
Ghosh, Saurav; Chakraborty, Prithwish; Nsoesie, Elaine O.; Cohn, Emily; Mekaru, Sumiko R.; Brownstein, John S.; Ramakrishnan, Naren
2017-01-01
In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.
Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid
2016-09-01
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.
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
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 probabi
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 probabi
Energy Technology Data Exchange (ETDEWEB)
Kim, S [Advocate Lutheran General Hospital, Park Ridge, IL (United States); Alaei, P [Univ Minnesota, Minneapolis, MN (United States)
2015-06-15
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.
Energy Technology Data Exchange (ETDEWEB)
Page, Ralph H.; Doty, Patrick F.
2017-08-01
The various technologies presented herein relate to a tiled filter array that can be used in connection with performance of spatial sampling of optical signals. The filter array comprises filter tiles, wherein a first plurality of filter tiles are formed from a first material, the first material being configured such that only photons having wavelengths in a first wavelength band pass therethrough. A second plurality of filter tiles is formed from a second material, the second material being configured such that only photons having wavelengths in a second wavelength band pass therethrough. The first plurality of filter tiles and the second plurality of filter tiles can be interspersed to form the filter array comprising an alternating arrangement of first filter tiles and second filter tiles.
Anticorrelated observed and modeled trends in dissolved oceanic oxygen over the last 50 years
Directory of Open Access Journals (Sweden)
L. Stramma
2012-04-01
Full Text Available Observations and model runs indicate trends in dissolved oxygen (DO associated with current and ongoing global warming. However, a large-scale observation-to-model comparison has been missing and is presented here. This study presents a first global compilation of DO measurements covering the last 50 years. It shows declining upper-ocean DO levels in many regions, especially the tropical oceans, whereas areas with increasing trends are found in the subtropics and in some subpolar regions. For the Atlantic Ocean south of 20° N, the DO history could even be extended back to about 70 years, showing decreasing DO in the subtropical South Atlantic. The global mean DO trend between 50° S and 50° N at 300 dbar for the period 1960 to 2010 is −0.063 μmol kg^{−1} yr^{−1}. Results of a numerical biogeochemical Earth system model reveal that the magnitude of the observed change is consistent with CO_{2}-induced climate change. However, the correlation between simulated and observed patterns of past DO change is negative, indicating that the model does not correctly reproduce the processes responsible for observed regional oxygen changes in the past 50 years. A negative pattern correlation is also obtained for model configurations with particularly low and particularly high diapycnal mixing, for a configuration that assumes a CO_{2}-induced enhancement of the C:N ratios of exported organic matter and irrespective of whether climatological or realistic winds from reanalysis products are used to force the model. Depending on the model configuration the 300 dbar DO trend between 50° S and 50° N is −0.026 to −0.046 μmol kg^{−1} yr^{−1}. Although numerical models reproduce the overall sign and, to some extent, magnitude of observed ocean deoxygenation, this degree of realism does not necessarily apply to simulated regional patterns and the representation of processes involved in their generation
Directory of Open Access Journals (Sweden)
Javad Faiz
2011-01-01
Full Text Available A UPS inverter operates in wide load impedance ranges from resistive to capacitive or inductive load. At the same time, fast transient load response, good load regulation and good switching frequency suppression is required. The variation of the load impedance changes the filter transfer characteristic and thus the output voltage value. In this paper, an analysis and simulation of the single phase voltage source uninterruptible power supply (UPS with fourth order filter (multiple-filter in output inverter, based on the state space averaging and small signal linearization technique, is proposed. The simulation results show the high quality sinusoidal output voltage at different loads, with THD less than %5.
Muñoz-Carpena, Rafael; Ritter, Amy; Fox, Garey A; Perez-Ovilla, Oscar
2015-11-01
Vegetative filter strips (VFS) are a widely adopted practice for limiting pesticide transport from adjacent fields to receiving waterbodies. The efficacy of VFS depends on site-specific input factors. To elucidate the complex and non-linear relationships among these factors requires a process-based modeling framework. Previous research proposed linking existing higher-tier environmental exposure models with a well-tested VFS model (VFSMOD). However, the framework assumed pesticide mass stored in the VFS was not available for transport in subsequent storm events. A new pesticide mass balance component was developed to estimate surface pesticide residue trapped in the VFS and its degradation between consecutive runoff events. The influence and necessity of the updated framework on acute and chronic estimated environmental concentrations (EECs) and percent reductions in EECs were investigated across three, 30-year U.S. EPA scenarios: Illinois corn, California tomato, and Oregon wheat. The updated framework with degradation predicted higher EECs than the existing framework without degradation for scenarios with greater sediment transport, longer VFS lengths, and highly sorbing and persistent pesticides. Global sensitivity analysis (GSA) assessed the relative importance of mass balance and degradation processes in the context of other input factors like VFS length (VL), organic-carbon sorption coefficient (Koc), and soil and water half-lives. Considering VFS pesticide residue and degradation was not important if single, large runoff events controlled transport, as is typical for higher percentiles considered in exposure assessments. Degradation processes become more important when considering percent reductions in acute or chronic EECs, especially under scenarios with lower pesticide losses.
A "Dressed" Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific
Institute of Scientific and Technical Information of China (English)
WAN Liying; ZHU Jiang; WANG Hui; YAN Changxiang; Laurent BERTINO
2009-01-01
The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of some simpler assimilation schemes,such as Optimal Interpolation (OI) or three-dimension variational assimilation (3DVAR).Ensemble optimal interpolation (EnOI),a crudely simplified implementation of EnKF,is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF.In this paper,to compromise between computational cost and dynamic covaxiance,we use the idea of "dressing" a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covaxiance.The term "dressing" means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles.This dressing EnKF (DrEnKF for short) scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model (HYCOM) over a four-year period.Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble.Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members.Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset.The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE)at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.
Evaluation of Local Ensemble Transform Kalman Filter System for the Global FSU Atmospheric Model
Cintra, R. S.; Cocke, S.
2014-12-01
This paper shows the results of a implementation of the data assimilation system to obtain the initial condition to the atmospheric general circulation model (AGCM) for the Florida State University/USA. The better quality of forecasts is given the more accurate the estimate of the initial conditions. The process of combining observations and short-range forecast to obtain an analysis is called data assimilation. The data assimilation system called "Local ensemble transform Kalman filter (LETKF) is implemented. A prediction estimates ensemble in state space represents the model errors in that scheme. The LETKF is tested with the AGCM Florida State University Global Spectral Model (FSUGSM). The model is a multilevel (27 vertical levels) spectral primitive equation model with a vertical σ-coordinate. All variables are expanded horizontally in a truncated series of spherical harmonic functions (at resolution T63) and a transform technique is applied to calculate the physical processes in real space. The LETKF data assimilation experiments are based in two types of the synthetic observations data (surface pressure, absolute temperature, zonal component wind, meridional component wind and humidity) to evaluate the LETKF system for FSUGSM. The data assimilation experiments are based on observational systems simulation experiments where the "nature" is assumed to be known, and adding random noise to the nature run. The first experiment, the "nature" fields are the FSUGSM forecasts without data assimilation, afterwards, we use the "National Centers for Environment Prediction" reanalysis to obtain the "nature" fields. The observations are localized at every other grid point of the model. The forecast ensemble size is 20 members. The numerical experiments have a one-month assimilation cycle, for the period 01/01/2001 to 31/01/2001 at (00, 06, 12 and 18 GMT) for each day. We compare the behavior of the model by comparing with its forecast, observations and nature fields. A
Model simulated trend of surface carbon monoxide for the 2001-2010 decade
Yoon, J.; Pozzer, A.
2014-05-01
We present decadal trend estimates of surface carbon monoxide (CO), simulated using the atmospheric chemistry general circulation model ECHAM5/MESSy (EMAC) based on the emission scenarios, Representative Concentration Pathways (RCP) 8.5 for anthropogenic activity and Global Fire Emissions Database (GFED) v3.1 for biomass burning from 2001 through 2010. The spatial distribution of the modelled surface CO is evaluated with monthly Measurements Of Pollution In The Troposphere (MOPITT) thermal infrared product. The global means of correlation coefficient and relative bias for the 2001-2010 are 0.95 and -4.29%, respectively. We also find a reasonable correlation (R = 0.78) between the trends of EMAC surface CO and full 10 year monthly records from ground-based observation (World Data Centre for Greenhouse Gases, WDCGG). Over Western Europe, Eastern USA, and Northern Australia, the significant decreases of EMAC surface CO are estimated at -35.5 ± 5.8, -59.6 ± 9.1, and -13.7 ± 9.5 ppbv decade-1, respectively, with a 95% confidence interval. In contrast, the surface CO increases by +8.9 ± 4.8 ppbv decade-1 over South Asia. A high correlation (R = 0.92) between the significant changes in EMAC-simulated surface CO and total emission flux shows that the significant regional trends are attributed to the changes in primary/direct emissions from both anthropogenic activity and biomass burning. In particular, increasing trends of surface hydroxyl radical (OH) partially contribute to the decreasing trends of surface CO in Western Europe and Eastern USA.
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.
Fensham, Roderick John; Low Choy, Sama J; Fairfax, Russell James; Cavallaro, Paul C
2003-08-01
Accounting of carbon stocks in woody vegetation for greenhouse purposes requires definition of medium term trends with accurate error assessment. Tree and shrub cover was sampled through time at randomly located sites over a large area of central Queensland, Australia using aerial photography from 1945 to 1999. Calibration models developed from field data for the same land types as those represented within the study area allowed for the extrapolation of overstorey and understorey cover, basal area and biomass values and these were modelled as trends over the latter half of the 20th century. These structural attributes have declined over the region because of land clearing with values for biomass changing from a mean of 58.0(+/-1.2)t/ha in 1953 to 41.1(+/-1.0)t/ha in 1991. The biomass of Acacia on clay and Eucalypt on texture contrast soils land types has declined most dramatically. Within uncleared vegetation there was an overall trend of increase from 56.1(+/-1.2)t/ha in 1951 to 67.6(+/-1.3)t/ha in 1995. The increase in structural attributes within uncleared vegetation was most pronounced for the Eucalypt on texture contrast soils and Eucalypt on clay land types. It was demonstrated that the sites sampled were representative of their land types and that spatial bias of the photography, undetected tree-killing, sampling error, inherent variability of structural attributes and measurement error should not have impacted greatly on bias or precision of trend estimates for well-sampled land types. Certainly the errors are not likely to be substantial for trends averaged over all land types and they provide an accurate assessment of the magnitude and direction of change. The technique presented here would appear to be a robust means of accounting for the above-ground woody component of woodlands and open forests and will also contribute to a broader understanding of savanna dynamics.
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
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.
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.
Modelling of the filter-adsorber type air cleaner by using neural network
Directory of Open Access Journals (Sweden)
Raos Miomir
2009-01-01
Full Text Available It is well known that most air purifying methods imply the passing of air flow, as a pollutant carrier, through a control unit which retains impurities. Properties of the air control unit and the purifying process itself therefore differ depending on the nature of present impurities, as well as on flow-thermal properties of air as the carrier of those impurities. For the assumed conditions, in terms of production of a pollution source and presence of different polluting substances in the form of dust, aerosols, gas, vapor in the exhaust gas, etc., an integrated gas purifier has been designed and tested, comprising a module for purification of mechanical impurities and a module for purification of gaseous impurities. The purifier is compact and has a universal application while simultaneously retaining several different pollutants. These requirements were met through application of the filtration and adsorption methods. On the formed experimental line with an adequate system of acquisition, filter-adsorber type gas cleaners in the function of flow-thermal parameters of gas mixture were tested simultaneously. Experimental data were used for training the radial basis function neural network, which was then used to model properties of the process and gas cleaner.
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.
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.
De Biase, C.
2012-01-01
Vertical flow filters (unplanted) and vertical flow constructed wetlands (planted), simple and inexpensive technologies to treat effectively volatile organic compounds (VOCs) contaminated water, consist of containers filled with granular material which is intermittently fed with contaminated water.
A high-order spatial filter for a cubed-sphere spectral element model
Kang, Hyun-Gyu; Cheong, Hyeong-Bin
2017-04-01
A high-order spatial filter is developed for the spectral-element-method dynamical core on the cubed-sphere grid which employs the Gauss-Lobatto Lagrange interpolating polynomials (GLLIP) as orthogonal basis functions. The filter equation is the high-order Helmholtz equation which corresponds to the implicit time-differencing of a diffusion equation employing the high-order Laplacian. The Laplacian operator is discretized within a cell which is a building block of the cubed sphere grid and consists of the Gauss-Lobatto grid. When discretizing a high-order Laplacian, due to the requirement of C0 continuity along the cell boundaries the grid-points in neighboring cells should be used for the target cell: The number of neighboring cells is nearly quadratically proportional to the filter order. Discrete Helmholtz equation yields a huge-sized and highly sparse matrix equation whose size is N*N with N the number of total grid points on the globe. The number of nonzero entries is also almost in quadratic proportion to the filter order. Filtering is accomplished by solving the huge-matrix equation. While requiring a significant computing time, the solution of global matrix provides the filtered field free of discontinuity along the cell boundaries. To achieve the computational efficiency and the accuracy at the same time, the solution of the matrix equation was obtained by only accounting for the finite number of adjacent cells. This is called as a local-domain filter. It was shown that to remove the numerical noise near the grid-scale, inclusion of 5*5 cells for the local-domain filter was found sufficient, giving the same accuracy as that obtained by global domain solution while reducing the computing time to a considerably lower level. The high-order filter was evaluated using the standard test cases including the baroclinic instability of the zonal flow. Results indicated that the filter performs better on the removal of grid-scale numerical noises than the explicit
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.
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.
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.
Directory of Open Access Journals (Sweden)
V. R. N. Pauwels
2013-04-01
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. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.
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.
Modeling the Air Flow in the 3410 Building Filtered Exhaust Stack System
Energy Technology Data Exchange (ETDEWEB)
Recknagle, Kurtis P.; Barnett, J. Matthew; Suffield, Sarah R.
2013-01-23
Additional ventilation capacity has been designed for the 3410 Building filtered exhaust stack system. The updated system will increase the number of fans from two to three and will include ductwork to incorporate the new fan into the existing stack. Stack operations will involve running various two-fan combinations at any given time. The air monitoring system of the existing two-fan stack was previously found to be in compliance with the ANSI/HPS N13.1-1999 standard, however it is not known if the modified (three-fan) system will comply. Subsequently, a full-scale three-dimensional (3-D) computational fluid dynamics (CFD) model of the modified stack system has been created to examine the sampling location for compliance with the standard. The CFD modeling results show good agreement with testing data collected from the existing 3410 Building stack and suggest that velocity uniformity and flow angles will remain well within acceptance criteria when the third fan and associated ductwork is installed. This includes two-fan flow rates up to 31,840 cfm for any of the two-fan combinations. For simulation cases in which tracer gas and particles are introduced in the main duct, the model predicts that both particle and tracer gas coefficients of variance (COVs) may be larger than the acceptable 20 percent criterion of the ANSI/HPS N13.1-1999 standard for each of the two-fan, 31,840 cfm combinations. Simulations in which the tracers are introduced near the fans result in improved, though marginally acceptable, COV values for the tracers. Due to the remaining uncertainty that the stack will qualify with the addition of the third fan and high flow rates, a stationary air blender from Blender Products, Inc. is considered for inclusion in the stack system. A model of the air blender has been developed and incorporated into the CFD model. Simulation results from the CFD model that includes the air blender show striking improvements in tracer gas mixing and tracer particle
Kery, M.; Royle, J. Andrew; Schmid, Hans; Schaub, M.; Volet, B.; Hafliger, G.; Zbinden, N.
2010-01-01
Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis. ) and Sparrowhawk (Accipiter nisus. ) and the scarce Rock Thrush (Monticola saxatilis. ) and Wallcreeper (Tichodroma muraria. ). Our method requires that all observed species are recorded. Detectability was biodiversity monitoring and modeling of species distributions. ?? 2010 Society for Conservation Biology.
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.
Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories.
Chen, Xiaojian; Cui, Tingting; Fu, Jianhong; Peng, Jianwei; Shan, Jie
2016-12-01
Low-cost GPS (receiver) has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a) it is able to model trajectories and detect outliers adaptively; (b) only one critical value for outlier scores needs to be set; (c) it is able to robustly detect unapparent outliers; and (d) it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques.
Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories
Directory of Open Access Journals (Sweden)
Xiaojian Chen
2016-12-01
Full Text Available Low-cost GPS (receiver has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a it is able to model trajectories and detect outliers adaptively; (b only one critical value for outlier scores needs to be set; (c it is able to robustly detect unapparent outliers; and (d it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques.
Poletika, N N; Coody, P N; Fox, G A; Sabbagh, G J; Dolder, S C; White, J
2009-01-01
Runoff volume and flow concentration are hydrological factors that limit effectiveness of vegetated filter strips (VFS) in removing pesticides from surface runoff. Empirical equations that predict VFS pesticide effectiveness based solely on physical characteristics are insufficient on the event scale because they do not completely account for hydrological processes. This research investigated the effect of drainage area ratio (i.e., the ratio of field area to VFS area) and flow concentration (i.e., uniform versus concentrated flow) on pesticide removal efficiency of a VFS and used these data to provide further field verification of a recently proposed numerical/empirical modeling procedure for predicting removal efficiency under variable flow conditions. Runoff volumes were used to simulate drainage area ratios of 15:1 and 30:1. Flow concentration was investigated based on size of the VFS by applying artificial runoff to 10% of the plot width (i.e., concentrated flow) or the full plot width (i.e., uniform flow). Artificial runoff was metered into 4.6-m long VFS plots for 90 min after a simulated rainfall of 63 mm applied over 2 h. The artificial runoff contained sediment and was dosed with chlorpyrifos and atrazine. Pesticide removal efficiency of VFS for uniform flow conditions (59% infiltration; 88% sediment removal) was 85% for chlorpyrifos and 62% for atrazine. Flow concentration reduced removal efficiencies regardless of drainage area ratio (i.e., 16% infiltration, 31% sediment removal, 21% chlorpyrifos removal, and 12% atrazine removal). Without calibration, the predictive modeling based on the integrated VFSMOD and empirical hydrologic-based pesticide trapping efficiency equation predicted atrazine and chlorpyrifos removal efficiency under uniform and concentrated flow conditions. Consideration for hydrological processes, as opposed to statistical relationships based on buffer physical characteristics, is required to adequately predict VFS pesticide trapping
Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model
Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P. O.
2013-06-01
This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this
Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model
Directory of Open Access Journals (Sweden)
S. Barthélémy
2013-06-01
Full Text Available This study describes the emulation of an Ensemble Kalman Filter (EnKF algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length
Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zechang
2015-10-01
The accurate monitoring of battery cell temperature is indispensible to the design of battery thermal management system. To obtain the internal temperature of a battery cell online, an adaptive temperature estimation method based on Kalman filtering and an equivalent time-variant electrical network thermal (EENT) model is proposed. The EENT model uses electrical components to simulate the battery thermodynamics, and the model parameters are obtained with a least square algorithm. With a discrete state-space description of the EENT model, a Kalman filtering (KF) based internal temperature estimator is developed. Moreover, considering the possible time-varying external heat exchange coefficient, a joint Kalman filtering (JKF) based estimator is designed to simultaneously estimate the internal temperature and the external thermal resistance. Several experiments using the hard-cased LiFePO4 cells with embedded temperature sensors have been conducted to validate the proposed method. Validation results show that, the EENT model expresses the battery thermodynamics well, the KF based temperature estimator tracks the real central temperature accurately even with a poor initialization, and the JKF based estimator can simultaneously estimate both central temperature and external thermal resistance precisely. The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
Cao, Lu; Li, Hengnian
2016-10-01
For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).
Yoon, Heesung; Park, Eungyu; Yoon, Pilsun; Lee, Eunhee; Kim, Gyoo-Bum
2016-04-01
A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river, South Korea. First, one-step ahead direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Based on the direct prediction models, recursive prediction models for the simulation of groundwater level fluctuations were designed. The effect of river stage fluctuation on groundwater level data was filtered out by setting a constant value for river stage inputs of the recursive time series models. The hybrid water table fluctuation method was employed to estimate the groundwater recharge using the filtered data. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.
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.
Directory of Open Access Journals (Sweden)
Adina-Eliza CROITORU
2014-11-01
Full Text Available This paper investigates summer temperature trends in the Romanian Carpathian Mountains, for three types of topographies: summit, slope and depression. We used a change-point regression model with serially correlated errors and compared it with a mainstream change-point model with independent errors. Statistical theory ensures that the former model gives a more accurate trend analysis than the latter model. For both models we identified strongly decreasing trends before the change-point and strongly increasing trends afterwards for most summer temperature series. The change-points are more consistent with each other, in the early 80’s, when using the former model. These general results occur for all topography types. A separate multiple regression model reveals that the temperature dynamics in the Romanian Carpathians can be explained by a linear effect of several major atmospheric circulation patterns
Trends and Challenges of Electronic Auctions as a New Business Model
Directory of Open Access Journals (Sweden)
Margarita Janeska
2014-11-01
Full Text Available The virtual internet based market allowed different forms of trading. Electronic business opens up new markets and market segments. Auctions are a very reliable and competitive trading model which allows to achieve fair prices and to choose the optimal business partners. The purpose of this model is that the system can extend the duration of the auction until they met various commercial applications.The basic motivation for research in this paper is the increasing trend of the use of electronic auctions in the process of selling and buying products and services.
Real-time modeling and online filtering of the stochastic error in a fiber optic current transducer
Wang, Lihui; Wei, Guangjin; Zhu, Yunan; Liu, Jian; Tian, Zhengqi
2016-10-01
The stochastic error characteristics of a fiber optic current transducer (FOCT) influence the relay protection, electric-energy metering, and other devices in the spacer layer. Real-time modeling and online filtering of the FOCT’s stochastic error tends to be an effective method for improving the measurement accuracy of the FOCT. This paper first pretreats and inspects the FOCT data, statistically. Then, the model order is set by the AIC principle to establish an ARMA (2,1) model and model’s applicability is tested. Finally, a Kalman filter is adopted to reduce the noise in the FOCT data. The results of the experiment and the simulation demonstrate that there is a notable decrease in the stochastic error after time series modeling and Kalman filtering. Besides, the mean-variance is decreased by two orders. All the stochastic error coefficients are decreased by the total variance method; the BI is decreased by 41.4%, the RRW is decreased by 67.5%, and the RR is decreased by 53.4%. Consequently, the method can reduce the stochastic error and improve the measurement accuracy of the FOCT, effectively.
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.
Prognosticating fault development rate in wind turbine generator bearings using local trend models
DEFF Research Database (Denmark)
Skrimpas, Georgios Alexandros; Palou, Jonel; Sweeney, Christian Walsted;
2016-01-01
Generator bearing defects, e.g. ball, inner and outer race defects, are ranked among the most frequent mechanical failures encountered in wind turbines. Diagnosis and prognosis of bearing faults can be successfully implemented using vibration based condition monitoring systems, where tracking...... the signal energy between 10Hz to 1000Hz is utilized as feature to characterize the severity of developing bearing faults. Furthermore, local trend models are employed to predict the progression of bearing defects from a vibration standpoint in accordance with the limits suggested in ISO 10816. Predictions...... of vibration trends from multi-megawatt wind turbine generators are presented, showing the effectiveness of the suggested approach on the calculation of the RUL and fault progression rate....
Modeling & Analysis of Shunt Active Power Filter Using IRP Theory Fed to Induction Drive
Directory of Open Access Journals (Sweden)
PABBISETTY SAI SUJATHA
2014-10-01
Full Text Available Utility distribution networks have sensitive industrial loads and critical commercial operations suffer from various types of outages and service interruptions which can cost significant financial losses. Because of sensitivity of consumers on power quality and advancement in power electronics. Active power filter technology is the most efficient way to compensate reactive power and cancel out low order harmonics generated by nonlinear loads. The shunt active power filter was considered to be the most basic configuration for the APF. This paper reviews the basic principle of shunt active power filter, along with the current tracking circuit based on the instantaneous reactive power theory and the main circuit performing as an inverter with PWM hysteresis control. The instantaneous active and reactive current component (id-iq method and instantaneous active and reactive power (p-q method are two control strategies which are extensively used in active filters. A shunt active filter based on the instantaneous active and reactive current component (id-iq method is proposed. This method aims to compensate harmonic and first harmonic unbalance. A Comprehensive control method is analyzed and a harmonic Compensation simulation is conducted, the result of which verifies The harmonic detection algorithm is well-proposed and the power Quality of the grid is overall-enhanced. The results are obtained using MATLAB/SIMULINK software.
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.
State-space modeling of population sizes and trends in Nihoa Finch and Millerbird
Gorresen, P. Marcos; Brinck, Kevin W.; Camp, Richard J.; Farmer, Chris; Plentovich, Sheldon M.; Banko, Paul C.
2016-01-01
Both of the 2 passerines endemic to Nihoa Island, Hawai‘i, USA—the Nihoa Millerbird (Acrocephalus familiaris kingi) and Nihoa Finch (Telespiza ultima)—are listed as endangered by federal and state agencies. Their abundances have been estimated by irregularly implemented fixed-width strip-transect sampling from 1967 to 2012, from which area-based extrapolation of the raw counts produced highly variable abundance estimates for both species. To evaluate an alternative survey method and improve abundance estimates, we conducted variable-distance point-transect sampling between 2010 and 2014. We compared our results to those obtained from strip-transect samples. In addition, we applied state-space models to derive improved estimates of population size and trends from the legacy time series of strip-transect counts. Both species were fairly evenly distributed across Nihoa and occurred in all or nearly all available habitat. Population trends for Nihoa Millerbird were inconclusive because of high within-year variance. Trends for Nihoa Finch were positive, particularly since the early 1990s. Distance-based analysis of point-transect counts produced mean estimates of abundance similar to those from strip-transects but was generally more precise. However, both survey methods produced biologically unrealistic variability between years. State-space modeling of the long-term time series of abundances obtained from strip-transect counts effectively reduced uncertainty in both within- and between-year estimates of population size, and allowed short-term changes in abundance trajectories to be smoothed into a long-term trend.