Diffusion Filters for Variational Data Assimilation of Sea Surface Temperature in an Intermediate Climate Model

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.

Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model

KAUST Repository

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.

Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model

KAUST Repository

Subramanian, Aneesh C.; Hoteit, Ibrahim; Cornuelle, Bruce; Miller, Arthur J.; Song, Hajoon

2012-01-01

This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.

Nonlinear Statistical Signal Processing: A Particle Filtering Approach

International Nuclear Information System (INIS)

Candy, J.

2007-01-01

A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It is shown how the underlying hidden or state variables are easily assimilated into this Bayesian construct. Importance sampling methods are then discussed and shown how they can be extended to sequential solutions implemented using Markovian state-space models as a natural evolution. With this in mind, the idea of a particle filter, which is a discrete representation of a probability distribution, is developed and shown how it can be implemented using sequential importance sampling/resampling methods. Finally, an application is briefly discussed comparing the performance of the particle filter designs with classical nonlinear filter implementations

Advances in sequential data assimilation and numerical weather forecasting: An Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic ensemble square root filters, and a test of a new time stepping scheme in an atmospheric model

Science.gov (United States)

Amezcua, Javier

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion

An operator model-based filtering scheme

International Nuclear Information System (INIS)

Sawhney, R.S.; Dodds, H.L.; Schryer, J.C.

1990-01-01

This paper presents a diagnostic model developed at Oak Ridge National Laboratory (ORNL) for off-normal nuclear power plant events. The diagnostic model is intended to serve as an embedded module of a cognitive model of the human operator, one application of which could be to assist control room operators in correctly responding to off-normal events by providing a rapid and accurate assessment of alarm patterns and parameter trends. The sequential filter model is comprised of two distinct subsystems --- an alarm analysis followed by an analysis of interpreted plant signals. During the alarm analysis phase, the alarm pattern is evaluated to generate hypotheses of possible initiating events in order of likelihood of occurrence. Each hypothesis is further evaluated through analysis of the current trends of state variables in order to validate/reject (in the form of increased/decreased certainty factor) the given hypothesis. 7 refs., 4 figs

Particle filters, a quasi-Monte-Carlo-solution for segmentation of coronaries.

Science.gov (United States)

Florin, Charles; Paragios, Nikos; Williams, Jim

2005-01-01

In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.

Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

KAUST Repository

Khaki, M.

2017-07-06

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

Bayesian signal processing classical, modern, and particle filtering methods

CERN Document Server

Candy, James V

2016-01-01

This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed an...

Sequential Probability Ratio Test for Spacecraft Collision Avoidance Maneuver Decisions

Science.gov (United States)

Carpenter, J. Russell; Markley, F. Landis

2013-01-01

A document discusses sequential probability ratio tests that explicitly allow decision-makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models the null hypotheses that the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming, highly elliptical orbit formation flying mission.

Sequential and simultaneous choices: testing the diet selection and sequential choice models.

Science.gov (United States)

Freidin, Esteban; Aw, Justine; Kacelnik, Alex

2009-03-01

We investigate simultaneous and sequential choices in starlings, using Charnov's Diet Choice Model (DCM) and Shapiro, Siller and Kacelnik's Sequential Choice Model (SCM) to integrate function and mechanism. During a training phase, starlings encountered one food-related option per trial (A, B or R) in random sequence and with equal probability. A and B delivered food rewards after programmed delays (shorter for A), while R ('rejection') moved directly to the next trial without reward. In this phase we measured latencies to respond. In a later, choice, phase, birds encountered the pairs A-B, A-R and B-R, the first implementing a simultaneous choice and the second and third sequential choices. The DCM predicts when R should be chosen to maximize intake rate, and SCM uses latencies of the training phase to predict choices between any pair of options in the choice phase. The predictions of both models coincided, and both successfully predicted the birds' preferences. The DCM does not deal with partial preferences, while the SCM does, and experimental results were strongly correlated to this model's predictions. We believe that the SCM may expose a very general mechanism of animal choice, and that its wider domain of success reflects the greater ecological significance of sequential over simultaneous choices.

Characteristics of Quoit filter, a digital filter developed for the extraction of circumscribed shadows, and its applications to mammograms

International Nuclear Information System (INIS)

Isobe, Yoshiaki; Ohkubo, Natsumi; Yamamoto, Shinji; Toriwaki, Jun-ichiro; Kobatake, Hidefumi.

1993-01-01

This paper presents a newly developed filter called Quoit filter, which detects circumscribed shadows (concentric circular isolated image), like typical cancer regions. This Quoit filter is based on the mathematical morphology and is found to have interesting facts as follows. (1) Output of this filter can be analytically expressible when an input image is assumed to be a concentric circular model (output is expectable for typical inputs). (2) This filter has an ability to reconstruct original isolated models mentioned in (1) selectively, when this filter is applied sequentially twice. This filter was tested on the detection of cancer regions in X-ray mammograms, and for 12 cancer mammograms, this filter achieved a true-positive cancer detection rate of 100 %. (author)

Particle rejuvenation of Rao-Blackwellized sequential Monte Carlo smoothers for conditionally linear and Gaussian models

Science.gov (United States)

Nguyen, Ngoc Minh; Corff, Sylvain Le; Moulines, Éric

2017-12-01

This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization: particle approximation is used to sample sequences of hidden regimes while the Gaussian states are explicitly integrated conditional on the sequence of regimes and observations, using variants of the Kalman filter/smoother. The first successful attempt to use Rao-Blackwellization for smoothing extends the Bryson-Frazier smoother for Gaussian linear state space models using the generalized two-filter formula together with Kalman filters/smoothers. More recently, a forward-backward decomposition of smoothing distributions mimicking the Rauch-Tung-Striebel smoother for the regimes combined with backward Kalman updates has been introduced. This paper investigates the benefit of introducing additional rejuvenation steps in all these algorithms to sample at each time instant new regimes conditional on the forward and backward particles. This defines particle-based approximations of the smoothing distributions whose support is not restricted to the set of particles sampled in the forward or backward filter. These procedures are applied to commodity markets which are described using a two-factor model based on the spot price and a convenience yield for crude oil data.

Sensitivity Analysis in Sequential Decision Models.

Science.gov (United States)

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration.

Science.gov (United States)

Carrière, Olivier; Hermand, Jean-Pierre

2012-04-01

Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

Science.gov (United States)

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

2018-05-01

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

Precomputing Process Noise Covariance for Onboard Sequential Filters

Science.gov (United States)

Olson, Corwin G.; Russell, Ryan P.; Carpenter, J. Russell

2017-01-01

Process noise is often used in estimation filters to account for unmodeled and mismodeled accelerations in the dynamics. The process noise covariance acts to inflate the state covariance over propagation intervals, increasing the uncertainty in the state. In scenarios where the acceleration errors change significantly over time, the standard process noise covariance approach can fail to provide effective representation of the state and its uncertainty. Consider covariance analysis techniques provide a method to precompute a process noise covariance profile along a reference trajectory using known model parameter uncertainties. The process noise covariance profile allows significantly improved state estimation and uncertainty representation over the traditional formulation. As a result, estimation performance on par with the consider filter is achieved for trajectories near the reference trajectory without the additional computational cost of the consider filter. The new formulation also has the potential to significantly reduce the trial-and-error tuning currently required of navigation analysts. A linear estimation problem as described in several previous consider covariance analysis studies is used to demonstrate the effectiveness of the precomputed process noise covariance, as well as a nonlinear descent scenario at the asteroid Bennu with optical navigation.

Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances

Institute of Scientific and Technical Information of China (English)

QI Wen-Juan; ZHANG Peng; DENG Zi-Li

2014-01-01

This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.

Particle filtering for passive fathometer tracking.

Science.gov (United States)

Michalopoulou, Zoi-Heleni; Yardim, Caglar; Gerstoft, Peter

2012-01-01

Seabed interface depths and fathometer amplitudes are tracked for an unknown and changing number of sub-bottom reflectors. This is achieved by incorporating conventional and adaptive fathometer processors into sequential Monte Carlo methods for a moving vertical line array. Sediment layering information and time-varying fathometer response amplitudes are tracked by using a multiple model particle filter with an uncertain number of reflectors. Results are compared to a classical particle filter where the number of reflectors is considered to be known. Reflector tracking is demonstrated for both conventional and adaptive processing applied to the drifting array data from the Boundary 2003 experiment. The layering information is successfully tracked by the multiple model particle filter even for noisy fathometer outputs. © 2012 Acoustical Society of America.

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter.

Science.gov (United States)

Rastetter, Edward B; Williams, Mathew; Griffin, Kevin L; Kwiatkowski, Bonnie L; Tomasky, Gabrielle; Potosnak, Mark J; Stoy, Paul C; Shaver, Gaius R; Stieglitz, Marc; Hobbie, John E; Kling, George W

2010-07-01

Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter

Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation

Directory of Open Access Journals (Sweden)

M. Morzfeld

2012-06-01

Full Text Available Implicit particle filtering is a sequential Monte Carlo method for data assimilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by minimizing, for each particle, a scalar function F of the state variables. Some previous implementations of the implicit filter rely on finding the Hessians of these functions. The calculation of the Hessians can be cumbersome if the state dimension is large or if the underlying physics are such that derivatives of F are difficult to calculate, as happens in many geophysical applications, in particular in models with partial noise, i.e. with a singular state covariance matrix. Examples of models with partial noise include models where uncertain dynamic equations are supplemented by conservation laws with zero uncertainty, or with higher order (in time stochastic partial differential equations (PDE or with PDEs driven by spatially smooth noise processes. We make the implicit particle filter applicable to such situations by combining gradient descent minimization with random maps and show that the filter is efficient, accurate and reliable because it operates in a subspace of the state space. As an example, we consider a system of nonlinear stochastic PDEs that is of importance in geomagnetic data assimilation.

Sequential and Simultaneous Logit: A Nested Model.

NARCIS (Netherlands)

van Ophem, J.C.M.; Schram, A.J.H.C.

1997-01-01

A nested model is presented which has both the sequential and the multinomial logit model as special cases. This model provides a simple test to investigate the validity of these specifications. Some theoretical properties of the model are discussed. In the analysis a distribution function is

Proposed hardware architectures of particle filter for object tracking

Science.gov (United States)

Abd El-Halym, Howida A.; Mahmoud, Imbaby Ismail; Habib, SED

2012-12-01

In this article, efficient hardware architectures for particle filter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.

Empirical intrinsic geometry for nonlinear modeling and time series filtering.

Science.gov (United States)

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.

Ensemble Kalman Filter Assimilation of ERT Data for Numerical Modeling of Seawater Intrusion in a Laboratory Experiment

Directory of Open Access Journals (Sweden)

Véronique Bouzaglou

2018-03-01

Full Text Available Seawater intrusion in coastal aquifers is a worldwide problem exacerbated by aquifer overexploitation and climate changes. To limit the deterioration of water quality caused by saline intrusion, research studies are needed to identify and assess the performance of possible countermeasures, e.g., underground barriers. Within this context, numerical models are fundamental to fully understand the process and for evaluating the effectiveness of the proposed solutions to contain the saltwater wedge; on the other hand, they are typically affected by uncertainty on hydrogeological parameters, as well as initial and boundary conditions. Data assimilation methods such as the ensemble Kalman filter (EnKF represent promising tools that can reduce such uncertainties. Here, we present an application of the EnKF to the numerical modeling of a laboratory experiment where seawater intrusion was reproduced in a specifically designed sandbox and continuously monitored with electrical resistivity tomography (ERT. Combining EnKF and the SUTRA model for the simulation of density-dependent flow and transport in porous media, we assimilated the collected ERT data by means of joint and sequential assimilation approaches. In the joint approach, raw ERT data (electrical resistances are assimilated to update both salt concentration and soil parameters, without the need for an electrical inversion. In the sequential approach, we assimilated electrical conductivities computed from a previously performed electrical inversion. Within both approaches, we suggest dual-step update strategies to minimize the effects of spurious correlations in parameter estimation. The results show that, in both cases, ERT data assimilation can reduce the uncertainty not only on the system state in terms of salt concentration, but also on the most relevant soil parameters, i.e., saturated hydraulic conductivity and longitudinal dispersivity. However, the sequential approach is more prone to

Imaging spectrometer using a liquid crystal tunable filter

Science.gov (United States)

Chrien, Thomas G.; Chovit, Christopher; Miller, Peter J.

1993-09-01

A demonstration imaging spectrometer using a liquid crystal tunable filter (LCTF) was built and tested on a hot air balloon platform. The LCTF is a tunable polarization interference or Lyot filter. The LCTF enables a small, light weight, low power, band sequential imaging spectrometer design. An overview of the prototype system is given along with a description of balloon experiment results. System model performance predictions are given for a future LCTF based imaging spectrometer design. System design considerations of LCTF imaging spectrometers are discussed.

Modelling modulation perception : modulation low-pass filter or modulation filter bank?

NARCIS (Netherlands)

Dau, T.; Kollmeier, B.; Kohlrausch, A.G.

1995-01-01

In current models of modulation perception, the stimuli are first filtered and nonlinearly transformed (mostly half-wave rectified). In order to model the low-pass characteristic of measured modulation transfer functions, the next stage in the models is a first-order low-pass filter with a typical

Comparing a recursive digital filter with the moving-average and sequential probability-ratio detection methods for SNM portal monitors

International Nuclear Information System (INIS)

Fehlau, P.E.

1993-01-01

The author compared a recursive digital filter proposed as a detection method for French special nuclear material monitors with the author's detection methods, which employ a moving-average scaler or a sequential probability-ratio test. Each of these nine test subjects repeatedly carried a test source through a walk-through portal monitor that had the same nuisance-alarm rate with each method. He found that the average detection probability for the test source is also the same for each method. However, the recursive digital filter may have on drawback: its exponentially decreasing response to past radiation intensity prolongs the impact of any interference from radiation sources of radiation-producing machinery. He also examined the influence of each test subject on the monitor's operation by measuring individual attenuation factors for background and source radiation, then ranked the subjects' attenuation factors against their individual probabilities for detecting the test source. The one inconsistent ranking was probably caused by that subject's unusually long stride when passing through the portal

Sequential neural models with stochastic layers

DEFF Research Database (Denmark)

Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

2016-01-01

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

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

Tracking speckle displacement by double Kalman filtering

Institute of Scientific and Technical Information of China (English)

Donghui Li; Li Guo

2006-01-01

@@ A tracking technique using two sequentially-connected Kalman filter for tracking laser speckle displacement is presented. One Kalman filter tracks temporal speckle displacement, while another Kalman filter tracks spatial speckle displacement. The temporal Kalman filter provides a prior for the spatial Kalman filter, and the spatial Kalman filter provides measurements for the temporal Kalman filter. The contribution of a prior to estimations of the spatial Kalman filter is analyzed. An optical analysis system was set up to verify the double-Kalman-filter tracker's ability of tracking laser speckle's constant displacement.

Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions.

Directory of Open Access Journals (Sweden)

Thomas H B FitzGerald

2017-05-01

Full Text Available Normative models of human cognition often appeal to Bayesian filtering, which provides optimal online estimates of unknown or hidden states of the world, based on previous observations. However, in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state. Importantly, Bayesian filtering and sequential inference strategies make different predictions about beliefs and subsequent choices, rendering them behaviourally dissociable. Taking data from a probabilistic reversal task we show that subjects' choices provide strong evidence that they are representing short sequences of states. Between-subject measures of this implicit sequential inference strategy had a neurobiological underpinning and correlated with grey matter density in prefrontal and parietal cortex, as well as the hippocampus. Our findings provide, to our knowledge, the first evidence for sequential inference in human cognition, and by exploiting between-subject variation in this measure we provide pointers to its neuronal substrates.

An assessment of particle filtering methods and nudging for climate state reconstructions

NARCIS (Netherlands)

S. Dubinkina (Svetlana); H. Goosse

2013-01-01

htmlabstractUsing the climate model of intermediate complexity LOVECLIM in an idealized framework, we assess three data-assimilation methods for reconstructing the climate state. The methods are a nudging, a particle filter with sequential importance resampling, and a nudging proposal particle

Mixtures of skewed Kalman filters

KAUST Repository

Kim, Hyoungmoon

2014-01-01

Normal state-space models are prevalent, but to increase the applicability of the Kalman filter, we propose mixtures of skewed, and extended skewed, Kalman filters. To do so, the closed skew-normal distribution is extended to a scale mixture class of closed skew-normal distributions. Some basic properties are derived and a class of closed skew. t distributions is obtained. Our suggested family of distributions is skewed and has heavy tails too, so it is appropriate for robust analysis. Our proposed special sequential Monte Carlo methods use a random mixture of the closed skew-normal distributions to approximate a target distribution. Hence it is possible to handle skewed and heavy tailed data simultaneously. These methods are illustrated with numerical experiments. © 2013 Elsevier Inc.

Test models for improving filtering with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.

MapReduce particle filtering with exact resampling and deterministic runtime

Science.gov (United States)

Thiyagalingam, Jeyarajan; Kekempanos, Lykourgos; Maskell, Simon

2017-12-01

Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduce. In this paper, we describe an implementation of a particle filter using MapReduce. We focus on a component that what would otherwise be a bottleneck to parallel execution, the resampling component. We devise a new implementation of this component, which requires no approximations, has O( N) spatial complexity and deterministic O((log N)2) time complexity. Results demonstrate the utility of this new component and culminate in consideration of a particle filter with 224 particles being distributed across 512 processor cores.

Comparing Consider-Covariance Analysis with Sigma-Point Consider Filter and Linear-Theory Consider Filter Formulations

Science.gov (United States)

Lisano, Michael E.

2007-01-01

Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to

Testing particle filters on convective scale dynamics

Science.gov (United States)

Haslehner, Mylene; Craig, George. C.; Janjic, Tijana

2014-05-01

Particle filters have been developed in recent years to deal with highly nonlinear dynamics and non Gaussian error statistics that also characterize data assimilation on convective scales. In this work we explore the use of the efficient particle filter (P.v. Leeuwen, 2011) for convective scale data assimilation application. The method is tested in idealized setting, on two stochastic models. The models were designed to reproduce some of the properties of convection, for example the rapid development and decay of convective clouds. The first model is a simple one-dimensional, discrete state birth-death model of clouds (Craig and Würsch, 2012). For this model, the efficient particle filter that includes nudging the variables shows significant improvement compared to Ensemble Kalman Filter and Sequential Importance Resampling (SIR) particle filter. The success of the combination of nudging and resampling, measured as RMS error with respect to the 'true state', is proportional to the nudging intensity. Significantly, even a very weak nudging intensity brings notable improvement over SIR. The second model is a modified version of a stochastic shallow water model (Würsch and Craig 2013), which contains more realistic dynamical characteristics of convective scale phenomena. Using the efficient particle filter and different combination of observations of the three field variables (wind, water 'height' and rain) allows the particle filter to be evaluated in comparison to a regime where only nudging is used. Sensitivity to the properties of the model error covariance is also considered. Finally, criteria are identified under which the efficient particle filter outperforms nudging alone. References: Craig, G. C. and M. Würsch, 2012: The impact of localization and observation averaging for convective-scale data assimilation in a simple stochastic model. Q. J. R. Meteorol. Soc.,139, 515-523. Van Leeuwen, P. J., 2011: Efficient non-linear data assimilation in geophysical

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.

Nonlinear consider covariance analysis using a sigma-point filter formulation

Science.gov (United States)

Lisano, Michael E.

2006-01-01

The research reported here extends the mathematical formulation of nonlinear, sigma-point estimators to enable consider covariance analysis for dynamical systems. This paper presents a novel sigma-point consider filter algorithm, for consider-parameterized nonlinear estimation, following the unscented Kalman filter (UKF) variation on the sigma-point filter formulation, which requires no partial derivatives of dynamics models or measurement models with respect to the parameter list. It is shown that, consistent with the attributes of sigma-point estimators, a consider-parameterized sigma-point estimator can be developed entirely without requiring the derivation of any partial-derivative matrices related to the dynamical system, the measurements, or the considered parameters, which appears to be an advantage over the formulation of a linear-theory sequential consider estimator. It is also demonstrated that a consider covariance analysis performed with this 'partial-derivative-free' formulation yields equivalent results to the linear-theory consider filter, for purely linear problems.

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.

Comparison of Sequential and Variational Data Assimilation

Science.gov (United States)

Alvarado Montero, Rodolfo; Schwanenberg, Dirk; Weerts, Albrecht

2017-04-01

Data assimilation is a valuable tool to improve model state estimates by combining measured observations with model simulations. It has recently gained significant attention due to its potential in using remote sensing products to improve operational hydrological forecasts and for reanalysis purposes. This has been supported by the application of sequential techniques such as the Ensemble Kalman Filter which require no additional features within the modeling process, i.e. it can use arbitrary black-box models. Alternatively, variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function describes the trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables. While sequential techniques have been commonly applied to hydrological processes, variational techniques are seldom used. In our believe, this is mainly attributed to the required computation of first order sensitivities by algorithmic differentiation techniques and related model enhancements, but also to lack of comparison between both techniques. We contribute to filling this gap and present the results from the assimilation of streamflow data in two basins located in Germany and Canada. The assimilation introduces noise to precipitation and temperature to produce better initial estimates of an HBV model. The results are computed for a hindcast period and assessed using lead time performance metrics. The study concludes with a discussion of the main features of each technique and their advantages/disadvantages in hydrological applications.

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...

A continuous-time neural model for sequential action.

Science.gov (United States)

Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Kalman filter-based gap conductance modeling

International Nuclear Information System (INIS)

Tylee, J.L.

1983-01-01

Geometric and thermal property uncertainties contribute greatly to the problem of determining conductance within the fuel-clad gas gap of a nuclear fuel pin. Accurate conductance values are needed for power plant licensing transient analysis and for test analyses at research facilities. Recent work by Meek, Doerner, and Adams has shown that use of Kalman filters to estimate gap conductance is a promising approach. A Kalman filter is simply a mathematical algorithm that employs available system measurements and assumed dynamic models to generate optimal system state vector estimates. This summary addresses another Kalman filter approach to gap conductance estimation and subsequent identification of an empirical conductance model

Attitude Modeling Using Kalman Filter Approach for Improving the Geometric Accuracy of Cartosat-1 Data Products

Directory of Open Access Journals (Sweden)

Nita H. SHAH

2010-07-01

Full Text Available This paper deals with the rigorous photogrammetric solution to model the uncertainty in the orientation parameters of Indian Remote Sensing Satellite IRS-P5 (Cartosat-1. Cartosat-1 is a three axis stabilized spacecraft launched into polar sun-synchronous circular orbit at an altitude of 618 km. The satellite has two panchromatic (PAN cameras with nominal resolution of ~2.5 m. The camera looking ahead is called FORE mounted with +26 deg angle and the other looking near nadir is called AFT mounted with -5 deg, in along track direction. Data Product Generation Software (DPGS system uses the rigorous photogrammetric Collinearity model in order to utilize the full system information, together with payload geometry & control points, for estimating the uncertainty in attitude parameters. The initial orbit, attitude knowledge is obtained from GPS bound orbit measurement, star tracker and gyros. The variations in satellite attitude with time are modelled using simple linear polynomial model. Also, based on this model, Kalman filter approach is studied and applied to improve the uncertainty in the orientation of spacecraft with high quality ground control points (GCPs. The sequential estimator (Kalman filter is used in an iterative process which corrects the parameters at each time of observation rather than at epoch time. Results are presented for three stereo data sets. The accuracy of model depends on the accuracy of the control points.

Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise.

Science.gov (United States)

Vuković, Najdan; Miljković, Zoran

2015-03-01

Feedforward neural networks (FFNN) are among the most used neural networks for modeling of various nonlinear problems in engineering. In sequential and especially real time processing all neural networks models fail when faced with outliers. Outliers are found across a wide range of engineering problems. Recent research results in the field have shown that to avoid overfitting or divergence of the model, new approach is needed especially if FFNN is to run sequentially or in real time. To accommodate limitations of FFNN when training data contains a certain number of outliers, this paper presents new learning algorithm based on improvement of conventional extended Kalman filter (EKF). Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is not constant; the sequence of noise measurement covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is modeled as inverse Wishart distribution. In each iteration EKF-OR simultaneously estimates noise estimates and current best estimate of FFNN parameters. Bayesian framework enables one to mathematically derive expressions, while analytical intractability of the Bayes' update step is solved by using structured variational approximation. All mathematical expressions in the paper are derived using the first principles. Extensive experimental study shows that FFNN trained with developed learning algorithm, achieves low prediction error and good generalization quality regardless of outliers' presence in training data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling, simulation, and design of SAW grating filters

Science.gov (United States)

Schwelb, Otto; Adler, E. L.; Slaboszewicz, J. K.

1990-05-01

A systematic procedure for modeling, simulating, and designing SAW (surface acoustic wave) grating filters, taking losses into account, is described. Grating structures and IDTs (interdigital transducers) coupling to SAWs are defined by cascadable transmission-matrix building blocks. Driving point and transfer characteristics (immittances) of complex architectures consisting of gratings, transducers, and coupling networks are obtained by chain-multiplying building-block matrices. This modular approach to resonator filter analysis and design combines the elements of lossy filter synthesis with the transmission-matrix description of SAW components. A multipole filter design procedure based on a lumped-element-model approximation of one-pole two-port resonator building blocks is given and the range of validity of this model examined. The software for simulating the performance of SAW grating devices based on this matrix approach is described, and its performance, when linked to the design procedure to form a CAD/CAA (computer-aided design and analysis) multiple-filter design package, is illustrated with a resonator filter design example.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

Energy Technology Data Exchange (ETDEWEB)

Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

A Distributional Representation Model For Collaborative Filtering

OpenAIRE

Junlin, Zhang; Heng, Cai; Tongwen, Huang; Huiping, Xue

2015-01-01

In this paper, we propose a very concise deep learning approach for collaborative filtering that jointly models distributional representation for users and items. The proposed framework obtains better performance when compared against current state-of-art algorithms and that made the distributional representation model a promising direction for further research in the collaborative filtering.

Inverse problems in 1D hemodynamics on systemic networks: a sequential approach.

Science.gov (United States)

Lombardi, D

2014-02-01

In this work, a sequential approach based on the unscented Kalman filter is applied to solve inverse problems in 1D hemodynamics, on a systemic network. For instance, the arterial stiffness is estimated by exploiting cross-sectional area and mean speed observations in several locations of the arteries. The results are compared with those ones obtained by estimating the pulse wave velocity and the Moens-Korteweg formula. In the last section, a perspective concerning the identification of the terminal models parameters and peripheral circulation (modeled by a Windkessel circuit) is presented. Copyright © 2013 John Wiley & Sons, Ltd.

Models of sequential decision making in consumer lending

OpenAIRE

Kanshukan Rajaratnam; Peter A. Beling; George A. Overstreet

2016-01-01

Abstract In this paper, we introduce models of sequential decision making in consumer lending. From the definition of adverse selection in static lending models, we show that homogenous borrowers take-up offers at different instances of time when faced with a sequence of loan offers. We postulate that bounded rationality and diverse decision heuristics used by consumers drive the decisions they make about credit offers. Under that postulate, we show how observation of early decisions in a seq...

Transient Heating and Thermomechanical Stress Modeling of Ceramic HEPA Filters

Energy Technology Data Exchange (ETDEWEB)

Bogle, Brandon [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kelly, James [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Haslam, Jeffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-09-29

The purpose of this report is to showcase an initial finite-element analysis model of a ceramic High-Efficiency Particulate (HEPA) Air filter design. Next generation HEPA filter assemblies are being developed at LLNL to withstand high-temperature fire scenarios by use of ceramics and advanced materials. The filters are meant for use in radiological and nuclear facilities, and are required to survive 500°C fires over an hour duration. During such conditions, however, collecting data under varying parameters can be challenging; therefore, a Finite Element Analysis model of the filter was conducted using COMSOL ® Multiphysics to analyze the effects of fire. Finite Element Analysis (FEA) modelling offers several opportunities: researchers can quickly and easily consider impacts of potential design changes, material selection, and flow characterization on filter performance. Specifically, this model provides stress references for the sealant at high temperatures. Modeling of full filter assemblies was deemed inefficient given the computational requirements, so a section of three tubes from the assembly was modeled. The model looked at the transient heating and thermomechanical stress development during a 500°C air flow at 6 CFM. Significant stresses were found at the ceramic-metal interfaces of the filter, and conservative temperature profiles at locations of interest were plotted. The model can be used for the development of sealants that minimize stresses at the ceramic-metal interface. Further work on the model would include the full filter assembly and consider heat losses to make more accurate predictions.

Joint Data Assimilation and Parameter Calibration in on-line groundwater modelling using Sequential Monte Carlo techniques

Science.gov (United States)

Ramgraber, M.; Schirmer, M.

2017-12-01

As computational power grows and wireless sensor networks find their way into common practice, it becomes increasingly feasible to pursue on-line numerical groundwater modelling. The reconciliation of model predictions with sensor measurements often necessitates the application of Sequential Monte Carlo (SMC) techniques, most prominently represented by the Ensemble Kalman Filter. In the pursuit of on-line predictions it seems advantageous to transcend the scope of pure data assimilation and incorporate on-line parameter calibration as well. Unfortunately, the interplay between shifting model parameters and transient states is non-trivial. Several recent publications (e.g. Chopin et al., 2013, Kantas et al., 2015) in the field of statistics discuss potential algorithms addressing this issue. However, most of these are computationally intractable for on-line application. In this study, we investigate to what extent compromises between mathematical rigour and computational restrictions can be made within the framework of on-line numerical modelling of groundwater. Preliminary studies are conducted in a synthetic setting, with the goal of transferring the conclusions drawn into application in a real-world setting. To this end, a wireless sensor network has been established in the valley aquifer around Fehraltorf, characterized by a highly dynamic groundwater system and located about 20 km to the East of Zürich, Switzerland. By providing continuous probabilistic estimates of the state and parameter distribution, a steady base for branched-off predictive scenario modelling could be established, providing water authorities with advanced tools for assessing the impact of groundwater management practices. Chopin, N., Jacob, P.E. and Papaspiliopoulos, O. (2013): SMC2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75 (3), p. 397-426. Kantas, N., Doucet, A., Singh, S

Infrared image background modeling based on improved Susan filtering

Science.gov (United States)

Yuehua, Xia

2018-02-01

When SUSAN filter is used to model the infrared image, the Gaussian filter lacks the ability of direction filtering. After filtering, the edge information of the image cannot be preserved well, so that there are a lot of edge singular points in the difference graph, increase the difficulties of target detection. To solve the above problems, the anisotropy algorithm is introduced in this paper, and the anisotropic Gauss filter is used instead of the Gauss filter in the SUSAN filter operator. Firstly, using anisotropic gradient operator to calculate a point of image's horizontal and vertical gradient, to determine the long axis direction of the filter; Secondly, use the local area of the point and the neighborhood smoothness to calculate the filter length and short axis variance; And then calculate the first-order norm of the difference between the local area of the point's gray-scale and mean, to determine the threshold of the SUSAN filter; Finally, the built SUSAN filter is used to convolution the image to obtain the background image, at the same time, the difference between the background image and the original image is obtained. The experimental results show that the background modeling effect of infrared image is evaluated by Mean Squared Error (MSE), Structural Similarity (SSIM) and local Signal-to-noise Ratio Gain (GSNR). Compared with the traditional filtering algorithm, the improved SUSAN filter has achieved better background modeling effect, which can effectively preserve the edge information in the image, and the dim small target is effectively enhanced in the difference graph, which greatly reduces the false alarm rate of the image.

Face identification with frequency domain matched filtering in mobile environments

Science.gov (United States)

Lee, Dong-Su; Woo, Yong-Hyun; Yeom, Seokwon; Kim, Shin-Hwan

2012-06-01

Face identification at a distance is very challenging since captured images are often degraded by blur and noise. Furthermore, the computational resources and memory are often limited in the mobile environments. Thus, it is very challenging to develop a real-time face identification system on the mobile device. This paper discusses face identification based on frequency domain matched filtering in the mobile environments. Face identification is performed by the linear or phase-only matched filter and sequential verification stages. The candidate window regions are decided by the major peaks of the linear or phase-only matched filtering outputs. The sequential stages comprise a skin-color test and an edge mask filtering test, which verify color and shape information of the candidate regions in order to remove false alarms. All algorithms are built on the mobile device using Android platform. The preliminary results show that face identification of East Asian people can be performed successfully in the mobile environments.

Ensemble Kalman filtering with one-step-ahead smoothing

KAUST Repository

Raboudi, Naila F.

2018-01-11

The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.

Filtering observations without the initial guess

Science.gov (United States)

Chin, T. M.; Abbondanza, C.; Gross, R. S.; Heflin, M. B.; Parker, J. W.; Soja, B.; Wu, X.

2017-12-01

Noisy geophysical observations sampled irregularly over space and time are often numerically "analyzed" or "filtered" before scientific usage. The standard analysis and filtering techniques based on the Bayesian principle requires "a priori" joint distribution of all the geophysical parameters of interest. However, such prior distributions are seldom known fully in practice, and best-guess mean values (e.g., "climatology" or "background" data if available) accompanied by some arbitrarily set covariance values are often used in lieu. It is therefore desirable to be able to exploit efficient (time sequential) Bayesian algorithms like the Kalman filter while not forced to provide a prior distribution (i.e., initial mean and covariance). An example of this is the estimation of the terrestrial reference frame (TRF) where requirement for numerical precision is such that any use of a priori constraints on the observation data needs to be minimized. We will present the Information Filter algorithm, a variant of the Kalman filter that does not require an initial distribution, and apply the algorithm (and an accompanying smoothing algorithm) to the TRF estimation problem. We show that the information filter allows temporal propagation of partial information on the distribution (marginal distribution of a transformed version of the state vector), instead of the full distribution (mean and covariance) required by the standard Kalman filter. The information filter appears to be a natural choice for the task of filtering observational data in general cases where prior assumption on the initial estimate is not available and/or desirable. For application to data assimilation problems, reduced-order approximations of both the information filter and square-root information filter (SRIF) have been published, and the former has previously been applied to a regional configuration of the HYCOM ocean general circulation model. Such approximation approaches are also briefed in the

Microwave Ablation: Comparison of Simultaneous and Sequential Activation of Multiple Antennas in Liver Model Systems.

Science.gov (United States)

Harari, Colin M; Magagna, Michelle; Bedoya, Mariajose; Lee, Fred T; Lubner, Meghan G; Hinshaw, J Louis; Ziemlewicz, Timothy; Brace, Christopher L

2016-01-01

To compare microwave ablation zones created by using sequential or simultaneous power delivery in ex vivo and in vivo liver tissue. All procedures were approved by the institutional animal care and use committee. Microwave ablations were performed in both ex vivo and in vivo liver models with a 2.45-GHz system capable of powering up to three antennas simultaneously. Two- and three-antenna arrays were evaluated in each model. Sequential and simultaneous ablations were created by delivering power (50 W ex vivo, 65 W in vivo) for 5 minutes per antenna (10 and 15 minutes total ablation time for sequential ablations, 5 minutes for simultaneous ablations). Thirty-two ablations were performed in ex vivo bovine livers (eight per group) and 28 in the livers of eight swine in vivo (seven per group). Ablation zone size and circularity metrics were determined from ablations excised postmortem. Mixed effects modeling was used to evaluate the influence of power delivery, number of antennas, and tissue type. On average, ablations created by using the simultaneous power delivery technique were larger than those with the sequential technique (P Simultaneous ablations were also more circular than sequential ablations (P = .0001). Larger and more circular ablations were achieved with three antennas compared with two antennas (P simultaneous power delivery creates larger, more confluent ablations with greater temperatures than those created with sequential power delivery. © RSNA, 2015.

On Modeling Large-Scale Multi-Agent Systems with Parallel, Sequential and Genuinely Asynchronous Cellular Automata

International Nuclear Information System (INIS)

Tosic, P.T.

2011-01-01

We study certain types of Cellular Automata (CA) viewed as an abstraction of large-scale Multi-Agent Systems (MAS). We argue that the classical CA model needs to be modified in several important respects, in order to become a relevant and sufficiently general model for the large-scale MAS, and so that thus generalized model can capture many important MAS properties at the level of agent ensembles and their long-term collective behavior patterns. We specifically focus on the issue of inter-agent communication in CA, and propose sequential cellular automata (SCA) as the first step, and genuinely Asynchronous Cellular Automata (ACA) as the ultimate deterministic CA-based abstract models for large-scale MAS made of simple reactive agents. We first formulate deterministic and nondeterministic versions of sequential CA, and then summarize some interesting configuration space properties (i.e., possible behaviors) of a restricted class of sequential CA. In particular, we compare and contrast those properties of sequential CA with the corresponding properties of the classical (that is, parallel and perfectly synchronous) CA with the same restricted class of update rules. We analytically demonstrate failure of the studied sequential CA models to simulate all possible behaviors of perfectly synchronous parallel CA, even for a very restricted class of non-linear totalistic node update rules. The lesson learned is that the interleaving semantics of concurrency, when applied to sequential CA, is not refined enough to adequately capture the perfect synchrony of parallel CA updates. Last but not least, we outline what would be an appropriate CA-like abstraction for large-scale distributed computing insofar as the inter-agent communication model is concerned, and in that context we propose genuinely asynchronous CA. (author)

Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

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Noor M. Khan

2017-01-01

Full Text Available In this paper, a novel processing-efficient architecture of a group of inexpensive and computationally incapable small platforms is proposed for a parallely distributed adaptive signal processing (PDASP operation. The proposed architecture runs computationally expensive procedures like complex adaptive recursive least square (RLS algorithm cooperatively. The proposed PDASP architecture operates properly even if perfect time alignment among the participating platforms is not available. An RLS algorithm with the application of MIMO channel estimation is deployed on the proposed architecture. Complexity and processing time of the PDASP scheme with MIMO RLS algorithm are compared with sequentially operated MIMO RLS algorithm and liner Kalman filter. It is observed that PDASP scheme exhibits much lesser computational complexity parallely than the sequential MIMO RLS algorithm as well as Kalman filter. Moreover, the proposed architecture provides an improvement of 95.83% and 82.29% decreased processing time parallely compared to the sequentially operated Kalman filter and MIMO RLS algorithm for low doppler rate, respectively. Likewise, for high doppler rate, the proposed architecture entails an improvement of 94.12% and 77.28% decreased processing time compared to the Kalman and RLS algorithms, respectively.

State Space Models and the Kalman-Filter in Stochastic Claims Reserving: Forecasting, Filtering and Smoothing

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.

Methodology for modeling the microbial contamination of air filters.

Science.gov (United States)

Joe, Yun Haeng; Yoon, Ki Young; Hwang, Jungho

2014-01-01

In this paper, we propose a theoretical model to simulate microbial growth on contaminated air filters and entrainment of bioaerosols from the filters to an indoor environment. Air filter filtration and antimicrobial efficiencies, and effects of dust particles on these efficiencies, were evaluated. The number of bioaerosols downstream of the filter could be characterized according to three phases: initial, transitional, and stationary. In the initial phase, the number was determined by filtration efficiency, the concentration of dust particles entering the filter, and the flow rate. During the transitional phase, the number of bioaerosols gradually increased up to the stationary phase, at which point no further increase was observed. The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively. It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases. The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.

Methodology for modeling the microbial contamination of air filters.

Directory of Open Access Journals (Sweden)

Yun Haeng Joe

Full Text Available In this paper, we propose a theoretical model to simulate microbial growth on contaminated air filters and entrainment of bioaerosols from the filters to an indoor environment. Air filter filtration and antimicrobial efficiencies, and effects of dust particles on these efficiencies, were evaluated. The number of bioaerosols downstream of the filter could be characterized according to three phases: initial, transitional, and stationary. In the initial phase, the number was determined by filtration efficiency, the concentration of dust particles entering the filter, and the flow rate. During the transitional phase, the number of bioaerosols gradually increased up to the stationary phase, at which point no further increase was observed. The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively. It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases. The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.

A nested sampling particle filter for nonlinear data assimilation

KAUST Repository

Elsheikh, Ahmed H.

2014-04-15

We present an efficient nonlinear data assimilation filter that combines particle filtering with the nested sampling algorithm. Particle filters (PF) utilize a set of weighted particles as a discrete representation of probability distribution functions (PDF). These particles are propagated through the system dynamics and their weights are sequentially updated based on the likelihood of the observed data. Nested sampling (NS) is an efficient sampling algorithm that iteratively builds a discrete representation of the posterior distributions by focusing a set of particles to high-likelihood regions. This would allow the representation of the posterior PDF with a smaller number of particles and reduce the effects of the curse of dimensionality. The proposed nested sampling particle filter (NSPF) iteratively builds the posterior distribution by applying a constrained sampling from the prior distribution to obtain particles in high-likelihood regions of the search space, resulting in a reduction of the number of particles required for an efficient behaviour of particle filters. Numerical experiments with the 3-dimensional Lorenz63 and the 40-dimensional Lorenz96 models show that NSPF outperforms PF in accuracy with a relatively smaller number of particles. © 2013 Royal Meteorological Society.

Hybrid Models of Alternative Current Filter for Hvdc

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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.

A Framework of Finite-model Kalman Filter with Case Study: MVDP-FMKF Algorithm%A Framework of Finite-model Kalman Filter with Case Study:MVDP-FMKF Algorithm

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.

A Particle Smoother with Sequential Importance Resampling for soil hydraulic parameter estimation: A lysimeter experiment

Science.gov (United States)

Montzka, Carsten; Hendricks Franssen, Harrie-Jan; Moradkhani, Hamid; Pütz, Thomas; Han, Xujun; Vereecken, Harry

2013-04-01

An adequate description of soil hydraulic properties is essential for a good performance of hydrological forecasts. So far, several studies showed that data assimilation could reduce the parameter uncertainty by considering soil moisture observations. However, these observations and also the model forcings were recorded with a specific measurement error. It seems a logical step to base state updating and parameter estimation on observations made at multiple time steps, in order to reduce the influence of outliers at single time steps given measurement errors and unknown model forcings. Such outliers could result in erroneous state estimation as well as inadequate parameters. This has been one of the reasons to use a smoothing technique as implemented for Bayesian data assimilation methods such as the Ensemble Kalman Filter (i.e. Ensemble Kalman Smoother). Recently, an ensemble-based smoother has been developed for state update with a SIR particle filter. However, this method has not been used for dual state-parameter estimation. In this contribution we present a Particle Smoother with sequentially smoothing of particle weights for state and parameter resampling within a time window as opposed to the single time step data assimilation used in filtering techniques. This can be seen as an intermediate variant between a parameter estimation technique using global optimization with estimation of single parameter sets valid for the whole period, and sequential Monte Carlo techniques with estimation of parameter sets evolving from one time step to another. The aims are i) to improve the forecast of evaporation and groundwater recharge by estimating hydraulic parameters, and ii) to reduce the impact of single erroneous model inputs/observations by a smoothing method. In order to validate the performance of the proposed method in a real world application, the experiment is conducted in a lysimeter environment.

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Science.gov (United States)

Schön, Thomas B.; Svensson, Andreas; Murray, Lawrence; Lindsten, Fredrik

2018-05-01

Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to one of the state-of-the-art methods-the particle Metropolis-Hastings algorithm-which has proven to offer a practical approximation. This is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space. One of the key merits of the particle Metropolis-Hastings algorithm is that it is guaranteed to converge to the "true solution" under mild assumptions, despite being based on a particle filter with only a finite number of particles. We will also provide a motivating numerical example illustrating the method using a modeling language tailored for sequential Monte Carlo methods. The intention of modeling languages of this kind is to open up the power of sophisticated Monte Carlo methods-including particle Metropolis-Hastings-to a large group of users without requiring them to know all the underlying mathematical details.

Model Adaptation for Prognostics in a Particle Filtering Framework

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Bhaskar Saha

2011-01-01

Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Model Adaptation for Prognostics in a Particle Filtering Framework

Science.gov (United States)

Saha, Bhaskar; Goebel, Kai Frank

2011-01-01

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models

KAUST Repository

El Gharamti, Mohamad

2010-01-01

Understanding the geology and the hydrology of the subsurface is important to model the fluid flow and the behavior of the contaminant. It is essential to have an accurate knowledge of the movement of the contaminants in the porous media in order to track them and later extract them from the aquifer. A two-dimensional flow model is studied and then applied on a linear contaminant transport model in the same porous medium. Because of possible different sources of uncertainties, the deterministic model by itself cannot give exact estimations for the future contaminant state. Incorporating observations in the model can guide it to the true state. This is usually done using the Kalman filter (KF) when the system is linear and the extended Kalman filter (EKF) when the system is nonlinear. To overcome the high computational cost required by the KF, we use the singular evolutive Kalman filter (SEKF) and the singular evolutive extended Kalman filter (SEEKF) approximations of the KF operating with low-rank covariance matrices. The SEKF can be implemented on large dimensional contaminant problems while the usage of the KF is not possible. Experimental results show that with perfect and imperfect models, the low rank filters can provide as much accurate estimates as the full KF but at much less computational cost. Localization can help the filter analysis as long as there are enough neighborhood data to the point being analyzed. Estimating the permeabilities of the aquifer is successfully tackled using both the EKF and the SEEKF.

Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models

KAUST Repository

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.

A SEQUENTIAL MODEL OF INNOVATION STRATEGY—COMPANY NON-FINANCIAL PERFORMANCE LINKS

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Wakhid Slamet Ciptono

2006-05-01

Full Text Available This study extends the prior research (Zahra and Das 1993 by examining the association between a company’s innovation strategy and its non-financial performance in the upstream and downstream strategic business units (SBUs of oil and gas companies. The sequential model suggests a causal sequence among six dimensions of innovation strategy (leadership orientation, process innovation, product/service innovation, external innovation source, internal innovation source, and investment that may lead to higher company non-financial performance (productivity and operational reliability. The study distributed a questionnaire (by mail, e-mailed web system, and focus group discussion to three levels of managers (top, middle, and first-line of 49 oil and gas companies with 140 SBUs in Indonesia. These qualified samples fell into 47 upstream (supply-chain companies with 132 SBUs, and 2 downstream (demand-chain companies with 8 SBUs. A total of 1,332 individual usable questionnaires were returned thus qualified for analysis, representing an effective response rate of 50.19 percent. The researcher conducts structural equation modeling (SEM and hierarchical multiple regression analysis to assess the goodness-of-fit between the research models and the sample data and to test whether innovation strategy mediates the impact of leadership orientation on company non-financial performance. SEM reveals that the models have met goodness-of-fit criteria, thus the interpretation of the sequential models fits with the data. The results of SEM and hierarchical multiple regression: (1 support the importance of innovation strategy as a determinant of company non-financial performance, (2 suggest that the sequential model is appropriate for examining the relationships between six dimensions of innovation strategy and company non-financial performance, and (3 show that the sequential model provides additional insights into the indirect contribution of the individual

Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter

KAUST Repository

Luo, Xiaodong

2011-12-01

A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞ filter is more robust than the Kalman filter, in the sense that the estimation error in the H∞ filter in general has a finite growth rate with respect to the uncertainties in assimilation, except for a special case that corresponds to the Kalman filter. The original form of the H∞ filter contains global constraints in time, which may be inconvenient for sequential data assimilation problems. Therefore a variant is introduced that solves some time-local constraints instead, and hence it is called the time-local H∞ filter (TLHF). By analogy to the ensemble Kalman filter (EnKF), the concept of ensemble time-local H∞ filter (EnTLHF) is also proposed. The general form of the EnTLHF is outlined, and some of its special cases are discussed. In particular, it is shown that an EnKF with certain covariance inflation is essentially an EnTLHF. In this sense, the EnTLHF provides a general framework for conducting covariance inflation in the EnKF-based methods. Some numerical examples are used to assess the relative robustness of the TLHF–EnTLHF in comparison with the corresponding KF–EnKF method.

Employees’ Perceptions of Corporate Social Responsibility and Job Performance: A Sequential Mediation Model

Directory of Open Access Journals (Sweden)

Inyong Shin

2016-05-01

Full Text Available In spite of the increasing importance of corporate social responsibility (CSR and employee job performance, little is still known about the links between the socially responsible actions of organizations and the job performance of their members. In order to explain how employees’ perceptions of CSR influence their job performance, this study first examines the relationships between perceived CSR, organizational identification, job satisfaction, and job performance, and then develops a sequential mediation model by fully integrating these links. The results of structural equation modeling analyses conducted for 250 employees at hotels in South Korea offered strong support for the proposed model. We found that perceived CSR was indirectly and positively associated with job performance sequentially mediated first through organizational identification and then job satisfaction. This study theoretically contributes to the CSR literature by revealing the sequential mechanism through which employees’ perceptions of CSR affect their job performance, and offers practical implications by stressing the importance of employees’ perceptions of CSR. Limitations of this study and future research directions are discussed.

Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter

DEFF Research Database (Denmark)

Andreasen, Martin Møller

This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second....... The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported...

Hydraulic modeling of clay ceramic water filters for point-of-use water treatment.

Science.gov (United States)

Schweitzer, Ryan W; Cunningham, Jeffrey A; Mihelcic, James R

2013-01-02

The acceptability of ceramic filters for point-of-use water treatment depends not only on the quality of the filtered water, but also on the quantity of water the filters can produce. This paper presents two mathematical models for the hydraulic performance of ceramic water filters under typical usage. A model is developed for two common filter geometries: paraboloid- and frustum-shaped. Both models are calibrated and evaluated by comparison to experimental data. The hydraulic models are able to predict the following parameters as functions of time: water level in the filter (h), instantaneous volumetric flow rate of filtrate (Q), and cumulative volume of water produced (V). The models' utility is demonstrated by applying them to estimate how the volume of water produced depends on factors such as the filter shape and the frequency of filling. Both models predict that the volume of water produced can be increased by about 45% if users refill the filter three times per day versus only once per day. Also, the models predict that filter geometry affects the volume of water produced: for two filters with equal volume, equal wall thickness, and equal hydraulic conductivity, a filter that is tall and thin will produce as much as 25% more water than one which is shallow and wide. We suggest that the models can be used as tools to help optimize filter performance.

Modeling the sustainability of a ceramic water filter intervention.

Science.gov (United States)

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

Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering

KAUST Repository

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

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.

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Science.gov (United States)

Rußwurm, Marc; Körner, Marco

2018-03-01

Earth observation (EO) sensors deliver data with daily or weekly temporal resolution. Most land use and land cover (LULC) approaches, however, expect cloud-free and mono-temporal observations. The increasing temporal capabilities of today's sensors enables the use of temporal, along with spectral and spatial features. Domains, such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells, which reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, we achieved in our experiments state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing compared to other classification approaches.

Linear theory for filtering nonlinear multiscale systems with model error.

Science.gov (United States)

Berry, Tyrus; Harlim, John

2014-07-08

In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online , as part of a filtering

A modified RRSQRT-filter for assimilating data in atmospheric chemistry models

NARCIS (Netherlands)

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

Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics.

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

Model for optimising the execution of anti-spam filters

Directory of Open Access Journals (Sweden)

David Ruano-Ordás

2016-12-01

Full Text Available During last years, the combination of several filtering techniques for the development of anti-spam systems has gained a enormous popularity. However, although the accuracy achieved by these models has increased considerably, its use has entailed the emergence of new challenges such as the need to reduce the excessive use of computational resources, the increase of filtering speed and the adjustment of the weights used for the combination of several filtering techniques. In order to achieve this goal we have been refined several aspects including: (i the design and development of small technical improvements to increase the overall performance of the filter, (ii application of genetic algorithms to increase filtering accuracy and (iii the use of scheduling algorithms to improve filtering throughput.

Image Quality of 3rd Generation Spiral Cranial Dual-Source CT in Combination with an Advanced Model Iterative Reconstruction Technique: A Prospective Intra-Individual Comparison Study to Standard Sequential Cranial CT Using Identical Radiation Dose.

Science.gov (United States)

Wenz, Holger; Maros, Máté E; Meyer, Mathias; Förster, Alex; Haubenreisser, Holger; Kurth, Stefan; Schoenberg, Stefan O; Flohr, Thomas; Leidecker, Christianne; Groden, Christoph; Scharf, Johann; Henzler, Thomas

2015-01-01

To prospectively intra-individually compare image quality of a 3rd generation Dual-Source-CT (DSCT) spiral cranial CT (cCT) to a sequential 4-slice Multi-Slice-CT (MSCT) while maintaining identical intra-individual radiation dose levels. 35 patients, who had a non-contrast enhanced sequential cCT examination on a 4-slice MDCT within the past 12 months, underwent a spiral cCT scan on a 3rd generation DSCT. CTDIvol identical to initial 4-slice MDCT was applied. Data was reconstructed using filtered backward projection (FBP) and 3rd-generation iterative reconstruction (IR) algorithm at 5 different IR strength levels. Two neuroradiologists independently evaluated subjective image quality using a 4-point Likert-scale and objective image quality was assessed in white matter and nucleus caudatus with signal-to-noise ratios (SNR) being subsequently calculated. Subjective image quality of all spiral cCT datasets was rated significantly higher compared to the 4-slice MDCT sequential acquisitions (pspiral compared to sequential cCT datasets with mean SNR improvement of 61.65% (p*Bonferroni0.05spiral cCT with an advanced model IR technique significantly improves subjective and objective image quality compared to a standard sequential cCT acquisition acquired at identical dose levels.

Neural Model for Left-Handed CPW Bandpass Filter Loaded Split Ring Resonator

Science.gov (United States)

Liu, Haiwen; Wang, Shuxin; Tan, Mingtao; Zhang, Qijun

2010-02-01

Compact left-handed coplanar waveguide (CPW) bandpass filter loaded split ring resonator (SRR) is presented in this paper. The proposed filter exhibits a quasi-elliptic function response and its circuit size occupies only 12 × 11.8 mm2 (≈0.21 λg × 0.20 λg). Also, a simple circuit model is given and the parametric study of this filter is discussed. Then, with the aid of NeuroModeler software, a five-layer feed-forward perceptron neural networks model is built up to optimize the proposed filter design fast and accurately. Finally, this newly left-handed CPW bandpass filter was fabricated and measured. A good agreement between simulations and measurement verifies the proposed left-handed filter and the validity of design methodology.

A low-complexity interacting multiple model filter for maneuvering target tracking

KAUST Repository

Khalid, Syed Safwan; Abrar, Shafayat

2017-01-01

In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.

A low-complexity interacting multiple model filter for maneuvering target tracking

KAUST Repository

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.

Efficient image duplicated region detection model using sequential block clustering

Czech Academy of Sciences Publication Activity Database

Sekeh, M. A.; Maarof, M. A.; Rohani, M. F.; Mahdian, Babak

2013-01-01

Roč. 10, č. 1 (2013), s. 73-84 ISSN 1742-2876 Institutional support: RVO:67985556 Keywords : Image forensic * Copy–paste forgery * Local block matching Subject RIV: IN - Informatics, Computer Science Impact factor: 0.986, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/mahdian-efficient image duplicated region detection model using sequential block clustering.pdf

Formal modelling and verification of interlocking systems featuring sequential release

DEFF Research Database (Denmark)

Vu, Linh Hong; Haxthausen, Anne Elisabeth; Peleska, Jan

2017-01-01

In this article, we present a method and an associated toolchain for the formal verification of the new Danish railway interlocking systems that are compatible with the European Train Control System (ETCS) Level 2. We have made a generic and reconfigurable model of the system behaviour and generic...... safety properties. This model accommodates sequential release - a feature in the new Danish interlocking systems. To verify the safety of an interlocking system, first a domain-specific description of interlocking configuration data is constructed and validated. Then the generic model and safety...

Modeling the filtration ability of stockpiled filtering facepiece

Science.gov (United States)

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.

Convergence Analysis for the SMC-MeMBer and SMC-CBMeMBer Filters

Directory of Open Access Journals (Sweden)

Feng Lian

2012-01-01

Full Text Available The convergence for the sequential Monte Carlo (SMC implementations of the multitarget multi-Bernoulli (MeMBer filter and cardinality-balanced MeMBer (CBMeMBer filters is studied here. This paper proves that the SMC-MeMBer and SMC-CBMeMBer filters, respectively, converge to the true MeMBer and CBMeMBer filters in the mean-square sense and the corresponding bounds for the mean-square errors are given. The significance of this paper is in theory to present the convergence results of the SMC-MeMBer and SMC-CBMeMBer filters and the conditions under which the two filters satisfy mean-square convergence.

Mathematical Model for the Sequential Action of Radiation and Heat on Yeast Cells

International Nuclear Information System (INIS)

Kim, Jin Kyu; Lee, Yun Jong; Kim, Su Hyoun; Nili, Mohammad; Zhurakovskaya, Galina P.; Petin, Vladislav G.

2009-01-01

It is well known that the synergistic interaction of hyperthermia with ionizing radiation and other agents is widely used in hyperthermic oncology. Interaction between two agents may be considered as synergistic or antagonistic when the effect produced is greater or smaller than the sum of the two single responses. It has long be considered that the mechanism of synergistic interaction of hyperthermia and ionizing radiation may be brought about by an inhibition of the repair from sublethal and potentially lethal damage at the cellular level. The inhibition of the recovery process after combined treatments cannot be considered as a reason for the synergy, but rather would be the expected and predicted consequence of the production of irreversible damage. On the basis of it, a simple mathematical model of the synergistic interaction of two agents acting simultaneously has been proposed. However, the model has not been applied to predict the degree of interaction of heat and ionizing radiation after their sequential action. Extension of the model to the sequential treatment of heat and ionizing radiation seems to be of interest for theoretical and practical reasons. Thus, the purposes of the present work is to suggest the simplest mathematical model which would be able to account for the results obtained and currently available experimental information on the sequential action of radiation and heat.

Multilevel ensemble Kalman filtering

KAUST Repository

Hoel, Haakon

2016-01-08

The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

Multilevel ensemble Kalman filtering

KAUST Repository

Hoel, Haakon; Chernov, Alexey; Law, Kody; Nobile, Fabio; Tempone, Raul

2016-01-01

The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

Optimal Sequential Rules for Computer-Based Instruction.

Science.gov (United States)

Vos, Hans J.

1998-01-01

Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…

Sequential memory: Binding dynamics

Science.gov (United States)

Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

2015-10-01

Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories—episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR Soil Moisture Retrieval Errors

Directory of Open Access Journals (Sweden)

Ju Hyoung Lee

2016-04-01

Full Text Available Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE, while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

Comparison of ERBS orbit determination accuracy using batch least-squares and sequential methods

Science.gov (United States)

Oza, D. H.; Jones, T. L.; Fabien, S. M.; Mistretta, G. D.; Hart, R. C.; Doll, C. E.

1991-10-01

The Flight Dynamics Div. (FDD) at NASA-Goddard commissioned a study to develop the Real Time Orbit Determination/Enhanced (RTOD/E) system as a prototype system for sequential orbit determination of spacecraft on a DOS based personal computer (PC). An overview is presented of RTOD/E capabilities and the results are presented of a study to compare the orbit determination accuracy for a Tracking and Data Relay Satellite System (TDRSS) user spacecraft obtained using RTOS/E on a PC with the accuracy of an established batch least squares system, the Goddard Trajectory Determination System (GTDS), operating on a mainframe computer. RTOD/E was used to perform sequential orbit determination for the Earth Radiation Budget Satellite (ERBS), and the Goddard Trajectory Determination System (GTDS) was used to perform the batch least squares orbit determination. The estimated ERBS ephemerides were obtained for the Aug. 16 to 22, 1989, timeframe, during which intensive TDRSS tracking data for ERBS were available. Independent assessments were made to examine the consistencies of results obtained by the batch and sequential methods. Comparisons were made between the forward filtered RTOD/E orbit solutions and definitive GTDS orbit solutions for ERBS; the solution differences were less than 40 meters after the filter had reached steady state.

Comparison of ERBS orbit determination accuracy using batch least-squares and sequential methods

Science.gov (United States)

Oza, D. H.; Jones, T. L.; Fabien, S. M.; Mistretta, G. D.; Hart, R. C.; Doll, C. E.

1991-01-01

The Flight Dynamics Div. (FDD) at NASA-Goddard commissioned a study to develop the Real Time Orbit Determination/Enhanced (RTOD/E) system as a prototype system for sequential orbit determination of spacecraft on a DOS based personal computer (PC). An overview is presented of RTOD/E capabilities and the results are presented of a study to compare the orbit determination accuracy for a Tracking and Data Relay Satellite System (TDRSS) user spacecraft obtained using RTOS/E on a PC with the accuracy of an established batch least squares system, the Goddard Trajectory Determination System (GTDS), operating on a mainframe computer. RTOD/E was used to perform sequential orbit determination for the Earth Radiation Budget Satellite (ERBS), and the Goddard Trajectory Determination System (GTDS) was used to perform the batch least squares orbit determination. The estimated ERBS ephemerides were obtained for the Aug. 16 to 22, 1989, timeframe, during which intensive TDRSS tracking data for ERBS were available. Independent assessments were made to examine the consistencies of results obtained by the batch and sequential methods. Comparisons were made between the forward filtered RTOD/E orbit solutions and definitive GTDS orbit solutions for ERBS; the solution differences were less than 40 meters after the filter had reached steady state.

The importance of examining movements within the US health care system: sequential logit modeling

Directory of Open Access Journals (Sweden)

Lee Chioun

2010-09-01

Full Text Available Abstract Background Utilization of specialty care may not be a discrete, isolated behavior but rather, a behavior of sequential movements within the health care system. Although patients may often visit their primary care physician and receive a referral before utilizing specialty care, prior studies have underestimated the importance of accounting for these sequential movements. Methods The sample included 6,772 adults aged 18 years and older who participated in the 2001 Survey on Disparities in Quality of Care, sponsored by the Commonwealth Fund. A sequential logit model was used to account for movement in all stages of utilization: use of any health services (i.e., first stage, having a perceived need for specialty care (i.e., second stage, and utilization of specialty care (i.e., third stage. In the sequential logit model, all stages are nested within the previous stage. Results Gender, race/ethnicity, education and poor health had significant explanatory effects with regard to use of any health services and having a perceived need for specialty care, however racial/ethnic, gender, and educational disparities were not present in utilization of specialty care. After controlling for use of any health services and having a perceived need for specialty care, inability to pay for specialty care via income (AOR = 1.334, CI = 1.10 to 1.62 or health insurance (unstable insurance: AOR = 0.26, CI = 0.14 to 0.48; no insurance: AOR = 0.12, CI = 0.07 to 0.20 were significant barriers to utilization of specialty care. Conclusions Use of a sequential logit model to examine utilization of specialty care resulted in a detailed representation of utilization behaviors and patient characteristics that impact these behaviors at all stages within the health care system. After controlling for sequential movements within the health care system, the biggest barrier to utilizing specialty care is the inability to pay, while racial, gender, and educational disparities

Model-Based Engine Control Architecture with an Extended Kalman Filter

Science.gov (United States)

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.

Collaborative Filtering Recommendation on Users' Interest Sequences.

Directory of Open Access Journals (Sweden)

Weijie Cheng

Full Text Available As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS and the count of users' total common sub-IS (ACSIS. Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

Collaborative Filtering Recommendation on Users' Interest Sequences.

Science.gov (United States)

Cheng, Weijie; Yin, Guisheng; Dong, Yuxin; Dong, Hongbin; Zhang, Wansong

2016-01-01

As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS) that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS) and the count of users' total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

Collaborative Filtering Recommendation on Users’ Interest Sequences

Science.gov (United States)

Cheng, Weijie; Yin, Guisheng; Dong, Yuxin; Dong, Hongbin; Zhang, Wansong

2016-01-01

As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction. PMID:27195787

TH-CD-202-04: Evaluation of Virtual Non-Contrast Images From a Novel Split-Filter Dual-Energy CT Technique

International Nuclear Information System (INIS)

Huang, J; Szczykutowicz, T; Bayouth, J; Miller, J

2016-01-01

Purpose: To compare the ability of two dual-energy CT techniques, a novel split-filter single-source technique of superior temporal resolution against an established sequential-scan technique, to remove iodine contrast from images with minimal impact on CT number accuracy. Methods: A phantom containing 8 tissue substitute materials and vials of varying iodine concentrations (1.7–20.1 mg I /mL) was imaged using a Siemens Edge CT scanner. Dual-energy virtual non-contrast (VNC) images were generated using the novel split-filter technique, in which a 120kVp spectrum is filtered by tin and gold to create high- and low-energy spectra with < 1 second temporal separation between the acquisition of low- and high-energy data. Additionally, VNC images were generated with the sequential-scan technique (80 and 140kVp) for comparison. CT number accuracy was evaluated for all materials at 15, 25, and 35mGy CTDIvol. Results: The spectral separation was greater for the sequential-scan technique than the split-filter technique with dual-energy ratios of 2.18 and 1.26, respectively. Both techniques successfully removed iodine contrast, resulting in mean CT numbers within 60HU of 0HU (split-filter) and 40HU of 0HU (sequential-scan) for all iodine concentrations. Additionally, for iodine vials of varying diameter (2–20 mm) with the same concentration (9.9 mg I /mL), the system accurately detected iodine for all sizes investigated. Both dual-energy techniques resulted in reduced CT numbers for bone materials (by >400HU for the densest bone). Increasing the imaging dose did not improve the CT number accuracy for bone in VNC images. Conclusion: VNC images from the split-filter technique successfully removed iodine contrast. These results demonstrate a potential for improving dose calculation accuracy and reducing patient imaging dose, while achieving superior temporal resolution in comparison sequential scans. For both techniques, inaccuracies in CT numbers for bone materials

TH-CD-202-04: Evaluation of Virtual Non-Contrast Images From a Novel Split-Filter Dual-Energy CT Technique

Energy Technology Data Exchange (ETDEWEB)

Huang, J; Szczykutowicz, T; Bayouth, J; Miller, J [University of Wisconsin, Madison, WI (United States)

2016-06-15

Purpose: To compare the ability of two dual-energy CT techniques, a novel split-filter single-source technique of superior temporal resolution against an established sequential-scan technique, to remove iodine contrast from images with minimal impact on CT number accuracy. Methods: A phantom containing 8 tissue substitute materials and vials of varying iodine concentrations (1.7–20.1 mg I /mL) was imaged using a Siemens Edge CT scanner. Dual-energy virtual non-contrast (VNC) images were generated using the novel split-filter technique, in which a 120kVp spectrum is filtered by tin and gold to create high- and low-energy spectra with < 1 second temporal separation between the acquisition of low- and high-energy data. Additionally, VNC images were generated with the sequential-scan technique (80 and 140kVp) for comparison. CT number accuracy was evaluated for all materials at 15, 25, and 35mGy CTDIvol. Results: The spectral separation was greater for the sequential-scan technique than the split-filter technique with dual-energy ratios of 2.18 and 1.26, respectively. Both techniques successfully removed iodine contrast, resulting in mean CT numbers within 60HU of 0HU (split-filter) and 40HU of 0HU (sequential-scan) for all iodine concentrations. Additionally, for iodine vials of varying diameter (2–20 mm) with the same concentration (9.9 mg I /mL), the system accurately detected iodine for all sizes investigated. Both dual-energy techniques resulted in reduced CT numbers for bone materials (by >400HU for the densest bone). Increasing the imaging dose did not improve the CT number accuracy for bone in VNC images. Conclusion: VNC images from the split-filter technique successfully removed iodine contrast. These results demonstrate a potential for improving dose calculation accuracy and reducing patient imaging dose, while achieving superior temporal resolution in comparison sequential scans. For both techniques, inaccuracies in CT numbers for bone materials

A filter-mediated communication model for design collaboration in building construction.

Science.gov (United States)

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 model for transient analysis of a multiple-medium confinement filter system

International Nuclear Information System (INIS)

Hyder, M.L.; Ellison, P.G.; Leonard, M.T.; Louie, D.L.Y.; Donbroski, E.L.; Wagner, K.C.

1990-01-01

A computational model is described that calculates the transient behavior of aerosol and vapor (adsorption) filter compartments such as those used in the Savannah River Site (SRS) production reactor confinement system. The principal application of the model is in the analysis of confinement response to hypothetical severe (core melt) accidents. Under these conditions, aerosol and radio-iodine deposition on filter compartments may be substantial. Attendant filter degradation mechanisms are modeled. Sample calculations are included to illustrate model performance. 6 refs., 14 figs., 1 tab

State and parameter estimation of the heat shock response system using Kalman and particle filters.

Science.gov (United States)

Liu, Xin; Niranjan, Mahesan

2012-06-01

Traditional models of systems biology describe dynamic biological phenomena as solutions to ordinary differential equations, which, when parameters in them are set to correct values, faithfully mimic observations. Often parameter values are tweaked by hand until desired results are achieved, or computed from biochemical experiments carried out in vitro. Of interest in this article, is the use of probabilistic modelling tools with which parameters and unobserved variables, modelled as hidden states, can be estimated from limited noisy observations of parts of a dynamical system. Here we focus on sequential filtering methods and take a detailed look at the capabilities of three members of this family: (i) extended Kalman filter (EKF), (ii) unscented Kalman filter (UKF) and (iii) the particle filter, in estimating parameters and unobserved states of cellular response to sudden temperature elevation of the bacterium Escherichia coli. While previous literature has studied this system with the EKF, we show that parameter estimation is only possible with this method when the initial guesses are sufficiently close to the true values. The same turns out to be true for the UKF. In this thorough empirical exploration, we show that the non-parametric method of particle filtering is able to reliably estimate parameters and states, converging from initial distributions relatively far away from the underlying true values. Software implementation of the three filters on this problem can be freely downloaded from http://users.ecs.soton.ac.uk/mn/HeatShock

Geometric Models for Collaborative Search and Filtering

Science.gov (United States)

Bitton, Ephrat

2011-01-01

This dissertation explores the use of geometric and graphical models for a variety of information search and filtering applications. These models serve to provide an intuitive understanding of the problem domains and as well as computational efficiencies to our solution approaches. We begin by considering a search and rescue scenario where both…

Real-time skin feature identification in a time-sequential video stream

Science.gov (United States)

Kramberger, Iztok

2005-04-01

Skin color can be an important feature when tracking skin-colored objects. Particularly this is the case for computer-vision-based human-computer interfaces (HCI). Humans have a highly developed feeling of space and, therefore, it is reasonable to support this within intelligent HCI, where the importance of augmented reality can be foreseen. Joining human-like interaction techniques within multimodal HCI could, or will, gain a feature for modern mobile telecommunication devices. On the other hand, real-time processing plays an important role in achieving more natural and physically intuitive ways of human-machine interaction. The main scope of this work is the development of a stereoscopic computer-vision hardware-accelerated framework for real-time skin feature identification in the sense of a single-pass image segmentation process. The hardware-accelerated preprocessing stage is presented with the purpose of color and spatial filtering, where the skin color model within the hue-saturation-value (HSV) color space is given with a polyhedron of threshold values representing the basis of the filter model. An adaptive filter management unit is suggested to achieve better segmentation results. This enables the adoption of filter parameters to the current scene conditions in an adaptive way. Implementation of the suggested hardware structure is given at the level of filed programmable system level integrated circuit (FPSLIC) devices using an embedded microcontroller as their main feature. A stereoscopic clue is achieved using a time-sequential video stream, but this shows no difference for real-time processing requirements in terms of hardware complexity. The experimental results for the hardware-accelerated preprocessing stage are given by efficiency estimation of the presented hardware structure using a simple motion-detection algorithm based on a binary function.

Sequential models for coarsening and missingness

NARCIS (Netherlands)

Gill, R.D.; Robins, J.M.

1997-01-01

In a companion paper we described what intuitively would seem to be the most general possible way to generate Coarsening at Random mechanisms a sequential procedure called randomized monotone coarsening Counterexamples showed that CAR mechanisms exist which cannot be represented in this way Here we

Moiré volume Bragg grating filter with tunable bandwidth.

Science.gov (United States)

Mokhov, Sergiy; Ott, Daniel; Divliansky, Ivan; Zeldovich, Boris; Glebov, Leonid

2014-08-25

We propose a monolithic large-aperture narrowband optical filter based on a moiré volume Bragg grating formed by two sequentially recorded gratings with slightly different resonant wavelengths. Such recording creates a spatial modulation of refractive index with a slowly varying sinusoidal envelope. By cutting a specimen at a small angle, to a thickness of one-period of this envelope, the longitudinal envelope profile will shift from a sine profile to a cosine profile across the face of the device. The transmission peak of the filter has a tunable bandwidth while remaining at a fixed resonant wavelength by a transversal shift of incidence position. Analytical expressions for the tunable bandwidth of such a filter are calculated and experimental data from a filter operating at 1064 nm with bandwidth range 30-90 pm is demonstrated.

Biotrickling filter modeling for styrene abatement. Part 1: Model development, calibration and validation on an industrial scale.

Science.gov (United States)

San-Valero, Pau; Dorado, Antonio D; Martínez-Soria, Vicente; Gabaldón, Carmen

2018-01-01

A three-phase dynamic mathematical model based on mass balances describing the main processes in biotrickling filtration: convection, mass transfer, diffusion, and biodegradation was calibrated and validated for the simulation of an industrial styrene-degrading biotrickling filter. The model considered the key features of the industrial operation of biotrickling filters: variable conditions of loading and intermittent irrigation. These features were included in the model switching from the mathematical description of periods with and without irrigation. Model equations were based on the mass balances describing the main processes in biotrickling filtration: convection, mass transfer, diffusion, and biodegradation. The model was calibrated with steady-state data from a laboratory biotrickling filter treating inlet loads at 13-74 g C m -3 h -1 and at empty bed residence time of 30-15 s. The model predicted the dynamic emission in the outlet of the biotrickling filter, simulating the small peaks of concentration occurring during irrigation. The validation of the model was performed using data from a pilot on-site biotrickling filter treating styrene installed in a fiber-reinforced facility. The model predicted the performance of the biotrickling filter working under high-oscillating emissions at an inlet load in a range of 5-23 g C m -3 h -1 and at an empty bed residence time of 31 s for more than 50 days, with a goodness of fit of 0.84. Copyright © 2017 Elsevier Ltd. All rights reserved.

NEW APPROACH TO MODELLING OF SAND FILTER CLOGGING BY SEPTIC TANK EFFLUENT

Directory of Open Access Journals (Sweden)

Jakub Nieć

2016-04-01

Full Text Available The deep bed filtration model elaborated by Iwasaki has many applications, e.g. solids removal from wastewater. Its main parameter, filter coefficient, is directly related to removal efficiency and depends on filter depth and time of operation. In this paper the authors have proposed a new approach to modelling, describing dry organic mass from septic tank effluent and biomass distribution in a sand filter. In this approach the variable filter coefficient value was used as affected by depth and time of operation and the live biomass concentration distribution was approximated by a logistic function. Relatively stable biomass contents in deeper beds compartments were observed in empirical studies. The Iwasaki equations associated with the logistic function can predict volatile suspended solids deposition and biomass content in sand filters. The comparison between the model and empirical data for filtration lasting 10 and 20 days showed a relatively good agreement.

Sequential estimation of surface water mass changes from daily satellite gravimetry data

Science.gov (United States)

Ramillien, G. L.; Frappart, F.; Gratton, S.; Vasseur, X.

2015-03-01

We propose a recursive Kalman filtering approach to map regional spatio-temporal variations of terrestrial water mass over large continental areas, such as South America. Instead of correcting hydrology model outputs by the GRACE observations using a Kalman filter estimation strategy, regional 2-by-2 degree water mass solutions are constructed by integration of daily potential differences deduced from GRACE K-band range rate (KBRR) measurements. Recovery of regional water mass anomaly averages obtained by accumulation of information of daily noise-free simulated GRACE data shows that convergence is relatively fast and yields accurate solutions. In the case of cumulating real GRACE KBRR data contaminated by observational noise, the sequential method of step-by-step integration provides estimates of water mass variation for the period 2004-2011 by considering a set of suitable a priori error uncertainty parameters to stabilize the inversion. Spatial and temporal averages of the Kalman filter solutions over river basin surfaces are consistent with the ones computed using global monthly/10-day GRACE solutions from official providers CSR, GFZ and JPL. They are also highly correlated to in situ records of river discharges (70-95 %), especially for the Obidos station where the total outflow of the Amazon River is measured. The sparse daily coverage of the GRACE satellite tracks limits the time resolution of the regional Kalman filter solutions, and thus the detection of short-term hydrological events.

Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise

Directory of Open Access Journals (Sweden)

Bowen Hou

2017-11-01

Full Text Available As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α -jerk model.

Quantum model for a periodically driven selectivity filter in a K+ ion channel

International Nuclear Information System (INIS)

Cifuentes, A A; Semião, F L

2014-01-01

In this work, we present a quantum transport model for the selectivity filter in the KcsA potassium ion channel. This model is fully consistent with the fact that two conduction pathways are involved in the translocation of ions through the filter, and we show that the presence of a second path may actually bring advantages for the filter as a result of quantum interference. To highlight interferences and resonances in the model, we consider the selectivity filter to be driven by a controlled time-dependent external field, which changes the free-energy scenario and consequently the conduction of the ions. In particular, we demonstrate that the two-pathway conduction mechanism is more advantageous for the filter when dephasing in the transient configurations is lower than in the main configurations. As a matter of fact, K + ions in the main configurations are highly coordinated by oxygen atoms of the filter backbone, and this increases noise. Moreover, we also show that for a wide range of dephasing rates and driving frequencies, the two-pathway conduction used by the filter leads to higher ionic currents than the single–path model. (paper)

Improved Collaborative Filtering Algorithm using Topic Model

Directory of Open Access Journals (Sweden)

Liu Na

2016-01-01

Full Text Available Collaborative filtering algorithms make use of interactions rates between users and items for generating recommendations. Similarity among users or items is calculated based on rating mostly, without considering explicit properties of users or items involved. In this paper, we proposed collaborative filtering algorithm using topic model. We describe user-item matrix as document-word matrix and user are represented as random mixtures over item, each item is characterized by a distribution over users. The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on Movie Lens data sets.

Modeling eye gaze patterns in clinician-patient interaction with lag sequential analysis.

Science.gov (United States)

Montague, Enid; Xu, Jie; Chen, Ping-Yu; Asan, Onur; Barrett, Bruce P; Chewning, Betty

2011-10-01

The aim of this study was to examine whether lag sequential analysis could be used to describe eye gaze orientation between clinicians and patients in the medical encounter. This topic is particularly important as new technologies are implemented into multiuser health care settings in which trust is critical and nonverbal cues are integral to achieving trust. This analysis method could lead to design guidelines for technologies and more effective assessments of interventions. Nonverbal communication patterns are important aspects of clinician-patient interactions and may affect patient outcomes. The eye gaze behaviors of clinicians and patients in 110 videotaped medical encounters were analyzed using the lag sequential method to identify significant behavior sequences. Lag sequential analysis included both event-based lag and time-based lag. Results from event-based lag analysis showed that the patient's gaze followed that of the clinician, whereas the clinician's gaze did not follow the patient's. Time-based sequential analysis showed that responses from the patient usually occurred within 2 s after the initial behavior of the clinician. Our data suggest that the clinician's gaze significantly affects the medical encounter but that the converse is not true. Findings from this research have implications for the design of clinical work systems and modeling interactions. Similar research methods could be used to identify different behavior patterns in clinical settings (physical layout, technology, etc.) to facilitate and evaluate clinical work system designs.

Development and sensitivity analysis of a fullykinetic model of sequential reductive dechlorination in subsurface

DEFF Research Database (Denmark)

Malaguerra, Flavio; Chambon, Julie Claire Claudia; Albrechtsen, Hans-Jørgen

2010-01-01

and natural degradation of chlorinated solvents frequently occurs in the subsurface through sequential reductive dechlorination. However, the occurrence and the performance of natural sequential reductive dechlorination strongly depends on environmental factor such as redox conditions, presence of fermenting...... organic matter / electron donors, presence of specific biomass, etc. Here we develop a new fully-kinetic biogeochemical reactive model able to simulate chlorinated solvents degradation as well as production and consumption of molecular hydrogen. The model is validated using batch experiment data......Chlorinated hydrocarbons originating from point sources are amongst the most prevalent contaminants of ground water and often represent a serious threat to groundwater-based drinking water resources. Natural attenuation of contaminant plumes can play a major role in contaminated site management...

Conditions for Model Matching of Switched Asynchronous Sequential Machines with Output Feedback

OpenAIRE

Jung–Min Yang

2016-01-01

Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the ...

On low-frequency errors of uniformly modulated filtered white-noise models for ground motions

Science.gov (United States)

Safak, Erdal; Boore, David M.

1988-01-01

Low-frequency errors of a commonly used non-stationary stochastic model (uniformly modulated filtered white-noise model) for earthquake ground motions are investigated. It is shown both analytically and by numerical simulation that uniformly modulated filter white-noise-type models systematically overestimate the spectral response for periods longer than the effective duration of the earthquake, because of the built-in low-frequency errors in the model. The errors, which are significant for low-magnitude short-duration earthquakes, can be eliminated by using the filtered shot-noise-type models (i. e. white noise, modulated by the envelope first, and then filtered).

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study.

Science.gov (United States)

Hosseinyalamdary, Siavash

2018-04-24

Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.

Exploring the sequential lineup advantage using WITNESS.

Science.gov (United States)

Goodsell, Charles A; Gronlund, Scott D; Carlson, Curt A

2010-12-01

Advocates claim that the sequential lineup is an improvement over simultaneous lineup procedures, but no formal (quantitatively specified) explanation exists for why it is better. The computational model WITNESS (Clark, Appl Cogn Psychol 17:629-654, 2003) was used to develop theoretical explanations for the sequential lineup advantage. In its current form, WITNESS produced a sequential advantage only by pairing conservative sequential choosing with liberal simultaneous choosing. However, this combination failed to approximate four extant experiments that exhibited large sequential advantages. Two of these experiments became the focus of our efforts because the data were uncontaminated by likely suspect position effects. Decision-based and memory-based modifications to WITNESS approximated the data and produced a sequential advantage. The next step is to evaluate the proposed explanations and modify public policy recommendations accordingly.

RB Particle Filter Time Synchronization Algorithm Based on the DPM Model.

Science.gov (United States)

Guo, Chunsheng; Shen, Jia; Sun, Yao; Ying, Na

2015-09-03

Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.

A comparison of nonlinear filtering approaches in the context of an HIV model.

Science.gov (United States)

Banks, H Thomas; Hu, Shuhua; Kenz, Zackary R; Tran, Hien T

2010-04-01

In this paper three different filtering methods, the Extended Kalman Filter (EKF), the Gauss-Hermite Filter (GHF), and the Unscented Kalman Filter (UKF), are compared for state-only and coupled state and parameter estimation when used with log state variables of a model of the immunologic response to the human immunodeficiency virus (HIV) in individuals. The filters are implemented to estimate model states as well as model parameters from simulated noisy data, and are compared in terms of estimation accuracy and computational time. Numerical experiments reveal that the GHF is the most computationally expensive algorithm, while the EKF is the least expensive one. In addition, computational experiments suggest that there is little difference in the estimation accuracy between the UKF and GHF. When measurements are taken as frequently as every week to two weeks, the EKF is the superior filter. When measurements are further apart, the UKF is the best choice in the problem under investigation.

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Directory of Open Access Journals (Sweden)

Siavash Hosseinyalamdary

2018-04-01

Full Text Available Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU, have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.

Interacting Multiple Model (IMM Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

Directory of Open Access Journals (Sweden)

Hua Liu

2017-06-01

Full Text Available For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF. The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF, the interacting multiple model cubature Kalman filter (IMMCKF and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF.

Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

Science.gov (United States)

Liu, Hua; Wu, Wen

2017-06-13

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).

Improving multiple-point-based a priori models for inverse problems by combining Sequential Simulation with the Frequency Matching Method

DEFF Research Database (Denmark)

Cordua, Knud Skou; Hansen, Thomas Mejer; Lange, Katrine

In order to move beyond simplified covariance based a priori models, which are typically used for inverse problems, more complex multiple-point-based a priori models have to be considered. By means of marginal probability distributions ‘learned’ from a training image, sequential simulation has...... proven to be an efficient way of obtaining multiple realizations that honor the same multiple-point statistics as the training image. The frequency matching method provides an alternative way of formulating multiple-point-based a priori models. In this strategy the pattern frequency distributions (i.......e. marginals) of the training image and a subsurface model are matched in order to obtain a solution with the same multiple-point statistics as the training image. Sequential Gibbs sampling is a simulation strategy that provides an efficient way of applying sequential simulation based algorithms as a priori...

Modelling of air flows in pleated filters and of their clogging by solid particles

International Nuclear Information System (INIS)

Del Fabbro, L.

2002-01-01

The devices of air cleaning against particles are widely spread in various branches of industry: nuclear, motor, food, electronic,...; among these devices, numerous are constituted by pleated porous media to increase the surface of filtration and thus to reduce the pressure drop, for given air flow. The objective of our work is to compensate a lack evident of knowledge on the evolution of the pressure drop of pleated filter during the clogging and to deduct a modelling from it, on the basis of experiments concerning industrial filters of nuclear and car types. The obtained model is a function of characteristics of the filtering medium and pleats, of the characteristics of solid particles deposited on the filter, of the mass of particles and of the aeraulic conditions of air flow. It also depends on data on the clogging of flat filters of equivalent medium. To elaborate this model of pressure drop, an initial stage was carried out in order to characterize, experimentally and numerically, the pressure drop and the distribution of air flow in clean pleated filters of nuclear (high efficiency particulate air filter, in fiberglasses) and car (mean efficiency filter, in fibers of cellulose) types. The numerical model allowed to understand the fundamental role played by the aeraulic resistance of the filtering medium. From an non-dimensional approach, we established a semi-empirical model of pressure drop for a clean pleated filter valid for both studied types of medium; this model is used of first base for the development of the final model of clogging. The study of the clogging of the filters showed the complexity of the phenomenon dependent mainly on a reduction of the surface of filtration. This observation brings us to propose a clogging of pleated filters in three phases. Both first phases are similar in those observed for flat filters, while last phase corresponds to a reduction of the surface of filtration and leads a strong increase of the filter pressure drop

A generalized model via random walks for information filtering

Science.gov (United States)

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.

Particle filters for object tracking: enhanced algorithm and efficient implementations

International Nuclear Information System (INIS)

Abd El-Halym, H.A.

2010-01-01

Object tracking and recognition is a hot research topic. In spite of the extensive research efforts expended, the development of a robust and efficient object tracking algorithm remains unsolved due to the inherent difficulty of the tracking problem. Particle filters (PFs) were recently introduced as a powerful, post-Kalman filter, estimation tool that provides a general framework for estimation of nonlinear/ non-Gaussian dynamic systems. Particle filters were advanced for building robust object trackers capable of operation under severe conditions (small image size, noisy background, occlusions, fast object maneuvers ..etc.). The heavy computational load of the particle filter remains a major obstacle towards its wide use.In this thesis, an Excitation Particle Filter (EPF) is introduced for object tracking. A new likelihood model is proposed. It depends on multiple functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modified the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. This modification is based on re-exciting the particles if their weights fall below a memorized weight value. The proposed enhanced PF is implemented in software and evaluated. Its results are compared with a single likelihood function PF tracker, Particle Swarm Optimization (PSO) tracker, a correlation tracker, as well as, an edge tracker. The experimental results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness, and occlusion compared with other methods Efficient novel hardware architectures of the Sample Important Re sample Filter (SIRF) and the EPF are implemented. Three novel hardware architectures of the SIRF for object tracking are introduced. The first architecture is a two-step sequential PF machine, where particle generation, weight calculation and normalization are carried out in parallel during the first step followed by a sequential re

Adaptive filtering for hidden node detection and tracking in networks.

Science.gov (United States)

Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L

2017-07-01

The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.

Sequential updating of a new dynamic pharmacokinetic model for caffeine in premature neonates.

Science.gov (United States)

Micallef, Sandrine; Amzal, Billy; Bach, Véronique; Chardon, Karen; Tourneux, Pierre; Bois, Frédéric Y

2007-01-01

Caffeine treatment is widely used in nursing care to reduce the risk of apnoea in premature neonates. To check the therapeutic efficacy of the treatment against apnoea, caffeine concentration in blood is an important indicator. The present study was aimed at building a pharmacokinetic model as a basis for a medical decision support tool. In the proposed model, time dependence of physiological parameters is introduced to describe rapid growth of neonates. To take into account the large variability in the population, the pharmacokinetic model is embedded in a population structure. The whole model is inferred within a Bayesian framework. To update caffeine concentration predictions as data of an incoming patient are collected, we propose a fast method that can be used in a medical context. This involves the sequential updating of model parameters (at individual and population levels) via a stochastic particle algorithm. Our model provides better predictions than the ones obtained with models previously published. We show, through an example, that sequential updating improves predictions of caffeine concentration in blood (reduce bias and length of credibility intervals). The update of the pharmacokinetic model using body mass and caffeine concentration data is studied. It shows how informative caffeine concentration data are in contrast to body mass data. This study provides the methodological basis to predict caffeine concentration in blood, after a given treatment if data are collected on the treated neonate.

Modeling and Predicting AD Progression by Regression Analysis of Sequential Clinical Data

KAUST Repository

Xie, Qing

2016-02-23

Alzheimer\\'s Disease (AD) is currently attracting much attention in elders\\' care. As the increasing availability of massive clinical diagnosis data, especially the medical images of brain scan, it is highly significant to precisely identify and predict the potential AD\\'s progression based on the knowledge in the diagnosis data. In this paper, we follow a novel sequential learning framework to model the disease progression for AD patients\\' care. Different from the conventional approaches using only initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the sequential diagnosis information in different disease stages to jointly simulate the disease progression. The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable. We examined our approach by extensive experiments on the clinical data provided by the Alzheimer\\'s Disease Neuroimaging Initiative (ADNI). The results indicate that the proposed approach is more effective to simulate and predict the disease progression compared with the existing methods.

Modeling and Predicting AD Progression by Regression Analysis of Sequential Clinical Data

KAUST Repository

Xie, Qing; Wang, Su; Zhu, Jia; Zhang, Xiangliang

2016-01-01

Alzheimer's Disease (AD) is currently attracting much attention in elders' care. As the increasing availability of massive clinical diagnosis data, especially the medical images of brain scan, it is highly significant to precisely identify and predict the potential AD's progression based on the knowledge in the diagnosis data. In this paper, we follow a novel sequential learning framework to model the disease progression for AD patients' care. Different from the conventional approaches using only initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the sequential diagnosis information in different disease stages to jointly simulate the disease progression. The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable. We examined our approach by extensive experiments on the clinical data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results indicate that the proposed approach is more effective to simulate and predict the disease progression compared with the existing methods.

Integrating retention soil filters into urban hydrologic models - Relevant processes and important parameters

Science.gov (United States)

Bachmann-Machnik, Anna; Meyer, Daniel; Waldhoff, Axel; Fuchs, Stephan; Dittmer, Ulrich

2018-04-01

Retention Soil Filters (RSFs), a form of vertical flow constructed wetlands specifically designed for combined sewer overflow (CSO) treatment, have proven to be an effective tool to mitigate negative impacts of CSOs on receiving water bodies. Long-term hydrologic simulations are used to predict the emissions from urban drainage systems during planning of stormwater management measures. So far no universally accepted model for RSF simulation exists. When simulating hydraulics and water quality in RSFs, an appropriate level of detail must be chosen for reasonable balancing between model complexity and model handling, considering the model input's level of uncertainty. The most crucial parameters determining the resultant uncertainties of the integrated sewer system and filter bed model were identified by evaluating a virtual drainage system with a Retention Soil Filter for CSO treatment. To determine reasonable parameter ranges for RSF simulations, data of 207 events from six full-scale RSF plants in Germany were analyzed. Data evaluation shows that even though different plants with varying loading and operation modes were examined, a simple model is sufficient to assess relevant suspended solids (SS), chemical oxygen demand (COD) and NH4 emissions from RSFs. Two conceptual RSF models with different degrees of complexity were assessed. These models were developed based on evaluation of data from full scale RSF plants and column experiments. Incorporated model processes are ammonium adsorption in the filter layer and degradation during subsequent dry weather period, filtration of SS and particulate COD (XCOD) to a constant background concentration and removal of solute COD (SCOD) by a constant removal rate during filter passage as well as sedimentation of SS and XCOD in the filter overflow. XCOD, SS and ammonium loads as well as ammonium concentration peaks are discharged primarily via RSF overflow not passing through the filter bed. Uncertainties of the integrated

Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model

KAUST Repository

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.

Stochastic global optimization as a filtering problem

International Nuclear Information System (INIS)

Stinis, Panos

2012-01-01

We present a reformulation of stochastic global optimization as a filtering problem. The motivation behind this reformulation comes from the fact that for many optimization problems we cannot evaluate exactly the objective function to be optimized. Similarly, we may not be able to evaluate exactly the functions involved in iterative optimization algorithms. For example, we may only have access to noisy measurements of the functions or statistical estimates provided through Monte Carlo sampling. This makes iterative optimization algorithms behave like stochastic maps. Naive global optimization amounts to evolving a collection of realizations of this stochastic map and picking the realization with the best properties. This motivates the use of filtering techniques to allow focusing on realizations that are more promising than others. In particular, we present a filtering reformulation of global optimization in terms of a special case of sequential importance sampling methods called particle filters. The increasing popularity of particle filters is based on the simplicity of their implementation and their flexibility. We utilize the flexibility of particle filters to construct a stochastic global optimization algorithm which can converge to the optimal solution appreciably faster than naive global optimization. Several examples of parametric exponential density estimation are provided to demonstrate the efficiency of the approach.

A SEQUENTIAL MODEL OF INNOVATION STRATEGY—COMPANY NON-FINANCIAL PERFORMANCE LINKS

OpenAIRE

Ciptono, Wakhid Slamet

2006-01-01

This study extends the prior research (Zahra and Das 1993) by examining the association between a company’s innovation strategy and its non-financial performance in the upstream and downstream strategic business units (SBUs) of oil and gas companies. The sequential model suggests a causal sequence among six dimensions of innovation strategy (leadership orientation, process innovation, product/service innovation, external innovation source, internal innovation source, and investment) that may ...

Nonlinear Kalman Filtering in Affine Term Structure Models

DEFF Research Database (Denmark)

Christoffersen, Peter; Dorion, Christian; Jacobs, Kris

When the relationship between security prices and state variables in dynamic term structure models is nonlinear, existing studies usually linearize this relationship because nonlinear fi…ltering is computationally demanding. We conduct an extensive investigation of this linearization and analyze...... the potential of the unscented Kalman …filter to properly capture nonlinearities. To illustrate the advantages of the unscented Kalman …filter, we analyze the cross section of swap rates, which are relatively simple non-linear instruments, and cap prices, which are highly nonlinear in the states. An extensive...

Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time

KAUST Repository

Kelly, D. T B

2014-09-22

The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz \\'63 and \\'96 models, together with the incompressible Navier-Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier-Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise.

Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time

International Nuclear Information System (INIS)

Kelly, D T B; Stuart, A M; Law, K J H

2014-01-01

The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz '63 and '96 models, together with the incompressible Navier–Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier–Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise. (paper)

Solution to the spectral filter problem of residual terrain modelling (RTM)

Science.gov (United States)

Rexer, Moritz; Hirt, Christian; Bucha, Blažej; Holmes, Simon

2018-06-01

In physical geodesy, the residual terrain modelling (RTM) technique is frequently used for high-frequency gravity forward modelling. In the RTM technique, a detailed elevation model is high-pass-filtered in the topography domain, which is not equivalent to filtering in the gravity domain. This in-equivalence, denoted as spectral filter problem of the RTM technique, gives rise to two imperfections (errors). The first imperfection is unwanted low-frequency (LF) gravity signals, and the second imperfection is missing high-frequency (HF) signals in the forward-modelled RTM gravity signal. This paper presents new solutions to the RTM spectral filter problem. Our solutions are based on explicit modelling of the two imperfections via corrections. The HF correction is computed using spectral domain gravity forward modelling that delivers the HF gravity signal generated by the long-wavelength RTM reference topography. The LF correction is obtained from pre-computed global RTM gravity grids that are low-pass-filtered using surface or solid spherical harmonics. A numerical case study reveals maximum absolute signal strengths of ˜ 44 mGal (0.5 mGal RMS) for the HF correction and ˜ 33 mGal (0.6 mGal RMS) for the LF correction w.r.t. a degree-2160 reference topography within the data coverage of the SRTM topography model (56°S ≤ φ ≤ 60°N). Application of the LF and HF corrections to pre-computed global gravity models (here the GGMplus gravity maps) demonstrates the efficiency of the new corrections over topographically rugged terrain. Over Switzerland, consideration of the HF and LF corrections reduced the RMS of the residuals between GGMplus and ground-truth gravity from 4.41 to 3.27 mGal, which translates into ˜ 26% improvement. Over a second test area (Canada), our corrections reduced the RMS of the residuals between GGMplus and ground-truth gravity from 5.65 to 5.30 mGal (˜ 6% improvement). Particularly over Switzerland, geophysical signals (associated, e.g. with

Concrete ensemble Kalman filters with rigorous catastrophic filter divergence.

Science.gov (United States)

Kelly, David; Majda, Andrew J; Tong, Xin T

2015-08-25

The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature.

Accounting for model error due to unresolved scales within ensemble Kalman filtering

OpenAIRE

Mitchell, Lewis; Carrassi, Alberto

2014-01-01

We propose a method to account for model error due to unresolved scales in the context of the ensemble transform Kalman filter (ETKF). The approach extends to this class of algorithms the deterministic model error formulation recently explored for variational schemes and extended Kalman filter. The model error statistic required in the analysis update is estimated using historical reanalysis increments and a suitable model error evolution law. Two different versions of the method are describe...

Sensitivity Filters In Topology Optimisation As A Solution To Helmholtz Type Differential Equation

DEFF Research Database (Denmark)

Lazarov, Boyan Stefanov; Sigmund, Ole

2009-01-01

The focus of the study in this article is on the use of a Helmholtz type differential equation as a filter for topology optimisation problems. Until now various filtering schemes have been utilised in order to impose mesh independence in this type of problems. The usual techniques require topology...... information about the neighbour sub-domains is an expensive operation. The proposed filtering technique requires only mesh information necessary for the finite element discretisation of the problem. The main idea is to define the filtered variable implicitly as a solution of a Helmholtz type differential...... equation with homogeneous Neumann boundary conditions. The properties of the filter are demonstrated for various 2D and 3D topology optimisation problems in linear elasticity, solved on sequential and parallel computers....

Modeling of memristor-based chaotic systems using nonlinear Wiener adaptive filters based on backslash operator

International Nuclear Information System (INIS)

Zhao, Yibo; Jiang, Yi; Feng, Jiuchao; Wu, Lifu

2016-01-01

Highlights: • A novel nonlinear Wiener adaptive filters based on the backslash operator are proposed. • The identification approach to the memristor-based chaotic systems using the proposed adaptive filters. • The weight update algorithm and convergence characteristics for the proposed adaptive filters are derived. - Abstract: Memristor-based chaotic systems have complex dynamical behaviors, which are characterized as nonlinear and hysteresis characteristics. Modeling and identification of their nonlinear model is an important premise for analyzing the dynamical behavior of the memristor-based chaotic systems. This paper presents a novel nonlinear Wiener adaptive filtering identification approach to the memristor-based chaotic systems. The linear part of Wiener model consists of the linear transversal adaptive filters, the nonlinear part consists of nonlinear adaptive filters based on the backslash operator for the hysteresis characteristics of the memristor. The weight update algorithms for the linear and nonlinear adaptive filters are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics. Comparing with the adaptive nonlinear polynomial filters, the proposed nonlinear adaptive filters have less identification error.

Sequential determination of important ecotoxic radionuclides in nuclear waste samples

International Nuclear Information System (INIS)

Bilohuscin, J.

2016-01-01

In the dissertation thesis we focused on the development and optimization of a sequential determination method for radionuclides 93 Zr, 94 Nb, 99 Tc and 126 Sn, employing extraction chromatography sorbents TEVA (R) Resin and Anion Exchange Resin, supplied by Eichrom Industries. Prior to the attestation of sequential separation of these proposed radionuclides from radioactive waste samples, a unique sequential procedure of 90 Sr, 239 Pu, 241 Am separation from urine matrices was tried, using molecular recognition sorbents of AnaLig (R) series and extraction chromatography sorbent DGA (R) Resin. On these experiments, four various sorbents were continually used for separation, including PreFilter Resin sorbent, which removes interfering organic materials present in raw urine. After the acquisition of positive results of this sequential procedure followed experiments with a 126 Sn separation using TEVA (R) Resin and Anion Exchange Resin sorbents. Radiochemical recoveries obtained from samples of radioactive evaporate concentrates and sludge showed high efficiency of the separation, while values of 126 Sn were under the minimum detectable activities MDA. Activity of 126 Sn was determined after ingrowth of daughter nuclide 126m Sb on HPGe gamma detector, with minimal contamination of gamma interfering radionuclides with decontamination factors (D f ) higher then 1400 for 60 Co and 47000 for 137 Cs. Based on the acquired experiments and results of these separation procedures, a complex method of sequential separation of 93 Zr, 94 Nb, 99 Tc and 126 Sn was proposed, which included optimization steps similar to those used in previous parts of the dissertation work. Application of the sequential separation method for sorbents TEVA (R) Resin and Anion Exchange Resin on real samples of radioactive wastes provided satisfactory results and an economical, time sparing, efficient method. (author)

Popularity Modeling for Mobile Apps: A Sequential Approach.

Science.gov (United States)

Zhu, Hengshu; Liu, Chuanren; Ge, Yong; Xiong, Hui; Chen, Enhong

2015-07-01

The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with mobile Apps, learn the process of adoption of mobile Apps, and thus enables better mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of mobile Apps toward mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Experimental study of filter cake formation on different filter media

International Nuclear Information System (INIS)

Saleem, M.

2009-01-01

Removal of particulate matter from gases generated in the process industry is important for product recovery as well as emission control. Dynamics of filtration plant depend on operating conditions. The models, that predict filter plant behaviour, involve empirical resistance parameters which are usually derived from limited experimental data and are characteristics of the filter media and filter cake (dust deposited on filter medium). Filter cake characteristics are affected by the nature of filter media, process parameters and mode of filter regeneration. Removal of dust particles from air is studied in a pilot scale jet pulsed bag filter facility resembling closely to the industrial filters. Limestone dust and ambient air are used in this study with two widely different filter media. All important parameters like pressure drop, gas flow rate, dust settling, are recorded continuously at 1s interval. The data is processed for estimation of the resistance parameters. The pressure drop rise on test filter media is compared. Results reveal that the surface of filter media has an influence on pressure drop rise (concave pressure drop rise). Similar effect is produced by partially jet pulsed filter surface. Filter behaviour is also simulated using estimated parameters and a simplified model and compared with the experimental results. Distribution of cake area load is therefore an important aspect of jet pulse cleaned bag filter modeling. Mean specific cake resistance remains nearly constant on thoroughly jet pulse cleaned membrane coated filter bags. However, the trend can not be confirmed without independent cake height and density measurements. Thus the results reveal the importance of independent measurements of cake resistance. (author)

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.

Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

Directory of Open Access Journals (Sweden)

Simionescu Mihaela

2015-06-01

Full Text Available This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component.

An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking

Science.gov (United States)

Raihan A. V, Dilshad; Chakravorty, Suman

2018-03-01

Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.

Capacity of textile filters for wastewater Treatment at changeable wastewater level – a hydraulic model

Directory of Open Access Journals (Sweden)

Marcin Spychała

2016-12-01

Full Text Available The aim of the study was to describe in a mathematical manner the hydraulic capacity of textile filters for wastewater treatment at changeable wastewater levels during a period between consecutive doses, taking into consideration the decisive factors for flow-conditions of filtering media. Highly changeable and slightly changeable flow-conditions tests were performed on reactors equipped with non-woven geo-textile filters. Hydraulic conductivity of filter material coupons was determined. The dry mass covering the surface and contained in internal space of filtering material was then indicated and a mathematical model was elaborated. Flow characteristics during the highly changeable flow-condition test were sensitivity to differentiated values of hydraulic conductivity in horizontal zones of filtering layer. During the slightly changeable flow-conditions experiment the differences in permeability and hydraulic conductivity of different filter (horizontal zones height regions were much smaller. The proposed modelling approach in spite of its simplicity provides a satisfactory agreement with empirical data and therefore enables to simulate the hydraulic capacity of vertically oriented textile filters. The mathematical model reflects the significant impact of the filter characteristics (textile permeability at different filter height and operational conditions (dosing frequency on the textile filters hydraulic capacity.

A generalized model via random walks for information filtering

Energy Technology Data Exchange (ETDEWEB)

Ren, Zhuo-Ming, E-mail: zhuomingren@gmail.com [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland); Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, ChongQing, 400714 (China); Kong, Yixiu [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland); Shang, Ming-Sheng, E-mail: msshang@cigit.ac.cn [Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, ChongQing, 400714 (China); Zhang, Yi-Cheng [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland)

2016-08-06

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation. - Highlights: • We propose a generalized recommendation model employing the random walk dynamics. • The proposed model with single and hybrid of degree information is analyzed. • A strategy with the hybrid degree information improves precision of recommendation.

A generalized model via random walks for information filtering

International Nuclear Information System (INIS)

Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng

2016-01-01

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation. - Highlights: • We propose a generalized recommendation model employing the random walk dynamics. • The proposed model with single and hybrid of degree information is analyzed. • A strategy with the hybrid degree information improves precision of recommendation.

Bias aware Kalman filters

DEFF Research Database (Denmark)

Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan

2006-01-01

This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state....... The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques...... are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 2005 Elsevier Ltd. All rights reserved....

Differences in radial expansion force among inferior vena cava filter models support documented perforation rates.

Science.gov (United States)

Robins, J Eli; Ragai, Ihab; Yamaguchi, Dean J

2018-05-01

Inferior vena cava (IVC) filters are used in patients at risk for pulmonary embolism who cannot be anticoagulated. Unfortunately, these filters are not without risk, and complications include perforation, migration, and filter fracture. The most prevalent complication is filter perforation of the IVC, with incidence varying among filter models. To our knowledge, the mechanical properties of IVC filters have not been evaluated and are not readily available through the manufacturer. This study sought to determine whether differences in mechanical properties are similar to differences in documented perforation rates. The radial expansion forces of Greenfield (Boston Scientific, Marlborough, Mass), Cook Celect (Cook Medical, Bloomington, Ind), and Cook Platinum filters were analyzed with three replicates per group. The intrinsic force exerted by the filter on the measuring device was collected in real time during controlled expansion. Replicates were averaged and significance was determined by calculating analysis of covariance using SAS software (SAS Institute, Cary, NC). Each filter model generated a significantly different radial expansion force (P filter, followed by the Cook Celect and Greenfield filters. Radial force dispersion during expansion was greatest in the Cook Celect, followed by the Cook Platinum and Greenfield filters. Differences in radial expansion forces among IVC filter models are consistent with documented perforation rates. Cook Celect IVC filters have a higher incidence of perforation compared with Greenfield filters when they are left in place for >90 days. Evaluation of Cook Celect filters yielded a significantly higher radial expansion force at minimum caval diameter, with greater force dispersion during expansion. Copyright © 2018 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

Science.gov (United States)

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2017-08-01

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimum filter selection for Dual Energy X-ray Applications through Analytical Modeling

International Nuclear Information System (INIS)

Koukou, V; Martini, N; Sotiropoulou, P; Nikiforidis, G; Michail, C; Kalyvas, N; Kandarakis, I; Fountos, G

2015-01-01

In this simulation study, an analytical model was used in order to determine the optimal acquisition parameters for a dual energy breast imaging system. The modeled detector system, consisted of a 33.91mg/cm 2 Gd 2 O 2 S:Tb scintillator screen, placed in direct contact with a high resolution CMOS sensor. Tungsten anode X-ray spectra, filtered with various filter materials and filter thicknesses were examined for both the low- and high-energy beams, resulting in 3375 combinations. The selection of these filters was based on their K absorption edge (K-edge filtering). The calcification signal-to-noise ratio (SNR tc ) and the mean glandular dose (MGD) were calculated. The total mean glandular dose was constrained to be within acceptable levels. Optimization was based on the maximization of the SNR tc /MGD ratio. The results showed that the optimum spectral combination was 40kVp with added beam filtration of 100 μm Ag and 70kVp Cu filtered spectrum of 1000 μm for the low- and high-energy, respectively. The minimum detectable calcification size was 150 μm. Simulations demonstrate that this dual energy X-ray technique could enhance breast calcification detection. (paper)

A meta-analysis of response-time tests of the sequential two-systems model of moral judgment.

Science.gov (United States)

Baron, Jonathan; Gürçay, Burcu

2017-05-01

The (generalized) sequential two-system ("default interventionist") model of utilitarian moral judgment predicts that utilitarian responses often arise from a system-two correction of system-one deontological intuitions. Response-time (RT) results that seem to support this model are usually explained by the fact that low-probability responses have longer RTs. Following earlier results, we predicted response probability from each subject's tendency to make utilitarian responses (A, "Ability") and each dilemma's tendency to elicit deontological responses (D, "Difficulty"), estimated from a Rasch model. At the point where A = D, the two responses are equally likely, so probability effects cannot account for any RT differences between them. The sequential two-system model still predicts that many of the utilitarian responses made at this point will result from system-two corrections of system-one intuitions, hence should take longer. However, when A = D, RT for the two responses was the same, contradicting the sequential model. Here we report a meta-analysis of 26 data sets, which replicated the earlier results of no RT difference overall at the point where A = D. The data sets used three different kinds of moral judgment items, and the RT equality at the point where A = D held for all three. In addition, we found that RT increased with A-D. This result holds for subjects (characterized by Ability) but not for items (characterized by Difficulty). We explain the main features of this unanticipated effect, and of the main results, with a drift-diffusion model.

A Practical Core Loss Model for Filter Inductors of Power Electronic Converters

DEFF Research Database (Denmark)

Matsumori, Hiroaki; Shimizu, Toshihisa; Wang, Xiongfei

2018-01-01

This paper proposes a core loss model for filter inductors of power electronic converters. The model allows a computationally efficient analysis on the core loss of the inductor under the square voltage excitation and the premagnetization condition. First, the core loss of the filter inductor under...... buck chopper excitation is evaluated with the proposed model and compared with the conventional methods. The comparison shows that the proposed method results in a better core loss prediction under the premagnetized condition than that of conventional alternatives. Then, the core loss of the filter...... inductor with the pulsewidth modulated inverter excitation is evaluated, which shows that the proposed model not only accurately predicts the core loss but also identifies the hysteresis loss part. These results demonstrate that the approach can further be used for the development of magnetic materials...

Mining Emerging Sequential Patterns for Activity Recognition in Body Sensor Networks

DEFF Research Database (Denmark)

Gu, Tao; Wang, Liang; Chen, Hanhua

2010-01-01

Body Sensor Networks oer many applications in healthcare, well-being and entertainment. One of the emerging applications is recognizing activities of daily living. In this paper, we introduce a novel knowledge pattern named Emerging Sequential Pattern (ESP)|a sequential pattern that discovers...... signicant class dierences|to recognize both simple (i.e., sequential) and complex (i.e., interleaved and concurrent) activities. Based on ESPs, we build our complex activity models directly upon the sequential model to recognize both activity types. We conduct comprehensive empirical studies to evaluate...

Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter

Science.gov (United States)

Raitoharju, Matti; Nurminen, Henri; Piché, Robert

2015-12-01

Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.

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.

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

Efficient Scalable Median Filtering Using Histogram-Based Operations.

Science.gov (United States)

Green, Oded

2018-05-01

Median filtering is a smoothing technique for noise removal in images. While there are various implementations of median filtering for a single-core CPU, there are few implementations for accelerators and multi-core systems. Many parallel implementations of median filtering use a sorting algorithm for rearranging the values within a filtering window and taking the median of the sorted value. While using sorting algorithms allows for simple parallel implementations, the cost of the sorting becomes prohibitive as the filtering windows grow. This makes such algorithms, sequential and parallel alike, inefficient. In this work, we introduce the first software parallel median filtering that is non-sorting-based. The new algorithm uses efficient histogram-based operations. These reduce the computational requirements of the new algorithm while also accessing the image fewer times. We show an implementation of our algorithm for both the CPU and NVIDIA's CUDA supported graphics processing unit (GPU). The new algorithm is compared with several other leading CPU and GPU implementations. The CPU implementation has near perfect linear scaling with a speedup on a quad-core system. The GPU implementation is several orders of magnitude faster than the other GPU implementations for mid-size median filters. For small kernels, and , comparison-based approaches are preferable as fewer operations are required. Lastly, the new algorithm is open-source and can be found in the OpenCV library.

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.

Modeling sequential context effects in diagnostic interpretation of screening mammograms.

Science.gov (United States)

Alamudun, Folami; Paulus, Paige; Yoon, Hong-Jun; Tourassi, Georgia

2018-07-01

Prior research has shown that physicians' medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist's visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist's current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski-Bouligand box-counting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further, to examine the predictive value of previous perceptual behavior and decisions, we trained and evaluated a predictive model for radiologists' current diagnostic decisions. ANOVA tests showed that previous visual behavior, characterized by fractal analysis, previous diagnostic decisions, and image characteristics of previous cases are significant predictors of current diagnostic decisions. Additionally, predictive modeling of diagnostic decisions showed an overall improvement in prediction error when the model is trained on additional information about

An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

International Nuclear Information System (INIS)

Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

2015-01-01

The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment

An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

Energy Technology Data Exchange (ETDEWEB)

Manoli, Gabriele, E-mail: manoli@dmsa.unipd.it [Department of Mathematics, University of Padova, Via Trieste 63, 35121 Padova (Italy); Nicholas School of the Environment, Duke University, Durham, NC 27708 (United States); Rossi, Matteo [Department of Geosciences, University of Padova, Via Gradenigo 6, 35131 Padova (Italy); Pasetto, Damiano [Department of Mathematics, University of Padova, Via Trieste 63, 35121 Padova (Italy); Deiana, Rita [Dipartimento dei Beni Culturali, University of Padova, Piazza Capitaniato 7, 35139 Padova (Italy); Ferraris, Stefano [Interuniversity Department of Regional and Urban Studies and Planning, Politecnico and University of Torino, Viale Mattioli 39, 10125 Torino (Italy); Cassiani, Giorgio [Department of Geosciences, University of Padova, Via Gradenigo 6, 35131 Padova (Italy); Putti, Mario [Department of Mathematics, University of Padova, Via Trieste 63, 35121 Padova (Italy)

2015-02-15

The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models

International Nuclear Information System (INIS)

Shin, Hoyoung; Jae, Moosung

2016-01-01

In this study, a probabilistic assessment of the severe accident management strategy through a filtered containment venting system was performed by using decision tree models. In Korea, the filtered containment venting system has been installed for the first time in Wolsong unit 1 as a part of Fukushima follow-up steps, and it is planned to be applied gradually for all the remaining reactors. Filtered containment venting system, one of severe accident countermeasures, prevents a gradual pressurization of the containment building exhausting noncondensable gas and vapor to the outside of the containment building. In this study, a probabilistic assessment of the filtered containment venting strategy, one of the severe accident management strategies, was performed by using decision tree models. Containment failure frequencies of each decision were evaluated by the developed decision tree model. The optimum accident management strategies were evaluated by comparing the results. Various strategies in severe accident management guidelines (SAMG) could be improved by utilizing the methodology in this study and the offsite risk analysis methodology

An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models

Energy Technology Data Exchange (ETDEWEB)

Shin, Hoyoung; Jae, Moosung [Hanyang University, Seoul (Korea, Republic of)

2016-10-15

In this study, a probabilistic assessment of the severe accident management strategy through a filtered containment venting system was performed by using decision tree models. In Korea, the filtered containment venting system has been installed for the first time in Wolsong unit 1 as a part of Fukushima follow-up steps, and it is planned to be applied gradually for all the remaining reactors. Filtered containment venting system, one of severe accident countermeasures, prevents a gradual pressurization of the containment building exhausting noncondensable gas and vapor to the outside of the containment building. In this study, a probabilistic assessment of the filtered containment venting strategy, one of the severe accident management strategies, was performed by using decision tree models. Containment failure frequencies of each decision were evaluated by the developed decision tree model. The optimum accident management strategies were evaluated by comparing the results. Various strategies in severe accident management guidelines (SAMG) could be improved by utilizing the methodology in this study and the offsite risk analysis methodology.

Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor

Directory of Open Access Journals (Sweden)

Ye Li

2017-01-01

Full Text Available Recommended system is beneficial to e-commerce sites, which provides customers with product information and recommendations; the recommendation system is currently widely used in many fields. In an era of information explosion, the key challenges of the recommender system is to obtain valid information from the tremendous amount of information and produce high quality recommendations. However, when facing the large mount of information, the traditional collaborative filtering algorithm usually obtains a high degree of sparseness, which ultimately lead to low accuracy recommendations. To tackle this issue, we propose a novel algorithm named Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor, which is based on the trust model and is combined with the user similarity. The novel algorithm takes into account the degree of interest overlap between the two users and results in a superior performance to the recommendation based on Trust Model in criteria of Precision, Recall, Diversity and Coverage. Additionally, the proposed model can effectively improve the efficiency of collaborative filtering algorithm and achieve high performance.

Low-rank Kalman filtering for efficient state estimation of subsurface advective contaminant transport models

KAUST Repository

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.

Corpus Callosum Analysis using MDL-based Sequential Models of Shape and Appearance

DEFF Research Database (Denmark)

Stegmann, Mikkel Bille; Davies, Rhodri H.; Ryberg, Charlotte

2004-01-01

are proposed, but all remain applicable to other domain problems. The well-known multi-resolution AAM optimisation is extended to include sequential relaxations on texture resolution, model coverage and model parameter constraints. Fully unsupervised analysis is obtained by exploiting model parameter...... that show that the method produces accurate, robust and rapid segmentations in a cross sectional study of 17 subjects, establishing its feasibility as a fully automated clinical tool for analysis and segmentation.......This paper describes a method for automatically analysing and segmenting the corpus callosum from magnetic resonance images of the brain based on the widely used Active Appearance Models (AAMs) by Cootes et al. Extensions of the original method, which are designed to improve this specific case...

Collaborative QoS Prediction for Mobile Service with Data Filtering and SlopeOne Model

Directory of Open Access Journals (Sweden)

Yuyu Yin

2017-01-01

Full Text Available The mobile service is a widely used carrier for mobile applications. With the increase of the number of mobile services, for service recommendation and selection, the nonfunctional properties (also known as quality of service, QoS become increasingly important. However, in many cases, the number of mobile services invoked by a user is quite limited, which leads to the large number of missing QoS values. In recent years, many prediction algorithms, such as algorithms extended from collaborative filtering (CF, are proposed to predict QoS values. However, the ideas of most existing algorithms are borrowed from the recommender system community, not specific for mobile service. In this paper, we first propose a data filtering-extended SlopeOne model (filtering-based CF, which is based on the characteristics of a mobile service and considers the relation with location. Also, using the data filtering technique in FB-CF and matrix factorization (MF, this paper proposes another model FB-MF (filtering-based MF. We also build an ensemble model, which combines the prediction results of FB-CF model and FB-MF model. We conduct sufficient experiments, and the experimental results demonstrate that our models outperform all compared methods and achieve good results in high data sparsity scenario.

Accounting for Heterogeneous Returns in Sequential Schooling Decisions

NARCIS (Netherlands)

Zamarro, G.

2006-01-01

This paper presents a method for estimating returns to schooling that takes into account that returns may be heterogeneous among agents and that educational decisions are made sequentially.A sequential decision model is interesting because it explicitly considers that the level of education of each

Neural Network-Based Passive Filtering for Delayed Neutral-Type Semi-Markovian Jump Systems.

Science.gov (United States)

Shi, Peng; Li, Fanbiao; Wu, Ligang; Lim, Cheng-Chew

2017-09-01

This paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.

Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter

KAUST Repository

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.

Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter

KAUST Repository

Dreano, Denis; Mallick, Bani; Hoteit, Ibrahim

2015-01-01

A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.

Virtual microphone sensing through vibro-acoustic modelling and Kalman filtering

Science.gov (United States)

van de Walle, A.; Naets, F.; Desmet, W.

2018-05-01

This work proposes a virtual microphone methodology which enables full field acoustic measurements for vibro-acoustic systems. The methodology employs a Kalman filtering framework in order to combine a reduced high-fidelity vibro-acoustic model with a structural excitation measurement and small set of real microphone measurements on the system under investigation. By employing model order reduction techniques, a high order finite element model can be converted in a much smaller model which preserves the desired accuracy and maintains the main physical properties of the original model. Due to the low order of the reduced-order model, it can be effectively employed in a Kalman filter. The proposed methodology is validated experimentally on a strongly coupled vibro-acoustic system. The virtual sensor vastly improves the accuracy with respect to regular forward simulation. The virtual sensor also allows to recreate the full sound field of the system, which is very difficult/impossible to do through classical measurements.

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa

2016-08-12

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

Application of Bayesian Maximum Entropy Filter in parameter calibration of groundwater flow model in PingTung Plain

Science.gov (United States)

Cheung, Shao-Yong; Lee, Chieh-Han; Yu, Hwa-Lung

2017-04-01

Due to the limited hydrogeological observation data and high levels of uncertainty within, parameter estimation of the groundwater model has been an important issue. There are many methods of parameter estimation, for example, Kalman filter provides a real-time calibration of parameters through measurement of groundwater monitoring wells, related methods such as Extended Kalman Filter and Ensemble Kalman Filter are widely applied in groundwater research. However, Kalman Filter method is limited to linearity. This study propose a novel method, Bayesian Maximum Entropy Filtering, which provides a method that can considers the uncertainty of data in parameter estimation. With this two methods, we can estimate parameter by given hard data (certain) and soft data (uncertain) in the same time. In this study, we use Python and QGIS in groundwater model (MODFLOW) and development of Extended Kalman Filter and Bayesian Maximum Entropy Filtering in Python in parameter estimation. This method may provide a conventional filtering method and also consider the uncertainty of data. This study was conducted through numerical model experiment to explore, combine Bayesian maximum entropy filter and a hypothesis for the architecture of MODFLOW groundwater model numerical estimation. Through the virtual observation wells to simulate and observe the groundwater model periodically. The result showed that considering the uncertainty of data, the Bayesian maximum entropy filter will provide an ideal result of real-time parameters estimation.

Model Adaptation for Prognostics in a Particle Filtering Framework

Data.gov (United States)

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....

[Mathematical modeling of synergistic interaction of sequential thermoradiation action on mammalian cells].

Science.gov (United States)

Belkina, S V; Semkina, M A; Kritskiĭ, R O; Petin, V G

2010-01-01

Data obtained by other authors for mammalian cells treated by sequential action of ionizing radiation and hyperthermia were used to estimate the dependence of synergistic enhancement ratio on the ratio of damages induced by these agents. Experimental results were described and interpreted by means of the mathematical model of synergism in accordance with which the synergism is expected to result from the additional lethal damage arising from the interaction of sublesions induced by both agents.

Iodine filter imaging system for subtraction angiography using synchrotron radiation

Science.gov (United States)

Umetani, K.; Ueda, K.; Takeda, T.; Itai, Y.; Akisada, M.; Nakajima, T.

1993-11-01

A new type of real-time imaging system was developed for transvenous coronary angiography. A combination of an iodine filter and a single energy broad-bandwidth X-ray produces two-energy images for the iodine K-edge subtraction technique. X-ray images are sequentially converted to visible images by an X-ray image intensifier. By synchronizing the timing of the movement of the iodine filter into and out of the X-ray beam, two output images of the image intensifier are focused side by side on the photoconductive layer of a camera tube by an oscillating mirror. Both images are read out by electron beam scanning of a 1050-scanning-line video camera within a camera frame time of 66.7 ms. One hundred ninety two pairs of iodine-filtered and non-iodine-filtered images are stored in the frame memory at a rate of 15 pairs/s. In vivo subtracted images of coronary arteries in dogs were obtained in the form of motion pictures.

Nonlinear Kalman filtering in affine term structure models

DEFF Research Database (Denmark)

Christoffersen, Peter; Dorion, Christian; Jacobs, Kris

2014-01-01

The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed-income applications. We investigate whether the unscented Kalman filter should be used to capture nonlinearities and compare the performance of the Kalman filter...... with that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in the states, and cap prices, which are highly nonlinear. When caps are used to filter the states, the unscented Kalman filter significantly outperforms its extended counterpart. The unscented Kalman filter also...... performs well when compared with the much more computationally intensive particle filter. These findings suggest that the unscented Kalman filter may be a good approach for a variety of problems in fixed-income pricing....

Development, characterization, and modeling of a tunable filter camera

Science.gov (United States)

Sartor, Mark Alan

1999-10-01

This paper describes the development, characterization, and modeling of a Tunable Filter Camera (TFC). The TFC is a new multispectral instrument with electronically tuned spectral filtering and low-light-level sensitivity. It represents a hybrid between hyperspectral and multispectral imaging spectrometers that incorporates advantages from each, addressing issues such as complexity, cost, lack of sensitivity, and adaptability. These capabilities allow the TFC to be applied to low- altitude video surveillance for real-time spectral and spatial target detection and image exploitation. Described herein are the theory and principles of operation for the TFC, which includes a liquid crystal tunable filter, an intensified CCD, and a custom apochromatic lens. The results of proof-of-concept testing, and characterization of two prototype cameras are included, along with a summary of the design analyses for the development of a multiple-channel system. A significant result of this effort was the creation of a system-level model, which was used to facilitate development and predict performance. It includes models for the liquid crystal tunable filter and intensified CCD. Such modeling was necessary in the design of the system and is useful for evaluation of the system in remote-sensing applications. Also presented are characterization data from component testing, which included quantitative results for linearity, signal to noise ratio (SNR), linearity, and radiometric response. These data were used to help refine and validate the model. For a pre-defined source, the spatial and spectral response, and the noise of the camera, system can now be predicted. The innovation that sets this development apart is the fact that this instrument has been designed for integrated, multi-channel operation for the express purpose of real-time detection/identification in low- light-level conditions. Many of the requirements for the TFC were derived from this mission. In order to provide

The role of model dynamics in ensemble Kalman filter performance for chaotic systems

Science.gov (United States)

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.

Collaborative filtering recommendation model based on fuzzy clustering algorithm

Science.gov (United States)

Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

Human visual modeling and image deconvolution by linear filtering

International Nuclear Information System (INIS)

Larminat, P. de; Barba, D.; Gerber, R.; Ronsin, J.

1978-01-01

The problem is the numerical restoration of images degraded by passing through a known and spatially invariant linear system, and by the addition of a stationary noise. We propose an improvement of the Wiener's filter to allow the restoration of such images. This improvement allows to reduce the important drawbacks of classical Wiener's filter: the voluminous data processing, the lack of consideration of the vision's characteristivs which condition the perception by the observer of the restored image. In a first paragraph, we describe the structure of the visual detection system and a modelling method of this system. In the second paragraph we explain a restoration method by Wiener filtering that takes the visual properties into account and that can be adapted to the local properties of the image. Then the results obtained on TV images or scintigrams (images obtained by a gamma-camera) are commented [fr

The Rao-Blackwellized Particle Filter: A Filter Bank Implementation

Directory of Open Access Journals (Sweden)

Karlsson Rickard

2010-01-01

Full Text Available For computational efficiency, it is important to utilize model structure in particle filtering. One of the most important cases occurs when there exists a linear Gaussian substructure, which can be efficiently handled by Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF. This contribution suggests an alternative formulation of this well-known result that facilitates reuse of standard filtering components and which is also suitable for object-oriented programming. Our RBPF formulation can be seen as a Kalman filter bank with stochastic branching and pruning.

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim

2016-01-01

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

Complex step-based low-rank extended Kalman filtering for state-parameter estimation in subsurface transport models

KAUST Repository

El Gharamti, Mohamad; Hoteit, Ibrahim

2014-01-01

The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.

Complex step-based low-rank extended Kalman filtering for state-parameter estimation in subsurface transport models

KAUST Repository

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.

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.

Application of the extended Kalman filtering for the estimation of core coolant flow rate in pressurized water reactors

International Nuclear Information System (INIS)

Shieh, D.J.; Upadhyaya, B.R.

1986-01-01

In-core neutron detector and core-exit temperature signals in a pressurized water reactor (PWR) satisfy the condition of observability of the core dynamic system, and can be used to estimate nonmeasurable state variables and model parameters. The extension of the Kalman filtering technique is very useful for direct parameter estimation. This approach is applied to the determination of core coolant mass flow rate in PWRs and is evaluated using in-core measurements at the Loss-of-Fluid Test (LOFT) reactor. The influence of model uncertainties on the estimation accuracy was studied using the ambiguity function analysis. A sequential discretization method was developed to achieve faster convergence to the true value, avoiding model discretization at each sample point. The performance of the extended Kalman filter and the computational innovations were evaluated using a reduced order core dynamic model of the LOFT reactor and random data simulation. The technique was then applied to the determination of LOFT core coolant flow rate from operational data at 100% and 65% flow conditions

Sequential Blood Filtration for Extracorporeal Circulation: Initial Results from a Proof-of-Concept Prototype

Science.gov (United States)

Herbst, Daniel P.

2014-01-01

Abstract: Micropore filters are used during extracorporeal circulation to prevent gaseous and solid particles from entering the patient’s systemic circulation. Although these devices improve patient safety, limitations in current designs have prompted the development of a new concept in micropore filtration. A prototype of the new design was made using 40-μm filter screens and compared against four commercially available filters for performance in pressure loss and gross air handling. Pre- and postfilter bubble counts for 5- and 10-mL bolus injections in an ex vivo test circuit were recorded using a Doppler ultrasound bubble counter. Statistical analysis of results for bubble volume reduction between test filters was performed with one-way repeated-measures analysis of variance using Bonferroni post hoc tests. Changes in filter performance with changes in microbubble load were also assessed with dependent t tests using the 5- and 10-mL bolus injections as the paired sample for each filter. Significance was set at p < .05. All filters in the test group were comparable in pressure loss performance, showing a range of 26–33 mmHg at a flow rate of 6 L/min. In gross air-handling studies, the prototype showed improved bubble volume reduction, reaching statistical significance with three of the four commercial filters. All test filters showed decreased performance in bubble volume reduction when the microbubble load was increased. Findings from this research support the underpinning theories of a sequential arterial-line filter design and suggest that improvements in microbubble filtration may be possible using this technique. PMID:26357790

Non-Pilot-Aided Sequential Monte Carlo Method to Joint Signal, Phase Noise, and Frequency Offset Estimation in Multicarrier Systems

Directory of Open Access Journals (Sweden)

Christelle Garnier

2008-05-01

Full Text Available We address the problem of phase noise (PHN and carrier frequency offset (CFO mitigation in multicarrier receivers. In multicarrier systems, phase distortions cause two effects: the common phase error (CPE and the intercarrier interference (ICI which severely degrade the accuracy of the symbol detection stage. Here, we propose a non-pilot-aided scheme to jointly estimate PHN, CFO, and multicarrier signal in time domain. Unlike existing methods, non-pilot-based estimation is performed without any decision-directed scheme. Our approach to the problem is based on Bayesian estimation using sequential Monte Carlo filtering commonly referred to as particle filtering. The particle filter is efficiently implemented by combining the principles of the Rao-Blackwellization technique and an approximate optimal importance function for phase distortion sampling. Moreover, in order to fully benefit from time-domain processing, we propose a multicarrier signal model which includes the redundancy information induced by the cyclic prefix, thus leading to a significant performance improvement. Simulation results are provided in terms of bit error rate (BER and mean square error (MSE to illustrate the efficiency and the robustness of the proposed algorithm.

Modeling Adsorption Based Filters (Bio-remediation of Heavy Metal Contaminated Water)

Science.gov (United States)

McCarthy, Chris

I will discuss kinetic models of adsorption, as well as models of filters based on those mechanisms. These mathematical models have been developed in support of our interdisciplinary lab group, which is centered at BMCC/CUNY (City University of New York). Our group conducts research into bio-remediation of heavy metal contaminated water via filtration. The filters are constructed out of biomass, such as spent tea leaves. The spent tea leaves are available in large quantities as a result of the industrial production of tea beverages. The heavy metals bond with the surfaces of the tea leaves (adsorption). The models involve differential equations, stochastic methods, and recursive functions. I will compare the models' predictions to data obtained from computer simulations and experimentally by our lab group. Funding: CUNY Collaborative Incentive Research Grant (Round 12); CUNY Research Scholars Program.

Non-linear DSGE Models and The Central Difference Kalman Filter

DEFF Research Database (Denmark)

Andreasen, Martin Møller

This paper introduces a Quasi Maximum Likelihood (QML) approach based on the Cen- tral Difference Kalman Filter (CDKF) to estimate non-linear DSGE models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models...

Modeling the system dynamics for nutrient removal in an innovative septic tank media filter.

Science.gov (United States)

Xuan, Zhemin; Chang, Ni-Bin; Wanielista, Martin

2012-05-01

A next generation septic tank media filter to replace or enhance the current on-site wastewater treatment drainfields was proposed in this study. Unit operation with known treatment efficiencies, flow pattern identification, and system dynamics modeling was cohesively concatenated in order to prove the concept of a newly developed media filter. A multicompartmental model addressing system dynamics and feedbacks based on our assumed microbiological processes accounting for aerobic, anoxic, and anaerobic conditions in the media filter was constructed and calibrated with the aid of in situ measurements and the understanding of the flow patterns. Such a calibrated system dynamics model was then applied for a sensitivity analysis under changing inflow conditions based on the rates of nitrification and denitrification characterized through the field-scale testing. This advancement may contribute to design such a drainfield media filter in household septic tank systems in the future.

The sequential structure of brain activation predicts skill.

Science.gov (United States)

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

Science.gov (United States)

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

Work–Family Conflict and Mental Health Among Female Employees: A Sequential Mediation Model via Negative Affect and Perceived Stress

Science.gov (United States)

Zhou, Shiyi; Da, Shu; Guo, Heng; Zhang, Xichao

2018-01-01

After the implementation of the universal two-child policy in 2016, more and more working women have found themselves caught in the dilemma of whether to raise a baby or be promoted, which exacerbates work–family conflicts among Chinese women. Few studies have examined the mediating effect of negative affect. The present study combined the conservation of resources model and affective events theory to examine the sequential mediating effect of negative affect and perceived stress in the relationship between work–family conflict and mental health. A valid sample of 351 full-time Chinese female employees was recruited in this study, and participants voluntarily answered online questionnaires. Pearson correlation analysis, structural equation modeling, and multiple mediation analysis were used to examine the relationships between work–family conflict, negative affect, perceived stress, and mental health in full-time female employees. We found that women’s perceptions of both work-to-family conflict and family-to-work conflict were significant negatively related to mental health. Additionally, the results showed that negative affect and perceived stress were negatively correlated with mental health. The 95% confidence intervals indicated the sequential mediating effect of negative affect and stress in the relationship between work–family conflict and mental health was significant, which supported the hypothesized sequential mediation model. The findings suggest that work–family conflicts affected the level of self-reported mental health, and this relationship functioned through the two sequential mediators of negative affect and perceived stress. PMID:29719522

Work-Family Conflict and Mental Health Among Female Employees: A Sequential Mediation Model via Negative Affect and Perceived Stress.

Science.gov (United States)

Zhou, Shiyi; Da, Shu; Guo, Heng; Zhang, Xichao

2018-01-01

After the implementation of the universal two-child policy in 2016, more and more working women have found themselves caught in the dilemma of whether to raise a baby or be promoted, which exacerbates work-family conflicts among Chinese women. Few studies have examined the mediating effect of negative affect. The present study combined the conservation of resources model and affective events theory to examine the sequential mediating effect of negative affect and perceived stress in the relationship between work-family conflict and mental health. A valid sample of 351 full-time Chinese female employees was recruited in this study, and participants voluntarily answered online questionnaires. Pearson correlation analysis, structural equation modeling, and multiple mediation analysis were used to examine the relationships between work-family conflict, negative affect, perceived stress, and mental health in full-time female employees. We found that women's perceptions of both work-to-family conflict and family-to-work conflict were significant negatively related to mental health. Additionally, the results showed that negative affect and perceived stress were negatively correlated with mental health. The 95% confidence intervals indicated the sequential mediating effect of negative affect and stress in the relationship between work-family conflict and mental health was significant, which supported the hypothesized sequential mediation model. The findings suggest that work-family conflicts affected the level of self-reported mental health, and this relationship functioned through the two sequential mediators of negative affect and perceived stress.

Work–Family Conflict and Mental Health Among Female Employees: A Sequential Mediation Model via Negative Affect and Perceived Stress

Directory of Open Access Journals (Sweden)

Shiyi Zhou

2018-04-01

Full Text Available After the implementation of the universal two-child policy in 2016, more and more working women have found themselves caught in the dilemma of whether to raise a baby or be promoted, which exacerbates work–family conflicts among Chinese women. Few studies have examined the mediating effect of negative affect. The present study combined the conservation of resources model and affective events theory to examine the sequential mediating effect of negative affect and perceived stress in the relationship between work–family conflict and mental health. A valid sample of 351 full-time Chinese female employees was recruited in this study, and participants voluntarily answered online questionnaires. Pearson correlation analysis, structural equation modeling, and multiple mediation analysis were used to examine the relationships between work–family conflict, negative affect, perceived stress, and mental health in full-time female employees. We found that women’s perceptions of both work-to-family conflict and family-to-work conflict were significant negatively related to mental health. Additionally, the results showed that negative affect and perceived stress were negatively correlated with mental health. The 95% confidence intervals indicated the sequential mediating effect of negative affect and stress in the relationship between work–family conflict and mental health was significant, which supported the hypothesized sequential mediation model. The findings suggest that work–family conflicts affected the level of self-reported mental health, and this relationship functioned through the two sequential mediators of negative affect and perceived stress.

Tracking the business cycle of the Euro area: A multivariate model-based band-pass filter

NARCIS (Netherlands)

Azevedo, J.M.; Koopman, S.J.; Rua, A.

2006-01-01

This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties.

A discrete event modelling framework for simulation of long-term outcomes of sequential treatment strategies for ankylosing spondylitis

NARCIS (Netherlands)

A. Tran-Duy (An); A. Boonen (Annelies); M.A.F.J. van de Laar (Mart); A. Franke (Andre); J.L. Severens (Hans)

2011-01-01

textabstractObjective: To develop a modelling framework which can simulate long-term quality of life, societal costs and cost-effectiveness as affected by sequential drug treatment strategies for ankylosing spondylitis (AS). Methods: Discrete event simulation paradigm was selected for model

A discrete event modelling framework for simulation of long-term outcomes of sequential treatment strategies for ankylosing spondylitis

NARCIS (Netherlands)

Tran-Duy, A.; Boonen, A.; Laar, M.A.F.J.; Franke, A.C.; Severens, J.L.

2011-01-01

Objective To develop a modelling framework which can simulate long-term quality of life, societal costs and cost-effectiveness as affected by sequential drug treatment strategies for ankylosing spondylitis (AS). Methods Discrete event simulation paradigm was selected for model development. Drug

Generalised Filtering

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.

Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter

Czech Academy of Sciences Publication Activity Database

Dedecius, Kamil; Hofman, Radek

2012-01-01

Roč. 41, č. 5 (2012), s. 582-589 ISSN 0361-0918 R&D Projects: GA MV VG20102013018; GA ČR GA102/08/0567 Grant - others:ČVUT(CZ) SGS 10/099/OHK3/1T/16 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian methods * Particle filters * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.295, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf

Retina-Inspired Filter.

Science.gov (United States)

Doutsi, Effrosyni; Fillatre, Lionel; Antonini, Marc; Gaulmin, Julien

2018-07-01

This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper. This filter is connected to the dynamic behavior of the retina, which enables the retina to increase the sharpness of the visual stimulus during filtering before its transmission to the brain. We establish that this retina-inspired transform forms a group of spatio-temporal Weighted Difference of Gaussian (WDoG) filters when it is applied to a still image visible for a given time. We analyze the spatial frequency bandwidth of the retina-inspired filter with respect to time. It is shown that the WDoG spectrum varies from a lowpass filter to a bandpass filter. Therefore, while time increases, the retina-inspired filter enables to extract different kinds of information from the input image. Finally, we discuss the benefits of using the retina-inspired filter in image processing applications such as edge detection and compression.

A hand tracking algorithm with particle filter and improved GVF snake model

Science.gov (United States)

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.

Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.

Science.gov (United States)

de Oliveira, Saulo H P; Law, Eleanor C; Shi, Jiye; Deane, Charlotte M

2018-04-01

Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy. Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2. saulo.deoliveira@dtc.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data

DEFF Research Database (Denmark)

Herckenrath, Daan; Fiandaca, G.; Auken, Esben

2013-01-01

hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM) and electrical resistivity tomography (ERT) data. In a sequential hydrogeophysical inversion (SHI) a groundwater model is calibrated with geophysical data by coupling groundwater model parameters...... with the inverted geophysical models. We subsequently compare the SHI with a joint hydrogeophysical inversion (JHI). In the JHI, a geophysical model is simultaneously inverted with a groundwater model by coupling the groundwater and geophysical parameters to explicitly account for an established petrophysical...

Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data

DEFF Research Database (Denmark)

Herckenrath, Daan; Fiandaca, G.; Auken, Esben

2013-01-01

with the inverted geophysical models. We subsequently compare the SHI with a joint hydrogeophysical inversion (JHI). In the JHI, a geophysical model is simultaneously inverted with a groundwater model by coupling the groundwater and geophysical parameters to explicitly account for an established petrophysical...... hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM) and electrical resistivity tomography (ERT) data. In a sequential hydrogeophysical inversion (SHI) a groundwater model is calibrated with geophysical data by coupling groundwater model parameters...

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

Science.gov (United States)

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.

In vivo comparison of simultaneous versus sequential injection technique for thermochemical ablation in a porcine model.

Science.gov (United States)

Cressman, Erik N K; Shenoi, Mithun M; Edelman, Theresa L; Geeslin, Matthew G; Hennings, Leah J; Zhang, Yan; Iaizzo, Paul A; Bischof, John C

2012-01-01

To investigate simultaneous and sequential injection thermochemical ablation in a porcine model, and compare them to sham and acid-only ablation. This IACUC-approved study involved 11 pigs in an acute setting. Ultrasound was used to guide placement of a thermocouple probe and coaxial device designed for thermochemical ablation. Solutions of 10 M acetic acid and NaOH were used in the study. Four injections per pig were performed in identical order at a total rate of 4 mL/min: saline sham, simultaneous, sequential, and acid only. Volume and sphericity of zones of coagulation were measured. Fixed specimens were examined by H&E stain. Average coagulation volumes were 11.2 mL (simultaneous), 19.0 mL (sequential) and 4.4 mL (acid). The highest temperature, 81.3°C, was obtained with simultaneous injection. Average temperatures were 61.1°C (simultaneous), 47.7°C (sequential) and 39.5°C (acid only). Sphericity coefficients (0.83-0.89) had no statistically significant difference among conditions. Thermochemical ablation produced substantial volumes of coagulated tissues relative to the amounts of reagents injected, considerably greater than acid alone in either technique employed. The largest volumes were obtained with sequential injection, yet this came at a price in one case of cardiac arrest. Simultaneous injection yielded the highest recorded temperatures and may be tolerated as well as or better than acid injection alone. Although this pilot study did not show a clear advantage for either sequential or simultaneous methods, the results indicate that thermochemical ablation is attractive for further investigation with regard to both safety and efficacy.

Sequential super-stereotypy of an instinctive fixed action pattern in hyper-dopaminergic mutant mice: a model of obsessive compulsive disorder and Tourette's

Directory of Open Access Journals (Sweden)

Houchard Kimberly R

2005-02-01

Full Text Available Abstract Background Excessive sequential stereotypy of behavioral patterns (sequential super-stereotypy in Tourette's syndrome and obsessive compulsive disorder (OCD is thought to involve dysfunction in nigrostriatal dopamine systems. In sequential super-stereotypy, patients become trapped in overly rigid sequential patterns of action, language, or thought. Some instinctive behavioral patterns of animals, such as the syntactic grooming chain pattern of rodents, have sufficiently complex and stereotyped serial structure to detect potential production of overly-rigid sequential patterns. A syntactic grooming chain is a fixed action pattern that serially links up to 25 grooming movements into 4 predictable phases that follow 1 syntactic rule. New mutant mouse models allow gene-based manipulation of brain function relevant to sequential patterns, but no current animal model of spontaneous OCD-like behaviors has so far been reported to exhibit sequential super-stereotypy in the sense of a whole complex serial pattern that becomes stronger and excessively rigid. Here we used a hyper-dopaminergic mutant mouse to examine whether an OCD-like behavioral sequence in animals shows sequential super-stereotypy. Knockdown mutation of the dopamine transporter gene (DAT causes extracellular dopamine levels in the neostriatum of these adult mutant mice to rise to 170% of wild-type control levels. Results We found that the serial pattern of this instinctive behavioral sequence becomes strengthened as an entire entity in hyper-dopaminergic mutants, and more resistant to interruption. Hyper-dopaminergic mutant mice have stronger and more rigid syntactic grooming chain patterns than wild-type control mice. Mutants showed sequential super-stereotypy in the sense of having more stereotyped and predictable syntactic grooming sequences, and were also more likely to resist disruption of the pattern en route, by returning after a disruption to complete the pattern from the

Multiple sequential failure model: A probabilistic approach to quantifying human error dependency

International Nuclear Information System (INIS)

Samanta

1985-01-01

This paper rpesents a probabilistic approach to quantifying human error dependency when multiple tasks are performed. Dependent human failures are dominant contributors to risks from nuclear power plants. An overview of the Multiple Sequential Failure (MSF) model developed and its use in probabilistic risk assessments (PRAs) depending on the available data are discussed. A small-scale psychological experiment was conducted on the nature of human dependency and the interpretation of the experimental data by the MSF model show remarkable accommodation of the dependent failure data. The model, which provides an unique method for quantification of dependent failures in human reliability analysis, can be used in conjunction with any of the general methods currently used for performing the human reliability aspect in PRAs

Encoding Sequential Information in Semantic Space Models: Comparing Holographic Reduced Representation and Random Permutation

Directory of Open Access Journals (Sweden)

Gabriel Recchia

2015-01-01

Full Text Available Circular convolution and random permutation have each been proposed as neurally plausible binding operators capable of encoding sequential information in semantic memory. We perform several controlled comparisons of circular convolution and random permutation as means of encoding paired associates as well as encoding sequential information. Random permutations outperformed convolution with respect to the number of paired associates that can be reliably stored in a single memory trace. Performance was equal on semantic tasks when using a small corpus, but random permutations were ultimately capable of achieving superior performance due to their higher scalability to large corpora. Finally, “noisy” permutations in which units are mapped to other units arbitrarily (no one-to-one mapping perform nearly as well as true permutations. These findings increase the neurological plausibility of random permutations and highlight their utility in vector space models of semantics.

Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

Directory of Open Access Journals (Sweden)

Buddhi Arachchige

2017-11-01

Full Text Available This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery’s State of Charge (SOC and State of Life (SOL by utilizing the battery’s physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge time with cycling has also been included, integrating both EOL (End of Life and EOD prediction models in order to get more accuracy in the estimations.

Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

Science.gov (United States)

Ma, Hongchao; Cai, Zhan; Zhang, Liang

2018-01-01

This paper discusses airborne light detection and ranging (LiDAR) point cloud filtering (a binary classification problem) from the machine learning point of view. We compared three supervised classifiers for point cloud filtering, namely, Adaptive Boosting, support vector machine, and random forest (RF). Nineteen features were generated from raw LiDAR point cloud based on height and other geometric information within a given neighborhood. The test datasets issued by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to evaluate the performance of the three filtering algorithms; RF showed the best results with an average total error of 5.50%. The paper also makes tentative exploration in the application of transfer learning theory to point cloud filtering, which has not been introduced into the LiDAR field to the authors' knowledge. We performed filtering of three datasets from real projects carried out in China with RF models constructed by learning from the 15 ISPRS datasets and then transferred with little to no change of the parameters. Reliable results were achieved, especially in rural area (overall accuracy achieved 95.64%), indicating the feasibility of model transfer in the context of point cloud filtering for both easy automation and acceptable accuracy.

Campbell and moment measures for finite sequential spatial processes

NARCIS (Netherlands)

M.N.M. van Lieshout (Marie-Colette)

2006-01-01

textabstractWe define moment and Campbell measures for sequential spatial processes, prove a Campbell-Mecke theorem, and relate the results to their counterparts in the theory of point processes. In particular, we show that any finite sequential spatial process model can be derived as the vector

Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

DEFF Research Database (Denmark)

Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh

2015-01-01

Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...... and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data...

The AGILE on-board Kalman filter

International Nuclear Information System (INIS)

Giuliani, A.; Cocco, V.; Mereghetti, S.; Pittori, C.; Tavani, M.

2006-01-01

On-board reduction of particle background is one of the main challenges of space instruments dedicated to gamma-ray astrophysics. We present in this paper a discussion of the method and main simulation results of the on-board background filter of the Gamma-Ray Imaging Detector (GRID) of the AGILE mission. The GRID is capable of detecting and imaging with optimal point spread function gamma-ray photons in the range 30MeV-30GeV. The AGILE planned orbit is equatorial, with an altitude of 550km. This is an optimal orbit from the point of view of the expected particle background. For this orbit, electrons and positrons of kinetic energies between 20MeV and hundreds of MeV dominate the particle background, with significant contributions from high-energy (primary) and low-energy protons, and gamma-ray albedo-photons. We present here the main results obtained by extensive simulations of the on-board AGILE-GRID particle/photon background rejection algorithms based on a special application of Kalman filter techniques. This filter is applied (Level-2) sequentially after other data processing techniques characterizing the Level-1 processing. We show that, in conjunction with the Level-1 processing, the adopted Kalman filtering is expected to reduce the total particle/albedo-photon background rate to a value (=<10-30Hz) that is compatible with the AGILE telemetry. The AGILE on-board Kalman filter is also effective in reducing the Earth-albedo-photon background rate, and therefore contributes to substantially increase the AGILE exposure for celestial gamma-ray sources

Generalized infimum and sequential product of quantum effects

International Nuclear Information System (INIS)

Li Yuan; Sun Xiuhong; Chen Zhengli

2007-01-01

The quantum effects for a physical system can be described by the set E(H) of positive operators on a complex Hilbert space H that are bounded above by the identity operator I. For A, B(set-membership sign)E(H), the operation of sequential product A(convolution sign)B=A 1/2 BA 1/2 was proposed as a model for sequential quantum measurements. A nice investigation of properties of the sequential product has been carried over [Gudder, S. and Nagy, G., 'Sequential quantum measurements', J. Math. Phys. 42, 5212 (2001)]. In this note, we extend some results of this reference. In particular, a gap in the proof of Theorem 3.2 in this reference is overcome. In addition, some properties of generalized infimum A sqcap B are studied

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

Science.gov (United States)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Modeling the Performance of Biological Rapid Sand Filters Used to Remove Ammonium, Iron, and Manganese From Drinking Water

DEFF Research Database (Denmark)

Lee, Carson; Albrechtsen, Hans-Jørgen; Smets, Barth F.

activated carbon and are often used following ozonation to remove additional biodegradable organics created during ozonation. In Europe, biological filters are also used to remove ammonium and reduced forms of iron and manganese. These compounds can cause biological instability in the distribution system...... tracer, are performed during an operational cycle of a filter to examine how the filter flow changes with time. The data is used to validate a mathematical model that can both predict process performance and to gain an understanding of how dynamic conditions can influence filter performance....... The mathematical model developed is intended to assist in the design of new filters, set up of pilot plant studies, and as a tool to troubleshoot existing problems in full scale filters. Unlike previous models, the model developed accounts for the effects of particle/precipitate accumulation and its effects...

Hardware-efficient implementation of digital FIR filter using fast first-order moment algorithm

Science.gov (United States)

Cao, Li; Liu, Jianguo; Xiong, Jun; Zhang, Jing

2018-03-01

As the digital finite impulse response (FIR) filter can be transformed into the shift-add form of multiple small-sized firstorder moments, based on the existing fast first-order moment algorithm, this paper presents a novel multiplier-less structure to calculate any number of sequential filtering results in parallel. The theoretical analysis on its hardware and time-complexities reveals that by appropriately setting the degree of parallelism and the decomposition factor of a fixed word width, the proposed structure may achieve better area-time efficiency than the existing two-dimensional (2-D) memoryless-based filter. To evaluate the performance concretely, the proposed designs for different taps along with the existing 2-D memoryless-based filters, are synthesized by Synopsys Design Compiler with 0.18-μm SMIC library. The comparisons show that the proposed design has less area-time complexity and power consumption when the number of filter taps is larger than 48.

A rational workflow for sequential virtual screening of chemical libraries on searching for new tyrosinase inhibitors.

Science.gov (United States)

Le-Thi-Thu, Huong; Casanola-Martín, Gerardo M; Marrero-Ponce, Yovani; Rescigno, Antonio; Abad, Concepcion; Khan, Mahmud Tareq Hassan

2014-01-01

The tyrosinase is a bifunctional, copper-containing enzyme widely distributed in the phylogenetic tree. This enzyme is involved in the production of melanin and some other pigments in humans, animals and plants, including skin pigmentations in mammals, and browning process in plants and vegetables. Therefore, enzyme inhibitors has been under the attention of the scientist community, due to its broad applications in food, cosmetic, agricultural and medicinal fields, to avoid the undesirable effects of abnormal melanin overproduction. However, the research of novel chemical with antityrosinase activity demands the use of more efficient tools to speed up the tyrosinase inhibitors discovery process. This chapter is focused in the different components of a predictive modeling workflow for the identification and prioritization of potential new compounds with activity against the tyrosinase enzyme. In this case, two structure chemical libraries Spectrum Collection and Drugbank are used in this attempt to combine different virtual screening data mining techniques, in a sequential manner helping to avoid the usually expensive and time consuming traditional methods. Some of the sequential steps summarize here comprise the use of drug-likeness filters, similarity searching, classification and potency QSAR multiclassifier systems, modeling molecular interactions systems, and similarity/diversity analysis. Finally, the methodologies showed here provide a rational workflow for virtual screening hit analysis and selection as a promissory drug discovery strategy for use in target identification phase.

Modelling of the modified-LLCL-filter-based single-phase grid-tied Aalborg inverter

DEFF Research Database (Denmark)

Liu, Zifa; Wu, Huiyun; Liu, Yuan

2017-01-01

Owing to less conduction and switching power losses, the recently proposed Aalborg inverter has high efficiency within a wide range of input DC voltage for single-phase DC/AC power conversion. In theory, the conduction power losses can be further decreased, if an LLCL-filter is adopted instead...... of an LCL-filter for a voltage source inverter, mainly due to the reduced inductance. The Aalborg inverter shows the characteristic of a current source inverter, when working in the `boost' state. Whether the LLCL-filter can meet the control requirement of this type inverter needs to be further explored....... In this study, the small signal analysis for the modified-LLCL-filter-based Aalborg inverter is addressed. Through the modelling, it can be proven that compared with the LCL-filter, the modified-LLCL-filter causes no extra control challenge for the Aalborg inverter, and therefore more inductance in the power...

Marginalized approximate filtering of state-space models

Czech Academy of Sciences Publication Activity Database

Dedecius, Kamil

2018-01-01

Roč. 32, č. 1 (2018), s. 1-12 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GA16-09848S Institutional support: RVO:67985556 Keywords : approximate filtering * marginalized filters * particle filtering Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.708, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/dedecius-0478074.pdf

Theoretical model for a background noise limited laser-excited optical filter for doubled Nd lasers

Science.gov (United States)

Shay, Thomas M.; Garcia, Daniel F.

1990-01-01

A simple theoretical model for the calculation of the dependence of filter quantum efficiency versus laser pump power in an atomic Rb vapor laser-excited optical filter is reported. Calculations for Rb filter transitions that can be used to detect the practical and important frequency-doubled Nd lasers are presented. The results of these calculations show the filter's quantum efficiency versus the laser pump power. The required laser pump powers required range from 2.4 to 60 mW/sq cm of filter aperture.

Dynamics-based sequential memory: Winnerless competition of patterns

International Nuclear Information System (INIS)

Seliger, Philip; Tsimring, Lev S.; Rabinovich, Mikhail I.

2003-01-01

We introduce a biologically motivated dynamical principle of sequential memory which is based on winnerless competition (WLC) of event images. This mechanism is implemented in a two-layer neural model of sequential spatial memory. We present the learning dynamics which leads to the formation of a WLC network. After learning, the system is capable of associative retrieval of prerecorded sequences of patterns

Behavioral Modeling of WSN MAC Layer Security Attacks: A Sequential UML Approach

DEFF Research Database (Denmark)

Pawar, Pranav M.; Nielsen, Rasmus Hjorth; Prasad, Neeli R.

2012-01-01

is the vulnerability to security attacks/threats. The performance and behavior of a WSN are vastly affected by such attacks. In order to be able to better address the vulnerabilities of WSNs in terms of security, it is important to understand the behavior of the attacks. This paper addresses the behavioral modeling...... of medium access control (MAC) security attacks in WSNs. The MAC layer is responsible for energy consumption, delay and channel utilization of the network and attacks on this layer can introduce significant degradation of the individual sensor nodes due to energy drain and in performance due to delays....... The behavioral modeling of attacks will be beneficial for designing efficient and secure MAC layer protocols. The security attacks are modeled using a sequential diagram approach of Unified Modeling Language (UML). Further, a new attack definition, specific to hybrid MAC mechanisms, is proposed....

Sequential Power-Dependence Theory

NARCIS (Netherlands)

Buskens, Vincent; Rijt, Arnout van de

2008-01-01

Existing methods for predicting resource divisions in laboratory exchange networks do not take into account the sequential nature of the experimental setting. We extend network exchange theory by considering sequential exchange. We prove that Sequential Power-Dependence Theory—unlike

A Low Cost Structurally Optimized Design for Diverse Filter Types

Science.gov (United States)

Kazmi, Majida; Aziz, Arshad; Akhtar, Pervez; Ikram, Nassar

2016-01-01

A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to 75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image

Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy.

Science.gov (United States)

Sil Kar, Sudeshna; Maity, Santi P

2016-09-01

Extraction of blood vessels on retinal images plays a significant role for screening of different opthalmologic diseases. However, accurate extraction of the entire and individual type of vessel silhouette from the noisy images with poorly illuminated background is a complicated task. To this aim, an integrated system design platform is suggested in this work for vessel extraction using a sequential bandpass filter followed by fuzzy conditional entropy maximization on matched filter response. At first noise is eliminated from the image under consideration through curvelet based denoising. To include the fine details and the relatively less thick vessel structures, the image is passed through a bank of sequential bandpass filter structure optimized for contrast enhancement. Fuzzy conditional entropy on matched filter response is then maximized to find the set of multiple optimal thresholds to extract the different types of vessel silhouettes from the background. Differential Evolution algorithm is used to determine the optimal gain in bandpass filter and the combination of the fuzzy parameters. Using the multiple thresholds, retinal image is classified as the thick, the medium and the thin vessels including neovascularization. Performance evaluated on different publicly available retinal image databases shows that the proposed method is very efficient in identifying the diverse types of vessels. Proposed method is also efficient in extracting the abnormal and the thin blood vessels in pathological retinal images. The average values of true positive rate, false positive rate and accuracy offered by the method is 76.32%, 1.99% and 96.28%, respectively for the DRIVE database and 72.82%, 2.6% and 96.16%, respectively for the STARE database. Simulation results demonstrate that the proposed method outperforms the existing methods in detecting the various types of vessels and the neovascularization structures. The combination of curvelet transform and tunable bandpass

Validation studies on indexed sequential modeling for the Colorado River Basin

International Nuclear Information System (INIS)

Labadie, J.W.; Fontane, D.G.; Salas, J.D.; Ouarda, T.

1991-01-01

This paper reports on a method called indexed sequential modeling (ISM) that has been developed by the Western Area Power Administration to estimate reliable levels of project dependable power capacity (PDC) and applied to several federal hydro systems in the Western U.S. The validity of ISM in relation to more commonly accepted stochastic modeling approaches is analyzed by applying it to the Colorado River Basin using the Colorado River Simulation System (CRSS) developed by the U.S. Bureau of Reclamation. Performance of ISM is compared with results from input of stochastically generated data using the LAST Applied Stochastic Techniques Package. Results indicate that output generated from ISM synthetically generated sequences display an acceptable correspondence with results obtained from final convergent stochastically generated hydrology for the Colorado River Basin

Semi-analytical model of filtering effects in microwave phase shifters based on semiconductor optical amplifiers

DEFF Research Database (Denmark)

Chen, Yaohui; Xue, Weiqi; Öhman, Filip

2008-01-01

We present a model to interpret enhanced microwave phase shifts based on filter assisted slow and fast light effects in semiconductor optical amplifiers. The model also demonstrates the spectral phase impact of input optical signals.......We present a model to interpret enhanced microwave phase shifts based on filter assisted slow and fast light effects in semiconductor optical amplifiers. The model also demonstrates the spectral phase impact of input optical signals....

Ensemble Kalman filter for the reconstruction of the Earth's mantle circulation

Science.gov (United States)

Bocher, Marie; Fournier, Alexandre; Coltice, Nicolas

2018-02-01

Recent advances in mantle convection modeling led to the release of a new generation of convection codes, able to self-consistently generate plate-like tectonics at their surface. Those models physically link mantle dynamics to surface tectonics. Combined with plate tectonic reconstructions, they have the potential to produce a new generation of mantle circulation models that use data assimilation methods and where uncertainties in plate tectonic reconstructions are taken into account. We provided a proof of this concept by applying a suboptimal Kalman filter to the reconstruction of mantle circulation (Bocher et al., 2016). Here, we propose to go one step further and apply the ensemble Kalman filter (EnKF) to this problem. The EnKF is a sequential Monte Carlo method particularly adapted to solve high-dimensional data assimilation problems with nonlinear dynamics. We tested the EnKF using synthetic observations consisting of surface velocity and heat flow measurements on a 2-D-spherical annulus model and compared it with the method developed previously. The EnKF performs on average better and is more stable than the former method. Less than 300 ensemble members are sufficient to reconstruct an evolution. We use covariance adaptive inflation and localization to correct for sampling errors. We show that the EnKF results are robust over a wide range of covariance localization parameters. The reconstruction is associated with an estimation of the error, and provides valuable information on where the reconstruction is to be trusted or not.

Adaptive filters and internal models: multilevel description of cerebellar function.

Science.gov (United States)

Porrill, John; Dean, Paul; Anderson, Sean R

2013-11-01

Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Bayesian sequential processor approach to spectroscopic portal system decisions

Energy Technology Data Exchange (ETDEWEB)

Sale, K; Candy, J; Breitfeller, E; Guidry, B; Manatt, D; Gosnell, T; Chambers, D

2007-07-31

The development of faster more reliable techniques to detect radioactive contraband in a portal type scenario is an extremely important problem especially in this era of constant terrorist threats. Towards this goal the development of a model-based, Bayesian sequential data processor for the detection problem is discussed. In the sequential processor each datum (detector energy deposit and pulse arrival time) is used to update the posterior probability distribution over the space of model parameters. The nature of the sequential processor approach is that a detection is produced as soon as it is statistically justified by the data rather than waiting for a fixed counting interval before any analysis is performed. In this paper the Bayesian model-based approach, physics and signal processing models and decision functions are discussed along with the first results of our research.

HMM filtering and parameter estimation of an electricity spot price model

International Nuclear Information System (INIS)

Erlwein, Christina; Benth, Fred Espen; Mamon, Rogemar

2010-01-01

In this paper we develop a model for electricity spot price dynamics. The spot price is assumed to follow an exponential Ornstein-Uhlenbeck (OU) process with an added compound Poisson process. In this way, the model allows for mean-reversion and possible jumps. All parameters are modulated by a hidden Markov chain in discrete time. They are able to switch between different economic regimes representing the interaction of various factors. Through the application of reference probability technique, adaptive filters are derived, which in turn, provide optimal estimates for the state of the Markov chain and related quantities of the observation process. The EM algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters. We implement this self-calibrating model on a deseasonalised series of daily spot electricity prices from the Nordic exchange Nord Pool. On the basis of one-step ahead forecasts, we found that the model is able to capture the empirical characteristics of Nord Pool spot prices. (author)

Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters

Directory of Open Access Journals (Sweden)

Juan G. Gonzalez

2002-01-01

Full Text Available Linear filtering theory has been largely motivated by the characteristics of Gaussian signals. In the same manner, the proposed Myriad Filtering methods are motivated by the need for a flexible filter class with high statistical efficiency in non-Gaussian impulsive environments that can appear in practice. Myriad filters have a solid theoretical basis, are inherently more powerful than median filters, and are very general, subsuming traditional linear FIR filters. The foundation of the proposed filtering algorithms lies in the definition of the myriad as a tunable estimator of location derived from the theory of robust statistics. We prove several fundamental properties of this estimator and show its optimality in practical impulsive models such as the α-stable and generalized-t. We then extend the myriad estimation framework to allow the use of weights. In the same way as linear FIR filters become a powerful generalization of the mean filter, filters based on running myriads reach all of their potential when a weighting scheme is utilized. We derive the “normal” equations for the optimal myriad filter, and introduce a suboptimal methodology for filter tuning and design. The strong potential of myriad filtering and estimation in impulsive environments is illustrated with several examples.

A sequential threshold cure model for genetic analysis of time-to-event data

DEFF Research Database (Denmark)

Ødegård, J; Madsen, Per; Labouriau, Rodrigo S.

2011-01-01

In analysis of time-to-event data, classical survival models ignore the presence of potential nonsusceptible (cured) individuals, which, if present, will invalidate the inference procedures. Existence of nonsusceptible individuals is particularly relevant under challenge testing with specific...... pathogens, which is a common procedure in aquaculture breeding schemes. A cure model is a survival model accounting for a fraction of nonsusceptible individuals in the population. This study proposes a mixed cure model for time-to-event data, measured as sequential binary records. In a simulation study...... survival data were generated through 2 underlying traits: susceptibility and endurance (risk of dying per time-unit), associated with 2 sets of underlying liabilities. Despite considerable phenotypic confounding, the proposed model was largely able to distinguish the 2 traits. Furthermore, if selection...

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods.

Science.gov (United States)

Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J

2017-03-03

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Numerical modelling of the erosion and deposition of sand inside a filter layer

DEFF Research Database (Denmark)

Jacobsen, Niels Gjøl; van Gent, Marcel R. A.; Fredsøe, Jørgen

2017-01-01

This paper treats the numerical modelling of the behaviour of a sand core covered by rocks and exposed to waves. The associated displacement of the rock is also studied. A design that allows for erosion and deposition of the sand core beneath a rock layer in a coastal structure requires an accurate...... prediction method to assure that the amount of erosion remains within acceptable limits. This work presents a numerical model that is capable of describing the erosion and deposition patterns inside of an open filter of rock on top of sand. The hydraulic loading is that of incident irregular waves...... and the open filters are surface piercing. Due to the few experimental data sets on sediment transport inside of rock layers, a sediment transport formulation has been proposed based on a matching between the numerical model and experimental data on the profile deformation inside an open filter. The rock layer...

Selection vector filter framework

Science.gov (United States)

Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.

2003-10-01

We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.

A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters

Science.gov (United States)

Zhu, Gaofeng; Li, Xin; Ma, Jinzhu; Wang, Yunquan; Liu, Shaomin; Huang, Chunlin; Zhang, Kun; Hu, Xiaoli

2018-04-01

Sequential Monte Carlo (SMC) samplers have become increasing popular for estimating the posterior parameter distribution with the non-linear dependency structures and multiple modes often present in hydrological models. However, the explorative capabilities and efficiency of the sampler depends strongly on the efficiency in the move step of SMC sampler. In this paper we presented a new SMC sampler entitled the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which is well suited to handle unknown static parameters of hydrologic model. The PEM-SMC sampler is inspired by the works of Liang and Wong (2001) and operates by incorporating the strengths of the genetic algorithm, differential evolution algorithm and Metropolis-Hasting algorithm into the framework of SMC. We also prove that the sampler admits the target distribution to be a stationary distribution. Two case studies including a multi-dimensional bimodal normal distribution and a conceptual rainfall-runoff hydrologic model by only considering parameter uncertainty and simultaneously considering parameter and input uncertainty show that PEM-SMC sampler is generally superior to other popular SMC algorithms in handling the high dimensional problems. The study also indicated that it may be important to account for model structural uncertainty by using multiplier different hydrological models in the SMC framework in future study.

Bootstrap Sequential Determination of the Co-integration Rank in VAR Models

DEFF Research Database (Denmark)

Guiseppe, Cavaliere; Rahbæk, Anders; Taylor, A.M. Robert

with empirical rejection frequencies often very much in excess of the nominal level. As a consequence, bootstrap versions of these tests have been developed. To be useful, however, sequential procedures for determining the co-integrating rank based on these bootstrap tests need to be consistent, in the sense...... in the literature by proposing a bootstrap sequential algorithm which we demonstrate delivers consistent cointegration rank estimation for general I(1) processes. Finite sample Monte Carlo simulations show the proposed procedure performs well in practice....

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.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Science.gov (United States)

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015

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.

An improved protocol for the preparation of total genomic DNA from isolates of yeast and mould using Whatman FTA filter papers.

Science.gov (United States)

Borman, Andrew M; Fraser, Mark; Linton, Christopher J; Palmer, Michael D; Johnson, Elizabeth M

2010-06-01

Here, we present a significantly improved version of our previously published method for the extraction of fungal genomic DNA from pure cultures using Whatman FTA filter paper matrix technology. This modified protocol is extremely rapid, significantly more cost effective than our original method, and importantly, substantially reduces the problem of potential cross-contamination between sequential filters when employing FTA technology.

L70 life prediction for solid state lighting using Kalman Filter and Extended Kalman Filter based models

Energy Technology Data Exchange (ETDEWEB)

Lall, Pradeep; Wei, Junchao; Davis, Lynn

2013-08-08

Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying damage using physics-based models. Life

Hemodynamic analysis of sequential graft from right coronary system to left coronary system.

Science.gov (United States)

Wang, Wenxin; Mao, Boyan; Wang, Haoran; Geng, Xueying; Zhao, Xi; Zhang, Huixia; Xie, Jinsheng; Zhao, Zhou; Lian, Bo; Liu, Youjun

2016-12-28

Sequential and single grafting are two surgical procedures of coronary artery bypass grafting. However, it remains unclear if the sequential graft can be used between the right and left coronary artery system. The purpose of this paper is to clarify the possibility of right coronary artery system anastomosis to left coronary system. A patient-specific 3D model was first reconstructed based on coronary computed tomography angiography (CCTA) images. Two different grafts, the normal multi-graft (Model 1) and the novel multi-graft (Model 2), were then implemented on this patient-specific model using virtual surgery techniques. In Model 1, the single graft was anastomosed to right coronary artery (RCA) and the sequential graft was adopted to anastomose left anterior descending (LAD) and left circumflex artery (LCX). While in Model 2, the single graft was anastomosed to LAD and the sequential graft was adopted to anastomose RCA and LCX. A zero-dimensional/three-dimensional (0D/3D) coupling method was used to realize the multi-scale simulation of both the pre-operative and two post-operative models. Flow rates in the coronary artery and grafts were obtained. The hemodynamic parameters were also showed, including wall shear stress (WSS) and oscillatory shear index (OSI). The area of low WSS and OSI in Model 1 was much less than that in Model 2. Model 1 shows optimistic hemodynamic modifications which may enhance the long-term patency of grafts. The anterior segments of sequential graft have better long-term patency than the posterior segments. With rational spatial position of the heart vessels, the last anastomosis of sequential graft should be connected to the main branch.

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

Directory of Open Access Journals (Sweden)

Laurent Dewasme

2017-02-01

Full Text Available Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb. For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition.

Attack Trees with Sequential Conjunction

NARCIS (Netherlands)

Jhawar, Ravi; Kordy, Barbara; Mauw, Sjouke; Radomirović, Sasa; Trujillo-Rasua, Rolando

2015-01-01

We provide the first formal foundation of SAND attack trees which are a popular extension of the well-known attack trees. The SAND at- tack tree formalism increases the expressivity of attack trees by intro- ducing the sequential conjunctive operator SAND. This operator enables the modeling of

Improving the precision of the keyword-matching pornographic text filtering method using a hybrid model.

Science.gov (United States)

Su, Gui-yang; Li, Jian-hua; Ma, Ying-hua; Li, Sheng-hong

2004-09-01

With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applications which can block or filter such information are used. Approaches in those systems can be roughly classified into two kinds: metadata based and content based. With the development of distributed technologies, content based filtering technologies will play a more and more important role in filtering systems. Keyword matching is a content based method used widely in harmful text filtering. Experiments to evaluate the recall and precision of the method showed that the precision of the method is not satisfactory, though the recall of the method is rather high. According to the results, a new pornographic text filtering model based on reconfirming is put forward. Experiments showed that the model is practical, has less loss of recall than the single keyword matching method, and has higher precision.

The effect of bathymetric filtering on nearshore process model results

Science.gov (United States)

Plant, N.G.; Edwards, K.L.; Kaihatu, J.M.; Veeramony, J.; Hsu, L.; Holland, K.T.

2009-01-01

Nearshore wave and flow model results are shown to exhibit a strong sensitivity to the resolution of the input bathymetry. In this analysis, bathymetric resolution was varied by applying smoothing filters to high-resolution survey data to produce a number of bathymetric grid surfaces. We demonstrate that the sensitivity of model-predicted wave height and flow to variations in bathymetric resolution had different characteristics. Wave height predictions were most sensitive to resolution of cross-shore variability associated with the structure of nearshore sandbars. Flow predictions were most sensitive to the resolution of intermediate scale alongshore variability associated with the prominent sandbar rhythmicity. Flow sensitivity increased in cases where a sandbar was closer to shore and shallower. Perhaps the most surprising implication of these results is that the interpolation and smoothing of bathymetric data could be optimized differently for the wave and flow models. We show that errors between observed and modeled flow and wave heights are well predicted by comparing model simulation results using progressively filtered bathymetry to results from the highest resolution simulation. The damage done by over smoothing or inadequate sampling can therefore be estimated using model simulations. We conclude that the ability to quantify prediction errors will be useful for supporting future data assimilation efforts that require this information.

A vector Wiener filter for dual-radionuclide imaging

International Nuclear Information System (INIS)

Links, J.M.; Prince, J.L.; Gupta, S.N.

1996-01-01

The routine use of a single radionuclide for patient imaging in nuclear medicine can be complemented by studies employing two tracers to examine two different processes in a single organ, most frequently by simultaneous imaging of both radionuclides in two different energy windows. In addition, simultaneous transmission/emission imaging with dual-radionuclides has been described, with one radionuclide used for the transmission study and a second for the emission study. There is thus currently considerable interest in dual-radionuclide imaging. A major problem with all dual-radionuclide imaging is the crosstalk between the two radionuclides. Such crosstalk frequently occurs, because scattered radiation from the higher energy radionuclide is detected in the lower energy window, and because the lower energy radionuclide may have higher energy emissions which are detected in the higher energy window. The authors have previously described the use of Fourier-based restoration filtering in single photon emission computed tomography (SPECT) and positron emission tomography (PET) to improve quantitative accuracy by designing a Wiener or other Fourier filter to partially restore the loss of contrast due to scatter and finite spatial resolution effects. The authors describe here the derivation and initial validation of an extension of such filtering for dual-radionuclide imaging that simultaneously (1) improves contrast in each radionuclide's direct image, (2) reduces image noise, and (3) reduces the crosstalk contribution from the other radionuclide. This filter is based on a vector version of the Wiener filter, which is shown to be superior [in the minimum mean square error (MMSE) sense] to the sequential application of separate crosstalk and restoration filters

Prediction of Lumen Output and Chromaticity Shift in LEDs Using Kalman Filter and Extended Kalman Filter Based Models

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

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

Directory of Open Access Journals (Sweden)

Anthony Hoak

2017-03-01

Full Text Available We develop an interactive likelihood (ILH for sequential Monte Carlo (SMC methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL and TUD-Stadtmitte using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA and classification of events, activities and relationships for multi-object trackers (CLEAR MOT. In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Sequential Logic Model Deciphers Dynamic Transcriptional Control of Gene Expressions

Science.gov (United States)

Yeo, Zhen Xuan; Wong, Sum Thai; Arjunan, Satya Nanda Vel; Piras, Vincent; Tomita, Masaru; Selvarajoo, Kumar; Giuliani, Alessandro; Tsuchiya, Masa

2007-01-01

Background Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. Methodology Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. Principal Findings SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. Conclusions/Significance The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological

Sequential logic model deciphers dynamic transcriptional control of gene expressions.

Directory of Open Access Journals (Sweden)

Zhen Xuan Yeo

Full Text Available BACKGROUND: Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. METHODOLOGY: Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. PRINCIPAL FINDINGS: SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. CONCLUSIONS/SIGNIFICANCE: The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet

Time scale of random sequential adsorption.

Science.gov (United States)

Erban, Radek; Chapman, S Jonathan

2007-04-01

A simple multiscale approach to the diffusion-driven adsorption from a solution to a solid surface is presented. The model combines two important features of the adsorption process: (i) The kinetics of the chemical reaction between adsorbing molecules and the surface and (ii) geometrical constraints on the surface made by molecules which are already adsorbed. The process (i) is modeled in a diffusion-driven context, i.e., the conditional probability of adsorbing a molecule provided that the molecule hits the surface is related to the macroscopic surface reaction rate. The geometrical constraint (ii) is modeled using random sequential adsorption (RSA), which is the sequential addition of molecules at random positions on a surface; one attempt to attach a molecule is made per one RSA simulation time step. By coupling RSA with the diffusion of molecules in the solution above the surface the RSA simulation time step is related to the real physical time. The method is illustrated on a model of chemisorption of reactive polymers to a virus surface.

Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

Science.gov (United States)

Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

2015-01-01

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

SPRINT: A Tool to Generate Concurrent Transaction-Level Models from Sequential Code

Directory of Open Access Journals (Sweden)

Richard Stahl

2007-01-01

Full Text Available A high-level concurrent model such as a SystemC transaction-level model can provide early feedback during the exploration of implementation alternatives for state-of-the-art signal processing applications like video codecs on a multiprocessor platform. However, the creation of such a model starting from sequential code is a time-consuming and error-prone task. It is typically done only once, if at all, for a given design. This lack of exploration of the design space often leads to a suboptimal implementation. To support our systematic C-based design flow, we have developed a tool to generate a concurrent SystemC transaction-level model for user-selected task boundaries. Using this tool, different parallelization alternatives have been evaluated during the design of an MPEG-4 simple profile encoder and an embedded zero-tree coder. Generation plus evaluation of an alternative was possible in less than six minutes. This is fast enough to allow extensive exploration of the design space.

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

Science.gov (United States)

Croce, Pierpaolo; Zappasodi, Filippo; Merla, Arcangelo; Chiarelli, Antonio Maria

2017-08-01

Objective. Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. Approach. Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). Main results. We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements. Significance. The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building walls

KAUST Repository

Iglesias, Marco; Sawlan, Zaid A; Scavino, Marco; Tempone, Raul; Wood, Christopher

2017-01-01

In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analogous to our previously proposed approach [1,2], for estimating the state and parameters of linear parabolic partial differential equations

Laboratory for filter testing

Energy Technology Data Exchange (ETDEWEB)

Paluch, W.

1987-07-01

Filters used for mine draining in brown coal surface mines are tested by the Mine Draining Department of Poltegor. Laboratory tests of new types of filters developed by Poltegor are analyzed. Two types of tests are used: tests of scale filter models and tests of experimental units of new filters. Design and operation of the test stands used for testing mechanical properties and hydraulic properties of filters for coal mines are described: dimensions, pressure fluctuations, hydraulic equipment. Examples of testing large-diameter filters for brown coal mines are discussed.

Comparisons of adaptive TIN modelling filtering method and threshold segmentation filtering method of LiDAR point cloud

International Nuclear Information System (INIS)

Chen, Lin; Fan, Xiangtao; Du, Xiaoping

2014-01-01

Point cloud filtering is the basic and key step in LiDAR data processing. Adaptive Triangle Irregular Network Modelling (ATINM) algorithm and Threshold Segmentation on Elevation Statistics (TSES) algorithm are among the mature algorithms. However, few researches concentrate on the parameter selections of ATINM and the iteration condition of TSES, which can greatly affect the filtering results. First the paper presents these two key problems under two different terrain environments. For a flat area, small height parameter and angle parameter perform well and for areas with complex feature changes, large height parameter and angle parameter perform well. One-time segmentation is enough for flat areas, and repeated segmentations are essential for complex areas. Then the paper makes comparisons and analyses of the results by these two methods. ATINM has a larger I error in both two data sets as it sometimes removes excessive points. TSES has a larger II error in both two data sets as it ignores topological relations between points. ATINM performs well even with a large region and a dramatic topology while TSES is more suitable for small region with flat topology. Different parameters and iterations can cause relative large filtering differences

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

Science.gov (United States)

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Mathematical modelling of tissue formation in chondrocyte filter cultures.

Science.gov (United States)

Catt, C J; Schuurman, W; Sengers, B G; van Weeren, P R; Dhert, W J A; Please, C P; Malda, J

2011-12-17

In the field of cartilage tissue engineering, filter cultures are a frequently used three-dimensional differentiation model. However, understanding of the governing processes of in vitro growth and development of tissue in these models is limited. Therefore, this study aimed to further characterise these processes by means of an approach combining both experimental and applied mathematical methods. A mathematical model was constructed, consisting of partial differential equations predicting the distribution of cells and glycosaminoglycans (GAGs), as well as the overall thickness of the tissue. Experimental data was collected to allow comparison with the predictions of the simulation and refinement of the initial models. Healthy mature equine chondrocytes were expanded and subsequently seeded on collagen-coated filters and cultured for up to 7 weeks. Resulting samples were characterised biochemically, as well as histologically. The simulations showed a good representation of the experimentally obtained cell and matrix distribution within the cultures. The mathematical results indicate that the experimental GAG and cell distribution is critically dependent on the rate at which the cell differentiation process takes place, which has important implications for interpreting experimental results. This study demonstrates that large regions of the tissue are inactive in terms of proliferation and growth of the layer. In particular, this would imply that higher seeding densities will not significantly affect the growth rate. A simple mathematical model was developed to predict the observed experimental data and enable interpretation of the principal underlying mechanisms controlling growth-related changes in tissue composition.

Particle filtering with path sampling and an application to a bimodal ocean current model

International Nuclear Information System (INIS)

Weare, Jonathan

2009-01-01

This paper introduces a recursive particle filtering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particle filters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particle filter but have been inaccessible due to the limitations of standard particle filtering strategies.

Stack filter classifiers

Energy Technology Data Exchange (ETDEWEB)

Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory

2009-01-01

Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

Asynchronous Operators of Sequential Logic Venjunction & Sequention

CERN Document Server

Vasyukevich, Vadim

2011-01-01

This book is dedicated to new mathematical instruments assigned for logical modeling of the memory of digital devices. The case in point is logic-dynamical operation named venjunction and venjunctive function as well as sequention and sequentional function. Venjunction and sequention operate within the framework of sequential logic. In a form of the corresponding equations, they organically fit analytical expressions of Boolean algebra. Thus, a sort of symbiosis is formed using elements of asynchronous sequential logic on the one hand and combinational logic on the other hand. So, asynchronous

Hydrodynamics of microbial filter feeding.

Science.gov (United States)

Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia; Walther, Jens H; Kiørboe, Thomas; Andersen, Anders

2017-08-29

Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate amounts of water. Also, the trade-off in the filter spacing remains unexplored, despite its simple formulation: A filter too coarse will allow suitably sized prey to pass unintercepted, whereas a filter too fine will cause strong flow resistance. We quantify the feeding flow of the filter-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet), something notoriously difficult to visualize but sporadically observed in the related choanocytes (sponges). A CFD model with a flagellar vane correctly predicts the filtration rate of D. grandis , and using a simple model we can account for the filtration rates of other microbial filter feeders. We finally predict how optimum filter mesh size increases with cell size in microbial filter feeders, a prediction that accords very well with observations. We expect our results to be of significance for small-scale biophysics and trait-based ecological modeling.

Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering.

Science.gov (United States)

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).

ETV TEST REPORT OF CONTROL OF BIOAEROSOLS IN HVAC SYSTEMS GLASFLOSS INDUSTRIES EXCEL FILTER, MODEL SBG24242898

Science.gov (United States)

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...

Ensemble Kalman filter for the reconstruction of the Earth's mantle circulation

Directory of Open Access Journals (Sweden)

M. Bocher

2018-02-01

Full Text Available Recent advances in mantle convection modeling led to the release of a new generation of convection codes, able to self-consistently generate plate-like tectonics at their surface. Those models physically link mantle dynamics to surface tectonics. Combined with plate tectonic reconstructions, they have the potential to produce a new generation of mantle circulation models that use data assimilation methods and where uncertainties in plate tectonic reconstructions are taken into account. We provided a proof of this concept by applying a suboptimal Kalman filter to the reconstruction of mantle circulation (Bocher et al., 2016. Here, we propose to go one step further and apply the ensemble Kalman filter (EnKF to this problem. The EnKF is a sequential Monte Carlo method particularly adapted to solve high-dimensional data assimilation problems with nonlinear dynamics. We tested the EnKF using synthetic observations consisting of surface velocity and heat flow measurements on a 2-D-spherical annulus model and compared it with the method developed previously. The EnKF performs on average better and is more stable than the former method. Less than 300 ensemble members are sufficient to reconstruct an evolution. We use covariance adaptive inflation and localization to correct for sampling errors. We show that the EnKF results are robust over a wide range of covariance localization parameters. The reconstruction is associated with an estimation of the error, and provides valuable information on where the reconstruction is to be trusted or not.

Making Career Decisions--A Sequential Elimination Approach.

Science.gov (United States)

Gati, Itamar

1986-01-01

Presents a model for career decision making based on the sequential elimination of occupational alternatives, an adaptation for career decisions of Tversky's (1972) elimination-by-aspects theory of choice. The expected utility approach is reviewed as a representative compensatory model for career decisions. Advantages, disadvantages, and…

Sequential Computerized Mastery Tests--Three Simulation Studies

Science.gov (United States)

Wiberg, Marie

2006-01-01

A simulation study of a sequential computerized mastery test is carried out with items modeled with the 3 parameter logistic item response theory model. The examinees' responses are either identically distributed, not identically distributed, or not identically distributed together with estimation errors in the item characteristics. The…

Flatness-based control and Kalman filtering for a continuous-time macroeconomic model

Science.gov (United States)

Rigatos, G.; Siano, P.; Ghosh, T.; Busawon, K.; Binns, R.

2017-11-01

The article proposes flatness-based control for a nonlinear macro-economic model of the UK economy. The differential flatness properties of the model are proven. This enables to introduce a transformation (diffeomorphism) of the system's state variables and to express the state-space description of the model in the linear canonical (Brunowsky) form in which both the feedback control and the state estimation problem can be solved. For the linearized equivalent model of the macroeconomic system, stabilizing feedback control can be achieved using pole placement methods. Moreover, to implement stabilizing feedback control of the system by measuring only a subset of its state vector elements the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied on the linearized equivalent model of the financial system and of an inverse transformation that is based again on differential flatness theory. The asymptotic stability properties of the control scheme are confirmed.

MODEL-ORIENTED METHOD OF DESIGN IMPLEMENTATION WHEN CREATING DIGITAL FILTERS

Directory of Open Access Journals (Sweden)

V. Levinskyi

2016-12-01

Full Text Available This article discusses the example of model-oriented method of design and development of digital low-pass filters (LPF for automatic control systems (ACS. Typically, high frequency noise and disturbance attenuation is carried out by analogue LPF. However, technical implementation of analogue filters higher than the second order arouse certain difficulties related with the need of precise passive components ratings selection (resistors, capacitors. If the noise and disturbances spectral composition is known, it is possible to build digital LPF with the Nyquist frequency greater than the maximum frequency in the noise spectrum. Such possibility has appeared because of cheap, energy-efficient, high-speed 32-bit microcontrollers market entry. They have analogue signals sampling rate of 30 kHz and above. The traditional approach using the “manual” method of filter parameters calculation, obtaining their recurrence expressions and further program implementation requires high qualification and a lot of time consumption from the developer. An alternative to this approach is the model-oriented method of design (MOMD in MatLab environment when in the one environment the design of digital LPF, verificaton of its performance as a part of the ACS, generation and compilation of program codes for selected microcontroller family take place. MOMD can also be used in the designs of bandpass and bandstop filters for adaptive control systems or systems of technical diagnostics. If during the commissioning or the operation of ACS there is a need in digital LPF parameters change then this operation can be performed within half an hour. MOMD technology allows to significantly reduce the time for developing a specific product without loss of quality in its design ‘cause of extensive possibilities of MatLab development environment.

AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.

Science.gov (United States)

Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo

2017-09-21

Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.

Adaptive Filtering Using Recurrent Neural Networks

Science.gov (United States)

Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

2005-01-01

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

The Application of Barnes Filter to Positioning the Center of Landed Tropical Cyclone in Numerical Models

Directory of Open Access Journals (Sweden)

Haibo Zou

2018-01-01

Full Text Available After a tropical cyclone (TC making landfall, the numerical model output sea level pressure (SLP presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85% the DDs are small (between −30 km and 30 km, while in a few cases (about 15% the DDs are large (greater than 30 km even 70 km. This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter.

Modelling and measurement of wear particle flow in a dual oil filter system for condition monitoring

DEFF Research Database (Denmark)

Henneberg, Morten; Eriksen, René Lynge; Fich, Jens

2016-01-01

. The quantity of wear particles in gear oil is analysed with respect to system running conditions. It is shown that the model fits the data in terms of startup “particle burst” phenomenon, quasi-stationary conditions during operation, and clean-up filtration when placed out of operation. In order to establish...... boundary condition for particle burst phenomenon, the release of wear particles from a pleated mesh filter is measured in a test rig and included in the model. The findings show that a dual filter model, with startup phenomenon included, can describe trends in the wear particle flow observed in the gear...... particle generation is made possible by model parameter estimation and identification of an unintended lack of filter change. The model may also be used to optimise system and filtration performance, and to enable continuous condition monitoring....

Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

Directory of Open Access Journals (Sweden)

Jan eKneissler

2015-04-01

Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

Physical Modeling of the Polyfrequency Filter-Compensating Device Based on the Capacitor-Coil

Science.gov (United States)

Butyrin, P. A.; Gusev, G. G.; Mikheev, D. V.; Shakirzianov, F. N.

2017-12-01

The paper presents the results of physical modeling and experimental study of the frequency characteristics of the polyfrequency filter-compensating device (PFCD) based on a capacitor-coil. The amplitude- frequency and phase-frequency characteristics of the physical PFCD model were constructed and its equivalent parameters were identified. The feasibility of a PFCD in the form of a single technical device with high technical and economic characteristics was experimentally proven. In the paper, recommendations for practical applications of the capacitor-coil-based PFCD are made and the advantages of the device over known standard passive filter-compensating devices are evaluated.

Theoretical aspects of spatial-temporal modeling

CERN Document Server

Matsui, Tomoko

2015-01-01

This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alph...

Optimal Filtering in Mass Transport Modeling From Satellite Gravimetry Data

Science.gov (United States)

Ditmar, P.; Hashemi Farahani, H.; Klees, R.

2011-12-01

Monitoring natural mass transport in the Earth's system, which has marked a new era in Earth observation, is largely based on the data collected by the GRACE satellite mission. Unfortunately, this mission is not free from certain limitations, two of which are especially critical. Firstly, its sensitivity is strongly anisotropic: it senses the north-south component of the mass re-distribution gradient much better than the east-west component. Secondly, it suffers from a trade-off between temporal and spatial resolution: a high (e.g., daily) temporal resolution is only possible if the spatial resolution is sacrificed. To make things even worse, the GRACE satellites enter occasionally a phase when their orbit is characterized by a short repeat period, which makes it impossible to reach a high spatial resolution at all. A way to mitigate limitations of GRACE measurements is to design optimal data processing procedures, so that all available information is fully exploited when modeling mass transport. This implies, in particular, that an unconstrained model directly derived from satellite gravimetry data needs to be optimally filtered. In principle, this can be realized with a Wiener filter, which is built on the basis of covariance matrices of noise and signal. In practice, however, a compilation of both matrices (and, therefore, of the filter itself) is not a trivial task. To build the covariance matrix of noise in a mass transport model, it is necessary to start from a realistic model of noise in the level-1B data. Furthermore, a routine satellite gravimetry data processing includes, in particular, the subtraction of nuisance signals (for instance, associated with atmosphere and ocean), for which appropriate background models are used. Such models are not error-free, which has to be taken into account when the noise covariance matrix is constructed. In addition, both signal and noise covariance matrices depend on the type of mass transport processes under

Numerical study of canister filters with alternatives filter cap configurations

Science.gov (United States)

Mohammed, A. N.; Daud, A. R.; Abdullah, K.; Seri, S. M.; Razali, M. A.; Hushim, M. F.; Khalid, A.

2017-09-01

Air filtration system and filter play an important role in getting a good quality air into turbo machinery such as gas turbine. The filtration system and filter has improved the quality of air and protect the gas turbine part from contaminants which could bring damage. During separation of contaminants from the air, pressure drop cannot be avoided but it can be minimized thus helps to reduce the intake losses of the engine [1]. This study is focused on the configuration of the filter in order to obtain the minimal pressure drop along the filter. The configuration used is the basic filter geometry provided by Salutary Avenue Manufacturing Sdn Bhd. and two modified canister filter cap which is designed based on the basic filter model. The geometries of the filter are generated by using SOLIDWORKS software and Computational Fluid Dynamics (CFD) software is used to analyse and simulates the flow through the filter. In this study, the parameters of the inlet velocity are 0.032 m/s, 0.063 m/s, 0.094 m/s and 0.126 m/s. The total pressure drop produce by basic, modified filter 1 and 2 is 292.3 Pa, 251.11 Pa and 274.7 Pa. The pressure drop reduction for the modified filter 1 is 41.19 Pa and 14.1% lower compared to basic filter and the pressure drop reduction for modified filter 2 is 17.6 Pa and 6.02% lower compared to the basic filter. The pressure drops for the basic filter are slightly different with the Salutary Avenue filter due to limited data and experiment details. CFD software are very reliable in running a simulation rather than produces the prototypes and conduct the experiment thus reducing overall time and cost in this study.

The statistical decay of very hot nuclei: from sequential decay to multifragmentation

International Nuclear Information System (INIS)

Carlson, B.V.; Donangelo, R.; Universidad de la Republica, Montevideo; Souza, S.R.; Universidade Federal do Rio Grande do Sul; Lynch, W.G.; Steiner, A.W.; Tsang, M.B.

2010-01-01

Full text. At low excitation energies, the compound nucleus typically decays through the sequential emission of light particles. As the energy increases, the emission probability of heavier fragments increases until, at sufficiently high energies, several heavy complex fragments are emitted during the decay. The extent to which this fragment emission is simultaneous or sequential has been a subject of theoretical and experimental study for almost 30 years. The Statistical Multifragmentation Model, an equilibrium model of simultaneous fragment emission, uses the configurations of a statistical ensemble to determine the distribution of primary fragments of a compound nucleus. The primary fragments are then assumed to decay by sequential compound emission or Fermi breakup. As the first step toward a more unified model of these processes, we demonstrate the equivalence of a generalized Fermi breakup model, in which densities of excited states are taken into account, to the microcanonical version of the statistical multifragmentation model. We then establish a link between this unified Fermi breakup / statistical multifragmentation model and the well-known process of compound nucleus emission, which permits to consider simultaneous and sequential emission on the same footing. Within this unified framework, we analyze the increasing importance of simultaneous, multifragment decay with increasing excitation energy and decreasing lifetime of the compound nucleus. (author)

Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task

Directory of Open Access Journals (Sweden)

Cristóbal Moënne-Loccoz

2017-09-01

Full Text Available Our daily interaction with the world is plagued of situations in which we develop expertise through self-motivated repetition of the same task. In many of these interactions, and especially when dealing with computer and machine interfaces, we must deal with sequences of decisions and actions. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion and a specific sequence of choices must be performed in order to produce the expected outcome. But, as we become experts in the use of such interfaces, is it possible to identify specific search and learning strategies? And if so, can we use this information to predict future actions? In addition to better understanding the cognitive processes underlying sequential decision making, this could allow building adaptive interfaces that can facilitate interaction at different moments of the learning curve. Here we tackle the question of modeling sequential decision-making behavior in a simple human-computer interface that instantiates a 4-level binary decision tree (BDT task. We record behavioral data from voluntary participants while they attempt to solve the task. Using a Hidden Markov Model-based approach that capitalizes on the hierarchical structure of behavior, we then model their performance during the interaction. Our results show that partitioning the problem space into a small set of hierarchically related stereotyped strategies can potentially capture a host of individual decision making policies. This allows us to follow how participants learn and develop expertise in the use of the interface. Moreover, using a Mixture of Experts based on these stereotyped strategies, the model is able to predict the behavior of participants that master the task.

Filtering a statistically exactly solvable test model for turbulent tracers from partial observations

International Nuclear Information System (INIS)

Gershgorin, B.; Majda, A.J.

2011-01-01

A statistically exactly solvable model for passive tracers is introduced as a test model for the authors' Nonlinear Extended Kalman Filter (NEKF) as well as other filtering algorithms. The model involves a Gaussian velocity field and a passive tracer governed by the advection-diffusion equation with an imposed mean gradient. The model has direct relevance to engineering problems such as the spread of pollutants in the air or contaminants in the water as well as climate change problems concerning the transport of greenhouse gases such as carbon dioxide with strongly intermittent probability distributions consistent with the actual observations of the atmosphere. One of the attractive properties of the model is the existence of the exact statistical solution. In particular, this unique feature of the model provides an opportunity to design and test fast and efficient algorithms for real-time data assimilation based on rigorous mathematical theory for a turbulence model problem with many active spatiotemporal scales. Here, we extensively study the performance of the NEKF which uses the exact first and second order nonlinear statistics without any approximations due to linearization. The role of partial and sparse observations, the frequency of observations and the observation noise strength in recovering the true signal, its spectrum, and fat tail probability distribution are the central issues discussed here. The results of our study provide useful guidelines for filtering realistic turbulent systems with passive tracers through partial observations.

A Relational Account of Call-by-Value Sequentiality

DEFF Research Database (Denmark)

Riecke, Jon Gary; Sandholm, Anders Bo

2002-01-01

We construct a model for FPC, a purely functional, sequential, call-by-value language. The model is built from partial continuous functions, in the style of Plotkin, further constrained to be uniform with respect to a class of logical relations. We prove that the model is fully abstract....

A comparison of the effects of filtering and sensorineural hearing loss on patients of consonant confusions.

Science.gov (United States)

Wang, M D; Reed, C M; Bilger, R C

1978-03-01

It has been found that listeners with sensorineural hearing loss who show similar patterns of consonant confusions also tend to have similar audiometric profiles. The present study determined whether normal listeners, presented with filtered speech, would produce consonant confusions similar to those previously reported for the hearing-impaired listener. Consonant confusion matrices were obtained from eight normal-hearing subjects for four sets of CV and VC nonsense syllables presented under six high-pass and six-low pass filtering conditions. Patterns of consonant confusion for each condition were described using phonological features in sequential information analysis. Severe low-pass filtering produced consonant confusions comparable to those of listeners with high-frequency hearing loss. Severe high-pass filtering gave a result comparable to that of patients with flat or rising audiograms. And, mild filtering resulted in confusion patterns comparable to those of listeners with essentially normal hearing. An explanation in terms of the spectrum, the level of speech, and the configuration of this individual listener's audiogram is given.

Nonlinear filtering for LIDAR signal processing

Directory of Open Access Journals (Sweden)

D. G. Lainiotis

1996-01-01

Full Text Available LIDAR (Laser Integrated Radar is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF, which has been the standard nonlinear filter in past engineering applications.

Modeling of Mixing Behavior in a Combined Blowing Steelmaking Converter with a Filter-Based Euler-Lagrange Model

Science.gov (United States)

Li, Mingming; Li, Lin; Li, Qiang; Zou, Zongshu

2018-05-01

A filter-based Euler-Lagrange multiphase flow model is used to study the mixing behavior in a combined blowing steelmaking converter. The Euler-based volume of fluid approach is employed to simulate the top blowing, while the Lagrange-based discrete phase model that embeds the local volume change of rising bubbles for the bottom blowing. A filter-based turbulence method based on the local meshing resolution is proposed aiming to improve the modeling of turbulent eddy viscosities. The model validity is verified through comparison with physical experiments in terms of mixing curves and mixing times. The effects of the bottom gas flow rate on bath flow and mixing behavior are investigated and the inherent reasons for the mixing result are clarified in terms of the characteristics of bottom-blowing plumes, the interaction between plumes and top-blowing jets, and the change of bath flow structure.

Environmentally adaptive processing for shallow ocean applications: A sequential Bayesian approach.

Science.gov (United States)

Candy, J V

2015-09-01

The shallow ocean is a changing environment primarily due to temperature variations in its upper layers directly affecting sound propagation throughout. The need to develop processors capable of tracking these changes implies a stochastic as well as an environmentally adaptive design. Bayesian techniques have evolved to enable a class of processors capable of performing in such an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean environment. A solution to this problem is addressed by developing a sequential Bayesian processor capable of providing a joint solution to the modal function tracking and environmental adaptivity problem. Here, the focus is on the development of both a particle filter and an unscented Kalman filter capable of providing reasonable performance for this problem. These processors are applied to hydrophone measurements obtained from a vertical array. The adaptivity problem is attacked by allowing the modal coefficients and/or wavenumbers to be jointly estimated from the noisy measurement data along with tracking of the modal functions while simultaneously enhancing the noisy pressure-field measurements.

An object-oriented language-database integration model: The composition filters approach

NARCIS (Netherlands)

Aksit, Mehmet; Bergmans, Lodewijk; Vural, Sinan; Vural, S.

1991-01-01

This paper introduces a new model, based on so-called object-composition filters, that uniformly integrates database-like features into an object-oriented language. The focus is on providing persistent dynamic data structures, data sharing, transactions, multiple views and associative access,

An Object-Oriented Language-Database Integration Model: The Composition-Filters Approach

NARCIS (Netherlands)

Aksit, Mehmet; Bergmans, Lodewijk; Vural, S.; Vural, Sinan; Lehrmann Madsen, O.

1992-01-01

This paper introduces a new model, based on so-called object-composition filters, that uniformly integrates database-like features into an object-oriented language. The focus is on providing persistent dynamic data structures, data sharing, transactions, multiple views and associative access,

An extended Kalman filter approach to non-stationary Bayesian estimation of reduced-order vocal fold model parameters.

Science.gov (United States)

Hadwin, Paul J; Peterson, Sean D

2017-04-01

The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.

Development of a predictive model for 6 month survival in patients with venous thromboembolism and solid malignancy requiring IVC filter placement.

Science.gov (United States)

Huang, Steven Y; Odisio, Bruno C; Sabir, Sharjeel H; Ensor, Joe E; Niekamp, Andrew S; Huynh, Tam T; Kroll, Michael; Gupta, Sanjay

2017-07-01

Our purpose was to develop a predictive model for short-term survival (i.e. filter placement in patients with venous thromboembolism (VTE) and solid malignancy. Clinical and laboratory parameters were retrospectively reviewed for patients with solid malignancy who received a filter between January 2009 and December 2011 at a tertiary care cancer center. Multivariate Cox proportional hazards modeling was used to assess variables associated with 6 month survival following filter placement in patients with VTE and solid malignancy. Significant variables were used to generate a predictive model. 397 patients with solid malignancy received a filter during the study period. Three variables were associated with 6 month survival: (1) serum albumin [hazard ratio (HR) 0.496, P filter placement can be predicted from three patient variables. Our predictive model could be used to help physicians decide whether a permanent or retrievable filter may be more appropriate as well as to assess the risks and benefits for filter retrieval within the context of survival longevity in patients with cancer.

Assessing filtering of mountaintop CO2 mole fractions for application to inverse models of biosphere-atmosphere carbon exchange

Directory of Open Access Journals (Sweden)

S. L. Heck

2012-02-01

Full Text Available There is a widely recognized need to improve our understanding of biosphere-atmosphere carbon exchanges in areas of complex terrain including the United States Mountain West. CO2 fluxes over mountainous terrain are often difficult to measure due to unusual and complicated influences associated with atmospheric transport. Consequently, deriving regional fluxes in mountain regions with carbon cycle inversion of atmospheric CO2 mole fraction is sensitive to filtering of observations to those that can be represented at the transport model resolution. Using five years of CO2 mole fraction observations from the Regional Atmospheric Continuous CO2 Network in the Rocky Mountains (Rocky RACCOON, five statistical filters are used to investigate a range of approaches for identifying regionally representative CO2 mole fractions. Test results from three filters indicate that subsets based on short-term variance and local CO2 gradients across tower inlet heights retain nine-tenths of the total observations and are able to define representative diel variability and seasonal cycles even for difficult-to-model sites where the influence of local fluxes is much larger than regional mole fraction variations. Test results from two other filters that consider measurements from previous and following days using spline fitting or sliding windows are overly selective. Case study examples showed that these windowing-filters rejected measurements representing synoptic changes in CO2, which suggests that they are not well suited to filtering continental CO2 measurements. We present a novel CO2 lapse rate filter that uses CO2 differences between levels in the model atmosphere to select subsets of site measurements that are representative on model scales. Our new filtering techniques provide guidance for novel approaches to assimilating mountain-top CO2 mole fractions in carbon cycle inverse models.

The development rainfall forecasting using kalman filter

Science.gov (United States)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*

KAUST Repository

Hoteit, Ibrahim

2012-02-01

This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an “ensemble of Kalman filters” operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, the authors consider the construction of the PKF through an “ensemble” of ensemble Kalman filters (EnKFs) instead, and call the implementation the particle EnKF (PEnKF). It is shown that different types of the EnKFs can be considered as special cases of the PEnKF. Similar to the situation in the particle filter, the authors also introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.

Making Faces - State-Space Models Applied to Multi-Modal Signal Processing

DEFF Research Database (Denmark)

Lehn-Schiøler, Tue

2005-01-01

The two main focus areas of this thesis are State-Space Models and multi modal signal processing. The general State-Space Model is investigated and an addition to the class of sequential sampling methods is proposed. This new algorithm is denoted as the Parzen Particle Filter. Furthermore...... optimizer can be applied to speed up convergence. The linear version of the State-Space Model, the Kalman Filter, is applied to multi modal signal processing. It is demonstrated how a State-Space Model can be used to map from speech to lip movements. Besides the State-Space Model and the multi modal...... application an information theoretic vector quantizer is also proposed. Based on interactions between particles, it is shown how a quantizing scheme based on an analytic cost function can be derived....

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.

Multi-agent sequential hypothesis testing

KAUST Repository

Kim, Kwang-Ki K.

2014-12-15

This paper considers multi-agent sequential hypothesis testing and presents a framework for strategic learning in sequential games with explicit consideration of both temporal and spatial coordination. The associated Bayes risk functions explicitly incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well-defined value functions with respect to (a) the belief states for the case of conditional independent private noisy measurements that are also assumed to be independent identically distributed over time, and (b) the information states for the case of correlated private noisy measurements. A sequential investment game of strategic coordination and delay is also discussed as an application of the proposed strategic learning rules.

Predicting the Hydraulic Conductivity of Metallic Iron Filters: Modeling Gone Astray

Directory of Open Access Journals (Sweden)

Chicgoua Noubactep

2016-04-01

Full Text Available Since its introduction about 25 years ago, metallic iron (Fe0 has shown its potential as the key component of reactive filtration systems for contaminant removal in polluted waters. Technical applications of such systems can be enhanced by numerical simulation of a filter design to improve, e.g., the service time or the minimum permeability of a prospected system to warrant the required output water quality. This communication discusses the relevant input quantities into such a simulation model, illustrates the possible simplifications and identifies the lack of relevant thermodynamic and kinetic data. As a result, necessary steps are outlined that may improve the numerical simulation and, consequently, the technical design of Fe0 filters. Following a general overview on the key reactions in a Fe0 system, the importance of iron corrosion kinetics is illustrated. Iron corrosion kinetics, expressed as a rate constant kiron, determines both the removal rate of contaminants and the average permeability loss of the filter system. While the relevance of a reasonable estimate of kiron is thus obvious, information is scarce. As a conclusion, systematic experiments for the determination of kiron values are suggested to improve the database of this key input parameter to Fe0 filters.

Design of Microwave Multibandpass Filters with Quasilumped Resonators

Directory of Open Access Journals (Sweden)

Dejan Miljanović

2015-01-01

Full Text Available Design of RF and microwave filters has always been the challenging engineering field. Modern filter design techniques involve the use of the three-dimensional electromagnetic (3D EM solvers for predicting filter behavior, yielding the most accurate filter characteristics. However, the 3D EM simulations are time consuming. In this paper, we propose electric-circuit models, instead of 3D EM models, suitable for design of RF and microwave filters with quasilumped coupled resonators. Using the diakoptic approach, the 3D filter structure is decomposed into domains that are modeled by electric networks. The coupling between these domains is modeled by capacitors and coupled inductors. Furthermore, we relate the circuit-element values to the physical dimensions of the 3D filter structure. We propose the filter design procedure that is based on the circuit models and fast circuit-level simulations, yielding the element values from which the physical dimensions can be obtained. The obtained dimensions should be slightly refined for achieving the desired filter characteristics. The mathematical problems encountered in the procedure are solved by numerical and symbolic computations. The procedure is exemplified by designing a triple-bandpass filter and validated by measurements on the fabricated filter. The simulation and experimental results are in good agreement.

Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model

International Nuclear Information System (INIS)

Xu Long; Wang Junping; Chen Quanshi

2012-01-01

Highlights: ► A novel extended Kalman Filtering SOC estimation method based on a stochastic fuzzy neural network (SFNN) battery model is proposed. ► The SFNN which has filtering effect on noisy input can model the battery nonlinear dynamic with high accuracy. ► A robust parameter learning algorithm for SFNN is studied so that the parameters can converge to its true value with noisy data. ► The maximum SOC estimation error based on the proposed method is 0.6%. - Abstract: Extended Kalman filtering is an intelligent and optimal means for estimating the state of a dynamic system. In order to use extended Kalman filtering to estimate the state of charge (SOC), we require a mathematical model that can accurately capture the dynamics of battery pack. In this paper, we propose a stochastic fuzzy neural network (SFNN) instead of the traditional neural network that has filtering effect on noisy input to model the battery nonlinear dynamic. Then, the paper studies the extended Kalman filtering SOC estimation method based on a SFNN model. The modeling test is realized on an 80 Ah Ni/MH battery pack and the Federal Urban Driving Schedule (FUDS) cycle is used to verify the SOC estimation method. The maximum SOC estimation error is 0.6% compared with the real SOC obtained from the discharging test.

A 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.

On the origin of reproducible sequential activity in neural circuits

Science.gov (United States)

Afraimovich, V. S.; Zhigulin, V. P.; Rabinovich, M. I.

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

The Bacterial Sequential Markov Coalescent.

Science.gov (United States)

De Maio, Nicola; Wilson, Daniel J

2017-05-01

Bacteria can exchange and acquire new genetic material from other organisms directly and via the environment. This process, known as bacterial recombination, has a strong impact on the evolution of bacteria, for example, leading to the spread of antibiotic resistance across clades and species, and to the avoidance of clonal interference. Recombination hinders phylogenetic and transmission inference because it creates patterns of substitutions (homoplasies) inconsistent with the hypothesis of a single evolutionary tree. Bacterial recombination is typically modeled as statistically akin to gene conversion in eukaryotes, i.e. , using the coalescent with gene conversion (CGC). However, this model can be very computationally demanding as it needs to account for the correlations of evolutionary histories of even distant loci. So, with the increasing popularity of whole genome sequencing, the need has emerged for a faster approach to model and simulate bacterial genome evolution. We present a new model that approximates the coalescent with gene conversion: the bacterial sequential Markov coalescent (BSMC). Our approach is based on a similar idea to the sequential Markov coalescent (SMC)-an approximation of the coalescent with crossover recombination. However, bacterial recombination poses hurdles to a sequential Markov approximation, as it leads to strong correlations and linkage disequilibrium across very distant sites in the genome. Our BSMC overcomes these difficulties, and shows a considerable reduction in computational demand compared to the exact CGC, and very similar patterns in simulated data. We implemented our BSMC model within new simulation software FastSimBac. In addition to the decreased computational demand compared to previous bacterial genome evolution simulators, FastSimBac provides more general options for evolutionary scenarios, allowing population structure with migration, speciation, population size changes, and recombination hotspots. FastSimBac is

Application of Multi-Hypothesis Sequential Monte Carlo for Breakup Analysis

Science.gov (United States)

Faber, W. R.; Zaidi, W.; Hussein, I. I.; Roscoe, C. W. T.; Wilkins, M. P.; Schumacher, P. W., Jr.

As more objects are launched into space, the potential for breakup events and space object collisions is ever increasing. These events create large clouds of debris that are extremely hazardous to space operations. Providing timely, accurate, and statistically meaningful Space Situational Awareness (SSA) data is crucial in order to protect assets and operations in space. The space object tracking problem, in general, is nonlinear in both state dynamics and observations, making it ill-suited to linear filtering techniques such as the Kalman filter. Additionally, given the multi-object, multi-scenario nature of the problem, space situational awareness requires multi-hypothesis tracking and management that is combinatorially challenging in nature. In practice, it is often seen that assumptions of underlying linearity and/or Gaussianity are used to provide tractable solutions to the multiple space object tracking problem. However, these assumptions are, at times, detrimental to tracking data and provide statistically inconsistent solutions. This paper details a tractable solution to the multiple space object tracking problem applicable to space object breakup events. Within this solution, simplifying assumptions of the underlying probability density function are relaxed and heuristic methods for hypothesis management are avoided. This is done by implementing Sequential Monte Carlo (SMC) methods for both nonlinear filtering as well as hypothesis management. This goal of this paper is to detail the solution and use it as a platform to discuss computational limitations that hinder proper analysis of large breakup events.

Sequential charged particle reaction

International Nuclear Information System (INIS)

Hori, Jun-ichi; Ochiai, Kentaro; Sato, Satoshi; Yamauchi, Michinori; Nishitani, Takeo

2004-01-01

The effective cross sections for producing the sequential reaction products in F82H, pure vanadium and LiF with respect to the 14.9-MeV neutron were obtained and compared with the estimation ones. Since the sequential reactions depend on the secondary charged particles behavior, the effective cross sections are corresponding to the target nuclei and the material composition. The effective cross sections were also estimated by using the EAF-libraries and compared with the experimental ones. There were large discrepancies between estimated and experimental values. Additionally, we showed the contribution of the sequential reaction on the induced activity and dose rate in the boundary region with water. From the present study, it has been clarified that the sequential reactions are of great importance to evaluate the dose rates around the surface of cooling pipe and the activated corrosion products. (author)

Eigenimage filtering of nuclear medicine image sequences

International Nuclear Information System (INIS)

Windham, J.P.; Froelich, J.W.; Abd-Allah, M.

1985-01-01

In many nuclear medicine imaging sequences the localization of radioactivity in organs other than the target organ interferes with imaging of the desired anatomical structure or physiological process. A filtering technique has been developed which suppresses the interfering process while enhancing the desired process. This technique requires the identification of temporal sequential signatures for both the interfering and desired processes. These signatures are placed in the form of signature vectors. Signature matrices, M/sub D/ and M/sub U/, are formed by taking the outer product expansion of the temporal signature vectors for the desired and interfering processes respectively. By using the transformation from the simultaneous diagonalization of these two signature matrices a weighting vector is obtained. The technique is shown to maximize the projection of the desired process while minimizing the interfering process based upon an extension of Rayleigh's Principle. The technique is demonstrated for first pass renal and cardiac flow studies. This filter offers a potential for simplifying and extending the accuracy of diagnostic nuclear medicine procedures

Kalman-filter model for determining block and trickle SNM losses

International Nuclear Information System (INIS)

Barlow, R.E.; Durst, M.J.; Smiriga, N.G.

1982-07-01

This paper describes an integrated decision procedure for deciding whether a diversion of SNM has occurred. Two possible types of diversion are considered: a block loss during a single time period and a cumulative trickle loss over several time periods. The methodology used is based on a compound Kalman filter model. Numerical examples illustrate our approach

Automatic synthesis of sequential control schemes

International Nuclear Information System (INIS)

Klein, I.

1993-01-01

Of all hard- and software developed for industrial control purposes, the majority is devoted to sequential, or binary valued, control and only a minor part to classical linear control. Typically, the sequential parts of the controller are invoked during startup and shut-down to bring the system into its normal operating region and into some safe standby region, respectively. Despite its importance, fairly little theoretical research has been devoted to this area, and sequential control programs are therefore still created manually without much theoretical support to obtain a systematic approach. We propose a method to create sequential control programs automatically. The main ideas is to spend some effort off-line modelling the plant, and from this model generate the control strategy, that is the plan. The plant is modelled using action structures, thereby concentrating on the actions instead of the states of the plant. In general the planning problem shows exponential complexity in the number of state variables. However, by focusing on the actions, we can identify problem classes as well as algorithms such that the planning complexity is reduced to polynomial complexity. We prove that these algorithms are sound, i.e., the generated solution will solve the stated problem, and complete, i.e., if the algorithms fail, then no solution exists. The algorithms generate a plan as a set of actions and a partial order on this set specifying the execution order. The generated plant is proven to be minimal and maximally parallel. For a larger class of problems we propose a method to split the original problem into a number of simple problems that can each be solved using one of the presented algorithms. It is also shown how a plan can be translated into a GRAFCET chart, and to illustrate these ideas we have implemented a planing tool, i.e., a system that is able to automatically create control schemes. Such a tool can of course also be used on-line if it is fast enough. This

Lexical decoder for continuous speech recognition: sequential neural network approach

International Nuclear Information System (INIS)

Iooss, Christine

1991-01-01

The work presented in this dissertation concerns the study of a connectionist architecture to treat sequential inputs. In this context, the model proposed by J.L. Elman, a recurrent multilayers network, is used. Its abilities and its limits are evaluated. Modifications are done in order to treat erroneous or noisy sequential inputs and to classify patterns. The application context of this study concerns the realisation of a lexical decoder for analytical multi-speakers continuous speech recognition. Lexical decoding is completed from lattices of phonemes which are obtained after an acoustic-phonetic decoding stage relying on a K Nearest Neighbors search technique. Test are done on sentences formed from a lexicon of 20 words. The results are obtained show the ability of the proposed connectionist model to take into account the sequentiality at the input level, to memorize the context and to treat noisy or erroneous inputs. (author) [fr

Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations

Directory of Open Access Journals (Sweden)

Nah-Oak Song

2015-08-01

Full Text Available We propose an optimal electric energy management of a cooperative multi-microgrid community with sequentially coordinated operations. The sequentially coordinated operations are suggested to distribute computational burden and yet to make the optimal 24 energy management of multi-microgrids possible. The sequential operations are mathematically modeled to find the optimal operation conditions and illustrated with physical interpretation of how to achieve optimal energy management in the cooperative multi-microgrid community. This global electric energy optimization of the cooperative community is realized by the ancillary internal trading between the microgrids in the cooperative community which reduces the extra cost from unnecessary external trading by adjusting the electric energy production amounts of combined heat and power (CHP generators and amounts of both internal and external electric energy trading of the cooperative community. A simulation study is also conducted to validate the proposed mathematical energy management models.

Sequential Foreign Investments, Regional Technology Platforms and the Evolution of Japanese Multinationals in East Asia

OpenAIRE

Song, Jaeyong

2001-01-01

IVABSTRACTIn this paper, we investigate the firm-level mechanisms that underlie the sequential foreign direct investment (FDI) decisions of multinational corporations (MNCs). To understand inter-firm heterogeneity in the sequential FDI behaviors of MNCs, we develop a firm capability-based model of sequential FDI decisions. In the setting of Japanese electronics MNCs in East Asia, we empirically examine how prior investments in firm capabilities affect sequential investments into existingprodu...

Eyewitness confidence in simultaneous and sequential lineups: a criterion shift account for sequential mistaken identification overconfidence.

Science.gov (United States)

Dobolyi, David G; Dodson, Chad S

2013-12-01

Confidence judgments for eyewitness identifications play an integral role in determining guilt during legal proceedings. Past research has shown that confidence in positive identifications is strongly associated with accuracy. Using a standard lineup recognition paradigm, we investigated accuracy using signal detection and ROC analyses, along with the tendency to choose a face with both simultaneous and sequential lineups. We replicated past findings of reduced rates of choosing with sequential as compared to simultaneous lineups, but notably found an accuracy advantage in favor of simultaneous lineups. Moreover, our analysis of the confidence-accuracy relationship revealed two key findings. First, we observed a sequential mistaken identification overconfidence effect: despite an overall reduction in false alarms, confidence for false alarms that did occur was higher with sequential lineups than with simultaneous lineups, with no differences in confidence for correct identifications. This sequential mistaken identification overconfidence effect is an expected byproduct of the use of a more conservative identification criterion with sequential than with simultaneous lineups. Second, we found a steady drop in confidence for mistaken identifications (i.e., foil identifications and false alarms) from the first to the last face in sequential lineups, whereas confidence in and accuracy of correct identifications remained relatively stable. Overall, we observed that sequential lineups are both less accurate and produce higher confidence false identifications than do simultaneous lineups. Given the increasing prominence of sequential lineups in our legal system, our data argue for increased scrutiny and possibly a wholesale reevaluation of this lineup format. PsycINFO Database Record (c) 2013 APA, all rights reserved.

How to include frequency dependent complex permeability Into SPICE models to improve EMI filters design?

Science.gov (United States)

Sixdenier, Fabien; Yade, Ousseynou; Martin, Christian; Bréard, Arnaud; Vollaire, Christian

2018-05-01

Electromagnetic interference (EMI) filters design is a rather difficult task where engineers have to choose adequate magnetic materials, design the magnetic circuit and choose the size and number of turns. The final design must achieve the attenuation requirements (constraints) and has to be as compact as possible (goal). Alternating current (AC) analysis is a powerful tool to predict global impedance or attenuation of any filter. However, AC analysis are generally performed without taking into account the frequency-dependent complex permeability behaviour of soft magnetic materials. That's why, we developed two frequency-dependent complex permeability models able to be included into SPICE models. After an identification process, the performances of each model are compared to measurements made on a realistic EMI filter prototype in common mode (CM) and differential mode (DM) to see the benefit of the approach. Simulation results are in good agreement with the measured ones especially in the middle frequency range.

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

Science.gov (United States)

Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

2016-11-01

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R 2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

Simulation model of harmonics reduction technique using shunt active filter by cascade multilevel inverter method

Science.gov (United States)

Andreh, Angga Muhamad; Subiyanto, Sunardiyo, Said

2017-01-01

Development of non-linear loading in the application of industry and distribution system and also harmonic compensation becomes important. Harmonic pollution is an urgent problem in increasing power quality. The main contribution of the study is the modeling approach used to design a shunt active filter and the application of the cascade multilevel inverter topology to improve the power quality of electrical energy. In this study, shunt active filter was aimed to eliminate dominant harmonic component by injecting opposite currents with the harmonic component system. The active filter was designed by shunt configuration with cascaded multilevel inverter method controlled by PID controller and SPWM. With this shunt active filter, the harmonic current can be reduced so that the current wave pattern of the source is approximately sinusoidal. Design and simulation were conducted by using Power Simulator (PSIM) software. Shunt active filter performance experiment was conducted on the IEEE four bus test system. The result of shunt active filter installation on the system (IEEE four bus) could reduce THD current from 28.68% to 3.09%. With this result, the active filter can be applied as an effective method to reduce harmonics.

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO2 storage monitoring

Science.gov (United States)

Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.

2017-08-01

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

Utility of silicone filtering for diffusive model CO2 sensors in field experiments

Directory of Open Access Journals (Sweden)

Shinjiro Ohkubo

2013-05-01

Full Text Available Installing a diffusive model CO2 sensor in the soil is a direct and useful method to observe the time variation of gas CO2 concentration in soil. Furthermore, it requires no bulky measurement system. A hydrophobic silicone filter prevents water infiltration. Therefore, a sensor whose detection element is covered with a silicone filter can be durable in the field even when experiencing inundation (e.g. farmland with snow melting, wetland with varying water level. The utility of a diffusive model of CO2 sensor covered with silicone filter was examined in laboratory and field experiments. Applying the silicone filter delays the response to change in ambient CO2 concentration, which results from lower gas permeability than those of other conventionally used filters made of materials, such as polytetrafluoroethylene. Theoretically, apart from the precision of the sensor itself, diurnal variation of soil gas CO2 concentration is calculable from obtained series of data with a silicone-covered sensor with negligible error. The error is estimated at approximately 1% of the diurnal amplitude in most cases of a 10-min logging interval. Drastic changes that occur, such as those of a rainfall event, cause a larger gap separating calculated and real values. However, the proportion of this gap to the extent of the drastic increase was extremely small (0.43% for a 10-min logging interval. For accurate estimation, a smoothly varied data series must be prepared as input data. Using a moving average or applying a fitting curve can be useful when using a sensor or data logger with low resolution. Estimating the gas permeability coefficient is crucial for calculation. The gas permeability coefficient can be estimated through laboratory experiments. This study revealed the possibility of evaluating the time variation of soil gas CO2 concentration by installing a diffusive model of silicone-covered sensor in an inundated field.

Relative resilience to noise of standard and sequential approaches to measurement-based quantum computation

Science.gov (United States)

Gallagher, C. B.; Ferraro, A.

2018-05-01

A possible alternative to the standard model of measurement-based quantum computation (MBQC) is offered by the sequential model of MBQC—a particular class of quantum computation via ancillae. Although these two models are equivalent under ideal conditions, their relative resilience to noise in practical conditions is not yet known. We analyze this relationship for various noise models in the ancilla preparation and in the entangling-gate implementation. The comparison of the two models is performed utilizing both the gate infidelity and the diamond distance as figures of merit. Our results show that in the majority of instances the sequential model outperforms the standard one in regard to a universal set of operations for quantum computation. Further investigation is made into the performance of sequential MBQC in experimental scenarios, thus setting benchmarks for possible cavity-QED implementations.

Geostationary Coastal and Air Pollution Events (GeoCAPE) Filter Radiometer (FR)

Science.gov (United States)

Kotecki, Carl; Chu, Martha; Wilson, Mark; Clark, Mike; Nanan, Bobby; Matson, Liz; McBirney, Dick; Smith, Jay; Earle, Paul; Choi, Mike;

Methamphetamine-alcohol interactions in murine models of sequential and simultaneous oral drug-taking.

Science.gov (United States)

Fultz, Elissa K; Martin, Douglas L; Hudson, Courtney N; Kippin, Tod E; Szumlinski, Karen K

2017-08-01

A high degree of co-morbidity exists between methamphetamine (MA) addiction and alcohol use disorders and both sequential and simultaneous MA-alcohol mixing increases risk for co-abuse. As little preclinical work has focused on the biobehavioral interactions between MA and alcohol within the context of drug-taking behavior, we employed simple murine models of voluntary oral drug consumption to examine how prior histories of either MA- or alcohol-taking influence the intake of the other drug. In one study, mice with a 10-day history of binge alcohol-drinking [5,10, 20 and 40% (v/v); 2h/day] were trained to self-administer oral MA in an operant-conditioning paradigm (10-40mg/L). In a second study, mice with a 10-day history of limited-access oral MA-drinking (5, 10, 20 and 40mg/L; 2h/day) were presented with alcohol (5-40% v/v; 2h/day) and then a choice between solutions of 20% alcohol, 10mg/L MA or their mix. Under operant-conditioning procedures, alcohol-drinking mice exhibited less MA reinforcement overall, than water controls. However, when drug availability was not behaviorally-contingent, alcohol-drinking mice consumed more MA and exhibited greater preference for the 10mg/L MA solution than drug-naïve and combination drug-experienced mice. Conversely, prior MA-drinking history increased alcohol intake across a range of alcohol concentrations. These exploratory studies indicate the feasibility of employing procedurally simple murine models of sequential and simultaneous oral MA-alcohol mixing of relevance to advancing our biobehavioral understanding of MA-alcohol co-abuse. Copyright © 2017 Elsevier B.V. All rights reserved.

Sequential Path Model for Grain Yield in Soybean

Directory of Open Access Journals (Sweden)

Mohammad SEDGHI

2010-09-01

Full Text Available This study was performed to determine some physiological traits that affect soybean,s grain yield via sequential path analysis. In a factorial experiment, two cultivars (Harcor and Williams were sown under four levels of nitrogen and two levels of weed management at the research station of Tabriz University, Iran, during 2004 and 2005. Grain yield, some yield components and physiological traits were measured. Correlation coefficient analysis showed that grain yield had significant positive and negative association with measured traits. A sequential path analysis was done in order to evaluate associations among grain yield and related traits by ordering the various variables in first, second and third order paths on the basis of their maximum direct effects and minimal collinearity. Two first-order variables, namely number of pods per plant and pre-flowering net photosynthesis revealed highest direct effect on total grain yield and explained 49, 44 and 47 % of the variation in grain yield based on 2004, 2005, and combined datasets, respectively. Four traits i.e. post-flowering net photosynthesis, plant height, leaf area index and intercepted radiation at the bottom layer of canopy were found to fit as second-order variables. Pre- and post-flowering chlorophyll content, main root length and intercepted radiation at the middle layer of canopy were placed at the third-order path. From the results concluded that, number of pods per plant and pre-flowering net photosynthesis are the best selection criteria in soybean for grain yield.

Relevance Models for Collaborative Filtering

NARCIS (Netherlands)

J. Wang (Jun)

2008-01-01

htmlabstractCollaborative filtering is the common technique of predicting the interests of a user by collecting preference information from many users. Although it is generally regarded as a key information retrieval technique, its relation to the existing information retrieval theory is unclear.

Implicit LES using adaptive filtering

Science.gov (United States)

Sun, Guangrui; Domaradzki, Julian A.

2018-04-01

In implicit large eddy simulations (ILES) numerical dissipation prevents buildup of small scale energy in a manner similar to the explicit subgrid scale (SGS) models. If spectral methods are used the numerical dissipation is negligible but it can be introduced by applying a low-pass filter in the physical space, resulting in an effective ILES. In the present work we provide a comprehensive analysis of the numerical dissipation produced by different filtering operations in a turbulent channel flow simulated using a non-dissipative, pseudo-spectral Navier-Stokes solver. The amount of numerical dissipation imparted by filtering can be easily adjusted by changing how often a filter is applied. We show that when the additional numerical dissipation is close to the subgrid-scale (SGS) dissipation of an explicit LES the overall accuracy of ILES is also comparable, indicating that periodic filtering can replace explicit SGS models. A new method is proposed, which does not require any prior knowledge of a flow, to determine the filtering period adaptively. Once an optimal filtering period is found, the accuracy of ILES is significantly improved at low implementation complexity and computational cost. The method is general, performing well for different Reynolds numbers, grid resolutions, and filter shapes.

A path-level exact parallelization strategy for sequential simulation

Science.gov (United States)

Peredo, Oscar F.; Baeza, Daniel; Ortiz, Julián M.; Herrero, José R.

2018-01-01

Sequential Simulation is a well known method in geostatistical modelling. Following the Bayesian approach for simulation of conditionally dependent random events, Sequential Indicator Simulation (SIS) method draws simulated values for K categories (categorical case) or classes defined by K different thresholds (continuous case). Similarly, Sequential Gaussian Simulation (SGS) method draws simulated values from a multivariate Gaussian field. In this work, a path-level approach to parallelize SIS and SGS methods is presented. A first stage of re-arrangement of the simulation path is performed, followed by a second stage of parallel simulation for non-conflicting nodes. A key advantage of the proposed parallelization method is to generate identical realizations as with the original non-parallelized methods. Case studies are presented using two sequential simulation codes from GSLIB: SISIM and SGSIM. Execution time and speedup results are shown for large-scale domains, with many categories and maximum kriging neighbours in each case, achieving high speedup results in the best scenarios using 16 threads of execution in a single machine.

Spectral information enhancement using wavelet-based iterative filtering for in vivo gamma spectrometry.

Science.gov (United States)

Paul, Sabyasachi; Sarkar, P K

2013-04-01

Use of wavelet transformation in stationary signal processing has been demonstrated for denoising the measured spectra and characterisation of radionuclides in the in vivo monitoring analysis, where difficulties arise due to very low activity level to be estimated in biological systems. The large statistical fluctuations often make the identification of characteristic gammas from radionuclides highly uncertain, particularly when interferences from progenies are also present. A new wavelet-based noise filtering methodology has been developed for better detection of gamma peaks in noisy data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for noise rejection and an inverse transform after soft 'thresholding' over the generated coefficients. Analyses of in vivo monitoring data of (235)U and (238)U were carried out using this method without disturbing the peak position and amplitude while achieving a 3-fold improvement in the signal-to-noise ratio, compared with the original measured spectrum. When compared with other data-filtering techniques, the wavelet-based method shows the best results.

A Data-Driven Method for Selecting Optimal Models Based on Graphical Visualisation of Differences in Sequentially Fitted ROC Model Parameters

Directory of Open Access Journals (Sweden)

K S Mwitondi

2013-05-01

Full Text Available Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models in the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index. Moving differences between sequential fitted parameters are then extracted, and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity, and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance.

Exposure assessment of mobile phone base station radiation in an outdoor environment using sequential surrogate modeling.

Science.gov (United States)

Aerts, Sam; Deschrijver, Dirk; Joseph, Wout; Verloock, Leen; Goeminne, Francis; Martens, Luc; Dhaene, Tom

2013-05-01

Human exposure to background radiofrequency electromagnetic fields (RF-EMF) has been increasing with the introduction of new technologies. There is a definite need for the quantification of RF-EMF exposure but a robust exposure assessment is not yet possible, mainly due to the lack of a fast and efficient measurement procedure. In this article, a new procedure is proposed for accurately mapping the exposure to base station radiation in an outdoor environment based on surrogate modeling and sequential design, an entirely new approach in the domain of dosimetry for human RF exposure. We tested our procedure in an urban area of about 0.04 km(2) for Global System for Mobile Communications (GSM) technology at 900 MHz (GSM900) using a personal exposimeter. Fifty measurement locations were sufficient to obtain a coarse street exposure map, locating regions of high and low exposure; 70 measurement locations were sufficient to characterize the electric field distribution in the area and build an accurate predictive interpolation model. Hence, accurate GSM900 downlink outdoor exposure maps (for use in, e.g., governmental risk communication and epidemiological studies) are developed by combining the proven efficiency of sequential design with the speed of exposimeter measurements and their ease of handling. Copyright © 2013 Wiley Periodicals, Inc.

Model-based extended quaternion Kalman filter to inertial orientation tracking of arbitrary kinematic chains.

Science.gov (United States)

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.

Mind-to-mind heteroclinic coordination: Model of sequential episodic memory initiation

Science.gov (United States)

Afraimovich, V. S.; Zaks, M. A.; Rabinovich, M. I.

2018-05-01

Retrieval of episodic memory is a dynamical process in the large scale brain networks. In social groups, the neural patterns, associated with specific events directly experienced by single members, are encoded, recalled, and shared by all participants. Here, we construct and study the dynamical model for the formation and maintaining of episodic memory in small ensembles of interacting minds. We prove that the unconventional dynamical attractor of this process—the nonsmooth heteroclinic torus—is structurally stable within the Lotka-Volterra-like sets of equations. Dynamics on this torus combines the absence of chaos with asymptotic instability of every separate trajectory; its adequate quantitative characteristics are length-related Lyapunov exponents. Variation of the coupling strength between the participants results in different types of sequential switching between metastable states; we interpret them as stages in formation and modification of the episodic memory.

Cascade ultrafiltering of 210Pb and 210Po in freshwater using a tangential flow filtering system

International Nuclear Information System (INIS)

Ohtsuka, Y.; Takaku, Y.; Hisamatsu, S.; Inaba, J.; Yamamoto, M.

2006-01-01

A rapid method was developed using ultrafilters with a tangential flow filtering system for molecular size separation of naturally occurring 210 Pb and 210 Po in a freshwater sample. Generally, ultrafiltering of a large volume water sample for measuring the nuclides was too time consuming and not practical. The tangential flow filtering system made the filtering time short enough to adapt for in-situ ultrafiltering the large volume sample. In this method, a 20 liter water sample was at first passed through the 0.45 μm pore size membrane filter immediately after sample collection to obtain suspended particle matter [>0.45 μm particulate fraction (PRT)]. Two ultrafilters (Millipore Pellicon 2 R ) were used sequentially. The nuclides in the filtrate were separated into three fractions: high molecular mass (100 kDa-0.45μm; HMM), low molecular mass (10 k-100 kDa; LMM) and ionic ( 210 Pb and 210 Po in an oligotrophic lake, Lake Towada located in the northern area of Japan. (author)

Pulse cleaning flow models and numerical computation of candle ceramic filters.

Science.gov (United States)

Tian, Gui-shan; Ma, Zhen-ji; Zhang, Xin-yi; Xu, Ting-xiang

2002-04-01

Analytical and numerical computed models are developed for reverse pulse cleaning system of candle ceramic filters. A standard turbulent model is demonstrated suitably to the designing computation of reverse pulse cleaning system from the experimental and one-dimensional computational result. The computed results can be used to guide the designing of reverse pulse cleaning system, which is optimum Venturi geometry. From the computed results, the general conclusions and the designing methods are obtained.

Measurement of filtration efficiency of Nuclepore filters challenged with polystyrene latex nanoparticles: experiments and modeling

International Nuclear Information System (INIS)

Ling, Tsz Yan; Wang Jing; Pui, David Y. H.

2011-01-01

Membrane filtration has been demonstrated to be effective for the removal of liquid-borne nanoparticles (NPs). Such technique can be applied to purify and disinfect drinking water as well as remove NPs in highly pure chemicals used in the industries. This study aims to study the filtration process of a model membrane filter, the Nuclepore filter. Experiments were carried out using standard filtration tools and the nanoparticle tracking analysis (NTA) technique was used to measure particle (50–500 nm) concentration upstream and downstream of the filter to determine the filtration efficiency. The NTA technique has been calibrated using 150-nm polystyrene latex particles to determine its accuracy of particle concentration measurement. Measurements were found reliable within a certain concentration limit (about 10 8 –10 10 particles/cm 3 ), which is dependent on the camera settings during the measurement. Experimental results are comparable with previously published data obtained using the aerosolization method, validating the capability of the NTA technique. The capillary tube model modified from that developed for aerosol filtration was found to be useful to represent the experimental results, when a sticking coefficient of 0.15 is incorporated. This suggests that only 15% of the particle collisions with the filter results in successful attachment. The small sticking coefficient found can be explained by the unfavorable surface interactions between the particles and the filter medium.

Modelling and simulation of lamp-pumped thallium atomic line filters

International Nuclear Information System (INIS)

Molisch, A.F.

1994-06-01

Atomic Line Filters (ALFs) are ultra-narrow-band, wide-field-of-view optical filters for the detection of weak optical signals embedded in broadband background noise. The central component is a quartz cell filled with atomic vapor where signal photons are absorbed and subsequently re-emitted at a different wavelength. At the 'Institut fuer Nachrichtentechnik und Hochfrequenztechnik', an ALF based on Thallium (Tl) vapor, which is pumped by a Tl spectral lamp, has been under development. The aim of this thesis is to model the physical processes in this filter (especially in the vapor cell) and to make simulations in order to find the optimum design. For this purpose, a theoretical 'toolbox' is to be created, which should be capable of describing quantitatively the various physical effects. The accuracy of the simulation should be about ±10 %, i.e. about the accuracy of the available atomic data. In part I, the physics that form the basis of ALFs are briefly explained. In chapter 1, the principle of an ALF is explained, and the parameters that describe such filters are defined. In the next two chapters, atomic energy levels and atomic line shapes are described. We then summarize the data of the UV and green resonance lines of Thallium. After giving an overview over the methods of description for trapping problems, (Holstein equation, equation-of-radiative-transfer plus rate-equation, Monte Carlo simulation), we describe the (generalized) Milne theory, an approximate method which allows a description of trapping by a differential equation. In part II, we then make use of these formalisms to describe the Tl ALF mathematically. After giving a description of the whole filter system, we show the various influences on the lifetime of the metastable Tl atoms. Then the pump phase of the filter is described. In that phase, we have non-linear trapping in a 3-level system. This problem is solved by a combination of finite-difference solution of the equation of radiative

Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

Science.gov (United States)

Huang, Lei

2015-01-01

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409

Remarks on sequential designs in risk assessment

International Nuclear Information System (INIS)

Seidenfeld, T.

1982-01-01

The special merits of sequential designs are reviewed in light of particular challenges that attend risk assessment for human population. The kinds of ''statistical inference'' are distinguished and the problem of design which is pursued is the clash between Neyman-Pearson and Bayesian programs of sequential design. The value of sequential designs is discussed and the Neyman-Pearson vs. Bayesian sequential designs are probed in particular. Finally, warnings with sequential designs are considered, especially in relation to utilitarianism

Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference.

Directory of Open Access Journals (Sweden)

Dario Cuevas Rivera

2015-10-01

Full Text Available The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an 'intelligent coincidence detector', which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena.

Modeling astronomical adaptive optics performance with temporally filtered Wiener reconstruction of slope data

Science.gov (United States)

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.

Sequential ethanol fermentation and anaerobic digestion increases bioenergy yields from duckweed.

Science.gov (United States)

Calicioglu, O; Brennan, R A

2018-06-01

The potential for improving bioenergy yields from duckweed, a fast-growing, simple, floating aquatic plant, was evaluated by subjecting the dried biomass directly to anaerobic digestion, or sequentially to ethanol fermentation and then anaerobic digestion, after evaporating ethanol from the fermentation broth. Bioethanol yields of 0.41 ± 0.03 g/g and 0.50 ± 0.01 g/g (glucose) were achieved for duckweed harvested from the Penn State Living-Filter (Lemna obscura) and Eco-Machine™ (Lemna minor/japonica and Wolffia columbiana), respectively. The highest biomethane yield, 390 ± 0.1 ml CH 4 /g volatile solids added, was achieved in a reactor containing fermented duckweed from the Living-Filter at a substrate-to-inoculum (S/I) ratio (i.e., duckweed to microorganism ratio) of 1.0. This value was 51.2% higher than the biomethane yield of a replicate reactor with raw (non-fermented) duckweed. The combined bioethanol-biomethane process yielded 70.4% more bioenergy from duckweed, than if anaerobic digestion had been run alone. Copyright © 2018 Elsevier Ltd. All rights reserved.

SU-D-207-07: Implementation of Full/half Bowtie Filter Model in a Commercial Treatment Planning System for Kilovoltage X-Ray Imaging Dose Estimation

International Nuclear Information System (INIS)

Kim, S; Alaei, P

2015-01-01

Purpose: To implement full/half bowtie filter models in a commercial treatment planning system (TPS) to calculate kilovoltage (kV) x-ray imaging dose of Varian On-Board Imager (OBI) cone beam CT (CBCT) system. Methods: Full/half bowtie filters of Varian OBI were created as compensator models in Pinnacle TPS (version 9.6) using Matlab software (version 2011a). The profiles of both bowtie filters were acquired from the manufacturer, imported into the Matlab system and hard coded in binary file format. A Pinnacle script was written to import each bowtie filter data into a Pinnacle treatment plan as a compensator. A kV x-ray beam model without including the compensator model was commissioned per each bowtie filter setting based on percent depth dose and lateral profile data acquired from Monte Carlo simulations. To validate the bowtie filter models, a rectangular water phantom was generated in the planning system and an anterior/posterior beam with each bowtie filter was created. Using the Pinnacle script, each bowtie filter compensator was added to the treatment plan. Lateral profile at the depth of 3cm and percent depth dose were measured using an ion chamber and compared with the data extracted from the treatment plans. Results: The kV x-ray beams for both full and half bowtie filter have been modeled in a commercial TPS. The difference of lateral and depth dose profiles between dose calculations and ion chamber measurements were within 6%. Conclusion: Both full/half bowtie filter models provide reasonable results in kV x-ray dose calculations in the water phantom. This study demonstrates the possibility of using a model-based treatment planning system to calculate the kV imaging dose for both full and half bowtie filter modes. Further study is to be performed to evaluate the models in clinical situations

Offline estimation of decay time for an optical cavity with a low pass filter cavity model.

Science.gov (United States)

Kallapur, Abhijit G; Boyson, Toby K; Petersen, Ian R; Harb, Charles C

2012-08-01

This Letter presents offline estimation results for the decay-time constant for an experimental Fabry-Perot optical cavity for cavity ring-down spectroscopy (CRDS). The cavity dynamics are modeled in terms of a low pass filter (LPF) with unity DC gain. This model is used by an extended Kalman filter (EKF) along with the recorded light intensity at the output of the cavity in order to estimate the decay-time constant. The estimation results using the LPF cavity model are compared to those obtained using the quadrature model for the cavity presented in previous work by Kallapur et al. The estimation process derived using the LPF model comprises two states as opposed to three states in the quadrature model. When considering the EKF, this means propagating two states and a (2×2) covariance matrix using the LPF model, as opposed to propagating three states and a (3×3) covariance matrix using the quadrature model. This gives the former model a computational advantage over the latter and leads to faster execution times for the corresponding EKF. It is shown in this Letter that the LPF model for the cavity with two filter states is computationally more efficient, converges faster, and is hence a more suitable method than the three-state quadrature model presented in previous work for real-time estimation of the decay-time constant for the cavity.

ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS

OpenAIRE

W. Nakanishi; T. Fuse; T. Ishikawa

2015-01-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation ...

Sequential lineup laps and eyewitness accuracy.

Science.gov (United States)

Steblay, Nancy K; Dietrich, Hannah L; Ryan, Shannon L; Raczynski, Jeanette L; James, Kali A

2011-08-01

Police practice of double-blind sequential lineups prompts a question about the efficacy of repeated viewings (laps) of the sequential lineup. Two laboratory experiments confirmed the presence of a sequential lap effect: an increase in witness lineup picks from first to second lap, when the culprit was a stranger. The second lap produced more errors than correct identifications. In Experiment 2, lineup diagnosticity was significantly higher for sequential lineup procedures that employed a single versus double laps. Witnesses who elected to view a second lap made significantly more errors than witnesses who chose to stop after one lap or those who were required to view two laps. Witnesses with prior exposure to the culprit did not exhibit a sequential lap effect.

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.

Robustness of the Sequential Lineup Advantage

Science.gov (United States)

Gronlund, Scott D.; Carlson, Curt A.; Dailey, Sarah B.; Goodsell, Charles A.

2009-01-01

A growing movement in the United States and around the world involves promoting the advantages of conducting an eyewitness lineup in a sequential manner. We conducted a large study (N = 2,529) that included 24 comparisons of sequential versus simultaneous lineups. A liberal statistical criterion revealed only 2 significant sequential lineup…

Implicit particle filtering for equations with partial noise and application to geomagnetic data assimilation

Science.gov (United States)

Morzfeld, M.; Atkins, E.; Chorin, A. J.

2011-12-01

The task in data assimilation is to identify the state of a system from an uncertain model supplemented by a stream of incomplete and noisy data. The model is typically given in form of a discretization of an Ito stochastic differential equation (SDE), x(n+1) = R(x(n))+ G W(n), where x is an m-dimensional vector and n=0,1,2,.... The m-dimensional vector function R and the m x m matrix G depend on the SDE as well as on the discretization scheme, and W is an m-dimensional vector whose elements are independent standard normal variates. The data are y(n) = h(x(n))+QV(n) where h is a k-dimensional vector function, Q is a k x k matrix and V is a vector whose components are independent standard normal variates. One can use statistics of the conditional probability density (pdf) of the state given the observations, p(n+1)=p(x(n+1)|y(1), ... , y(n+1)), to identify the state x(n+1). Particle filters approximate p(n+1) by sequential Monte Carlo and rely on the recursive formulation of the target pdf, p(n+1)∝p(x(n+1)|x(n)) p(y(n+1)|x(n+1)). The pdf p(x(n+1)|x(n)) can be read off of the model equations to be a Gaussian with mean R(x(n)) and covariance matrix Σ = GG^T, where the T denotes a transposed; the pdf p(y(n+1)|x(n+1)) is a Gaussian with mean h(x(n+1)) and covariance QQ^T. In a sampling-importance-resampling (SIR) filter one samples new values for the particles from a prior pdf and then one weighs these samples with weights determined by the observations, to yield an approximation to p(n+1). Such weighting schemes often yield small weights for many of the particles. Implicit particle filtering overcomes this problem by using the observations to generate the particles, thus focusing attention on regions of large probability. A suitable algebraic equation that depends on the model and the observations is constructed for each particle, and its solution yields high probability samples of p(n+1). In the current formulation of the implicit particle filter, the state

Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter

Directory of Open Access Journals (Sweden)

Jing Li

2016-01-01

Full Text Available Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF, which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF, which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.

Filtering Photogrammetric Point Clouds Using Standard LIDAR Filters Towards DTM Generation

Science.gov (United States)

Zhang, Z.; Gerke, M.; Vosselman, G.; Yang, M. Y.

2018-05-01

Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.