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1

Algorithmic and Architectural Optimizations for Computationally Efficient Particle Filtering  

E-print Network

filtering especially to video sequences. Particle filtering is a technique used for filtering non-linear, Auxillary variable, Design Methodologies, Visual Tracking I. INTRODUCTION Filtering is the problem scenarios. For example, Kalman filtering [1] is an optimal analytic filter when the models are linear

Chellappa, Rama

2

Neuromuscular fiber segmentation through particle filtering and discrete optimization  

NASA Astrophysics Data System (ADS)

We present an algorithm to segment a set of parallel, intertwined and bifurcating fibers from 3D images, targeted for the identification of neuronal fibers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calculation of fiber probabilities, seed detection, tracking by particle filtering, global supervised seed clustering and final voxel segmentation. The preprocessing uses a novel random local probability filtering (RLPF). The fiber probabilities computation is performed by means of SVM using steerable filters and the RLPF outputs as features. The global segmentation is solved by discrete optimization. The combination of global and local approaches makes the segmentation robust, yet the individual data blocks can be processed sequentially, limiting memory consumption. The method is automatic but efficient manual interactions are possible if needed. The method is validated on the Neuromuscular Projection Fibers dataset from the Diadem Challenge. On the 15 first blocks present, our method has a 99.4% detection rate. We also compare our segmentation results to a state-of-the-art method. On average, the performances of our method are either higher or equivalent to that of the state-of-the-art method but less user interactions is needed in our approach.

Dietenbeck, Thomas; Varray, François; Kybic, Jan; Basset, Olivier; Cachard, Christian

2014-03-01

3

Annealed particle filter based on particle swarm optimization for articulated three-dimensional human motion tracking  

NASA Astrophysics Data System (ADS)

Three-dimensional articulated human motion tracking is challenging due to the high-dimensional parameter space and poor image observations. When particle swarm optimization (PSO) is used for human motion tracking, due to unreliable image likelihood, particles may be misled and be unable to find the most plausible pose space. This paper proposes a new PSO-based algorithm for human motion tracking, annealed PSO-based particle filter (APSOPF). The sampling covariance and annealing factor are incorporated into the velocity-updating equation of PSO; they are initialized with appropriate values at the beginning of the PSO iteration, and decreased (annealed) in reasonable steps. Through the sampling covariance, the motion prior is introduced into APSOPF, constraining particles to the most likely region of pose space and reducing the generation of invalid particles. By adopting decreasing coefficients in the updating principle, the leading effects of the local and global best on particles decrease with generations, making particles preserve their own divergence and self-exploration capabilities before convergence. Hence the problem of insufficiently reliable image likelihood can be mitigated in some degree. We compare APSOPF quantitatively with an annealed particle filter and a standard particle filter on the challenging HumanEvaI data set. Experimental results show that the proposed algorithm achieves lower estimation error in tracking real-world 3-D human motion.

Wang, Xiangyang; Wan, Wanggen; Zhang, Xiaoqin; Yu, Xiaoqing

2010-01-01

4

Design of application specific long period waveguide grating filters using adaptive particle swarm optimization algorithms  

NASA Astrophysics Data System (ADS)

We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra.

Semwal, Girish; Rastogi, Vipul

2014-01-01

5

Particle swarm optimization and its application to the design of diffraction grating filters  

NASA Astrophysics Data System (ADS)

Particle swarm optimization (PSO) is an evolutionary, easy-to-implement technique to design optical diffraction gratings. Design of reflection and transmission guided-mode resonance (GMR) grating filters using PSO is reported. The spectra of the designed filters are in good agreement with the design targets in a reasonable computation time. Also, filters are designed with a genetic algorithm (GA) and the results obtained by the GA and PSO are compared.

Shokooh-Saremi, Mehrdad; Magnusson, Robert

2007-04-01

6

Optimal noise reduction in 3D reconstructions of single particles using a volume-normalized filter.  

PubMed

The high noise level found in single-particle electron cryo-microscopy (cryo-EM) image data presents a special challenge for three-dimensional (3D) reconstruction of the imaged molecules. The spectral signal-to-noise ratio (SSNR) and related Fourier shell correlation (FSC) functions are commonly used to assess and mitigate the noise-generated error in the reconstruction. Calculation of the SSNR and FSC usually includes the noise in the solvent region surrounding the particle and therefore does not accurately reflect the signal in the particle density itself. Here we show that the SSNR in a reconstructed 3D particle map is linearly proportional to the fractional volume occupied by the particle. Using this relationship, we devise a novel filter (the "single-particle Wiener filter") to minimize the error in a reconstructed particle map, if the particle volume is known. Moreover, we show how to approximate this filter even when the volume of the particle is not known, by optimizing the signal within a representative interior region of the particle. We show that the new filter improves on previously proposed error-reduction schemes, including the conventional Wiener filter as well as figure-of-merit weighting, and quantify the relationship between all of these methods by theoretical analysis as well as numeric evaluation of both simulated and experimentally collected data. The single-particle Wiener filter is applicable across a broad range of existing 3D reconstruction techniques, but is particularly well suited to the Fourier inversion method, leading to an efficient and accurate implementation. PMID:22613568

Sindelar, Charles V; Grigorieff, Nikolaus

2012-10-01

7

Particle swarm optimization-based approach for optical finite impulse response filter design  

E-print Network

algorithms such as a genetic algorithm have been attempted feasibly but the genetic operators by the optimization algorithm. This structure determines the output that if N plates are concerned, there will be N 1 than some previous optimiz- The authors are with the Optical Engineering Department, State Key

Wu, Shin-Tson

8

Optimal filtering and filter stability of linear stochastic delay systems  

NASA Technical Reports Server (NTRS)

Optimal filtering equations are obtained for very general linear stochastic delay systems. Stability of the optimal filter is studied in the case where there are no delays in the observations. Using the duality between linear filtering and control, asymptotic stability of the optimal filter is proved. Finally, the cascade of the optimal filter and the deterministic optimal quadratic control system is shown to be asymptotically stable as well.

Kwong, R. H.-S.; Willsky, A. S.

1977-01-01

9

Variational Particle Filter for Imperfect Models  

NASA Astrophysics Data System (ADS)

Whereas classical data processing techniques work with perfect models geophysical sciences have to deal with imperfect models with spatially structured errors. For the perfect model cases, in terms of Mean-Field Markovian processes, the optimal filter is known: the Kalman estimator is the answer to the linearGaussian problem and in the general case Particle approximations are the empirical solutions to the optimal estimator. We will present another way to decompose the Bayes rule, using an one step ahead observation. This method is well adapted to the strong nonlinear or chaotic systems. Then, in order to deal with imperfect model, we suggest in this presentation to learn the (large scale) model errors using a variational correction before the resampling step of the non-linear filtering. This procedure replace the a-priori Markovian transition by a kernel conditioned to the observations. This supplementary step may be read as the use of variational particles approximation. For the numerical applications, we have chosen to show the impact of our method, first on a simple marked Poisson process with Gaussian observation noises (the time-exponential jumps are considered as model errors) and then on a 2D shallow water experiment in a closed basin, with some falling droplets as model errors.; Marked Poisson process with Gaussian observation noise filtered by four methods: classical Kalman filter, genetic particle filter, trajectorial particle filter and Kalman-particle filter. All use only 10 particles. ; 2D Shallow Water simulation with droplets errors. Results of a classical 3DVAR and of our VarPF (10 particles).

Baehr, C.

2012-12-01

10

Particle Filters for State Estimation of Jump Markov Linear Systems  

Microsoft Academic Search

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively com- pute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem.

Arnaud Doucet; Neil J. Gordon; Vikram Krishnamurthy

1999-01-01

11

Particle filters for state estimation of jump Markov linear systems  

Microsoft Academic Search

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our

Arnaud Doucet; Neil J. Gordon; Vikram Krishnamurthy

2001-01-01

12

OPTIMIZATION OF ADVANCED FILTER SYSTEMS  

SciTech Connect

Reliable, maintainable and cost effective hot gas particulate filter technology is critical to the successful commercialization of advanced, coal-fired power generation technologies, such as IGCC and PFBC. In pilot plant testing, the operating reliability of hot gas particulate filters have been periodically compromised by process issues, such as process upsets and difficult ash cake behavior (ash bridging and sintering), and by design issues, such as cantilevered filter elements damaged by ash bridging, or excessively close packing of filtering surfaces resulting in unacceptable pressure drop or filtering surface plugging. This test experience has focused the issues and has helped to define advanced hot gas filter design concepts that offer higher reliability. Westinghouse has identified two advanced ceramic barrier filter concepts that are configured to minimize the possibility of ash bridge formation and to be robust against ash bridges should they occur. The ''inverted candle filter system'' uses arrays of thin-walled, ceramic candle-type filter elements with inside-surface filtering, and contains the filter elements in metal enclosures for complete separation from ash bridges. The ''sheet filter system'' uses ceramic, flat plate filter elements supported from vertical pipe-header arrays that provide geometry that avoids the buildup of ash bridges and allows free fall of the back-pulse released filter cake. The Optimization of Advanced Filter Systems program is being conducted to evaluate these two advanced designs and to ultimately demonstrate one of the concepts in pilot scale. In the Base Contract program, the subject of this report, Westinghouse has developed conceptual designs of the two advanced ceramic barrier filter systems to assess their performance, availability and cost potential, and to identify technical issues that may hinder the commercialization of the technologies. A plan for the Option I, bench-scale test program has also been developed based on the issues identified. The two advanced barrier filter systems have been found to have the potential to be significantly more reliable and less expensive to operate than standard ceramic candle filter system designs. Their key development requirements are the assessment of the design and manufacturing feasibility of the ceramic filter elements, and the small-scale demonstration of their conceptual reliability and availability merits.

R.A. Newby; G.J. Bruck; M.A. Alvin; T.E. Lippert

1998-04-30

13

Distributed SLAM using improved particle filter for mobile robot localization.  

PubMed

The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness. PMID:24883362

Pei, Fujun; Wu, Mei; Zhang, Simin

2014-01-01

14

Particle Swarm Optimization Toolbox  

NASA Technical Reports Server (NTRS)

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Grant, Michael J.

2010-01-01

15

A PSO Accelerated Immune Particle Filter for Dynamic State Estimation  

Microsoft Academic Search

Particle Filter (PF) is a flexible and powerful Sequential Monte Carlo (SMC) technique to solve the nonlinear state\\/parameter estimation problems. The generic PF suffers due to degeneracy or sample impoverishment, which adversely affects its performance. In order to overcome this issue of the generic PF, a Particle Swarm Optimization accelerated Immune Particle Filter (PSO-acc-IPF) is proposed in this work. It

S. Akhtar; A. R. Ahmad; E. M. Abdel-Rahman; T. Naqvi

2011-01-01

16

Westinghouse Advanced Particle Filter System  

SciTech Connect

Integrated Gasification Combined Cycles (IGCC) and Pressurized Fluidized Bed Combustion (PFBC) are being developed and demonstrated for commercial, power generation application. Hot gas particulate filters are key components for the successful implementation of IGCC and PFBC in power generation gas turbine cycles. The objective of this work is to develop and qualify through analysis and testing a practical hot gas ceramic barrier filter system that meets the performance and operational requirements of PFBC and IGCC systems. This paper reports on the development and status of testing of the Westinghouse Advanced Hot Gas Particle Filter (W-APF) including: W-APF integrated operation with the American Electric Power, 70 MW PFBC clean coal facility--approximately 6000 test hours completed; approximately 2500 hours of testing at the Hans Ahlstrom 10 MW PCFB facility located in Karhula, Finland; over 700 hours of operation at the Foster Wheeler 2 MW 2nd generation PFBC facility located in Livingston, New Jersey; status of Westinghouse HGF supply for the DOE Southern Company Services Power System Development Facility (PSDF) located in Wilsonville, Alabama; the status of the Westinghouse development and testing of HGF`s for Biomass Power Generation; and the status of the design and supply of the HGF unit for the 95 MW Pinon Pine IGCC Clean Coal Demonstration.

Lippert, T.E.; Bruck, G.J.; Sanjana, Z.N.; Newby, R.A.; Bachovchin, D.M. [Westinghouse Electric Corp., Pittsburgh, PA (United States). Science and Technology Center

1996-12-31

17

Filtering via simulation: auxiliary particle filters  

Microsoft Academic Search

In this article we model a time series Yt, t = 1,. .. ,n, as being conditionally independent given an unobserved suffi- cient state °t> which is itself assumed to be Markovian. The task is to use simulation to carry out on-line filtering-tbat is, to learn about the state given contemporaneously avail- able information. We do this by estimating the

Michael K Pitt; Neil Shephard

1997-01-01

18

A New Sampling Approach in Particle Filtering  

NASA Astrophysics Data System (ADS)

A hybrid deterministic-stochastic (HSD) sampling approach for particle filters is studied in the context of the Lorenz96 model. HSD sampling uses prior knowledge of model dynamics and current and future observations to reduce the notorious sampling inefficiency of particle filters when applied to spatial models. Results from Observation System Simulation Experiments with a HSD particle filter will be shown and improvements in sampling efficiency and aspects of forecast skill will be discussed.

de Vries, John

2014-05-01

19

OPTIMIZATION OF ADVANCED FILTER SYSTEMS  

Microsoft Academic Search

Two advanced, hot gas, barrier filter system concepts have been proposed by the Siemens Westinghouse Power Corporation to improve the reliability and availability of barrier filter systems in applications such as PFBC and IGCC power generation. The two hot gas, barrier filter system concepts, the inverted candle filter system and the sheet filter system, were the focus of bench-scale testing,

R. A. Newby; M. A. Alvin; G. J. Bruck; T. E. Lippert; E. E. Smeltzer; M. E. Stampahar

2002-01-01

20

Particle filter tracking for the banana problem  

NASA Astrophysics Data System (ADS)

In this paper we present an approach for tracking with a high-bandwidth active sensor in very long range scenarios. We show that in these scenarios the extended Kalman filter is not desirable as it suffers from major consistency problems; and most flavors of particle filter suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and the divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter is shown to avoid this diversity problem while producing consistent results. The regularization is accomplished using a modified version of the Epanechnikov kernel.

Romeo, Kevin; Willett, Peter; Bar-Shalom, Yaakov

2013-09-01

21

Variable rate particle filters for tracking applications  

Microsoft Academic Search

Here we describe recent advances in particle filtering algorithms and models for tracking of manoeuvring objects in clutter. The methods develop on the basic variable dimension particle filtering algorithms introduced in S.J. Godsill and J. Vermaak (2004), in which a new type of dynamical model is introduced whose state variables arrive at unknown times relative to the observation process (hence

Simon Godsill; Jaco Vermaak

2005-01-01

22

Particle filters for positioning, navigation, and tracking  

Microsoft Academic Search

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and

Fredrik Gustafsson; Fredrik Gunnarsson; Niclas Bergman; Urban Forssell; Jonas Jansson; Rickard Karlsson; Per-Johan Nordlund

2002-01-01

23

Particle Filters for Mobile Robot Localization  

E-print Network

Particle Filters for Mobile Robot Localization Dieter Fox, Sebastian Thrun, Wolfram Bur­ gard of mobile robotics. In particular, we report results of applying particle filters to the problem of mobile environment. The localization problem is a key problem in mobile robotics, as it plays a fundamental role

Burgard, Wolfram

24

Rickard Karlsson ISIS Particle Filtering in Practice  

E-print Network

Rickard Karlsson ISIS 2004-11-04 Particle Filtering in Practice Sensor fusion, Positioning and Tracking Rickard Karlsson Automatic Control Linköping University, SWEDEN rickard@isy.liu.se #12;Rickard Karlsson ISIS Linköping 2004-11-05 Particle Filtering within ISIS from my perspective #12;Rickard Karlsson

Zhao, Yuxiao

25

OPTIMIZATION OF ADVANCED FILTER SYSTEMS  

SciTech Connect

Two advanced, hot gas, barrier filter system concepts have been proposed by the Siemens Westinghouse Power Corporation to improve the reliability and availability of barrier filter systems in applications such as PFBC and IGCC power generation. The two hot gas, barrier filter system concepts, the inverted candle filter system and the sheet filter system, were the focus of bench-scale testing, data evaluations, and commercial cost evaluations to assess their feasibility as viable barrier filter systems. The program results show that the inverted candle filter system has high potential to be a highly reliable, commercially successful, hot gas, barrier filter system. Some types of thin-walled, standard candle filter elements can be used directly as inverted candle filter elements, and the development of a new type of filter element is not a requirement of this technology. Six types of inverted candle filter elements were procured and assessed in the program in cold flow and high-temperature test campaigns. The thin-walled McDermott 610 CFCC inverted candle filter elements, and the thin-walled Pall iron aluminide inverted candle filter elements are the best candidates for demonstration of the technology. Although the capital cost of the inverted candle filter system is estimated to range from about 0 to 15% greater than the capital cost of the standard candle filter system, the operating cost and life-cycle cost of the inverted candle filter system is expected to be superior to that of the standard candle filter system. Improved hot gas, barrier filter system availability will result in improved overall power plant economics. The inverted candle filter system is recommended for continued development through larger-scale testing in a coal-fueled test facility, and inverted candle containment equipment has been fabricated and shipped to a gasifier development site for potential future testing. Two types of sheet filter elements were procured and assessed in the program through cold flow and high-temperature testing. The Blasch, mullite-bonded alumina sheet filter element is the only candidate currently approaching qualification for demonstration, although this oxide-based, monolithic sheet filter element may be restricted to operating temperatures of 538 C (1000 F) or less. Many other types of ceramic and intermetallic sheet filter elements could be fabricated. The estimated capital cost of the sheet filter system is comparable to the capital cost of the standard candle filter system, although this cost estimate is very uncertain because the commercial price of sheet filter element manufacturing has not been established. The development of the sheet filter system could result in a higher reliability and availability than the standard candle filter system, but not as high as that of the inverted candle filter system. The sheet filter system has not reached the same level of development as the inverted candle filter system, and it will require more design development, filter element fabrication development, small-scale testing and evaluation before larger-scale testing could be recommended.

R.A. Newby; M.A. Alvin; G.J. Bruck; T.E. Lippert; E.E. Smeltzer; M.E. Stampahar

2002-06-30

26

The Marginalized Auxiliary Particle Filter Carsten Fritsche, Thomas B. Schon and Anja Klein  

E-print Network

.g, for linear Gaussian models, where the Kalman filter provides the optimal solution [2]. However] and the variable rate particle filter [10]. How- ever, when the dimension of the state space is high, the comThe Marginalized Auxiliary Particle Filter Carsten Fritsche, Thomas B. Sch¨on and Anja Klein

Schön, Thomas

27

Particle Filtering on the Euclidean Group  

Microsoft Academic Search

Abstract— We address general filtering problems on the Eu- clidean group SE(3). We first generalize, to stochastic nonlinear systems evolving on SE(3), the particle filter of Liu and West [1] for simultaneously estimating the state and covariance. The filter is constructed in a coordinate-invariant way, and explicitly takes into account the geometry of SE(3) and P(n) ,t he space of

Junghyun Kwon; Minseok Choi; Changmook Chun; F. C. Park

2007-01-01

28

Westinghouse advanced particle filter system  

SciTech Connect

Integrated Gasification Combined Cycles (IGCC), Pressurized Fluidized Bed Combustion (PFBC) and Advanced PFBC (APFB) are being developed and demonstrated for commercial power generation application. Hot gas particulate filters are key components for the successful implementation of IGCC, PFBC and APFB in power generation gas turbine cycles. The objective of this work is to develop and qualify through analysis and testing a practical hot gas ceramic barrier filter system that meets the performance and operational requirements of these advanced, solid fuel power generation cycles.

Lippert, T.E.; Bruck, G.J.; Sanjana, Z.N.; Newby, R.A.

1995-11-01

29

OPTIMIZATION OF ADVANCED FILTER SYSTEMS  

Microsoft Academic Search

Reliable, maintainable and cost effective hot gas particulate filter technology is critical to the successful commercialization of advanced, coal-fired power generation technologies, such as IGCC and PFBC. In pilot plant testing, the operating reliability of hot gas particulate filters have been periodically compromised by process issues, such as process upsets and difficult ash cake behavior (ash bridging and sintering), and

R. A. Newby; G. J. Bruck; M. A. Alvin; T. E. Lippert

1998-01-01

30

Westinghouse advanced particle filter system  

SciTech Connect

Integrated Gasification Combined Cycles (IGCC) and Pressurized Fluidized Bed Combustion (PFBC) are being developed and demonstrated for commercial, power generation application. Hot gas particulate filters are key components for the successful implementation of IGCC and PFBC in power generation gas turbine cycles. The objective of this work is to develop and qualify through analysis and testing a practical hot gas ceramic barrier filter system that meets the performance and operational requirements of PFBC and IGCC systems. This paper updates the assessment of the Westinghouse hot gas filter design based on ongoing testing and analysis. Results are summarized from recent computational fluid dynamics modeling of the plenum flow during back pulse, analysis of candle stressing under cleaning and process transient conditions and testing and analysis to evaluate potential flow induced candle vibration.

Lippert, T.E.; Bruck, G.J.; Sanjana, Z.N.; Newby, R.A.

1994-10-01

31

Introducing the Laplace approximation in particle filtering  

E-print Network

important. Indeed, when the state space area with a high a priori probability differs too much from the area in online estimation of the distribution of the hidden state condi- tionally to observations, with the help filtering is as follows. A population of weighted particles is propagated over time. Each particle un

Del Moral , Pierre

32

Testing particle filters on convective scale dynamics  

NASA Astrophysics Data System (ADS)

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 fluid dynamics. - Computers and Fluids, doi:10,1016/j.compfluid.2010.11.011, 1096 2011. Würsch, M. and G. C. Craig, 2013: A simple dynamical model of cumulus convection for data assimilation research, submitted to Met. Zeitschrift.

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

2014-05-01

33

MEDOF - MINIMUM EUCLIDEAN DISTANCE OPTIMAL FILTER  

NASA Technical Reports Server (NTRS)

The Minimum Euclidean Distance Optimal Filter program, MEDOF, generates filters for use in optical correlators. The algorithm implemented in MEDOF follows theory put forth by Richard D. Juday of NASA/JSC. This program analytically optimizes filters on arbitrary spatial light modulators such as coupled, binary, full complex, and fractional 2pi phase. MEDOF optimizes these modulators on a number of metrics including: correlation peak intensity at the origin for the centered appearance of the reference image in the input plane, signal to noise ratio including the correlation detector noise as well as the colored additive input noise, peak to correlation energy defined as the fraction of the signal energy passed by the filter that shows up in the correlation spot, and the peak to total energy which is a generalization of PCE that adds the passed colored input noise to the input image's passed energy. The user of MEDOF supplies the functions that describe the following quantities: 1) the reference signal, 2) the realizable complex encodings of both the input and filter SLM, 3) the noise model, possibly colored, as it adds at the reference image and at the correlation detection plane, and 4) the metric to analyze, here taken to be one of the analytical ones like SNR (signal to noise ratio) or PCE (peak to correlation energy) rather than peak to secondary ratio. MEDOF calculates filters for arbitrary modulators and a wide range of metrics as described above. MEDOF examines the statistics of the encoded input image's noise (if SNR or PCE is selected) and the filter SLM's (Spatial Light Modulator) available values. These statistics are used as the basis of a range for searching for the magnitude and phase of k, a pragmatically based complex constant for computing the filter transmittance from the electric field. The filter is produced for the mesh points in those ranges and the value of the metric that results from these points is computed. When the search is concluded, the values of amplitude and phase for the k whose metric was largest, as well as consistency checks, are reported. A finer search can be done in the neighborhood of the optimal k if desired. The filter finally selected is written to disk in terms of drive values, not in terms of the filter's complex transmittance. Optionally, the impulse response of the filter may be created to permit users to examine the response for the features the algorithm deems important to the recognition process under the selected metric, limitations of the filter SLM, etc. MEDOF uses the filter SLM to its greatest potential, therefore filter competence is not compromised for simplicity of computation. MEDOF is written in C-language for Sun series computers running SunOS. With slight modifications, it has been implemented on DEC VAX series computers using the DEC-C v3.30 compiler, although the documentation does not currently support this platform. MEDOF can also be compiled using Borland International Inc.'s Turbo C++ v1.0, but IBM PC memory restrictions greatly reduce the maximum size of the reference images from which the filters can be calculated. MEDOF requires a two dimensional Fast Fourier Transform (2DFFT). One 2DFFT routine which has been used successfully with MEDOF is a routine found in "Numerical Recipes in C: The Art of Scientific Programming," which is available from Cambridge University Press, New Rochelle, NY 10801. The standard distribution medium for MEDOF is a .25 inch streaming magnetic tape cartridge (Sun QIC-24) in UNIX tar format. MEDOF was developed in 1992-1993.

Barton, R. S.

1994-01-01

34

OPTIMAL FILTERING TECHNIQUES FOR ANALYTICAL STREAMFLOW FORECASTING  

E-print Network

over the rocks, hard soil or ponds, lakes, and streams, produces direct runoff. Some water gets describes the development of a streamflow forecasting model based on the the Sacramento Soil Moisture Accounting Model and applies optimal filtering techniques to sequentially update watershed-scale soil

Simon, Dan

35

A modified PSO based particle filter algorithm for object tracking  

NASA Astrophysics Data System (ADS)

In this paper, a modified particle swarm optimization (PSO) approach, particle swarm optimization with ?- greedy exploration ?PSO), is used to tackle the object tracking. In the modified ?PSO algorithm, the cooperative learning mechanism among individuals has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best individuals according to certain probability. This kind of biologically-inspired mutual-learning behavior can help to find the global optimum solution with better convergence speed and accuracy. The ?PSO algorithm has been tested on benchmark function and demonstrated its effectiveness in high-dimension multi-modal optimization. In addition to the standard benchmark study, we also combined our new ?PSO approach with the traditional particle filter (PF) algorithm on the object tracking task, such as car tracking in complex environment. Comparative studies between our ?PSO combined PF algorithm with those of existing techniques, such as the particle filter (PF) and classic PSO combined PF will be used to verify and validate the performance of our approach.

Tang, Yufei; Fu, Siyao; Tang, Bo; He, Haibo

2013-05-01

36

Small curvature particle flow for nonlinear filters  

NASA Astrophysics Data System (ADS)

We derive five new particle flow algorithms for nonlinear filters based on the small curvature approximation inspired by fluid dynamics. We find it extremely interesting that this physically motivated approximation generalizes two of our previous exact flow algorithms, namely incompressible flow and Gaussian flow. We derive a new algorithm to compute the inverse of the sum of two linear differential operators using a second homotopy, similar to Feynman's perturbation theory for quantum electrodynamics as well as Gromov's h-principle.

Daum, Fred; Huang, Jim

2012-05-01

37

Zero curvature particle flow for nonlinear filters  

NASA Astrophysics Data System (ADS)

We derive a new algorithm for computing Bayes' rule using particle flow that has zero curvature. The flow is computed by solving a vector Riccati equation exactly in closed form rather than solving a PDE, with a significant reduction in computational complexity. Our theory is valid for any smooth nowhere vanishing probability densities, including highly multimodal non-Gaussian densities. We show that this new flow is similar to the extended Kalman filter in the special case of nonlinear measurements with Gaussian noise. We also outline more general particle flows, including: constant curvature, geodesic flow, non-constant curvature, piece-wise constant curvature, etc.

Daum, Fred; Huang, Jim

2013-05-01

38

Particle swarm optimization in electromagnetics  

Microsoft Academic Search

The particle swarm optimization (PSO), new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper introduces a conceptual overview and detailed explanation of the PSO algorithm, as well as how it can be used for electromagnetic optimizations. This paper also presents several results illustrating the swarm behavior in

Jacob Robinson; Yahya Rahmat-Samii

2004-01-01

39

Point Set Registration via Particle Filtering and Stochastic Dynamics  

PubMed Central

In this paper, we propose a particle filtering approach for the problem of registering two point sets that differ by a rigid body transformation. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in pose parameters obtained by running a few iterations of a certain local optimizer. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer approaches for registration. Thus, the novelty of our method is threefold: First, we employ a particle filtering scheme to drive the point set registration process. Second, we present a local optimizer that is motivated by the correlation measure. Third, we increase the robustness of the registration performance by introducing a dynamic model of uncertainty for the transformation parameters. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity (with respect to particle size) as well as maintains the temporal coherency of the state (no loss of information). Also unlike some alternative approaches for point set registration, we make no geometric assumptions on the two data sets. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures, and/or differing point densities in each set, on several challenging 2D and 3D registration scenarios. PMID:20558877

Sandhu, Romeil; Dambreville, Samuel; Tannenbaum, Allen

2013-01-01

40

Optimal digital filtering for tremor suppression.  

PubMed

Remote manually operated tasks such as those found in teleoperation, virtual reality, or joystick-based computer access, require the generation of an intermediate electrical signal which is transmitted to the controlled subsystem (robot arm, virtual environment, or a cursor in a computer screen). When human movements are distorted, for instance, by tremor, performance can be improved by digitally filtering the intermediate signal before it reaches the controlled device. This paper introduces a novel tremor filtering framework in which digital equalizers are optimally designed through pursuit tracking task experiments. Due to inherent properties of the man-machine system, the design of tremor suppression equalizers presents two serious problems: 1) performance criteria leading to optimizations that minimize mean-squared error are not efficient for tremor elimination and 2) movement signals show ill-conditioned autocorrelation matrices, which often result in useless or unstable solutions. To address these problems, a new performance indicator in the context of tremor is introduced, and the optimal equalizer according to this new criterion is developed. Ill-conditioning of the autocorrelation matrix is overcome using a novel method which we call pulled-optimization. Experiments performed with artificially induced vibrations and a subject with Parkinson's disease show significant improvement in performance. Additional results, along with MATLAB source code of the algorithms, and a customizable demo for PC joysticks, are available on the Internet at http:¿tremor-suppression.com. PMID:10851810

Gonzalez, J G; Heredia, E A; Rahman, T; Barner, K E; Arce, G R

2000-05-01

41

Gaussian Particle Filtering for Concurrent Hybrid Models Gaussian Particle Filtering for Concurrent Hybrid Models  

E-print Network

. Robotic systems often face unpredictable, harsh physical environments and must continue performing in the physical world, robotic systems must handle the uncertainty and partial observability inherent in most real estimation that combines Rao-Blackwellised particle filtering with a Gaussian representation. Conceptually

Williams, Brian C.

42

FIR Filter Design via Spectral Factorization and Convex Optimization 1 FIR Filter Design via Spectral Factorization  

E-print Network

is optimization variable fi are convex: for 0 1, fix + 1 ,y fix + 1 ,fiy examples: linear & convex quadratic 1000s variables, 10000s constraints feasible on PC FIR Filter Design via Spectral FactorizationFIR Filter Design via Spectral Factorization and Convex Optimization 1 FIR Filter Design via

43

Particle swarm optimization recommender system  

Microsoft Academic Search

Recommender systems are new types of Internet-based software tools, designed to help users find their way through today's complex on-line shops and entertainment Web sites. This paper describes a new recommender system, which employs a particle swarm optimization (PSO) algorithm to learn personal preferences of users and provide tailored suggestions. Experiments are carried out to observe the performance of the

Supiya Ujjin; Peter J. Bentley

2003-01-01

44

Filtering and Particle Systems Pierre Del Moral  

E-print Network

. . . . . . . . . . . . . . . . . . . . . . . 56 3 Filtering 61 3.1 Filtering linear/Gaussian signals . . . . . . . . . . . . . . . . . . . 63 3.1.1 Gaussian variables . . . . . . . . . . . . . . . . . . . . . . 63 3.1.2 Kalman�Bucy filter . . . . . . . . . . . . . . . . . . . . . . 65 3.2 Non linear filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2.1 Non linear

Del Moral , Pierre

45

Metal finishing wastewater pressure filter optimization  

SciTech Connect

The 300-M Area Liquid Effluent Treatment Facility (LETF) of the Savannah River Site (SRS) is an end-of-pipe industrial wastewater treatment facility, that uses precipitation and filtration which is the EPA Best Available Technology economically achievable for a Metal Finishing and Aluminum Form Industries. The LETF consists of three close-coupled treatment facilities: the Dilute Effluent Treatment Facility (DETF), which uses wastewater equalization, physical/chemical precipitation, flocculation, and filtration; the Chemical Treatment Facility (CTF), which slurries the filter cake generated from the DETF and pumps it to interim-StatuS RCRA storage tanks; and the Interim Treatment/Storage Facility (IT/SF) which stores the waste from the CTF until the waste is stabilized/solidified for permanent disposal, 85% of the stored waste is from past nickel plating and aluminum canning of depleted uranium targets for the SRS nuclear reactors. Waste minimization and filtration efficiency are key to cost effective treatment of the supernate, because the waste filter cake generated is returned to the IT/SF. The DETF has been successfully optimized to achieve maximum efficiency and to minimize waste generation.

Norford, S.W.; Diener, G.A.; Martin, H.L.

1992-12-31

46

Metal finishing wastewater pressure filter optimization  

SciTech Connect

The 300-M Area Liquid Effluent Treatment Facility (LETF) of the Savannah River Site (SRS) is an end-of-pipe industrial wastewater treatment facility, that uses precipitation and filtration which is the EPA Best Available Technology economically achievable for a Metal Finishing and Aluminum Form Industries. The LETF consists of three close-coupled treatment facilities: the Dilute Effluent Treatment Facility (DETF), which uses wastewater equalization, physical/chemical precipitation, flocculation, and filtration; the Chemical Treatment Facility (CTF), which slurries the filter cake generated from the DETF and pumps it to interim-StatuS RCRA storage tanks; and the Interim Treatment/Storage Facility (IT/SF) which stores the waste from the CTF until the waste is stabilized/solidified for permanent disposal, 85% of the stored waste is from past nickel plating and aluminum canning of depleted uranium targets for the SRS nuclear reactors. Waste minimization and filtration efficiency are key to cost effective treatment of the supernate, because the waste filter cake generated is returned to the IT/SF. The DETF has been successfully optimized to achieve maximum efficiency and to minimize waste generation.

Norford, S.W.; Diener, G.A.; Martin, H.L.

1992-01-01

47

Particle Size Distribution to Assess the Performance of Trickling Filters  

Microsoft Academic Search

Particle size distributions by laser scatter analysis were compared with other solids settlement performance indicators from trickling filters. Field and laboratory pilot plant data indicated smaller less flocculated solids from trickling filters than activated sludge or rotating biological contractors (RBC). Analysis of utility company treatment plants indicated settlement characteristics were linked to less consistent performance from the trickling filters compared

R. Marquet; N. Muhammad; K. Vairavamoorthy; A. Wheatley

2007-01-01

48

Sampling Strategies for Particle Filtering SLAM Kristopher R. Beevers  

E-print Network

Sampling Strategies for Particle Filtering SLAM Kristopher R. Beevers Department of Computer strategies for Rao-Blackwellized particle filtering SLAM. Two of the strategies, called fixed-lag roughening sampling tech- niques such as the improved proposal distribution of FastSLAM 2. In addition, we examine

Bystroff, Chris

49

On Incremental Sigma-Delta Modulation with Optimal Filtering  

E-print Network

On Incremental Sigma-Delta Modulation with Optimal Filtering Sam Kavusi, Student Member, IEEE derivation of the Zoomer algorithm. Index Terms Sigma-Delta (), incremental A/D converter, optimal filter@isl.stanford.edu). #12;IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS­I: REGULAR PAPERS, 1 On Incremental Sigma-Delta

El Gamal, Abbas

50

Human-Manipulator Interface Using Particle Filter  

PubMed Central

This paper utilizes a human-robot interface system which incorporates particle filter (PF) and adaptive multispace transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. PF is used to estimate the translation of the human hand. Although a PF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an adaptive multispace transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator. PMID:24757430

Wang, Xueqian

2014-01-01

51

Tractable particle filters for robot fault diagnosis  

NASA Astrophysics Data System (ADS)

Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainly or use linear approximations of nonlinear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions. Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Classical Monte Carlo methods, such as particle filters, suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults. The thesis presents a set of complementary algorithms that provide an approach for computationally tractable fault diagnosis. These algorithms leverage probabilistic approaches to decision theory and information theory to efficiently track a large number of faults in a general dynamic system with noisy measurements. The problem of fault diagnosis is represented as hybrid (discrete/continuous) state estimation. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.

Verma, Vandi

52

Human-manipulator interface using particle filter.  

PubMed

This paper utilizes a human-robot interface system which incorporates particle filter (PF) and adaptive multispace transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. PF is used to estimate the translation of the human hand. Although a PF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an adaptive multispace transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator. PMID:24757430

Du, Guanglong; Zhang, Ping; Wang, Xueqian

2014-01-01

53

Blended particle filters for large-dimensional chaotic dynamical systems.  

PubMed

A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886

Majda, Andrew J; Qi, Di; Sapsis, Themistoklis P

2014-05-27

54

A hybrid method for optimization of the adaptive Goldstein filter  

NASA Astrophysics Data System (ADS)

The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.

Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue

2014-12-01

55

Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization  

NASA Astrophysics Data System (ADS)

Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC) methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP), is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF) and the sequential importance resampling (SIR) particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.

Noh, S. J.; Tachikawa, Y.; Shiiba, M.; Kim, S.

2011-10-01

56

Optimal PHD filter for single-target detection and tracking  

NASA Astrophysics Data System (ADS)

The PHD filter has attracted much international interest since its introduction in 2000. It is based on two approximations. First, it is a first-order approximation of the multitarget Bayes filter. Second, to achieve closed-form formulas for the Bayes data-update step, the predicted multitarget probability distribution must be assumed Poisson. In this paper we show how to derive an optimal PHD (OPHD) filter, given that target number does not exceed one. (That is, we restrict ourselves to the single-target detection and tracking problem.) We further show that, assuming no more than a single target, the following are identical: (1) the multitarget Bayes filter; (2) the OPHD filter; (3) the CPHD filter; and (4) the multi-hypothesis correlation (MHC) filter. We also note that all of these are generalizations of the probabilistic data association (IPDA) filter of Musicki, Evans, and Stankovic.

Maher, Ronald

2007-09-01

57

PSO algorithm particle filters for improving the performance of lane detection and tracking systems in difficult roads.  

PubMed

In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options. PMID:23235453

Cheng, Wen-Chang

2012-01-01

58

Efficient Particle Filtering for Road-Constrained Target Tracking  

E-print Network

estimation falls into the category of non- linear filtering even if every single model is a linear systemEfficient Particle Filtering for Road-Constrained Target Tracking Yang Cheng Department at Buffalo, State University of New York Amherst, NY 14260 Email: tsingh@buffalo.edu Abstract-- The variable

Singh, Tarunraj

59

Continuous Time Particle Filtering for fMRI Lawrence Murray  

E-print Network

, and using a much more flexible Kalman filter xt = axt-1 + c + t, yt = xt + t (where xt is a latent variable dependent variable may be a linear combination of both indepen- dent and other dependent variables. ItsContinuous Time Particle Filtering for fMRI Lawrence Murray School of Informatics University

Storkey, Amos

60

Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours  

Microsoft Academic Search

Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time. Tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape, but these are dependent on the chosen parametrization and cannot handle changes in curve topology. Geometric active contours provide a framework which is

Yogesh Rathi; Namrata Vaswani; Allen Tannenbaum; Anthony J. Yezzi

2007-01-01

61

Optimal filter bandwidth for pulse oximetry  

NASA Astrophysics Data System (ADS)

Pulse oximeters contain one or more signal filtering stages between the photodiode and microcontroller. These filters are responsible for removing the noise while retaining the useful frequency components of the signal, thus improving the signal-to-noise ratio. The corner frequencies of these filters affect not only the noise level, but also the shape of the pulse signal. Narrow filter bandwidth effectively suppresses the noise; however, at the same time, it distorts the useful signal components by decreasing the harmonic content. In this paper, we investigated the influence of the filter bandwidth on the accuracy of pulse oximeters. We used a pulse oximeter tester device to produce stable, repetitive pulse waves with digitally adjustable R ratio and heart rate. We built a pulse oximeter and attached it to the tester device. The pulse oximeter digitized the current of its photodiode directly, without any analog signal conditioning. We varied the corner frequency of the low-pass filter in the pulse oximeter in the range of 0.66-15 Hz by software. For the tester device, the R ratio was set to R = 1.00, and the R ratio deviation measured by the pulse oximeter was monitored as a function of the corner frequency of the low-pass filter. The results revealed that lowering the corner frequency of the low-pass filter did not decrease the accuracy of the oxygen level measurements. The lowest possible value of the corner frequency of the low-pass filter is the fundamental frequency of the pulse signal. We concluded that the harmonics of the pulse signal do not contribute to the accuracy of pulse oximetry. The results achieved by the pulse oximeter tester were verified by human experiments, performed on five healthy subjects. The results of the human measurements confirmed that filtering out the harmonics of the pulse signal does not degrade the accuracy of pulse oximetry.

Stuban, Norbert; Niwayama, Masatsugu

2012-10-01

62

Optimal filter bandwidth for pulse oximetry.  

PubMed

Pulse oximeters contain one or more signal filtering stages between the photodiode and microcontroller. These filters are responsible for removing the noise while retaining the useful frequency components of the signal, thus improving the signal-to-noise ratio. The corner frequencies of these filters affect not only the noise level, but also the shape of the pulse signal. Narrow filter bandwidth effectively suppresses the noise; however, at the same time, it distorts the useful signal components by decreasing the harmonic content. In this paper, we investigated the influence of the filter bandwidth on the accuracy of pulse oximeters. We used a pulse oximeter tester device to produce stable, repetitive pulse waves with digitally adjustable R ratio and heart rate. We built a pulse oximeter and attached it to the tester device. The pulse oximeter digitized the current of its photodiode directly, without any analog signal conditioning. We varied the corner frequency of the low-pass filter in the pulse oximeter in the range of 0.66-15 Hz by software. For the tester device, the R ratio was set to R = 1.00, and the R ratio deviation measured by the pulse oximeter was monitored as a function of the corner frequency of the low-pass filter. The results revealed that lowering the corner frequency of the low-pass filter did not decrease the accuracy of the oxygen level measurements. The lowest possible value of the corner frequency of the low-pass filter is the fundamental frequency of the pulse signal. We concluded that the harmonics of the pulse signal do not contribute to the accuracy of pulse oximetry. The results achieved by the pulse oximeter tester were verified by human experiments, performed on five healthy subjects. The results of the human measurements confirmed that filtering out the harmonics of the pulse signal does not degrade the accuracy of pulse oximetry. PMID:23126791

Stuban, Norbert; Niwayama, Masatsugu

2012-10-01

63

Adaptive MIMO time-varying channel equalization using particle filtering  

Microsoft Academic Search

This paper addresses the problem of semi-blind multi-input multi-output (MIMO) equalization of non-Gaussian time-varying channels by employing particle filter methods. Based on the Middleton Class A noise model, we derive a state-space model that characterizes the behavior of the channel in time. The channel estimation and tracking are performed using particle filter, and a decision feedback equalizer derived using MMSE

Du Zheng-cong; Tang Bin; Li Ke

2005-01-01

64

Robustness of optimal binary filters: analysis and design  

E-print Network

and these are governed by parameterized probability laws. The optimal filter is found relative to these laws. Qualitatively, a filter is said to be robust when its performance degradation is acceptable for processes statistically close to the one for which it has been...

Grigoryan, Artyom M

2012-06-07

65

A Filter-Based Evolutionary Algorithm for Constrained Optimization  

Microsoft Academic Search

We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent

Lauren M. Clevenger; Lauren Ferguson; William E. Hart

2005-01-01

66

Optimization of tunable silicon compatible microring filters  

E-print Network

Microring resonators can be used as pass-band filters for wavelength division demultiplexing in electronic-photonic integrated circuits for applications such as analog-to-digital converters (ADCs). For high quality signal ...

Amatya, Reja

2008-01-01

67

VLSI floorplanning based on Particle Swarm Optimization  

Microsoft Academic Search

Floorplanning is an important problem in the very large integrated circuit (VLSI) design automation. It¿s an NP-hard combinatorial optimization problem. The particle swarm optimization (PSO) has been proved to be a good optimization algorithm with outstanding global performance. However, PSO cannot be directly used in the combinatorial optimization problem due to its continuous characteristic. In this paper a novel floorplanning

Guolong Chen; Wenzhong Guo; Hongju Cheng; Xiang Fen; Xiaotong Fang

2008-01-01

68

Particle filter for long range radar in RUV  

NASA Astrophysics Data System (ADS)

In this paper we present an approach for tracking with a high-bandwidth active radar in long range scenarios with 3-D measurements in r-u-v coordinates. The 3-D low-process-noise scenarios considered are much more difficult than the ones we have previously investigated where measurements were in 2-D (i.e., polar coordinates). We show that in these 3-D scenarios the extended Kalman filter and its variants are not desirable as they suffer from either major consistency problems or degraded range accuracy, and most flavors of particle filter suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter is shown to avoid this diversity problem while producing consistent results. The regularization is accomplished using a modified version of the Epanechnikov kernel.

Romeo, Kevin; Willett, Peter; Bar-Shalom, Yaakov

2014-06-01

69

On particle filters applied to electricity load forecasting Tristan Launay1,2 Anne Philippe1 Sophie Lamarche2  

E-print Network

we use such a particle filter to estimate a state-space model that includes exogenous variables sequences of independent random variables and {Fn}n1 and {Gn}n1 are sequences of (possibly non linear-called optimal filtering problem. For simple models such as the linear Gaussian state-space model the problem can

70

A Tutorial on Particle Filtering and Smoothing: Fifteen years later  

E-print Network

A Tutorial on Particle Filtering and Smoothing: Fifteen years later Arnaud Doucet The Institute. The objective of this tutorial is to provide a complete, up-to-date survey of this field as of 2008. Basic of particularly simple cases. The "particle" methods described by this tutorial are a broad and popular class

Del Moral , Pierre

71

Fast and Accurate SLAM with Rao-Blackwellized Particle Filters  

E-print Network

localization and mapping (SLAM) problem [1, 2, 3, 4, 5, 6, 7, 8]. In general, SLAM is a complex problem because to localize the robot. This dependency between the pose and the map estimate makes the SLAM problem hard localization and mapping problem. This technique applies a particle filter in which each particle carries

Stachniss, Cyrill

72

COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS  

EPA Science Inventory

The paper discusses computations on the performance of particle filters and electronic air cleaners (EACs). he collection efficiency of particle filters and ACs is calculable if certain factors can be assumed or calibrated. or fibrous particulate filters, measurement of collectio...

73

COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS  

EPA Science Inventory

The paper discusses computations on the performance of particle filters and electronic air cleaners (EACs). The collection efficiency of particle filters and ACs is calculable if certain factors can be assumed or calibrated. For fibrous particulate filters, measurement of colle...

74

Optimal wavelet filter construction using X and Y data  

Microsoft Academic Search

It has been recently shown that the predictive ability of wavelet models in multivariate calibration problems can be improved by optimizing the filters employed in the Discrete Wavelet Transform (DWT) with respect to the statistics of the matrix of instrumental responses. However, no attempt has been made at exploiting the statistics of the matrix of predicted parameters in the optimization

Roberto Kawakami Harrop Galvão; Gledson Em??dio José; Heronides Adonias Dantas Filho; Mario Cesar Ugulino Araujo; Edvan Cirino da Silva; Henrique Mohallem Paiva; Teresa Cristina Bezerra Saldanha; Ênio Sartre Oliveira Nunes de Souza

2004-01-01

75

Optimal digital filtering for tremor suppression  

Microsoft Academic Search

Remote manually operated tasks such as those found in teleoperation, virtual reality, or joystick-based computer access, require the generation of an intermediate electrical signal which is transmitted to the controlled subsystem (robot arm, virtual environment, or a cursor in a computer screen). When human movements are distorted, for instance, by tremor, performance can be improved by digitally filtering the intermediate

Juan G. Gonzalez; Edwin A. Heredia; Tariq Rahman; Kenneth E. Barner; Gonzalo R. Arce

2000-01-01

76

Road-constrained target tracking and identification a particle filter  

NASA Astrophysics Data System (ADS)

Sequential Monte Carlo methods have attracted the attention of the tracking community as a solution to Bayesian estimation particularly for nonlinear problems. Several attributes of particle filters support their use in jointly tracking and identifying ground targets in a road-constrained network. First, since the target dynamics are simulated, propagating a target within a constrained state space is handled quite naturally since the particle filter is not restricted to propagating Gaussian PDFs. Furthermore, a particle filter can approximate a PDF which is a mixture of continuous random variables (the target kinematic state) and discrete random variables (the target ID) which is necessary for the joint tracking and identification problem. Given HRRGMTI measurements of a target, we propose to jointly estimate a target's kinematic state and identification by propagating the joint PDF of the target kinematic state (position and velocity) and target ID. In this way, we capitalize on the inherent coupling between the target's feature measurement (the HRR profile) and the target's kinematic state. In addition to the coupling between a target's feature measurement and the target's kinematic state, there exists a coupling between a target's dynamics and the target's ID which can also be exploited through particle filtering methods. We develop the particle filtering algorithm for tracking and identifying ground targets in a road-constrained environment and present simulation results for a two-class problem.

Agate, Craig S.; Sullivan, Kevin J.

2003-12-01

77

Road-constrained target tracking and identification a particle filter  

NASA Astrophysics Data System (ADS)

Sequential Monte Carlo methods have attracted the attention of the tracking community as a solution to Bayesian estimation particularly for nonlinear problems. Several attributes of particle filters support their use in jointly tracking and identifying ground targets in a road-constrained network. First, since the target dynamics are simulated, propagating a target within a constrained state space is handled quite naturally since the particle filter is not restricted to propagating Gaussian PDFs. Furthermore, a particle filter can approximate a PDF which is a mixture of continuous random variables (the target kinematic state) and discrete random variables (the target ID) which is necessary for the joint tracking and identification problem. Given HRRGMTI measurements of a target, we propose to jointly estimate a target's kinematic state and identification by propagating the joint PDF of the target kinematic state (position and velocity) and target ID. In this way, we capitalize on the inherent coupling between the target's feature measurement (the HRR profile) and the target's kinematic state. In addition to the coupling between a target's feature measurement and the target's kinematic state, there exists a coupling between a target's dynamics and the target's ID which can also be exploited through particle filtering methods. We develop the particle filtering algorithm for tracking and identifying ground targets in a road-constrained environment and present simulation results for a two-class problem.

Agate, Craig S.; Sullivan, Kevin J.

2004-01-01

78

A Genetic Binary Particle Swarm Optimization Model  

Microsoft Academic Search

In this paper, a genetic binary particle swarm optimization (GBPSO) model is proposed, and its performance is compared with the regular binary particle swarm optimizer (PSO), introduced by Kennedy and Eberhart. In the original model, the size of the swarm was fixed. In our model, we introduce birth and death operations in order to make the population very dynamic. Since

Javad Sadri; Ching Y. Suen

2006-01-01

79

Square Root Unscented Particle Filtering for Grid Mapping  

NASA Astrophysics Data System (ADS)

In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [12]. Various filtering methods - Particle Filtering, and derived Kalman Filter methods (Extended, Unscented) - have been applied successfully to SLAM. We present a new algorithm that adapts the Square Root Unscented Transformation [13], previously only applied to feature based maps [5], to grid mapping. We also present a new method for the so-called pose-correction step in the algorithm. Experimental results show improved computational performance on more complex grid maps compared to an existing grid based particle filtering algorithm.

Zandara, Simone; Nicholson, Ann

80

Effect of particle-fiber friction coefficient on ultrafine aerosol particles clogging in nanofiber based filter  

NASA Astrophysics Data System (ADS)

Realistic SEM image based 3D filter model considering transition/free molecular flow regime, Brownian diffusion, aerodynamic slip, particle-fiber and particle-particle interactions together with a novel Euclidian distance map based methodology for the pressure drop calculation has been utilized for a polyurethane nanofiber based filter prepared via electrospinning process in order to more deeply understand the effect of particle-fiber friction coefficient on filter clogging and basic filter characteristics. Based on the performed theoretical analysis, it has been revealed that the increase in the fiber-particle friction coefficient causes, firstly, more weaker particle penetration in the filter, creation of dense top layers and generation of higher pressure drop (surface filtration) in comparison with lower particle-fiber friction coefficient filter for which deeper particle penetration takes place (depth filtration), secondly, higher filtration efficiency, thirdly, higher quality factor and finally, higher quality factor sensitivity to the increased collected particle mass. Moreover, it has been revealed that even if the particle-fiber friction coefficient is different, the cake morphology is very similar.

Sambaer, Wannes; Zatloukal, Martin; Kimmer, Dusan

2013-04-01

81

Spectral optimized asymmetric segmented phase-only correlation filter.  

PubMed

We suggest a new type of optimized composite filter, i.e., the asymmetric segmented phase-only filter (ASPOF), for improving the effectiveness of a VanderLugt correlator (VLC) when used for face identification. Basically, it consists in merging several reference images after application of a specific spectral optimization method. After segmentation of the spectral filter plane to several areas, each area is assigned to a single winner reference according to a new optimized criterion. The point of the paper is to show that this method offers a significant performance improvement on standard composite filters for face identification. We first briefly revisit composite filters [adapted, phase-only, inverse, compromise optimal, segmented, minimum average correlation energy, optimal trade-off maximum average correlation, and amplitude-modulated phase-only (AMPOF)], which are tools of choice for face recognition based on correlation techniques, and compare their performances with those of the ASPOF. We illustrate some of the drawbacks of current filters for several binary and grayscale image identifications. Next, we describe the optimization steps and introduce the ASPOF that can overcome these technical issues to improve the quality and the reliability of the correlation-based decision. We derive performance measures, i.e., PCE values and receiver operating characteristic curves, to confirm consistency of the results. We numerically find that this filter increases the recognition rate and decreases the false alarm rate. The results show that the discrimination of the ASPOF is comparable to that of the AMPOF, but the ASPOF is more robust than the trade-off maximum average correlation height against rotation and various types of noise sources. Our method has several features that make it amenable to experimental implementation using a VLC. PMID:22614484

Leonard, I; Alfalou, A; Brosseau, C

2012-05-10

82

Estimate the Electromechanical States Using Particle Filtering and Smoothing  

SciTech Connect

Accurate knowledge of electromechanical states is critical for efficient and reliable control of a power system. This paper proposes a particle filtering approach to estimate the electromechanical states of power systems from Phasor Measurement Unit (PMU) data. Without having to go through laborious linearization procedure, the proposed particle filtering techniques can estimate states of a complex power system, which is often non-linear and has non-Gaussian noise. The proposed method is evaluated using a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance are also presented to show the robustness of the proposed method. The inherent decoupling properties of particle filtering make it highly scalable and the potential to reduce computational time through parallel implementation is very promising.

Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang

2012-07-22

83

Particle Swarm Optimizers with Growing Tree Topology  

NASA Astrophysics Data System (ADS)

This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.

Miyagawa, Eiji; Saito, Toshimichi

84

Statistics of optimal particle streak photography  

Microsoft Academic Search

A number of statistical problems pertinent to the optimal use of particle streak photography are examined with the goal of deriving error bars and practical rules of thumb. The seeding density of particles is analyzed with the goal of maximizing the number of isolated streaks from randomly distributed particles. The number of overlapped streaks, the main source of ‘‘noise’’ in

Donald B. Altman

1991-01-01

85

Statistics of optimal particle streak photography  

Microsoft Academic Search

To derive error bars and practical rules of thumb, statistical problems of optimal particle streak photography (PSP) have been examined. Four practical problems are discussed, including the seeding density of particles necessary for maximizing the number of isolated streaks from randomly distributed particles, estimation of the mean of an arbitrary velocity field with randomly placed noise-free measurements, determination of the

Donald B. Altman

1991-01-01

86

Particle filtering algorithm for tracking multiple road-constrained targets  

NASA Astrophysics Data System (ADS)

We propose a particle filtering algorithm for tracking multiple ground targets in a road-constrained environment through the use of GMTI radar measurements. Particle filters approximate the probability density function (PDF) of a target's state by a set of discrete points in the state space. The particle filter implements the step of propagating the target dynamics by simulating them. Thus, the dynamic model is not limited to that of a linear model with Gaussian noise, and the state space is not limited to linear vector spaces. Indeed, the road network is a subset (not even a vector space) of R2. Constraining the target to lie on the road leads to adhoc approaches for the standard Kalman filter. However, since the particle filter simulates the dynamics, it is able to simply sample points in the road network. Furthermore, while the target dynamics are modeled with a parasitic acceleration, a non-Gaussian discrete random variable noise process is used to simulate the target going through an intersection and choosing the next segment in the road network on which to travel. The algorithm is implemented in the SLAMEM simulation (an extensive simulation which models roads, terrain, sensors and vehicles using GVS). Tracking results from the simulation are presented.

Agate, Craig S.; Sullivan, Kevin J.

2003-08-01

87

Improved particle filter algorithm for robot localization  

Microsoft Academic Search

For solving the problems of mobile robot SLAM (Simultaneous Localization and Mapping) in unknown environments, this paper presents an optimized RBPF algorithm. The method employs the UKF algorithm instead of the EKF algorithm to estimate landmarks, so it can avoid the derivation of complicated Jacobian Matrix and reduce the error generated by linearizing the nonlinear system. Using the Euclidean distance

Chunlei Ji; Haijun Wang; Qiang Sun

2010-01-01

88

Identifying Optimal Measurement Subspace for the Ensemble Kalman Filter  

SciTech Connect

To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimization algorithm based on the generalized eigenvalue decomposition method is proposed for identifying the most informative measurement subspace. When the number of measurements is large, the proposed algorithm can be used to make an effective tradeoff between computational complexity and estimation accuracy. This algorithm also can be extended to other Kalman filters for measurement subspace selection.

Zhou, Ning; Huang, Zhenyu; Welch, Greg; Zhang, J.

2012-05-24

89

Design of optimal correlation filters for hybrid vision systems  

NASA Technical Reports Server (NTRS)

Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.

Rajan, Periasamy K.

1990-01-01

90

Optimal Filtering Methods to Structural Damage Estimation under Ground Excitation  

PubMed Central

This paper considers the problem of shear building damage estimation subject to earthquake ground excitation using the Kalman filtering approach. The structural damage is assumed to take the form of reduced elemental stiffness. Two damage estimation algorithms are proposed: one is the multiple model approach via the optimal two-stage Kalman estimator (OTSKE), and the other is the robust two-stage Kalman filter (RTSKF), an unbiased minimum-variance filtering approach to determine the locations and extents of the damage stiffness. A numerical example of a six-storey shear plane frame structure subject to base excitation is used to illustrate the usefulness of the proposed results. PMID:24453869

Hsieh, Chien-Shu; Liaw, Der-Cherng; Lin, Tzu-Hsuan

2013-01-01

91

Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method  

PubMed Central

The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model's parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation. PMID:24453865

Hao, Chengpeng

2013-01-01

92

Optimal Recursive Digital Filters for Active Bending Stabilization  

NASA Technical Reports Server (NTRS)

In the design of flight control systems for large flexible boosters, it is common practice to utilize active feedback control of the first lateral structural bending mode so as to suppress transients and reduce gust loading. Typically, active stabilization or phase stabilization is achieved by carefully shaping the loop transfer function in the frequency domain via the use of compensating filters combined with the frequency response characteristics of the nozzle/actuator system. In this paper we present a new approach for parameterizing and determining optimal low-order recursive linear digital filters so as to satisfy phase shaping constraints for bending and sloshing dynamics while simultaneously maximizing attenuation in other frequency bands of interest, e.g. near higher frequency parasitic structural modes. By parameterizing the filter directly in the z-plane with certain restrictions, the search space of candidate filter designs that satisfy the constraints is restricted to stable, minimum phase recursive low-pass filters with well-conditioned coefficients. Combined with optimal output feedback blending from multiple rate gyros, the present approach enables rapid and robust parametrization of autopilot bending filters to attain flight control performance objectives. Numerical results are presented that illustrate the application of the present technique to the development of rate gyro filters for an exploration-class multi-engined space launch vehicle.

Orr, Jeb S.

2013-01-01

93

Optimization of filtering schemes for broadband astro-combs.  

PubMed

To realize a broadband, large-line-spacing astro-comb, suitable for wavelength calibration of astrophysical spectrographs, from a narrowband, femtosecond laser frequency comb ("source-comb"), one must integrate the source-comb with three additional components: (1) one or more filter cavities to multiply the source-comb's repetition rate and thus line spacing; (2) power amplifiers to boost the power of pulses from the filtered comb; and (3) highly nonlinear optical fiber to spectrally broaden the filtered and amplified narrowband frequency comb. In this paper we analyze the interplay of Fabry-Perot (FP) filter cavities with power amplifiers and nonlinear broadening fiber in the design of astro-combs optimized for radial-velocity (RV) calibration accuracy. We present analytic and numeric models and use them to evaluate a variety of FP filtering schemes (labeled as identical, co-prime, fraction-prime, and conjugate cavities), coupled to chirped-pulse amplification (CPA). We find that even a small nonlinear phase can reduce suppression of filtered comb lines, and increase RV error for spectrograph calibration. In general, filtering with two cavities prior to the CPA fiber amplifier outperforms an amplifier placed between the two cavities. In particular, filtering with conjugate cavities is able to provide <1 cm/s RV calibration error with >300 nm wavelength coverage. Such superior performance will facilitate the search for and characterization of Earth-like exoplanets, which requires <10 cm/s RV calibration error. PMID:23187265

Chang, Guoqing; Li, Chih-Hao; Phillips, David F; Szentgyorgyi, Andrew; Walsworth, Ronald L; Kärtner, Franz X

2012-10-22

94

Westinghouse hot gas particle filter system  

SciTech Connect

Integrated Gasification Combined Cycles (IGCC) and Pressurized Circulating Fluidized Bed Cycles (PCFB) are being developed and demonstrated for commercial power generation applications. Hot gas particulate filters (HGPF) are key components for the successful implementation of IGCC and PCFB in power generation gas turbine cycles. The objective is to develop and qualify through analysis and testing a practical HGPF system that meets the performance and operational requirements of PCFB and IGCC systems. This paper reports on the status of Westinghouse`s HGPF commercialization programs including: A quick summary of past gasification based HGPF test programs; A summary of the integrated HGPF operation at the American Electric Power, Tidd Pressurized Fluidized Bed Combustion (PFBC) Demonstration Project with approximately 6000 hours of HGPF testing completed; A summary of approximately 3200 hours of HGPF testing at the Foster Wheeler (FW) 10 MW{sub e} facility located in Karhula, Finland; A summary of over 700 hours of HGPF operation at the FW 2 MW{sub e} topping PCFB facility located in Livingston, New Jersey; A summary of the design of the HGPFs for the DOE/Southern Company Services, Power System Development Facility (PSDF) located in Wilsonville, Alabama; A summary of the design of the commercial-scale HGPF system for the Sierra Pacific, Pinon Pine IGCC Project; A review of completed testing and a summary of planned testing of Westinghouse HGPFs in Biomass IGCC applications; and A brief summary of the HGPF systems for the City of Lakeland, McIntosh Unit 4 PCFB Demonstration Project.

Lippert, T.E.; Bruck, G.J.; Newby, R.A.; Bachovchin, D.M. [Westinghouse Electric Corp., Pittsburgh, PA (United States). Science and Technology Center; Debski, V.L.; Morehead, H.T. [Westinghouse Electric Corp., Orlando, FL (United States). Power Generation Business Unit

1997-12-31

95

Hot gas particle filter systems: Commercialization status  

SciTech Connect

Integrated Gasification Combined Cycles (IGCCs) and Pressurized Circulating Fluidized Bed Cycles (PCFBs) are being developed and demonstrated for commercial power generation applications. Hot gas particulate filters (HGPFs) are key components for the successful implementation of advanced IGCC and PCFB power generation cycles. The objective is to develop and qualify through analysis and testing a practical HGPF system that meets the performance and operational requirements of PCFB and IGCC systems. This paper reports on the status of Westinghouse`s HGPF commercialization programs including: A quick summary of past gasification based HGPF test programs; A summary of the integrated HGPF operation at the American Electric Power, Tidd Pressurized Fluidized Bed Combustion (PFBC) Demonstration Project with approximately 6,000 hours of HGPF testing completed; A summary of approximately 3,200 hours of HGPF testing at the Foster Wheeler (FW) 10 MWe PCFB facility located in Karhula, Finland; A summary of over 700 hours of HGPF operation at the FW 2 MWe topping PCFB facility located in Livingston, New Jersey; A summary of the design of the HGPFs for the DOE/Southern Company Services, Power System Development Facility (PSDF) located in Wilsonville, Alabama; A summary of the design of the commercial-scale HGPF system for the Sierra Pacific, Pinon Pine IGCC Project; A review of completed testing and a summary of planned testing of Westinghouse HGPFs in Biomass IGCC applications; and A brief summary of the HGPF systems for the City of Lakeland, McIntosh Unit 4 PCFB Demonstration Project.

Morehead, H.T.; Adams, V.L. [Westinghouse Electric Corp., Orlando, FL (United States). Power Generation Business Unit; Yang, W.C.; Lippert, T.E. [Westinghouse Electric Corp., Pittsburgh, PA (United States). Science and Technology Center

1997-12-31

96

Lubricant wear particle analysis by filter patch extraction  

SciTech Connect

Lubricating Oil Analysis (LOA) has become an important part of a comprehensive Reliability Centered Maintenance (RCM) program. However, knowing the condition of the lubricant alone does not provide a complete description of equipment reliability. Condition monitoring for equipment can be accomplished through Wear Particle Analysis (WPA). This usually involves separating suspended materials and wear products from the lubricant by magnetic (ferrographic) means. This paper will present a simple, low-cost, alternate method of particle acquisition called Filter Patch Extraction (FPE). This method removes solids, regardless of their composition, from the lubricant by vacuum filtration and deposits them onto a filter for microscopic examination similar to that of analytical ferrography. A large filter pore size retains suspended materials and permits rapid filtration of large volumes of lubricant thereby increasing the accuracy of the wear and cleanliness profile that can be established for a given machine. Qualitative trending of equipment wear and lubricant system cleanliness are easily performed with FPE. Equipment condition is determined by then characterizing the metal particles which are recovered. Examined filters are easily archived in filter holders for future reference. Equipment for FPE is inexpensive and readily available. The technique is field-portable, allowing WPA to be performed on-site, eliminating delays with remote laboratories while building customer participation and support. There are numerous advantages for using FPE in a machine condition monitoring program.

Smart, C.L. [Public Service Company of Colorado, Englewood, CO (United States)

1996-07-01

97

Nonlinear Statistical Signal Processing: A Particle Filtering Approach  

SciTech Connect

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.

Candy, J

2007-09-19

98

Estimating the full posterior pdf with particle filters  

NASA Astrophysics Data System (ADS)

The majority of data assimilation schemes rely on linearity assumptions. However as the resolution and complexity of both the numerical models and observations increases, these linearity assumptions become less appropriate. A need is arising for fully non-linear data assimilation schemes, such as particle filters. Recently, new particle filter schemes have been generated that explore the freedom in proposal densities and that are quite effective in estimating the mean of the posterior probability density function (pdf), even in very high dimensional systems. However, in non-linear data assimilation the solution to the data assimilation problem is the full posterior pdf. At the same time we can only afford a limited number of particles. Here we concentrate on the equivalent weights particle filter in conjunction with a 65,000 dimensional Barotropic Vorticity model. Specifically we test the ability of the scheme to represent the posterior in three important areas. In many actual geophysical applications, observations will be sparse and may well be unevenly distributed. We discuss the effect of changing the frequency, number and distribution of the observed variables on the ensemble representation of the posterior pdf. Specifically we show that the filter has remarkably good convergence in marginal and joint pdfs with ensemble size, and the rank histograms are quite flat, even with low observation numbers and low observation frequencies. Only when the observation frequency is much larger than the typical decorrelation time scale of the system do we see underdispersive ensembles when using 32 particles. The second area attempts to replicate the realistic situation of using a geophysical model designed without a full understanding of the error statistics of the truth. This is done by using deliberately erroneous error statistics in the ensemble equations compared to those used to generate the truth. Specifically we consider changes in the correlation length-scales and variances in the model error statistics. Again the filter is remarkably successful in generating correct posterior pdfs, although rank histograms tend to point to under- or overdispersive ensembles. One of the interesting results is that when we overestimate the model error amplitude the ensemble is underdispersive. We present an explanation for this counter-intuitive phenomenon. Finally we show that the computational effort involved in the equivalent-weights particle filter is comparable to running a simple resampling particle filter with the same number of particles.

Ades, Melanie; van Leeuwen, Peter Jan

2013-04-01

99

Iterative gradient technique for the design of least squares optimal FIR magnitude squared Nyquist filters  

Microsoft Academic Search

Recently, much attention has been given to the design of optimal finite impulse response (FIR) compaction filters. Such filters, which arise in the design of optimal signal-adapted orthonormal FIR filter banks, satisfy a magnitude squared Nyquist constraint in addition to the inherent FIR assumption. In this paper, we focus on the least squares optimal design of FIR filters whose magnitude

Andre Tkacenko; P. P. Vaidyanathan

2004-01-01

100

A Tutorial on Particle Filtering and Smoothing: Fifteen years later  

E-print Network

A Tutorial on Particle Filtering and Smoothing: Fifteen years later Arnaud Doucet The Institute vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a complete described by this tutorial are a broad and popular class of Monte Carlo algorithms which have been developed

Johansen, Adam

101

ESTIMATION AND CONTROL OF INDUSTRIAL PROCESSES WITH PARTICLE FILTERS  

E-print Network

an automatic control system. Keyword: State Estimation, Control, Particle Filtering, Jump Markov Linear with different linear regimes of operation. A discrete state variable controls the switching between the various to the continuous state variables corresponding to each linear process, we have a discrete state variable

de Freitas, Nando

102

Fast, parallel implementation of particle filtering on the GPU architecture  

NASA Astrophysics Data System (ADS)

In this paper, we introduce a modified cellular particle filter (CPF) which we mapped on a graphics processing unit (GPU) architecture. We developed this filter adaptation using a state-of-the art CPF technique. Mapping this filter realization on a highly parallel architecture entailed a shift in the logical representation of the particles. In this process, the original two-dimensional organization is reordered as a one-dimensional ring topology. We proposed a proof-of-concept measurement on two models with an NVIDIA Fermi architecture GPU. This design achieved a 411- ?s kernel time per state and a 77-ms global running time for all states for 16,384 particles with a 256 neighbourhood size on a sequence of 24 states for a bearing-only tracking model. For a commonly used benchmark model at the same configuration, we achieved a 266- ?s kernel time per state and a 124-ms global running time for all 100 states. Kernel time includes random number generation on the GPU with curand. These results attest to the effective and fast use of the particle filter in high-dimensional, real-time applications.

Gelencsér-Horváth, Anna; Tornai, Gábor János; Horváth, András; Cserey, György

2013-12-01

103

Decision Theoretic Particle Filters Sebastian Thrun, John Langford, Vandi Verma  

E-print Network

Decision Theoretic Particle Filters Sebastian Thrun, John Langford, Vandi Verma School of Computer generally comes in two flavors: controls (e.g., robot motion commands) and measure­ ments (e.g., camera images). The measurement at time t will denoted z t , and u t denotes the control asserted in the time

Langford, John

104

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks  

Microsoft Academic Search

Particle filters (PFs) are powerful sampling- based inference\\/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probabil- ity distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as \\

Arnaud Doucet; Nando De Freitas; Kevin P. Murphy; Stuart J. Russell

2000-01-01

105

Localization of acoustic sources utilizing a decentralized particle filter  

E-print Network

localization scheme. Several sensors are embedded in an acoustic wave field. We assume that the field variables random vector wc k models the process noise of the acoustic wave, vector wd k the discrete positionLocalization of acoustic sources utilizing a decentralized particle filter Florian Xaver, Gerald

Gerstoft, Peter

106

Distributed State and Field Estimation Using a Particle Filter  

E-print Network

). At every time k a data vector yk RNR is measured by NR sensors according to the measurement equation, yk differential equation using a particle filter (PF). We focus on localizing an acoustic source in a given region of a wireless sensor network (WSN) without a fusion center. I. BACKGROUND Approaches to source tracking [1], [2

Gerstoft, Peter

107

Tracking the small object through clutter with adaptive particle filter  

Microsoft Academic Search

Cluttered background and occlusion cause large ambiguity in the tracking of video objects. When the object is small (like a soccer ball in broadcast game video signals), the ambiguity gets even more severe. In this paper, we propose an adaptive particle filter with effective proposal distribution to handle these situations. In the proposed tracking approach, motion estimation is embedded into

Yu Huang; Joan Llach

2008-01-01

108

Collaborative emitter tracking using Rao-Blackwellized random exchange diffusion particle filtering  

NASA Astrophysics Data System (ADS)

We introduce in this paper the fully distributed, random exchange diffusion particle filter (ReDif-PF) to track a moving emitter using multiple received signal strength (RSS) sensors. We consider scenarios with both known and unknown sensor model parameters. In the unknown parameter case, a Rao-Blackwellized (RB) version of the random exchange diffusion particle filter, referred to as the RB ReDif-PF, is introduced. In a simulated scenario with a partially connected network, the proposed ReDif-PF outperformed a PF tracker that assimilates local neighboring measurements only and also outperformed a linearized random exchange distributed extended Kalman filter (ReDif-EKF). Furthermore, the novel ReDif-PF matched the tracking error performance of alternative suboptimal distributed PFs based respectively on iterative Markov chain move steps and selective average gossiping with an inter-node communication cost that is roughly two orders of magnitude lower than the corresponding cost for the Markov chain and selective gossip filters. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker or its equivalent (exact) consensus-based implementations, ReDif-PF showed a degradation in steady-state error performance. However, compared to the optimal consensus-based trackers, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurement arrivals.

Bruno, Marcelo G. S.; Dias, Stiven S.

2014-12-01

109

Ensemble Kalman Filter vs Particle Filter in a Physically Based Coupled Model of Surface-Subsurface Flow (Invited)  

NASA Astrophysics Data System (ADS)

Data assimilation (DA) has recently received growing interest by the hydrological modeling community due to its capability to merge observations into model prediction. Among the many DA methods available, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF) are suitable alternatives for applications to detailed physically-based hydrological models. For each assimilation period, both methods use a Monte Carlo approach to approximate the state probability distribution (in terms of mean and covariance matrix) by a finite number of independent model trajectories, also called particles or realizations. The two approaches differ in the way the filtering distribution is evaluated. EnKF implements the classical Kalman filter, optimal only for linear dynamics and Gaussian error statistics. Particle filters, instead, use directly the recursive formula of the sequential Bayesian framework and approximate the posterior probability distributions by means of appropriate weights associated to each realization. We use the Sequential Importance Resampling (SIR) technique, which retains only the most probable particles, in practice the trajectories closest in a statistical sense to the observations, and duplicates them when needed. In contrast to EnKF, particle filters make no assumptions on the form of the prior distribution of the model state, and convergence to the true state is ensured for large enough ensemble size. In this study EnKF and PF have been implemented in a physically based catchment simulator that couples a three-dimensional finite element Richards equation solver with a finite difference diffusion wave approximation based on a digital elevation data for surface water dynamics. We report on the retrieval performance of the two schemes using a three-dimensional tilted v-catchment synthetic test case in which multi-source observations are assimilated (pressure head, soil moisture, and streamflow data). The comparison between the results of the two approaches allows to discuss some of the strengths and weaknesses, both physical and numerical, of EnKF and PF and to learn the implications related to the choice of the statistics used to build the ensemble of realizations.

Putti, M.; Camporese, M.; Pasetto, D.

2010-12-01

110

Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences  

Microsoft Academic Search

This paper investigates the philosophical and performance differences of particle swarm and evolutionary optimization. The method of processing employed in each technique are first reviewed followed by a summary of their philosophical differences. Comparison experiments involving four non-linear functions well studied in the evolutionary optimization literature are used to highlight some performance differences between the techniques.

Peter J. Angeline

1998-01-01

111

Optimal Design of --() Filters Dirk Tenne Tarunraj Singh,  

E-print Network

. An optimal selection of , and parameters is provided for various penalties on the noise filtering. 1 select the set of the smoothing parameters which minimize the noise transmission ca- pability to #12;lie within the unit circle to guarantee stability. Jury's Stability Test [5] yields

Singh, Tarunraj

112

An automated framework for multicriteria optimization of analog filter designs  

Microsoft Academic Search

This paper presents an extensible framework for designing analog filters that exhibit several desired behavioral properties after being realized in circuits. In the framework, we model the constrained nonlinear optimization problem as a sequential quadratic programming (SQP) problem. SQP requires real-valued constraints and objective functions that are differentiable with respect to the free parameters (pole-zero locations). We derive the differentiable

Niranjan Damera-Venkata; Brian L. Evans

1999-01-01

113

THE GAUSSIAN PARTICLE FILTER FOR DIAGNOSIS OF NON-LINEAR SYSTEMS  

E-print Network

THE GAUSSIAN PARTICLE FILTER FOR DIAGNOSIS OF NON-LINEAR SYSTEMS Frank Hutter Richard Dearden particle filter (GPF), an efficient variant on the particle filtering algorithm for non-linear hybrid systems. Each particle samples a discrete mode and approximates the continuous variables by a multivariate

Hutter, Frank

114

A Binary Particle Swarm Optimization for Structural Topology Optimization  

Microsoft Academic Search

Binary particle swarm optimization (BPSO) algorithm is applied to continuum structural topology design. An overview of the PSO and binary PSO algorithms are first described. A discretized topology design representation and the method for mapping each binary particle into toplogy representation are then detailed. Subsequently, a modified BPSO algorithm adopting binary bit-string type code and logic AND, OR and XOR

Guan-Chun Luh; Chun-Yi Lin

2010-01-01

115

Na-Faraday rotation filtering: The optimal point.  

PubMed

Narrow-band optical filtering is required in many spectroscopy applications to suppress unwanted background light. One example is quantum communication where the fidelity is often limited by the performance of the optical filters. This limitation can be circumvented by utilizing the GHz-wide features of a Doppler broadened atomic gas. The anomalous dispersion of atomic vapours enables spectral filtering. These, so-called, Faraday anomalous dispersion optical filters (FADOFs) can be by far better than any commercial filter in terms of bandwidth, transition edge and peak transmission. We present a theoretical and experimental study on the transmission properties of a sodium vapour based FADOF with the aim to find the best combination of optical rotation and intrinsic loss. The relevant parameters, such as magnetic field, temperature, the related optical depth, and polarization state are discussed. The non-trivial interplay of these quantities defines the net performance of the filter. We determine analytically the optimal working conditions, such as transmission and the signal to background ratio and validate the results experimentally. We find a single global optimum for one specific optical path length of the filter. This can now be applied to spectroscopy, guide star applications, or sensing. PMID:25298251

Kiefer, Wilhelm; Löw, Robert; Wrachtrup, Jörg; Gerhardt, Ilja

2014-01-01

116

Na-Faraday rotation filtering: The optimal point  

PubMed Central

Narrow-band optical filtering is required in many spectroscopy applications to suppress unwanted background light. One example is quantum communication where the fidelity is often limited by the performance of the optical filters. This limitation can be circumvented by utilizing the GHz-wide features of a Doppler broadened atomic gas. The anomalous dispersion of atomic vapours enables spectral filtering. These, so-called, Faraday anomalous dispersion optical filters (FADOFs) can be by far better than any commercial filter in terms of bandwidth, transition edge and peak transmission. We present a theoretical and experimental study on the transmission properties of a sodium vapour based FADOF with the aim to find the best combination of optical rotation and intrinsic loss. The relevant parameters, such as magnetic field, temperature, the related optical depth, and polarization state are discussed. The non-trivial interplay of these quantities defines the net performance of the filter. We determine analytically the optimal working conditions, such as transmission and the signal to background ratio and validate the results experimentally. We find a single global optimum for one specific optical path length of the filter. This can now be applied to spectroscopy, guide star applications, or sensing. PMID:25298251

Kiefer, Wilhelm; Low, Robert; Wrachtrup, Jorg; Gerhardt, Ilja

2014-01-01

117

Using selection to improve particle swarm optimization  

Microsoft Academic Search

This paper describes a evolutionary optimization algorithm that is a hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations. A comparison is performed between the hybrid swarm and the ordinary particle swarm that shows selection to provide an advantage for some (but not all) complex functions

Peter J. Angeline

1998-01-01

118

A 3-Component Inverse Depth Parameterization for Particle Filter SLAM  

NASA Astrophysics Data System (ADS)

The non-Gaussianity of the depth estimate uncertainty degrades the performance of monocular extended Kalman filter SLAM (EKF-SLAM) systems employing a 3-component Cartesian landmark parameterization, especially in low-parallax configurations. Even particle filter SLAM (PF-SLAM) approaches are affected, as they utilize EKF for estimating the map. The inverse depth parameterization (IDP) alleviates this problem through a redundant representation, but at the price of increased computational complexity. The authors show that such a redundancy does not exist in PF-SLAM, hence the performance advantage of the IDP comes almost without an increase in the computational cost.

Imre, Evren; Berger, Marie-Odile

119

Degeneracy, frequency response and filtering in IMRT optimization.  

PubMed

This paper attempts to provide an answer to some questions that remain either poorly understood, or not well documented in the literature, on basic issues related to intensity modulated radiation therapy (IMRT). The questions examined are: the relationship between degeneracy and frequency response of optimizations, effects of initial beamlet fluence assignment and stopping point, what does filtering of an optimized beamlet map actually do and how could image analysis help to obtain better optimizations? Two target functions are studied, a quadratic cost function and the log likelihood function of the dynamically penalized likelihood (DPL) algorithm. The algorithms used are the conjugate gradient, the stochastic adaptive simulated annealing and the DPL. One simple phantom is used to show the development of the analysis tools used and two clinical cases of medium and large dose matrix size (a meningioma and a prostate) are studied in detail. The conclusions reached are that the high number of iterations that is needed to avoid degeneracy is not warranted in clinical practice, as the quality of the optimizations, as judged by the DVHs and dose distributions obtained, does not improve significantly after a certain point. It is also shown that the optimum initial beamlet fluence assignment for analytical iterative algorithms is a uniform distribution, but such an assignment does not help a stochastic method of optimization. Stopping points for the studied algorithms are discussed and the deterioration of DVH characteristics with filtering is shown to be partially recoverable by the use of space-variant filtering techniques. PMID:15285252

Llacer, Jorge; Agazaryan, Nzhde; Solberg, Timothy D; Promberger, Claus

2004-07-01

120

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement  

NASA Astrophysics Data System (ADS)

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

121

Optimal State Discrimination Using Particle Statistics  

E-print Network

We present an application of particle statistics to the problem of optimal ambiguous discrimination of quantum states. The states to be discriminated are encoded in the internal degrees of freedom of identical particles, and we use the bunching and antibunching of the external degrees of freedom to discriminate between various internal states. We show that we can achieve the optimal single-shot discrimination probability using only the effects of particle statistics. We discuss interesting applications of our method to detecting entanglement and purifying mixed states. Our scheme can easily be implemented with the current technology.

S. Bose; A. Ekert; Y. Omar; N. Paunkovic; V. Vedral

2003-09-10

122

Acoustic Radiation Optimization Using the Particle Swarm Optimization Algorithm  

NASA Astrophysics Data System (ADS)

The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary population-based heuristic algorithm called the particle swarm optimization algorithm (PSOA). The particle swarm optimization algorithm is a parallel evolutionary computation technique proposed by Kennedy and Eberhart in 1995. This algorithm is based on the social behavior models for bird flocking, fish schooling and other models investigated by zoologists. Optimal structural design problems to reduce noise are highly nonlinear, so that most conventional methods are difficult to apply. The present paper investigates the applicability of PSOA to such problems. Optimal bending design of a vibrating plate using PSOA is performed in order to minimize noise radiation. PSOA can be effectively applied to such nonlinear acoustic radiation optimization.

Jeon, Jin-Young; Okuma, Masaaki

123

A novel method for retinal vessel tracking using particle filters.  

PubMed

Extraction of a proper map from the vessel paths in the retinal images is a prerequisite for many applications such as identification. In this paper, we present a new approach based on particle filtering to determine and locally track the vessel paths in retina. Particle filter needs to use an acceptable probability density function (PDF) describing the blood vessels which must be provided by the retinal image. For this purpose, the product of the green and blue channels of the RGB retinal images is considered and after a median filtering stage, it is used as a PDF for tracking procedure. Then a stage of optic disc localization is performed to localize the starting points around the optic disc. With a proper set of starting points, the iterative tracking procedure initiates. First, a uniform propagation of the particles on an annular ring around each point (including starting points or ones determined as central points in the previous iteration) is performed. The particle weights are evaluated and accordingly, each particle is decided to be inside or outside the vessel. The subsequent stage is to analyze the hypothetical vectors between a central point and each of the inside vessel particles to find ones located inside vessel. Afterwards, the particles are clustered using quality threshold clustering method. Finally, each cluster introduces a central point for pursuing the tracking procedure in the next iteration. The tracking proceeds towards a bifurcation or the end of the vessels. We introduced two criteria: automatic/manually tracked ratio (AMTR) and false/manually tracked ratio (FMTR) for evaluating the tracking results. Apart from the labeling accuracy, the average values of AMTR and FMTR were 0.7746 and 0.2091, respectively. The proposed method successfully deals with the bifurcations with robustness against noise and tracks the thin vessels. PMID:23434235

Nayebifar, B; Abrishami Moghaddam, H

2013-06-01

124

IMM/MHT tracking with an unscented particle filter with application to ground targets  

NASA Astrophysics Data System (ADS)

Particle filter tracking, a type of sequential Monte Carlo method, has long been considered to be a very promising but time-consuming tracking technique. Methods have been developed to include a particle filter as part of a Variable Structure, Interactive Multiple Model (VS-IMM) structure and to integrate it into the Multiple Hypothesis Tracker (MHT) scoring structure. By integrating a particle filter as just one of many filters in Raytheon's MHT, the particle filter is applied sparingly on difficult off-road targets. This dramatically reduces the computation time as well as improves tracking performance in circumstances in which the other filters do not excel. Moreover, terrain information may be taken into account in the particle propagation process. In particular, an Unscented Particle Filter (UPF) was implemented in order to address the potential dominance of a small set of degenerate particles and/or poor prior distribution sampling from hampering the ability of the particle filter to accurately handle a maneuver. The Unscented Particle Filter treats every particle as its own Kalman filter. After the distribution of particles is adjusted in order to take into account the terrain, each particle is divided into sigma point states. These sigma points are propagated forward in time and then recombined to form a new composite particle state and covariance. These reformed particles are used in scoring and can be updated with a new observation. Since the Unscented Particle Filter includes the covariances in these calculations, this particle filter approach is more accurate and potentially requires fewer particles than an ordinary particle filter. By adding an Unscented Particle Filter to the other filters in an MHT tracker, the advantages of the UPF can be utilized in an efficient manner in order to enhance tracking performance.

Lancaster, J.; Blackman, S.; Yu, L.

2007-09-01

125

A Kalman-Particle Kernel Filter and its Application to Terrain Navigation  

E-print Network

. Keywords: Kalman filter, kernel density estimator, regularized particle filter, Inertial navigation SystemA Kalman-Particle Kernel Filter and its Application to Terrain Navigation Dinh-Tuan Pham causes undesirable Monte Carlo fluctuations. This new filter is applied to terrain navigation, which

Del Moral , Pierre

126

Cubature Gaussian Particle Filter for Initial Alignment of Strapdown Inertial Navigation System  

Microsoft Academic Search

The error model of the initial alignment of the marine strap down inertial navigation system on the swaying base is nonlinear, while the azimuth angle error is large. For this nonlinear model, a new nonlinear filter called as the cubature Gaussian Particle filter is proposed, which is based on the cubature Kalman filter and the Gaussian Particle filter. The cubature

Weisheng Wu; Chunlei Song; Junhou Wang; Zhenzhen Long

2010-01-01

127

Central difference Gaussian Particle filter for initial alignment of strapdown inertial navigation system  

Microsoft Academic Search

The error model of the initial alignment of the marine strapdown inertial navigation system is nonlinear, while the azimuth angle error is large on the swaying base. For this nonlinear model, a new nonlinear filter called as the central difference Gaussian Particle filter is proposed, which is based on the central difference Kalman filter and the Gaussian Particle filter. The

Shoucai Sun; Chunlei Song; Junhou Wang; Xingtai Yao; Ling Xie

2010-01-01

128

Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study  

PubMed Central

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30?Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30?Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings. PMID:25298967

Peyrodie, Laurent; Szurhaj, William; Bolo, Nicolas; Pinti, Antonio; Gallois, Philippe

2014-01-01

129

Dual adaptive filtering by optimal projection applied to filter muscle artifacts on EEG and comparative study.  

PubMed

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings. PMID:25298967

Boudet, Samuel; Peyrodie, Laurent; Szurhaj, William; Bolo, Nicolas; Pinti, Antonio; Gallois, Philippe

2014-01-01

130

Measurement of particle sulfate from micro-aethalometer filters  

NASA Astrophysics Data System (ADS)

The micro-aethalometer (AE51) was designed for high time resolution black carbon (BC) measurements and the process collects particles on a filter inside the instrument. Here we examine the potential for saving these filters for subsequent sulfate (SO42-) measurement. For this purpose, a series lab and field blanks were analyzed to characterize blank levels and variability and then collocated 24-h aerosol sampling was conducted in Beijing with the AE51 and a dual-channel filterpack sampler that collects fine particles (PM2.5). AE51 filters and the filters from the filterpacks sampled for 24 h were extracted with ultrapure water and then analyzed by Ion Chromatography (IC) to determine integrated SO42- concentration. Blank corrections were essential and the estimated detection limit for 24 h AE51 sampling of SO42- was estimated to be 1.4 ?g/m3. The SO42- measured from the AE51 based upon blank corrections using batch-average field blank SO42- values was found to be in reasonable agreement with the filterpack results (R2 > 0.87, slope = 1.02) indicating that it is possible to determine both BC and SO42- concentrations using the AE51 in Beijing. This result suggests that future comparison of the relative health impacts of BC and SO42- could be possible when the AE51 is used for personal exposure measurement.

Wang, Qingqing; Yang, Fumo; Wei, Lianfang; Zheng, Guangjie; Fan, Zhongjie; Rajagopalan, Sanjay; Brook, Robert D.; Duan, Fengkui; He, Kebin; Sun, Yele; Brook, Jeffrey R.

2014-10-01

131

Linear multistep methods, particle filtering and sequential Monte Carlo  

NASA Astrophysics Data System (ADS)

Numerical integration is the main bottleneck in particle filter methodologies for dynamic inverse problems to estimate model parameters, initial values, and non-observable components of an ordinary differential equation (ODE) system from partial, noisy observations, because proposals may result in stiff systems which first slow down or paralyze the time integration process, then end up being discarded. The immediate advantage of formulating the problem in a sequential manner is that the integration is carried out on shorter intervals, thus reducing the risk of long integration processes followed by rejections. We propose to solve the ODE systems within a particle filter framework with higher order numerical integrators which can handle stiffness and to base the choice of the variance of the innovation on estimates of the discretization errors. The application of linear multistep methods to particle filters gives a handle on the stability and accuracy of the propagation, and linking the innovation variance to the accuracy estimate helps keep the variance of the estimate as low as possible. The effectiveness of the methodology is demonstrated with a simple ODE system similar to those arising in biochemical applications.

Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

2013-08-01

132

Weak sense Lp error bounds for leader-node distributed particle filters  

Microsoft Academic Search

The leader node particle filter is a partially distributed approach to tracking in a sensor network, in which the node performing the particle filter computations (the leader node) changes over time. The primary advantage is that the position of the leader node can follow the target, improving the efficiency of data collection. When the leader node changes, the particle filter

Boris N. Oreshkin; Mark Coates

2008-01-01

133

An Emotional Particle Swarm Optimization Algorithm  

Microsoft Academic Search

\\u000a This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology\\u000a factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model,\\u000a each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards\\u000a the sense

Yang Ge; Zhang Rubo

2005-01-01

134

NURBS Curve Approximation Using Particle Swarm Optimization  

Microsoft Academic Search

This paper presents curve approximation problem using Particle Swarm Optimization (PSO). The proposed algorithm will be used to develop a skinning surface with PSO to keep the number of surface control points minimum. The experiments are conducted on various parameterization methods for approximating the curves. By implementing PSO on NURBS curve approximation, the weights of the curve can be adjusted

D. I. S. Adi; S. M. b. Shamsuddin; Siti Zaiton Mohd Hashim

2010-01-01

135

Loading characteristics of filter pretreated with anionic surfactant for monodisperse solid particles  

Microsoft Academic Search

Anionic surfactant (sodium oleate, SO) was used to pretreat polypropylene fibrous filters to make them negatively charged. This work examines the effects on particle loading of an anionic surfactant-pretreated filter. Also, the effects of various factors, such as the particle size, the face velocity, concentration of the surfactant, and particle distribution (mono and poly) on the particle loading characteristics were

Shinhao Yang; Grace W. M. Lee; Chin-Hsiang Luo; Chih-Cheng Wu; Kuo-Pin Yu

2005-01-01

136

A multi-dimensional procedure for BNCT filter optimization  

SciTech Connect

An initial version of an optimization code utilizing two-dimensional radiation transport methods has been completed. This code is capable of predicting material compositions of a beam tube-filter geometry which can be used in a boron neutron capture therapy treatment facility to improve the ratio of the average radiation dose in a brain tumor to that in the healthy tissue surrounding the tumor. The optimization algorithm employed by the code is very straightforward. After an estimate of the gradient of the dose ratio with respect to the nuclide densities in the beam tube-filter geometry is obtained, changes in the nuclide densities are made based on: (1) the magnitude and sign of the components of the dose ratio gradient, (2) the magnitude of the nuclide densities, (3) the upper and lower bound of each nuclide density, and (4) the linear constraint that the sum of the nuclide density fractions in each material zone be less than or equal to 1.0. A local optimal solution is assumed to be found when one of the following conditions is satisfied in every material zone: (1) the maximum positive component of the gradient corresponds to a nuclide at its maximum density and the sum of the density fractions equals 1.0 or, and (2) the positive and negative components of the gradient correspond to nuclides densities at their upper and lower bounds, respectively, and the remaining components of the gradient are sufficiently small. The optimization procedure has been applied to a beam tube-filter geometry coupled to a simple tumor-patient head model and an improvement of 50% in the dose ratio was obtained.

Lille, R.A.

1998-02-01

137

Ridge filter design for a particle therapy line  

NASA Astrophysics Data System (ADS)

The beam irradiation system for particle therapy can use a passive or an active beam irradiation method. In the case of an active beam irradiation, using a ridge filter would be appropriate to generate a spread-out Bragg peak (SOBP) through a large scanning area. For this study, a ridge filter was designed as an energy modulation device for a prototype active scanning system at MC-50 in Korea Institute of Radiological And Medical Science (KIRAMS). The ridge filter was designed to create a 10 mm of SOBP for a 45-MeV proton beam. To reduce the distal penumbra and the initial dose, [DM] determined the weighting factor for Bragg Peak by applying an in-house iteration code and the Minuit Fit package of Root. A single ridge bar shape and its corresponding thickness were obtained through 21 weighting factors. Also, a ridge filter was fabricated to cover a large scanning area (300 × 300 mm2) by Polymethyl Methacrylate (PMMA). The fabricated ridge filter was tested at the prototype active beamline of MC-50. The SOBP and the incident beam distribution were obtained by using HD-810 GaF chromatic film placed at a right triangle to the PMMA block. The depth dose profile for the SOBP can be obtained precisely by using the flat field correction and measuring the 2-dimensional distribution of the incoming beam. After the flat field correction is used, the experimental results show that the SOBP region matches with design requirement well, with 0.62% uniformity.

Kim, Chang Hyeuk; Han, Garam; Lee, Hwa-Ryun; Kim, Hyunyong; Jang, Hong Suk; Kim, Jeong Hwan; Park, Dong Wook; Jang, Sea Duk; Hwang, Won Taek; Kim, Geun-Beom; Yang, Tae-Keun

2014-05-01

138

Solving constrained optimization problems with hybrid particle swarm optimization  

NASA Astrophysics Data System (ADS)

Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.

Zahara, Erwie; Hu, Chia-Hsin

2008-11-01

139

Identification of Backlash Type Hysteretic Systems Based on Particle Filter  

NASA Astrophysics Data System (ADS)

This paper considers the system identification problem for hysteresis systems. This problem plays an important role in achieving better control performance, because many actuators have hysteresis property. This paper proposes a method to identify linear dynamical systems having input hysteresis property of backlash type. The method is based on particle filter, which is known for its applicability to a wide class of nonlinear systems. Numerical examples are given to demonstrate the effectiveness of the proposed method in detail. Furthermore, experimental validation is performed for a DC servo motor system.

Masuda, Tetsuya; Sugie, Toshiharu

140

Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem  

NASA Astrophysics Data System (ADS)

In this paper we consider a nonlinear bearing-only target tracking problem using three different methods and compare their performances. The study is motivated by a ground surveillance problem where a target is tracked from an airborne sensor at an approximately known altitude using depression angle observations. Two nonlinear suboptimal estimators, namely, the extended Kalman Filter (EKF) and the pseudomeasurement tracking filter are applied in a 2-D bearing-only tracking scenario. The EKF is based on the linearization of the nonlinearities in the dynamic and/or the measurement equations. The pseudomeasurement tracking filter manipulates the original nonlinear measurement algebraically to obtain the linear-like structures measurement. Finally, the particle filter, which is a Monte Carlo integration based optimal nonlinear filter and has been presented in the literature as a better alternative to linearization via EKF, is used on the same problem. The performances of these three different techniques in terms of accuracy and computational load are presented in this paper. The results demonstrate the limitations of these algorithms on this deceptively simple tracking problem.

Lin, Xiangdong; Kirubarajan, Thiagalingam; Bar-Shalom, Yaakov; Maskell, Simon

2002-08-01

141

Performance of blast furnace dust clay sodium silicate ceramic particles (BCSCP) for brewery wastewater treatment in a biological aerated filter  

Microsoft Academic Search

Blast furnace dust clay sodium silicate ceramic particles (BCSCP) and commercial ceramic particles (CCP) were applied to treat brewery wastewater in two lab-scale up-flow biological aerated filters (BAF) to compare their abilities to act as biofilm supports. The optimal preparation conditions of BCSCP were obtained through orthogonal tests. The influence of hydraulic retention time (HRT) and air–liquid ratio (A\\/L) on

Shanping Li; Jiangjie Cui; Qilei Zhang; Jing Fu; Junfeng Lian; Chong Li

2010-01-01

142

Bayesian approach of nearfield acoustic reconstruction with particle filters.  

PubMed

This paper demonstrates that inverse source reconstruction can be performed using a methodology of particle filters that relies primarily on the Bayesian approach of parameter estimation. In particular, the proposed approach is applied in the context of nearfield acoustic holography based on the equivalent source method (ESM). A state-space model is formulated in light of the ESM. The parameters to estimate are amplitudes and locations of the equivalent sources. The parameters constitute the state vector which follows a first-order Markov process with the transition matrix being the identity for every frequency-domain data frame. Filtered estimates of the state vector obtained are assigned weights adaptively. The implementation of recursive Bayesian filters involves a sequential Monte Carlo sampling procedure that treats the estimates as point masses with a discrete probability mass function (PMF) which evolves with iteration. The weight update equation governs the evolution of this PMF and depends primarily on the likelihood function and the prior distribution. It is evident from the simulation results that the inclusion of the appropriate prior distribution is crucial in the parameter estimation. PMID:23742356

Bai, Mingsian R; Agarwal, Amal; Chen, Ching-Cheng; Wang, Yen-Chih

2013-06-01

143

Optimal Nonnegative Color Scanning Filters Gaurav Sharma \\Lambda H. Joel Trussell y Michael J. Vrhel z  

E-print Network

. Vrhel z Abstract In this correspondence, the problem of designing color scanning filters for multiOptimal Nonnegative Color Scanning Filters Gaurav Sharma \\Lambda H. Joel Trussell y Michael J­form solutions for optimal scanning filters at various signal­to­noise ratios (SNRs) were determined, \\Lambda

Sharma, Gaurav

144

Multi-Object Optimal Design of Analog Filter Based on Improved Genetic Algorithm  

Microsoft Academic Search

A multi-object optimization method for analog filter design based on genetic algorithm (GA) is proposed. The complete objective function is a weighted sum of deviations between the properties of designed filter and these of desired filter, including the magnitude, phase responses and step response, etc. The optimization is achieved by GA to minimize the complete objective. For overcoming the disadvantages

Xie Qinlan; Chen Hong

2009-01-01

145

A nearly optimal variable fractional delay filter with extracted Chebyshev window  

Microsoft Academic Search

A novel technique for designing a nearly optimal (in the Chebyshev sense) FIR variable fractional delay filter is presented that offers rapid recalculation of the filter coefficients according to the varying fractional delay parameter. The idea of the technique lies in using a fixed window extracted from a strictly optimal linear-phase design and in updating the filter coefficients in a

Ewa Hermanowicz

1998-01-01

146

Nonlinear EEG Decoding Based on a Particle Filter Model  

PubMed Central

While the world is stepping into the aging society, rehabilitation robots play a more and more important role in terms of both rehabilitation treatment and nursing of the patients with neurological diseases. Benefiting from the abundant contents of movement information, electroencephalography (EEG) has become a promising information source for rehabilitation robots control. Although the multiple linear regression model was used as the decoding model of EEG signals in some researches, it has been considered that it cannot reflect the nonlinear components of EEG signals. In order to overcome this shortcoming, we propose a nonlinear decoding model, the particle filter model. Two- and three-dimensional decoding experiments were performed to test the validity of this model. In decoding accuracy, the results are comparable to those of the multiple linear regression model and previous EEG studies. In addition, the particle filter model uses less training data and more frequency information than the multiple linear regression model, which shows the potential of nonlinear decoding models. Overall, the findings hold promise for the furtherance of EEG-based rehabilitation robots. PMID:24949420

Hong, Jun

2014-01-01

147

Particle Swarm Optimization in Exploratory Data Analysis  

Microsoft Academic Search

\\u000a We discuss extensions of particle swarm based optimization (PSO) algorithms in the context of exploratory data analysis. In\\u000a particular, we apply these extensions to principal component analysis, exploratory projection pursuit and topology preserving\\u000a mappings. Our extensions include combining PSO algorithms with stochastic sampling and a form of reinforcement learning known\\u000a as Q-learning. We illustrate on a variety of artificial data

Ying Wu; Colin Fyfe

2010-01-01

148

Filter screen apparatus for the air outlet of a particle production apparatus  

SciTech Connect

This patent describes a filter screen apparatus for use in an air outlet of a particle production apparatus. It comprises: closely spaced resiliently deformable filter elements comprising coil springs mountable across the air outlet effective to collectively releasably receive and hold fines carried in the air passing through the air outlet of the particle production apparatus; and vibrating means for resiliently deforming the filter elements effective to dislodge and release fines held on mounted filter elements.

Bakker, J.

1990-08-07

149

An optimal nonorthogonal separation of the anisotropic Gaussian convolution filter.  

PubMed

We give an analytical and geometrical treatment of what it means to separate a Gaussian kernel along arbitrary axes in R(n), and we present a separation scheme that allows us to efficiently implement anisotropic Gaussian convolution filters for data of arbitrary dimensionality. Based on our previous analysis we show that this scheme is optimal with regard to the number of memory accesses and interpolation operations needed. The proposed method relies on nonorthogonal convolution axes and works completely in image space. Thus, it avoids the need for a fast Fourier transform (FFT)-subroutine. Depending on the accuracy and speed requirements, different interpolation schemes and methods to implement the one-dimensional Gaussian (finite impulse response and infinite impulse response) can be integrated. Special emphasis is put on analyzing the performance and accuracy of the new method. In particular, we show that without any special optimization of the source code, it can perform anisotropic Gaussian filtering faster than methods relying on the FFT. PMID:17076408

Lampert, Christoph H; Wirjadi, Oliver

2006-11-01

150

Optimization of counting times for short-lived gamma-ray emitters in air filter samples.  

PubMed

A methodology for the optimization of the counting times in a series of measurements of gamma-ray emitters in air filters is presented. In the optimal measurement regime in measurements of all the filters in a batch, the same minimum detectable activity is attained. It is shown how the number of filters, the properties of the gamma-ray emitter and the equipment influence the measurement time of the batch of filters and the minimum detectable activity attained. PMID:16554169

Korun, M

2006-01-01

151

Optimizing the use of inferior vena cava filters in oncology patients: are all filters created equally?  

PubMed

Many studies have supported the efficacy of inferior vena cava filters (IVCF) in the setting of venous thromboembolic disease, particularly in oncologic patients who are at increased risk. The advent of retrievable IVCF designs has prompted dramatically expanded use for patients with widely accepted indications but also disproportionately so in patients with so-called extended indications. At the same time, an alarming increase in filter-related complications has been reported both in the literature and through regulatory agencies, leading to government agency-issued warnings. The synergistic effect of these two interconnected phenomena is explained through a careful review of the evolution of IVCF device design. Critical differences exist when comparing retrievable IVCF and permanent IVCF. IVCF utilization can be optimized by prospectively identifying which patients are best served by a specific IVCF device. Careful follow-up strategies are also needed to ensure that all IVCFs are removed as soon as they are no longer needed. Finally, adjunctive techniques for removing "difficult" filters help maximize the number of IVCF removed and minimize IVCF left implanted needlessly. PMID:24610401

Ryu, Robert K; Lewandowski, Robert J

2014-04-01

152

Wet particle source identification and reduction using a new filter cleaning process  

NASA Astrophysics Data System (ADS)

Wet particle reduction during filter installation and start-up aligns closely with initiatives to reduce both chemical consumption and preventative maintenance time. The present study focuses on the effects of filter materials cleanliness on wet particle defectivity through evaluation of filters that have been treated with a new enhanced cleaning process focused on organic compounds reduction. Little difference in filter performance is observed between the two filter types at a size detection threshold of 60 nm, while clear differences are observed at that of 26 nm. It can be suggested that organic compounds can be identified as a potential source of wet particles. Pall recommends filters that have been treated with the special cleaning process for applications with a critical defect size of less than 60 nm. Standard filter products are capable to satisfy wet particle defect performance criteria in less critical lithography applications.

Umeda, Toru; Morita, Akihiko; Shimizu, Hideki; Tsuzuki, Shuichi

2014-03-01

153

Numerical simulation of DPF filter for selected regimes with deposited soot particles  

NASA Astrophysics Data System (ADS)

For the purpose of accumulation of particulate matter from Diesel engine exhaust gas, particle filters are used (referred to as DPF or FAP filters in the automotive industry). However, the cost of these filters is quite high. As the emission limits become stricter, the requirements for PM collection are rising accordingly. Particulate matters are very dangerous for human health and these are not invisible for human eye. They can often cause various diseases of the respiratory tract, even what can cause lung cancer. Performed numerical simulations were used to analyze particle filter behavior under various operating modes. The simulations were especially focused on selected critical states of particle filter, when engine is switched to emergency regime. The aim was to prevent and avoid critical situations due the filter behavior understanding. The numerical simulations were based on experimental analysis of used diesel particle filters.

Lávi?ka, David; Kova?ík, Petr

2012-04-01

154

Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling  

Microsoft Academic Search

Abstract: Recently Rao-Blackwellized particle filters havebeen introduced as effective means to solve the simultaneouslocalization and mapping (SLAM) problem. This approach usesa particle filter in which each particle carries an individualmap of the environment. Accordingly, a key question is howto reduce the number of particles. In this paper we presentadaptive techniques to reduce the number of particles in a RaoBlackwellizedparticle filter

Giorgio Grisetti; Cyrill Stachniss; Wolfram Burgard

2005-01-01

155

Equal correlation peak optimization of the filter-feature-based synthetic discriminant reference image  

NASA Astrophysics Data System (ADS)

A modified equal correlation peak (ECP) optimization is proposed and introduced into the filter-feature-based joint transform correlator (FFB JTC) used for distortion-invariant pattern recognition. Instead of one fixed and arbitrarily chosen point in the classical ECP optimization, the maximum point in the correlation output plane is selected in our method. The modified ECP optimization is quite efficient when involving filter modulation. When compared with other ECP optimization methods related to the filter-modulation, our optimization method is linear and can easily obtain an exact solution. Simulation results show that after ECP optimization the stability of the correlation output of the FFB JTC may be greatly improved.

Zhong, Sheng; Liu, Shutian; Zhang, Xueru; Li, Chunfei

1998-02-01

156

Sparsity Optimization in Design of Multidimensional Filter Networks  

E-print Network

filters whose low sparsity ensures fast image processing. The filter network ..... solves, for alternating index j, the linear least-squares problem that consists in minimizing ..... the speed-up in the signal processing time provided by filter networks.

2014-02-02

157

Investigation of Particle Swarm Optimization for Job Shop Scheduling Problem  

Microsoft Academic Search

Job shop scheduling problem has stronger processing constraints, and it is a kind of well-known combination optimization problem. Particle swarm optimization algorithm is employed to solve the job shop scheduling problem, and the objective is minimizing the maximum completion time of all the jobs. The particle representation based on operation-particle position sequence is proposed. In the particle representation, the mapping

Zhixiong Liu

2007-01-01

158

Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing Loops  

Microsoft Academic Search

Abstract: Acquiring models of the environment belongs tothe fundamental tasks of mobile robots. Approaches addressingthe problem of simultaneous localization and mapping (SLAM)typically process the perceived sensor data and do not influencethe motion of the mobile robot. In this paper, we present anapproach to actively closing loops during exploration. It applies aRao-Blackwellized particle filter to maintain multiple hypothesesabout potential trajectories of

Cyrill Stachniss; Giorgio Grisetti; Wolfram Burgard

2005-01-01

159

Polymer Optimization of Pigmented Photoresists for Color Filter Production  

NASA Astrophysics Data System (ADS)

The lithographic performance of pigmented photoresists for color filter production is affected by the structure of the employed polymer. Four polymers with acrylate backbones and pendant reactive acrylate/methacrylate groups were prepared, and the effects of their molecular weights and acid values on the pixel pattern quality, development time, sensitivity and development mode were elucidated. ECHIPTM, a statistical experimental design program was used for optimization studies revealing that the red resist performs best, when polymers with relatively low acid values (<40 mg KOH/g polymer) and high molecular weights >50,000 are used. The green and the blue resists yielded optimal patterns at molecular weights in the range of 20,000 30,000 with acid values of about 50 60 mg KOH/g polymer. The sensitivity of resists containing polymers with pendant acryloyl groups is in general higher than that of the corresponding methacryloyl derivatives. Polymers having butyl acrylate-methacrylic acid backbone units showed the highest sensitivity among the polymers investigated. When developed with an optimized tetramethyl ammonium hydroxide (TMAH) based developer, resists using polymers with methyl methacrylate units showed peeling type development, while butyl acrylate copolymers effected homogeneous dissolution yielding higher resolution.

Kudo, Takanori; Nanjo, Yuki; Nozaki, Yuko; Yamaguchi, Hidemasa; Kang, Wen-Bing; Pawlowski, Georg

1998-03-01

160

Using triaxial magnetic fields to create optimal particle composites.  

SciTech Connect

The properties of a particle composite can be controlled by organizing the particles into assemblies. The properties of the composite will depend on the structure of the particle assemblies, and for any give property there is some optimal structure. Through simulation and experiment we show that the application of heterodyned triaxial magnetic or electric fields generates structures that optimize the magnetic and dielectric properties of particle composites. We suggest that optimizing these properties optimizes other properties, such as transport properties, and we give as one example of this optimization the magnetostriction of magnetic particle composites formed in a silicone elastomer.

Martin, James Ellis

2004-05-01

161

Optimization of astigmatic particle tracking velocimeters  

NASA Astrophysics Data System (ADS)

Astigmatic particle tracking velocimetry (APTV) has been developed in the last years to measure the three-dimensional displacement of tracer particles using a single-camera view. The measurement principle relies on an astigmatic optical system that provides aberrated particle images with a characteristic elliptical shape univocally related to the corresponding particle depth position. Because of the precision of this method, this concept is well established for measuring and controlling the distance between a CD/DVD and the reading head. The optical arrangement of an APTV system essentially consists of a primary stigmatic optics (e.g., a microscope, or a camera objective) and an astigmatic optics, typically a cylindrical lens placed in front of the camera sensor. This paper focuses on the uncertainty of APTV in the depth direction. First, an approximated analytical model is derived and experimentally validated. From the model, a set of three non-dimensional parameters that are the most significant in the optimization of the APTV performance are identified. Finally, the effect of different parameter settings and calibration approaches are studied systematically using numerical Monte Carlo simulations. The results allow for the derivation of general criteria to minimize the overall error in APTV measurements and provide the basis for reliable uncertainty estimation for a wide range of applications.

Rossi, Massimiliano; Kähler, Christian J.

2014-09-01

162

A Hybrid Particle-Ensemble Kalman Filter for Lagrangian Data Assimilation  

E-print Network

linear flow variables. Previous work on hybrid schemes includes the ensemble Kalman-particle filter (En experiments on the linear shallow water equations. In these experiments, the hybrid filter consistently nonlinear Lagrangian coordinate variables and an ensemble Kalman filter in the high-dimensional, relatively

Sandstede, Björn

163

PEOPLE TRACKING WITH A MOBILE ROBOT: A COMPARISON OF KALMAN AND PARTICLE FILTERS  

Microsoft Academic Search

People tracking is an essential part for modern service robots. In this paper we compare three different Bayesian estimators to perform such task: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Im- portance Resampling (SIR) Particle Filter. We give a brief explanation of each technique and describe the system im- plemented to perform people tracking with a mobile

Nicola Bellotto; Huosheng Hu

164

Human Behavior-Based Particle Swarm Optimization  

PubMed Central

Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c1 and c2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost. PMID:24883357

Xu, Gang; Ding, Gui-yan; Sun, Yu-bo

2014-01-01

165

Filtering by optimal projection and application to automatic artifact removal from EEG  

Microsoft Academic Search

A new approach to filter multi-channel signals is presented, called filtering by optimal projection (FOP) in this paper. This approach is based on common spatial subspace decomposition (CSSD) theory. Moreover, an evolution of this method for non-stationary signals is also introduced which is called adaptative FOP (AFOP). As ICA, a filtering matrix is set up in the best way to

Samuel Boudet; Laurent Peyrodie; Philippe Gallois; Christian Vasseur

2007-01-01

166

Actin Filament Tracking Based on Particle Filters and Stretching Open Active Contour  

E-print Network

Actin Filament Tracking Based on Particle Filters and Stretching Open Active Contour Models a novel algorithm for actin filament tracking and elongation measurement. Particle Filters (PF sequences with very low SNRs demonstrates the accuracy and robustness of this approach. 1 Introduction Actin

Huang, Xiaolei

167

Adapting the Sample Size in Particle Filters Through KLD-Sampling  

Microsoft Academic Search

Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation pro- cess. The key idea of the KLD-sampling method is to bound the approximation error intro-

Dieter Fox

2003-01-01

168

Tracking Football Player Movement From a Single Moving Camera Using Particle Filters  

E-print Network

Tracking Football Player Movement From a Single Moving Camera Using Particle Filters Anthony Soccer, Tracking, Particle Filter Abstract This paper deals with the problem of tracking football players in a football match using data from a single mov- ing camera. Tracking footballers from a single video source

Demiris, Yiannis

169

SLAM: Stereo Vision SLAM Using the Rao-Blackwellised Particle Filter and a Novel  

E-print Network

SLAM: Stereo Vision SLAM Using the Rao-Blackwellised Particle Filter and a Novel Mixture Proposal the problem of Simultaneous Localiza- tion and Mapping (SLAM) using the Rao-Blackwellised Particle Filter, the problem of Simultaneous Localization and Mapping (SLAM) is that of estimating both a robot's location

Little, Jim

170

A new multi-target state estimation algorithm for PHD particle filter  

Microsoft Academic Search

Probability hypothesis density (PHD) filter is a new practical method to solve the unknown time-varying multi-target tracking problem. Particle filter implementation of the PHD filter has demonstrated a feasible suboptimal method for tracking multi-target in real-time. To obtain the target states, the peak-extraction from the posterior PHD particles needs to be implemented. A new state estimation method is proposed in

Lingling Zhao; Peijun Ma; Xiaohong Su; Hongtao Zhang

2010-01-01

171

Particles shed from syringe filters and their effects on agitation-induced protein aggregation.  

PubMed

We tested the hypothesis that foreign particles shed from filters can accelerate the rate of protein aggregation and particle formation during agitation stress. Various types and brands of syringe filters were tested. Particle counts and size distribution (?1 µm) in buffer alone or in solutions of keratinocyte growth factor 2 (KGF-2) were determined with a micro-flow imaging. Submicron particle populations were characterized by dynamic light scattering. Loss of soluble protein during filtration or postfiltration incubation was determined by ultraviolet spectroscopy and bicinchoninic acid protein assay. There was a wide range (from essentially none to >100,000/mL) in the counts for at least 1 µm particles shed into buffer or KGF-2 solution from the different syringe filters (with or without borosilicate glass microfibers). Filtration of KGF-2 with units containing glass microfibers above the membrane resulted in 20%-80% loss of protein due to adsorption to filter components. Filtration with systems containing a membrane alone resulted in 0%-20% loss of KGF-2. Effects of 24-h postfiltration incubation were tested on KGF-2 solution filtered with polyether sulfone membrane filters. Loss of soluble protein and formation of particles during agitation were much greater than that in control, unfiltered KGF-2 solutions. Similar acceleration of protein aggregation and particle formation was observed when unfiltered KGF-2 solution was mixed with filtered buffer and agitated. Particle shedding from syringe filters--and the resulting acceleration of protein aggregation during agitation--varied greatly among the different syringe filters and individual units of a given filter type. Our results demonstrate that nanoparticles and microparticles shed from the filters can accelerate protein aggregation and particle formation, especially during agitation. PMID:22674153

Liu, Lu; Randolph, Theodore W; Carpenter, John F

2012-08-01

172

Fuzzy membership function optimization for system identification using an extended Kalman filter  

E-print Network

Fuzzy membership function optimization for system identification using an extended Kalman filter an extended Kalman filter to optimize the membership functions for system modeling, or system identification identification. The ideas described in this paper are illustrated for system identification of a nonlinear

Simon, Dan

173

Resolving power and encircled energy in aberrated optical systems with filters optimized for the Strehl ratio  

Microsoft Academic Search

Results of the optimization of the axial response of optical systems by filters of non-uniform transmission are presented. Optical systems with different types of residual aberrations are studied. The image quality criterion considered in the optimization procedure is the Strehl Ratio (SR). The effect of these filters on the point spread function (PSF), the resolving power and the encircled energy

J. Campos; F. Calvo; M. J. Yzuel

1988-01-01

174

Characterization of filtration and regeneration behavior of rigid ceramic filters and particle properties at high temperatures  

SciTech Connect

For power generation with combined cycles or production of so called advanced materials by vapor phase synthesis particle separation at high temperatures is of crucial importance. There, systems working with rigid ceramic barrier filters are either of thermodynamical benefit to the process or essential for producing materials with certain properties. A hot gas filter test rig has been installed to investigate the influence of different parameters e.g. temperature, dust properties, filter media and filtration and regeneration conditions into particle separation at high temperatures. These tests were conducted both with commonly used filter candles and with filter discs made out of the same material. The filter disc is mounted at one side of the test rig. That is why both filters face the same raw gas conditions. The filter disc is flown through by a cross flow arrangement. This bases upon the conviction that for comparison of filtration characteristics of candles with filter discs or other model filters the structure of the dust cakes have to be equal. This way of conducting investigations into the influence of the above mentioned parameters on dust separation at high temperatures follows the new standard VDI 3926. There, test procedures for the characterization of filter media at ambient conditions are prescribed. The paper mainly focuses then on the influence of particle properties (e.g. stickiness etc.) upon the filtration and regeneration behavior of fly ashes with rigid ceramic filters.

Pilz, T. [Univ. of Karlsruhe (Germany). Inst. fuer Mechanische Verfahrenstechnik und Mechanik

1995-12-31

175

SEARCH OPTIMIZATION USING HYBRID PARTICLE SUB SWARMS AND EVOLUTIONARY ALGORITHMS  

Microsoft Academic Search

Particle Swarm Optimization (PSO) technique proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a variant of the PSO technique named Independent Neighborhoods Particle Swarm Optimization (INPSO) dealing with sub-swarms for solving the well known geometrical place problems. Finding the geometrical place can be sometimes

CRINA GROSAN; AJITH ABRAHAM; MONICA NICOARA

2005-01-01

176

ASME AG-1 Section FC Qualified HEPA Filters; a Particle Loading Comparison - 13435  

SciTech Connect

High Efficiency Particulate Air (HEPA) Filters used to protect personnel, the public and the environment from airborne radioactive materials are designed, manufactured and qualified in accordance with ASME AG-1 Code section FC (HEPA Filters) [1]. The qualification process requires that filters manufactured in accordance with this ASME AG-1 code section must meet several performance requirements. These requirements include performance specifications for resistance to airflow, aerosol penetration, resistance to rough handling, resistance to pressure (includes high humidity and water droplet exposure), resistance to heated air, spot flame resistance and a visual/dimensional inspection. None of these requirements evaluate the particle loading capacity of a HEPA filter design. Concerns, over the particle loading capacity, of the different designs included within the ASME AG-1 section FC code[1], have been voiced in the recent past. Additionally, the ability of a filter to maintain its integrity, if subjected to severe operating conditions such as elevated relative humidity, fog conditions or elevated temperature, after loading in use over long service intervals is also a major concern. Although currently qualified HEPA filter media are likely to have similar loading characteristics when evaluated independently, filter pleat geometry can have a significant impact on the in-situ particle loading capacity of filter packs. Aerosol particle characteristics, such as size and composition, may also have a significant impact on filter loading capacity. Test results comparing filter loading capacities for three different aerosol particles and three different filter pack configurations are reviewed. The information presented represents an empirical performance comparison among the filter designs tested. The results may serve as a basis for further discussion toward the possible development of a particle loading test to be included in the qualification requirements of ASME AG-1 Code sections FC and FK[1]. (authors)

Stillo, Andrew [Camfil Farr, 1 North Corporate Drive, Riverdale, NJ 07457 (United States)] [Camfil Farr, 1 North Corporate Drive, Riverdale, NJ 07457 (United States); Ricketts, Craig I. [New Mexico State University, Department of Engineering Technology and Surveying Engineering, P.O. Box 30001 MSC 3566, Las Cruces, NM 88003-8001 (United States)] [New Mexico State University, Department of Engineering Technology and Surveying Engineering, P.O. Box 30001 MSC 3566, Las Cruces, NM 88003-8001 (United States)

2013-07-01

177

PARTICLE TRANSPORTATION AND DEPOSITION IN HOT GAS FILTER VESSELS - A COMPUTATIONAL AND EXPERIMENTAL MODELING APPROACH  

SciTech Connect

In this project, a computational modeling approach for analyzing flow and ash transport and deposition in filter vessels was developed. An Eulerian-Lagrangian formulation for studying hot-gas filtration process was established. The approach uses an Eulerian analysis of gas flows in the filter vessel, and makes use of the Lagrangian trajectory analysis for the particle transport and deposition. Particular attention was given to the Siemens-Westinghouse filter vessel at Power System Development Facility in Wilsonville in Alabama. Details of hot-gas flow in this tangential flow filter vessel are evaluated. The simulation results show that the rapidly rotation flow in the spacing between the shroud and the vessel refractory acts as cyclone that leads to the removal of a large fraction of the larger particles from the gas stream. Several alternate designs for the filter vessel are considered. These include a vessel with a short shroud, a filter vessel with no shroud and a vessel with a deflector plate. The hot-gas flow and particle transport and deposition in various vessels are evaluated. The deposition patterns in various vessels are compared. It is shown that certain filter vessel designs allow for the large particles to remain suspended in the gas stream and to deposit on the filters. The presence of the larger particles in the filter cake leads to lower mechanical strength thus allowing for the back-pulse process to more easily remove the filter cake. A laboratory-scale filter vessel for testing the cold flow condition was designed and fabricated. A laser-based flow visualization technique is used and the gas flow condition in the laboratory-scale vessel was experimental studied. A computer model for the experimental vessel was also developed and the gas flow and particle transport patterns are evaluated.

Goodarz Ahmadi

2002-07-01

178

Uniform design and inertia mutation based particle swarm optimization  

NASA Astrophysics Data System (ADS)

Particle swarm optimization (PSO) is a population-based stochastic optimization technique. It shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). But compared with GA, it has simpler model, fewer parameters, higher intelligence, faster computation, which makes it attractive to some researchers. This paper presents a new particle swarm optimization based on uniform design and inertia mutation (UMPSO). It uses uniform designs (UD) to initialize particles, which makes some particles stay at or near the position where the global optimal solution stays with more probability. So the new PSO can find global optimal solution with more probability and more speed. Particles can keep diverse through mutating inertia particle with the probability of 1 in the process of evolution, which makes the new PSO find more precise solution. The results of simulation verify that the new PSO can find more precise solution with higher speed than the standard one.

Zhang, Boquan; Yang, Yimin; Wang, Jianbin

2007-12-01

179

Penetration of 4.5 nm to 10 ? m aerosol particles through fibrous filters  

Microsoft Academic Search

This study presents the experimental results of penetration of aerosol particles with diameters between 4.5nm and 10?m through fibrous filters. Three particle size spectrometers: the TSI 3080 electrostatic classifier equipped with nano- or long differential mobility analyzer, and the TSI 3321 aerodynamic particle sizer, were used to measure nanometer, submicron, and micron-sized particles. NaCl aerosol particles were generated by using

Sheng-Hsiu Huang; Chun-Wan Chen; Cheng-Ping Chang; Chane-Yu Lai; Chih-Chieh Chen

2007-01-01

180

Thin film characterization for modeling and optimization of silver-dielectric color filters.  

PubMed

We investigate the most appropriate way to optically characterize the materials and predict the spectral responses of metal-dielectric filters in the visible range. Special attention is given to thin silver layers that have a major impact on the filter's spectral transmittance and reflectance. Two characterization approaches are compared, based either on single layers, or on multilayer stacks, in approaching the filter design. The second approach is preferred, because it gives the best way to predict filter characteristics. Meanwhile, it provides a stack model and dispersion relations that can be used for filter design optimization. PMID:24663425

Frey, Laurent; Parrein, Pascale; Virot, Léopold; Pellé, Catherine; Raby, Jacques

2014-03-10

181

Particle filter-based data assimilation for a three-dimensional biological ocean model and satellite observations  

NASA Astrophysics Data System (ADS)

We assimilate satellite observations of surface chlorophyll into a three-dimensional biological ocean model in order to improve its state estimates using a particle filter referred to as sequential importance resampling (SIR). Particle Filters represent an alternative to other, more commonly used ensemble-based state estimation techniques like the ensemble Kalman filter (EnKF). Unlike the EnKF, Particle Filters do not require normality assumptions about the model error structure and are thus suitable for highly nonlinear applications. However, their application in oceanographic contexts is typically hampered by the high dimensionality of the model's state space. We apply SIR to a high-dimensional model with a small ensemble size (20) and modify the standard SIR procedure to avoid complications posed by the high dimensionality of the model state. Two extensions to the SIR include a simple smoother to deal with outliers in the observations, and state-augmentation which provides the SIR with parameter memory. Our goal is to test the feasibility of biological state estimation with SIR for realistic models. For this purpose we compare the SIR results to a model simulation with optimal parameters with respect to the same set of observations. By running replicates of our main experiments, we assess the robustness of our SIR implementation. We show that SIR is suitable for satellite data assimilation into biological models and that both extensions, the smoother and state-augmentation, are required for robust results and improved fit to the observations.

Mattern, Jann Paul; Dowd, Michael; Fennel, Katja

2013-05-01

182

Joint optimization of multiple behavioral and implementation properties of digital IIR filter designs  

Microsoft Academic Search

This paper presents an extensible framework for the simultaneous constrained optimization of multiple properties of digital IIR filters. The framework optimizes the pole-zero locations for behavioral properties of magnitude and phase response, and the implementation property of quality factors, subject to constraints on the same properties. We formulate the constrained nonlinear optimization problem as a sequential quadratic programming (SQP) problem.

M. Valliappan; Brian L. Evans; M. Gzara; M. D. Lutovac; D. V. Tosic

2000-01-01

183

Hybrid particle swarm optimization and convergence analysis for scheduling problems  

Microsoft Academic Search

This paper proposes a hybrid particle swarm optimization algorithm and for solving Flow Shop Scheduling Problems (FSSP) and Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. By reasonably combining these three different search algorithms, we develop a robust, fast

Xue-Feng Zhang; Miyuki Koshimura; Hiroshi Fujita; Ryuzo Hasegawa

2012-01-01

184

Optimal and unbiased FIR filtering in discrete time state space with smoothing and predictive properties  

NASA Astrophysics Data System (ADS)

We address p-shift finite impulse response optimal (OFIR) and unbiased (UFIR) algorithms for predictive filtering ( p > 0), filtering ( p = 0), and smoothing filtering ( p < 0) at a discrete point n over N neighboring points. The algorithms were designed for linear time-invariant state-space signal models with white Gaussian noise. The OFIR filter self-determines the initial mean square state function by solving the discrete algebraic Riccati equation. The UFIR one represented both in the batch and iterative Kalman-like forms does not require the noise covariances and initial errors. An example of applications is given for smoothing and predictive filtering of a two-state polynomial model. Based upon this example, we show that exact optimality is redundant when N ? 1 and still a nice suboptimal estimate can fairly be provided with a UFIR filter at a much lower cost.

Shmaliy, Yuriy S.; Ibarra-Manzano, Oscar

2012-12-01

185

Combined particle and smooth variable structure filtering for nonlinear estimation problems  

Microsoft Academic Search

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the smooth variable structure filter (SVSF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SVSF is a relatively new estimation strategy based on sliding

S. Andrew Gadsden; Darcy Dunne; Saeid R. Habibi; Thia Kirubarajan

2011-01-01

186

ON LOW-POWER ANALOG IMPLEMENTATION OF PARTICLE FILTERS FOR TARGET TRACKING  

E-print Network

] are used in state estimation, where the underlying state-space models can be nonlinear and non involved in the estimation. Because parti- cle filters do not approximate the nonlinearities in the state-spaceON LOW-POWER ANALOG IMPLEMENTATION OF PARTICLE FILTERS FOR TARGET TRACKING Rajbabu Velmurugan

Odam, Kofi

187

Robust 3D visual tracking using particle filtering on the SE(3) group  

Microsoft Academic Search

In this paper, we present a 3D model-based object tracking approach using edge and keypoint features in a parti- cle filtering framework. Edge points provide 1D information for pose estimation and it is natural to consider multiple hypotheses. Recently, particle filtering based approaches have been proposed to integrate multiple hypotheses and have shown good performance, but most of the work

Changhyun Choi; Henrik I. Christensen

2011-01-01

188

Optease Vena Cava Filter Optimal Indwelling Time and Retrievability  

SciTech Connect

The purpose of this study was to assess the indwelling time and retrievability of the Optease IVC filter. Between 2002 and 2009, a total of 811 Optease filters were inserted: 382 for prophylaxis in multitrauma patients and 429 for patients with venous thromboembolic (VTE) disease. In 139 patients [97 men and 42 women; mean age, 36 (range, 17-82) years], filter retrieval was attempted. They were divided into two groups to compare change in retrieval policy during the years: group A, 60 patients with filter retrievals performed before December 31 2006; and group B, 79 patients with filter retrievals from January 2007 to October 2009. A total of 128 filters were successfully removed (57 in group A, and 71 in group B). The mean filter indwelling time in the study group was 25 (range, 3-122) days. In group A the mean indwelling time was 18 (range, 7-55) days and in group B 31 days (range, 8-122). There were 11 retrieval failures: 4 for inability to engage the filter hook and 7 for inability to sheathe the filter due to intimal overgrowth. The mean indwelling time of group A retrieval failures was 16 (range, 15-18) days and in group B 54 (range, 17-122) days. Mean fluoroscopy time for successful retrieval was 3.5 (range, 1-16.6) min and for retrieval failures 25.2 (range, 7.2-62) min. Attempts to retrieve the Optease filter can be performed up to 60 days, but more failures will be encountered with this approach.

Rimon, Uri, E-mail: rimonu@sheba.health.gov.il; Bensaid, Paul, E-mail: paulbensaid@hotmail.com; Golan, Gil, E-mail: gilgolan201@gmail.com; Garniek, Alexander, E-mail: garniek@gmail.com; Khaitovich, Boris, E-mail: borislena@012.net.il [Chaim Sheba Medical Center (Affiliated to the Sackler School of Medicine, Tel-Aviv University, Tel-Aviv), Department of Diagnostic Imaging (Israel); Dotan, Zohar, E-mail: Zohar.Dotan@sheba.health.gov.il [Chaim Sheba Medical Center (Affiliated to the Sackler School of Medicine, Tel-Aviv University, Tel-Aviv), Department of Urology (Israel); Konen, Eli, E-mail: Eli.Konen@sheba.health.gov.il [Chaim Sheba Medical Center (Affiliated to the Sackler School of Medicine, Tel-Aviv University, Tel-Aviv), Department of Diagnostic Imaging (Israel)

2011-06-15

189

Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering  

E-print Network

a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter based state estimation algorithm is studied instead of the contemporary linear approximation based approaches. The parameters signifying the regulatory...

Noor, Amina

2012-10-19

190

An adaptive non-local means filter for denoising live-cell images and improving particle detection  

PubMed Central

Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data. PMID:20599512

Yang, Lei; Parton, Richard; Ball, Graeme; Qiu, Zhen; Greenaway, Alan H.; Davis, Ilan; Lu, Weiping

2010-01-01

191

Particle filters and RAP-MUSIC in MEG source modelling: A comparison  

Microsoft Academic Search

We compared the novel particle filtering approach to an established MEG inverse modelling algorithm “recursively applied and projected (RAP) MUSIC”. Both methods are able to reconstruct temporally correlated sources in an automatic manner. We evaluated the performance of the two methods in critical yet neurophysiologically plausible source configurations. RAP-MUSIC outperformed particle filters when modelling sources of low signal-to-noise ratio or

A. Pascarella; A. Sorrentino; M. Piana; L. Parkkonen

2007-01-01

192

Array of micro-machined mass energy micro-filters for charged particles  

NASA Technical Reports Server (NTRS)

An energy filter for charged particles includes a stack of micro-machined wafers including plural apertures passing through the stack of wafers, focusing electrodes bounding charged particle paths through the apertures, an entrance orifice to each of the plural apertures and an exit orifice from each of the plural apertures and apparatus for biasing the focusing electrodes with an electrostatic potential corresponding to an energy pass band of the filter.

Stalder, Roland E. (Inventor); Van Zandt, Thomas R. (Inventor); Hecht, Michael H. (Inventor); Grunthaner, Frank J. (Inventor)

1996-01-01

193

PARTICLE TRANSPORT AND DEPOSITION IN THE HOT-GAS FILTER AT WILSONVILLE  

SciTech Connect

Particle transport and deposition in the Wilsonville hot-gas filter vessel is studied. The filter vessel contains a total of 72 filters, which are arranged in two tiers. These are modeled by six upper and one lower cylindrical effective filters. An unstructured grid of 312,797 cells generated by GAMBIT is used in the simulations. The Reynolds stress model of FLUENT{trademark} (version 5.0) code is used for evaluating the gas mean velocities and root mean-square fluctuation velocities in the vessel. The particle equation of motion includes the drag, the gravitational and the lift forces. The turbulent instantaneous fluctuation velocity is simulated by a filtered Gaussian white-noise model provided by the FLUENT code. The particle deposition patterns are evaluated, and the effect of particle size is studied. The effect of turbulent dispersion, the lift force and the gravitational force are analyzed. The results show that the deposition pattern depends on particle size. Turbulent dispersion plays an important role in transport and deposition of particles. Lift and gravitational forces affect the motion of large particles, but has no effect on small particles.

Goodarz Ahmadi

1999-06-24

194

Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared  

E-print Network

Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared mid-infrared reflectance filters based on guided-mode resonance (GMR) in waveguide gratings@illinois.edu Abstract: An alternative to the well-established Fourier transform infrared (FT-IR) spectrometry, termed

Cunningham, Brian

195

Structured design of a 288-tap FIR filter by optimized partial product tree compression  

Microsoft Academic Search

A compact 10-b, 288-tap finite impulse response (FIR) filter is designed by adopting structured architecture that employs an optimized partial product tree compression method. The new scheme is based on the addition of equally weighted partial products resulted from 288 multiplications of the filter coefficients and the inputs. The 288 multiplication and 287 addition operations are decomposed to add 1440

Jun Rim Choi; Lak Hyun Jang; Seong Wook Jung; Jin Ho Choi

1997-01-01

196

Environmentally realistic fingerprint-image generation with evolutionary filter-bank optimization  

E-print Network

Environmentally realistic fingerprint-image generation with evolutionary filter-bank optimization t i c l e i n f o Keywords: Fingerprint image generation Evolutionary algorithm Image filters Input pressure a b s t r a c t Constructing a fingerprint database is important to evaluate the performance

Cho, Sung-Bae

197

DMT bit rate maximization with optimal time domain equalizer filter bank architecture  

Microsoft Academic Search

In a multicarrier modulation system, a time domain equalizer (TEQ) traditionally shortens the transmission channel impulse response (CIR) to mitigate intersymbol interference (ISI). In this paper, we propose a data-rate optimal TEQ filter bank whose data rates at the equalizer output of this filter bank are significantly better than those of the Maximum Bit Rate and Minimum ISI methods and

Milos Milosevic; Lucio F. C. Pessoa; Brian L. Evans; Ross Baldick

2002-01-01

198

Speed estimation of an induction motor drive using an optimized extended Kalman filter  

Microsoft Academic Search

This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V\\/Hz controller and a

K. L. Shi; T. F. Chan; Y. K. Wong; S. L. Ho

2002-01-01

199

Near-Optimal deterministic filtering on the Rotation Mohammad Zamani, Jochen Trumpf, Member, IEEE,  

E-print Network

1 Near-Optimal deterministic filtering on the Rotation Group Mohammad Zamani, Jochen Trumpf, Member are with the School of Engineering, Aus- tralian National University, Canberra, ACT, Australia. Mohammad.Zamani

Trumpf, Jochen

200

Optimization of Al Matrix Reinforced with B4C Particles  

NASA Astrophysics Data System (ADS)

In the current study, abrasive wear resistance and mechanical properties of A356 composite reinforced with B4C particulates were investigated. A center particle swarm optimization algorithm (CenterPSO) is proposed to predict the optimal process conditions in fabrication of aluminum matrix composites. Unlike other ordinary particles, the center particle has no explicit velocity and is set to the center of the swarm at every iteration. Other aspects of the center particle are the same as that of the ordinary particle, such as fitness evaluation and competition for the best particle of the swarm. Because the center of the swarm is a promising position, the center particle generally gets good fitness value. More importantly, due to frequent appearance as the best particle of swarm, it often attracts other particles and guides the search direction of the whole swarm.

Shabani, Mohsen Ostad; Mazahery, Ali

2013-02-01

201

Optimal Filters with Multiple Packet Losses and its Application in Wireless Sensor Networks  

PubMed Central

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs. PMID:22319301

Liu, Yonggui; Xu, Bugong; Feng, Linfang; Li, Shanbin

2010-01-01

202

Comparison of optimal and local search methods for designing finite wordlength FIR digital filters  

NASA Astrophysics Data System (ADS)

This paper presents a comparison between an optimal (branch-and-bound) algorithm and a suboptimal (local search) algorithm for the design of finite wordlength finite-impulse-response (FIR) digital filters. Experimental results are described for 11 examples of length 15 to 35. It is concluded that when computer resources are not available for the optimal method, it is still worth applying the local search method to the filter with rounded coefficients.

Kodek, D.; Steiglitz, K.

1981-01-01

203

Optimized cut of LiTaO3 for resonator filters with improved performance  

Microsoft Academic Search

In resonator filters, it is often desirable to minimize propagation loss simultaneously at resonant and anti-resonant frequencies. Using this criterion, we found an optimal dependence of rotation angle on electrode thickness in wavelengths, in rotated YX cuts of LiTaO3 with At grating. In particular, 48°YX cut was found to be optimal for resonator filters with thick Al electrodes, about 10%

N. Naumenko; B. Abbot

2002-01-01

204

Optimizing a continuously variable filter in a hybrid optical correlator  

NASA Technical Reports Server (NTRS)

In contrast to binary filters, continuously variable optical filters offer an ability to conpensate for certain imperfections in the optics of a hybrid correlator. Arbitrary static phase errors are introduced into a model of a phase-only filtering hybrid correlator, and a method of discovering a correction for them simulated. By a recursive technique a first approximation to the impulse's matched filter is adjusted (allowed to relax) so as to produce successively more localized distribution of the output in the correlation plane. The method is motivated by the development of continuously-variable phase-only spatial light modulators, but it is applicable to amplitude modulators and -with appropriate modification -- to binary modulators as well. The technique is robust against the form of the system's departure from ideal behavior.

Juday, Richard D.

1987-01-01

205

Optimizing a continuously variable filter in a hybrid optical correlator  

NASA Astrophysics Data System (ADS)

In contrast to binary filters, continuously variable optical filters offer an ability to conpensate for certain imperfections in the optics of a hybrid correlator. Arbitrary static phase errors are introduced into a model of a phase-only filtering hybrid correlator, and a method of discovering a correction for them simulated. By a recursive technique a first approximation to the impulse's matched filter is adjusted (allowed to relax) so as to produce successively more localized distribution of the output in the correlation plane. The method is motivated by the development of continuously-variable phase-only spatial light modulators, but it is applicable to amplitude modulators and -with appropriate modification -- to binary modulators as well. The technique is robust against the form of the system's departure from ideal behavior.

Juday, Richard D.

206

Assessing consumption of bioactive micro-particles by filter-feeding Asian carp  

USGS Publications Warehouse

Silver carp Hypophthalmichthys molitrix (SVC) and bighead carp H. nobilis (BHC) have impacted waters in the US since their escape. Current chemical controls for aquatic nuisance species are non-selective. Development of a bioactive micro-particle that exploits filter-feeding habits of SVC or BHC could result in a new control tool. It is not fully understood if SVC or BHC will consume bioactive micro-particles. Two discrete trials were performed to: 1) evaluate if SVC and BHC consume the candidate micro-particle formulation; 2) determine what size they consume; 3) establish methods to evaluate consumption of filter-feeders for future experiments. Both SVC and BHC were exposed to small (50-100 ?m) and large (150-200 ?m) micro-particles in two 24-h trials. Particles in water were counted electronically and manually (microscopy). Particles on gill rakers were counted manually and intestinal tracts inspected for the presence of micro-particles. In Trial 1, both manual and electronic count data confirmed reductions of both size particles; SVC appeared to remove more small particles than large; more BHC consumed particles; SVC had fewer overall particles in their gill rakers than BHC. In Trial 2, electronic counts confirmed reductions of both size particles; both SVC and BHC consumed particles, yet more SVC consumed micro-particles compared to BHC. Of the fish that ate micro-particles, SVC consumed more than BHC. It is recommended to use multiple metrics to assess consumption of candidate micro-particles by filter-feeders when attempting to distinguish differential particle consumption. This study has implications for developing micro-particles for species-specific delivery of bioactive controls to help fisheries, provides some methods for further experiments with bioactive micro-particles, and may also have applications in aquaculture.

Jensen, Nathan R.; Amberg, Jon J.; Luoma, James A.; Walleser, Liza R.; Gaikowski, Mark P.

2012-01-01

207

Optimal Filter Estimation for Lucas-Kanade Optical Flow  

PubMed Central

Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm.

Sharmin, Nusrat; Brad, Remus

2012-01-01

208

Use of Nuclepore filters for ambient and workplace nanoparticle exposure assessment-Spherical particles  

NASA Astrophysics Data System (ADS)

Nuclepore filter collection with subsequent electron microscopy analysis for nanoparticles was carried out to examine the feasibility of the method to assess the nanoparticle exposure. The number distribution of nanoparticles collected on the filter surface was counted visually and converted to the distribution in the air using existing filtration models for Nuclepore filters. To search for a proper model, this paper studied the overall penetrations of three different nanoparticles (PSL, Ag and NaCl), covering a wide range of particle sizes (20-800 nm) and densities (1.05-10.5 g cm-3), through Nuclepore filters with two different pore diameters (1 and 3 ?m) and different face velocities (2-15 cm s-1). The data were compared with existing particle deposition models and modified models proposed by this study, which delivered different results because of different deposition processes considered. It was found that a parameter associated with flow condition and filter geometry (density of fluid medium, particle density, filtration face velocity, filter porosity and pore diameter) should be taken into account to verify the applicability of the models. The data of the overall penetration were in very good agreement with the properly applied models. A good agreement of filter surface collection between the validated model and the SEM analysis was obtained, indicating a correct nanoparticle number distribution in the air can be converted from the Nuclepore filter surface collection and this method can be applied for nanoparticle exposure assessment.

Chen, Sheng-Chieh; Wang, Jing; Fissan, Heinz; Pui, David Y. H.

2013-10-01

209

Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic.  

PubMed

We present general methodology for sequential inference in nonlinear stochastic state-space models to simultaneously estimate dynamic states and fixed parameters. We show that basic particle filters may fail due to degeneracy in fixed parameter estimation and suggest the use of a kernel density approximation to the filtered distribution of the fixed parameters to allow the fixed parameters to regenerate. In addition, we show that "seemingly" uninformative uniform priors on fixed parameters can affect posterior inferences and suggest the use of priors bounded only by the support of the parameter. We show the negative impact of using multinomial resampling and suggest the use of either stratified or residual resampling within the particle filter. As a motivating example, we use a model for tracking and prediction of a disease outbreak via a syndromic surveillance system. Finally, we use this improved particle filtering methodology to relax prior assumptions on model parameters yet still provide reasonable estimates for model parameters and disease states. PMID:25016201

Sheinson, Daniel M; Niemi, Jarad; Meiring, Wendy

2014-09-01

210

Wide-Band Optical Filter Optimized for Deep Imaging of Small Solar System Bodies  

NASA Astrophysics Data System (ADS)

This paper describes a newly designed wide-band optical filter. It is optimized for deep imaging of small solar-system bodies. The new filter, which we denote as W i, is designed to reduce contamination by light pollution from street lamps, especially strong mercury and sodium emission lines. It is also useful for reducing unwanted scattered moonlight. Compared with the use of a commercially available long-wave cut wide-band filter, the signal-to-noise ratios in the detection of asteroids are improved by about 6% by using the W i filter.

Okumura, Shin-ichiro; Nishiyama, Kota; Urakawa, Seitaro; Sakamoto, Tsuyoshi; Takahashi, Noritsugu; Yoshikawa, Makoto

2012-06-01

211

Nonlinear temporal filtering of time-resolved digital particle image velocimetry data  

NASA Astrophysics Data System (ADS)

Nonlinear filtering methods have been developed to identify and replace outlying data points in velocity time series obtained with time-resolved digital particle image velocimetry (PIV) of the flow around a surface-mounted cube at a Reynolds number of 20,000. Nuances associated with the spectral computation of the cross-correlation are highlighted, including the requirement of zero-padding an image interrogation area to eliminate the circular components of the cross-correlation. Three nonlinear filtering methods for the replacement of outliers are applied to the velocity time series sampled at 1,000 Hz: a median filter, a decision-based Hampel filter, and a PIV-specific Hampel filter. The particular benefit of the PIV-specific Hampel filter is that it allows the retention of actual measured data, sometimes derived from alternate peaks in the cross-correlation function, while still providing for the removal of outliers when a consistent, nonoutlying measurement is not available.

Fore, L. B.; Tung, A. T.; Buchanan, J. R.; Welch, J. W.

2005-07-01

212

Distributed optimal consensus filter for target tracking in heterogeneous sensor networks.  

PubMed

This paper is concerned with the problem of filter design for target tracking over sensor networks. Different from most existing works on sensor networks, we consider the heterogeneous sensor networks with two types of sensors different on processing abilities (denoted as type-I and type-II sensors, respectively). However, questions of how to deal with the heterogeneity of sensors and how to design a filter for target tracking over such kind of networks remain largely unexplored.We propose in this paper a novel distributed consensus filter to solve the target tracking problem. Two criteria, namely, unbiasedness and optimality, are imposed for the filter design. The so-called sequential design scheme is then presented to tackle the heterogeneity of sensors. The minimum principle of Pontryagin is adopted for type-I sensors to optimize the estimation errors. As for type-II sensors, the Lagrange multiplier method coupled with the generalized inverse of matrices is then used for filter optimization. Furthermore, it is proven that convergence property is guaranteed for the proposed consensus filter in the presence of process and measurement noise. Simulation results have validated the performance of the proposed filter. It is also demonstrated that the heterogeneous sensor networks with the proposed filter outperform the homogenous counterparts in light of reduction in the network cost, with slight degradation of estimation performance. PMID:23757586

Zhu, Shanying; Chen, Cailian; Li, Wenshuang; Yang, Bo; Guan, Xinping

2013-12-01

213

Terrain Aided Underwater Navigation Using Point Mass and Particle Filters  

Microsoft Academic Search

This paper focuses on obtaining submerged position fixes for underwater vehicles from comparing bathymetric mea- surements with a bathymetric map. Our algorithms are tested on real data, collected by a HUGIN AUV equipped with a multibeam echo sounder (MBE). Due to our strongly non-linear and non-Gaussian problem, local linearization methods such as the extended Kalman filter (EKF), has proven unsuitable

Kjetil Bergh; Oddvar Hallingstad

214

Particle emission characteristics of filter-equipped vacuum cleaners.  

PubMed

Industrial vacuum cleaners with final high-efficiency particulate air (HEPA) filters traditionally have been used for cleanup operations in which all of the nozzle-entrained dust must be collected with high efficiency, for example, after lead-based paint abatement in homes. In this study household vacuum cleaners ranging from $70 to $650 and an industrial vacuum cleaner costing more than $1400 were evaluated relative to their collection efficiency immediately after installing new primary dust collectors in them. Using newly developed testing technology, some of the low-cost household vacuum cleaners equipped with a final HEPA filter were found to have initial overall filtration efficiencies comparable to those of industrial vacuum cleaners equipped with a final HEPA filter. The household vacuum cleaners equipped with a final HEPA filter efficiently collect about 100% of the dry dust entrained by the nozzle. For extensive cleaning efforts and for vacuum cleaning of wet surfaces, however, industrial vacuum cleaners may have an advantage, including ruggedness and greater loading capacity. The methods and findings of this study are applicable to field evaluations of vacuum cleaners. PMID:11549143

Trakumas, S; Willeke, K; Grinshpun, S A; Reponen, T; Mainelis, G; Friedman, W

2001-01-01

215

Adaptive Multi-Modal Particle Filtering for Probabilistic Tractography  

E-print Network

white mat- ter (WM) neural network [16]. Yet, the tractography problem of inferring the WM neural system capturing the multi-modality of the target distribution. For brain white matter tractography, this means by formulating the filtering distribution as an adaptive M-component non-parametric mixture model. Such a for

Paris-Sud XI, Université de

216

An optimal numerical filter for wide-field-of-view measurements of earth-emitted radiation  

NASA Technical Reports Server (NTRS)

A technique is described in which all data points along an arc of the orbit may be used in an optimal numerical filter for wide-field-of-view measurements of earth emitted radiation. The statistical filter design is derived whereby the filter is required to give a minimum variance estimate of the radiative exitance at discrete points along the ground track of the satellite. An equation for the optimal numerical filter is given by minimizing the estimate error variance equation with respect to the filter weights, resulting in a discrete form of the Wiener-Hopf equation. Finally, variances of the errors in the radiant exitance can be computed along the ground track and in the cross track directions.

Smith, G. L.; House, F. B.

1981-01-01

217

Optimization of multiplexed holographic gratings in PQ-PMMA for spectral-spatial imaging filters.  

PubMed

Holographic gratings formed in thick phenanthrenquinone- (PQ-) doped poly(methyl methacrylate) (PMMA) can be made to have narrowband spectral and spatial transmittance filtering properties. We present the design and performance of angle-multiplexed holographic filters formed in PQ-PMMA at 488 nm and reconstructed with a LED operated at approximately 630 nm. The dark delay time between exposure and the preillumination exposure of the polymer prior to exposure of the holographic area are varied to optimize the diffraction efficiency of multiplexed holographic filters. The resultant holographic filters can enhance the performance of four-dimensional spatial-spectral imaging systems. The optimized filters are used to simultaneously sample spatial and spectral information at five different depths separated by 50 microm within biological tissue samples. PMID:18347711

Luo, Yuan; Gelsinger, Paul J; Barton, Jennifer K; Barbastathis, George; Kostuk, Raymond K

2008-03-15

218

Particle filtering for tracking of GLUT4 vesicles in TIRF microscpy  

NASA Astrophysics Data System (ADS)

GLUT4 is responsible for insulin-stimulated glucose uptake into fat cells and description of the dynamic behavior of it can give insight in some working mechanisms and structures of these cells. Quantitative analysis of the dynamical process requires tracking of hundreds of GLUT4 vesicles characterized as bright spots in noisy image sequences. In this paper, a 3D tracking algorithm built in Bayesian probabilistic framework is put forward, combined with the unique features of the TIRF microscopy. A brightness-correction procedure is firstly applied to ensure that the intensity of a vesicle is constant along time and is only affected by spatial factors. Then, tracking is formalized as a state estimation problem and a developed particle filter integrated by a sub-optimizer that steers the particles towards a region with high likelihood is used. Once each tracked vesicle is located in image plane, the depth information of a granule can be indirectly inferred according to the exponential relationship between its intensity and its vertical position. The experimental results indicate that the vesicles are tracked well under different motion styles. More, the algorithm provides the depth information of the tracked vesicle.

Wu, Xiangping; Liu, Xiaofang; Xu, Wenglong; Yan, Dandan; Chen, Yongli

2009-10-01

219

The design of an optimal filter for monthly GRACE gravity models  

NASA Astrophysics Data System (ADS)

Most applications of the publicly released Gravity Recovery and Climate Experiment monthly gravity field models require the application of a spatial filter to help suppressing noise and other systematic errors present in the data. The most common approach makes use of a simple Gaussian averaging process, which is often combined with a `destriping' technique in which coefficient correlations within a given degree are removed. As brute force methods, neither of these techniques takes into consideration the statistical information from the gravity solution itself and, while they perform well overall, they can often end up removing more signal than necessary. Other optimal filters have been proposed in the literature; however, none have attempted to make full use of all information available from the monthly solutions. By examining the underlying principles of filter design, a filter has been developed that incorporates the noise and full signal variance-covariance matrix to tailor the filter to the error characteristics of a particular monthly solution. The filter is both anisotropic and non-symmetric, meaning it can accommodate noise of an arbitrary shape, such as the characteristic stripes. The filter minimizes the mean-square error and, in this sense, can be considered as the most optimal filter possible. Through both simulated and real data scenarios, this improved filter will be shown to preserve the highest amount of gravity signal when compared to other standard techniques, while simultaneously minimizing leakage effects and producing smooth solutions in areas of low signal.

Klees, R.; Revtova, E. A.; Gunter, B. C.; Ditmar, P.; Oudman, E.; Winsemius, H. C.; Savenije, H. H. G.

2008-11-01

220

AIR FILTER PARTICLE-SIZE EFFICIENCY TESTING FOR DIAMETERS GREATER THAN 1UM  

EPA Science Inventory

The paper discusses tests of air filter particle-size efficiency for diameters greater than 1 micrometer. valuation of air cleaner efficiencies in this size range can be quite demanding, depending on the required accuracy. uch particles have sufficient mass to require considerati...

221

Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps  

E-print Network

Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps Austin I. Eliazar.duke.edu Abstract We present an improvement to the DP-SLAM algorithm for simultane- ous localization and mapping (SLAM) that maintains multiple hypothe- ses about densely populated maps (one full map per particle

Parr, Ronald

222

Reaction filters. Charged-particle multiplicity and linear momentum transfer  

NASA Astrophysics Data System (ADS)

The relation between charged-particle multiplicity and linear momentum transfer to heavy reaction residues has been investigated with a 4? charged-particle detector for the reactions 36Ar+ 238U at {E}/{A}=35 MeV and 14N+ 238U at {E}/{A}=50 MeV. The multiplicity of charged particles at backward angles ( ? > 35°) incrreases linear momentum transfer while the multiplicity of charged particles in the forward direction is almost independent of the linear momentum transfer.

Tsang, M. B.; Kim, Y. D.; Carlin, N.; Chen, Z.; Fox, R.; Gelbke, C. K.; Gong, W. G.; Lynch, W. G.; Murakami, T.; Nayak, T. K.; Ronningen, R. M.; Xu, H. M.; Zhu, F.; Sobotka, L.; Stracener, D.; Sarantites, D. G.; Majka, Z.; Abenante, V.; Griffin, H.

1989-04-01

223

Optimized Loading for Particle-in-cell Gyrokinetic Simulations  

SciTech Connect

The problem of particle loading in particle-in-cell gyrokinetic simulations is addressed using a quadratic optimization algorithm. Optimized loading in configuration space dramatically reduces the short wavelength modes in the electrostatic potential that are partly responsible for the non-conservation of total energy; further, the long wavelength modes are resolved with good accuracy. As a result, the conservation of energy for the optimized loading is much better that the conservation of energy for the random loading. The method is valid for any geometry and can be coupled to optimization algorithms in velocity space.

J.L.V. Lewandowski

2004-05-13

224

Weinberg angle, current coupling constant, and mass of particles as properties of culminating-point filters - consequences for particle astrophysics  

E-print Network

Culminating-point filter construction for particle points is distinguished from torus construction for wave functions in the tangent objects of their neighborhoods. Both constructions are not united by a general manifold diffeomorphism, but are united by a map of a hidden conformal $S^{1}\\times S^{3}$ charge with harmonic (Maxwell) potentials into a physical space formed by culminating points, tangent objects, and Feynman connections. The particles are obtained from three classes of eigensolutions of the homogeneous potential equations on $S^{1}\\times S^{3}$. The map of the $u(2)$ invariant vector fields into the Dirac phase factors of the connections yields the electro-weak Lagrangian with explicit mass operators for the massive leptons. The spectrum of massive particles is restricted by the small, manageable number of eigensolution classes and an instability of the model for higher mass values. This instability also defines the huge numbers of filter elements needed for the culminating points. Weinberg angle, current coupling constant, and lepton masses are calculated or estimated from the renormalization of filter properties. Consequences for particle astrophysics follow, on the one hand, from the restriction of particle classes and, on the other hand, from the suggestion of new particles from the three classes e.g. of dark matter, of a confinon for the hadrons, and of a prebaryon. Definitely excluded are e.g. SUSY constructions, Higgs particles, and a quark gluon plasma: three-piece phenoma from the confinons are always present.

E. Donth

2009-01-08

225

A Dwindling Filter Line Search Method for Unconstrained Optimization  

E-print Network

multidimensional filter technique, which is a modification and improvement of .... Suppose that f(x) is an one-variable function, i.e., n = 1, and assume that xk belongs to ..... linear feasibility problems, Report 03/17, Dept of Mathematics, FUNDP, ...

2011-01-12

226

On-road vehicle detection using evolutionary Gabor filter optimization  

Microsoft Academic Search

Past work on vehicle detection has emphasized the issues of feature extraction and classification, however, less attention has been given on the critical issue of feature selection. The focus of this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters that have been specifically customized for the problem of vehicle detection. The

Zehang Sun; George Bebis; Ronald Miller

2005-01-01

227

Polymer Optimization of Pigmented Photoresists for Color Filter Production  

Microsoft Academic Search

The lithographic performance of pigmented photoresists for color filter production is affected by the structure of the employed polymer. Four polymers with acrylate backbones and pendant reactive acrylate\\/methacrylate groups were prepared, and the effects of their molecular weights and acid values on the pixel pattern quality, development time, sensitivity and development mode were elucidated. ECHIPTM, a statistical experimental design program

Takanori Kudo; Yuki Nanjo; Yuko Nozaki; Hidemasa Yamaguchi; Wen-Bing Kang; Georg Pawlowski

1998-01-01

228

OpenFilters: open-source software for the design, optimization, and synthesis of optical filters.  

PubMed

The design of optical filters relies on powerful computer-assisted methods. Many of these methods are provided by commercial programs, but, in order to adapt and improve them, or to develop new methods, one needs to create his own software. To help people interested in such a process, we decided to release our in-house software, called OpenFilters, under the GNU General Public License, an open-source license. It is programmed in Python and C++, and the graphical user interface is implemented with wxPython. It allows creation of multilayer and graded-index filters and calculation of reflection, transmission, absorption, phase, group delay, group delay dispersion, color, ellipsometric variables, admittance diagram, circle diagram, electric field distribution, and generation of reflection, transmission, and ellipsometric monitoring curves. It also provides the refinement, needle, step, and Fourier transform methods. PMID:18449250

Larouche, Stéphane; Martinu, Ludvik

2008-05-01

229

Particle Swarm Optimization for Optimal Operational Planning of Energy Plants  

Microsoft Academic Search

In this chapter, three PSO based methods: Original PSO, Evolutionary PSO, and Adaptive PSO are compared for optimal operational\\u000a planning problems of energy plants, which are formulated as Mixed-Intger Nonlinear Problems (MINLPs). The three methods are\\u000a compared using typical energy plant operational planning problems. We have been developed an optimal operational planning\\u000a and control system of energy plants using PSO

Yoshikazu Fukuyama; Hideyuki Nishida; Yuji Todaka

2009-01-01

230

Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error  

SciTech Connect

This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.

Sun, W.Y. [Lawrence Berkeley Lab., CA (United States)]|[California Univ., Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences

1993-04-01

231

Pumped-Storage Scheduling Using Evolutionary Particle Swarm Optimization  

Microsoft Academic Search

This paper presents new solution algorithms based on an evolutionary particle swarm optimization (EPSO) for solving the pumped-storage (P\\/S) scheduling problem. The proposed EPSO approach combines a basic particle swarm optimization (PSO) with binary encoding\\/decoding techniques as well as a mutation operation. The binary encoding\\/decoding techniques are adopted to model the discrete characteristics of a P\\/S plant. The mutation operation

Po-Hung Chen

2008-01-01

232

A Gaussian process guided particle filter for tracking 3D human pose in video.  

PubMed

In this paper, we propose a hybrid method that combines Gaussian process learning, a particle filter, and annealing to track the 3D pose of a human subject in video sequences. Our approach, which we refer to as annealed Gaussian process guided particle filter, comprises two steps. In the training step, we use a supervised learning method to train a Gaussian process regressor that takes the silhouette descriptor as an input and produces multiple output poses modeled by a mixture of Gaussian distributions. In the tracking step, the output pose distributions from the Gaussian process regression are combined with the annealed particle filter to track the 3D pose in each frame of the video sequence. Our experiments show that the proposed method does not require initialization and does not lose tracking of the pose. We compare our approach with a standard annealed particle filter using the HumanEva-I dataset and with other state of the art approaches using the HumanEva-II dataset. The evaluation results show that our approach can successfully track the 3D human pose over long video sequences and give more accurate pose tracking results than the annealed particle filter. PMID:23846470

Sedai, Suman; Bennamoun, Mohammed; Huynh, Du Q

2013-11-01

233

Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared spectroscopy.  

PubMed

An alternative to the well-established Fourier transform infrared (FT-IR) spectrometry, termed discrete frequency infrared (DFIR) spectrometry, has recently been proposed. This approach uses narrowband mid-infrared reflectance filters based on guided-mode resonance (GMR) in waveguide gratings, but filters designed and fabricated have not attained the spectral selectivity (? 32 cm(-1)) commonly employed for measurements of condensed matter using FT-IR spectroscopy. With the incorporation of dispersion and optical absorption of materials, we present here optimal design of double-layer surface-relief silicon nitride-based GMR filters in the mid-IR for various narrow bandwidths below 32 cm(-1). Both shift of the filter resonance wavelengths arising from the dispersion effect and reduction of peak reflection efficiency and electric field enhancement due to the absorption effect show that the optical characteristics of materials must be taken into consideration rigorously for accurate design of narrowband GMR filters. By incorporating considerations for background reflections, the optimally designed GMR filters can have bandwidth narrower than the designed filter by the antireflection equivalence method based on the same index modulation magnitude, without sacrificing low sideband reflections near resonance. The reported work will enable use of GMR filters-based instrumentation for common measurements of condensed matter, including tissues and polymer samples. PMID:22109445

Liu, Jui-Nung; Schulmerich, Matthew V; Bhargava, Rohit; Cunningham, Brian T

2011-11-21

234

Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared spectroscopy  

PubMed Central

An alternative to the well-established Fourier transform infrared (FT-IR) spectrometry, termed discrete frequency infrared (DFIR) spectrometry, has recently been proposed. This approach uses narrowband mid-infrared reflectance filters based on guided-mode resonance (GMR) in waveguide gratings, but filters designed and fabricated have not attained the spectral selectivity (? 32 cm?1) commonly employed for measurements of condensed matter using FT-IR spectroscopy. With the incorporation of dispersion and optical absorption of materials, we present here optimal design of double-layer surface-relief silicon nitride-based GMR filters in the mid-IR for various narrow bandwidths below 32 cm?1. Both shift of the filter resonance wavelengths arising from the dispersion effect and reduction of peak reflection efficiency and electric field enhancement due to the absorption effect show that the optical characteristics of materials must be taken into consideration rigorously for accurate design of narrowband GMR filters. By incorporating considerations for background reflections, the optimally designed GMR filters can have bandwidth narrower than the designed filter by the antireflection equivalence method based on the same index modulation magnitude, without sacrificing low sideband reflections near resonance. The reported work will enable use of GMR filters-based instrumentation for common measurements of condensed matter, including tissues and polymer samples. PMID:22109445

Liu, Jui-Nung; Schulmerich, Matthew V.; Bhargava, Rohit; Cunningham, Brian T.

2011-01-01

235

An Analysis of Field-Aged Diesel Particulate Filter Performance: Particle Emissions before, during, and after Regeneration  

Microsoft Academic Search

A field-aged, passive diesel particulate filter (DPF) used in a school bus retrofit program was evaluated for emissions of particle mass and number concentration before, during, and after regeneration. For the particle mass measurements, filter samples were collected for gravimetric analysis with a partial flow sampling system, which sampled proportionally to the exhaust flow. A condensation particle counter and scanning

Teresa L. Barone; John Morse Storey; Norberto Domingo; Shannon Serre; Shawn Ryan; Emily Snyder; Abderrahmane Touati; Matthew Clayton; Tsung-Wen Chien; Hsin-Ta Hsueh; Hsin Chu; Wei-Chieh Hsu; Yueh-Yuan Tu; Hsien-Shiou Tsai; Kuo-Yi Chen; Richard Derwent; Michael Jenkin; Michael Pilling; William Carter; Ajith Kaduwela; Hanna Kierzkowska-Pawlak; Andrzej Chacuk; Andrzej Chmielewski; Anna Ostapczuk; Janusz Licki; Kenneth Casey; Richard Gates; Richard Shores; Eben Thoma; D. Harris; Tomasz Mroz; Ana Elías; Astrid Barona; Gorka Gallastegi; Naiara Rojo; Luis Gurtubay; Gabriel Ibarra-Berastegi; Amnon Bar-Ilan; Jeremiah Johnson; Allison DenBleyker; Lit-Mian Chan; Gregory Yarwood; David Hitchcock; Joseph Pinto; Katarzyna Piekarska; Andrey Zagoruiko; Bair Balzhinimaev; Sergey Vanag; Vladimir Goncharov; Sergey Lopatin; Alexander Zykov; Sergey Anichkov; Yurii Zhukov; Vassily Yankilevich; Nikolay Proskokov; Nick Hutson; Michal Glomba

2010-01-01

236

Melanie Ades, Peter Jan van Leeuwen | m.ades@student.reading.ac.uk Particle filtering using continuous guiding  

E-print Network

continuous guiding Conclusions · The particle filter using the proposal density is a clear improvement over the proposal density as we wish but one option is to use it to guide particles towards future observations the standard particle filter · Although there are still problems with the proposal density method the solutions

237

Particle swarm optimization with a leader and followers  

Microsoft Academic Search

Referring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO,

Junwei Wang; Dingwei Wang

2008-01-01

238

Multiobjective particle swarm optimization for environmental\\/economic dispatch problem  

Microsoft Academic Search

A new multiobjective particle swarm optimization (MOPSO) technique for environmental\\/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposed MOPSO technique has been implemented to solve the EED problem with competing and non-commensurable cost and

M. A. Abido

2009-01-01

239

Hybrid Particle Swarm - Evolutionary Algorithm for Search and Optimization  

Microsoft Academic Search

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search prob- lems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO - evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations

Crina Grosan; Ajith Abraham; Sangyong Han; Alexander F. Gelbukh

2005-01-01

240

Cooperative learning in neural networks using particle swarm optimizers  

Microsoft Academic Search

This paper presents a method to employ particle swarms optim izers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each w hich is optimized cooperatively in its own swarm. The applic ation of this technique to neural network training is investigate d, with promising results.

F. Van Den Bergh; Andries Petrus Engelbrecht

2000-01-01

241

Terrain Aided Underwater Navigation Using Point Mass and Particle Filters  

Microsoft Academic Search

Abstract—This paper focuses on obtaining submerged,position fixes for underwater,vehicles from,comparing,bathymetric,mea- surements,with a bathymetric,map. Our algorithms,are tested on real data, collected by a HUGIN AUV equipped with a multibeam,echo sounder,(MBE). Due to our strongly non-linear and non-Gaussian problem, local linearization methods such as the extended Kalman filter (EKF), has proven unsuitable in many,terrain types. We therefore focus on two different recursive

Kjetil Bergh Anonsen; Oddvar Hallingstad

2006-01-01

242

Energy resolution improvement of superconducting tunnel junction particle detectors with infrared-blocking filters  

Microsoft Academic Search

The energy resolution of a superconducting tunnel junction particle detector was significantly improved by the addition of a metal mesh infrared-blocking filter. The energy resolution was 138–215eV full width at half maximum for 1.5–3.0keV Ar ions. The infrared-blocking filter reduces the loss of energy resolution due to infrared radiation at room temperature.

Shigetomo Shiki; Masahiro Ukibe; Ryutaro Maeda; Masataka Ohkubo; Yuki Sato; Shigeo Tomita

2008-01-01

243

Infrared-Blocking Filters for Superconducting-Tunnel-Junction Particle Detector  

Microsoft Academic Search

The prevention of detector performance degradation due to infrared radiation is a serious issue in superconducting detectors. For this purpose, we designed and fabricated metal-mesh infrared-blocking filters. The infrared-blocking filters are extremely effective for superconducting-tunnel-junction (STJ) particle detectors. One of the best mesh structures consists of a free-standing 500-nm-thick Cr\\/Cu film, having a membrane size of 12 × 5 mm2,

Shigetomo Shiki; Masahiro Ukibe; Ryutaro Maeda; Masataka Ohkubo

2008-01-01

244

Kinetics of reduction and oxidation reactions for application in catalytic gas–particle-filters  

Microsoft Academic Search

Catalytic filters offer the possibility to combine the functions of a catalytic reactor and a gas–particle filter within one process step. In this way, plant complexity may be reduced, which is of economical interest for small- to medium-size biomass combustion plants. SiC materials seem attractive for catalytic activation as they exhibit high heat conductivity and good thermal shock resistance properties.

Marius Hackel; Georg Schaub; Manfred Nacken; Steffen Heidenreich

2008-01-01

245

Particle swarm optimization with particles having quantum behavior  

Microsoft Academic Search

In this paper, inspired by the analysis of convergence of PSO, we study the individual particle of a PSO system moving in a quantum multidimensional space and establish a quantum delta potential well model for PSO. After that, a trial method of parameter control and QDPSO is proposed. The experiment result shows much advantage of QDPSO to the traditional PSO.

Jun Sun; Bin Feng; Wenbo Xu

2004-01-01

246

Optimization of microfluidic particle sorters based on dielectrophoresis  

Microsoft Academic Search

In this paper, improved dielectrophoretic particle sorters are introduced for application in microfluidics. The optimal shape of the electrodes is briefly discussed and two new sorter topologies are introduced. Based on the theoretical considerations, four sorter configurations are analyzed in detail. The devices are modeled by calculating the particle trajectories from a combination of finite-element simulations and analytical calculations. The

Jeroen H. Nieuwenhuis; Artur Jachimowicz; Peter Svasek; Michiel J. Vellekoop

2005-01-01

247

Fiber Bragg grating filter using evaporated induced self assembly of silica nano particles  

NASA Astrophysics Data System (ADS)

In the present work we conduct a study of fiber filters produced by evaporation of silica particles upon a MM-fiber core. A band filter was designed and theoretically verified using a 2D Comsol simulation model of a 3D problem, and calculated in the frequency domain in respect to refractive index. The fiber filters were fabricated by stripping and chemically etching the middle part of an MM-fiber until the core was exposed. A mono layer of silica nano particles were evaporated on the core using an Evaporation Induced Self-Assembly (EISA) method. The experimental results indicated a broader bandwidth than indicated by the simulations which can be explained by the mismatch in the particle size distributions, uneven particle packing and finally by effects from multiple mode angles. Thus, there are several closely connected Bragg wavelengths that build up the broader bandwidth. The experimental part shows that it is possible by narrowing the particle size distributing and better control of the particle packing, the filter effectiveness can be greatly improved.

Hammarling, Krister; Zhang, Renyung; Manuilskiy, Anatoliy; Nilsson, Hans-Erik

2014-03-01

248

Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems  

E-print Network

and the algorithm is modified to preserve feasibility of the encountered solutions. The algorithm is illustrated efficiently and effectively. Due to the nature of these applications, the solutions usually need. On the other hand, stochastic optimization algorithms such as Genetic Algorithms, Evolution Strategies

Parsopoulos, Konstantinos

249

Design of multichannel DWDM fiber Bragg grating filters by Lagrange multiplier constrained optimization.  

PubMed

We present the synthesis of multi-channel fiber Bragg grating (MCFBG) filters for dense wavelength-division-multiplexing (DWDM) application by using a simple optimization approach based on a Lagrange multiplier optimization (LMO) method. We demonstrate for the first time that the LMO method can be used to constrain various parameters of the designed MCFBG filters for practical application demands and fabrication requirements. The designed filters have a number of merits, i.e., flat-top and low dispersion spectral response as well as single stage. Above all, the maximum amplitude of the index modulation profiles of the designed MCFBGs can be substantially reduced under the applied constrained condition. The simulation results demonstrate that the LMO algorithm can provide a potential alternative for complex fiber grating filter design problems. PMID:19529515

Lee, Cheng-Ling; Lee, Ray-Kuang; Kao, Yee-Mou

2006-11-13

250

Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization  

NASA Astrophysics Data System (ADS)

Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of composite correlation filters designed with multi-objective combinatorial optimization. Given a large set of available face images having variations in pose, gesticulations, and global illumination, a proposed algorithm synthesizes composite correlation filters by optimization of several performance criteria. The resultant filters are able to reliably detect and correctly classify face images of different subjects even when they are corrupted with additive noise and nonhomogeneous illumination. Computer simulation results obtained with the proposed approach are presented and discussed in terms of efficiency in face detection and reliability of facial classification. These results are also compared with those obtained with existing composite filters.

Cuevas, Andres; Diaz-Ramirez, Victor H.; Kober, Vitaly; Trujillo, Leonardo

2014-09-01

251

On the application of optimal wavelet filter banks for ECG signal classification  

NASA Astrophysics Data System (ADS)

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

Hadjiloucas, S.; Jannah, N.; Hwang, F.; Galvão, R. K. H.

2014-03-01

252

A Novel Particle Swarm Optimization Approach for Grid Job Scheduling  

NASA Astrophysics Data System (ADS)

This paper represents a Particle Swarm Optimization (PSO) algorithm, for grid job scheduling. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. In this paper we used a PSO approach for grid job scheduling. The scheduler aims at minimizing makespan and flowtime simultaneously. Experimental studies show that the proposed novel approach is more efficient than the PSO approach reported in the literature.

Izakian, Hesam; Tork Ladani, Behrouz; Zamanifar, Kamran; Abraham, Ajith

253

Auto-Clustering Using Particle Swarm Optimization and Bacterial Foraging  

NASA Astrophysics Data System (ADS)

This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid mechanism outperforms earlier PSO-based approaches by using simplistic communication between agents.

Olesen, Jakob R.; Cordero H., Jorge; Zeng, Yifeng

254

Particle filtering for obstacle tracking in UAS sense and avoid applications.  

PubMed

Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering methodologies, such as particle filters, can provide more accurate estimates of the target state in case of nonlinear problems, thus improving system performance in terms of collision risk estimation. The paper focuses on algorithm development and performance evaluation for an obstacle tracking system based on a particle filter. The particle filter algorithm was tested in off-line simulations based on data gathered during flight tests. In particular, radar-based tracking was considered in order to evaluate the impact of particle filtering in a single sensor framework. The analysis shows some accuracy improvements in the estimation of Distance at Closest Point of Approach, thus reducing the delay in collision detection. PMID:25105154

Tirri, Anna Elena; Fasano, Giancarmine; Accardo, Domenico; Moccia, Antonio

2014-01-01

255

A particle-filtering approach to convoy tracking in the midst of civilian traffic  

NASA Astrophysics Data System (ADS)

In the battlefield surveillance domain, ground target tracking is used to evaluate the threat. Data used for tracking is given by a Ground Moving Target Indicator (GMTI) sensor which only detects moving targets. Multiple target tracking has been widely studied but most of the algorithms have weaknesses when targets are close together, as they are in a convoy. In this work, we propose a filtering approach for convoys in the midst of civilian traffic. Inspired by particle filtering, our specific algorithm cannot be applied to all the targets because of its complexity. That is why well discriminated targets are tracked using an Interacting Multiple Model-Multiple Hypothesis Tracking (IMM-MHT), whereas the convoy targets are tracked with a specific particle filter. We make the assumption that the convoy is detected (position and number of targets). Our approach is based on an Independent Partition Particle Filter (IPPF) incorporating constraint-regions. The originality of our approach is to consider a velocity constraint (all the vehicles belonging to the convoy have the same velocity) and a group constraint. Consequently, the multitarget state vector contains all the positions of the individual targets and a single convoy velocity vector. When another target is detected crossing or overtaking the convoy, a specific algorithm is used and the non-cooperative target is tracked down an adapted particle filter. As demonstrated by our simulations, a high increase in convoy tracking performance is obtained with our approach.

Pollard, Evangeline; Pannetier, Benjamin; Rombaut, Michèle

2008-04-01

256

Evaluation of filter media for particle number, surface area and mass penetrations.  

PubMed

The National Institute for Occupational Safety and Health (NIOSH) developed a standard for respirator certification under 42 CFR Part 84, using a TSI 8130 automated filter tester with photometers. A recent study showed that photometric detection methods may not be sensitive for measuring engineered nanoparticles. Present NIOSH standards for penetration measurement are mass-based; however, the threshold limit value/permissible exposure limit for an engineered nanoparticle worker exposure is not yet clear. There is lack of standardized filter test development for engineered nanoparticles, and development of a simple nanoparticle filter test is indicated. To better understand the filter performance against engineered nanoparticles and correlations among different tests, initial penetration levels of one fiberglass and two electret filter media were measured using a series of polydisperse and monodisperse aerosol test methods at two different laboratories (University of Minnesota Particle Technology Laboratory and 3M Company). Monodisperse aerosol penetrations were measured by a TSI 8160 using NaCl particles from 20 to 300 nm. Particle penetration curves and overall penetrations were measured by scanning mobility particle sizer (SMPS), condensation particle counter (CPC), nanoparticle surface area monitor (NSAM), and TSI 8130 at two face velocities and three layer thicknesses. Results showed that reproducible, comparable filtration data were achieved between two laboratories, with proper control of test conditions and calibration procedures. For particle penetration curves, the experimental results of monodisperse testing agreed well with polydisperse SMPS measurements. The most penetrating particle sizes (MPPSs) of electret and fiberglass filter media were ~50 and 160 nm, respectively. For overall penetrations, the CPC and NSAM results of polydisperse aerosols were close to the penetration at the corresponding median particle sizes. For each filter type, power-law correlations between the penetrations measured by different instruments show that the NIOSH TSI 8130 test may be used to predict penetrations at the MPPS as well as the CPC and NSAM results with polydisperse aerosols. It is recommended to use dry air (<20% RH) as makeup air in the test system to prevent sodium chloride particle deliquescing and minimizing the challenge particle dielectric constant and to use an adequate neutralizer to fully neutralize the polydisperse challenge aerosol. For a simple nanoparticle penetration test, it is recommended to use a polydisperse aerosol challenge with a geometric mean of ~50 nm with the CPC or the NSAM as detectors. PMID:22752097

Li, Lin; Zuo, Zhili; Japuntich, Daniel A; Pui, David Y H

2012-07-01

257

Ensemble Kalman filter versus particle filter for a physically-based coupled surface-subsurface model  

NASA Astrophysics Data System (ADS)

The ensemble Kalman filter (EnKF) and sequential importance resampling (SIR) are two Monte Carlo-based sequential data assimilation (DA) methods developed to solve the filtering problem in nonlinear systems. Both methods present drawbacks when applied to physically-based nonlinear models: the EnKF update is affected by the inherent Gaussian approximation, while SIR may require a large number of Monte Carlo realizations to ensure consistent updates. In this work we implemented EnKF and SIR into a physically-based coupled surface-subsurface flow model and applied it to a synthetic test case that considers a uniform soil v-shaped catchment subject to rainfall and evaporation events. After a sensitivity analysis on the number of Monte Carlo realizations and the correlation time of the atmospheric forcing, the comparison between the two filters is done on the basis of different simulation scenarios varying observations (outlet streamflow and/or pressure head), assimilation frequency, and type of bias (atmospheric forcing or initial conditions). The results demonstrate that both EnKF and SIR are suitable DA methods for detailed physically-based hydrological modeling using the same, relatively small, ensemble size. We highlight that the Gaussian approximation in the EnKF updates leads to a state estimation that can be not consistent with the physics of the model, resulting in a slowdown of the numerical solver. SIR instead duplicates physically consistent realizations, but can display difficulties in updates when the realizations are far from the true state. We propose and test a modification of the SIR algorithm to overcome this issue and preserve assimilation efficiency.

Pasetto, Damiano; Camporese, Matteo; Putti, Mario

2012-10-01

258

Segmentation of 3D tubular structures based on 3D intensity models and particle filter tracking  

NASA Astrophysics Data System (ADS)

We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based on a novel combination of a 3D cylindrical intensity model and particle filter tracking. In comparison to earlier work we utilize a 3D intensity model as the measurement model of the particle filter, thus a more realistic 3D appearance model is used that directly represents the image intensities of 3D tubular structures within semiglobal regions-of-interest. We have successfully applied our approach using 3D synthetic images and real 3D MRA image data of the human pelvis.

Wörz, Stefan; Godinez, William J.; Rohr, Karl

2009-02-01

259

FPGA Implementation of Optimal Filtering Algorithm for TileCal ROD System  

E-print Network

Traditionally, Optimal Filtering Algorithm has been implemented using general purpose programmable DSP chips. Alternatively, new FPGAs provide a highly adaptable and flexible system to develop this algorithm. TileCal ROD is a multi-channel system, where similar data arrives at very high sampling rates and is subject to simultaneous tasks. It include different FPGAs with high I/O and with parallel structures that provide a benefit at a data analysis. The Optical Multiplexer Board is one of the elements presents in TileCal ROD System. It has FPGAs devices that present an ideal platform for implementing Optimal Filtering Algorithm. Actually this algorithm is performing in the DSPs included at ROD Motherboard. This work presents an alternative to implement Optimal Filtering Algorithm.

Torres, J; Castillo, V; Cuenca, C; Ferrer, A; Fullana, E; González, V; Higón, E; Poveda, J; Ruiz-Martinez, A; Salvachúa, B; Sanchis, E; Solans, C; Valero, A; Valls, J A

2008-01-01

260

Ares-I Bending Filter Design using a Constrained Optimization Approach  

NASA Technical Reports Server (NTRS)

The Ares-I launch vehicle represents a challenging flex-body structural environment for control system design. Software filtering of the inertial sensor output is required to ensure adequate stable response to guidance commands while minimizing trajectory deviations. This paper presents a design methodology employing numerical optimization to develop the Ares-I bending filters. The design objectives include attitude tracking accuracy and robust stability with respect to rigid body dynamics, propellant slosh, and flex. Under the assumption that the Ares-I time-varying dynamics and control system can be frozen over a short period of time, the bending filters are designed to stabilize all the selected frozen-time launch control systems in the presence of parameter uncertainty. To ensure adequate response to guidance command, step response specifications are introduced as constraints in the optimization problem. Imposing these constrains minimizes performance degradation caused by the addition of the bending filters. The first stage bending filter design achieves stability by adding lag to the first structural frequency to phase stabilize the first flex mode while gain stabilizing the higher modes. The upper stage bending filter design gain stabilizes all the flex bending modes. The bending filter designs provided here have been demonstrated to provide stable first and second stage control systems in both Draper Ares Stability Analysis Tool (ASAT) and the MSFC MAVERIC 6DOF nonlinear time domain simulation.

Hall, Charles; Jang, Jiann-Woei; Hall, Robert; Bedrossian, Nazareth

2008-01-01

261

Near-Optimal Deterministic Attitude Filtering Mohammad Zamani, Jochen Trumpf, Member, IEEE, and Robert Mahony, Senior Member, IEEE  

E-print Network

Near-Optimal Deterministic Attitude Filtering Mohammad Zamani, Jochen Trumpf, Member, IEEE are with the School of Engineering, Australian National University, Canberra, ACT, Australia. Mohammad.Zamani

Trumpf, Jochen

262

A COMPARISON OF MODEL BASED AND DIRECT OPTIMIZATION BASED FILTERING ALGORITHMS FOR SHEARWAVE VELOCITY RECONSTRUCTION FOR ELECTRODE VIBRATION ELASTOGRAPHY  

PubMed Central

Tissue stiffness estimation plays an important role in cancer detection and treatment. The presence of stiffer regions in healthy tissue can be used as an indicator for the possibility of pathological changes. Electrode vibration elastography involves tracking of a mechanical shear wave in tissue using radio-frequency ultrasound echoes. Based on appropriate assumptions on tissue elasticity, this approach provides a direct way of measuring tissue stiffness from shear wave velocity, and enabling visualization in the form of tissue stiffness maps. In this study, two algorithms for shear wave velocity reconstruction in an electrode vibration setup are presented. The first method models the wave arrival time data using a hidden Markov model whose hidden states are local wave velocities that are estimated using a particle filter implementation. This is compared to a direct optimization-based function fitting approach that uses sequential quadratic programming to estimate the unknown velocities and locations of interfaces. The mean shear wave velocities obtained using the two algorithms are within 10%of each other. Moreover, the Young’s modulus estimates obtained from an incompressibility assumption are within 15 kPa of those obtained from the true stiffness data obtained from mechanical testing. Based on visual inspection of the two filtering algorithms, the particle filtering method produces smoother velocity maps.

Ingle, Atul; Varghese, Tomy

2014-01-01

263

Intelligent Parallel Particle Swarm Optimization Algorithms  

Microsoft Academic Search

Some social systems of natural species, such as flocks of birds and schools of fish, possess interesting collective behavior.\\u000a In these systems, globally sophisticated behavior emerges from local, indirect communication amongst simple agents with only\\u000a limited capabilities. In an attempt to simulate this flocking behavior by computers, Kennedy and Eberthart (1995) realized\\u000a that an optimization problem can be formulated as

Shu-chuan Chu; Jeng-shyang Pan

2006-01-01

264

Improvements of Adaptive Filtering by Optimal Projection to filter different artifact types on long duration EEG recordings.  

PubMed

Adaptive Filtering by Optimal Projection (AFOP) is an automatic method for reducing ocular and muscular artifacts on electro-encephalographic (EEG) recordings. This paper presents two additions to this method: an improvement of the stability of ocular artifact filtering and an adaptation of the method for filtering electrode artifacts. With these improvements, it is possible to reduce almost all the current types of artifacts, while preserving brain signals, particularly those characterising epilepsy. This generalised method consists of dividing the signal into several time-frequency windows, and in applying different spatial filters to each. Two steps are required to define one of these spatial filters: the first step consists of defining artifact spatial projection using the Common Spatial Pattern (CSP) method and the second consists of defining EEG spatial projection via regression. For this second step, a progressive orthogonalisation process is proposed to improve stability. This method has been tested on long-duration EEG recordings of epileptic patients. A neurologist quantified the ratio of removed artifacts and the ratio of preserved EEG. Among the 330 artifacted pages used for evaluation, readability was judged better for 78% of pages, equal for 20% of pages, and worse for 2%. Artifact amplitudes were reduced by 80% on average. At the same time, brain sources were preserved in amplitude from 70% to 95% depending on the type of waves (alpha, theta, delta, spikes, etc.). A blind comparison with manual Independent Component Analysis (ICA) was also realised. The results show that this method is competitive and useful for routine clinical practice. PMID:22717094

Boudet, S; Peyrodie, L; Forzy, G; Pinti, A; Toumi, H; Gallois, P

2012-10-01

265

Optimal performance of charged particle telescopes in space  

NASA Astrophysics Data System (ADS)

A Bayesian probabilistic data analysis method for energetic proton and ion data from charged particle telescopes in space is described. The telescope is assumed to consist of only a series of planar silicon detectors with graduated thicknesses. The method is based on a range-straggling function and makes optimal use of energy loss measurements in each detector. It provides accurate incidence angle estimates for particles stopping in the telescope, allowing accurate element identification and possible isotope identification. It also provides energy estimates for high-energy particles going through the telescope without stopping. Examples are shown for simulated telescope design performance tests and application to real space-particle data.

Selesnick, R. S.

2014-10-01

266

Optimal filter parameters for low SNR seismograms as a function of station and event location  

NASA Astrophysics Data System (ADS)

Global seismic monitoring requires deployment of seismic sensors worldwide, in many areas that have not been studied or have few useable recordings. Using events with lower signal-to-noise ratios (SNR) would increase the amount of data from these regions. Lower SNR events can add significant numbers to data sets, but recordings of these events must be carefully filtered. For a given region, conventional methods of filter selection can be quite subjective and may require intensive analysis of many events. To reduce this laborious process, we have developed an automated method to provide optimal filters for low SNR regional or teleseismic events. As seismic signals are often localized in frequency and time with distinct time-frequency characteristics, our method is based on the decomposition of a time series into a set of subsignals, each representing a band with f/? f constant (constant Q). The SNR is calculated on the pre-event noise and signal window. The band pass signals with high SNR are used to indicate the cutoff filter limits for the optimized filter. Results indicate a significant improvement in SNR, particularly for low SNR events. The method provides an optimum filter which can be immediately applied to unknown regions. The filtered signals are used to map the seismic frequency response of a region and may provide improvements in travel-time picking, azimuth estimation, regional characterization, and event detection. For example, when an event is detected and a preliminary location is determined, the computer could automatically select optimal filter bands for data from non-reporting stations. Results are shown for a set of low SNR events as well as 379 regional and teleseismic events recorded at stations ABKT, KIV, and ANTO in the Middle East.

Leach, Richard R.; Dowla, Farid U.; Schultz, Craig A.

1999-06-01

267

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization  

PubMed Central

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

268

Optimal design of Biquad Switched-Capacitor Active Filters  

E-print Network

? ncasas ? nsn's + nsnsar ? &tla2& and D(=) = ='(1 + ae) + =(asn as s ) 9 ( + as) 16 H(Z) f'0 N('TH 1&0TI0 N AND 00EFFIC'IENT MATCHING T&i &l&sig&i a S(' ?&' tive filter. the =-domain (digital domain) transfer function H(=) which?n&eets the d.... For M&&d& s 2-4: S&ui&e as (4-&d) &xcept that hs(P?) hss three more equations, that is, each op'11&n&sation &s p&'i'for&lied with DMC' ? DAf C:*=-0, or S~?? S~, *=O, or j, /fr = ( f, /fr )* where * denot& s an assumed valne. But in each case a...

Jo, Han Cheol

2012-06-07

269

Stochastic adaptive particle-beam tracker using Meer-filter feedback. Master's thesis  

Microsoft Academic Search

This research develops a realizable proportional-plus-integral (PI) feedback tracker to control a neutral-particle beam. The design is based on detecting the photoelectron events that are emitted from a laser-excited particle beam and the observed events are used by a Meer filter to locate the beam's centerline. The observed events are modeled by a Poisson space time process and are composed

Johnson

1986-01-01

270

High-efficiency particulate air filter test stand and aerosol generator for particle loading studies  

NASA Astrophysics Data System (ADS)

This manuscript describes the design, characterization, and operational range of a test stand and high-output aerosol generator developed to evaluate the performance of 30×30×29cm3 nuclear grade high-efficiency particulate air (HEPA) filters under variable, highly controlled conditions. The test stand system is operable at volumetric flow rates ranging from 1.5to12standardm3/min. Relative humidity levels are controllable from 5%-90% and the temperature of the aerosol stream is variable from ambient to 150°C. Test aerosols are produced through spray drying source material solutions that are introduced into a heated stainless steel evaporation chamber through an air-atomizing nozzle. Regulation of the particle size distribution of the aerosol challenge is achieved by varying source solution concentrations and through the use of a postgeneration cyclone. The aerosol generation system is unique in that it facilitates the testing of standard HEPA filters at and beyond rated media velocities by consistently providing, into a nominal flow of 7standardm3/min, high mass concentrations (˜25mg/m3) of dry aerosol streams having count mean diameters centered near the most penetrating particle size for HEPA filters (120-160nm). Aerosol streams that have been generated and characterized include those derived from various concentrations of KCl, NaCl, and sucrose solutions. Additionally, a water insoluble aerosol stream in which the solid component is predominantly iron (III) has been produced. Multiple ports are available on the test stand for making simultaneous aerosol measurements upstream and downstream of the test filter. Types of filter performance related studies that can be performed using this test stand system include filter lifetime studies, filtering efficiency testing, media velocity testing, evaluations under high mass loading and high humidity conditions, and determination of the downstream particle size distributions.

Arunkumar, R.; Hogancamp, Kristina U.; Parsons, Michael S.; Rogers, Donna M.; Norton, Olin P.; Nagel, Brian A.; Alderman, Steven L.; Waggoner, Charles A.

2007-08-01

271

Tracking and recognizing actions of multiple hockey players using the boosted particle filter  

E-print Network

Tracking and recognizing actions of multiple hockey players using the boosted particle filter Wei track multiple hockey players and simultaneously recognize their actions given a single broadcast video in the challenge of inventing a system that tracks multiple hockey players in a video sequence and simul- taneously

Little, Jim

272

Particle Filter Based Method for Maneuvering Target Tracking Norikazu Ikoma*, Naoyuki Ichimuray, Tomoyuki Higuchiz, Hiroshi Maeda*  

E-print Network

Particle Filter Based Method for Maneuvering Target Tracking Norikazu Ikoma*, Naoyuki Ichimuray dynamics and observa- tion model for the radar observation process with non- linear formula. Using heavy in polar coordinate with independent mea- surement noises for each observation variable. Due

Higuchi, Tomoyuki

273

A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior  

Microsoft Academic Search

Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle fil- ters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a

Yogesh Rathi; Namrata Vaswani; Allen Tannenbaum

2007-01-01

274

Facial Action Coding Using Multiple Visual Cues and a Hierarchy of Particle Filters  

Microsoft Academic Search

In this paper we present a framework for tracking nonrigid facial landmarks by combining various visual cues at multiple levels of detail. Using a probabilistic framework consisting of a hierarchy of particle filters, we are able to track individual facial landmarks using multiple visual cues at the local level, as well as tracking results at more coarse level of detail.

Joel C. McCall; Mohan M. Trivedi

2006-01-01

275

Estimation of sea-ice parameters using a local particle filter  

E-print Network

Estimation of sea-ice parameters using a local particle filter Arjen Terwisscha van Scheltinga1@ualberta.ca #12;Abstract The modeling of sea-ice (thermo-)dynamics and sea-ice-ocean interaction has proven to be difficult due to the highly nonlinear viscous-elastic properties of the sea-ice. In sea-ice-ocean models

van Leeuwen, Peter Jan

276

ECG Denoising Using a Dynamical Model and a Marginalized Particle Filter  

E-print Network

ECG Denoising Using a Dynamical Model and a Marginalized Particle Filter Chao Lin1,3, M of robust ECG denoising tech- niques is important for automatic diagnoses of cardiac diseases. Based on a previously suggested nonlinear dynamic model for the generation of realistic synthetic ECG, we introduce

Tourneret, Jean-Yves

277

X-RAY FLUORESCENCE ANALYSIS OF FILTER-COLLECTED AEROSOL PARTICLES  

EPA Science Inventory

X-ray fluorescence (XRF) has become an effective technique for determining the elemental content of aerosol samples. For quantitative analysis, the aerosol particles must be collected as uniform deposits on the surface of Teflon membrane filters. An energy dispersive XRF spectrom...

278

A dynamic grouping strategy for implementation of the particle filter on a massively parallel computer  

E-print Network

computer Shin'ya Nakano The Institute of Statistical Mathematics Tachikawa, Tokyo, Japan. shiny@ism.ac.jp Abstract ­ A practical way to implement the parti- cle filter (PF) on a massively parallel computer is dis to be computation- ally expensive in applying to high-dimensional problems because a enormous number of particles

Nakano, Shin'ya

279

The Influence of Multi-Sensor Video Fusion on Object Tracking Using a Particle Filter  

E-print Network

investigates how the object tracking performance is affected by the fusion quality of videos from visible (VIZThe Influence of Multi-Sensor Video Fusion on Object Tracking Using a Particle Filter L. Mihaylova similarity measure [LMCD06], can capture spatial information from the image and can provide better

Mihaylova, Lyudmila

280

A fast atmospheric turbulent parameters estimation using particle filtering. Application to LIDAR  

E-print Network

A fast atmospheric turbulent parameters estimation using particle filtering. Application to LIDAR. Doppler LIDAR, is typically used to get this kind of information because it can make fast, distant on simulated Doppler LIDAR measurements, in tree-dimensional modeling. 1. Introduction In various activities

Baehr, Christophe

281

The Influence of MultiSensor Video Fusion on Object Tracking Using a Particle Filter  

Microsoft Academic Search

This paper investigates how the object tracking performance is affected by the fusion quality of videos from visible (VIZ) and infrared (IR) surveillance cameras, as compared to tracking in single modality videos. The videos have been fused us- ing the simple averaging, and various multiresolution techniques. Tracking has been accomplished by means of a particle filter using colour and edge

Lyudmila Mihaylova; Artur Loza; Stavri G. Nikolov; John J. Lewis; Eduardo Fernández Canga; J. Li; Timothy D. Dixon; Cedric Nishan Canagarajah; David R. Bull

2006-01-01

282

Multiple cues fusion for object tracking with particle filter in infrared image  

Microsoft Academic Search

Object tracking in infrared image sequences is a challenging research topic due to the extremely low signal to noise ratio of IR image. In this paper, a new tracking method based on multiple cues fusion particle filter framework is proposed. In order to make full use of the object appearance information, both the spatial distribution and the gray distribution of

Ting Jin; Fu-Gen Zhou; Xiang-Zhi Bai; Ken Chen

2009-01-01

283

Automated Intruder Tracking using Particle Filtering and a Network of Binary Motion Sensors  

E-print Network

goldberg@berkeley.edu Abstract-- Our objective is to automatically track and capture photos of an intruderAutomated Intruder Tracking using Particle Filtering and a Network of Binary Motion Sensors Jeremy infrared (PIR) motion sensors that suggest that our estimator is effective and degrades gracefully

Goldberg, Ken

284

ON PARTICLE FILTERS FOR LANDMINE DETECTION USING IMPULSE GROUND PENETRATING RADAR  

E-print Network

ON PARTICLE FILTERS FOR LANDMINE DETECTION USING IMPULSE GROUND PENETRATING RADAR William Ng detection based on ground penetrating radar (GPR) signals using sequential Monte Carlo (SMC) methods. Since resolution and excellent de- tection of metallic and nonmetallic objects, ground penetrating radar (GPR) has

So, Hing-Cheung

285

Vision-based SLAM using the Rao-Blackwellised Particle Filter  

Microsoft Academic Search

We consider the problem of Simultaneous Localization and Mapping (SLAM) from a Bayesian point of view using the Rao-Blackwellised Particle Filter (RBPF). We focus on the class of indoor mobile robots equipped with only a stereo vision sensor. Our goal is to construct dense metric maps of natural 3D point landmarks for large cyclic environments in the absence of accurate

Robert Sim; Matt Griffin; James J. Little

2005-01-01

286

Rao-Blackwellised Particle Filter with Adaptive System Noise and its Evaluation for Tracking in Surveillance  

Microsoft Academic Search

In the visual tracking domain, Particle Filtering (PF) can become quite inefficient when being applied into high dimensional state space. Rao-Blackwellisation (1) has been shown to be an effective method to reduce the size of the state space by marginalizing out some of the variables analytically (2) . In this paper based on our previous work (3) we propose an

Xinyu Xu; Baoxin Li

287

Optimal filtering of complex turbulent systems with memory depth through consistency constraints  

NASA Astrophysics Data System (ADS)

In this article, we develop a linear theory for optimal filtering of complex turbulent signals with model errors through linear autoregressive models. We will show that when the autoregressive model parameters are chosen such that they satisfy absolute stability and consistency conditions of at least order-2 of the classical multistep method for solving initial value problems, the filtered solutions with autoregressive models of order p?2 are optimal in the sense that they are comparable to the estimates obtained from the true filter with perfect model. This result is reminiscent of the Lax-equivalence fundamental theorem in the analysis of finite difference discretization scheme for the numerical solutions of partial differential equations. We will apply this linear theory to filter two nonlinear problems, the slowest mode of the truncated Burgers-Hopf and the Lorenz-96 model. On these nonlinear problems, we will show that whenever these linear conditions are satisfied, the filtered solutions accuracy is significantly improved. Finally, we will also apply the recently developed offline test criteria to understand the robustness of the multistep filter on various turbulent nature, including the stochastically forced linear advection-diffusion equation and a toy model for barotropic turbulent Rossby waves.

Bakunova, Eugenia S.; Harlim, John

2013-03-01

288

A STUDY ON ASH PARTICLE DISTRIBUTION CHARACTERISITICS OF CANDLE FILTER SURFACE REGENERATION AT ROOM TEMPERATURE  

SciTech Connect

Ceramic barrier filtration is a leading technology employed in hot gas filtration. Hot gases loaded with ash particle flow through the ceramic candle filters and deposit ash on their outer surface. The deposited ash is periodically removed using back pulse cleaning jet, known as surface regeneration. The cleaning done by this technique still leaves some residual ash on the filter surface, which over a period of time sinters, forms a solid cake and leads to mechanical failure of the candle filter. A room temperature testing facility (RTTF) was built to gain more insight into the surface regeneration process before testing commenced at high temperature. RTTF was instrumented to obtain pressure histories during the surface regeneration process and a high-resolution high-speed imaging system was integrated in order to obtain pictures of the surface regeneration process. The objective of this research has been to utilize the RTTF to study the surface regeneration process at the convenience of room temperature conditions. The face velocity of the fluidized gas, the regeneration pressure of the back pulse and the time to build up ash on the surface of the candle filter were identified as the important parameters to be studied. Two types of ceramic candle filters were used in the study. Each candle filter was subjected to several cycles of ash build-up followed by a thorough study of the surface regeneration process at different parametric conditions. The pressure histories in the chamber and filter system during build-up and regeneration were then analyzed. The size distribution and movement of the ash particles during the surface regeneration process was studied. Effect of each of the parameters on the performance of the regeneration process is presented. A comparative study between the two candle filters with different characteristics is presented.

Vasudevan, V.; Kang, B.S-J.; Johnson, E.K.

2002-09-19

289

Nonlinear optimal filter technique for analyzing energy depositions in TES sensors driven into saturation  

NASA Astrophysics Data System (ADS)

We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs) connected to quasiparticle (qp) traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search) Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.

Shank, B.; Yen, J. J.; Cabrera, B.; Kreikebaum, J. M.; Moffatt, R.; Redl, P.; Young, B. A.; Brink, P. L.; Cherry, M.; Tomada, A.

2014-11-01

290

IEEE TRANSACTIONS ON SIGN-L PROCESSING, VOL. 43, NO. 3, MARCH 1995 59I Optimal Weighted Median Filtering  

E-print Network

attenuation and some structural constraints on the filter's behavior. In certain special cases, the optimal the same advantages as- the median filter: edge preservation and efficient suppression of impulsive noise filters under the MSE and the MAE criteria, [5], [12], [20], [21]. These algorithms were motivated

Gabbouj, Moncef

291

Boundary filters for vector particles passing parity breaking domains  

NASA Astrophysics Data System (ADS)

The electrodynamics supplemented with a Lorenz and CPT invariance violating Chern-Simons (CS) action (Carrol-Field-Jackiw electrodynamics) is studied when the parity-odd medium is bounded by a hyperplane separating it from the vacuum. The solutions in both half-spaces are carefully discussed and for space-like boundary stitched on the boundary with help of the Bogolubov transformations. The presence of two different Fock vacua is shown. The passage of photons and massive vector mesons through a boundary between the CS medium and the vacuum of conventional Maxwell electrodynamics is investigated. Effects of reflection from a boundary (up to the total one) are revealed when vector particles escape to vacuum and income from vacuum passing the boundary.

Kolevatov, S. S.; Andrianov, A. A.

2014-07-01

292

Nonlinear Motion Cueing Algorithm: Filtering at Pilot Station and Development of the Nonlinear Optimal Filters for Pitch and Roll  

NASA Technical Reports Server (NTRS)

Telban and Cardullo have developed and successfully implemented the non-linear optimal motion cueing algorithm at the Visual Motion Simulator (VMS) at the NASA Langley Research Center in 2005. The latest version of the non-linear algorithm performed filtering of motion cues in all degrees-of-freedom except for pitch and roll. This manuscript describes the development and implementation of the non-linear optimal motion cueing algorithm for the pitch and roll degrees of freedom. Presented results indicate improved cues in the specified channels as compared to the original design. To further advance motion cueing in general, this manuscript describes modifications to the existing algorithm, which allow for filtering at the location of the pilot's head as opposed to the centroid of the motion platform. The rational for such modification to the cueing algorithms is that the location of the pilot's vestibular system must be taken into account as opposed to the off-set of the centroid of the cockpit relative to the center of rotation alone. Results provided in this report suggest improved performance of the motion cueing algorithm.

Zaychik, Kirill B.; Cardullo, Frank M.

2012-01-01

293

IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 7, JULY 2005 2279 Marginalized Particle Filters for Mixed  

E-print Network

particle filter is applied to an integrated navigation system for aircraft. It is demonstrated system in the Swedish fighter aircraft Gripen consists of an inertial navigation system (INS), a terrain, marginalization, navigation sys- tems, nonlinear systems, particle filter, state estimation. I. INTRODUCTION

LeGland, François

294

Optimization of high speed pipelining in FPGA-based FIR filter design using genetic algorithm  

NASA Astrophysics Data System (ADS)

This paper compares FPGA-based full pipelined multiplierless FIR filter design options. Comparison of Distributed Arithmetic (DA), Common Sub-Expression (CSE) sharing and n-dimensional Reduced Adder Graph (RAG-n) multiplierless filter design methods in term of size, speed, and A*T product are provided. Since DA designs are table-based and CSE/RAG-n designs are adder-based, FPGA synthesis design data are used for a realistic comparison. Superior results of a genetic algorithm based optimization of pipeline registers and non-output fundamental coefficients are shown. FIR filters (posted as open source by Kastner et al.) for filters in the length from 6 to 151 coefficients are used.

Meyer-Baese, Uwe; Botella, Guillermo; Romero, David E. T.; Kumm, Martin

2012-06-01

295

Common model analysis and improvement of particle swarm optimizer  

Microsoft Academic Search

Particle swarm optimizer (PSO), a new evolutionary computation algorithm, exhibits good performance for optimization problems,\\u000a although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters\\u000a and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying\\u000a dynamic system, and the sufficient

Feng Pan; Jie Chen; Minggang Gan; Guanghui Wang; Tao Cai

2007-01-01

296

Optimal filtering versus regularization methods in the Fourier precompensation based proximity neurocorrection in Electron Beam Lithography  

Microsoft Academic Search

In this paper proximity effects correction in Electron Beam Lithography by means of an artificial neural network is presented. Supporting approximations to cope with negative doses inherent in Gibbs oscillations which occur from step-like function representation in the Fourier space are introduced. Miller regularization theory as better alternative to Tikhonov one is presented. Optimal filtering with prolate spheriodal wave functions

P. Jedrasik; J. Garcia; B. De Boeck; D Van Dyck

1998-01-01

297

DMT Bit Rate Maximization With Optimal Time Domain Equalizer Filter Bank Architecture  

E-print Network

DMT Bit Rate Maximization With Optimal Time Domain Equalizer Filter Bank Architecture Milos-tone (DMT) is a multicarrier modula- tion method in which the available bandwidth of a com- munication create nearly orthogonal subchannels. DMT has been standardized in [1, 2, 3, 4]. A similar multi- carrier

Evans, Brian L.

298

Performance Optimization of a Photovoltaic Generator with an Active Power Filter Application  

E-print Network

1 Performance Optimization of a Photovoltaic Generator with an Active Power Filter Application (MPPT) for photovoltaic (PV) systems maximizes the power that can be transferred from the PV system), a constant voltage on the DC side of the inverter was proposed, it is a photovoltaic generator which is used

Paris-Sud XI, Université de

299

Optimal steering vector adaptation for linear filters leading to robust beamforming  

E-print Network

. However, the actual steering vector might represent a target which is moving in space, as for exampleOptimal steering vector adaptation for linear filters leading to robust beamforming Michal Natora steering vector is not know, and thus, robust beamforming methods have to be used. In this contribution

Wichmann, Felix

300

Sixth-moment method for multi-channel Bragg filter optimization  

NASA Astrophysics Data System (ADS)

A new method for the crest factor minimization of for multi-tone signal based on the sampling functional is proposed. The minimum of sixth moment is used as an initial value for the minimax search. For high number of tones the optimal values are better than previously reported. Application for fiber Bragg multi-channel filters for telecommunications is discussed.

Belai, Oleg V.; Nemykin, Anton V.; Shapiro, David A.

2011-03-01

301

Sixth-moment method for multi-channel Bragg filter optimization  

NASA Astrophysics Data System (ADS)

A new method for the crest factor minimization of for multi-tone signal based on the sampling functional is proposed. The minimum of sixth moment is used as an initial value for the minimax search. For high number of tones the optimal values are better than previously reported. Application for fiber Bragg multi-channel filters for telecommunications is discussed.

Belai, Oleg V.; Nemykin, Anton V.; Shapiro, David A.

2010-07-01

302

Optimal Characteristic of Optical Filter for White-Light Interferometry based on  

E-print Network

Optimal Characteristic of Optical Filter for White-Light Interferometry based on Sampling Theory. hidemitsu-ogawa@kuramae.ne.jp, a-hira@yamaguchi-u.ac.jp Abstract: White-light interferometry is a technique White-light interferometry is a technique of profiling sur- face topography of objects

Boyer, Edmond

303

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

NASA Astrophysics Data System (ADS)

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 ? = 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 covariance matrix ? is assumed to be non-singular. In the present work we consider the case where the covariance ? is singular. This happens in particular when the noise is spatially smooth and can be represented by a small number of Fourier coefficients, as is often the case in geophysical applications. We derive an implicit filter for this problem and show that it is very efficient, because the filter operates in a space whose dimension is the rank of ?, rather than the full model dimension. We compare the implicit filter to SIR, to the Ensemble Kalman Filter and to variational methods, and also study how information from data is propagated from observed to unobserved variables. We illustrate the theory on two coupled nonlinear PDE's in one space dimension that have been used as a test-bed for geomagnetic data assimilation. We observe that the implicit filter gives good results with few (2-10) particles, while SIR requires thousands of particles for similar accuracy. We also find lower limits to the accuracy of the filter's reconstruction as a function of data availability.

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

2011-12-01

304

Abstract--A new technique for designing EMG spatial filters with optimized spatial selectivity is described. Simulations  

E-print Network

. Additionally, the surface EMG signal is distorted compared to indwelling EMG [11]. Spatial filtering has been-adapted to non-invasive surface EMG. Farina and Rainoldi [8] created an optimal spatial Supported in partAbstract--A new technique for designing EMG spatial filters with optimized spatial selectivity

Clancy, Ted

305

Evaluation of the effect of media velocity on filter efficiency and most penetrating particle size of nuclear grade high-efficiency particulate air filters.  

PubMed

High-efficiency particulate air (HEPA) filters are widely used to control particulate matter emissions from processes that involve management or treatment of radioactive materials. Section FC of the American Society of Mechanical Engineers AG-1 Code on Nuclear Air and Gas Treatment currently restricts media velocity to a maximum of 2.5 cm/sec in any application where this standard is invoked. There is some desire to eliminate or increase this media velocity limit. A concern is that increasing media velocity will result in higher emissions of ultrafine particles; thus, it is unlikely that higher media velocities will be allowed without data to demonstrate the effect of media velocity on removal of ultrafine particles. In this study, the performance of nuclear grade HEPA filters, with respect to filter efficiency and most penetrating particle size, was evaluated as a function of media velocity. Deep-pleat nuclear grade HEPA filters (31 cm x 31 cm x 29 cm) were evaluated at media velocities ranging from 2.0 to 4.5 cm/sec using a potassium chloride aerosol challenge having a particle size distribution centered near the HEPA filter most penetrating particle size. Filters were challenged under two distinct mass loading rate regimes through the use of or exclusion of a 3 microm aerodynamic diameter cut point cyclone. Filter efficiency and most penetrating particle size measurements were made throughout the duration of filter testing. Filter efficiency measured at the onset of aerosol challenge was noted to decrease with increasing media velocity, with values ranging from 99.999 to 99.977%. The filter most penetrating particle size recorded at the onset of testing was noted to decrease slightly as media velocity was increased and was typically in the range of 110-130 nm. Although additional testing is needed, these findings indicate that filters operating at media velocities up to 4.5 cm/sec will meet or exceed current filter efficiency requirements. Additionally, increased emission of ultrafine particles is seemingly negligible. PMID:18726819

Alderman, Steven L; Parsons, Michael S; Hogancamp, Kristina U; Waggoner, Charles A

2008-11-01

306

Preparation and optimization of the laser thin film filter  

NASA Astrophysics Data System (ADS)

A co-colored thin film device for laser-induced damage threshold test system is presented in this paper, to make the laser-induced damage threshold tester operating at 532nm and 1064nm band. Through TFC simulation software, a film system of high-reflection, high -transmittance, resistance to laser damage membrane is designed and optimized. Using thermal evaporation technique to plate film, the optical properties of the coating and performance of the laser-induced damage are tested, and the reflectance and transmittance and damage threshold are measured. The results show that, the measured parameters, the reflectance R >= 98%@532nm, the transmittance T >= 98%@1064nm, the laser-induced damage threshold LIDT >= 4.5J/cm2 , meet the design requirements, which lays the foundation of achieving laser-induced damage threshold multifunction tester.

Su, Jun-hong; Wang, Wei; Xu, Jun-qi; Cheng, Yao-jin; Wang, Tao

2014-08-01

307

Particle swarm optimization-based algorithm for lightning location estimation  

Microsoft Academic Search

Lightning early warning requires lightning location systems to process sensors' measurements in near real time. A new algorithm based on particle swarm optimization (PSO) is developed to provide reliable and immediate solutions of lightning location and occurrence time. Comparing with iterative-type algorithms, the PSO-based algorithm does not require initial value and is easy to program. Different parameter choice schemes for

Zhixiang Hu; Yinping Wen; Wenguang Zhao; Hongping Zhu

2010-01-01

308

Comparing inertia weights and constriction factors in particle swarm optimization  

Microsoft Academic Search

The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance

R. C. Eberhart; Y. Shi

2000-01-01

309

Generating Optimal Initial Conditions for Smooth Particle Hydrodynamics Simulations  

E-print Network

We present a new optimal method to set up initial conditions for Smooth Particle Hydrodynamics (SPH) simulations, which may also be of interest for N-body simulations. This new method is based on weighted Voronoi tesselations (WVTs) and can meet arbitrarily complex spatial resolution requirements. We conduct a comprehensive review of existing SPH setup methods, and outline their advantages, limitations and drawbacks.

Diehl, Steven; Fryer, Christopher L; Riethmiller, David; Statler, Thomas S

2012-01-01

310

Generating optimal initial conditions for smooth particle hydrodynamics (SPH) simulations  

Microsoft Academic Search

We present a new optimal method to set up initial conditions for Smooth Particle Hydrodynamics Simulations, which may also be of interest for N-body simulations. This new method is based on weighted Voronoi tesselations (WVTs) and can meet arbitrarily complex spatial resolution requirements. We conduct a comprehensive review of existing SPH setup methods, and outline their advantages, limitations and drawbacks.

Steven Diehl; Gabriel M Rockefeller; Christopher L Fryer

2008-01-01

311

Particle Swarm Optimization for clustering short-text corpora1  

E-print Network

Particle Swarm Optimization for clustering short-text corpora1 Diego INGARAMO a , Marcelo ERRECALDE, Spain. e-mail: prosso@dsic.upv.es Abstract. Clustering of short-text collections is a very relevant: the Expected Density Measure ¯ and the Global Silhouette coefficient. In recent works on short-text clustering

Rosso, Paolo

312

Coverage in wireless sensor networks based on individual particle optimization  

Microsoft Academic Search

Effective sensor coverage is one of the key topics addressed in wireless sensor networks (WSNs) study, which refers to the deployment and detection probability of WSNs. This paper proposes a novel deployment algorithm for mobile sensor networks, based on individual particle optimization (IPO). The algorithm is designed for real-time online deployment for the purpose of maximum coverage in the environment.

S. M. A Salehizadeh; A. Dirafzoon; M. B. Menhaj; A. Afshar

2010-01-01

313

Optimal nonlinear filtering for track-before-detect in IR image sequences  

NASA Astrophysics Data System (ADS)

The 3D matched filter proposed by Reed et al. and its generalizations provide a powerful processing technique for detecting moving low observable targets. This technique is a centerpiece of various track-before-detect (TBD) systems. However, the 3D matched filter was designed for constant velocity targets and its applicability to more complicated patterns of target dynamics is not obvious. In this paper the 3D matched filter and BAVF are extended to the case of switching multiple models of target dynamics. We demonstrate that the 3D matched filtering can be cast into a general framework of optimal spatio-temporal nonlinear filtering for hidden Markov models. A robust and computationally efficient Bayesian algorithm for detection and tracking of low observable agile targets in IR Search and Track (IRST) systems is presented. The proposed algorithm is fully sequential. It facilitates optimal fusion of sensor measurements and prior information regarding possible threats. The algorithm is implemented as a TBD subsystem for IRST, however the general methodology is equally applicable for other imaging sensors.

Rozovskii, Boris L.; Petrov, Anton

1999-10-01

314

Linear variable filter optimization for emergency response chemical detection and discrimination  

NASA Astrophysics Data System (ADS)

Linear variable filter design and fabrication for LWIR is now commercially available for use in the development of remote sensing systems. The linear variable filter is attached directly to the cold shield of the focal plane array. The resulting compact spectrometer assemblies are completely contained in the Dewar system. This approach eliminates many of the wavelength calibration problems associated with current prism and grating systems and also facilitates the cost effective design and fabrication of aerial sensing systems for specific applications. This paper describes a study that was conducted with the following three objectives: 1) Determine if a multi-channel linear-variable-filter-based line scanner system can be used to discriminate a set of chemical vapors that represent a high probability of occurrence during a typical emergency response chemical incident; 2) Determine which multi-channel linear variable filter design is optimal; and 3) Determine the acceptable instrument noise equivalent spectral radiance for this application. A companion paper describes a separate study that was conducted to determine the concentration levels at which detection and discrimination can be achieved for the various chemicals based on the optimal filter design under various degrees of imperfect atmospheric correction.

Shen, Sylvia S.; Lewis, Paul E.

2010-08-01

315

Design Optimization of Vena Cava Filters: An application to dual filtration devices  

SciTech Connect

Pulmonary embolism (PE) is a significant medical problem that results in over 300,000 fatalities per year. A common preventative treatment for PE is the insertion of a metallic filter into the inferior vena cava that traps thrombi before they reach the lungs. The goal of this work is to use methods of mathematical modeling and design optimization to determine the configuration of trapped thrombi that minimizes the hemodynamic disruption. The resulting configuration has implications for constructing an optimally designed vena cava filter. Computational fluid dynamics is coupled with a nonlinear optimization algorithm to determine the optimal configuration of trapped model thrombus in the inferior vena cava. The location and shape of the thrombus are parameterized, and an objective function, based on wall shear stresses, determines the worthiness of a given configuration. The methods are fully automated and demonstrate the capabilities of a design optimization framework that is broadly applicable. Changes to thrombus location and shape alter the velocity contours and wall shear stress profiles significantly. For vena cava filters that trap two thrombi simultaneously, the undesirable flow dynamics past one thrombus can be mitigated by leveraging the flow past the other thrombus. Streamlining the shape of thrombus trapped along the cava wall reduces the disruption to the flow, but increases the area exposed to abnormal wall shear stress. Computer-based design optimization is a useful tool for developing vena cava filters. Characterizing and parameterizing the design requirements and constraints is essential for constructing devices that address clinical complications. In addition, formulating a well-defined objective function that quantifies clinical risks and benefits is needed for designing devices that are clinically viable.

Singer, M A; Wang, S L; Diachin, D P

2009-12-03

316

Determination of filter-cake thicknesses from on-line flow measurements and gas/particle transport modeling  

SciTech Connect

The use of cylindrical candle filters to remove fine ({approx}0.005 mm) particles from hot ({approx}500- 900{degrees}C) gas streams currently is being developed for applications in advanced pressurized fluidized bed combustion (PFBC) and integrated gasification combined cycle (IGCC) technologies. Successfully deployed with hot-gas filtration, PFBC and IGCC technologies will allow the conversion of coal to electrical energy by direct passage of the filtered gases into non-ruggedized turbines and thus provide substantially greater conversion efficiencies with reduced environmental impacts. In the usual approach, one or more clusters of candle filters are suspended from a tubesheet in a pressurized (P {approx_lt}1 MPa) vessel into which hot gases and suspended particles enter, the gases pass through the walls of the cylindrical filters, and the filtered particles form a cake on the outside of each filter. The cake is then removed periodically by a backpulse of compressed air from inside the filter, which passes through the filter wall and filter cake. In various development or demonstration systems the thickness of the filter cake has proved to be an important, but unknown, process parameter. This paper describes a physical model for cake and pressure buildups between cleaning backpulses, and for longer term buildups of the ``baseline`` pressure drop, as caused by incomplete filter cleaning and/or re-entrainment. When combined with operating data and laboratory measurements of the cake porosity, the model may be used to calculate the (average) filter permeability, the filter-cake thickness and permeability, and the fraction of filter-cake left on the filter by the cleaning backpulse or re-entrained after the backpulse. When used for a variety of operating conditions (e.g., different coals, sorbents, temperatures, etc.), the model eventually may provide useful information on how the filter-cake properties depend on the various operating parameters.

Smith, D.H.; Powell, V. [USDOE Morgantown Energy Technology Center, WV (United States); Ibrahim, E. [Oak Ridge Inst. for Science and Education, TN (United States); Ferer, M. [West Virginia Univ., Morgantown, WV (United States). Dept. of Physics; Ahmadi, G. [National Research Council, Washington, DC (United States)

1996-12-31

317

Optimal control for a parallel hybrid hydraulic excavator using particle swarm optimization.  

PubMed

Optimal control using particle swarm optimization (PSO) is put forward in a parallel hybrid hydraulic excavator (PHHE). A power-train mathematical model of PHHE is illustrated along with the analysis of components' parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rule-based one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is off-line optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators. PMID:23818832

Wang, Dong-yun; Guan, Chen

2013-01-01

318

Optimal Control for a Parallel Hybrid Hydraulic Excavator Using Particle Swarm Optimization  

PubMed Central

Optimal control using particle swarm optimization (PSO) is put forward in a parallel hybrid hydraulic excavator (PHHE). A power-train mathematical model of PHHE is illustrated along with the analysis of components' parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rule-based one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is off-line optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators. PMID:23818832

Wang, Dong-yun; Guan, Chen

2013-01-01

319

Two-dimensional temperature measurements in particle loaded technical flames by filtered Rayleigh scattering.  

PubMed

Filtered Rayleigh scattering (FRS) is applied to determine two-dimensional temperature distributions in a hexamethyldisiloxane loaded propane/air flame intended for combustion chemical vapor deposition processes. An iodine cell as a molecular filter suppresses background scattering, e.g., by particles, while the wings of the spectrally broadened Rayleigh scattering can pass this filter. A frequency-doubled Nd:YAG laser is tuned to a strong absorption line of iodine. The gas temperature is deduced from the transmitted Rayleigh scattering signal. Since FRS also depends on molecule-specific scattering cross sections, the local gas composition of majority species is measured using the Raman scattering technique. Limits and restrictions are discussed. PMID:24663450

Müller, D; Pagel, R; Burkert, A; Wagner, V; Paa, W

2014-03-20

320

Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering  

PubMed Central

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics. Tested on the Audio-visual Emotion Recognition Challenge dataset, our single modality estimators achieve substantially higher scores than the official baseline method for every dimension of affect. Our filtering-based multi-modality fusion achieves correlation performance of 0.344 (baseline: 0.136) and 0.280 (baseline: 0.096) for the fully continuous and word level sub challenges, respectively.

Savran, Arman; Cao, Houwei; Shah, Miraj; Nenkova, Ani; Verma, Ragini

2013-01-01

321

Optimization and evaluation of fluorescent tracers for flare removal in gas-phase particle image velocimetry  

NASA Astrophysics Data System (ADS)

We report the development of optimized fluorescent dye-doped tracer particles for gas-phase particle image velocimetry (PIV) and their use to eliminate 'flare' from the images obtained. In such applications, micron-sized tracer particles are normally required to accurately follow the flow. However, as the tracer size is reduced the amount of light incident on the particle diminishes and consequently the intensity of emitted light (fluorescence). Hence, there is a requirement to identify dyes with high quantum yield that can be dissolved in conventional tracer media at high concentrations. We describe the selection and characterization of a highly fluorescent blue-emitting dye, Bis-MSB, using a novel method, employing stabilized micro-emulsions, to emulate the fluorescence properties of tracer particles. We present the results of PIV experiments, using 1 µm tracer particles of o-xylene doped with Bis-MSB, in which elastically scattered 'flare' has been successfully removed from the images using an appropriate optical filter.

Chennaoui, M.; Angarita-Jaimes, D.; Ormsby, M. P.; Angarita-Jaimes, N.; McGhee, E.; Towers, C. E.; Jones, A. C.; Towers, D. P.

2008-11-01

322

Planar straightness error evaluation based on particle swarm optimization  

NASA Astrophysics Data System (ADS)

The straightness error generally refers to the deviation between an actual line and an ideal line. According to the characteristics of planar straightness error evaluation, a novel method to evaluate planar straightness errors based on the particle swarm optimization (PSO) is proposed. The planar straightness error evaluation problem is formulated as a nonlinear optimization problem. According to minimum zone condition the mathematical model of planar straightness together with the optimal objective function and fitness function is developed. Compared with the genetic algorithm (GA), the PSO algorithm has some advantages. It is not only implemented without crossover and mutation but also has fast congruence speed. Moreover fewer parameters are needed to set up. The results show that the PSO method is very suitable for nonlinear optimization problems and provides a promising new method for straightness error evaluation. It can be applied to deal with the measured data of planar straightness obtained by the three-coordinates measuring machines.

Mao, Jian; Zheng, Huawen; Cao, Yanlong; Yang, Jiangxin

2006-11-01

323

Statistical Orbit Determination using the Particle Filter for Incorporating Non-Gaussian Uncertainties  

NASA Technical Reports Server (NTRS)

The tracking of space objects requires frequent and accurate monitoring for collision avoidance. As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full probability density function (PDF) of the random orbit state. Through representing the full PDF of the orbit state for orbit maintenance and collision avoidance, we can take advantage of the statistical information present in the heavy tailed distributions, more accurately representing the orbit states with low probability. The classical methods of orbit determination (i.e. Kalman Filter and its derivatives) provide state estimates based on only the second moments of the state and measurement errors that are captured by assuming a Gaussian distribution. Although the measurement errors can be accurately assumed to have a Gaussian distribution, errors with a non-Gaussian distribution could arise during propagation between observations. Moreover, unmodeled dynamics in the orbit model could introduce non-Gaussian errors into the process noise. A Particle Filter (PF) is proposed as a nonlinear filtering technique that is capable of propagating and estimating a more complete representation of the state distribution as an accurate approximation of a full PDF. The PF uses Monte Carlo runs to generate particles that approximate the full PDF representation. The PF is applied in the estimation and propagation of a highly eccentric orbit and the results are compared to the Extended Kalman Filter and Splitting Gaussian Mixture algorithms to demonstrate its proficiency.

Mashiku, Alinda; Garrison, James L.; Carpenter, J. Russell

2012-01-01

324

Unscented particle filtering for estimation of shipboard deformation based on inertial measurement units.  

PubMed

Shipboard is not an absolute rigid body. Many factors could cause deformations which lead to large errors of mounted devices, especially for the navigation systems. Such errors should be estimated and compensated effectively, or they will severely reduce the navigation accuracy of the ship. In order to estimate the deformation, an unscented particle filter method for estimation of shipboard deformation based on an inertial measurement unit is presented. In this method, a nonlinear shipboard deformation model is built. Simulations demonstrated the accuracy reduction due to deformation. Then an attitude plus angular rate match mode is proposed as a frame to estimate the shipboard deformation using inertial measurement units. In this frame, for the nonlinearity of the system model, an unscented particle filter method is proposed to estimate and compensate the deformation angles. Simulations show that the proposed method gives accurate and rapid deformation estimations, which can increase navigation accuracy after compensation of deformation. PMID:24248280

Wang, Bo; Xiao, Xuan; Xia, Yuanqing; Fu, Mengyin

2013-01-01

325

Particle Filters for Real-Time Fault Detection in Planetary Rovers  

NASA Technical Reports Server (NTRS)

Planetary rovers provide a considerable challenge for robotic systems in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have on-board fault detection and diagnosis capabilities in order to determine the actual state of the vehicle, and decide what actions are safe to perform. Traditional model-based diagnosis techniques are not suitable for rovers due to the tight coupling between the vehicle's performance and its environment. Hybrid diagnosis using particle filters is presented as an alternative, and its strengths and weakeners are examined. We also present some extensions to particle filters that are designed to make them more suitable for use in diagnosis problems.

Dearden, Richard; Clancy, Dan; Koga, Dennis (Technical Monitor)

2001-01-01

326

Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems  

NASA Technical Reports Server (NTRS)

Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.

Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel

2004-01-01

327

Unscented Particle Filtering for Estimation of Shipboard Deformation Based on Inertial Measurement Units  

PubMed Central

Shipboard is not an absolute rigid body. Many factors could cause deformations which lead to large errors of mounted devices, especially for the navigation systems. Such errors should be estimated and compensated effectively, or they will severely reduce the navigation accuracy of the ship. In order to estimate the deformation, an unscented particle filter method for estimation of shipboard deformation based on an inertial measurement unit is presented. In this method, a nonlinear shipboard deformation model is built. Simulations demonstrated the accuracy reduction due to deformation. Then an attitude plus angular rate match mode is proposed as a frame to estimate the shipboard deformation using inertial measurement units. In this frame, for the nonlinearity of the system model, an unscented particle filter method is proposed to estimate and compensate the deformation angles. Simulations show that the proposed method gives accurate and rapid deformation estimations, which can increase navigation accuracy after compensation of deformation. PMID:24248280

Wang, Bo; Xiao, Xuan; Xia, Yuanqing; Fu, Mengyin

2013-01-01

328

A HIGH TEMPERATURE TEST FACILITY FOR STUDYING ASH PARTICLE CHARACTERISTICS OF CANDLE FILTER DURING SURFACE REGENERATION  

SciTech Connect

Hot gas particulate filtration is a basic component in advanced power generation systems such as Integrated Gasification Combined Cycle (IGCC) and Pressurized Fluidized Bed Combustion (PFBC). These systems require effective particulate removal to protect the downstream gas turbine and also to meet environmental emission requirements. The ceramic barrier filter is one of the options for hot gas filtration. Hot gases flow through ceramic candle filters leaving ash deposited on the outer surface of the filter. A process known as surface regeneration removes the deposited ash periodically by using a high pressure back pulse cleaning jet. After this cleaning process has been done there may be some residual ash on the filter surface. This residual ash may grow and this may lead to mechanical failure of the filter. A High Temperature Test Facility (HTTF) was built to investigate the ash characteristics during surface regeneration at high temperatures. The system is capable of conducting surface regeneration tests of a single candle filter at temperatures up to 1500 F. Details of the HTTF apparatus as well as some preliminary test results are presented in this paper. In order to obtain sequential digital images of ash particle distribution during the surface regeneration process, a high resolution, high speed image acquisition system was integrated into the HTTF system. The regeneration pressure and the transient pressure difference between the inside of the candle filter and the chamber during regeneration were measured using a high speed PC data acquisition system. The control variables for the high temperature regeneration tests were (1) face velocity, (2) pressure of the back pulse, and (3) cyclic ash built-up time.

Kang, B.S-J.; Johnson, E.K.; Rincon, J.

2002-09-19

329

Creating protein models from electron-density maps using particle-filtering methods  

Microsoft Academic Search

Motivation: One bottleneck in high-throughput protein crystallography is interpreting an electron-density map; that is, fitting a molecular model to the 3D picture crystallography produces. Previously, we developed ACMI, an algorithm that uses a probabilistic model to infer an accurate protein backbone layout. Here we use a sampling method known as particle filtering to produce a set of all-atom protein models.

Frank Dimaio; Dmitry A. Kondrashov; Eduard Bitto; Ameet Soni; Craig A. Bingman; George N. Phillips Jr.; Jude W. Shavlik

2007-01-01

330

Particles in swimming pool filters--does pH determine the DBP formation?  

PubMed

The formation was investigated for different groups of disinfection byproducts (DBPs) during chlorination of filter particles from swimming pools at different pH-values and the toxicity was estimated. Specifically, the formation of the DBP group trihalomethanes (THMs), which is regulated in many countries, and the non-regulated haloacetic acids (HAAs) and haloacetonitriles (HANs) were investigated at 6.0?pH?8.0, under controlled chlorination conditions. The investigated particles were collected from a hot tub with a drum micro filter. In two series of experiments with either constant initial active or initial free chlorine concentrations the particles were chlorinated at different pH-values in the relevant range for swimming pools. THM and HAA formations were reduced by decreasing pH while HAN formation increased with decreasing pH. Based on the organic content the relative DBP formation from the particles was higher than previously reported for body fluid analogue and filling water. The genotoxicity and cytotoxicity estimated from formation of DBPs from the treated particle suspension increased with decreasing pH. Among the quantified DBP groups the HANs were responsible for the majority of the toxicity from the measured DBPs. PMID:22285035

Hansen, Kamilla M S; Willach, Sarah; Mosbæk, Hans; Andersen, Henrik R

2012-04-01

331

Robust dead reckoning system for mobile robots based on particle filter and raw range scan.  

PubMed

Robust dead reckoning is a complicated problem for wheeled mobile robots (WMRs), where the robots are faulty, such as the sticking of sensors or the slippage of wheels, for the discrete fault models and the continuous states have to be estimated simultaneously to reach a reliable fault diagnosis and accurate dead reckoning. Particle filters are one of the most promising approaches to handle hybrid system estimation problems, and they have also been widely used in many WMRs applications, such as pose tracking, SLAM, video tracking, fault identification, etc. In this paper, the readings of a laser range finder, which may be also interfered with by noises, are used to reach accurate dead reckoning. The main contribution is that a systematic method to implement fault diagnosis and dead reckoning in a particle filter framework concurrently is proposed. Firstly, the perception model of a laser range finder is given, where the raw scan may be faulty. Secondly, the kinematics of the normal model and different fault models for WMRs are given. Thirdly, the particle filter for fault diagnosis and dead reckoning is discussed. At last, experiments and analyses are reported to show the accuracy and efficiency of the presented method. PMID:25192318

Duan, Zhuohua; Cai, Zixing; Min, Huaqing

2014-01-01

332

Robust Dead Reckoning System for Mobile Robots Based on Particle Filter and Raw Range Scan  

PubMed Central

Robust dead reckoning is a complicated problem for wheeled mobile robots (WMRs), where the robots are faulty, such as the sticking of sensors or the slippage of wheels, for the discrete fault models and the continuous states have to be estimated simultaneously to reach a reliable fault diagnosis and accurate dead reckoning. Particle filters are one of the most promising approaches to handle hybrid system estimation problems, and they have also been widely used in many WMRs applications, such as pose tracking, SLAM, video tracking, fault identification, etc. In this paper, the readings of a laser range finder, which may be also interfered with by noises, are used to reach accurate dead reckoning. The main contribution is that a systematic method to implement fault diagnosis and dead reckoning in a particle filter framework concurrently is proposed. Firstly, the perception model of a laser range finder is given, where the raw scan may be faulty. Secondly, the kinematics of the normal model and different fault models for WMRs are given. Thirdly, the particle filter for fault diagnosis and dead reckoning is discussed. At last, experiments and analyses are reported to show the accuracy and efficiency of the presented method. PMID:25192318

Duan, Zhuohua; Cai, Zixing; Min, Huaqing

2014-01-01

333

Design Optimization of Passively Mode-Locked Semiconductor Lasers With Intracavity Grating Spectral Filters  

NASA Astrophysics Data System (ADS)

We consider design optimization of passively mode-locked two-section semiconductor lasers that incorporate intracavity grating spectral filters. Our goal is to develop a method for finding the optimal wavelength location for the filter in order to maximize the region of stable mode-locking as a function of drive current and reverse bias in the absorber section. In order to account for material dispersion in the two sections of the laser, we use analytic approximations for the gain and absorption as a function of carrier density and frequency. Fits to measured gain and absorption curves then provide inputs for numerical simulations based on a large signal accurate delay-differential model of the mode-locked laser. We show how a unique set of model parameters for each value of the drive current and reverse bias voltage can be selected based on the variation of the net gain along branches of steady-state solutions of the model. We demonstrate the validity of this approach by demonstrating qualitative agreement between numerical simulations and the measured current-voltage phase-space of a two-section Fabry-Perot laser. We then show how to adapt this method to determine an optimum location for the spectral filter in a notional device with the same material composition, based on the targeted locking range, and accounting for the modal selectivity of the filter.

O'Callaghan, Finbarr; Bitauld, David; O'Brien, Stephen

2014-11-01

334

Frequency Domain Identification Based on Particle Swarm Optimization  

NASA Astrophysics Data System (ADS)

The paper proposes a frequency domain identification method for linear continuous-time systems including delay ones. The model parameters are estimated by minimizing the cost functions which are appropriate for open/closed loop. The minimization is achieved through Particle Swarm Optimization, which attracts a lot of attention recently in the evolutionary computation area due to its empirical evidence of its superiority. Its effectiveness is demonstrated by numerical examples. In addition, a comparison with the existing optimization method is given to show the robustness of the proposed method. Furthermore, an experimental evaluation using a magnetic levitation system is performed.

Wada, Takashi; Sugie, Toshiharu

335

Optimal Pid Tuning for Power System Stabilizers Using Adaptive Particle Swarm Optimization Technique  

NASA Astrophysics Data System (ADS)

An application of the intelligent search technique to find optimal parameters of power system stabilizer (PSS) considering proportional-integral-derivative controller (PID) for a single-machine infinite-bus system is presented. Also, an efficient intelligent search technique, adaptive particle swarm optimization (APSO), is engaged to express usefulness of the intelligent search techniques in tuning of the PID—PSS parameters. Improve damping frequency of system is optimized by minimizing an objective function with adaptive particle swarm optimization. At the same operating point, the PID—PSS parameters are also tuned by the Ziegler-Nichols method. The performance of proposed controller compared to the conventional Ziegler-Nichols PID tuning controller. The results reveal superior effectiveness of the proposed APSO based PID controller.

Oonsivilai, Anant; Marungsri, Boonruang

2008-10-01

336

Optimization of adenovirus 40 and 41 recovery from tap water using small disk filters.  

PubMed

Currently, the U.S. Environmental Protection Agency's Information Collection Rule (ICR) for the primary concentration of viruses from drinking and surface waters uses the 1MDS filter, but a more cost effective option, the NanoCeram® filter, has been shown to recover comparable levels of enterovirus and norovirus from both matrices. In order to achieve the highest viral recoveries, filtration methods require the identification of optimal concentration conditions that are unique for each virus type. This study evaluated the effectiveness of 1MDS and NanoCeram filters in recovering adenovirus (AdV) 40 and 41 from tap water, and optimized two secondary concentration procedures the celite and organic flocculation method. Adjustments in pH were made to both virus elution solutions and sample matrices to determine which resulted in higher virus recovery. Samples were analyzed by quantitative PCR (qPCR) and Most Probable Number (MPN) techniques and AdV recoveries were determined by comparing levels of virus in sample concentrates to that in the initial input. The recovery of adenovirus was highest for samples in unconditioned tap water (pH 8) using the 1MDS filter and celite for secondary concentration. Elution buffer containing 0.1% sodium polyphosphate at pH 10.0 was determined to be most effective overall for both AdV types. Under these conditions, the average recovery for AdV40 and 41 was 49% and 60%, respectively. By optimizing secondary elution steps, AdV recovery from tap water could be improved at least two-fold compared to the currently used methodology. Identification of the optimal concentration conditions for human AdV (HAdV) is important for timely and sensitive detection of these viruses from both surface and drinking waters. PMID:23796954

McMinn, Brian R

2013-11-01

337

Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization  

NASA Technical Reports Server (NTRS)

Frequency selective surfaces are widely used in the microwave and millimeter wave regions of the spectrum for filtering signals. They are used in telecommunication systems for multi-frequency operation or in instrument detectors for spectroscopy. The frequency selective surface operation depends on a periodic array of elements resonating at prescribed wavelengths producing a filter response. The size of the elements is on the order of half the electrical wavelength, and the array period is typically less than a wavelength for efficient operation. When operating in the optical region, diffraction gratings are used for filtering. In this regime the period of the grating may be several wavelengths producing multiple orders of light in reflection or transmission. In regions between these bands (specifically in the infrared band) frequency selective filters consisting of patterned metal layers fabricated using electron beam lithography are beginning to be developed. The operation is completely analogous to surfaces made in the microwave and millimeter wave region except for the choice of materials used and the fabrication process. In addition, the lithography process allows an arbitrary distribution of patterns corresponding to resonances at various wavelengths to be produced. The design of sub-millimeter filters follows the design methods used in the microwave region. Exacting modal matching, integral equation or finite element methods can be used for design. A major difference though is the introduction of material parameters and thicknesses tha_ may not be important in longer wavelength designs. This paper describes the design of multi-bandwidth filters operating in the I-5 micrometer wavelength range. This work follows on previous design [1,2]. In this paper extensions based on further optimization and an examination of the specific shape of the element in the periodic cell will be reported. Results from the design, manufacture and test of linear wedge filters built using micro-lithographic techniques and used ir spectral imaging applications will be presented.

Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.

1999-01-01

338

Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization  

NASA Technical Reports Server (NTRS)

Frequency selective surfaces are widely used in the microwave and millimeter wave regions of the spectrum for filtering signals. They are used in telecommunication systems for multi-frequency operation or in instrument detectors for spectroscopy. The frequency selective surface operation depends on a periodic array of elements resonating at prescribed wavelengths producing a filter response. The size of the elements is on the order of half the electrical wavelength, and the array period is typically less than a wavelength for efficient operation. When operating in the optical region, diffraction gratings are used for filtering. In this regime the period of the grating may be several wavelengths producing multiple orders of light in reflection or transmission. In regions between these bands (specifically in the infrared band) frequency selective filters consisting of patterned metal layers fabricated using electron beam lithography are beginning to be developed. The operation is completely analogous to surfaces made in the microwave and millimeter wave region except for the choice of materials used and the fabrication process. In addition, the lithography process allows an arbitrary distribution of patterns corresponding to resonances at various wavelengths to be produced. The design of sub-millimeter filters follows the design methods used in the microwave region. Exacting modal matching, integral equation or finite element methods can be used for design. A major difference though is the introduction of material parameters and thicknesses that may not be important in longer wavelength designs. This paper describes the design of multi- bandwidth filters operating in the 1-5 micrometer wavelength range. This work follows on a previous design. In this paper extensions based on further optimization and an examination of the specific shape of the element in the periodic cell will be reported. Results from the design, manufacture and test of linear wedge filters built using microlithographic techniques and used in spectral imaging applications will be presented.

Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.

1998-01-01

339

Improving neural networks prediction accuracy using particle swarm optimization combiner.  

PubMed

This paper proposes a technique to improve Artificial Neural Network (ANN) prediction accuracy using Particle Swarm Optimization (PSO) combiner. A hybrid system consists of two stages with the first stage containing two ANNs. The first ANN predictor is a multi-layer feed-forward network trained with error back-propagation and the second predictor is a functional link network. These two predictors are combined in the second stage using PSO combiner in a linear and non-linear fashion. The proposed method is applied to problem of predicting daily natural gas consumption. The performance of ANN predictors and combination methods are tested on real data from four different gas utilities. The experimental results show that the proposed particle swarm optimization combiners results in more accurate prediction compared to using single ANN predictor. Prediction accuracy improvement of the proposed PSO combiners have been shown using hypothesis testing. PMID:19885966

Elragal, Hassan M

2009-10-01

340

Optimizing the Performance of the Entropic Splitter for Particle Separation  

E-print Network

Recently, it has been shown that entropy can be used to sort Brownian particles according to their size. In particular, a combination of a static and a time-dependent force applied on differently sized particles which are confined in an asymmetric periodic structure can be used to separate them efficiently, by forcing them to move in opposite directions. In this paper, we investigate the optimization of the performance of the 'entropic splitter'. Specifically, the splitting mechanism and how it depends on the geometry of the channel, and the frequency and strength of the periodic forcing is analyzed. Using numerical simulations, we demonstrate that a very efficient and fast separation with a practically 100% purity can be achieved by a proper optimization of the control variables. The results of this work could be useful for a more efficient separation of dispersed phases such as DNA fragments or colloids dependent on their size.

Motz, Thomas; Hänggi, Peter; Reguera, David; Rubí, J Miguel

2014-01-01

341

A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter  

NASA Astrophysics Data System (ADS)

SummaryHybrid data assimilation (DA) is greatly used in recent hydrology and water resources research. In this study, one newly introduced technique, the ensemble particle filter (EnPF), formed by coupling ensemble Kalman filter (EnKF) with particle filter (PF), is applied for a multi-layer soil moisture prediction in the Meilin watershed based on the support vector machines (SVMs). The data used in this paper includes six-layer soil moisture: 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm and 300 cm and five meteorological parameters: soil temperature at 5 cm and 20 cm, air temperature, relative humidity and solar radiation in the study area. In order to investigate this EnPF approach, another two filters, EnKF and PF are applied as another two data assimilation methods to conduct a comparison. In addition, the SVM model simulated data without updating with data assimilation technique is discussed as well to evaluate the data assimilation technique. Two experimental cases are explored here, one with 200 initial training ensemble members in the SVM training phase while the other with 1000 initial training ensemble members. Three main findings are obtained in this study: (1) the SVMs machine is a statistically sound and robust model for soil moisture prediction in both the surface and root zone layers, and the larger the initial training data ensemble, the more effective the operator derived; (2) data assimilation technique does improve the performance of SVM modeling; (3) EnPF outweighs the performance of other two filters as well as the SVM model; Moreover, the ability of EnPF and PF is not positively related to the resampling ensemble size, when the resampling size exceeds a certain amount, the performance of EnPF and PF would be degraded. Because the EnPF still performs well than EnKF, it can be used as a powerful data assimilation tool in the soil moisture prediction.

Yu, Zhongbo; Liu, Di; Lü, Haishen; Fu, Xiaolei; Xiang, Long; Zhu, Yonghua

2012-12-01

342

Design of an H ? PID controller using particle swarm optimization  

Microsoft Academic Search

This paper proposes a novel method to designing an H\\u000a ? PID controller with robust stability and disturbance attenuation. This method uses particle swarm optimization algorithm\\u000a to minimize a cost function subject to H\\u000a ?-norm to design robust performance PID controller. We propose two cost functions to design of a multiple-input, multiple-output\\u000a (MIMO) and single-input, single-output (SISO) robust performance PID

Majid Zamani; Nasser Sadati; Masoud Karimi Ghartemani

2009-01-01

343

Particle Swarm Optimization for Road Extraction in SAR Images  

Microsoft Academic Search

The paper proposes a new method for road extraction in SAR images. We regard that the road in SAR images can be represented\\u000a by the Bspline curve. Firstly, we manually select the road’s extremities. Secondly, we calculate the each pixels’s road membership\\u000a value using local road detector in the original SAR images. Thirdly, with particle swarm optimization that is one

Ge Xu; Hong Sun; Wen Yang

344

Optimal Long Binary Phase Code-Mismatched Filter Pairs with the Application to Ionospheric Radars  

NASA Astrophysics Data System (ADS)

Binary phase codes have been often used in radar systems. The most widely known binary phase codes are Barker codes. Families of binary phase codes, which are called alternating codes, have been also discovered]. In a radar system, which employs a binary phase code, a matched filter is usually used to obtain a very high range resolution without decreasing the average transmitted power. However, matched filtering of a binary phase code gives unwanted sidelobes at the filter output. The amplitude of the sidelobes depends on the phase patterns of the binary phase code. Significant research effort has gone to search binary phase patterns that give smallest possible sidelobes. Most often peak-to-sidelobe ratio (PSR), integrated sidelobe ratio (TSR) and merit factor (F) are used as criterions to search for the best binary phase codes. The Barker codes have relatively high PSR. Other kind of binary phases with improved PSR have been found, including the 28-element code by Turyn] and the 40-element code by Lindner]. Although binary phase codes with maximum PSR can be satisfactory for some applications, in some cases removing the sidelobes reveals new and important information. Key showed that weighting networks to be placed after the standard matched filter can be designed, which reduces the sidelobes to an arbitrary low level. For any periodic digital signal with linearly independent cyclical shifts, lpatov] has showed that a filter can be constructed that suppresses to a zero level all the sidelobes. However, the filter has associated SNR losses when compared to the corresponding matched filter. Lpatov carried out a computer search for a binary periodic signal-filter pair with minimum possible SNR losses. The search includes all binary codes of length up to 30 elements. A different approach for eliminating the sidelobes in periodical binary phase codes by using mismatched filter have been published by Rohling and Plagg]. Exhaustive search for optimal aperiodic binary phase codes and mismatched filter pairs up to length of 25 has been carried out by Lehtinen. The benefits of eliminating sidelobes are also demonstrated in using real radar measurements. In this paper we present mismatched filtering of aperiodic binary phase codes. This is done without creating any sidelobes. A mismatched filter has small losses in SNR when compared with the corresponding matched filter. We have selected the best binary phase codes with length from 26 to 39. The best codes are the ones which have the smallest SNR losses in mismatched filtering when compared with the corresponding matched filtering. We have chosen one best code from each length and this means we have selected 14 different length best binary phase codes. These codes were chosen from a total number of 5.4972 x 1011 investigated codes. We have found these codes have nearly similar losses in SNR and it is about 15 percent. We did not find a binary phase code that outperforms the well-known 13-element Barker code, which has 4.8 percent SYR losses .

Damtie, B.; Lehtinen, M.; Orispaã, M.; Vierinen, J.

2006-11-01

345

Parallel global optimization with the particle swarm algorithm.  

PubMed

Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available. PMID:17891226

Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D

2004-12-01

346

Optimizing Magnetite Nanoparticles for Mass Sensitivity in Magnetic Particle Imaging  

SciTech Connect

Purpose: Magnetic particle imaging (MPI), using magnetite nanoparticles (MNPs) as tracer material, shows great promise as a platform for fast tomographic imaging. To date, the magnetic properties of MNPs used in imaging have not been optimized. As nanoparticle magnetism shows strong size dependence, we explore how varying MNP size impacts imaging performance in order to determine optimal MNP characteristics for MPI at any driving field frequency, ?. Methods: Monodisperse MNPs of varying size were synthesized and their magnetic properties characterized. Their MPI response was measured experimentally, at an arbitrarily chosen ? = 250 kHz, using a custom-built MPI transceiver designed to detect the third harmonic of MNP magnetization. Results were interpreted using a model of dynamic MNP magnetization that is based on the Langevin theory of superparamagnetism and accounts for sample size distribution, and size-dependent magnetic relaxation. Results: Our experimental results show clear variation in the MPI signal intensity as a function of MNP size that is in good agreement with modeled results. A maxima in the plot of MPI signal vs. MNP size indicates there is a particular size that is optimal for the chosen frequency of 250 kHz. Conclusions: For MPI at any chosen frequency, there will exist a characteristic particle size that generates maximum signal amplitude. We illustrate this at 250 kHz with particles of 15 nm core diameter.

Ferguson, R. Matthew; Minard, Kevin R.; Khandhar, Amit P.; Krishnan, Kannan M.

2011-03-01

347

Optimal estimation of diffusion coefficients from single-particle trajectories  

NASA Astrophysics Data System (ADS)

How does one optimally determine the diffusion coefficient of a diffusing particle from a single-time-lapse recorded trajectory of the particle? We answer this question with an explicit, unbiased, and practically optimal covariance-based estimator (CVE). This estimator is regression-free and is far superior to commonly used methods based on measured mean squared displacements. In experimentally relevant parameter ranges, it also outperforms the analytically intractable and computationally more demanding maximum likelihood estimator (MLE). For the case of diffusion on a flexible and fluctuating substrate, the CVE is biased by substrate motion. However, given some long time series and a substrate under some tension, an extended MLE can separate particle diffusion on the substrate from substrate motion in the laboratory frame. This provides benchmarks that allow removal of bias caused by substrate fluctuations in CVE. The resulting unbiased CVE is optimal also for short time series on a fluctuating substrate. We have applied our estimators to human 8-oxoguanine DNA glycolase proteins diffusing on flow-stretched DNA, a fluctuating substrate, and found that diffusion coefficients are severely overestimated if substrate fluctuations are not accounted for.

Vestergaard, Christian L.; Blainey, Paul C.; Flyvbjerg, Henrik

2014-02-01

348

PCDD/F formation in an iron/potassium-catalyzed diesel particle filter.  

PubMed

Catalytic diesel particle filters (DPFs) have evolved to a powerful environmental technology. Several metal-based, fuel soluble catalysts, so-called fuel-borne catalysts (FBCs), were developed to catalyze soot combustion and support filter regeneration. Mainly iron- and cerium-based FBCs have been commercialized for passenger cars and heavy-duty vehicle applications. We investigated a new iron/potassium-based FBC used in combination with an uncoated silicon carbide filter and report effects on emissions of polychlorinated dibenzodioxins/furans (PCDD/Fs). The PCDD/F formation potential was assessed under best and worst case conditions, as required for filter approval under the VERT protocol. TEQ-weighted PCDD/F emissions remained low when using the Fe/K catalyst (37/7.5 ?g/g) with the filter and commercial, low-sulfur fuel. The addition of chlorine (10 ?g/g) immediately led to an intense PCDD/F formation in the Fe/K-DPF. TEQ-based emissions increased 51-fold from engine-out levels of 95 to 4800 pg I-TEQ/L after the DPF. Emissions of 2,3,7,8-TCDD, the most toxic congener (TEF = 1.0), increased 320-fold, those of 2,3,7,8-TCDF (TEF = 0.1) even 540-fold. Remarkable pattern changes were noticed, indicating a preferential formation of tetrachlorinated dibenzofurans. It has been shown that potassium acts as a structural promoter inducing the formation of magnetite (Fe3O4) rather than hematite (Fe2O3). This may alter the catalytic properties of iron. But the chemical nature of this new catalyst is yet unknown, and we are far from an established mechanism for this new pathway to PCDD/Fs. In conclusion, the iron/potassium-catalyzed DPF has a high PCDD/F formation potential, similar to the ones of copper-catalyzed filters, the latter are prohibited by Swiss legislation. PMID:23713673

Heeb, Norbert V; Zennegg, Markus; Haag, Regula; Wichser, Adrian; Schmid, Peter; Seiler, Cornelia; Ulrich, Andrea; Honegger, Peter; Zeyer, Kerstin; Emmenegger, Lukas; Bonsack, Peter; Zimmerli, Yan; Czerwinski, Jan; Kasper, Markus; Mayer, Andreas

2013-06-18

349

Filter feeders and plankton increase particle encounter rates through flow regime control  

PubMed Central

Collisions between particles or between particles and other objects are fundamental to many processes that we take for granted. They drive the functioning of aquatic ecosystems, the onset of rain and snow precipitation, and the manufacture of pharmaceuticals, powders and crystals. Here, I show that the traditional assumption that viscosity dominates these situations leads to consistent and large-scale underestimation of encounter rates between particles and of deposition rates on surfaces. Numerical simulations reveal that the encounter rate is Reynolds number dependent and that encounter efficiencies are consistent with the sparse experimental data. This extension of aerosol theory has great implications for understanding of selection pressure on the physiology and ecology of organisms, for example filter feeders able to gather food at rates up to 5 times higher than expected. I provide evidence that filter feeders have been strongly selected to take advantage of this flow regime and show that both the predicted peak concentration and the steady-state concentrations of plankton during blooms are ?33% of that predicted by the current models of particle encounter. Many ecological and industrial processes may be operating at substantially greater rates than currently assumed. PMID:19416879

Humphries, Stuart

2009-01-01

350

Initial parameters problem of WNN based on particle swarm optimization  

NASA Astrophysics Data System (ADS)

The stock price prediction by the wavelet neural network is about minimizing RMSE by adjusting the parameters of initial values of network, training data percentage, and the threshold value in order to predict the fluctuation of stock price in two weeks. The objective of this dissertation is to reduce the number of parameters to be adjusted for achieving the minimization of RMSE. There are three kinds of parameters of initial value of network: w , t , and d . The optimization of these three parameters will be conducted by the Particle Swarm Optimization method, and comparison will be made with the performance of original program, proving that RMSE can be even less than the one before the optimization. It has also been shown in this dissertation that there is no need for adjusting training data percentage and threshold value for 68% of the stocks when the training data percentage is set at 10% and the threshold value is set at 0.01.

Yang, Chi-I.; Wang, Kaicheng; Chang, Kueifang

2014-04-01

351

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 46, NO. 11, NOVEMBER 2008 3739 Particle Filtering Based Approach for Landmine  

E-print Network

Filtering Based Approach for Landmine Detection Using Ground Penetrating Radar William Ng, Thomas C. T. Chan present an online stochastic ap- proach for landmine detection based on ground penetrating radar (GPR to it. Index Terms--Ground penetrating radar (GPR), Kalman filter (KF), landmine detection, particle

So, Hing-Cheung

352

Optimization by marker removal for ?f particle simulations  

NASA Astrophysics Data System (ADS)

A marker removal optimization technique is developed for ?f particle simulations. The technique uses the linear eigenmode structure in the equilibrium constant-of-motion space to construct an importance function, then removes some markers based on the importance function and adjusts the weights of the leftover markers to optimize the marker distribution function, so as to save markers and computing time. The technique can be directly applied to single-mode linear simulations. For multi-mode or nonlinear simulations, the technique can still be directly applied if there is one most unstable mode that dominates the simulation and ?f does not change too much in the nonlinear stage, otherwise special care is needed, which is discussed in detail in this paper. The technique's effectiveness, e.g., marker saving factor, depends on how localized ?f is. The technique can be used for a phase space of arbitrary dimension, as long as the constants of motion in equilibrium can be found. In this paper, the technique is tested in a 2D bump-on-tail simulation and a 5D gyrokinetic toroidal Alfvén eigenmode (TAE) simulation and saves markers by factors of 4 and 19, respectively. The technique is not limited to particle-in-cell (PIC) simulations but could be applied to other approaches of marker particle simulations such as particle-in-wavelet (PIW) and grid-free treecode simulations.

Deng, Wenjun; Fu, Guo-Yong

2014-01-01

353

Effects of Particle Filters and Accelerated Engine Replacement on Heavy-Duty Diesel Vehicle Emissions of Black Carbon, Nitrogen Oxides, and Ultrafine Particles  

NASA Astrophysics Data System (ADS)

Diesel particle filters have become widely used in the United States since the introduction in 2007 of a more stringent exhaust particulate matter emission standard for new heavy-duty diesel vehicle engines. California has instituted additional regulations requiring retrofit or replacement of older in-use engines to accelerate emission reductions and air quality improvements. This presentation summarizes pollutant emission changes measured over several field campaigns at the Port of Oakland in the San Francisco Bay Area associated with diesel particulate filter use and accelerated modernization of the heavy-duty truck fleet. Pollutants in the exhaust plumes of hundreds of heavy-duty trucks en route to the Port were measured in 2009, 2010, 2011, and 2013. Ultrafine particle number, black carbon (BC), nitrogen oxides (NOx), and nitrogen dioxide (NO2) concentrations were measured at a frequency ? 1 Hz and normalized to measured carbon dioxide concentrations to quantify fuel-based emission factors (grams of pollutant emitted per kilogram of diesel consumed). The size distribution of particles in truck exhaust plumes was also measured at 1 Hz. In the two most recent campaigns, emissions were linked on a truck-by-truck basis to installed emission control equipment via the matching of transcribed license plates to a Port truck database. Accelerated replacement of older engines with newer engines and retrofit of trucks with diesel particle filters reduced fleet-average emissions of BC and NOx. Preliminary results from the two most recent field campaigns indicate that trucks without diesel particle filters emit 4 times more BC than filter-equipped trucks. Diesel particle filters increase emissions of NO2, however, and filter-equipped trucks have NO2/NOx ratios that are 4 to 7 times greater than trucks without filters. Preliminary findings related to particle size distribution indicate that (a) most trucks emitted particles characterized by a single mode of approximately 100 nm in diameter and (b) new trucks originally equipped with diesel particle filters were 5 to 6 times more likely than filter-retrofitted trucks and trucks without filters to emit particles characterized by a single mode in the range of 10 to 30 nm in diameter.

Kirchstetter, T.; Preble, C.; Dallmann, T. R.; DeMartini, S. J.; Tang, N. W.; Kreisberg, N. M.; Hering, S. V.; Harley, R. A.

2013-12-01

354

Optimizing performance of ceramic pot filters in Northern Ghana and modeling flow through paraboloid-shaped filters/  

E-print Network

This work aimed to inform the design of ceramic pot filters to be manufactured by the organization Pure Home Water (PHW) in Northern Ghana, and to model the flow through an innovative paraboloid-shaped ceramic pot filter. ...

Miller, Travis Reed

2010-01-01

355

Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation.  

PubMed

Based on a cascaded Kalman-Particle Filtering, gyroscope drift and robot attitude estimation method is proposed in this paper. Due to noisy and erroneous measurements of MEMS gyroscope, it is combined with Photogrammetry based vision navigation scenario. Quaternions kinematics and robot angular velocity dynamics with augmented drift dynamics of gyroscope are employed as system state space model. Nonlinear attitude kinematics, drift and robot angular movement dynamics each in 3 dimensions result in a nonlinear high dimensional system. To reduce the complexity, we propose a decomposition of system to cascaded subsystems and then design separate cascaded observers. This design leads to an easier tuning and more precise debugging from the perspective of programming and such a setting is well suited for a cooperative modular system with noticeably reduced computation time. Kalman Filtering (KF) is employed for the linear and Gaussian subsystem consisting of angular velocity and drift dynamics together with gyroscope measurement. The estimated angular velocity is utilized as input of the second Particle Filtering (PF) based observer in two scenarios of stochastic and deterministic inputs. Simulation results are provided to show the efficiency of the proposed method. Moreover, the experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method. PMID:24342270

Sadaghzadeh N, Nargess; Poshtan, Javad; Wagner, Achim; Nordheimer, Eugen; Badreddin, Essameddin

2014-03-01

356

Parameter estimation and asymptotic stability in stochastic filtering  

E-print Network

filtering is how to compute the optimal filter. With the exception of very few cases (for example, linear as part of the state variable and then use some variation of the Interactive Particle Filter to computeParameter estimation and asymptotic stability in stochastic filtering Anastasia Papavasiliou

Del Moral , Pierre

357

Particle swarm optimization of ascent trajectories of multistage launch vehicles  

NASA Astrophysics Data System (ADS)

Multistage launch vehicles are commonly employed to place spacecraft and satellites in their operational orbits. If the rocket characteristics are specified, the optimization of its ascending trajectory consists of determining the optimal control law that leads to maximizing the final mass at orbit injection. The numerical solution of a similar problem is not trivial and has been pursued with different methods, for decades. This paper is concerned with an original approach based on the joint use of swarming theory and the necessary conditions for optimality. The particle swarm optimization technique represents a heuristic population-based optimization method inspired by the natural motion of bird flocks. Each individual (or particle) that composes the swarm corresponds to a solution of the problem and is associated with a position and a velocity vector. The formula for velocity updating is the core of the method and is composed of three terms with stochastic weights. As a result, the population migrates toward different regions of the search space taking advantage of the mechanism of information sharing that affects the overall swarm dynamics. At the end of the process the best particle is selected and corresponds to the optimal solution to the problem of interest. In this work the three-dimensional trajectory of the multistage rocket is assumed to be composed of four arcs: (i) first stage propulsion, (ii) second stage propulsion, (iii) coast arc (after release of the second stage), and (iv) third stage propulsion. The Euler-Lagrange equations and the Pontryagin minimum principle, in conjunction with the Weierstrass-Erdmann corner conditions, are employed to express the thrust angles as functions of the adjoint variables conjugate to the dynamics equations. The use of these analytical conditions coming from the calculus of variations leads to obtaining the overall rocket dynamics as a function of seven parameters only, namely the unknown values of the initial state and costate components, the coast duration, and the upper stage thrust duration. In addition, a simple approach is introduced and successfully applied with the purpose of satisfying exactly the path constraint related to the maximum dynamical pressure in the atmospheric phase. The basic version of the swarming technique, which is used in this research, is extremely simple and easy to program. Nevertheless, the algorithm proves to be capable of yielding the optimal rocket trajectory with a very satisfactory numerical accuracy.

Pontani, Mauro

2014-02-01

358

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.  

PubMed

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics. PMID:20064026

Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

2010-01-01

359

Particle Swarm and Ant Colony Approaches in Multiobjective Optimization  

NASA Astrophysics Data System (ADS)

The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.

Rao, S. S.

2010-10-01

360

Optimal hydrograph separation filter to evaluate transport routines of hydrological models  

NASA Astrophysics Data System (ADS)

Hydrograph separation (HS) using recursive digital filter approaches focuses on trying to distinguish between the rapidly occurring discharge components like surface runoff, and the slowly changing discharge originating from interflow and groundwater. Filter approaches are mathematical procedures, which perform the HS using a set of separation parameters. The first goal of this study is an attempt to minimize the subjective influence that a user of the filter technique exerts on the results by the choice of such filter parameters. A simple optimal HS (OHS) technique for the estimation of the separation parameters was introduced, relying on measured stream hydrochemistry. The second goal is to use the OHS parameters to develop a benchmark model that can be used as a geochemical model itself, or to test the performance of process based hydro-geochemical models. The benchmark model quantifies the degree of knowledge that the stream flow time series itself contributes to the hydrochemical analysis. Results of the OHS show that the two HS fractions ("rapid" and "slow") differ according to the geochemical substances which were selected. The OHS parameters were then used to demonstrate how to develop benchmark model for hydro-chemical predictions. Finally, predictions of solute transport from a process-based hydrological model were compared to the proposed benchmark model. Our results indicate that the benchmark model illustrated and quantified the contribution of the modeling procedure better than only using traditional measures like r2 or the Nash-Sutcliffe efficiency.

Rimmer, Alon; Hartmann, Andreas

2014-05-01

361

[Characteristic wavelength variable optimization of near-infrared spectroscopy based on Kalman filtering].  

PubMed

Combining classical Kalman filter with NIR analysis technology, a new method of characteristic wavelength variable selection, namely Kalman filtering method, is presented. The principle of Kalman filter for selecting optimal wavelength variable was analyzed. The wavelength selection algorithm was designed and applied to NIR detection of soybean oil acid value. First, the PLS (partial leastsquares) models were established by using different absorption bands of oil. The 4 472-5 000 cm(-1) characteristic band of oil acid value, including 132 wavelengths, was selected preliminarily. Then the Kalman filter was used to select characteristic wavelengths further. The PLS calibration model was established using selected 22 characteristic wavelength variables, the determination coefficient R2 of prediction set and RMSEP (root mean squared error of prediction) are 0.970 8 and 0.125 4 respectively, equivalent to that of 132 wavelengths, however, the number of wavelength variables was reduced to 16.67%. This algorithm is deterministic iteration, without complex parameters setting and randomicity of variable selection, and its physical significance was well defined. The modeling using a few selected characteristic wavelength variables which affected modeling effect heavily, instead of total spectrum, can make the complexity of model decreased, meanwhile the robustness of model improved. The research offered important reference for developing special oil near infrared spectroscopy analysis instruments on next step. PMID:25007608

Wang, Li-Qi; Ge, Hui-Fang; Li, Gui-Bin; Yu, Dian-Yu; Hu, Li-Zhi; Jiang, Lian-Zhou

2014-04-01

362

Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems  

PubMed Central

A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants. PMID:24723827

Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka

2014-01-01

363

Representation of Probability Density Functions from Orbit Determination using the Particle Filter  

NASA Technical Reports Server (NTRS)

Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty. In order to obtain an accurate representation of the probability density function (PDF) that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. The Particle Filter (PF) is capable of providing a PDF representation of the state estimates whose accuracy is dependent on the number of particles or samples used. For this method to be applicable to real case scenarios, we need a way of accurately representing the PDF in a compressed manner with little information loss. Hence we propose using the Independent Component Analysis (ICA) as a non-Gaussian dimensional reduction method that is capable of maintaining higher order statistical information obtained using the PF. Methods such as the Principal Component Analysis (PCA) are based on utilizing up to second order statistics, hence will not suffice in maintaining maximum information content. Both the PCA and the ICA are applied to two scenarios that involve a highly eccentric orbit with a lower apriori uncertainty covariance and a less eccentric orbit with a higher a priori uncertainty covariance, to illustrate the capability of the ICA in relation to the PCA.

Mashiku, Alinda K.; Garrison, James; Carpenter, J. Russell

2012-01-01

364

Optimal interpolation schemes for particle tracking in turbulence.  

PubMed

An important aspect in numerical simulations of particle-laden turbulent flows is the interpolation of the flow field needed for the computation of the Lagrangian trajectories. The accuracy of the interpolation method has direct consequences for the acceleration spectrum of the fluid particles and is therefore also important for the correct evaluation of the hydrodynamic forces for almost neutrally buoyant particles, common in many environmental applications. In order to systematically choose the optimal tradeoff between interpolation accuracy and computational cost we focus on comparing errors: the interpolation error is compared with the discretization error of the flow field. In this way one can prevent unnecessary computations and still retain the accuracy of the turbulent flow simulation. From the analysis a practical method is proposed that enables direct estimation of the interpolation and discretization error from the energy spectrum. The theory is validated by means of direct numerical simulations (DNS) of homogeneous, isotropic turbulence using a spectral code, where the trajectories of fluid tracers are computed using several interpolation methods. We show that B-spline interpolation has the best accuracy given the computational cost. Finally, the optimal interpolation order for the different methods is shown as a function of the resolution of the DNS simulation. PMID:23679548

van Hinsberg, M A T; Boonkkamp, J H M ten Thije; Toschi, F; Clercx, H J H

2013-04-01

365

Optimal interpolation schemes for particle tracking in turbulence  

NASA Astrophysics Data System (ADS)

An important aspect in numerical simulations of particle-laden turbulent flows is the interpolation of the flow field needed for the computation of the Lagrangian trajectories. The accuracy of the interpolation method has direct consequences for the acceleration spectrum of the fluid particles and is therefore also important for the correct evaluation of the hydrodynamic forces for almost neutrally buoyant particles, common in many environmental applications. In order to systematically choose the optimal tradeoff between interpolation accuracy and computational cost we focus on comparing errors: the interpolation error is compared with the discretization error of the flow field. In this way one can prevent unnecessary computations and still retain the accuracy of the turbulent flow simulation. From the analysis a practical method is proposed that enables direct estimation of the interpolation and discretization error from the energy spectrum. The theory is validated by means of direct numerical simulations (DNS) of homogeneous, isotropic turbulence using a spectral code, where the trajectories of fluid tracers are computed using several interpolation methods. We show that B-spline interpolation has the best accuracy given the computational cost. Finally, the optimal interpolation order for the different methods is shown as a function of the resolution of the DNS simulation.

van Hinsberg, M. A. T.; Boonkkamp, J. H. M. ten Thije; Toschi, F.; Clercx, H. J. H.

2013-04-01

366

Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers  

NASA Astrophysics Data System (ADS)

Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our "matrix-free" approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image ?2 and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest ?2 is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.

Rogers, Adam; Fiege, Jason D.

2011-02-01

367

“Worst Case” Aerosol Testing Parameters: I. Sodium Chloride and Dioctyl Phthalate Aerosol Filter Efficiency as a Function of Particle Size and Flow Rate  

Microsoft Academic Search

The efficiency of filter media is dependent on the characteristics of the challenge aerosol and the filter's construction. Challenge aerosol Parameters, Such as Particle Size, density, shape, electrical charge, and flow rate, are influential in determining the filter's efficiency. In this regard, a so-called “worst case” set of conditions has been proposed for testing respirator filter efficiency in order to

GREGORY A. STEVENS; ERNEST S. MOYER

1989-01-01

368

Generalized Particle Swarm Algorithm for HCR Gearing Geometry Optimization  

NASA Astrophysics Data System (ADS)

Temperature scuffing evidenced by damage to teeth flanks of gears is one of the mostimportant problems needing to be solved in the process of gearing design and calculation. Accordingto current valid standards, such calculations can be resolved with a high level of reliability for all theusual gearing types. However, suitable calculations for HCR gears have not been adequatelyresearched to date. It has been identified that in HCR gears some different process of scuffingformation occurs during the gear`s operation. In this article, the authors describe a new method forfinding optimal solutions for * a1 h , * a 2 h and x1, using a Generalized Particle Swarm OptimizationAlgorithm.

Kuzmanovi?, Siniša; Vereš, Miroslav; Rackov, Milan

2012-12-01

369

Effect of ventilation systems and air filters on decay rates of particles produced by indoor sources in an occupied townhouse  

NASA Astrophysics Data System (ADS)

Several studies have shown the importance of particle losses in real homes due to deposition and filtration; however, none have quantitatively shown the impact of using a central forced air fan and in-duct filter on particle loss rates. In an attempt to provide such data, we measured the deposition of particles ranging from 0.3 to 10 ?m in an occupied townhouse and also in an unoccupied test house. Experiments were run with three different sources (cooking with a gas stove, citronella candle, pouring kitty litter), with the central heating and air conditioning (HAC) fan on or off, and with two different types of in-duct filters (electrostatic precipitator and ordinary furnace filter). Particle size, HAC fan operation, and the electrostatic precipitator had significant effects on particle loss rates. The standard furnace filter had no effect. Surprisingly, the type of source (combustion vs. mechanical generation) and the type of furnishings (fully furnished including carpet vs. largely unfurnished including mostly bare floor) also had no measurable effect on the deposition rates of particles of comparable size. With the HAC fan off, average deposition rates varied from 0.3 h -1 for the smallest particle range (0.3-0.5 ?m) to 5.2 h -1 for particles greater than 10 ?m. Operation of the central HAC fan approximately doubled these rates for particles <5 ?m, and increased rates by 2 h -1 for the larger particles. An in-duct electrostatic precipitator increased the loss rates compared to the fan-off condition by factors of 5-10 for particles <2.5 ?m, and by a factor of 3 for 2.5-5.0 ?m particles. In practical terms, use of the central fan alone could reduce indoor particle concentrations by 25-50%, and use of an in-duct ESP could reduce particle concentrations by 55-85% compared to fan-off conditions.

Howard-Reed, Cynthia; Wallace, Lance A.; Emmerich, Steven J.

370

Optimal steering of inertial particles diffusing anisotropically with losses  

E-print Network

Exploiting a fluid dynamic formulation for which a probabilistic counterpart might not be available, we extend the theory of Schroedinger bridges to the case of inertial particles with losses and general, possibly singular diffusion coefficient. We find that, as for the case of constant diffusion coefficient matrix, the optimal control law is obtained by solving a system of two p.d.e.'s involving adjoint operators and coupled through their boundary values. In the linear case with quadratic loss function, the system turns into two matrix Riccati equations with coupled split boundary conditions. An alternative formulation of the control problem as a semidefinite programming problem allows computation of suboptimal solutions. This is illustrated in one example of inertial particles subject to a constant rate killing.

Yongxin Chen; Tryphon T. Georgiou; Michele Pavon

2014-10-07

371

Parameter extraction of solar cells using particle swarm optimization  

NASA Astrophysics Data System (ADS)

In this article, particle swarm optimization (PSO) was applied to extract the solar cell parameters from illuminated current-voltage characteristics. The performance of the PSO was compared with the genetic algorithms (GAs) for the single and double diode models. Based on synthetic and experimental current-voltage data, it has been confirmed that the proposed method can obtain higher parameter precision with better computational efficiency than the GA method. Compared with conventional gradient-based methods, even without a good initial guess, the PSO method can obtain the parameters of solar cells as close as possible to the practical parameters only based on a broad range specified for each of the parameters.

Ye, Meiying; Wang, Xiaodong; Xu, Yousheng

2009-05-01

372

Multiuser Detection for Asynchronous Multicarrier CDMA Using Particle Swarm Optimization  

NASA Astrophysics Data System (ADS)

Due to the computational complexity of the optimum maximum likelihood detector (OMD) growing exponentially with the number of users, suboptimum techniques have received significant attention. We have proposed the particle swarm optimization (PSO) for the multiuser detection (MUD) in asynchronous multicarrier code division multiple access (MC-CDMA) system. The performance of PSO based MUD is near optimum, while its computational complexity is far less than OMD. Performance of PSO-MUD has also been shown to be better than that of genetic algorithm based MUD (GA-MUD) at practical SNR.

Zubair, Muhammad; Choudhry, Muhammad A. S.; Naveed, Aqdas; Qureshi, Ijaz Mansoor

373

PMSM Driver Based on Hybrid Particle Swarm Optimization and CMAC  

NASA Astrophysics Data System (ADS)

A novel hybrid particle swarm optimization (PSO) and cerebellar model articulation controller (CMAC) is introduced to the permanent magnet synchronous motor (PMSM) driver. PSO can simulate the random learning among the individuals of population and CMAC can simulate the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments and comparisons have been done in MATLAB/SIMULINK. Analysis among PSO, hybrid PSO-CMAC and CMAC feed-forward control is also given. The results prove that the electric torque ripple and torque disturbance of the PMSM driver can be reduced by using the hybrid PSO-CMAC algorithm.

Tu, Ji; Cao, Shaozhong

374

Generating optimal initial conditions for smooth particle hydrodynamics (SPH) simulations  

SciTech Connect

We present a new optimal method to set up initial conditions for Smooth Particle Hydrodynamics Simulations, which may also be of interest for N-body simulations. This new method is based on weighted Voronoi tesselations (WVTs) and can meet arbitrarily complex spatial resolution requirements. We conduct a comprehensive review of existing SPH setup methods, and outline their advantages, limitations and drawbacks. A serial version of our WVT setup method is publicly available and we give detailed instruction on how to easily implement the new method on top of an existing parallel SPH code.

Diehl, Steven [Los Alamos National Laboratory; Rockefeller, Gabriel M [Los Alamos National Laboratory; Fryer, Christopher L [Los Alamos National Laboratory

2008-01-01

375

Particle Swarm Optimization with Watts-Strogatz Model  

NASA Astrophysics Data System (ADS)

Particle swarm optimization (PSO) is a popular swarm intelligent methodology by simulating the animal social behaviors. Recent study shows that this type of social behaviors is a complex system, however, for most variants of PSO, all individuals lie in a fixed topology, and conflict this natural phenomenon. Therefore, in this paper, a new variant of PSO combined with Watts-Strogatz small-world topology model, called WSPSO, is proposed. In WSPSO, the topology is changed according to Watts-Strogatz rules within the whole evolutionary process. Simulation results show the proposed algorithm is effective and efficient.

Zhu, Zhuanghua

376

EFFECT OF VENTILATION SYSTEMS AND AIR FILTERS ON DECAY RATES OF PARTICLES PRODUCED BY INDOOR SOURCES IN AN OCCUPIED TOWNHOUSE  

EPA Science Inventory

Several studies have shown the importance of particle losses in real homes due to deposition and filtration; however, none have quantitatively shown the impact of using a central forced air fan and in-duct filter on particle loss rates. In an attempt to provide such data, we me...

377

Removal of iron oxide particles in a gas stream by means of a magnetically stabilized granular filter  

Microsoft Academic Search

The present study deals with the influence of diverse operating variables such as gas velocity, height of the bed, magnetic field strength, and particle bounce on separation of fine dust particles (iron oxide) in magnetically stabilized granular filters (MSF). The collection results are more effective when the height of the MSF and dust sizes increase. Investigations concerning the magnetic field

J. M. Rodriguez; A. Macias-Machin; A. Alvaro; J. R. Sanchez; A. M. Estevez

1999-01-01

378

1-D DC Resistivity Modeling and Interpretation in Anisotropic Media Using Particle Swarm Optimization  

NASA Astrophysics Data System (ADS)

We examine the one-dimensional direct current method in anisotropic earth formation. We derive an analytic expression of a simple, two-layered anisotropic earth model. Further, we also consider a horizontally layered anisotropic earth response with respect to the digital filter method, which yields a quasi-analytic solution over anisotropic media. These analytic and quasi-analytic solutions are useful tests for numerical codes. A two-dimensional finite difference earth model in anisotropic media is presented in order to generate a synthetic data set for a simple one-dimensional earth. Further, we propose a particle swarm optimization method for estimating the model parameters of a layered anisotropic earth model such as horizontal and vertical resistivities, and thickness. The particle swarm optimization is a naturally inspired meta-heuristic algorithm. The proposed method finds model parameters quite successfully based on synthetic and field data. However, adding 5 % Gaussian noise to the synthetic data increases the ambiguity of the value of the model parameters. For this reason, the results should be controlled by a number of statistical tests. In this study, we use probability density function within 95 % confidence interval, parameter variation of each iteration and frequency distribution of the model parameters to reduce the ambiguity. The result is promising and the proposed method can be used for evaluating one-dimensional direct current data in anisotropic media.

Pek?en, Ertan; Yas, Türker; K?yak, Alper

2014-09-01

379

Adaptive Square-Root Cubature-Quadrature Kalman Particle Filter for satellite attitude determination using vector observations  

NASA Astrophysics Data System (ADS)

A novel algorithm is presented in this study for estimation of spacecraft's attitudes and angular rates from vector observations. In this regard, a new cubature-quadrature particle filter (CQPF) is initially developed that uses the Square-Root Cubature-Quadrature Kalman Filter (SR-CQKF) to generate the importance proposal distribution. The developed CQPF scheme avoids the basic limitation of particle filter (PF) with regards to counting the new measurements. Subsequently, CQPF is enhanced to adjust the sample size at every time step utilizing the idea of confidence intervals, thus improving the efficiency and accuracy of the newly proposed adaptive CQPF (ACQPF). In addition, application of the q-method for filter initialization has intensified the computation burden as well. The current study also applies ACQPF to the problem of attitude estimation of a low Earth orbit (LEO) satellite. For this purpose, the undertaken satellite is equipped with a three-axis magnetometer (TAM) as well as a sun sensor pack that provide noisy geomagnetic field data and Sun direction measurements, respectively. The results and performance of the proposed filter are investigated and compared with those of the extended Kalman filter (EKF) and the standard particle filter (PF) utilizing a Monte Carlo simulation. The comparison demonstrates the viability and the accuracy of the proposed nonlinear estimator.

Kiani, Maryam; Pourtakdoust, Seid H.

2014-12-01

380

Security Constrained Optimal Power Flow with FACTS Devices Using Modified Particle Swarm Optimization  

NASA Astrophysics Data System (ADS)

This paper presents new computationally efficient improved Particle Swarm algorithms for solving Security Constrained Optimal Power Flow (SCOPF) in power systems with the inclusion of FACTS devices. The proposed algorithms are developed based on the combined application of Gaussian and Cauchy Probability distribution functions incorporated in Particle Swarm Optimization (PSO). The power flow algorithm with the presence of Static Var Compensator (SVC) Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC), has been formulated and solved. The proposed algorithms are tested on standard IEEE 30-bus system. The analysis using PSO and modified PSO reveals that the proposed algorithms are relatively simple, efficient, reliable and suitable for real-time applications. And these algorithms can provide accurate solution with fast convergence and have the potential to be applied to other power engineering problems.

Somasundaram, P.; Muthuselvan, N. B.

381

Survey on Applications of Particle Swarm Optimization in Electric Power Systems  

Microsoft Academic Search

The paper presents a survey of particle swarm optimization (PSO) applications in electric power systems. PSO, a novel population based stochastic optimizer with faster convergence speed and simpler implementation than genetic algorithm and ant colony optimization, has been successfully applied to solve electric power optimization problems such as optimal power flow, economic dispatch, reactive power dispatch, unit commitment, generation and

Bo Yang; Yunping Chen; Zunlian Zhao

2007-01-01

382

MODELING REFLECTANCE AND TRANSMITTANCE OF QUARTZ-FIBER FILTER SAMPLES CONTAINING ELEMENTAL CARBON PARTICLES: IMPLICATIONS FOR THERMAL/OPTICAL ANALYSIS. (R831086)  

EPA Science Inventory

A radiative transfer scheme that considers absorption, scattering, and distribution of light-absorbing elemental carbon (EC) particles collected on a quartz-fiber filter was developed to explain simultaneous filter reflectance and transmittance observations prior to and during...

383

Effects of fan cycling on the particle shedding of particulate air filters used for IAQ control. Report for January--July 1996  

SciTech Connect

The purpose of the present study is to investigate the effect of fan cycling on two types of bag filters. Total particle concentrations and viable bioaerosol concentrations were measured upstream and downstream of the filters.

Kuehn, T.H.; Yang, C.H.; Kulp, R.N.

1998-08-01

384

Filterable redox cycling activity: a comparison between diesel exhaust particles and secondary organic aerosol constituents.  

PubMed

The redox activity of diesel exhaust particles (DEP) collected from a light-duty diesel passenger car engine was examined using the dithiothreitol (DTT) assay. DEP was highly redox-active, causing DTT to decay at a rate of 23-61 pmol min(-1) ?g(-1) of particle used in the assay, which was an order of magnitude higher than ambient coarse and fine particulate matter (PM) collected from downtown Toronto. Only 2-11% of the redox activity was in the water-soluble portion, while the remainder occurred at the black carbon surface. This is in contrast to redox-active secondary organic aerosol constituents, in which upward of 90% of the activity occurs in the water-soluble fraction. The redox activity of DEP is not extractable by moderately polar (methanol) and nonpolar (dichloromethane) organic solvents, and is hypothesized to arise from redox-active moieties contiguous with the black carbon portion of the particles. These measurements illustrate that "Filterable Redox Cycling Activity" may therefore be useful to distinguish black carbon-based oxidative capacity from water-soluble organic-based activity. The difference in chemical environment leading to redox activity highlights the need to further examine the relationship between activity in the DTT assay and toxicology measurements across particles of different origins and composition. PMID:23470039

McWhinney, Robert D; Badali, Kaitlin; Liggio, John; Li, Shao-Meng; Abbatt, Jonathan P D

2013-04-01

385

Toward particle-level filtering of individual collision events at the Large Hadron Collider and beyond  

E-print Network

Low-energy strong interactions are a major source of background at hadron colliders, and methods of subtracting the associated energy flow are well established in the field. Traditional approaches treat the contamination as diffuse, and estimate background energy levels either by averaging over large data sets or by restricting to given kinematic regions inside individual collision events. On the other hand, more recent techniques take into account the discrete nature of background, most notably by exploiting the presence of substructure inside hard jets, i.e. inside collections of particles originating from scattered hard quarks and gluons. However, none of the existing methods subtract background at the level of individual particles inside events. We illustrate the use of an algorithm that will allow particle-by-particle background discrimination at the Large Hadron Collider, and we envisage this as the basis for a novel event filtering procedure upstream of the official reconstruction chains. Our hope is that this new technique will improve physics analysis when used in combination with state-of-the-art algorithms in high-luminosity hadron collider environments.

Federico Colecchia

2013-11-05

386

Effect of ventilation systems and air filters on decay rates of particles produced by indoor sources in an occupied townhouse  

Microsoft Academic Search

Several studies have shown the importance of particle losses in real homes due to deposition and filtration; however, none have quantitatively shown the impact of using a central forced air fan and in-duct filter on particle loss rates. In an attempt to provide such data, we measured the deposition of particles ranging from 0.3 to 10?m in an occupied townhouse

Cynthia Howard-Reed; Lance A. Wallace; Steven J. Emmerich

2003-01-01

387

Design optimization of passively mode-locked semiconductor lasers with intracavity grating spectral filters  

E-print Network

We consider design optimization of passively mode-locked two-section semiconductor lasers that incorporate intracavity grating spectral filters. Our goal is to develop a method for finding the optimal wavelength location for the filter in order to maximize the region of stable mode-locking as a function of drive current and reverse bias in the absorber section. In order to account for material dispersion in the two sections of the laser, we use analytic approximations for the gain and absorption as a function of carrier density and frequency. Fits to measured gain and absorption curves then provide inputs for numerical simulations based on a large signal accurate delay-differential model of the mode-locked laser. We show how a unique set of model parameters for each value of the drive current and reverse bias voltage can be selected based on the variation of the net gain along branches of steady-state solutions of the model. We demonstrate the validity of this approach by demonstrating qualitative agreement b...

O'Callaghan, Finbarr; O'Brien, Stephen

2014-01-01

388

Optimal hydrograph separation filter to evaluate transport routines of hydrological models  

NASA Astrophysics Data System (ADS)

Hydrograph separation (HS) using recursive digital filter approaches focuses on trying to distinguish between the rapidly occurring discharge components like surface runoff, and the slowly changing discharge originating from interflow and groundwater. Filter approaches are mathematical procedures, which perform the HS using a set of separation parameters. The first goal of this study is to minimize the subjective influence that a user of the filter technique exerts on the results by the choice of such filter parameters. A simple optimal HS (OHS) technique for the estimation of the separation parameters was introduced, relying on measured stream hydrochemistry. The second goal is to use the OHS parameters to benchmark the performance of process-based hydro-geochemical (HG) models. The new HG routine can be used to quantify the degree of knowledge that the stream flow time series itself contributes to the HG analysis, using newly developed benchmark geochemistry efficiency (BGE). Results of the OHS show that the two HS fractions (“rapid” and “slow”) differ according to the HG substances which were selected. The BFImax parameter (long-term ratio of baseflow to total streamflow) ranged from 0.26 to 0.94 for SO4-2 and total suspended solids, TSS, respectively. Then, predictions of SO4-2 transport from a process-based hydrological model were benchmarked with the proposed HG routine, in order to evaluate the significance of the HG routines in the process-based model. This comparison provides valuable quality test that would not be obvious when using the traditional measures like r2 or the NSE (Nash-Sutcliffe efficiency). The process-based model resulted in r2 = 0.65 and NSE = 0.65, while the benchmark routine results were slightly lower with r2 = 0.61 and NSE = 0.58. However, the comparison between the two model resulted in obvious advantage for the process-based model with BGE = 0.15.

Rimmer, Alon; Hartmann, Andreas

2014-06-01

389

Application of Simulated Annealing Particle Swarm Algorithm in Optimal Scheduling of Hydropower Plant  

Microsoft Academic Search

Taking Hongjiadu hydropower plant as an instance, an algorithm of Simulated Annealing Particle Swarm Optimization (SAPSO) is proposed to optimize the regulation of hydropower plant. Practical application demonstrates that SAPSO solve the regulation problem of hydropower plant well, which is a nonlinear problem with complicated constraints. Compared with Standard Particle Swarm Optimization (SPSO), SAPSO is more efficient and simple and

Li Bin; Rui Jun

2009-01-01

390

Automatic control of ball and beam system using Particle Swarm Optimization  

Microsoft Academic Search

Over the last few decades, many evolutionary algorithms have emerged. One such algorithm is Particle Swarm Optimization which emulates social and cognitive behavior of bird-flocks. In this paper, particle swarm optimization algorithm is presented as a robust and highly useful optimization technique to tune the gains of the PID controllers in the two feedback loops of the classic Ball and

Muhammad Asif Rana; Zubair Usman; Zeeshan Shareef

2011-01-01

391

In-line particle sizing for real-time process control by fibre-optical spatial filtering technique (SFT)  

Microsoft Academic Search

Sizing of particles in industrial processes is of great technical interest and therefore different physical-based techniques have been developed. The objective of this study was to review the characteristics of modern sizing instruments based on a modified fibre-optical spatial filtering technique (SFT). Fibre-optical spatial filtering velocimetry was modified by fibre-optical spot scanning in order to determine simultaneously the size and

Petrak Dieter; Dietrich Stefan; Eckardt Günter; Köhler Michael

2011-01-01

392

Anti-predatory particle swarm optimization: Solution to nonconvex economic dispatch problems  

Microsoft Academic Search

This paper proposes a new particle swarm optimization (PSO) strategy namely, anti-predatory particle swarm optimization (APSO) to solve nonconvex economic dispatch problems. In the classical PSO, the movement of a particle (bird) is governed by three behaviors: inertial, cognitive and social. The cognitive and social behaviors are the components of the foraging activity, which help the swarm of birds to

A. Immanuel Selvakumar; K. Thanushkodi

2008-01-01

393

Comparison of Particle Swarm Optimization and Genetic Algorithm in Rational Function Model Optimization  

NASA Astrophysics Data System (ADS)

Rational Function Models (RFM) are one of the most considerable approaches for spatial information extraction from satellite images especially where there is no access to the sensor parameters. As there is no physical meaning for the terms of RFM, in the conventional solution all the terms are involved in the computational process which causes over-parameterization errors. Thus in this paper, advanced optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are investigated to determine the optimal terms of RFM. As the optimization would reduce the number of required RFM terms, the possibility of using fewer numbers of Ground Control Points (GCPs) in the solution comparing to the conventional method is inspected. The results proved that both GA and PSO are able to determine the optimal terms of RFM to achieve rather the same accuracy. However, PSO shows to be more effective from computational time part of view. The other important achievement is that the algorithms are able to solve the RFM using less GCPs with higher accuracy in comparison to conventional RFM.

Yavari, S.; Zoej, M. J. V.; Mokhtarzade, M.; Mohammadzadeh, A.

2012-07-01

394

A Framework for 3D Model-Based Visual Tracking Using a GPU-Accelerated Particle Filter.  

PubMed

A novel framework for acceleration of particle filtering approaches to 3D model-based, markerless visual tracking in monocular video is described. Specifically, we present a methodology for partitioning and mapping the computationally expensive weight-update stage of a particle filter to a graphics processing unit (GPU) to achieve particle- and pixel-level parallelism. Nvidia CUDA and Direct3D are employed to harness the massively parallel computational power of modern GPUs for simulation (3D model rendering) and evaluation (segmentation, feature extraction, and weight calculation) of hundreds of particles at high speeds. The proposed framework addresses the computational intensity that is intrinsic to all particle filter approaches, including those that have been modified to minimize the number of particles required for a particular task. Performance and tracking quality results for rigid object and articulated hand tracking experiments demonstrate markerless, model-based visual tracking on consumer-grade graphics hardware with pixel-level accuracy up to 95 percent at 60+ frames per second. The framework accelerates particle evaluation up to 49 times over a comparable CPU-only implementation, providing an increased particle count while maintaining real-time frame rates. PMID:21301027

Brown, James Anthony; Capson, David W

2011-02-01

395

Stochastic Particle Advection in Hybrid Large Eddy Simulation/Filtered Density Function Methods  

NASA Astrophysics Data System (ADS)

We describe an efficient combination of interpolation and stochastic time integration schemes for the advection of computational particles in Large Eddy Simulation/Filtered Density Function (LES/FDF) methods. In this setting, particle positions evolve by a standard diffusion process whose drift and diffusion coefficients are determined from flow properties which are known in the form of face- and cell-average values. We demonstrate that a stochastic time integration scheme, developed by Cao and Pope in 2003, yields second-order accurate values of the particle position density function, provided that the interpolation schemes used reconstruct the diffusion and drift terms with second-order accuracy, and their first derivatives with first-order accuracy. Here, we present a velocity interpolation scheme, called the Polar Parabolic Edge Reconstruction Method (PPERM), and a scalar interpolation scheme, called the Multilinear Gradients Method (MLG), which satisfy these requirements, and we compare the performance of the Cao & Pope SDE integration scheme with that of a weak second-order accurate derivative-free scheme proposed by Tocino and Vigo-Aguiar in 2002.

Popov, Pavel P.; Pope, Stephen B.

2008-11-01

396

Optimal Estimation Of Voltage Phasors And Frequency Deviation Using Linear And Non-Linear Kalman Filtering: Theory And Limitations  

Microsoft Academic Search

This paper presents two techniques for optimal tracking of power system voltage phasors and frequency deviation. The first technique is based on a two-state linear Kalman filter model. The second technique is based on a three-state extended Kalman filter model. In the latter the frequency deviation is considered a third state variable and is recursively computed on-line. It is shown

Adly Girgis; T. Daniel Hwang

1984-01-01

397

Particle Swarm Optimization Approach in a Consignment Inventory System  

NASA Astrophysics Data System (ADS)

Consignment Inventory (CI) is a kind of inventory which is in the possession of the customer, but is still owned by the supplier. This creates a condition of shared risk whereby the supplier risks the capital investment associated with the inventory while the customer risks dedicating retail space to the product. This paper considers both the vendor's and the retailers' costs in an integrated model. The vendor here is a warehouse which stores one type of product and supplies it at the same wholesale price to multiple retailers who then sell the product in independent markets at retail prices. Our main aim is to design a CI system which generates minimum costs for the two parties. Here a Particle Swarm Optimization (PSO) algorithm is developed to calculate the proper values. Finally a sensitivity analysis is performed to examine the effects of each parameter on decision variables. Also PSO performance is compared with genetic algorithm.

Sharifyazdi, Mehdi; Jafari, Azizollah; Molamohamadi, Zohreh; Rezaeiahari, Mandana; Arshizadeh, Rahman

2009-09-01

398

Estimating behavioral parameters in animal movement models using a state-augmented particle filter.  

PubMed

Data on fine-scale animal movement are being collected worldwide, with the number of species being tagged and the resolution of data rapidly increasing. In this study, a general methodology is proposed to understand the patterns in these high-resolution movement time series that relate to marine animal behavior. The approach is illustrated with dive data from a northern fur seal (Callorhinus ursinus) tagged on the Pribilof Islands, Alaska, USA. We apply a state-space model composed of a movement model and corresponding high-resolution vertical movement data. The central goal is to estimate parameters of this movement model, particularly their variation on appropriate time scales, thereby providing a direct link to behavior. A particle filter with state augmentation is used to jointly estimate the movement parameters and the state. A multiple iterated filter using overlapping data segments is implemented to match the parameter time scale with the behavioral inference. The time variation in the auto-covariance function facilitates identification of a movement model, allows separation of observation and process noise, and provides for validation of results. The analysis yields fitted parameters that show distinct time-evolving changes in fur seal behavior over time, matching well what is observed in the original data set. PMID:21608465

Dowd, Michael; Joy, Ruth

2011-03-01

399

Particle velocity estimation based on a two-microphone array and Kalman filter.  

PubMed

A traditional method to measure particle velocity is based on the finite difference (FD) approximation of pressure gradient by using a pair of well matched pressure microphones. This approach is known to be sensitive to sensor noise and mismatch. Recently, a double hot-wire sensor termed Microflown became available in light of micro-electro-mechanical system technology. This sensor eliminates the robustness issue of the conventional FD-based methods. In this paper, an alternative two-microphone approach termed the u-sensor is developed from the perspective of robust adaptive filtering. With two ordinary microphones, the proposed u-sensor does not require novel fabrication technology. In the method, plane wave and spherical wave models are employed in the formulation of a Kalman filter with process and measurement noise taken into account. Both numerical and experimental investigations were undertaken to validate the proposed u-sensor technique. The results have shown that the proposed approach attained better performance than the FD method, and comparable performance to a Microflown sensor. PMID:23464014

Bai, Mingsian R; Juan, Shen-Wei; Chen, Ching-Cheng

2013-03-01

400

System and process for tracking an object state using a particle filter sensor fusion technique  

US Patent & Trademark Office Database

A system and process for tracking an object state over time using particle filter sensor fusion and a plurality of logical sensor modules is presented. This new fusion framework combines both the bottom-up and top-down approaches to sensor fusion to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers can be designed to generate effective proposals for the fuser. At the higher level, the fuser performs reliable tracking by verifying hypotheses over multiple likelihood models from multiple cues. Different from the traditional fusion algorithms, the present framework is a closed-loop system where the fuser and trackers coordinate their tracking information. Furthermore, to handle non-stationary situations, the present framework evaluates the performance of the individual trackers and dynamically updates their object states. A real-time speaker tracking system based on the proposed framework is feasible by fusing object contour, color and sound source location.

2006-04-25

401

Optimizing magnetite nanoparticles for mass sensitivity in magnetic particle imaging  

PubMed Central

Purpose: Magnetic particle imaging (MPI), using magnetite nanoparticles (MNPs) as tracer material, shows great promise as a platform for fast tomographic imaging. To date, the magnetic properties of MNPs used in imaging have not been optimized. As nanoparticle magnetism shows strong size dependence, the authors explore how varying MNP size impacts imaging performance in order to determine optimal MNP characteristics for MPI at any driving field frequency f0. Methods: Monodisperse MNPs of varying size were synthesized and their magnetic properties characterized. Their MPI response was measured experimentally using a custom-built MPI transceiver designed to detect the third harmonic of MNP magnetization. The driving field amplitude H0=6 mT ?0?1 and frequency f0=250 kHz were chosen to be suitable for imaging small animals. Experimental results were interpreted using a model of dynamic MNP magnetization that is based on the Langevin theory of superparamagnetism and accounts for sample size distribution and size-dependent magnetic relaxation. Results: The experimental results show a clear variation in the MPI signal intensity as a function of MNP diameter that is in agreement with simulated results. A maximum in the plot of MPI signal vs MNP size indicates there is a particular size that is optimal for the chosen f0. Conclusions: The authors observed that MNPs 15 nm in diameter generate maximum signal amplitude in MPI experiments at 250 kHz. The authors expect the physical basis for this result, the change in magnetic relaxation with MNP size, will impact MPI under other experimental conditions. PMID:21520874

Ferguson, R. Matthew; Minard, Kevin R.; Khandhar, Amit P.; Krishnan, Kannan M.

2011-01-01

402

Microwave-based medical diagnosis using particle swarm optimization algorithm  

NASA Astrophysics Data System (ADS)

This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level of complexity and randomness inherent to the selection of electromagnetic benchmark problems, a trend to resort to oversimplification in order to arrive at reasonable solutions has been taken in literature when utilizing analytical techniques. Here, an attempt has been made to avoid oversimplification when using the proposed swarm-based optimization algorithms.

Modiri, Arezoo

403

An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization.  

PubMed

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. PMID:24688370

Yu, Xiaobing; Cao, Jie; Shan, Haiyan; Zhu, Li; Guo, Jun

2014-01-01

404

Reliability Optimization of Radial Distribution Systems Employing Differential Evolution and Bare Bones Particle Swarm Optimization  

NASA Astrophysics Data System (ADS)

This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system.

Kela, K. B.; Arya, L. D.

2014-09-01

405

A NEW METHODOLOGY FOR EMERGENT SYSTEM IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION  

E-print Network

A NEW METHODOLOGY FOR EMERGENT SYSTEM IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION (PSO. Section 2 describes traditional System Identification and introduces the use of Particle Swarm from the results. A new methodology for Emergent System Identification is proposed in this paper

Fernandez, Thomas

406

Application of digital tomosynthesis (DTS) of optimal deblurring filters for dental X-ray imaging  

NASA Astrophysics Data System (ADS)

Digital tomosynthesis (DTS) is a limited-angle tomographic technique that provides some of the tomographic benefits of computed tomography (CT) but at reduced dose and cost. Thus, the potential for application of DTS to dental X-ray imaging seems promising. As a continuation of our dental radiography R&D, we developed an effective DTS reconstruction algorithm and implemented it in conjunction with a commercial dental CT system for potential use in dental implant placement. The reconstruction algorithm employed a backprojection filtering (BPF) method based upon optimal deblurring filters to suppress effectively both the blur artifacts originating from the out-focus planes and the high-frequency noise. To verify the usefulness of the reconstruction algorithm, we performed systematic simulation works and evaluated the image characteristics. We also performed experimental works in which DTS images of enhanced anatomical resolution were successfully obtained by using the algorithm and were promising to our ongoing applications to dental X-ray imaging. In this paper, our approach to the development of the DTS reconstruction algorithm and the results are described in detail.

Oh, J. E.; Cho, H. S.; Kim, D. S.; Choi, S. I.; Je, U. K.

2012-04-01

407

Structure optimization of an artificial neural filter detecting membrane-spanning amino acid sequences.  

PubMed

An artificial neural network has been developed for the recognition and prediction of transmembrane regions in the amino acid sequences of human integral membrane proteins. It provides an additional prediction method besides the common hydrophobicity analysis by statistical means. Membrane/nonmembrane transition regions are predicted with 92% accuracy in both training and independent test data. The method used for the development of the neural filter is the algorithm of structure evolution. It subjects both the architecture and parameters of the system to a systematical optimization process and carries out local search in the respective structure and parameter spaces. The training technique of incomplete induction as part of the structure evolution provides for a comparatively general solution of the problem that is described by input-output relations only. Seven physiochemical side-chain properties were used to encode the amino acid sequences. It was found that geometric parameters like side-chain volume, bulkiness, or surface area are of minor importance. The properties polarity, refractivity, and hydrophobicity, however, turned out to support feature extraction. It is concluded that membrane transition regions in proteins are encoded in sequences as a characteristic feature based on the respective side-chain properties. The method of structure evolution is described in detail for this particular application and suggestions for further development of amino acid sequence filters are made. PMID:8679941

Lohmann, R; Schneider, G; Wrede, P

1996-01-01

408

Construction of Fuzzy Models for Dynamic Systems Using Multi-population Cooperative Particle Swarm Optimizer  

Microsoft Academic Search

\\u000a A new fuzzy modeling method using Multi-population Cooperative Particle Swarm Optimizer (MCPSO) for identification and control\\u000a of nonlinear dynamic systems is presented in this paper. In MCPSO, the population consists of one master swarm and several\\u000a slave swarms. The slave swarms executeParticle Swarm Optimization (PSO) or its variants independently to maintain the diversity\\u000a of particles, while the particles in the

Ben Niu; Yunlong Zhu; Xiaoxian He

2005-01-01

409

Diesel passenger car PM emissions: From Euro 1 to Euro 4 with particle filter  

NASA Astrophysics Data System (ADS)

This paper examines the impact of the emission control and fuel technology development on the emissions of gaseous and, in particular, PM pollutants from diesel passenger cars. Three cars in five configurations in total were measured, and covered the range from Euro 1 to Euro 4 standards. The emission control ranged from no aftertreatment in the Euro 1 case, an oxidation catalyst in Euro 2, two oxidation catalysts and exhaust gas recirculation in Euro 3 and Euro 4, while a catalyzed diesel particle filter (DPF) fitted in the Euro 4 car led to a Euro 4 + DPF configuration. Both certification test and real-world driving cycles were employed. The results showed that CO and HC emissions were much lower than the emission standard over the hot-start real-world cycles. However, vehicle technologies from Euro 2 to Euro 4 exceeded the NOx and PM emission levels over at least one real-world cycle. The NOx emission level reached up to 3.6 times the certification level in case of the Euro 4 car. PM were up to 40% and 60% higher than certification level for the Euro 2 and Euro 3 cars, while the Euro 4 car emitted close or slightly below the certification level over the real-world driving cycles. PM mass reductions from Euro 1 to Euro 4 were associated with a relevant decrease in the total particle number, in particular over the certification test. This was not followed by a respective reduction in the solid particle number which remained rather constant between the four technologies at 0.86 × 10 14 km -1 (coefficient of variation 9%). As a result, the ratio of solid vs. total particle number ranged from ˜50% in Euro 1-100% in Euro 4. A significant reduction of more than three orders of magnitude in solid particle number is achieved with the introduction of the DPF. However, the potential for nucleation mode formation at high speed from the DPF car is an issue that needs to be considered in the over all assessment of its environmental benefit. Finally, comparison of the mobility and aerodynamic diameters of airborne particles led to fractal dimensions dropping from 2.60 (Euro 1) to 2.51 (Euro 4), denoting a more loose structure with improving technology.

Tzamkiozis, Theodoros; Ntziachristos, Leonidas; Samaras, Zissis

2010-03-01

410

Biologically Induced Deposition of Fine Suspended Particles by Filter-Feeding Bivalves in Land-Based Industrial Marine Aquaculture Wastewater  

PubMed Central

Industrial aquaculture wastewater contains large quantities of suspended particles that can be easily broken down physically. Introduction of macro-bio-filters, such as bivalve filter feeders, may offer the potential for treatment of fine suspended matter in industrial aquaculture wastewater. In this study, we employed two kinds of bivalve filter feeders, the Pacific oyster Crassostrea gigas and the blue mussel Mytilus galloprovincialis, to deposit suspended solids from marine fish aquaculture wastewater in flow-through systems. Results showed that the biodeposition rate of suspended particles by C. gigas (shell height: 8.67±0.99 cm) and M. galloprovincialis (shell height: 4.43±0.98 cm) was 77.84±7.77 and 6.37±0.67 mg ind?1•d?1, respectively. The total solid suspension (TSS) deposition rates of oyster and mussel treatments were 3.73±0.27 and 2.76±0.20 times higher than that of the control treatment without bivalves, respectively. The TSS deposition rates of bivalve treatments were significantly higher than the natural sedimentation rate of the control treatment (P<0.001). Furthermore, organic matter and C, N in the sediments of bivalve treatments were significantly lower than those in the sediments of the control (P<0.05). It was suggested that the filter feeders C. gigas and M. galloprovincialis had considerable potential to filter and accelerate the deposition of suspended particles from industrial aquaculture wastewater, and simultaneously yield value-added biological products. PMID:25250730

Zhou, Yi; Zhang, Shaojun; Liu, Ying; Yang, Hongsheng

2014-01-01

411

Video object tracking using improved chamfer matching and condensation particle filter  

NASA Astrophysics Data System (ADS)

Object tracking is an essential problem in the field of video and image processing. Although tracking algorithms working on gray video are convenient in actual applications, they are more difficult to be developed than those using color features, since less information is taken into account. Few researches have been dedicated to tracking object using edge information. In this paper, we proposed a novel video tracking algorithm based on edge information for gray videos. This method adopts the combination of a condensation particle filter and an improved chamfer matching. The improved chamfer matching is rotation invariant and capable of estimating the shift between an observed image patch and a template by an orientation distance transform. A modified discriminative likelihood measurement method that focuses on the difference is adopted. These values are normalized and used as the weights of particles which predict and track the object. Experiment results show that our modifications to chamfer matching improve its performance in video tracking problem. And the algorithm is stable, robust, and can effectively handle rotation distortion. Further work can be done on updating the template to adapt to significant viewpoint and scale changes of the appearance of the object during the tracking process.

Wu, Tao; Ding, Xiaoqing; Wang, Shengjin; Wang, Kongqiao

2008-02-01

412

A Bayesian Interpretation of the Particle Swarm Optimization and Its Kernel Extension  

PubMed Central

Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved–such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases. PMID:23144937

Andras, Peter

2012-01-01

413

The first on-site evaluation of a new filter optimized for TARC and developer  

NASA Astrophysics Data System (ADS)

In previous studies, we identified filter properties that have a strong effect on microbubble formation on the downstream side of the filter membrane. A new Highly Asymmetric Polyarylsulfone (HAPAS) filter was developed based on the findings. In the current study, we evaluated newly-developed HAPAS filter in environmentally preferred non-PFOS TARC in a laboratory setting. Test results confirmed that microbubble counts downstream of the filter were lower than those of a conventional HDPE filter. Further testing in a manufacturing environment confirmed that HAPAS filtration of TARC at point of use was able to reduce defectivity caused by microbubbles on both unpatterned and patterned wafers, compared with a HDPE filter.

Umeda, Toru; Ishibashi, Takeo; Nakamura, Atsushi; Ide, Junichi; Nagano, Masaru; Omura, Koichi; Tsuzuki, Shuichi; Numaguchi, Toru

2008-11-01

414

Comparison between GMM and KDE data fusion methods for particle filtering: Application to pedestrian detection from laser and video measurements  

Microsoft Academic Search

In urban environment, pedestrian detection is a challenging task in automotive research, which often suffers from the lack of reliability due to the occurrences of spurious detections. In order to answer multitarget multisensor tracking problem and more specifically pedestrian tracking, we propose to use an algorithm based on a stochastic recursive Bayesian framework also called particle filter. We aim to

S. Gidel; C. Blanc; T. Chateau; P. Checchin; L. Trassoudaine

2010-01-01

415

BEAT-TO-BEAT P AND T WAVE DELINEATION IN ECG SIGNALS USING A MARGINALIZED PARTICLE FILTER  

E-print Network

BEAT-TO-BEAT P AND T WAVE DELINEATION IN ECG SIGNALS USING A MARGINALIZED PARTICLE FILTER Chao Lin1-yves.tourneret,corinne.mailhes}@enseeiht.fr ABSTRACT The delineation of P and T waves is important for the interpretation of ECG signals. In this work model which exploits the sequential nature of the ECG by introducing a random walk model

Tourneret, Jean-Yves

416

www.cesos.ntnu.no Bo Zhao Centre for Ships and Ocean Structures Fault Diagnosis based on Particle Filter  

E-print Network

.cesos.ntnu.no Bo Zhao ­ Centre for Ships and Ocean Structures Data from: The Software Problem ++, Marine1 www.cesos.ntnu.no Bo Zhao ­ Centre for Ships and Ocean Structures Fault Diagnosis based on Particle Filter - with applications to marine crafts Bo Zhao CeSOS / Department of Marine Technology

Nørvåg, Kjetil

417

spl sigma\\/SLAM: Stereo Vision SLAM using the Rao-Blackwellised Particle Filter and a Novel Mixture Proposal Distribution  

Microsoft Academic Search

ó We consider the problem of Simultaneous Localiza- tion and Mapping (SLAM) using the Rao-Blackwellised Particle Filter (RBPF) for the class of indoor mobile robots equipped only with stereo vision. Our goal is to construct dense metric maps of natural 3D point landmarks for large cyclic environments in the absence of accurate landmark position measurements and motion estimates. Our work

Pantelis Elinas; Robert Sim; James J. Little

2006-01-01

418

A specification language for the optimal design of exotic FIR filters with second-order cone programs  

Microsoft Academic Search

Application-tailored individual and joint FIR-filter designs of remarkable complexity are elegantly coded using our MATLAB toolbox Opt, a research tool providing a DSP-oriented modeling language for driving ultra-efficient off-the-shelf numerical solvers of (linear and) second-order cone programs. Opt data types symbolically capture affine or (nonnegative definite) quadratic dependencies on optimization variables, which gain numeric values only later, when optimized. On

J. O. Coleman; D. P. Scholnik; J. J. Brandriss

2002-01-01

419

Design of Optimal Quincunx Filter Banks for Image Dept. of Elec. and Comp. Engineering, University of Victoria, Victoria, BC, V8W 3P6, Canada  

E-print Network

typically desires filter banks to have perfect reconstruc- tion (PR), linear phase, high coding gain, good methods have been proposed. Variable transformation methods are commonly used for the design of 2D filter1 Design of Optimal Quincunx Filter Banks for Image Coding Yi Chen Dept. of Elec. and Comp

Adams, Michael D.

420

Optimization of Temporal Filters in the Modulation Frequency Domain via Constrained Linear Discriminant Analysis (C-LDA) for Constructing Robust Features in Speech Recognition  

Microsoft Academic Search

Data-driven temporal filtering approaches based on a specific optimization criterion have been shown to be capable of enhancing the discrimination and robustness of speech features in speech recognition. The filters in these approaches are often obtained with the statistics of the features in the temporal domain. In this paper, we derive new data-driven temporal filters that employ the statistics of

Jeih-weih Hung; Nantou Hsien

2007-01-01

421

Fishing for Data: Using Particle Swarm Optimization to Search Data  

NASA Astrophysics Data System (ADS)

As the size of data and model sets continue to increase, more efficient ways are needed to sift through the available information. We present a computational method which will efficiently search large parameter spaces to either map the space or find individual data/models of interest. Particle swarm optimization (PSO) is a subclass of artificial life computer algorithms. The PSO algorithm attempts to leverage "swarm intelligence” against finding optimal solutions to a problem. This system is often based on a biological model of a swarm (e.g. schooling fish). These biological models are broken down into a few simple rules which govern the behavior of the system. "Agents” (e.g. fish) are introduced and the agents, following the rules, search out solutions much like a fish would seek out food. We have made extensive modifications to the standard PSO model which increase its efficiency as-well-as adding the capacity to map a parameter space and find multiple solutions. Our modified PSO is ideally suited to search and map large sets of data/models which are degenerate or to search through data/models which are too numerous to analyze by hand. One example of this would include radiative transfer models, which are inherently degenerate. Applying the PSO algorithm will allow the degeneracy space to be mapped and thus better determine limits on dust shell parameters. Another example is searching through legacy data from a survey for hints of Polycyclic Aromatic Hydrocarbon emission. What might have once taken years of searching (and many frustrated graduate students) can now be relegated to the task of a computer which will work day and night for only the cost of electricity. We hope this algorithm will allow fellow astronomers to more efficiently search data and models, thereby freeing them to focus on the physics of the Universe.

Caputo, Daniel P.; Dolan, R.

2010-01-01

422

An Explicit Linear Filtering Solution for the Optimization of Guidance Systems with Statistical Inputs  

NASA Technical Reports Server (NTRS)

The determination of optimum filtering characteristics for guidance system design is generally a tedious process which cannot usually be carried out in general terms. In this report a simple explicit solution is given which is applicable to many different types of problems. It is shown to be applicable to problems which involve optimization of constant-coefficient guidance systems and time-varying homing type systems for several stationary and nonstationary inputs. The solution is also applicable to off-design performance, that is, the evaluation of system performance for inputs for which the system was not specifically optimized. The solution is given in generalized form in terms of the minimum theoretical error, the optimum transfer functions, and the optimum transient response. The effects of input signal, contaminating noise, and limitations on the response are included. From the results given, it is possible in an interception problem, for example, to rapidly assess the effects on minimum theoretical error of such factors as target noise and missile acceleration. It is also possible to answer important questions regarding the effect of type of target maneuver on optimum performance.

Stewart, Elwood C.

1961-01-01

423

Optimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique  

NASA Astrophysics Data System (ADS)

The multiple-objective resource allocation problem (MORAP) seeks for an allocation of resource to a number of activities such that a set of objectives are optimized simultaneously and the resource constraints are satisfied. MORAP has many applications, such as resource distribution, project budgeting, software testing, health care resource allocation, etc. This paper addresses the nonlinear MORAP with integer decision variable constraint. To guarantee that all the resource constraints are satisfied, we devise an adaptive-resource-bound technique to construct feasible solutions. The proposed method employs the particle swarm optimization (PSO) paradigm and presents a hybrid execution plan which embeds a hill-climbing heuristic into the PSO for expediting the convergence. To cope with the optimization problem with multiple objectives, we evaluate the candidate solutions based on dominance relationship and a score function. Experimental results manifest that the hybrid PSO derives solution sets which are very close to the exact Pareto sets. The proposed method also outperforms several representatives of the state-of-the-art algorithms on a simulation data set of the MORAP.

Yin, Peng-Yeng; Wang, Jing-Yu

2008-06-01

424

Influence by small dispersive coal dust particles of different fractional consistence on characteristics of iodine air filter at nuclear power plant  

E-print Network

The main purpose of research is to determine the influence by the small dispersive coal dust particles of the different fractional consistence on the technical characteristics of the vertical iodine air filter at nuclear power plant. The research on the transport properties of the small dispersive coal dust particles in the granular filtering medium of absorber in the vertical iodine air filter is completed in the case, when the modeled aerodynamic conditions are similar to the real aerodynamic conditions. It is shown that the appearance of the different fractional consistence of small dispersive coal dust particles with the decreasing dimensions down to the micro and nano sizes at the action of the air dust aerosol stream normally results in a significant change of distribution of the small dispersive coal dust particles masses in the granular filtering medium of an absorber in the vertical iodine air filter, changing the vertical iodine air filter aerodynamic characteristics. The precise characterization of...

Neklyudov, I M; Fedorova, L I; Poltinin, P Ya

2013-01-01

425

Particle emission and operating characterization of an electrostatically enhanced fabric filter pilot plant: Final report  

SciTech Connect

Southern Research Institute performed an experimental study to investigate the Potential advantages of precharging fly ash before collection in a fabric filter baghouse. The study was performed on a pilot scale, reverse-gas cleaned, fabric filter at the Electric Power Research Institute (EPRI), Arapahoe Test Facility in Denver, Colorado. The system utilized a high intensity ionizer, developed jointly by EPRI and Air Pollution Systems. The ionizer was installed upstream of the baghouse to precharge the fly ash particles. While collection efficiencies were comparable to those of conventional baghouses (>99.9%), the tube sheet pressure drop observed during operation with the charger on was substantially lower than that obtained with the charger off. This was true especially at the higher air-to-cloth (A/C) ratios. Following long-term operations (in excess of 1200 hours) at an A/C value of 3, the average tube sheet pressure drop recorded while using charged ash (..delta..p = 4 in. H/sub 2/O) was found to be approximately 50% less than that using uncharged ash (..delta..p = 8 in. H/sub 2/O). Thus, it appears that a substantial benefit, in terms of reduced baghouse pressure drop, may be obtained by precharging the ash before filtration. As a result of the reduced pressure drop achieved with precharged ash, the baghouse may then be operated at a substantially higher air-to-cloth ratio. The direct result of this would be to reduce the physical size of the baghouse required for a given installation. Thus by requiring a smaller baghouse, substantial savings may be possible in construction costs and operating expenses.

Pyle, B.E.; Pontius, D.H.; Martin, C.E.

1987-09-01

426

Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments  

NASA Astrophysics Data System (ADS)

Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of interest is a weighted average of pdfs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts, and reflect the individual models skill over a training (calibration) period. The original BMA approach presented by Raftery et al. (2005) assumes that the conditional pdf of each individual model is adequately described with a rather standard Gaussian or Gamma statistical distribution, possibly with a heteroscedastic variance. Here we analyze the advantages of using BMA with a flexible representation of the conditional pdf. A joint particle filtering and Gaussian mixture modeling framework is presented to derive analytically, as closely and consistently as possible, the evolving forecast density (conditional pdf) of each constituent ensemble member. The median forecasts and evolving conditional pdfs of the constituent models are subsequently combined using BMA to derive one overall predictive distribution. This paper introduces the theory and concepts of this new ensemble postprocessing method, and demonstrates its usefulness and applicability by numerical simulation of the rainfall-runoff transformation using discharge data from three different catchments in the contiguous United States. The revised BMA method receives significantly lower-prediction errors than the original default BMA method (due to filtering) with predictive uncertainty intervals that are substantially smaller but still statistically coherent (due to the use of a time-variant conditional pdf).

Rings, Joerg; Vrugt, Jasper A.; Schoups, Gerrit; Huisman, Johan A.; Vereecken, Harry

2012-05-01

427

Bounds on the performance of optimal four-dimensional filters for detection of low-contrast IR point targets  

Microsoft Academic Search

This paper provides analytic expressions for the performance of optimal matched filters designed to utilize spatial, temporal, and spectral observations of point targets against cluttered backgrounds. The analysis explicitly treats the situation of bipolar low contrast target signatures typical in advanced infrared systems such as the infrared search and track systems. In these cases, one must include the temporal effects

Martin R. Wohlers

1991-01-01

428

High-Dimensional Adaptive Particle Swarm Optimization on Heterogeneous Systems  

NASA Astrophysics Data System (ADS)

Much work has recently been reported in parallel GPU-based particle swarm optimization (PSO). Motivated by the encouraging results of these investigations, while also recognizing the limitations of GPU-based methods for big problems using a large amount of data, this paper explores the efficacy of employing other types of parallel hardware for PSO. Most commodity systems feature a variety of architectures whose high-performance capabilities can be exploited. In this paper, high-dimensional problems and those that employ a large amount of external data are explored within the context of heterogeneous systems. Large problems are decomposed into constituent components, and analyses are undertaken of which components would benefit from multi-core or GPU parallelism. The current study therefore provides another demonstration that "supercomputing on a budget" is possible when subtasks of large problems are run on hardware most suited to these tasks. Experimental results show that large speedups can be achieved on high dimensional, data-intensive problems. Cost functions must first be analysed for parallelization opportunities, and assigned hardware based on the particular task.

Wachowiak, M. P.; Sarlo, B. B.; Lambe Foster, A. E.

2014-10-01

429

Cosmological parameter estimation using Particle Swarm Optimization (PSO)  

E-print Network

Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter space. Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid based approaches) with the dimensionality of the search space. MCMC methods sample the full joint probability distribution (posterior) from which one and two dimensional probability distributions, best fit (average) values of parameters and then error bars can be computed. In the present work we demonstrate the application of another stochastic method, named Particle Swarm Optimization (PSO), that is widely used in the field of engineering and artificial intelligence, for cosmological parameter estimation from WMAP seven years data. We find that there is a good agreement between the values of the best fit parameters obtained from PSO and publicly available code COSMOMC. However, there is a slight disagreement between error bars mainly due to the fact that errors are computed differently in PSO. Apart from presenting the results of our exercise, we also discuss the merits of PSO and explain its usefulness in more extensive search in higher dimensional parameter space.

Jayanti Prasad; Tarun Souradeep

2011-08-29

430

DNA Sequence Compression Using Adaptive Particle Swarm Optimization-Based Memetic Algorithm  

Microsoft Academic Search

With the rapid development of high-throughput DNA sequencing technologies, the amount of DNA sequence data is accumulating exponentially. The huge influx of data creates new challenges for storage and transmission. This paper proposes a novel adaptive particle swarm optimization-based memetic algorithm (POMA) for DNA sequence compression. POMA is a synergy of comprehensive learning particle swarm optimization (CLPSO) and an adaptive

Zexuan Zhu; Jiarui Zhou; Zhen Ji; Yu-Hui Shi

2011-01-01