Particle Filter with Swarm Move for Optimization
Yang, Shengxiang
method in particle swarm optimization (PSO). In this way, the PSO update equation is treated the ability of PSO in searching the optimal position can be embedded into the particle filter optimization in both convergence speed and final fitness in comparison with the PSO algorithm over a set of standard
Optimal proposal densities for particle filters
NASA Astrophysics Data System (ADS)
van Leeuwen, P. J.
2012-04-01
Most data-assimilation problems in the geosciences are of very large dimension and nonlinear, either through nonlinear models, and/or through nonlinear observation operators. Most present-day data-assimilation methods for large-dimensional problems are based on linearisations, such as (Ensemble) Kalman filters and variational methods like 4DVar. There is a growing need for fully nonlinear data-assimilation methods, and particle filters could in principle serve this goal. However, standard particle filters are notoriously inefficient in that they typically need millions or more model runs to represent the posterior pdf. It is easy to show that this large number is related to the number of independent observations that determine the weighting of the particles via the likelihood. These weights typically vary enormously, such that e.g. a weighted mean is effectively represented via one or a few particles. This problem has been reduced to some extend by using proposal densities that bring the particles closer to observations, and as such reduce the variance in the weights of particles. However, even if the so-called 'optimal proposal density' is used, in which the proposal density takes into account the future observations, the variance in the weights is so large that an astronomical number of particles is needed for any real-sized problem. In this presentation we discuss new proposal densities that solve this weight degeneracy problem. The idea is simply that the proposal densities can be used not only to bring particles close to observations, but also to ensure that the weights of the particles are very similar. Two examples of such proposal densities are discussed, the equivalent-weights particle filter, and the new Gaussian-peak particle filter. The first scheme determines a target weight at the last time step before the observations come in, and moves the particles such that each obtains a weight very close to that target weight. The second new scheme slightly perturbs the particles such that while sampled from a very narrow pdf, their weights are determined from a very broad pdf, ensuring that the weights are nearly equal. The methods are implemented and tested on a high-dimensional highly nonlinear one-dimensional problem and compared to the standard particle filter and the so-called 'optimal proposal density' scheme. It is shown that both the standard particle filter and the 'optimal proposal density' scheme are degenerate, while both new schemes properly represent the posterior pdf using a very small number of particles. These results show that particle filters can be made to work for real geophysical problems when the extra freedom in the proposal density related to the weights of the particles is explored.
Mandal, J K
2012-01-01
In this paper a novel approach for de noising images corrupted by random valued impulses has been proposed. Noise suppression is done in two steps. The detection of noisy pixels is done using all neighbor directional weighted pixels (ANDWP) in the 5 x 5 window. The filtering scheme is based on minimum variance of the four directional pixels. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) has also been used for searching the parameters of detection and filtering operators required for optimal performance. Results obtained shows better de noising and preservation of fine details for highly corrupted images.
Particle swarm optimization-based approach for optical finite impulse response filter design
Wu, Shin-Tson
Particle swarm optimization-based approach for optical finite impulse response filter design Ying method for the design of an optical finite impulse response FIR filter by employing a particle swarm- lished.8 Here, we employ the particle swarm opti- mization PSO technique as proposed by Kennedy
Junghyun Kwon; Kyoung Mu Lee; Frank Chongwoo Park
2009-01-01
We propose a geometric method for visual tracking, in which the 2-D affine motion of a given object template is estimated in a video sequence by means of coordinate- invariant particle filtering on the 2-D affine group Aff(2). Tracking performance is further enhanced through a geo- metrically defined optimal importance function, obtained explicitly via Taylor expansion of a principal component
Designing Linear Phase FIR Filters with Particle Swarm Optimization and Harmony Search
NASA Astrophysics Data System (ADS)
Shirvani, Abdolreza; Khezri, Kaveh; Razzazi, Farbod; Lucas, Caro
In recent years, evolutionary methods have shown great success in solving many combinatorial optimization problems such as FIR (Finite Impulse Response) filter design. An ordinary method in FIR filter design problem is Parks-McClellan, which is both difficult to implement and computationally expensive. The goal of this paper is to design a near optimal linear phase FIR filter using two recent evolutionary approaches; Particle Swarm Optimization (PSO) and Harmony Search (HS). These methods are robust, easy to implement, and they would not trap in local optima due to their stochastic behavior. In addition, they have distinguishing features such as less variance error and smaller overshoots in both stop and pass bands. To prove these benefits, two case studies are presented and obtained results are compared with previous implementations. In both cases, better and reliable results are achieved.
Generalized particle flow for nonlinear filters
Fred Daum; Jim Huang
2010-01-01
We generalize the theory of particle flow to stabilize the nonlinear filter. We have invented a new nonlinear filter that is vastly superior to the classic particle filter and the extended Kalman filter (EKF). In particular, the computational complexity of the new filter is many orders of magnitude less than the classic particle filter with optimal estimation accuracy for problems
Junghyun Kwon; Kyoung Mu Lee; Frank C. Park
2009-01-01
We propose a geometric method for visual tracking, in which the 2-D affine motion of a given object template is estimated in a video sequence by means of coordinate-invariant particle filtering on the 2-D affine group Aff(2). Tracking performance is further enhanced through a geometrically defined optimal importance function, obtained explicitly via Taylor expansion of a principal component analysis based
Optimizing Parameters of Process-Based Terrestrial Ecosystem Model with Particle Filter
NASA Astrophysics Data System (ADS)
Ito, A.
2014-12-01
Present terrestrial ecosystem models still contain substantial uncertainties, as model intercomparison studies have shown, because of poor model constraint by observational data. So, development of advanced methodology of data-model fusion, or data-assimilation, is an important task to reduce the uncertainties and improve model predictability. In this study, I apply the Particle filter (or Sequential Monte Carlo filer) to optimize parameters of a process-based terrestrial ecosystem model (VISIT). The Particle filter is one of the data-assimilation methods, in which probability distribution of model state is approximated by many samples of parameter set (i.e., particle). This is a computationally intensive method and applicable to nonlinear systems; this is an advantage of the method in comparison with other techniques like Ensemble Kalman filter and variational method. At several sites, I used flux measurement data of atmosphere-ecosystem CO2 exchange in sequential and non-sequential manners. In the sequential data assimilation, a time-series data at 30-min or daily steps were used to optimize gas-exchange-related parameters; this method would be also effective to assimilate satellite observational data. On the other hand, in the non-sequential case, annual or long-term mean budget was adjusted to observations; this method would be also effective to assimilate carbon stock data. Although there remain technical issues (e.g., appropriate number of particles and likelihood function), I demonstrate that the Partile filter is an effective method of data-assimilation for process-based models, enhancing collaboration between field and model researchers.
Particle flow for nonlinear filters
Fred Daum; Jim Huang
2011-01-01
We have solved the well known and important problem of particle degeneracy for particle filters. Our filter is roughly seven orders of magnitude faster than standard particle filters for the same estimation accuracy. The new filter is four orders of magnitude faster per particle, and it requires roughly three orders of magnitude fewer particles to achieve the same accuracy as
Robust evolutionary particle filter.
Havangi, R
2015-07-01
The particle filter (PF) has been widely applied for non-linear filtering owing to its ability to carry multiple hypotheses relaxing the linearity and Gaussian assumptions. However, PF is inconsistent over time due to the loss of particle diversity caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process and measurement noise. To overcome these problems, in this paper, robust evolutionary particle filter is proposed. The proposed method can work in unknown statistical noise and does not require a prior knowledge about the system. In addition, to increase diversity, a resampling process is done based on the differential evolution (DE). The effectiveness of the proposed algorithm is demonstrated through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method. PMID:25669842
Bounds on the performance of particle filters
NASA Astrophysics Data System (ADS)
Snyder, C.; Bengtsson, T.
2014-12-01
Particle filters rely on sequential importance sampling and it is well known that their performance can depend strongly on the choice of proposal distribution from which new ensemble members (particles) are drawn. The use of clever proposals has seen substantial recent interest in the geophysical literature, with schemes such as the implicit particle filter and the equivalent-weights particle filter. A persistent issue with all particle filters is degeneracy of the importance weights, where one or a few particles receive almost all the weight. Considering single-step filters such as the equivalent-weights or implicit particle filters (that is, those in which the particles and weights at time tk depend only on the observations at tk and the particles and weights at tk-1), two results provide a bound on their performance. First, the optimal proposal minimizes the variance of the importance weights not only over draws of the particles at tk, but also over draws from the joint proposal for tk-1 and tk. This shows that a particle filter using the optimal proposal will have minimal degeneracy relative to all other single-step filters. Second, the asymptotic results of Bengtsson et al. (2008) and Snyder et al. (2008) also hold rigorously for the optimal proposal in the case of linear, Gaussian systems. The number of particles necessary to avoid degeneracy must increase exponentially with the variance of the incremental importance weights. In the simplest examples, that variance is proportional to the dimension of the system, though in general it depends on other factors, including the characteristics of the observing network. A rough estimate indicates that single-step particle filter applied to global numerical weather prediction will require very large numbers of particles.
Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking
Ravi Kumar Jatoth; D. N. Rao; K. S. Kumar
2010-01-01
Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable
Probabilistic Robotics Discrete Filters and Particle Filters
Kosecka, Jana
SA-1 Probabilistic Robotics Discrete Filters and Particle Filters Models Some slides adopted from: Wolfram Burgard, Cyrill Stachniss, Maren Bennewitz, Kai Arras and Probabilistic Robotics Book #12 to monitor whether the robot is de-localized or not. · To achieve this, one can consider the likelihood
Particle flow for nonlinear filters with log-homotopy
Fred Daum; Jim Huang
2008-01-01
We describe a new nonlinear filter that is vastly superior to the classic particle filter. In particular, the computational complexity of the new filter is many orders of magnitude less than the classic particle filter with optimal estimation accuracy for problems with dimension greater than 2 or 3. We consider nonlinear estimation problems with dimensions varying from 1 to 20
A SIMULATION-BASED OPTIMIZATION APPROACH TO POLYMER EXTRUSION FILTER
Jenkins, Lea
A SIMULATION-BASED OPTIMIZATION APPROACH TO POLYMER EXTRUSION FILTER DESIGN K.R. Fowler1 S.M. La methods for finding optimal parameters for the filter such that its lifetime is maximized, while placing model that describes the deposition of debris particles in the filter. Optimization algorithms are used
Exact particle flow for nonlinear filters
Fred Daum; Jim Huang; Arjang Noushin
2010-01-01
We have invented a new theory of exact particle flow for nonlinear filters. This generalizes our theory of particle flow that is already many orders of magnitude faster than standard particle filters and which is several orders of magnitude more accurate than the extended Kalman filter for difficult nonlinear problems. The new theory generalizes our recent log-homotopy particle flow filters
Series expansions of Brownian motion and the unscented particle filter
Edinburgh, University of
Series expansions of Brownian motion and the unscented particle filter October 15, 2013 Abstract The discrete-time filtering problem for nonlinear diffusion processes is computationally intractable in general. For this reason, methods such as the bootstrap filter are particularly effective at approximating the optimal
OPTIMIZATION OF ADVANCED FILTER SYSTEMS
R.A. Newby; G.J. Bruck; M.A. Alvin; T.E. Lippert
1998-04-30
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.
Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization
Pei, Fujun; Wu, Mei; Zhang, Simin
2014-01-01
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
Distributed SLAM using improved particle filter for mobile robot localization.
Pei, Fujun; Wu, Mei; Zhang, Simin
2014-01-01
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
Application of Optimization Technique for GPS Navigation Kalman Filter Adaptation
Dah-jing Jwo; Shun-chieh Chang
2008-01-01
The position-velocity (PV) process model can be applied to the GPS Kalman filter adequately when navigating a vehicle with\\u000a constant speed. However, when an abrupt acceleration motion occurs, the filtering solution becomes very poor or even diverges.\\u000a To avoid the limitation of the Kalman filter, the particle swarm optimization can be incorporated into the filtering mechanism\\u000a as dynamic model corrector.
Nonlinear Filtering: Interacting Particle P. Del Moral
Del Moral , Pierre
so- lution of the nonlinear filtering equations based on the simulation of interacting particle for the numerical solution of the nonlinear filtering equations based on the simulation of interacting particle problems in Radar/Sonar signal processing and GPS/INS integration. Such particle nonlinear filters
NASA Technical Reports Server (NTRS)
Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw
2002-01-01
The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.
Angle only tracking with particle flow filters
Fred Daum; Jim Huang
2011-01-01
We show the results of numerical experiments for tracking ballistic missiles using only angle measurements. We compare the performance of an extended Kalman filter with a new nonlinear filter using particle flow to compute Bayes' rule. For certain difficult geometries, the particle flow filter is an order of magnitude more accurate than the EKF. Angle only tracking is of interest
BACTERIA-FILTERS: PERSISTENT PARTICLE FILTERS FOR BACKGROUND SUBTRACTION
Peleg, Shmuel
BACTERIA-FILTERS: PERSISTENT PARTICLE FILTERS FOR BACKGROUND SUBTRACTION Yair Movshovitz switch of bacteria between two states: A normal growing cell and a dormant but persistent cell after the stress is over, bacterial growth continues. Similar to bacteria, particles will switch between
Westinghouse Advanced Particle Filter System
Lippert, T.E.; Bruck, G.J.; Sanjana, Z.N.; Newby, R.A.; Bachovchin, D.M.
1996-12-31
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.
System and Apparatus for Filtering Particles
NASA Technical Reports Server (NTRS)
Agui, Juan H. (Inventor); Vijayakumar, Rajagopal (Inventor)
2015-01-01
A modular pre-filtration apparatus may be beneficial to extend the life of a filter. The apparatus may include an impactor that can collect a first set of particles in the air, and a scroll filter that can collect a second set of particles in the air. A filter may follow the pre-filtration apparatus, thus causing the life of the filter to be increased.
Coulomb's law particle flow for nonlinear filters
Fred Daum; Jim Huang; Arjang Noushin
2011-01-01
We have invented a new theory of exact particle flow for nonlinear filters. The flow of particles corresponding to Bayes' rule is computed from the gradient of the solution of Poisson's equation, and it is analogous to Coulomb's law. Our theory is a radical departure from other particle filters in several ways: (1) we compute Bayes' rule using a flow
Filtering via simulation: auxiliary particle filters
Michael K Pitt; Neil Shephard
1997-01-01
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
Multiple object tracking using particle filters
M. Jaward; L. Mihaylova; N. Canagarajah; D. Bull
2006-01-01
The particle filtering technique with multiple cues such as colour, texture and edges as observation features is a powerful technique for tracking deformable objects in image sequences with complex backgrounds. In this paper, our recent work (Brasnett et al., 2005) on single object tracking using particle filters is extended to multiple objects. In the proposed scheme, track initialisation is embedded
Angle only tracking with particle flow filters
NASA Astrophysics Data System (ADS)
Daum, Fred; Huang, Jim
2011-09-01
We show the results of numerical experiments for tracking ballistic missiles using only angle measurements. We compare the performance of an extended Kalman filter with a new nonlinear filter using particle flow to compute Bayes' rule. For certain difficult geometries, the particle flow filter is an order of magnitude more accurate than the EKF. Angle only tracking is of interest in several different sensors; for example, passive optics and radars in which range and Doppler data are spoiled by jamming.
Early maritime applications of particle filtering
NASA Astrophysics Data System (ADS)
Richardson, Henry R.; Stone, Lawrence D.; Monach, W. Reynolds; Discenza, Joseph H.
2003-12-01
This paper provides a brief history of some operational particle filters that were used by the U. S. Coast Guard and U. S. Navy. Starting in 1974 the Coast Guard system provided Search and Rescue Planning advice for objects lost at sea. The Navy systems were used to plan searches for Soviet submarines in the Atlantic, Pacific, and Mediterranean starting in 1972. The systems operated in a sequential, Bayesian manner. A prior distribution for the target"s location and movement was produced using both objective and subjective information. Based on this distribution, the search assets available, and their detection characteristics, a near-optimal search was planned. Typically, this involved visual searches by Coast Guard aircraft and sonobuoy searches by Navy antisubmarine warfare patrol aircraft. The searches were executed, and the feedback, both detections and lack of detections, was fed into a particle filter to produce the posterior distribution of the target"s location. This distribution was used as the prior for the next iteration of planning and search.
Early maritime applications of particle filtering
NASA Astrophysics Data System (ADS)
Richardson, Henry R.; Stone, Lawrence D.; Monach, W. Reynolds; Discenza, Joseph H.
2004-01-01
This paper provides a brief history of some operational particle filters that were used by the U. S. Coast Guard and U. S. Navy. Starting in 1974 the Coast Guard system provided Search and Rescue Planning advice for objects lost at sea. The Navy systems were used to plan searches for Soviet submarines in the Atlantic, Pacific, and Mediterranean starting in 1972. The systems operated in a sequential, Bayesian manner. A prior distribution for the target"s location and movement was produced using both objective and subjective information. Based on this distribution, the search assets available, and their detection characteristics, a near-optimal search was planned. Typically, this involved visual searches by Coast Guard aircraft and sonobuoy searches by Navy antisubmarine warfare patrol aircraft. The searches were executed, and the feedback, both detections and lack of detections, was fed into a particle filter to produce the posterior distribution of the target"s location. This distribution was used as the prior for the next iteration of planning and search.
Exact particle flow for nonlinear filters
NASA Astrophysics Data System (ADS)
Daum, Fred; Huang, Jim; Noushin, Arjang
2010-04-01
We have invented a new theory of exact particle flow for nonlinear filters. This generalizes our theory of particle flow that is already many orders of magnitude faster than standard particle filters and which is several orders of magnitude more accurate than the extended Kalman filter for difficult nonlinear problems. The new theory generalizes our recent log-homotopy particle flow filters in three ways: (1) the particle flow corresponds to the exact flow of the conditional probability density; (2) roughly speaking, the old theory was based on incompressible flow (like subsonic flight in air), whereas the new theory allows compressible flow (like supersonic flight in air); (3) the old theory suffers from obstruction of particle flow as well as singularities in the equations for flow, whereas the new theory has no obstructions and no singularities. Moreover, our basic filter theory is a radical departure from all other particle filters in three ways: (a) we do not use any proposal density; (b) we never resample; and (c) we compute Bayes' rule by particle flow rather than as a point wise multiplication.
OPTIMIZATION OF ADVANCED FILTER SYSTEMS
R.A. Newby; M.A. Alvin; G.J. Bruck; T.E. Lippert; E.E. Smeltzer; M.E. Stampahar
2002-06-30
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.
Particle Filtering on the Euclidean Group
Junghyun Kwon; Minseok Choi; Changmook Chun; F. C. Park
2007-01-01
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
Economical simulation in particle filtering using interpolation
Taylor, Joshua Adam
Sampling from the importance density is often a costly aspect of particle filters. We present a method by which to replace the most computationally expensive component of the importance density with an efficient approximation, ...
Westinghouse advanced particle filter system
Lippert, T.E.; Bruck, G.J.; Sanjana, Z.N.; Newby, R.A.
1995-11-01
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.
Westinghouse advanced particle filter system
Lippert, T.E.; Bruck, G.J.; Sanjana, Z.N.; Newby, R.A.
1994-10-01
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.
Optimal multiobjective design of digital filters using spiral optimization technique.
Ouadi, Abderrahmane; Bentarzi, Hamid; Recioui, Abdelmadjid
2013-01-01
The multiobjective design of digital filters using spiral optimization technique is considered in this paper. This new optimization tool is a metaheuristic technique inspired by the dynamics of spirals. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired frequency response while having minimum linear phase; hence, reducing the time response. The results demonstrate that the proposed problem solving approach blended with the use of the spiral optimization technique produced filters which fulfill the desired characteristics and are of practical use. PMID:24083108
A Marginalized Particle Filtering Framework for Simultaneous Localization and Mapping
Gustafsson, Fredrik
filter (MPF) or the Rao-Blackwellized particle filter in positioning and track- ing applications-Blackwellized particle filter) [4][9] enables estima- tion of velocity, acceleration, and sensor error modelsA Marginalized Particle Filtering Framework for Simultaneous Localization and Mapping Thomas B. Sch
The Marginalized Particle Filter Analysis, Applications and Generalizations
Gustafsson, Fredrik
The Marginalized Particle Filter Analysis, Applications and Generalizations Thomas B. Sch Link¨oping University, Sweden {schon, rickard, fredrik}@isy.liu.se Abstract-- The marginalized particle filter is a powerful com- bination of the particle filter and the Kalman filter, which can be used when
On Optimal Infinite Impulse Response Edge Detection Filters
Sudeep Sarkar; Kim L. Boyer
1991-01-01
The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated
Testing particle filters on convective scale dynamics
NASA Astrophysics Data System (ADS)
Haslehner, Mylene; Craig, George. C.; Janjic, Tijana
2014-05-01
Particle filters have been developed in recent years to deal with highly nonlinear dynamics and non Gaussian error statistics that also characterize data assimilation on convective scales. In this work we explore the use of the efficient particle filter (P.v. Leeuwen, 2011) for convective scale data assimilation application. The method is tested in idealized setting, on two stochastic models. The models were designed to reproduce some of the properties of convection, for example the rapid development and decay of convective clouds. The first model is a simple one-dimensional, discrete state birth-death model of clouds (Craig and Würsch, 2012). For this model, the efficient particle filter that includes nudging the variables shows significant improvement compared to Ensemble Kalman Filter and Sequential Importance Resampling (SIR) particle filter. The success of the combination of nudging and resampling, measured as RMS error with respect to the 'true state', is proportional to the nudging intensity. Significantly, even a very weak nudging intensity brings notable improvement over SIR. The second model is a modified version of a stochastic shallow water model (Würsch and Craig 2013), which contains more realistic dynamical characteristics of convective scale phenomena. Using the efficient particle filter and different combination of observations of the three field variables (wind, water 'height' and rain) allows the particle filter to be evaluated in comparison to a regime where only nudging is used. Sensitivity to the properties of the model error covariance is also considered. Finally, criteria are identified under which the efficient particle filter outperforms nudging alone. References: Craig, G. C. and M. Würsch, 2012: The impact of localization and observation averaging for convective-scale data assimilation in a simple stochastic model. Q. J. R. Meteorol. Soc.,139, 515-523. Van Leeuwen, P. J., 2011: Efficient non-linear data assimilation in geophysical 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.
Optimal stack filtering and classical Bayes decision
B. Zeng; M. Gabbouj; Y. Neuvo
1991-01-01
Optimal stack filtering under the mean absolute error (MAE) criterion is studied. It is first shown that this problem is equivalent to the classical a priori Bayes minimum-cost decision. Generally, a linear program (LP) with O(b2b) variables and constraints (b is the window width) is required for finding the best filter. Instead, the authors develop a suboptimal routine which renders
PARTICLE FILTERING AND CRAM ERRAO LOWER BOUND
Gustafsson, Fredrik
PARTICLE FILTERING AND CRAM â?? ERÂRAO LOWER BOUND FOR UNDERWATER NAVIGATION Rickard Karlsson limitations in navigaÂ tion uncertainty can be described in terms of the Cramâ?? erÂ Rao lower bound, which excitation. Hence, the Cramâ?? erÂRao lower bound can be interpreted and used in design for INS sysÂ tems
SKELETONIZATION WITH PARTICLE FILTERS YUCHUN TANG
Latecki, Longin Jan
SKELETONIZATION WITH PARTICLE FILTERS YUCHUN TANG Research Center of Sectional and Imaging Anatomy 29 31 33 35 37 39 41 #12;We present a novel method to obtain high quality skeletons of binary shapes. The obtained skeletons are connected and one pixel thick. They do not require any pruning or any other post
A Geometric Particle Filter for Template-Based Visual Tracking.
Junghyun Kwon; Hee Seok Lee; Park, Frank C; Kyoung Mu Lee
2014-04-01
Existing approaches to template-based visual tracking, in which the objective is to continuously estimate the spatial transformation parameters of an object template over video frames, have primarily been based on deterministic optimization, which as is well-known can result in convergence to local optima. To overcome this limitation of the deterministic optimization approach, in this paper we present a novel particle filtering approach to template-based visual tracking. We formulate the problem as a particle filtering problem on matrix Lie groups, specifically the three-dimensional Special Linear group SL(3) and the two-dimensional affine group Aff(2). Computational performance and robustness are enhanced through a number of features: (i) Gaussian importance functions on the groups are iteratively constructed via local linearization; (ii) the inverse formulation of the Jacobian calculation is used; (iii) template resizing is performed; and (iv) parent-child particles are developed and used. Extensive experimental results using challenging video sequences demonstrate the enhanced performance and robustness of our particle filtering-based approach to template-based visual tracking. We also show that our approach outperforms several state-of-the-art template-based visual tracking methods via experiments using the publicly available benchmark data set. PMID:26353190
Computationally efficient angles-only tracking with particle flow filters
NASA Astrophysics Data System (ADS)
Costa, Russell; Wettergren, Thomas A.
2015-05-01
Particle filters represent the current state of the art in nonlinear, non-Gaussian filtering. They are easy to implement and have been applied in numerous domains. That being said, particle filters can be impractical for problems with state dimensions greater than four, if some other problem specific efficiencies can't be identified. This "curse of dimensionality" makes particle filters a computationally burdensome approach, and the associated re-sampling makes parallel processing difficult. In the past several years an alternative to particle filters dubbed particle flows has emerged as a (potentially) much more efficient method to solving non-linear, non-Gaussian problems. Particle flow filtering (unlike particle filtering) is a deterministic approach, however, its implementation entails solving an under-determined system of partial differential equations which has infinitely many potential solutions. In this work we apply the filters to angles-only target motion analysis problems in order to quantify the (if any) computational gains over standard particle filtering approaches. In particular we focus on the simplest form of particle flow filter, known as the exact particle flow filter. This form assumes a Gaussian prior and likelihood function of the unknown target states and is then linearized as is standard practice for extended Kalman filters. We implement both particle filters and particle flows and perform numerous numerical experiments for comparison.
Adaptive Correlation Estimation With Particle Filtering For Distributed Video Coding
Cheng, Samuel
1 Adaptive Correlation Estimation With Particle Filtering For Distributed Video Coding Shuang Wang to exploit the robustness of DSC code designs, we integrate particle filtering with standard belief estimation) and the case without dynamic particle filtering tracking, due to improved knowledge of timely
A PARAMETERIZED DESIGN FRAMEWORK FOR HARDWARE IMPLEMENTATION OF PARTICLE FILTERS
Bhattacharyya, Shuvra S.
Particle filtering methods provide powerful techniques for solving non-linear state-estimation problems complexity, real-time hardware implementation of particle-filter-based systems is a challenging task. However, many particle filter applications share common characteristics, and the same system design can
PARTICLE FILTERING APPROACH TO STATE ESTIMATION IN BOOLEAN DYNAMICAL SYSTEMS
Braga-Neto, Ulisses
PARTICLE FILTERING APPROACH TO STATE ESTIMATION IN BOOLEAN DYNAMICAL SYSTEMS Ulisses Braga a particle filtering approach to address this problem. The methodology is illustrated through application to state track- ing in high-dimensional Boolean network models. The re- sults show that the particle filter
FIR Filter Design via Spectral Factorization and Convex Optimization 1 FIR Filter Design via UCSB 10 24 97 FIR Filter Design via Spectral Factorization and Convex Optimization 2 Outline Convex Spectral factorization methods Discretization #12;FIR Filter Design via Spectral Factorization and Convex
Origins of filter effluent particles: experimental study of particle deposition and detachment.
Kim, J; Tobiason, J E
2004-01-01
This paper investigates the relative roles of particle deposition and detachment in controlling the origin of filter effluent particles. A conceptual mathematical model was developed and laboratory-scale experiments were conducted. Laboratory experiments were performed using three sizes of fluorescent microspheres (FMs), to determine the fraction of filter effluent particles that are filter influent particles that were never removed, as well as the fraction of filter effluent particles that were detached after deposition. Experimental results indicated that particle detachment is significant beginning from the early phase of filtration. FM removal increased with filter run time, depth and particle size. For each size FM at one filter depth, FM removal increased with filter runtime to a maximum due to ripening and then decreased with filter runtime after ripening due to limited pore space remaining in the filter. The fraction of effluent particles that were detached particles increased with particle size and filter bed depth. PMID:15686024
Optimization of stack filters based on mirrored threshold decomposition
José L. Paredes; Gonzalo R. Arce
2001-01-01
An adaptive optimization algorithm for the design of a new class of stack filters is presented. Unlike stack smoothers, this new class of stack filters, based on mirrored threshold decomposition, has been empowered not only with lowpass filtering characteristics but with bandpass and highpass filtering characteristics as well. Therefore, these filters can be effectively used in applications where frequency selection
Design of Optimal Stack Filter Under MAE Criterion
Win-long Lee; Kuo-chin Fan; Zhi-ming Chen
1997-01-01
A deterministic algorithm is proposed to design the optimal stack filter. The proposed algorithm can generate the optimal stack filter in one second for a window size of 9 and it can still generate the optimal stack filter for a window size of 21 although it takes about 4 hours. Experimental results reveal the feasibility and efficiency of the proposed
Parallel asynchronous particle swarm optimization
Byung-Il Koh; Alan D. George; Raphael T. Haftka; Benjamin J. Fregly
2006-01-01
SUMMARY The high computational cost of complex engineering optimization problems has motivated the develop- ment of parallel optimization algorithms. A recent example is the parallel particle swarm optimization (PSO) algorithm, which is valuable due to its global search capabilities. Unfortunately, because existing parallel implementations are synchronous (PSPSO), they do not make efficient use of computational resources when a load imbalance
Real-Time Camera Tracking Using a Particle Filter
Mark Pupilli; Andrew Calway
2005-01-01
We describe a particle filtering method for vision based tracking of a hand held calibrated camera in real-time. The ability of the particle filter to deal with non-linearities and non-Gaussian statistics suggests the potential to pro- vide improved robustness over existing approaches, such as those based on the Kalman filter. In our approach, the particle filter provides recursive ap- proximations
Ensemble Particle Filter with Posterior Gaussian By X. Xiong1
Ensemble Particle Filter with Posterior Gaussian Resampling By X. Xiong1 and I. M. Navon1 1School March 2005 ABSTRACT An ensemble particle filter(EnPF) was recently developed as a fully nonlinear fil- ter of Bayesian conditional probability estimation, along with the well known ensemble Kalman filter
Design of optimal stack filters under the MAE criterion
Win-Long Lee; Kuo-Chin Fan; Zhi-Ming Chen
1999-01-01
The design of optimal stack filters under the MAE criterion is addressed in this paper. In our work, the Hasse diagram is adopted to represent the positive Boolean functions to solve the optimization problem. After problem transformation, the finding of the optimal stack filter is equivalent to the finding of the optimal on-set such that the total cost of the
Particle Methods for Filtering & Uncertainty Propagations P. Del Moral
Del Moral , Pierre
Particle Methods for Filtering & Uncertainty Propagations P. Del Moral INRIA team ALEA, INRIA Research Center, Bordeaux Some references : · Feynman-Kac formulae. Genealogical and interacting particle approximations. Springer New York, Series: Probability and Applications (04). · Particle Methods: An introduction
Optimal digital filtering for tremor suppression.
Gonzalez, J G; Heredia, E A; Rahman, T; Barner, K E; Arce, G R
2000-05-01
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
Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM
Teschner, Matthias
Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding maps. To prevent the particle filter from becoming overly confident, we present a technique to recover
Finding of optimal stack filter by graphic searching methods
Chin-Chuan Han; Kuo-Chin Fan
1997-01-01
An efficient process to filter noise via an optimal stack filter is proposed. The graphic search based techniques are employed to speed up the finding of the optimal stack filter. Experimental results and performance evaluation are demonstrated to show the efficiency of our proposed method
Optimal parallel stack filtering under the mean absolute error criterion
Bing Zeng; Yrjö Neuvo
1994-01-01
The authors extend the configuration of stack filtering to develop a new class of stack-type filters called parallel stack filters (PSFs). As a basis for the parallel stack filtering, the block threshold decomposition (BTD) is introduced, and its properties are investigated. The design of optimal PSHs under the mean absolute error (MAE) criterion is shown to be similar to the
Optimal filters with heuristic 1-norm sparsity constraints
NASA Astrophysics Data System (ADS)
Yazdani, Mehrdad; Hecht-Nielsen, Robert
2011-09-01
We present a design method for sparse optimal Finite Impulse Response (FIR) filters that improve the visibility of a desired stochastic signal corrupted with white Gaussian noise. We emphasize that the filters we seek are of high-order but sparse, thus significantly reducing computational complexity. An optimal FIR filter for the estimation of a desired signal corrupted with white noise can be designed by maximizing the signal-to-noise ratio (SNR) of the filter output with the constraint that the magnitude (in 2-norm) of the FIR filter coefficients are set to unity.1, 2 This optimization problem is in essence maximizing the Rayleigh quotient and is thus equivalent to finding the eigenvector with the largest eigenvalue.3 While such filters are optimal, they are rarely sparse. To ensure sparsity, one must introduce a cardinality constraint in the optimization procedure. For high order filters such constraints are computationally burdensome due to the combinatorial search space. We relax the cardinality constraint by using the 1-norm approximation of the cardinality function. This is a relaxation heuristic similar to the recent sparse filter design work of Baran, Wei, and Oppenheim.4 The advantage of this relaxation heuristic is that the solutions tend to be sparse and the optimization procedure reduces to a convex program, thus ensuring global optimality. In addition to our proposed optimization procedure for deriving sparse FIR filters, we show examples where sparse high-order filters significantly perform better than low-order filters, whereas complexity is reduced by a factor of 10.
Finding of optimal stack filter by using graphic searching methods
Zhi-ming Chen; Chin-Chuan Hunt; Kuo-Chin Fant
1995-01-01
A graphic searching algorithm is proposed to find the optimal stack filter. The search of the optimal stack filter is reduced to a problem of finding a minimal path from the root node to the optimal node in the error cone graph (ECG). Two graphic searching techniques, the greedy and A* algorithms, are applied to avoid the searching an extremely
Groupwise surface correspondence using particle filtering
NASA Astrophysics Data System (ADS)
Li, Guangxu; Kim, Hyoungseop; Tan, Joo Kooi; Ishikawa, Seiji
2015-03-01
To obtain an effective interpretation of organic shape using statistical shape models (SSMs), the correspondence of the landmarks through all the training samples is the most challenging part in model building. In this study, a coarse-tofine groupwise correspondence method for 3-D polygonal surfaces is proposed. We manipulate a reference model in advance. Then all the training samples are mapped to a unified spherical parameter space. According to the positions of landmarks of the reference model, the candidate regions for correspondence are chosen. Finally we refine the perceptually correct correspondences between landmarks using particle filter algorithm, where the likelihood of local surface features are introduced as the criterion. The proposed method was performed on the correspondence of 9 cases of left lung training samples. Experimental results show the proposed method is flexible and under-constrained.
Change Detection for Nonlinear Systems; A Particle Filtering Approach
Del Moral , Pierre
Change Detection for Nonlinear Systems; A Particle Filtering Approach B. Azimi-Sadjadi and P for nonlinear stochastic systems based on Projection Particle Filtering. The statistic for this method is chosen the linear case, change detection for nonlinear stochastic systems has not been investigated in any depth
Numerical experiments for Coulomb's law particle flow for nonlinear filters
Fred Daum; Jim Huang; Arjang Noushin
2011-01-01
We show numerical results for a new nonlinear filtering algorithm that is analogous to Coulomb's law. We have invented a new theory of exact particle flow for nonlinear filters. The flow of particles corresponding to Bayes' rule is computed from the gradient of the solution of Poisson's equation, and it is analogous to Coulomb's law. Our theory is a radical
Sampling Strategies for Particle Filtering SLAM Kristopher R. Beevers
Bystroff, Chris
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
Body Part Tracking with Random Forests and Particle Filters
Freitas, Nando de
050 051 052 053 Body Part Tracking with Random Forests and Particle Filters Anonymous Author per pixel classification of body parts with multiple particle filters for tracking critical body parts for a body part it can not make a prediction. An example of this would be one hand passing in front
ESTIMATION AND CONTROL OF INDUSTRIAL PROCESSES WITH PARTICLE FILTERS
Freitas, Nando de
of industrial processes. In particular, we adopt a jump Markov linear Gaussian (JMLG) model to describe-time, the state of operation of the heat exchanger. The particle filtering estimates are then used to drive an automatic control system. Keyword: State Estimation, Control, Particle Filtering, Jump Markov Linear
Evolutionary Gabor Filter Optimization with Application to Vehicle Detection
Bebis, George
1 Evolutionary Gabor Filter Optimization with Application to Vehicle Detection Zehang Sun1 , George of Gabor filters in pattern classification, their design and selection have been mostly done on a trial and error basis. Existing techniques are either only suitable for a small number of filters or less problem
Motion-compensated speckle tracking via particle filtering
NASA Astrophysics Data System (ADS)
Liu, Lixin; Yagi, Shin-ichi; Bian, Hongyu
2015-07-01
Recently, an improved motion compensation method that uses the sum of absolute differences (SAD) has been applied to frame persistence utilized in conventional ultrasonic imaging because of its high accuracy and relative simplicity in implementation. However, high time consumption is still a significant drawback of this space-domain method. To seek for a more accelerated motion compensation method and verify if it is possible to eliminate conventional traversal correlation, motion-compensated speckle tracking between two temporally adjacent B-mode frames based on particle filtering is discussed. The optimal initial density of particles, the least number of iterations, and the optimal transition radius of the second iteration are analyzed from simulation results for the sake of evaluating the proposed method quantitatively. The speckle tracking results obtained using the optimized parameters indicate that the proposed method is capable of tracking the micromotion of speckle throughout the region of interest (ROI) that is superposed with global motion. The computational cost of the proposed method is reduced by 25% compared with that of the previous algorithm and further improvement is necessary.
Metal finishing wastewater pressure filter optimization
Norford, S.W.; Diener, G.A.; Martin, H.L.
1992-01-01
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.
Metal finishing wastewater pressure filter optimization
Norford, S.W.; Diener, G.A.; Martin, H.L.
1992-12-31
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.
Optimal filters for detecting cosmic bubble collisions
NASA Astrophysics Data System (ADS)
McEwen, J. D.; Feeney, S. M.; Johnson, M. C.; Peiris, H. V.
2012-05-01
A number of well-motivated extensions of the ?CDM concordance cosmological model postulate the existence of a population of sources embedded in the cosmic microwave background. One such example is the signature of cosmic bubble collisions which arise in models of eternal inflation. The most unambiguous way to test these scenarios is to evaluate the full posterior probability distribution of the global parameters defining the theory; however, a direct evaluation is computationally impractical on large datasets, such as those obtained by the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck. A method to approximate the full posterior has been developed recently, which requires as an input a set of candidate sources which are most likely to give the largest contribution to the likelihood. In this article, we present an improved algorithm for detecting candidate sources using optimal filters, and apply it to detect candidate bubble collision signatures in WMAP 7-year observations. We show both theoretically and through simulations that this algorithm provides an enhancement in sensitivity over previous methods by a factor of approximately two. Moreover, no other filter-based approach can provide a superior enhancement of these signatures. Applying our algorithm to WMAP 7-year observations, we detect eight new candidate bubble collision signatures for follow-up analysis.
Optimization of photon correlations by frequency filtering
NASA Astrophysics Data System (ADS)
González-Tudela, Alejandro; del Valle, Elena; Laussy, Fabrice P.
2015-04-01
Photon correlations are a cornerstone of quantum optics. Recent works [E. del Valle, New J. Phys. 15, 025019 (2013), 10.1088/1367-2630/15/2/025019; A. Gonzalez-Tudela et al., New J. Phys. 15, 033036 (2013), 10.1088/1367-2630/15/3/033036; C. Sanchez Muñoz et al., Phys. Rev. A 90, 052111 (2014), 10.1103/PhysRevA.90.052111] have shown that by keeping track of the frequency of the photons, rich landscapes of correlations are revealed. Stronger correlations are usually found where the system emission is weak. Here, we characterize both the strength and signal of such correlations, through the introduction of the "frequency-resolved Mandel parameter." We study a plethora of nonlinear quantum systems, showing how one can substantially optimize correlations by combining parameters such as pumping, filtering windows and time delay.
Optimal filtering of the LISA data
Andrzej Krolak; Massimo Tinto; Michele Vallisneri
2007-07-19
The LISA time-delay-interferometry responses to a gravitational-wave signal are rewritten in a form that accounts for the motion of the LISA constellation around the Sun; the responses are given in closed analytic forms valid for any frequency in the band accessible to LISA. We then present a complete procedure, based on the principle of maximum likelihood, to search for stellar-mass binary systems in the LISA data. We define the required optimal filters, the amplitude-maximized detection statistic (analogous to the F statistic used in pulsar searches with ground-based interferometers), and discuss the false-alarm and detection probabilities. We test the procedure in numerical simulations of gravitational-wave detection.
Adaptive Statistical Optimization Techniques for Firewall Packet Filtering
Hazem Hamed; Adel El-atawy; Ehab Al-shaer
2006-01-01
Packet filtering plays a critical role in the perfor- mance of many network devices such as firewalls, IPSec gateways, DiffServ and QoS routers. A tremendous amount of research was proposed to optimize packet filters. However, most of the related works use deterministic techniques and do not exploit the traffic characteristics in their optimization schemes. In addition, most packet classifiers give
Optimal correlation filters for implementation on deformable mirror devices
NASA Astrophysics Data System (ADS)
Vijaya Kumar, B. V. K.; Carlson, Daniel
1991-11-01
A systematic procedure is presented for designing optimal correlation filters for implementation on deformable mirror devices (DMDs) exhibiting cross-coupled amplitude and phase characteristics. The utility of the algorithm for designing such filters is illustrated using five different device characteristics: phase-only filter, a binary phase-only filter, a diagonal line characteristic, a DMD zeroth-order characteristic, and a DMD first-order characteristic. Results are also presented regarding the signal-to-noise ratio and peak-to-correlation energy obtainable using these filters. The performance achievable using DMD type characteristics was found to be close to that of phase-only filter.
Cat Swarm Optimization algorithm for optimal linear phase FIR filter design.
Saha, Suman Kumar; Ghoshal, Sakti Prasad; Kar, Rajib; Mandal, Durbadal
2013-11-01
In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics. CSO is generated by observing the behaviour of cats and composed of two sub-models. In CSO, one can decide how many cats are used in the iteration. Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode. The final solution would be the best position of one of the cats. CSO keeps the best solution until it reaches the end of the iteration. The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE). The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems. The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE. The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters. PMID:23958491
Blended particle filters for large-dimensional chaotic dynamical systems.
Majda, Andrew J; Qi, Di; Sapsis, Themistoklis P
2014-05-27
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
Blended particle filters for large-dimensional chaotic dynamical systems
Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.
2014-01-01
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
Human-manipulator interface using particle filter.
Du, Guanglong; Zhang, Ping; Wang, Xueqian
2014-01-01
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
Human-Manipulator Interface Using Particle Filter
Wang, Xueqian
2014-01-01
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
NASA Astrophysics Data System (ADS)
Noh, S. J.; Tachikawa, Y.; Shiiba, M.; Kim, S.
2011-10-01
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.
Efficient object tracking by incremental self-tuning particle filtering on the affine group.
Li, Min; Tan, Tieniu; Chen, Wei; Huang, Kaiqi
2012-03-01
We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group, which can find the optimal state in a chainlike way with a very small number of particles. Unlike traditional particle filtering, which only relies on random sampling for state optimization, ISPF incrementally draws particles and utilizes an online-learned pose estimator (PE) to iteratively tune them to their neighboring best states according to some feedback appearance-similarity scores. Sampling is terminated if the maximum similarity of all tuned particles satisfies a target-patch similarity distribution modeled online or if the permitted maximum number of particles is reached. With the help of the learned PE and some appearance-similarity feedback scores, particles in ISPF become "smart" and can automatically move toward the correct directions; thus, sparse sampling is possible. The optimal state can be efficiently found in a step-by-step way in which some particles serve as bridge nodes to help others to reach the optimal state. In addition to the single-target scenario, the "smart" particle idea is also extended into a multitarget tracking problem. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of particles. PMID:21965203
Some issues and results on the EnKF and particle filters for meteorological models
Baehr, Christophe
Some issues and results on the EnKF and particle filters for meteorological models Chaos 2009KF and particle filters for meteorological models #12;The nonlinear filtering problem Particle Filter resolution C. Baehr & O. Pannekoucke EnKF and particle filters for meteorological models #12;2 / 26 Nonlinear
Gao, Shuang; Kim, Jinyong; Yermakov, Michael; Elmashae, Yousef; He, Xinjian; Reponen, Tiina; Grinshpun, Sergey A
2015-10-01
Filtering facepiece respirators (FFRs) are commonly worn by first responders, first receivers, and other exposed groups to protect against exposure to airborne particles, including those originated by combustion. Most of these FFRs are NIOSH-certified (e.g., N95-type) based on the performance testing of their filters against charge-equilibrated aerosol challenges, e.g., NaCl. However, it has not been examined if the filtration data obtained with the NaCl-challenged FFR filters adequately represent the protection against real aerosol hazards such as combustion particles. A filter sample of N95 FFR mounted on a specially designed holder was challenged with NaCl particles and three combustion aerosols generated in a test chamber by burning wood, paper, and plastic. The concentrations upstream (Cup) and downstream (Cdown) of the filter were measured with a TSI P-Trak condensation particle counter and a Grimm Nanocheck particle spectrometer. Penetration was determined as (Cdown/Cup) ×100%. Four test conditions were chosen to represent inhalation flows of 15, 30, 55, and 85 L/min. Results showed that the penetration values of combustion particles were significantly higher than those of the "model" NaCl particles (p < 0.05), raising a concern about applicability of the N95 filters performance obtained with the NaCl aerosol challenge to protection against combustion particles. Aerosol type, inhalation flow rate and particle size were significant (p < 0.05) factors affecting the performance of the N95 FFR filter. In contrast to N95 filters, the penetration of combustion particles through R95 and P95 FFR filters (were tested in addition to N95) were not significantly higher than that obtained with NaCl particles. The findings were attributed to several effects, including the degradation of an N95 filter due to hydrophobic organic components generated into the air by combustion. Their interaction with fibers is anticipated to be similar to those involving "oily" particles. The findings of this study suggest that the efficiency of N95 respirator filters obtained with the NaCl aerosol challenge may not accurately predict (and rather overestimate) the filter efficiency against combustion particles. PMID:26010982
Multi-Objective Particles Swarm Optimization Approaches
Parsopoulos, Konstantinos
method that roughly models the so- cial behavior of swarms (Kennedy & Eberhart, 2001). PSO shares many20 Chapter II Multi-Objective Particles Swarm Optimization Approaches Konstantinos E. Parsopoulos- rithmsthatcantacklesuchproblemseffectively,withthesmallestpossiblecomputationalburden.Particle Swarm Optimization has attracted the interest
Measurement of particle flow by optical spatial filtering
Yoshihisa Ichikura; Kajiro Watanabe
1994-01-01
This paper describes a new method to measure the flow of particles being conveyed pneumatically in a pipe. The proposed method determines the flow from the particle speed and also the mass concentration. The method requires only a light emitting source and light sensors on either side of a pipe. The particle speed is measured by the spatial filter. The
Particle filtering on the Euclidean group: framework and applications
Junghyun Kwon; Minseok Choi; Frank Chongwoo Park; Changmook Chun
2007-01-01
SUMMARY We address general filtering problems on the Euclidean group SE(3). We first generalize, to stochastic nonlinear systems evolving on SE(3), the particle filter of Liu and West17 for simultaneous estimation of the state and covariance. The filter is constructed in a coordinate-invariant way, and explicitly takes into account the geometry ofSE(3) and P (n), the space of symmetric positive
Design of passive filter circuit based on robust optimization
NASA Astrophysics Data System (ADS)
Zhao, Hong; Chen, Gang
2013-03-01
In view on this change of filter performance by the deviation of circuit component parameter values from its design values, the concept of robust optimization design for the passive filter circuit is presented. The function, that is to minimize the ripples and maximal variations of system performance is chosen as the objective function. The optimization strategy by combining random direction searching method with compound optimum was adopted for solving this nonlinear programming problem with two-level optimization. This theory is used on an 800MHz transmitter bandpass filter circuit. By comparing with original design and conventional optimization, passband performance of the robust optimized circuit is more flat and its fluctuation is more small when component parameters change within their rated tolerance. So filter performance of the circuit is improved, and the method mentioned in this paper is effective and superior.
Design guidelines for granular particles in a conical centrifugal filter
Fleck, Norman A.
Design guidelines for granular particles in a conical centrifugal filter A.F.M. Bizarda , D centrifugal filter are developed in a dimensionless fashion. Four criteria are considered: minimum flow and basket failure. The criteria are based on idealised physical models of the machine operation
Distributed Particle Filters for Sensor Networks Mark Coates
between #12;2 sensor nodes, becomes inapplicable. Extended Kalman filters, grid-based meth- odsDistributed Particle Filters for Sensor Networks Mark Coates Department of Electrical and Computer the monitored system. The goal of the proposed algorithms is to perform on-line, distributed estimation
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
Yogesh Rathi; Namrata Vaswani; Allen Tannenbaum; Anthony J. Yezzi
2007-01-01
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
MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION
Parsopoulos, Konstantinos
the social behavior of swarms [5]. PSO is characterized by its simplicity and straightforward applicabilityMULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION K Swarm Optimization (VEPSO) method for multiobjective problems. Experiments on well known and widely used
Fine-particle filter prevents damage to vacuum pumps
NASA Technical Reports Server (NTRS)
Harlamert, P., Jr.
1964-01-01
A filter system for mechanical pumps is designed with a baffle assembly that rotates in a circulating oil bath which traps destructive particles. This prevents severe damage to the pump and is serviceable for long periods before it requires cleaning.
Visual tracking via particle filtering on the affine group
Junghyun Kwon; Frank C. Park
2008-01-01
We propose a particle filtering-based visual tracker, in which the affine group is treated as the state. We first develop a general particle filtering algorithm that explicitly takes into account the geometry of the affine group. The tracking performance is further enhanced by the geometric auto-regressive process used for the state dynamics, combined state-covariance estimation, and robust measurement likelihood calculation
The Application Reserch of Unscented Particle Filter Algorithm to GPS\\/DR
Mei Zhao; Santong Zhang; Gang Zhu
2006-01-01
In this paper, a new particle filter algorithm - unscented particle filter (UPF) is given based on the analysis of extended Kalman filter (EKF), general particle filter (PF) and unscented Kalman filter (UKF), and then it is applied to GPS\\/DR integrated navigation system. Finally, the feasibility and accuracy of UPF algorithm are proved, with a simulation and compare of UPF
Silicon oxide nano-particles doped PQ-PMMA for volume holographic imaging filters
Luo, Yuan; Russo, Juan M.; Kostuk, Raymond K.; Barbastathis, George
2011-01-01
Holographic imaging filters are required to have high Bragg selectivity to obtain spatial-spectral information within a three-dimensional object. In this Letter, we present the design of holographic imaging filters formed using silicon oxide nano-particles (nano-SiO2) in PQ-PMMA polymer recording material. This combination offers greater angular and spectral selectivity and increases the diffraction efficiency of holographic filters. The holographic filters with optimized ratio of nano-SiO2 in PQ-PMMA can significantly improve the performance of Bragg selectivity and diffraction efficiency by 53% and 16%, respectively. We present experimental results and data analysis demonstrating this technique in use for holographic spatial-spectral imaging filters. PMID:20410989
MULTIOBJECTIVE OPTIMAL POWER PLANT OPERATION USING PARTICLE SWARM OPTIMIZATION TECHNIQUE
Jin S. Heo; Kwang Y. Lee; Raul Garduno-Ramirez
Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set-point in a Fossil Fuel Power Unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, Particle Swarm Optimization (PSO), to realize the optimal mapping by searching the
Particle filter for long range radar in RUV
NASA Astrophysics Data System (ADS)
Romeo, Kevin; Willett, Peter; Bar-Shalom, Yaakov
2014-06-01
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.
On the Distance to Optimality of the Geometric Approximate Minimum-Energy Attitude Filter
Trumpf, Jochen
On the Distance to Optimality of the Geometric Approximate Minimum-Energy Attitude Filter Mohammad-optimality of the recent geometric approximate minimum-energy (GAME) filter, an attitude filter for estimation on the rotation group SO(3). The GAME filter approximates the minimum-energy (optimal) filtering solution
Optimal filter bandwidth for pulse oximetry
NASA Astrophysics Data System (ADS)
Stuban, Norbert; Niwayama, Masatsugu
2012-10-01
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.
Optimal filter systems for photometric redshift estimation
N. Benitez; M. Moles; J. A. L. Aguerri; E. Alfaro; T. Broadhurst; J. Cabrera; F. J. Castander; J. Cepa; M. Cervino; D. Cristobal-Hornillos; A. Fernandez-Soto; R. M. Gonzalez-Delgado; L. Infante; I. Marquez; V. J. Martinez; J. Masegosa; A. Del Olmo; J. Perea; F. Prada; J. M. Quintana; S. F. Sanchez
2008-12-18
In the next years, several cosmological surveys will rely on imaging data to estimate the redshift of galaxies, using traditional filter systems with 4-5 optical broad bands; narrower filters improve the spectral resolution, but strongly reduce the total system throughput. We explore how photometric redshift performance depends on the number of filters n_f, characterizing the survey depth through the fraction of galaxies with unambiguous redshift estimates. For a combination of total exposure time and telescope imaging area of 270 hrs m^2, 4-5 filter systems perform significantly worse, both in completeness depth and precision, than systems with n_f >= 8 filters. Our results suggest that for low n_f, the color-redshift degeneracies overwhelm the improvements in photometric depth, and that even at higher n_f, the effective photometric redshift depth decreases much more slowly with filter width than naively expected from the reduction in S/N. Adding near-IR observations improves the performance of low n_f systems, but still the system which maximizes the photometric redshift completeness is formed by 9 filters with logarithmically increasing bandwidth (constant resolution) and half-band overlap, reaching ~0.7 mag deeper, with 10% better redshift precision, than 4-5 filter systems. A system with 20 constant-width, non-overlapping filters reaches only ~0.1 mag shallower than 4-5 filter systems, but has a precision almost 3 times better, dz = 0.014(1+z) vs. dz = 0.042(1+z). We briefly discuss a practical implementation of such a photometric system: the ALHAMBRA survey.
COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS
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...
COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS
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...
Li, Yangmin
Cooperative Particle Swarm Optimizer with Elimination Mechanism for Global Optimization particle swarm optimizer (PSO) that called the cooperative particle swarm optimizer with elimination. I. INTRODUCTION IN the past decade, particle swarm optimizer (PSO) has been applied and studied
F. Daum; J. Huang
2010-01-01
We solve the important and well known problem with particle filters, called “particle degeneracy” or “particle collapse”. This new filter theory is a radical departure from all other particle filters in five ways: (a) we do not use any proposal density; (b) we never resample particles; (c) we compute Bayes' rule by particle flow rather than as a pointwise multiplication;
CS685 -Project Homework, due December 16, Jana Kosecka Particle Filter.
Kosecka, Jana
exercises you will implement a complete particle filter. A framework contain- ing the motion model; plots this folder is used to store images generated by the visualization. To run the particle filter, change into the directory matlab and type particle filter to start the particle filter. Running
Fast and Accurate SLAM with Rao-Blackwellized Particle Filters
Grisetti, Giorgio
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
Robustness and accuracy in particle filtering
LeGland, François
of multimodality · Mixture filters · Application to terrain navigation · Analysis of the Monte Carlo error with the Laplace method · Importance density · Application to tracking #12;4 Multimodality : behavior of the PF In theory PF deals with multimodality when N is « large » enough. In practice we observe mode losses
Interacting Particle Filtering With Discrete Observations
Del Moral , Pierre
in the nonlinear filtering problem (in short NLF). That is, we want to find the one step predictor conditional the two types of NLF problems covered by our work. . Case A: The state signal (X n ) n#IN is an E A crucial practical advantage of the first category of NLF problems is that it leads to a natural IPS
Compound Particle Swarm Optimization in Dynamic Environments
Yang, Shengxiang
applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO behavior of particle swarms from the domain of physics is integrated into PSO and a compound particle swarm is a population based optimization technique with the inspiration from the social behavior of a swarm of birds
Optimal Sharpening of Compensated Comb Decimation Filters: Analysis and Design
Troncoso Romero, David Ernesto
2014-01-01
Comb filters are a class of low-complexity filters especially useful for multistage decimation processes. However, the magnitude response of comb filters presents a droop in the passband region and low stopband attenuation, which is undesirable in many applications. In this work, it is shown that, for stringent magnitude specifications, sharpening compensated comb filters requires a lower-degree sharpening polynomial compared to sharpening comb filters without compensation, resulting in a solution with lower computational complexity. Using a simple three-addition compensator and an optimization-based derivation of sharpening polynomials, we introduce an effective low-complexity filtering scheme. Design examples are presented in order to show the performance improvement in terms of passband distortion and selectivity compared to other methods based on the traditional Kaiser-Hamming sharpening and the Chebyshev sharpening techniques recently introduced in the literature. PMID:24578674
Optimization of OT-MACH Filter Generation for Target Recognition
NASA Technical Reports Server (NTRS)
Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin
2009-01-01
An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.
Estimate the Electromechanical States Using Particle Filtering and Smoothing
Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang
2012-07-22
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.
Geomagnetic modeling by optimal recursive filtering
NASA Technical Reports Server (NTRS)
Gibbs, B. P.; Estes, R. H.
1981-01-01
The results of a preliminary study to determine the feasibility of using Kalman filter techniques for geomagnetic field modeling are given. Specifically, five separate field models were computed using observatory annual means, satellite, survey and airborne data for the years 1950 to 1976. Each of the individual field models used approximately five years of data. These five models were combined using a recursive information filter (a Kalman filter written in terms of information matrices rather than covariance matrices.) The resulting estimate of the geomagnetic field and its secular variation was propogated four years past the data to the time of the MAGSAT data. The accuracy with which this field model matched the MAGSAT data was evaluated by comparisons with predictions from other pre-MAGSAT field models. The field estimate obtained by recursive estimation was found to be superior to all other models.
Effects of particle size and velocity on burial depth of airborne particles in glass fiber filters
Higby, D.P.
1984-11-01
Air sampling for particulate radioactive material involves collecting airborne particles on a filter and then determining the amount of radioactivity collected per unit volume of air drawn through the filter. The amount of radioactivity collected is frequently determined by directly measuring the radiation emitted from the particles collected on the filter. Counting losses caused by the particle becoming buried in the filter matrix may cause concentrations of airborne particulate radioactive materials to be underestimated by as much as 50%. Furthermore, the dose calculation for inhaled radionuclides will also be affected. The present study was designed to evaluate the extent to which particle size and sampling velocity influence burial depth in glass-fiber filters. Aerosols of high-fired /sup 239/PuO/sub 2/ were collected at various sampling velocities on glass-fiber filters. The fraction of alpha counts lost due to burial was determined as the ratio of activity detected by direct alpha count to the quantity determined by photon spectrometry. The results show that burial of airborne particles collected on glass-fiber filters appears to be a weak function of sampling velocity and particle size. Counting losses ranged from 0 to 25%. A correction that assumes losses of 10 to 15% would ensure that the concentration of airborne alpha-emitting radionuclides would not be underestimated when glass-fiber filters are used. 32 references, 21 figures, 11 tables.
Optimal digital filtering for tremor suppression
Juan G. Gonzalez; Edwin A. Heredia; Tariq Rahman; Kenneth E. Barner; Gonzalo R. Arce
2000-01-01
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
Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method
Hao, Chengpeng
2013-01-01
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
Recursive bayesian decoding of motor cortical signals by particle filtering.
Brockwell, A E; Rojas, A L; Kass, R E
2004-04-01
The population vector (PV) algorithm and optimal linear estimation (OLE) have been used to reconstruct movement by combining signals from multiple neurons in the motor cortex. While these linear methods are effective, recursive Bayesian decoding schemes, which are nonlinear, can be more powerful when probability model assumptions are satisfied. We have implemented a recursive Bayesian algorithm for reconstructing hand movement from neurons in the motor cortex. The algorithm uses a recently developed numerical method known as "particle filtering" and follows the same general strategy as that used by Brown et al. to reconstruct the path of a foraging rat from hippocampal place cells. We investigated the method in a numerical simulation study in which neural firing rate was assumed to be positive, but otherwise a linear function of movement velocity, and preferred directions were not uniformly distributed. In terms of mean-squared error, the approach was approximately 10 times more efficient than the PV algorithm and 5 times more efficient than OLE. Thus use of recursive Bayesian decoding can achieve the accuracy of the PV algorithm (or OLE) with approximately 10 times (or 5 times) fewer neurons. The method was also used to reconstruct hand movement in an ellipse-drawing task from 258 cells in the ventral premotor cortex. Recursive Bayesian decoding was again more efficient than the PV and OLE methods, by factors of roughly seven and three, respectively. PMID:15010499
Optimized Beam Sculpting with Generalized Fringe-Rate Filters
Parsons, Aaron R; Ali, Zaki S; Cheng, Carina
2015-01-01
We generalize the technique of fringe-rate filtering, whereby visibilities measured by a radio interferometer are re-weighted according to their temporal variation. As the Earth rotates, radio sources traverse through an interferometer's fringe pattern at rates that depend on their position on the sky. Capitalizing on this geometric interpretation of fringe rates, we employ time-domain convolution kernels to enact fringe-rate filters that sculpt the effective primary beam of antennas in an interferometer. As we show, beam sculpting through fringe-rate filtering can be used to optimize measurements for a variety of applications, including mapmaking, minimizing polarization leakage, suppressing instrumental systematics, and enhancing the sensitivity of power-spectrum measurements. We show that fringe-rate filtering arises naturally in minimum variance treatments of many of these problems, enabling optimal visibility-based approaches to analyses of interferometric data that avoid systematics potentially introduc...
Identifying Optimal Measurement Subspace for the Ensemble Kalman Filter
Zhou, Ning; Huang, Zhenyu; Welch, Greg; Zhang, J.
2012-05-24
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.
Design of optimal correlation filters for hybrid vision systems
NASA Technical Reports Server (NTRS)
Rajan, Periasamy K.
1990-01-01
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.
Collaborative emitter tracking using Rao-Blackwellized random exchange diffusion particle filtering
NASA Astrophysics Data System (ADS)
Bruno, Marcelo G. S.; Dias, Stiven S.
2014-12-01
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.
Optimal Filtering Methods to Structural Damage Estimation under Ground Excitation
Hsieh, Chien-Shu; Liaw, Der-Cherng; Lin, Tzu-Hsuan
2013-01-01
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
Optimal Recursive Digital Filters for Active Bending Stabilization
NASA Technical Reports Server (NTRS)
Orr, Jeb S.
2013-01-01
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.
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Candy, J
2007-09-19
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.
Estimating the full posterior pdf with particle filters
NASA Astrophysics Data System (ADS)
Ades, Melanie; van Leeuwen, Peter Jan
2013-04-01
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.
A New Particle Swarm Optimization Technique
Simon, Dan
· Particle Swarm Optimization (PSO) Invented by Eberhart & Kennedy in 1995 Motivated by social behavior PSO · Discrete PSO Flip each bit probabilistically Research has been done on benchmark functions · Individual and groups #12;9/8/2005 7 New Particle Swarm Optimization · Formulae are similar to PSO · pid
Particle Swarm Optimization for Integer Programming
Parsopoulos, Konstantinos
Particle Swarm Optimization for Integer Programming E.C. Laskari, K.E. Parsopoulos and M of the performance of the Particle Swarm Optimization (PSO) method in Integer Programming problems, is the main theme as a continuous one. The procedure is repeated until the real variables are xed to integer values. Evolutionary
Framework for Particle Swarm Optimization with
Stryk, Oskar von
#12;Abstract Particle swarm optimization (PSO) is a population-based, heuristic minimization technique function, within the PSO framework. We present numerical results to show that this hybrid approach can the computational efficiency of a general particle swarm optimization (PSO) algorithm [1]. The motivation
Discriminatively trained particle filters for complex multi-object tracking
Rob Hess; Alan Fern
2009-01-01
This work presents a discriminative training method for particle filters in the context of multi-object tracking. We are motivated by the difficulty of hand-tuning the many model parameters for such applications and also by results in many application domains indicating that discriminative training is often superior to generative training methods. Our learning approach is tightly integrated into the actual inference
Multi-robot Simultaneous Localization and Mapping using Particle Filters
Del Moral , Pierre
Multi-robot Simultaneous Localization and Mapping using Particle Filters Andrew Howard NASA Jet Propulsion Laboratory Pasadena, California 91109, U.S.A. Email: abhoward@robotics.jpl.nasa.gov Abstract-- This paper describes an on-line algorithm for multi- robot simultaneous localization and mapping (SLAM). We
Computing Stable Skeletons with Particle Filters , Xingwei Yang2
Latecki, Longin Jan
Computing Stable Skeletons with Particle Filters Xiang Bai1 , Xingwei Yang2 , Longin Jan Latecki2.yang,latecki}@temple.edu Abstract. We present a novel method to obtain high quality skeletons of binary shapes. The obtained skeletons are connected and one pixel thick. They do not require any pruning or any other post
Monitoring and Diagnosis of Hybrid Systems Using Particle Filtering Methods
Koutsoukos, Xenofon D.
of multiple models and the transitions between them. This paper presents a particle filtering based estima represented by hybrid models and present a number of interesting new challenges for diagnostic systems. Model-based diagnostic techniques are usually based upon a logical framework for diagnosis [3] and are thus discrete
MULTI-HYPOTHESIS MAP-MATCHING USING PARTICLE FILTERING
Paris-Sud XI, Université de
MULTI-HYPOTHESIS MAP-MATCHING USING PARTICLE FILTERING Ph. Bonnifait, J. Laneurit, C. Fouque and G this method implements a multi-hypothesis road-tracking strategy, it is able to handle ambiguous situations Advanced Driver Assistance Systems) or road charging. In this paper, a multi-hypothesis Bayesian "road
Model Adaptation for Prognostics in a Particle Filtering Framework
NASA Technical Reports Server (NTRS)
Saha, Bhaskar; Goebel, Kai Frank
2011-01-01
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Localization of acoustic sources utilizing a decentralized particle filter
Gerstoft, Peter
Localization of acoustic sources utilizing a decentralized particle filter Florian Xaver, Gerald localization scheme. Several sensors are embedded in an acoustic wave field. We assume that the field variables waves are emit- ted by sources and observed by sensors. For decentralized localization, a further
A Boosted Particle Filter: Multitarget Detection and Tracking
Little, Jim
. The system is demonstrated in the context of track- ing hockey players using video sequences. Our approach of hockey players on a sequence of digitized video from TV. Over the last few years, particle filters, also to multi-target tracking. Among others, Hue et. al [5] developed a system for multitarget tracking
Vehicle Detection under Various Lighting Conditions by Incorporating Particle Filter
Yi-Ming Chan; Shih-Shinh Huang; Li-Chen Fu; Pei-Yung Hsiao
2007-01-01
We propose an automatic system to detect preceding vehicles on the highway under various lighting and different weather conditions based on the computer vision technologies. To adapt to different characteristics of vehicle appearance at daytime and nighttime, four cues including underneath, vertical edge, symmetry and taillight are fused for the preceding vehicle detection. By using particle filter with four cues
Optimization of filtering schemes for broadband astro-combs.
Chang, Guoqing; Li, Chih-Hao; Phillips, David F; Szentgyorgyi, Andrew; Walsworth, Ronald L; Kärtner, Franz X
2012-10-22
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
Westinghouse hot gas particle filter system
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
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.
Hot gas particle filter systems: Commercialization status
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
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.
Numerical experiments for nonlinear filters with exact particle flow induced by log-homotopy
Fred Daum; Jim Huang
2010-01-01
We show numerical experiments on a new theory of exact particle flow for nonlinear filters. This generalizes our theory of particle flow that was already many orders of magnitude faster than standard particle filters and which is several orders of magnitude more accurate than the extended Kalman filter for difficult nonlinear problems. The new theory generalizes our recent log-homotopy particle
Na-Faraday rotation filtering: The optimal point
NASA Astrophysics Data System (ADS)
Kiefer, Wilhelm; Löw, Robert; Wrachtrup, Jörg; Gerhardt, Ilja
2014-10-01
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.
Effect of Pump Pulsation and Particle Loading on Membrane Filter Retention
Mark R. Litchy; Donald C. Grant; Reto Schoeb
Hydraulic shocks caused by flow stoppages through microporous membrane filters have been shown to dramatically increase particle release from the filters. The magnitude of the release can be mitigated by techniques like Stabilized Distribution™(1). In Stabilized Distribution™, a minimum flow rate is always maintained through system filters to minimize particle release. Changes in the flow rate through a filter have
Degeneracy, frequency response and filtering in IMRT optimization
NASA Astrophysics Data System (ADS)
Llacer, Jorge; Agazaryan, Nzhde; Solberg, Timothy D.; Promberger, Claus
2004-07-01
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.
Optimal color image restoration: Wiener filter and quaternion Fourier transform
NASA Astrophysics Data System (ADS)
Grigoryan, Artyom M.; Agaian, Sos S.
2015-03-01
In this paper, we consider the model of quaternion signal degradation when the signal is convoluted and an additive noise is added. The classical model of such a model leads to the solution of the optimal Wiener filter, where the optimality with respect to the mean square error. The characteristic of this filter can be found in the frequency domain by using the Fourier transform. For quaternion signals, the inverse problem is complicated by the fact that the quaternion arithmetic is not commutative. The quaternion Fourier transform does not map the convolution to the operation of multiplication. In this paper, we analyze the linear model of the signal and image degradation with an additive independent noise and the optimal filtration of the signal and images in the frequency domain and in the quaternion space.
Multiswarm Particle Swarm Optimization with Transfer of the Best Particle
Wei, Xiao-peng; Zhang, Jian-xia; Zhou, Dong-sheng; Zhang, Qiang
2015-01-01
We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems.
Multiswarm Particle Swarm Optimization with Transfer of the Best Particle.
Wei, Xiao-peng; Zhang, Jian-xia; Zhou, Dong-sheng; Zhang, Qiang
2015-01-01
We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems. PMID:26345200
Distributed soft-data-constrained multi-model particle filter.
Seifzadeh, Sepideh; Khaleghi, Bahador; Karray, Fakhri
2015-03-01
A distributed nonlinear estimation method based on soft-data-constrained multimodel particle filtering and applicable to a number of distributed state estimation problems is proposed. This method needs only local data exchange among neighboring sensor nodes and thus provides enhanced reliability, scalability, and ease of deployment. To make the multimodel particle filtering work in a distributed manner, a Gaussian approximation of the particle cloud obtained at each sensor node and a consensus propagation-based distributed data aggregation scheme are used to dynamically reweight the particles' weights. The proposed method can recover from failure situations and is robust to noise, since it keeps the same population of particles and uses the aggregated global Gaussian to infer constraints. The constraints are enforced by adjusting particles' weights and assigning a higher mass to those closer to the global estimate represented by the nodes in the entire sensor network after each communication step. Each sensor node experiences gradual change; i.e., if a noise occurs in the system, the node, its neighbors, and consequently the overall network are less affected than with other approaches, and thus recover faster. The efficiency of the proposed method is verified through extensive simulations for a target tracking system which can process both soft and hard data in sensor networks. PMID:24956539
FIR filter optimization for video processing on FPGAs
NASA Astrophysics Data System (ADS)
Kumm, Martin; Fanghänel, Diana; Möller, Konrad; Zipf, Peter; Meyer-Baese, Uwe
2013-12-01
Two-dimensional finite impulse response (FIR) filters are an important component in many image and video processing systems. The processing of complex video applications in real time requires high computational power, which can be provided using field programmable gate arrays (FPGAs) due to their inherent parallelism. The most resource-intensive components in computing FIR filters are the multiplications of the folding operation. This work proposes two optimization techniques for high-speed implementations of the required multiplications with the least possible number of FPGA components. Both methods use integer linear programming formulations which can be optimally solved by standard solvers. In the first method, a formulation for the pipelined multiple constant multiplication problem is presented. In the second method, also multiplication structures based on look-up tables are taken into account. Due to the low coefficient word size in video processing filters of typically 8 to 12 bits, an optimal solution is found for most of the filters in the benchmark used. A complexity reduction of 8.5% for a Xilinx Virtex 6 FPGA could be achieved compared to state-of-the-art heuristics.
A particle filter for joint detection and tracking of color objects
Jacek Czyz; Branko Ristic; Benoit M. Macq
2007-01-01
Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. The color particle filter has proven to be an efficient, simple and robust tracking algorithm. In this paper, we present a hybrid valued sequential state estimation algorithm, and its particle filter-based implementation, that extends the standard color particle filter in two ways. First, target detection
Ravindran, Binoy
Completely Distributed Particle Filters for Target Tracking in Sensor Networks Bo Jiang, Binoy: {bjiang,binoy}@vt.edu Abstract--Particle filters (or PFs) are widely used for the tracking problem, distributed particle filters (or DPFs) have been studied to distribute the computational workload onto
Particle Filtering Approach to Bayesian Formant Tracking Yanli Zheng, Mark Hasegawa-Johnson
Hasegawa-Johnson, Mark
Particle Filtering Approach to Bayesian Formant Tracking Yanli Zheng, Mark Hasegawa}@uiuc.edu This paper presents Particle Filtering Approach to Bayesian Formant Tracking. Explicit nonlinear formulas. Formant tracking is formulated as a non- linear Bayesian tracking problem and solved by particle filtering
IMAGE RESTORATION USING A HYBRID COMBINATION OF PARTICLE FILTERING AND WAVELET DENOISING
Havlicek, Joebob
IMAGE RESTORATION USING A HYBRID COMBINATION OF PARTICLE FILTERING AND WAVELET DENOISING Yan Zhai. Specifically, the particle filter acts to suppress outlier-rich components of the noise while, in a subsequent, these preliminary results suggest that the combination of particle filters with more traditional restoration
Adapting the Sample Size in Particle Filters Through KLD-Sampling
Washington at SeattleUniversity of
Adapting the Sample Size in Particle Filters Through KLD-Sampling Dieter Fox Department of Computer improvements over particle filters with fixed sample set sizes and over a previously introduced adaptation particle filters with fixed sample set sizes and over a previously introduced adaptation technique
Grisetti, Giorgio
A Comparative Analysis of Particle Filter based Localization Methods Luca Marchetti, Giorgio the problem of robot kidnapping. The goal of this paper is to provide a systematic analysis of Particle Filter is using a Particle Filter for tracking a probability distribution of the possible robot poses. Whereas
Measurement of particle sulfate from micro-aethalometer filters
NASA Astrophysics Data System (ADS)
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
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.
A filter-based evolutionary algorithm for constrained optimization.
Clevenger, Lauren M.; Hart, William Eugene; Ferguson, Lauren Ann
2004-02-01
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 convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.
Filtering of windborne particles by a natural windbreak
NASA Astrophysics Data System (ADS)
Bouvet, Thomas; Loubet, Benjamin; Wilson, John D.; Tuzet, Andree
2007-06-01
New measurements of the transport and deposition of artificial heavy particles (glass beads) to a thick ‘shelterbelt’ of maize (width/height ratio W/ H ? 1.6) are used to test numerical simulations with a Lagrangian stochastic trajectory model driven by the flow field from a RANS (Reynolds-averaged, Navier-Stokes) wind and turbulence model. We illustrate the ambiguity inherent in applying to such a thick windbreak the pre-existing (Raupach et al. 2001; Atmos. Environ. 35, 3373-3383) ‘thin windbreak’ theory of particle filtering by vegetation, and show that the present description, while much more laborious, provides a reasonably satisfactory account of what was measured. A sizeable fraction of the particle flux entering the shelterbelt across its upstream face is lifted out of its volume by the mean updraft induced by the deceleration of the flow in the near-upstream and entry region, and these particles thereby escape deposition in the windbreak.
Adaptive Learning Particle Swarm Optimizer-II for Global Optimization
Yang, Shengxiang
functions in comparison of the ALPSO algorithm as well as several state-of-the-art variant PSO algorithms and solution accuracy. I. INTRODUCTION Particle swarm optimization (PSO) is an effective op- timization tool, especially for solving global optimization problems. Since PSO was first proposed in 1995 [1], [4], it has
Unified Particle Swarm Optimization in Dynamic Environments
Parsopoulos, Konstantinos
Introduction Particle Swarm Optimization (PSO) is a stochastic optimization algorithm that belongs to the category of swarm intelligence methods [1,2]. PSO has attained in- creasing popularity due to its ability a thorough investigation of PSO on a large number of dynamic test problems. Modifications of PSO that can
A social learning particle swarm optimization algorithm for scalable optimization
Jin, Yaochu
into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter
An exact algorithm for optimal MAE stack filter design.
Dellamonica, Domingos; Silva, Paulo J S; Humes, Carlos; Hirata, Nina S T; Barrera, Junior
2007-02-01
We propose a new algorithm for optimal MAE stack filter design. It is based on three main ingredients. First, we show that the dual of the integer programming formulation of the filter design problem is a minimum cost network flow problem. Next, we present a decomposition principle that can be used to break this dual problem into smaller subproblems. Finally, we propose a specialization of the network Simplex algorithm based on column generation to solve these smaller subproblems. Using our method, we were able to efficiently solve instances of the filter problem with window size up to 25 pixels. To the best of our knowledge, this is the largest dimension for which this problem was ever solved exactly. PMID:17269638
Composite Particle Swarm Optimizer With Historical Memory for Function Optimization.
Li, Jie; Zhang, JunQi; Jiang, ChangJun; Zhou, MengChu
2015-10-01
Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms. PMID:26390177
Particle swarm optimization: developments, applications and resources
Russell C. Eberhart; Yuhui Shi
2001-01-01
This paper focuses on the engineering and computer science aspects of developments, applications, and resources related to particle swarm optimization. Developments in the particle swarm algorithm since its origin in 1995 are reviewed. Included are brief discussions of constriction factors, inertia weights, and tracking dynamic systems. Applications, both those already developed, and promising future application areas, are reviewed. Finally, resources
NASA Astrophysics Data System (ADS)
Onur Karsl?o?lu, Mahmut; Aghakarimi, Armin
2013-04-01
Ionosphere modeling is an important field of current studies because of its influences on the propagation of the electromagnetic signals. Among the various methods of obtaining ionospheric information, Global Positioning System (GPS) is the most prominent one because of extensive stations which are distributed all over the world. There are several studies in the literature related to the modeling of the ionosphere in terms of Total Electron Content (TEC). However, most of these studies investigate the ionosphere in the global and regional scales. On the other hand, complex dynamic of the ionosphere requires further studies in the local structure of the TEC distribution. In this work, Particle filter has been used for the investigation of the local character of the ionospheric Vertical Total Electron Content (VTEC). The GPS data of 29 ground based GPS stations, belonging to International GNSS Service (IGS) and Reference Frame Sub-commission for Europe (EUREF), for Europe have been used in this study. The data acquisition time is 18 February 2011 and the data is affected by the 15 February geomagnetic storm. In the preprocessing step, the observations of each satellite are examined for any possible cycle slip and also geometry-free linear combination of the observables are calculated for each continuous arc. Then, Pseudorange observations smoothed with the carrier to code leveling method. Particle filter is used for near-real time estimation of the VTEC and of the combined satellite and receiver biases. The Particle filter is implemented by recursively generating a set of weighted samples of the state variables. This filter has a flexible nature which can be more adaptive to some characteristics of the high dynamic systems. Besides, standard Kalman filter as an effective method for optimal state estimation is applied to the same data sets to compare the corresponding results with results of Particle filter. The comparison shows that Particle filter indicates better performance than the standard Kalman filter especially during the geomagnetic storm. Keywords: ionosphere, GPS, Kalman filter, Particle filer
Design and optimization of space-variant photonic crystal filters.
Rumpf, Raymond C; Mehta, Alok; Srinivasan, Pradeep; Johnson, Eric G
2007-08-10
A space-variant photonic crystal filter is designed and optimized that may be placed over a detector array to perform filtering functions tuned for each pixel. The photonic crystal is formed by etching arrays of holes through a multilayer stack of alternating high and low refractive index materials. Position of a narrow transmission notch within a wide reflection band is varied across the device aperture by adjusting the diameter of the holes. Numerical simulations are used to design and optimize the geometry of the photonic crystal. As a result of physics inherent in the etching process, the diameter of the holes reduces with depth, producing a taper. Optical performance was found to be sensitive to the taper, but a method for compensation was developed where film thickness is varied through the device. PMID:17694124
Microscopical examination of particles on smoked cigarette filters.
Linch, Charles A; Prahlow, Joseph A
2008-01-01
Cigarette butts collected from crime scenes can play an important role in forensic investigations by providing a DNA link to a victim or suspect. Microscopic particles can frequently be seen on smoked cigarette filters with stereomicroscopy. The authors are not aware of previous published attempts to identify this material. These particles were examined with transmission and scanning electron microscopy and were found to consist of two types of superficial epithelial tissue, consistent with two areas of the lip surface. The particles were often composed of several layers of non-nucleated and nucleated epithelium with the former being the most common. It was further determined that both of these cell types are easily transferred from the lip. The results of this study indicate that the most visible source of DNA obtained from cigarette butts and other objects in contact with the lip may be lip epithelial tissue. PMID:19291443
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters
Stachniss, Cyrill
in which each particle represents a potential trajectory. Each particle furthermore carries its own map which is computed based on the associated trajectory. Whereas a Rao-Blackwellized particle filterInformation Gain-based Exploration Using Rao-Blackwellized Particle Filters Cyrill Stachniss
Collection efficiencies of coarse and fine dust filter media for airborne biological particles
R. Maus; H. Umhauer
1997-01-01
A newly constructed test system, utilising two optical particle counters, has been used to determine the fractional efficiency of fibrous filter media for bioaerosols (bacteria, fungal spores and pollen) and non-biological particles (PSL and limestone). The collection efficiency of coarse and fine dust filters for bioaerosols was comparable with that of non-biological particles of the same aerodynamic diameter. Particle bounce
Selectively-informed particle swarm optimization
Gao, Yang; Du, Wenbo; Yan, Gang
2015-01-01
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315
Selectively-informed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
2015-01-01
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315
Ridge filter design for a particle therapy line
NASA Astrophysics Data System (ADS)
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
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.
An Object-Tracking Algorithm Based on Multiple-Model Particle Filtering With State Partitioning
Yan Zhai; Mark B. Yeary; Samuel Cheng; Nasser D. Kehtarnavaz
2009-01-01
As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partitioning with parallel filters. Traditionally, most tracking algorithms assume that a target operates according to a single dynamic model.
Vaswani, Namrata
. But track- ing involves estimating the global motion of the object and its local deformations as a function or the deformation. On the other hand, tracking algorithms us- ing Kalman filters or Particle filters have been, we propose a scheme which combines the advantages of particle filtering and geometric active contours
Independent motion detection with a rival penalized adaptive particle filter
NASA Astrophysics Data System (ADS)
Becker, Stefan; Hübner, Wolfgang; Arens, Michael
2014-10-01
Aggregation of pixel based motion detection into regions of interest, which include views of single moving objects in a scene is an essential pre-processing step in many vision systems. Motion events of this type provide significant information about the object type or build the basis for action recognition. Further, motion is an essential saliency measure, which is able to effectively support high level image analysis. When applied to static cameras, background subtraction methods achieve good results. On the other hand, motion aggregation on freely moving cameras is still a widely unsolved problem. The image flow, measured on a freely moving camera is the result from two major motion types. First the ego-motion of the camera and second object motion, that is independent from the camera motion. When capturing a scene with a camera these two motion types are adverse blended together. In this paper, we propose an approach to detect multiple moving objects from a mobile monocular camera system in an outdoor environment. The overall processing pipeline consists of a fast ego-motion compensation algorithm in the preprocessing stage. Real-time performance is achieved by using a sparse optical flow algorithm as an initial processing stage and a densely applied probabilistic filter in the post-processing stage. Thereby, we follow the idea proposed by Jung and Sukhatme. Normalized intensity differences originating from a sequence of ego-motion compensated difference images represent the probability of moving objects. Noise and registration artefacts are filtered out, using a Bayesian formulation. The resulting a posteriori distribution is located on image regions, showing strong amplitudes in the difference image which are in accordance with the motion prediction. In order to effectively estimate the a posteriori distribution, a particle filter is used. In addition to the fast ego-motion compensation, the main contribution of this paper is the design of the probabilistic filter for real-time detection and tracking of independently moving objects. The proposed approach introduces a competition scheme between particles in order to ensure an improved multi-modality. Further, the filter design helps to generate a particle distribution which is homogenous even in the presence of multiple targets showing non-rigid motion patterns. The effectiveness of the method is shown on exemplary outdoor sequences.
A multi-dimensional procedure for BNCT filter optimization
Lille, R.A.
1998-02-01
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.
Synthesis of optimal detail-restoring stack filters for image processing
Bing Zeng; Hongbing Zhou; YrjZi Neuvo
1991-01-01
A two-step method is presented to synthesize optimal stack filters under the mean absolute error (MAE) criterion. First, the probabilities needed in the optimal filter design are estimated based on images. Second, the linear program (LP) required for finding the best filter is avoided by a `reasonably good' suboptimal routine which only involves data comparisons. A sufficient condition under which
Stryker, Michael
Supplementary Discussion. Optimal filtering in a nonlinear system. The simple argument leading filter and ensemble are swapped, Fig. 3). The optimality argument (1) for a nonlinear system analyzing system, the redundancy reduction arguments predict40, 41, 47 that neural filters should completely remove
A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems
Stefan Janson; Martin Middendorf
2004-01-01
\\u000a Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic\\u000a variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions. Moreover, a new type of hierarchical\\u000a PSO, called Partitioned H-PSO (PH-PSO), is proposed. In this algorithm the hierarchy is partitioned into several sub-swarms\\u000a for a limited number of generations after
Loss of fine particle ammonium from denuded nylon filters
NASA Astrophysics Data System (ADS)
Yu, Xiao-Ying; Lee, Taehyoung; Ayres, Benjamin; Kreidenweis, Sonia M.; Malm, William; Collett, Jeffrey L.
Ammonium is an important constituent of fine particulate mass in the atmosphere, but can be difficult to quantify due to possible sampling artifacts. Losses of semivolatile species such as NH 4NO 3 can be particularly problematic. In order to evaluate ammonium losses from aerosol particles collected on filters, a series of field experiments was conducted using denuded nylon and Teflon filters at Bondville, IL (February 2003), San Gorgonio, CA (April 2003 and July 2004), Grand Canyon NP, AZ (May, 2003), Brigantine, NJ (November 2003), and Great Smoky Mountains National Park (NP), TN (July-August 2004). Samples were collected over 24 h periods. Losses from denuded nylon filters ranged from 10% (monthly average) in Bondville, IL to 28% in San Gorgonio, CA in summer. Losses on individual sample days ranged from 1% to 65%. Losses tended to increase with increasing diurnal temperature and relative humidity changes and with the fraction of ambient total N(-III) (particulate NH 4++gaseous NH 3) present as gaseous NH 3. The amount of ammonium lost at most sites could be explained by the amount of NH 4NO 3 present in the sampled aerosol. Ammonium losses at Great Smoky Mountains NP, however, significantly exceeded the amount of NH 4NO 3 collected. Ammoniated organic salts are suggested as additional important contributors to observed ammonium loss at this location.
Loss of Fine Particle Ammonium from Denuded Nylon Filters
Yu, Xiao-Ying; Lee, Taehyoung; Ayres, Benjamin; Kreidenweis, Sonia M.; Malm, William C.; Collett, Jeffrey L.
2006-08-01
Ammonium is an important constituent of fine particulate mass in the atmosphere, but can be difficult to quantify due to possible sampling artifacts. Losses of semivolatile species such as NH4NO3 can be particularly problematic. In order to evaluate ammonium losses from aerosol particles collected on filters, a series of field experiments was conducted using denuded nylon and Teflon filters at Bondville, Illinois (February 2003), San Gorgonio, California (April 2003 and July 2004), Grand Canyon National Park, Arizona (May, 2003), Brigantine, New Jersey (November 2003), and Great Smoky Mountains National Park (NP), Tennessee (July–August 2004). Samples were collected over 24-hr periods. Losses from denuded nylon filters ranged from 10% (monthly average) in Bondville, Illinois to 28% in San Gorgonio, California in summer. Losses on individual sample days ranged from 1% to 65%. Losses tended to increase with increasing diurnal temperature and relative humidity changes and with the fraction of ambient total N(--III) (particulate NH4+ plus gaseous NH3) present as gaseous NH3. The amount of ammonium lost at most sites could be explained by the amount of NH4NO3 present in the sampled aerosol. Ammonium losses at Great Smoky Mountains NP, however, significantly exceeded the amount of NH4NO3 collected. Ammoniated organic salts are suggested as additional important contributors to observed ammonium loss at this location.
Optimal design of one-dimensional photonic crystal filters using minimax optimization approach.
Hassan, Abdel-Karim S O; Mohamed, Ahmed S A; Maghrabi, Mahmoud M T; Rafat, Nadia H
2015-02-20
In this paper, we introduce a simulation-driven optimization approach for achieving the optimal design of electromagnetic wave (EMW) filters consisting of one-dimensional (1D) multilayer photonic crystal (PC) structures. The PC layers' thicknesses and/or material types are considered as designable parameters. The optimal design problem is formulated as a minimax optimization problem that is entirely solved by making use of readily available software tools. The proposed approach allows for the consideration of problems of higher dimension than usually treated before. In addition, it can proceed starting from bad initial design points. The validity, flexibility, and efficiency of the proposed approach is demonstrated by applying it to obtain the optimal design of two practical examples. The first is (SiC/Ag/SiO(2))(N) wide bandpass optical filter operating in the visible range. Contrarily, the second example is (Ag/SiO(2))(N) EMW low pass spectral filter, working in the infrared range, which is used for enhancing the efficiency of thermophotovoltaic systems. The approach shows a good ability to converge to the optimal solution, for different design specifications, regardless of the starting design point. This ensures that the approach is robust and general enough to be applied for obtaining the optimal design of all 1D photonic crystals promising applications. PMID:25968205
Symmetric Phase-Only Filtering in Particle-Image Velocimetry
NASA Technical Reports Server (NTRS)
Wemet, Mark P.
2008-01-01
Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes.
Nonlinear EEG Decoding Based on a Particle Filter Model
Hong, Jun
2014-01-01
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
Enhanced Particle Capture in Slow Sand Filters using a Filter Aid O b j e c t i v e s
was obtained by extracting an acid soluble polymer from surface water seston. The filter aid enhances particle removal from raw source waters by modifing the filter media surface properties and appearsEnhanced Particle Capture in Slow Sand Filters using a Filter Aid O b j e c t i v e s The main
Analytically-Guided-Sampling Particle Filter Applied to Range-only Target Tracking
Roumeliotis, Stergios I.
the variances of the particles' weights conditioned on the trajectory and all available measurements [6], manyAnalytically-Guided-Sampling Particle Filter Applied to Range-only Target Tracking Guoquan P. Huang and Stergios I. Roumeliotis Abstract-- Particle filtering (PF) is a popular nonlinear esti- mation technique
Design and implementation of embedded computer vision systems based on particle filters
Bhattacharyya, Shuvra S.
Design and implementation of embedded computer vision systems based on particle filters Sankalita. For example, in smart camera systems, object tracking is a very important application and particle filter and powerful methodology for computer vision applications especially in tracking based systems. Particle
Bhandarkar, Suchendra "Suchi" M.
A BOOSTED ADAPTIVE PARTICLE FILTER FOR FACE DETECTION AND TRACKING Wenlong Zheng and Suchendra M), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and an AdaBoost face detection algorithm. A novel Adaptive Particle
Optimizing filtering for fast measurements in circuit QED
NASA Astrophysics Data System (ADS)
Gambetta, Jay; Dial, Oliver; Cross, Andrew; McClure, Douglas; Chow, Jerry; Steffen, Matthias
2014-03-01
Quantum error correction schemes, for example the popular surface code, involve running interleaved gate operations and measurements on a set of physical qubits. For this reason it is important to have fast measurements. In a fast measurement most of the information will be in the transients of the signal. In this talk we present a filtering technique to extract optimal qubit state information from the transient response of the resonator. I will also discuss techniques for rapidly driving the readout resonator to its ground state independent of the qubit state. We acknowledge support from IARPA under contract W911NF-10-1-0324.
An Adaptive Learning Particle Swarm Optimizer for Function Optimization
Yang, Shengxiang
Yang Abstract-- Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a variant PSO learning strategy. In ALPSO, the learning mechanism of each
Performance optimization of Gaussian apodized fiber Bragg grating filters in WDM systems
João L. Rebola; A. V. T. Cartazo
2002-01-01
Fiber Bragg gratings (FBGs) with Gaussian apodization profiles and zero de index change are studied extensively and optimized for optical filtering in 40-Gb\\/s single-channel and WDM systems with channel spacing of 100 and 200 GHz, for a single filter and for a cascade of optical filters. In the single-filter case, the optimized FBG leads practically to the same performance for
Lattice-Boltzmann simulation of gas-particle flow in filters
O. Filippova; D. Hänel
1997-01-01
This paper deals with the numerical simulation of three-dimensional gas-particle flow through filters. Typical for filter flows is the deposition of particles on the surfaces of filter material, resulting in irregular shapes of surfaces, which in interaction with the flow field influence the next particle deposition. The investigations have shown that lattice-gas approaches perform very well for such interactive flow
Quantum demolition filtering and optimal control of unstable systems.
Belavkin, V P
2012-11-28
A brief account of the quantum information dynamics and dynamical programming methods for optimal control of quantum unstable systems is given to both open loop and feedback control schemes corresponding respectively to deterministic and stochastic semi-Markov dynamics of stable or unstable systems. For the quantum feedback control scheme, we exploit the separation theorem of filtering and control aspects as in the usual case of quantum stable systems with non-demolition observation. This allows us to start with the Belavkin quantum filtering equation generalized to demolition observations and derive the generalized Hamilton-Jacobi-Bellman equation using standard arguments of classical control theory. This is equivalent to a Hamilton-Jacobi equation with an extra linear dissipative term if the control is restricted to Hamiltonian terms in the filtering equation. An unstable controlled qubit is considered as an example throughout the development of the formalism. Finally, we discuss optimum observation strategies to obtain a pure quantum qubit state from a mixed one. PMID:23091216
Hollywood log-homotopy: movies of particle flow for nonlinear filters
Fred Daum; Jim Huang
2011-01-01
In this paper we show five movies of particle flow to provide insight and intuition about this new algorithm. The particles flow solves the well known and important problem of particle degeneracy. Bayes' rule is implemented by particle flow rather than as a pointwise multiplication. This theory is roughly seven orders of magnitude faster than standard particle filters, and it
A MultiScale Particle Filter Framework for Contour Detection.
Widynski, Nicolas; Mignotte, Max
2014-10-01
We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which jointly tracks at two scales small pieces of edges called edgelets. This multiscale edgelet structure naturally embeds semi-local information and is the basic element of the proposed recursive Bayesian modeling. Prior and transition distributions are learned offline using a shape database. Likelihood functions are learned online, thus are adaptive to an image, and integrate color and gradient information via local, textural, oriented, and profile gradient-based features. The underlying model is estimated using a sequential Monte Carlo approach, and the final soft contour detection map is retrieved from the approximated trajectory distribution. We also propose to extend the model to the interactive cut-out task. Experiments conducted on the Berkeley Segmentation data sets show that the proposed MultiScale Particle Filter Contour Detector method performs well compared to competing state-of-the-art methods. PMID:26352625
Object tracking with particle filter in UAV video
NASA Astrophysics Data System (ADS)
Yu, Wenshuai; Yin, Xiaodong; Chen, Bing; Xie, Jinhua
2013-10-01
Aerial surveillance is a main functionality of UAV, which is realized via video camera. During the operations, the mission assigned targets always are the kinetic objects, such as people or vehicles. Therefore, object tracking is taken as the key techniques for UAV sensor payload. Two difficulties for UAV object tracking are dynamic background and hardly predicting target's motion. To solve the problems, it employed the particle filter in the research. Modeling the target by its characteristics, for instance, color features, it approximates the possibility density of target state with weighting sample sets, and the state vector contains position, motion vector and region parameters. The experiments demonstrate the effectiveness and robustness of the proposed method in UAV video tracking.
Particle filter and EnKF as data assimilation methods for the Kuramoto-Sivashinsky
Particle filter and EnKF as data assimilation methods for the Kuramoto-Sivashinsky Equation M adjoint methods do- main and only Chorin and Krause [26] tested it using a sequential Bayesian filter approach. In this work we compare the usual ensemble Kalman filter (EnKF) ap- proach versus versions
Stretching technique for obtaining global minimizers through Particle Swarm Optimization
Parsopoulos, Konstantinos
Stretching technique for obtaining global minimizers through Particle Swarm Optimization K of Mathematics University of Patras GR{26110, Patras, Greece Abstract The Particle Swarm Optimizer, like many other evolutionary and classical minimization methods, su ers the problem of occasional convergence
Gan, Wei; Liu, Xuemin; Sun, Jing
2015-02-01
This paper presents a method of regression evaluation index intelligent filter method (REIFM) for quick optimization of chromatographic separation conditions. The hierarchical chromatography response function was used as the chromatography-optimization index. The regression model was established by orthogonal regression design. The chromatography-optimization index was filtered by the intelligent filter program, and the optimization of the separation conditions was obtained. The experimental results showed that the average relative deviation between the experimental values and the predicted values was 0. 18% at the optimum and the optimization results were satisfactory. PMID:25989685
Tracking Football Player Movement From a Single Moving Camera Using Particle Filters
Demiris, Yiannis
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
Analyzing Gaussian Proposal Distributions for Mapping with Rao-Blackwellized Particle Filters
Teschner, Matthias
Analyzing Gaussian Proposal Distributions for Mapping with Rao-Blackwellized Particle Filters to problems such as localization, mapping, or tracking. The particle filter framework allows the designer with situations in which the target distribution is multi-modal. Experimental results indicate that our
SLAM: Stereo Vision SLAM Using the Rao-Blackwellised Particle Filter and a Novel
Little, Jim
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
VIDEO TRACKING USING DUAL-TREE WAVELET POLAR MATCHING AND PARTICLE FILTERING
Nelson, James
- manned air vehicle (UAV) platform based image sensor as it attempts to track a ground vehicle traversingVIDEO TRACKING USING DUAL-TREE WAVELET POLAR MATCHING AND PARTICLE FILTERING S. K. Pang, J. D. B-Tree Wavelet,Polar Matching,Video Tracking, Particle Filtering Abstract In this paper, we describe a video
Cevher, Volkan
FAST INITIALIZATION OF PARTICLE FILTERS USING A MODIFIED METROPOLIS-HASTINGS ALGORITHM: MODE-0250 ABSTRACT As a recursive algorithm, the particle filter requires initial samples to track a state vector. These initial samples must be generated from the received data and usually obey a complicated distribution
Ravindran, Binoy
Energy Efficient Target Tracking in Wireless Sensor Networks: Sleep Scheduling, Particle Filtering and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Sleep Scheduling, Particle Filters, Constrained Flooding Copyright 2010, Bo Jiang #12;Energy Efficient
Wolfe, Patrick J.
Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic analysis using a simulated likelihood, often computed using a particle filter, for a number of well known of the approach we look at a standard cross-sectional Probit model. Our other examples come from time series
TRACKING MULTIPLE ACOUSTIC SOURCES IN REVERBERANT ENVIRONMENTS USING REGULARIZED PARTICLE FILTER
Tagliasacchi, Marco
with the tracking of multiple acoustic sources in reverberant environments. The localization is typically achieved al. [6] have shown that localization of multiple acoustic sources with particle filter is possibleTRACKING MULTIPLE ACOUSTIC SOURCES IN REVERBERANT ENVIRONMENTS USING REGULARIZED PARTICLE FILTER F
GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization
Majercik, Stephen Michael
GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization Stephen M. Majercik1 1: particle swarm optimization; swarm intelligence. Abstract: In the Particle Swarm Optimization (PSO the PSO algorithm more widely. Many function approximation techniques have been developed that address
OPTIMIZATION OF PARTICLE PRODUCTION FOR A STAGED NEUTRINO FACTORY
McDonald, Kirk
OPTIMIZATION OF PARTICLE PRODUCTION FOR A STAGED NEUTRINO FACTORY (NA-PAC13, THPMA11) X. Ding3, J of a carbon target for a Staged Neutrino Factory were optimized to maximize particle production by an incident). 2. Optimized target parameters and particle production for IDS120h with MARS15 default mode
Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN
Li, Yangmin
Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN Yangmin Li and Xin Chen presents a novel design for mobile robot using particle swarm optimization (PSO) and adaptive NN control Navigation Using Particle Swarm Optimization 629 2.2 Control Law for Individual Robot Let pd denote
Sub-filter models for large eddy simulation of particle-laden flows
Olivier Desjardins; Venkatramanan Raman; Sourabh Apte; Heinz Pitsch
2004-01-01
The Lagrangian representation of particle-laden flows requires sub-filter models for the fluid velocity experienced by the particles. The primary interest of the current study is to evaluate the need for sub-filter models in spray combustion. Current simulation strategies either neglect the sub-filter fluid velocity fluctuations or use simple stochastic models. In the current study, we use direct numerical simulation (DNS)
Pilz, T.
1995-12-31
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.
[Numerical simulation and operation optimization of biological filter].
Zou, Zong-Sen; Shi, Han-Chang; Chen, Xiang-Qiang; Xie, Xiao-Qing
2014-12-01
BioWin software and two sensitivity analysis methods were used to simulate the Denitrification Biological Filter (DNBF) + Biological Aerated Filter (BAF) process in Yuandang Wastewater Treatment Plant. Based on the BioWin model of DNBF + BAF process, the operation data of September 2013 were used for sensitivity analysis and model calibration, and the operation data of October 2013 were used for model validation. The results indicated that the calibrated model could accurately simulate practical DNBF + BAF processes, and the most sensitive parameters were the parameters related to biofilm, OHOs and aeration. After the validation and calibration of model, it was used for process optimization with simulating operation results under different conditions. The results showed that, the best operation condition for discharge standard B was: reflux ratio = 50%, ceasing methanol addition, influent C/N = 4.43; while the best operation condition for discharge standard A was: reflux ratio = 50%, influent COD = 155 mg x L(-1) after methanol addition, influent C/N = 5.10. PMID:25826934
Optimal filters for the construction of the ensemble pulsar time
NASA Astrophysics Data System (ADS)
Rodin, Alexander E.
2008-07-01
An algorithm of the ensemble pulsar time based on the optimal Wiener filtration method has been constructed. This algorithm allows the separation of the contributions to the post-fit pulsar timing residuals of the atomic clock and the pulsar itself. Filters were designed using the cross- and auto-covariance functions of the timing residuals. The method has been applied to the timing data of millisecond pulsars PSR B1855+09 and B1937+21 and allowed the filtering out of the atomic-scale component from the pulsar data. Direct comparison of the terrestrial time TT(BIPM06) and the ensemble pulsar time PTens revealed that the fractional instability of TT(BIPM06)-PTens is equal to ?z = (0.8 +/- 1.9) × 10-15. Based on the ?z statistics of TT(BIPM06)-PTens, a new limit of the energy density of the gravitational wave background was calculated to be equal to ?gh2 ~ 3 × 10-9.
Joint tracking algorithm using particle filter and mean shift with target model updating
NASA Astrophysics Data System (ADS)
Zhang, Bo; Tian, Weifeng; Jin, Zhihua
2006-10-01
Roughly, visual tracking algorithms can be divided into two main classes: deterministic tracking and stochastic tracking. Mean shift and particle filter are their typical representatives, respectively. Recently, a hybrid tracker, seamlessly integrating the respective advantages of mean shift and particle filter (MSPF) has achieved impressive success in robust tracking. The pivot of MSPF is to sample fewer particles using particle filter and then those particles are shifted to their respective local maximum of target searching space by mean shift. MSPF not only can greatly reduce the number of particles that particle filter required, but can remedy the deficiency of mean shift. Unfortunately, due to its inherent principle, MSPF is restricted to those applications with little changes of the target model. To make MSPF more flexible and robust, an adaptive target model is extended to MSPF in this paper. Experimental results show that MSPF with target model updating can robustly track the target through the whole sequences regardless of the change of target model.
Design and optimization of digital filters without multipliers
NASA Astrophysics Data System (ADS)
Lueder, E.
1983-10-01
A method to design digital filters without multipliers is presented. The process requires no approximations. First, a second order structure, such as the first canonic form, is realized with coefficients in the CSD-Code. As a rule, equivalent structures then permit the reduction of either the number of adders or the time tau sub y needed to calculate one sample at the output or, in some cases permit the reduction of both simultaneously. As an example a part of a PCM lowpass with 50 Hz suppression is designed and optimized. For this circuit tau sub y = 2(tau sub A), with tau sub A the time needed for one addition; tau sub A = 30 ns allows for a sampling frequency of up to 16.5 MHz.
Particle Swarm Optimization for Image Deblurring
NASA Astrophysics Data System (ADS)
Toumi, A.; Taleb-Ahmed, A.; Benmahammed, K.; Rechid, N.
2008-06-01
Within the framework of this first study we suggest the use of the Particle Swarm Optimization technique (PSO) in the image restoration field. In our knowledge, we did not still find works concerning the image deblurring (restoration) using the PSO. So, in this paper, we present the use of the PSO in two manners: (i) by taking as hypothesis the degraded image as the entire swarm and pixels as particles; (ii) the degraded image is taken as particle and we generate a population of images to buildup a swarm of variable size. The first results which we give were validated on real images degraded by a Gaussian blur only, and degraded by a Gaussian blur and an additive Gaussian noise. We finish with the comparison of our results with some classical restoration methods.
Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V.
2015-01-01
Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares. PMID:25983690
Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V
2015-01-01
Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares. PMID:25983690
Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter
Kang, In-Sik
Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter Yoo-Verlag 2009 Abstract A method for selecting optimal initial pertur- bations is developed within the framework of an ensemble Kalman filter (EnKF). Among the initial conditions gene- rated by EnKF, ensemble members with fast
Optimizing LPC filter parameters for multi-pulse excitation
Sharad Singhal; Bishnu S. Atal
1983-01-01
Present LPC analysis procedures assume that the input to the all-pole filter is white; the filter parameters are obtained by minimizing the mean-squared error between the filter output samples and their values obtained by linear prediction on the basis of past output samples. It is well known that these procedures often do not yield accurate filter parameters for periodic (or
Continuous and Discrete Space Particle Filters for Predictions in Acoustic Positioning
NASA Astrophysics Data System (ADS)
Bauer, Will; Kim, Surrey; Kouritzin, Michael A.
2002-12-01
Predicting the future state of a random dynamic signal based on corrupted, distorted, and partial observations is vital for proper real-time control of a system that includes time delay. Motivated by problems from Acoustic Positioning Research Inc., we consider the continual automated illumination of an object moving within a bounded domain, which requires object location prediction due to inherent mechanical and physical time lags associated with robotic lighting. Quality computational predictions demand high fidelity models for the coupled moving object signal and observation equipment pair. In our current problem, the signal represents the vector position, orientation, and velocity of a stage performer. Acoustic observations are formed by timing ultrasonic waves traveling from four perimeter speakers to a microphone attached to the performer. The goal is to schedule lighting movements that are coordinated with the performer by anticipating his/her future position based upon these observations using filtering theory. Particle system based methods have experienced rapid development and have become an essential technique of contemporary filtering strategies. Hitherto, researchers have largely focused on continuous state particle filters, ranging from traditional weighted particle filters to adaptive refining particle filters, readily able to perform path-space estimation and prediction. Herein, we compare the performance of a state-of-the-art refining particle filter to that of a novel discrete-space particle filter on the acoustic positioning problem. By discrete space particle filter we mean a Markov chain that counts particles in discretized cells of the signal state space in order to form an approximated unnormalized distribution of the signal state. For both filters mentioned above, we will examine issues like the mean time to localize a signal, the fidelity of filter estimates at various signal to noise ratios, computational costs, and the effect of signal fading; furthermore, we will provide visual demonstrations of filter performance.
A Clustering Particle Swarm Optimizer for Dynamic Optimization Changhe Li and Shengxiang Yang
Yang, Shengxiang
A Clustering Particle Swarm Optimizer for Dynamic Optimization Changhe Li and Shengxiang Yang that optimiza- tion algorithms need to not only find the global optimal solution but also track the trajectory particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical
Tracking non-Stationary Optimal Solution by Particle Swarm Optimizer , C. T. Hardin2
Cui, Xiaohui
but also track the trajectory of the optimal solution in a dynamic environment. Particle Swarm OptimizationTracking non-Stationary Optimal Solution by Particle Swarm Optimizer X. Cui1 , C. T. Hardin2 , R. K environment. This demands that the algorithm not only find the optimal solution but also track the trajectory
Particle filtering methods for georeferencing panoramic image sequence in complex urban scenes
NASA Astrophysics Data System (ADS)
Ji, Shunping; Shi, Yun; Shan, Jie; Shao, Xiaowei; Shi, Zhongchao; Yuan, Xiuxiao; Yang, Peng; Wu, Wenbin; Tang, Huajun; Shibasaki, Ryosuke
2015-07-01
Georeferencing image sequences is critical for mobile mapping systems. Traditional methods such as bundle adjustment need adequate and well-distributed ground control points (GCP) when accurate GPS data are not available in complex urban scenes. For applications of large areas, automatic extraction of GCPs by matching vehicle-born image sequences with geo-referenced ortho-images will be a better choice than intensive GCP collection with field surveying. However, such image matching generated GCP's are highly noisy, especially in complex urban street environments due to shadows, occlusions and moving objects in the ortho images. This study presents a probabilistic solution that integrates matching and localization under one framework. First, a probabilistic and global localization model is formulated based on the Bayes' rules and Markov chain. Unlike many conventional methods, our model can accommodate non-Gaussian observation. In the next step, a particle filtering method is applied to determine this model under highly noisy GCP's. Owing to the multiple hypotheses tracking represented by diverse particles, the method can balance the strength of geometric and radiometric constraints, i.e., drifted motion models and noisy GCP's, and guarantee an approximately optimal trajectory. Carried out tests are with thousands of mobile panoramic images and aerial ortho-images. Comparing with the conventional extended Kalman filtering and a global registration method, the proposed approach can succeed even under more than 80% gross errors in GCP's and reach a good accuracy equivalent to the traditional bundle adjustment with dense and precise control.
A Basic Study of The Adaptive Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Ide, Azuma; Yasuda, Keiichiro
This paper points out that meta-heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle Swarm Optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into particle swarm optimization on the basis of the Proximate Optimality Principle (POP). In this paper, we propose adaptive Particle Swarm Optimization and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems.
ASME AG-1 Section FC Qualified HEPA Filters; a Particle Loading Comparison - 13435
Stillo, Andrew; Ricketts, Craig I.
2013-07-01
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)
Multiobjective control of power plants using particle swarm optimization techniques
Jin S. Heo; Kwang Y. Lee; Raul Garduno-Ramirez
2006-01-01
Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set point in a fossil fuel power unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, particle swarm optimization (PSO), to realize the optimal mapping by searching
NASA Astrophysics Data System (ADS)
He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Dong, Hongxing
2014-12-01
Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.
NASA Astrophysics Data System (ADS)
Diaz-Ramirez, Victor H.; Cuevas, Andres; Kober, Vitaly; Trujillo, Leonardo; Awwal, Abdul
2015-03-01
Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.
Optimization of astigmatic particle tracking velocimeters
NASA Astrophysics Data System (ADS)
Rossi, Massimiliano; Kähler, Christian J.
2014-09-01
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.
Bhandarkar, Suchendra "Suchi" M.
Face detection and tracking using a Boosted Adaptive Particle Filter Wenlong Zheng a,*, Suchendra M online 17 September 2008 Keywords: Adaptive Particle Filter Particle filter Face detection Video tracking detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive
NASA Astrophysics Data System (ADS)
Singh, R.; Verma, H. K.
2013-12-01
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
Goodarz Ahmadi
2002-07-01
In this project, a computational modeling approach for analyzing flow and ash transport and deposition in filter vessels was developed. An Eulerian-Lagrangian formulation for studying hot-gas filtration process was established. The approach uses an Eulerian analysis of gas flows in the filter vessel, and makes use of the Lagrangian trajectory analysis for the particle transport and deposition. Particular attention was given to the Siemens-Westinghouse filter vessel at Power System Development Facility in Wilsonville in Alabama. Details of hot-gas flow in this tangential flow filter vessel are evaluated. The simulation results show that the rapidly rotation flow in the spacing between the shroud and the vessel refractory acts as cyclone that leads to the removal of a large fraction of the larger particles from the gas stream. Several alternate designs for the filter vessel are considered. These include a vessel with a short shroud, a filter vessel with no shroud and a vessel with a deflector plate. The hot-gas flow and particle transport and deposition in various vessels are evaluated. The deposition patterns in various vessels are compared. It is shown that certain filter vessel designs allow for the large particles to remain suspended in the gas stream and to deposit on the filters. The presence of the larger particles in the filter cake leads to lower mechanical strength thus allowing for the back-pulse process to more easily remove the filter cake. A laboratory-scale filter vessel for testing the cold flow condition was designed and fabricated. A laser-based flow visualization technique is used and the gas flow condition in the laboratory-scale vessel was experimental studied. A computer model for the experimental vessel was also developed and the gas flow and particle transport patterns are evaluated.
Optimal stack filters under rank selection and structural constraints
P. Kuosmanen; J. Astola
1995-01-01
A new expression for the moments about the origin of the output of stack filtered data is derived in this paper. This expression is based on the A and M vectors containing the well-known coefficients Ai of stack filters and numbers M(?, ?, N, i) defined in this paper. The noise attenuation capability of any stack filter can now be
Optimal Sidelobe Reduction of Matched Filter for Bistatic Sonar
Bo Lei; Kunde Yang; Yong Wang
2012-01-01
For bistatic sonar, the weak signal of target is often buried by the side lobes of strong direct blast after pulse compression. A method is proposed in this paper to suppress the side lobes of matched filter output. The basic idea is to design an FIR filter at the output of matched filter, so that to minimize the ISL (Integrated
Numerical experiments for nonlinear filters with exact particle flow induced by log-homotopy
NASA Astrophysics Data System (ADS)
Daum, Fred; Huang, Jim
2010-04-01
We show numerical experiments on a new theory of exact particle flow for nonlinear filters. This generalizes our theory of particle flow that was already many orders of magnitude faster than standard particle filters and which is several orders of magnitude more accurate than the extended Kalman filter for difficult nonlinear problems. The new theory generalizes our recent log-homotopy particle flow filters in three ways: (1) the particle flow corresponds to the exact flow of the conditional probability density; (2) roughly speaking, the old theory was based on incompressible flow (like subsonic flight in air), whereas the new theory allows compressible flow (like supersonic flight in air); (3) the old theory suffers from obstruction of particle flow as well as singularities in the equations for flow, whereas the new theory has no obstructions and no singularities. Moreover, our basic filter theory is a radical departure from all other particle filters in three ways: (a) we do not use any proposal density; (b) we never resample; and (c) we compute Bayes' rule by particle flow rather than as a point wise multiplication.
Human behavior-based particle swarm optimization.
Liu, Hao; Xu, Gang; Ding, Gui-Yan; Sun, Yu-Bo
2014-01-01
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 c 1 and c 2 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
Microstructure and particle-laden flow in diesel particulate filter
Kazuhiro Yamamoto; Shingo Satake; Hiroshi Yamashita
2009-01-01
Due to the public awareness with regard to harmful diesel emissions, more strict diesel emissions standards such as Euro V in 2008 are being set in the world. As one of the key technologies, a diesel particulate filter (DPF) has been developed to reduce particulate matters (PM) in the after-treatment of exhaust gas. Since the structure of the filter is
Thin film characterization for modeling and optimization of silver-dielectric color filters.
Frey, Laurent; Parrein, Pascale; Virot, Léopold; Pellé, Catherine; Raby, Jacques
2014-03-10
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
NASA Astrophysics Data System (ADS)
Zhang, Ning; Yuan, Xiaocong
2010-08-01
The authors report experimental results of optical edge enhancement using a modified filter, i.e. hybrid raised-cosine spiral phase filter (SPF). This filter is capable to produce optimized optical image processing results. Comparing with conventional SPF, the proposed filter is able to suppress redundant noise for better contrast and resolution of the edge-enhanced image with improved efficiency. The proposed filtering process is demonstrated using off-axis holograms displayed on a spatial light modulator (SLM) and can be readily incorporated with conventional microscopic system.
Design of waveguide E-plane filters with all-metal inserts by equal ripple optimization
NASA Astrophysics Data System (ADS)
Postoyalko, Vasil; Budimir, D. S.
1994-02-01
An optimization based approach to the design of E-plane filters is described. An optimization procedure based on Cohn's equal ripple optimization is developed. This vector procedure has several advantages over the general purpose optimization routines previously applied to the design of E-plane filters. The problem of local minima does not arise. Optimization is carried out with respect to the Chebyshev (or minimax) criteria. Less frequency sampling and therefore less calculation of the electrical parameters of E-plane discontinuities is required. The design of a symmetrical E-plane filter is considered. Higher order mode interaction between E-plane discontinuities is not included in the design. For the design example considered this is shown not to be significant. A numerically efficient method, requiring only real scalar arithmetic, for calculating the insertion loss of a symmetrical cascade of lossless symmetrical 2-ports is employed. Measurements on a fabricated filter confirm the accuracy of the design procedure.
Nir, Guy; Sahebjavaher, Ramin S; Kozlowski, Piotr; Chang, Silvia D; Jones, Edward C; Goldenberg, S Larry; Salcudean, Septimiu E
2014-08-01
Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to physical changes of the specimen during excision and fixation, and misalignment of the histological slices during preparation and digital scanning. In this work, we consider a multi-slice to volume registration method in which a stack of sparse, unaligned 2-D whole-mount histological slices is registered to a 3-D volumetric imaging of the prostate. We propose a particle filtering framework to contend with the high dimensionality of the search space and multimodal nature of the optimization. Such framework allows modeling of the uncertainty in the pose of the slices and in the imaged information, in order to derive optimal registration parameters in a Bayesian approach. Intensity-, region-, and point-based similarity metrics were incorporated into the registration algorithm to account for different imaging modalities. We demonstrate and evaluate our method on a diverse set of data that includes a synthetic volume, ex vivo and in vivo magnetic resonance imaging, and in vivo ultrasound. PMID:24771576
A study of nonlinear filters with particle flow induced by log-homotopy
Lingji Chen; Raman K. Mehra
2010-01-01
In this paper, a study of the particle flow filter proposed by Daum and Huang has been conducted. It is discovered that for certain initial conditions, the desired particle flow that brings one particle from a good location in the prior distribution to a good location in the posterior distribution with an equal value does not exist. This explains the
Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization
NASA Technical Reports Server (NTRS)
Birge, Brian
2013-01-01
The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.
A JOINT RADAR-ACOUSTIC PARTICLE FILTER TRACKER WITH ACOUSTIC PROPAGATION DELAY COMPENSATION
Cevher, Volkan
aligns the radar and acoustic data streams to account for acoustic propagation delays. The filter for a radar-acoustic sensor that also adaptively syn- chronizes its multi-modal data streams for robust targetA JOINT RADAR-ACOUSTIC PARTICLE FILTER TRACKER WITH ACOUSTIC PROPAGATION DELAY COMPENSATION Volkan
Schulz, Dirk
Tracking Multiple Moving Targets with a Mobile Robot using Particle Filters and Statistical Data data association filters to track fea- tures originating from individual objects and to solve features detected in the sensor data and the different objects to be tracked. Vir- tually all existing
A non-degenerate Rao-Blackwellised particle filter for estimating static
SchÃ¶n, Thomas
A non-degenerate Rao-Blackwellised particle filter for estimating static parameters in dynamical some static parameter. This is true also for the RBPF, even if the static states are marginalised analytically by a Kalman filter. The reason is that the posterior density of the static states is computed
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1 Particle Filtering Algorithms
Botea, Adi
acoustic source localization algorithms attempt to find the current location of the acoustic source using for acoustic source localization using parti- cle filters. We discuss four specific algorithms that fit within]. In this paper we formulate a general framework for acoustic source localization using particle filters. We
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
Jing J. Liang; A. Kai Qin; Ponnuthurai Nagaratnam Suganthan; S. Baskar
2006-01-01
This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes
Anderson, Andrew D. (Andrew David)
2006-01-01
This thesis considers possible solutions to sample impoverishment, a well-known failure mode of the Rao-Blackwellized particle filter (RBPF) in simultaneous localization and mapping (SLAMI) situations that arises when ...
Particle Filtering for Dynamic Agent Modelling in Simplified Poker Nolan Bard and Michael Bowling
Bowling, Michael
Particle Filtering for Dynamic Agent Modelling in Simplified Poker Nolan Bard and Michael Bowling Department of Computing Science University of Alberta Edmonton, Alberta, Canada T6G 2E8 {nolan,bowling
NASA Astrophysics Data System (ADS)
Shmaliy, Yuriy S.; Ibarra-Manzano, Oscar
2012-12-01
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.
Backus, Sterling J. (Erie, CO); Kapteyn, Henry C. (Boulder, CO)
2007-07-10
A method for optimizing multipass laser amplifier output utilizes a spectral filter in early passes but not in later passes. The pulses shift position slightly for each pass through the amplifier, and the filter is placed such that early passes intersect the filter while later passes bypass it. The filter position may be adjust offline in order to adjust the number of passes in each category. The filter may be optimized for use in a cryogenic amplifier.
Inertial deposition of aerosol particles from laminar flows in fibrous filters
V. A. Kirsh; D. A. Pripachkin; A. K. Budyka
2010-01-01
The deposition of aerosol particles onto filter fibers under the effect of inertial forces is studied in a wide range of Stokes\\u000a numbers (St) at Reynolds numbers close to unity (Re ? 1). Coefficients ? of the capture of inertial particles with finite\\u000a sizes in model filters composed of parallel rows of identical parallel fibers located normal to the direction
Target Tracking In a Sensor Network Based on Particle Filtering and Power-Aware Design
Y. Zhai; M. Yeary; J.-C. Noyer
2006-01-01
In this paper, we present a novel target tracking method applied to a distributed acoustic sensor network. The underlying tracking methodology is described as a multiple sensor tracking\\/fusion technique based on particle filtering (PF). As discussed in the most recent literature, particle filtering is defined as an emerging Monte-Carlo non-linear state estimation method. More specifically, in our proposed method each
Array of micro-machined mass energy micro-filters for charged particles
NASA Technical Reports Server (NTRS)
Stalder, Roland E. (Inventor); Van Zandt, Thomas R. (Inventor); Hecht, Michael H. (Inventor); Grunthaner, Frank J. (Inventor)
1996-01-01
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.
PARTICLE TRANSPORT AND DEPOSITION IN THE HOT-GAS FILTER AT WILSONVILLE
Goodarz Ahmadi
1999-06-24
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.
Multisensor fusion for 3D target tracking using track-before-detect particle filter
NASA Astrophysics Data System (ADS)
Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.
2015-05-01
This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.
Franke, Felix; Quian Quiroga, Rodrigo; Hierlemann, Andreas; Obermayer, Klaus
2015-06-01
Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy. PMID:25652689
Cosmological parameter estimation using Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Environmentally realistic fingerprint-image generation with evolutionary filter-bank optimization
Cho, Sung-Bae
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
An Optimal Frequency Domain Filter for Edge Detection in Digital Pictures
K. Sam Shanmugam; Fred M. Dickey; James A. Green
1979-01-01
Edge detection and enhancement are widely used in image processing applications. In this paper we consider the problem of optimizing spatial frequency domain filters for detecting edges in digital pictures. The filter is optimum in that it produces maximum energy within a resolution interval of specified width in the vicinity of the edge. We show that, in the continuous case,
Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared
Cunningham, Brian
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
Optimal Sharpening of CIC Filters and An Efficient Implementation Through Saramaki-Ritoniemi
Candan, Cagatay
1 Optimal Sharpening of CIC Filters and An Efficient Implementation Through Saram, Ankara, Turkey. email: ccandan@metu.edu.tr Abstract--Conventional sharpened cascaded-integrator-comb (CIC) filters use generic sharpening polynomials to improve the frequency response. In contrast to the existing
Optimization of 3D Shape Sharpening Filter Based on Geometric Statistical Values
Tokyo, University of
Optimization of 3D Shape Sharpening Filter Based on Geometric Statistical Values Masanari Yokomizo, by applying a sharpening filter to the 3D shape data of a plaster statue, highlighted contours compa- rable is to prepare a stone statue that is used as a reference and to sharpen the input data to match the histogram
Statistical Design and Optimization for Adaptive Post-silicon Tuning of MEMS Filters
Li, Xin
Statistical Design and Optimization for Adaptive Post-silicon Tuning of MEMS Filters Fa Wang, Gokce of microelectro-mechanical systems (MEMS) for RF (radio frequency) applications. In this paper we describe a novel technique of adaptive post-silicon tuning to reliably design MEMS filters that are robust to process
Designing optimal spatial filters for single-trial EEG classification in a movement task
Johannes Müller-gerking; Gert Pfurtscheller; Henrik Flyvbjerg
1998-01-01
We devise spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of left or right index finger or right foot. Best classification rates for 3 subjects were 94%, 90% and 84%, respectively. The filters are estimated from a
Inertial measurement unit calibration using Full Information Maximum Likelihood Optimal Filtering
Thompson, Gordon A. (Gordon Alexander)
2005-01-01
The robustness of Full Information Maximum Likelihood Optimal Filtering (FIMLOF) for inertial measurement unit (IMU) calibration in high-g centrifuge environments is considered. FIMLOF uses an approximate Newton's Method ...
as , and the importance weights of the particles are obtained from (1) where the superscript denotes the -th trajectory for Particle Filtering Yufei Huang, Member, IEEE, and Petar M. Djuric´, Senior Member, IEEE Abstract--Particle-Gaussian dy- namic problems. One crucial issue in particle filtering is the selec- tion of the importance
NASA Astrophysics Data System (ADS)
Chen, Sheng-Chieh; Wang, Jing; Fissan, Heinz; Pui, David Y. H.
2013-10-01
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.
Assessing consumption of bioactive micro-particles by filter-feeding Asian carp
Jensen, Nathan R.; Amberg, Jon J.; Luoma, James A.; Walleser, Liza R.; Gaikowski, Mark P.
2012-01-01
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.
Sheinson, Daniel M; Niemi, Jarad; Meiring, Wendy
2014-09-01
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
Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter
NASA Astrophysics Data System (ADS)
Huang, Jin-Wang; Feng, Jiu-Chao
2014-07-01
For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.
Particle Swarm Optimization and Its Applications in Power Systems
M. R. AlRashidi; M. F. AlHajri; A. K. Al-Othman; K. M. El-Naggar
\\u000a Optimization problems are widely encountered in various fields in science and technology. The fact that most optimization\\u000a problems, when modeled accurately, are of non-convex and sometimes discrete nature has encouraged many researchers to develop\\u000a new optimization techniques to overcome such difficulties. Particle Swarm Optimization (PSO) is one of the newly developed\\u000a optimization techniques with many attractive features. Early experimentations of
Particle filtering for tracking of GLUT4 vesicles in TIRF microscpy
NASA Astrophysics Data System (ADS)
Wu, Xiangping; Liu, Xiaofang; Xu, Wenglong; Yan, Dandan; Chen, Yongli
2009-10-01
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.
Optimization of atomic Faraday filters in the presence of homogeneous line broadening
NASA Astrophysics Data System (ADS)
Zentile, Mark A.; Keaveney, James; Mathew, Renju S.; Whiting, Daniel J.; Adams, Charles S.; Hughes, Ifan G.
2015-09-01
We show that homogeneous line broadening drastically affects the performance of atomic Faraday filters. We study the effects of cell length and find that the behaviour of ‘line-centre’ filters are quite different from ‘wing-type’ filters, where the effect of self-broadening is found to be particularly important. We use a computer optimization algorithm to find the best magnetic field and temperature for Faraday filters with a range of cell lengths, and experimentally realize one particular example using a micro-fabricated 87Rb vapour cell. We find excellent agreement between our theoretical model and experimental data.
Ioan Tabus; Doina Petrescu; Moncef Gabbouj
1996-01-01
A training framework is developed in this paper to design optimal nonlinear filters for various signal and image processing tasks. The targeted families of nonlinear filters are the Boolean filters and stack filters. The main merit of this framework at the implementation level is perhaps the absence of constraining models, making it nearly universal in terms of application areas. We
Cooperative MicroParticle Swarm Optimization Konstantinos E. Parsopoulos
Parsopoulos, Konstantinos
Cooperative MicroParticle Swarm Optimization Konstantinos E. Parsopoulos Department of Mathematics Keywords Particle Swarm Optimization, Cooperative, MicroEvolutio- nary Algorithms, Swarm Intelligence 1-dimensional complex problems through the coopera- tion of lowdimensional subpopulations. On the other hand, Micro
PARTICLE SWARM OPTIMIZER IN NOISY AND CONTINUOUSLY CHANGING ENVIRONMENTS
Parsopoulos, Konstantinos
. KEY WORDS Particle Swarm, Evolutionary Methods, Noisy Functions 1. INTRODUCTION Optimizationmotivated evolutionary computation tech- niques in that it is motivated from the simulation of birds' social behaviorPARTICLE SWARM OPTIMIZER IN NOISY AND CONTINUOUSLY CHANGING ENVIRONMENTS K.E. PARSOPOULOS
Inertial Geometric Particle Swarm Optimization Alberto Moraglio and Julian Togelius
Togelius, Julian
form of traditional particle swarm optimization (PSO) without the inertia term that applies naturally GPSO (IGPSO), that generalizes the traditional PSO endowed with the full equation of motion Particle Swarm Optimization (PSO) is a relatively recently devised popualtion-based stochastic global
Particle Swarm Optimization based Fuzzy Clustering Approach to Identify Optimal Number of Clusters
Ludwig, Simone
Particle Swarm Optimization based Fuzzy Clustering Approach to Identify Optimal Number of Clusters based on Particle Swarm Opti- mization (PSO). This PSO approach determines the optimal number for are astronomy, banking, customer relationship man- agement, climate modeling, ecology, finance, life sci- ences
Optimization of multiplexed holographic gratings in PQ-PMMA for spectral-spatial imaging filters.
Luo, Yuan; Gelsinger, Paul J; Barton, Jennifer K; Barbastathis, George; Kostuk, Raymond K
2008-03-15
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
Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps
Parr, Ronald
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
EFFICIENT PARTICLE-PAIR FILTERING FOR ACCELERATION OF MOLECULAR DYNAMICS SIMULATION
Herbordt, Martin
EFFICIENT PARTICLE-PAIR FILTERING FOR ACCELERATION OF MOLECULAR DYNAMICS SIMULATION Matt Chiu ABSTRACT The acceleration of molecular dynamics (MD) simulations using high performance reconfigurable: determining the short-range force between particle pairs. In particular, we present the first FPGA study
AIR FILTER PARTICLE-SIZE EFFICIENCY TESTING FOR DIAMETERS GREATER THAN 1UM
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...
Cosmological parameter estimation using particle swarm optimization
NASA Astrophysics Data System (ADS)
Prasad, Jayanti; Souradeep, Tarun
2012-06-01
Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit ? cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.
The design of an optimal filter for monthly GRACE gravity models
NASA Astrophysics Data System (ADS)
Klees, R.; Revtova, E. A.; Gunter, B. C.; Ditmar, P.; Oudman, E.; Winsemius, H. C.; Savenije, H. H. G.
2008-11-01
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.
PARTICLE FILTERING FOR QUANTIZED SENSOR INFORMATION Rickard Karlsson, Fredrik Gustafsson
Gustafsson, Fredrik
,fredrik}@isy.liu.se ABSTRACT The implication of quantized sensor information on filtering problems is studied. The Cram as x yg(x, y) = y( xg(x, y))T , g : Rn Ã? Rm R. (2) For an unbiased estimator, E (^x) = x, the Cram
Particle emission characteristics of filter-equipped vacuum cleaners.
Trakumas, S; Willeke, K; Grinshpun, S A; Reponen, T; Mainelis, G; Friedman, W
2001-01-01
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
An evolutionary game based particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
Liu, Wei-Bing; Wang, Xian-Jia
2008-04-01
Particle swarm optimization (PSO) is an evolutionary algorithm used extensively. This paper presented a new particle swarm optimizer based on evolutionary game (EGPSO). We map particles' finding optimal solution in PSO algorithm to players' pursuing maximum utility by choosing strategies in evolutionary games, using replicator dynamics to model the behavior of particlesE And in order to overcome premature convergence a multi-start technique was introduced. Experimental results show that EGPSO can overcome premature convergence and has great performance of convergence property over traditional PSO.
Expedite Particle Swarm Optimization Algorithm (EPSO) for Optimization of MSA
NASA Astrophysics Data System (ADS)
Rathi, Amit; Vijay, Ritu
This paper presents a new designing method of Rectangular patch Microstrip Antenna using an Artificial searches Algorithm with some constraints. It requires two stages for designing. In first stage, bandwidth of MSA is modeled using bench Mark function. In second stage, output of first stage give to modified Artificial search Algorithm which is Particle Swarm Algorithm (PSO) as input and get output in the form of five parameter- dimensions width, frequency range, dielectric loss tangent, length over a ground plane with a substrate thickness and electrical thickness. In PSO Cognition, factor and Social learning Factor give very important effect on balancing the local search and global search in PSO. Basing the modification of cognition factor and social learning factor, this paper presents the strategy that at the starting process cognition-learning factor has more effect then social learning factor. Gradually social learning factor has more impact after learning cognition factor for find out global best. The aim is to find out under above circumstances these modifications in PSO can give better result for optimization of microstrip Antenna (MSA).
hyjang@bi.snu.ac.kr, btzhang@bi.snu.ac.kr Bayesian Filtering Method using n-gram Particle
n-gram O hyjang@bi.snu.ac.kr, btzhang@bi.snu.ac.kr Bayesian Filtering Method using n-gram Particle University (Bayesian Filtering) (Markov Process) (Kalman Filter) (Particle Filter) . (Random Walk) , . n-gram n-gram , n-gram . n-gram . 1. , n-gram . [4] (Word Segmentation)[3] . . n-gram . n-gram [5] n-gram
E. Donth
2009-12-11
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.
NASA Astrophysics Data System (ADS)
Saini, Sanjay; Awang Rambli, Dayang Rohaya B.; Sulaiman, Suziah B.; B Zakaria, M. Nordin; B Tomi, Azfar
2015-03-01
In this paper, we address the problem of three dimensional human pose tracking and estimation using Particle Swarm Optimization (PSO) with an improved silhouette extraction mechanism. In this work, the tracking problem is formulated as a nonlinear function optimization problem so the main objective is to optimize the fitness function between the 3D human model and the image observations. In order to improve the tracking performance, new shadow detection, removal and a level-set mechanism are applied during silhouette extraction. Both the silhouette and edge likelihood are used in the fitness function. Experiments using HumanEva-II dataset demonstrate that the proposed approach performance is considerably better than baseline algorithm which uses the Annealed Particle Filter (APF).
DISTRIBUTED PARTICLE FILTER FOR TARGET TRACKING IN SENSOR NETWORKS
Hong-Qing Liu; F. K. W. Chan; K. W. K. Lui
2009-01-01
In this paper, we present a distributed particle fllter (DPF) for target tracking in a sensor network. The proposed DPF consists of two major steps. First, particle compression based on support vector machine is performed to reduce the cost of transmission among sensors. Second, each sensor fuses the compressed information from its neighboring nodes with use of consensus or gossip
NASAL FILTERING OF FINE PARTICLES IN CHILDREN VS. ADULTS
Nasal efficiency for removing fine particles may be affected by developmental changes in nasal structure associated with age. In healthy Caucasian children (age 6-13, n=17) and adults (age 18-28, n=11) we measured the fractional deposition (DF) of fine particles (1 and 2um MMAD)...
Bayesian-Optimal Image Reconstruction for Translational-Symmetric Filters
NASA Astrophysics Data System (ADS)
Tajima, Satohiro; Inoue, Masato; Okada, Masato
2008-05-01
Translational-symmetric filters provide a foundation for various kinds of image processing. When a filtered image containing noise is observed, the original one can be reconstructed by Bayesian inference. Furthermore, hyperparameters such as the smoothness of the image and the noise level in the communication channel through which the image observed can be estimated from the observed image by setting a criterion of maximizing marginalized likelihood. In this article we apply a diagonalization technique with the Fourier transform to this image reconstruction problem. This diagonalization not only reduces computational costs but also facilitates theoretical analyses of the estimation and reconstruction performances. We take as an example the Mexican-hat shaped neural cell receptive field seen in the early visual systems of animals, and we compare the reconstruction performances obtained under various hyperparameter and filter parameter conditions with each other and with the corresponding performances obtained under no-filter conditions. The results show that the using a Mexican-hat filter can reduce reconstruction error.
Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL
2007-01-01
In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing and noisy environment.
Liu, Jui-Nung; Schulmerich, Matthew V.; Bhargava, Rohit; Cunningham, Brian T.
2011-01-01
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
Particle capture theory and experiment of amorphous magnetic filter for superconducting HGMS
Ohara, T.; Koyama, K.I.; Komuro, K.; Onishi, T.
1982-05-01
A theoretical analysis of the capture processes of magnetic particles by cylindrical magnetic wires for a parallel flow filter has been carried out. A magnetic filter using amorphous magnetic tapes and experiments have been conducted in the low magnetic field produced by a water-cooled electromagnet and in a high magnetic field by a superconducting electromagnet. These results are compared with theoretical results and reported here.
Removal of Particles and Acid Gases (SO2 or HCl) with a Ceramic Filter by Addition of Dry Sorbents
Hemmer, G.; Kasper, G.; Wang, J.; Schaub, G.
2002-09-20
The present investigation intends to add to the fundamental process design know-how for dry flue gas cleaning, especially with respect to process flexibility, in cases where variations in the type of fuel and thus in concentration of contaminants in the flue gas require optimization of operating conditions. In particular, temperature effects of the physical and chemical processes occurring simultaneously in the gas-particle dispersion and in the filter cake/filter medium are investigated in order to improve the predictive capabilities for identifying optimum operating conditions. Sodium bicarbonate (NaHCO{sub 3}) and calcium hydroxide (Ca(OH){sub 2}) are known as efficient sorbents for neutralizing acid flue gas components such as HCl, HF, and SO{sub 2}. According to their physical properties (e.g. porosity, pore size) and chemical behavior (e.g. thermal decomposition, reactivity for gas-solid reactions), optimum conditions for their application vary widely. The results presented concentrate on the development of quantitative data for filtration stability and overall removal efficiency as affected by operating temperature. Experiments were performed in a small pilot unit with a ceramic filter disk of the type Dia-Schumalith 10-20 (Fig. 1, described in more detail in Hemmer 2002 and Hemmer et al. 1999), using model flue gases containing SO{sub 2} and HCl, flyash from wood bark combustion, and NaHCO{sub 3} as well as Ca(OH){sub 2} as sorbent material (particle size d{sub 50}/d{sub 84} : 35/192 {micro}m, and 3.5/16, respectively). The pilot unit consists of an entrained flow reactor (gas duct) representing the raw gas volume of a filter house and the filter disk with a filter cake, operating continuously, simulating filter cake build-up and cleaning of the filter medium by jet pulse. Temperatures varied from 200 to 600 C, sorbent stoichiometric ratios from zero to 2, inlet concentrations were on the order of 500 to 700 mg/m{sup 3}, water vapor contents ranged from zero to 20 vol%. The experimental program with NaHCO{sub 3} is listed in Table 1. In addition, model calculations were carried out based on own and published experimental results that estimate residence time and temperature effects on removal efficiencies.
A Rao-Blackwellized particle filter for magnetoencephalography
Piana, Michele
functional studies which measures non-invasively the magnetic field outside the head with outstanding computational effectiveness. However regularized reconstructions often present a significant drawback can be addressed by optimization methods like Multiple SIgnal Classification (MUSIC) [12
Khan, T.; Ramuhalli, Pradeep; Dass, Sarat
2011-06-30
Flaw profile characterization from NDE measurements is a typical inverse problem. A novel transformation of this inverse problem into a tracking problem, and subsequent application of a sequential Monte Carlo method called particle filtering, has been proposed by the authors in an earlier publication [1]. In this study, the problem of flaw characterization from multi-sensor data is considered. The NDE inverse problem is posed as a statistical inverse problem and particle filtering is modified to handle data from multiple measurement modes. The measurement modes are assumed to be independent of each other with principal component analysis (PCA) used to legitimize the assumption of independence. The proposed particle filter based data fusion algorithm is applied to experimental NDE data to investigate its feasibility.
NASA Astrophysics Data System (ADS)
Vrugt, J. A.
2009-04-01
Sequential Monte Carlo (SMC) approaches are increasingly being used in watershed hydrology to approximate the evolving posterior distribution of model parameters and states when new streamflow or other data are becoming available. The typical implementation of SMC requires the use of a set of particles to represent the posterior probability density function (pdf) of model parameters and states. These particles are propagated forward in time and/or space using the (nonlinear) model operator and updated when new observational data become available. Main difficulty in applying particle filters in practice is problems with ensemble degeneracy, in which an increasing number of particles is exploring unproductive parts of the posterior pdf and assigned a negligible weight. To ensure sufficient particle diversity at every stage during the simulation, I will present an efficient SMC scheme that combines particle filtering with importance resampling and DiffeRential Evolution Adaptive Metropolis (DREAM) sampling. Our method is based on the DREAM adaptive MCMC scheme presented in Vrugt et al. (2009), but implemented sequentially to facilitate posterior tracking of model parameters and states. Initial results using the Sacramento Soil Moisture Accounting (SAC-SMA) model have shown that our DREAM particle filter has the advantage of requiring far fewer particles than conventional SMC approaches. This significantly speeds up convergence to the evolving limiting distribution, and allows parameter and state inference in spatially distributed hydrologic models.
RB Particle Filter Time Synchronization Algorithm Based on the DPM Model.
Guo, Chunsheng; Shen, Jia; Sun, Yao; Ying, Na
2015-01-01
Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms. PMID:26404291
Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
Moccia, Antonio
2014-01-01
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
Particle filtering for obstacle tracking in UAS sense and avoid applications.
Tirri, Anna Elena; Fasano, Giancarmine; Accardo, Domenico; Moccia, Antonio
2014-01-01
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
AN ADAPTIVE PROJECTION ALGORITHM FOR MULTIRATE FILTER BANK OPTIMIZATION
Regalia, Phillip A.
the first step uses well-known Rayleigh quotient type algorithms (e.g., [6], [7]) to obtain an extremal eigenvector of the input autocorrelation matrix. The lossless filter is then adapted to fit one of its impulse, the algorithm aims to project the "error" in the eigenvector fit onto the orthogonal complement subspace
NASA Astrophysics Data System (ADS)
Cuevas, Andres; Diaz-Ramirez, Victor H.; Kober, Vitaly; Trujillo, Leonardo
2014-09-01
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.
On the application of optimal wavelet filter banks for ECG signal classification
NASA Astrophysics Data System (ADS)
Hadjiloucas, S.; Jannah, N.; Hwang, F.; Galvão, R. K. H.
2014-03-01
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.
Optimal morphological hit-or-miss filtering of gray-level images
NASA Astrophysics Data System (ADS)
Dougherty, Edward R.
1993-05-01
The binary hit-or-miss transform is applied to filter digital gray-scale signals. This is accomplished by applying a union of hit-or-miss transforms to an observed signal's umbra and then taking the surface of the filtered umbra as the estimate of the ideal signal. The hit-or-miss union is constructed to provide the optimal mean-absolute-error filter for both the ideal signal and its umbra. The method is developed in detail for thinning hit-or-miss filters and applies at once to the dual thickening filters. It requires the output of the umbra filter to be an umbra, which in general is not true. A key aspect of the paper is the complete characterization of umbra-preserving union-of-hit-or-miss thinning and thickening filters. Taken together, the mean-absolute-error theory and the umbra-preservation characterization provide a full characterization of binary hit-or-miss filtering as applied to digital gray-scale signals. The theory is at once applicable to hit-or-miss filtering of digital gray-scale signals via the three- dimensional binary hit-or-miss transform.
Optimized Loading for Particle-in-cell Gyrokinetic Simulations
J.L.V. Lewandowski
2004-05-13
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.
Visual tracking in high-dimensional state space by appearance-guided particle filtering.
Chang, Wen-Yan; Chen, Chu-Song; Jian, Yong-Dian
2008-07-01
In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adopts some known attractors in the state space and integrates both appearance and motion-transition information for visual tracking. A probability propagation model based on these two types of information is derived from a Bayesian formulation, and a particle filtering framework is developed to realize it. Experimental results demonstrate that the proposed method is effective for high degree-of-freedom visual tracking problems, such as articulated hand tracking and lip-contour tracking. PMID:18586623
Person tracking with a mobile robot using particle filters in complex environment
NASA Astrophysics Data System (ADS)
Kwon, Ho Sang; Kim, Young Joong; Lim, Myo Taeg
2005-12-01
Based on a particle filter, a method that mobile robots can track a person in complex environment was presented. The problem of person following for mobile robot has been researched many different areas. The main issues of following a person are real time constraint, motion change of person during the tracking and occlusion with other objects. Using appearance-adaptive models in a particle filter, a robust visual tracking algorithm was realized. The appearance-adaptive model can handle occlusion with other people while the target is moving.
Optimizing the Choice of Filter Sets for Space Based Imaging Instruments
NASA Astrophysics Data System (ADS)
Elliott, Rachel E.; Farrah, Duncan; Petty, Sara M.; Harris, Kathryn Amy
2015-01-01
We investigate the challenge of selecting a limited number of filters for space based imaging instruments such that they are able to address multiple heterogeneous science goals. The number of available filter slots for a mission is bounded by factors such as instrument size and cost. We explore methods used to extract the optimal group of filters such that they complement each other most effectively. We focus on three approaches; maximizing the separation of objects in two-dimensional color planes, SED fitting to select those filter sets that give the finest resolution in fitted physical parameters, and maximizing the orthogonality of physical parameter vectors in N-dimensional color-color space. These techniques are applied to a test-case, a UV/optical imager with space for five filters, with the goal of measuring the properties of local stars through to distant galaxies.
Chaotic Particle Swarm Optimization with Mutation for Classification
Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza
2015-01-01
In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937
Chaotic particle swarm optimization with mutation for classification.
Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza
2015-01-01
In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937
Design and optimization of high reflectance graded index optical filter with quintic apodization
NASA Astrophysics Data System (ADS)
Praveen Kumar, Vemuri S. R. S.; Sunita, Parinam; Kumar, Mukesh; Rao, Parinam Krishna; Kumari, Neelam; Karar, Vinod; Sharma, Amit L.
2015-06-01
Rugate filters are a special kind of graded-index films that may provide advantages in both, optical performance and mechanical properties of the optical coatings. In this work, design and optimization of a high reflection rugate filter having reflection peak at 540nm has been presented which has been further optimized for side-lobe suppression. A suitable number of apodization and matching layers, generated through Quintic function, were added to the basic sinusoidal refractive index profile to achieve high reflectance of around 80% in the rejection window for normal incidence. Smaller index contrast between successive layers in the present design leads to less residual stress in the thinfilm stack which enhances the adhesion and mechanical strength of the filter. The optimized results show excellent side lobe suppression achieved around the stopband.
Particle Count Statistics Applied to the Penetration of a Filter Challenged with Nanoparticles.
O'Shaughnessy, Patrick T; Schmoll, Linda H
2013-01-01
Statistical confidence in a single measure of filter penetration (P) is dependent on the low number of particle counts made downstream of the filter. This paper discusses methods for determining an upper confidence limit (UCL) for a single measure of penetration. The magnitude of the UCL was then compared to the P value, UCL ? 2P, as a penetration acceptance criterion (PAC). This statistical method was applied to penetration trials involving an N95 filtering facepiece respirator challenged with sodium chloride and four engineered nanoparticles: titanium dioxide, iron oxide, silicon dioxide and single-walled carbon nanotubes. Ten trials were performed for each particle type with the aim of determining the most penetrating particle size (MPPS) and the maximum penetration, Pmax. The PAC was applied to the size channel containing the MPPS. With those P values that met the PAC for a given set of trials, an average Pmax and MPPS was computed together with corresponding standard deviations. Because the size distribution of the silicon dioxide aerosol was shifted towards larger particles relative to the MPPS, none of the ten trials satisfied the PAC for that aerosol. The remaining four particle types resulted in at least 4 trials meeting the criterion. MPPS values ranged from 35 - 53 nm with average Pmax values varying from 4.0% for titanium dioxide to 7.0% for iron oxide. The use of the penetration acceptance criterion is suggested for determining the reliability of penetration measurements obtained to determine filter Pmax and MPPS. PMID:24678138
Particle Clogging in Filter Media of Embankment Dams: A Numerical and Experimental Study
NASA Astrophysics Data System (ADS)
Antoun, T.; Kanarska, Y.; Ezzedine, S. M.; Lomov, I.; Glascoe, L. G.; Smith, J.; Hall, R. L.; Woodson, S. C.
2013-12-01
The safety of dam structures requires the characterization of the granular filter ability to capture fine-soil particles and prevent erosion failure in the event of an interfacial dislocation. Granular filters are one of the most important protective design elements of large embankment dams. In case of cracking and erosion, if the filter is capable of retaining the eroded fine particles, then the crack will seal and the dam safety will be ensured. Here we develop and apply a numerical tool to thoroughly investigate the migration of fines in granular filters at the grain scale. The numerical code solves the incompressible Navier-Stokes equations and uses a Lagrange multiplier technique which enforces the correct in-domain computational boundary conditions inside and on the boundary of the particles. The numerical code is validated to experiments conducted at the US Army Corps of Engineering and Research Development Center (ERDC). These laboratory experiments on soil transport and trapping in granular media are performed in constant-head flow chamber filled with the filter media. Numerical solutions are compared to experimentally measured flow rates, pressure changes and base particle distributions in the filter layer and show good qualitative and quantitative agreement. To further the understanding of the soil transport in granular filters, we investigated the sensitivity of the particle clogging mechanism to various parameters such as particle size ratio, the magnitude of hydraulic gradient, particle concentration, and grain-to-grain contact properties. We found that for intermediate particle size ratios, the high flow rates and low friction lead to deeper intrusion (or erosion) depths. We also found that the damage tends to be shallower and less severe with decreasing flow rate, increasing friction and concentration of suspended particles. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was sponsored by the Department of Homeland Security (DHS), Science and Technology Directorate, Homeland Security Advanced Research Projects Agency (HSARPA).
Two-stage hybrid optimization of fiber Bragg gratings for design of linear phase filters
NASA Astrophysics Data System (ADS)
Zheng, Rui Tao; Ngo, Nam Quoc; Binh, Le Nguyen; Tjin, Swee Chuan
2004-12-01
We present a new hybrid optimization method for the synthesis of fiber Bragg gratings (FBGs) with complex characteristics. The hybrid optimization method is a two-tier search that employs a global optimization algorithm [i.e., the tabu search (TS) algorithm] and a local optimization method (i.e., the quasi-Netwon method). First the TS global optimization algorithm is used to find a ``promising'' FBG structure that has a spectral response as close as possible to the targeted spectral response. Then the quasi-Newton local optimization method is applied to further optimize the FBG structure obtained from the TS algorithm to arrive at a targeted spectral response. A dynamic mechanism for weighting of different requirements of the spectral response is employed to enhance the optimization efficiency. To demonstrate the effectiveness of the method, the synthesis of three linear-phase optical filters based on FBGs with different grating lengths is described.
Blended particle methods with adaptive subspaces for filtering turbulent dynamical systems
NASA Astrophysics Data System (ADS)
Qi, Di; Majda, Andrew J.
2015-04-01
It is a major challenge throughout science and engineering to improve uncertain model predictions by utilizing noisy data sets from nature. Hybrid methods combining the advantages of traditional particle filters and the Kalman filter offer a promising direction for filtering or data assimilation in high dimensional turbulent dynamical systems. In this paper, blended particle filtering methods that exploit the physical structure of turbulent dynamical systems are developed. Non-Gaussian features of the dynamical system are captured adaptively in an evolving-in-time low dimensional subspace through particle methods, while at the same time statistics in the remaining portion of the phase space are amended by conditional Gaussian mixtures interacting with the particles. The importance of both using the adaptively evolving subspace and introducing conditional Gaussian statistics in the orthogonal part is illustrated here by simple examples. For practical implementation of the algorithms, finding the most probable distributions that characterize the statistics in the phase space as well as effective resampling strategies is discussed to handle realizability and stability issues. To test the performance of the blended algorithms, the forty dimensional Lorenz 96 system is utilized with a five dimensional subspace to run particles. The filters are tested extensively in various turbulent regimes with distinct statistics and with changing observation time frequency and both dense and sparse spatial observations. In real applications perfect dynamical models are always inaccessible considering the complexities in both modeling and computation of high dimensional turbulent system. The effects of model errors from imperfect modeling of the systems are also checked for these methods. The blended methods show uniformly high skill in both capturing non-Gaussian statistics and achieving accurate filtering results in various dynamical regimes with and without model errors.
A Tutorial on Particle Filtering and Smoothing: Fifteen years later
Johansen, Adam
of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. Email: Arnaud@ism.ac.jp Adam M an extremely flexible framework for modelling time series. The great descriptive power of these models comes advantage of particle methods is that they do not rely on any local linearisation technique or any crude
Stochastic adaptive particle-beam tracker using Meer-filter feedback. Master's thesis
Johnson, B.A.
1986-12-01
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 of both signal- and noise-induced events. The Meer filter is a stochastic multiple model adaptive estimator which is composed of a bank of Snyder Fishman filters and is designed to distinguish the signal-induced events from the noise-induced events. A target model is developed from a Gauss-Markov acceleration process, and the target states are estimated by a Kalman filter. The objectives of the research were to (1) select the best cost weighting matrices that minimize the RMS tracker error and enhance robustness, (2) simplify the Meer filter for easier on-line usage, (3) complete full-scale sensitivity and robustness analyses over all the Kalman and Meer filter parameters, and (4) develop on-line adaptive estimation of those parameters that greatly affect stability robustness and tracker performance. A fifth objective is to identify the source of instability, and to propose a solution that will insure stability during parameter variations.
Seventeen dubious methods to approximate the gradient for nonlinear filters with particle flow
Fred Daum; Jim Huang; Misha Krichman; Talia Kohen
2009-01-01
We have investigated more than 17 distinct methods to approximate the gradient of the loghomotopy for nonlinear filters. This is a challenging problem because the data are given as function values at random points in high dimensional space. This general problem is important in optimization, financial engineering, quantum chemistry, chemistry, physics and engineering. The best general method that we have
Gradient estimation for particle flow induced by log-homotopy for nonlinear filters
Frederick Daum; Jim Huang; A. J. Noushin; Misha Krichman
2009-01-01
We study 17 distinct methods to approximate the gradient of the log-homotopy for nonlinear filters. This is a challenging problem because the data are given as function values at random points in high dimensional space. This general problem is important in optimization, financial engineering, quantum chemistry, chemistry, physics and engineering. The best general method that we have developed so far
Optimal-adaptive filters for modelling spectral shape, site amplification, and source scaling
Safak, Erdal
1989-01-01
This paper introduces some applications of optimal filtering techniques to earthquake engineering by using the so-called ARMAX models. Three applications are presented: (a) spectral modelling of ground accelerations, (b) site amplification (i.e., the relationship between two records obtained at different sites during an earthquake), and (c) source scaling (i.e., the relationship between two records obtained at a site during two different earthquakes). A numerical example for each application is presented by using recorded ground motions. The results show that the optimal filtering techniques provide elegant solutions to above problems, and can be a useful tool in earthquake engineering.
Georg Jäger; Ulrich Hohenester
2013-09-07
We theoretically investigate protocols based on optimal control theory (OCT) for manipulating Bose-Einstein condensates in magnetic microtraps, using the framework of the Gross-Pitaevskii equation. In our approach we explicitly account for filter functions that distort the computed optimal control, a situation inherent to many experimental OCT implementations. We apply our scheme to the shakeup process of a condensate from the ground to the first excited state, following a recent experimental and theoretical study, and demonstrate that the fidelity of OCT protocols is not significantly deteriorated by typical filters.
Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard
2002-01-01
The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.
NASA Astrophysics Data System (ADS)
Glascoe, L. G.; Ezzedine, S. M.; Kanarska, Y.; Lomov, I. N.; Antoun, T.; Smith, J.; Hall, R.; Woodson, S.
2014-12-01
Understanding the flow of fines, particulate sorting in porous media and fractured media during sediment transport is significant for industrial, environmental, geotechnical and petroleum technologies to name a few. For example, the safety of dam structures requires the characterization of the granular filter ability to capture fine-soil particles and prevent erosion failure in the event of an interfacial dislocation. Granular filters are one of the most important protective design elements of large embankment dams. In case of cracking and erosion, if the filter is capable of retaining the eroded fine particles, then the crack will seal and the dam safety will be ensured. Here we develop and apply a numerical tool to thoroughly investigate the migration of fines in granular filters at the grain scale. The numerical code solves the incompressible Navier-Stokes equations and uses a Lagrange multiplier technique. The numerical code is validated to experiments conducted at the USACE and ERDC. These laboratory experiments on soil transport and trapping in granular media are performed in constant-head flow chamber filled with the filter media. Numerical solutions are compared to experimentally measured flow rates, pressure changes and base particle distributions in the filter layer and show good qualitative and quantitative agreement. To further the understanding of the soil transport in granular filters, we investigated the sensitivity of the particle clogging mechanism to various parameters such as particle size ratio, the magnitude of hydraulic gradient, particle concentration, and grain-to-grain contact properties. We found that for intermediate particle size ratios, the high flow rates and low friction lead to deeper intrusion (or erosion) depths. We also found that the damage tends to be shallower and less severe with decreasing flow rate, increasing friction and concentration of suspended particles. We have extended these results to more realistic heterogeneous population particulates for sediment transport. This work performed under the auspices of the US DOE by LLNL under Contract DE-AC52-07NA27344 and was sponsored by the Department of Homeland Security, Science and Technology Directorate, Homeland Security Advanced Research Projects Agency.
Step-Optimized Particle Swarm Optimization Thomas Schoene
Ludwig, Simone
--Recent developments of Particle Swarm Optimiza- tion (PSO) have successfully trended towards Adaptive PSO (APSO). APSO and effectively. In classical PSO, all parameters remain constant for the entire swarm during the iterations PSO (SOPSO) algorithm in which every particle has its own velocity weights and an inner PSO iteration
Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah
2015-01-01
The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims. PMID:25978493
Accelerating Particle Filter Using Randomized Multiscale and Fast Multipole Type Methods.
Shabat, Gil; Shmueli, Yaniv; Bermanis, Amit; Averbuch, Amir
2015-07-01
Particle filter is a powerful tool for state tracking using non-linear observations. We present a multiscale based method that accelerates the tracking computation by particle filters. Unlike the conventional way, which calculates weights over all particles in each cycle of the algorithm, we sample a small subset from the source particles using matrix decomposition methods. Then, we apply a function extension algorithm that uses a particle subset to recover the density function for all the rest of the particles not included in the chosen subset. The computational effort is substantial especially when multiple objects are tracked concurrently. The proposed algorithm significantly reduces the computational load. By using the Fast Gaussian Transform, the complexity of the particle selection step is reduced to a linear time in n and k, where n is the number of particles and k is the number of particles in the selected subset. We demonstrate our method on both simulated and on real data such as object tracking in video sequences. PMID:26352448
An Estimation of Distribution Particle Swarm Optimization Algorithm
Kent, University of
evaluations. 1 Introduction The first Particle Swarm Optimization (PSO) algorithm was introduced by Kennedy other population-based optimization algorithms, PSO is initialized with a population of complete multiplication operator. Clerc and Kennedy [2] introduced the concept of constriction in PSO. Since it is based
Towards a Repulsive and Adaptive Particle Swarm Optimization Algorithm
Ludwig, Simone
proposes a Repulsive Adaptive PSO (RAPSO) variant that adaptively optimizes the velocity weights of ev- ery particle at every iteration. RAPSO optimizes the ve- locity weights during every outer PSO iteration, and opti- mizes the solution of the problem in an inner PSO iteration. We compare RAPSO to Global Best PSO
An Adaptive Mutation Operator for Particle Swarm Optimization
Yang, Shengxiang
.le.ac.uk Abstract Particle swarm optimization (PSO) is an efficient tool for optimization and search problems- search works have shown that mutation operators can help PSO prevent prema- ture convergence and compared for PSO. An adaptive muta- tion operator is designed. Experimental results show
Design of intelligent ship autopilots using particle swarm optimization
B. Samanta; C. Nataraj
2008-01-01
A study is presented on the application of particle swarm optimization (PSO) to design intelligent autopilots for ship steering. Two versions of PSO-conventional and anti-predatory (APSO) - have been used. The autopilot consists of a fuzzy logic controller (FLC) emulating the characteristics of manual ship steering. The parameters for the FLC are optimized using PSO and APSO. The robustness of
A parallel Particle swarm optimization algorithm for option pricing
Hari Prasain; Girish Kumar Jha; Parimala Thulasiraman; Ruppa K. Thulasiram
2010-01-01
Option pricing is one of the challenging problems of computational finance. Nature-inspired algorithms have gained prominence in real world optimization problems such as in mobile ad hoc networks. The option pricing problem fits very well into this category of problems due to the ad hoc nature of the market. Particle swarm optimization (PSO) is one of the novel global search
High-efficiency particulate air filter test stand and aerosol generator for particle loading studies
NASA Astrophysics Data System (ADS)
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
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.
Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter
Abd Rabbou, Mahmoud; El-Rabbany, Ahmed
2015-01-01
Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. PMID:25815446
Integration of GPS precise point positioning and MEMS-based INS using unscented particle filter.
Abd Rabbou, Mahmoud; El-Rabbany, Ahmed
2015-01-01
Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. PMID:25815446
Vasudevan, V.; Kang, B.S-J.; Johnson, E.K.
2002-09-19
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.
Convolution Particle Filter for Parameter Estimation in General State-Space Models
Rossi, Vivien
study. Index Terms Bayes procedures, Hidden Markov models, Marine vehicle detection and tracking, Monte1 Convolution Particle Filter for Parameter Estimation in General State-Space Models Fabien Campillo and Vivien Rossi Abstract The state-space modeling of partially observed dynamical systems
ON PARTICLE FILTERS FOR LANDMINE DETECTION USING IMPULSE GROUND PENETRATING RADAR
So, Hing-Cheung
ON PARTICLE FILTERS FOR LANDMINE DETECTION USING IMPULSE GROUND PENETRATING RADAR William Ng the exis- tence of true landmines is unknown and random, we propose to use the reversible jump Markov chain Monte Carlo (RJMCMC) in association with the SMC methods to jointly detect and local- ize landmines
A Study on Particle Filters for Single-Tone Frequency Tracking
So, Hing-Cheung
of a real sinusoid in additive zero-mean noise. The discrete-time signal model is yt = st + ut, t = 1A Study on Particle Filters for Single-Tone Frequency Tracking WILLIAM NG City University of Hong Member, IEEE City University of Hong Kong In this paper, we present an online approach for frequency
LS-N-IPS: an Improvement of Particle Filters by Means of Local Search
Szepesvari, Csaba
LS-N-IPS: an Improvement of Particle Filters by Means of Local Search P#19;eter Torma Eotvos Lor algorithm in the small sample size limit and when the observations are \\reliable". The algorithm called LS in a local search procedure that utilizes the most recent observation. The uniform stability of LS
Discriminatively Trained Particle Filters for Complex Multi-Object Tracking Rob Hess and Alan Fern
to track all 22 players throughout football plays. The training method is shown to significantly improve and the stage of the play. To date, there has been very limited success in tracking for American footballDiscriminatively Trained Particle Filters for Complex Multi-Object Tracking Rob Hess and Alan Fern
Local Symmetry Detection in Natural Images using a Particle Filtering Approach
Mignotte, Max
1 Local Symmetry Detection in Natural Images using a Particle Filtering Approach Nicolas Widynski symmetries and contours of ribbon-like objects in natural images. The detection is formulated as a spatial an adaptive local geometric model, the method can detect straight reflection symmetries in perfectly
PARTICLE FILTERING AND CRAM ER-RAO LOWER BOUND FOR UNDERWATER NAVIGATION
Gustafsson, Fredrik
PARTICLE FILTERING AND CRAM Â´ER-RAO LOWER BOUND FOR UNDERWATER NAVIGATION Rickard Karlsson, Fredrik, Sweden E-mail: tobias.karlsson.lith@dynamics.saab.se ABSTRACT We have studied a sea navigation method is interpreted in terms of the iner- tial navigation system (INS) error, the sensor accuracy and the terrain map
Tracking of 3D sound source location by particle filter with TDOA and signal power ratio
Norikazu Ikoma; Osamu Tokunaga; Hideaki Kawano; Hiroshi Maeda
2009-01-01
A new sound source tracking method in 3 dimensional space using state space modeling approach and particle filter as a state estimation method has been proposed. Not only TDOA (Time Difference Of Arrival) as our previous works including by the authors, but also using power ratio of two sound signals from microphone pair are effectively used in formulation of state
A Particle Filter Approach to WiFi Target Localization Neeti Wagle
Frew, Eric W.
A Particle Filter Approach to WiFi Target Localization Neeti Wagle , Eric Frew University localization using only received signal strength indicator (RSSI) measurements. It uses a model that exploits the behavior of wireless signals in free space and obtains position estimates from the signal strength
CONSTRAINED STATE ESTIMATION IN PARTICLE FILTERS Bradley Ebinger, Nidhal Bouaynaya and Robi Polikar
Bouaynaya, Nidhal
CONSTRAINED STATE ESTIMATION IN PARTICLE FILTERS Bradley Ebinger, Nidhal Bouaynaya and Robi Polikar University of Alabama at Birmingham Department of Mathematics Birmingham, AL ABSTRACT Dynamical systems are often required to satisfy certain con- straints arising from basic physical laws, mathematical prop
Nakano, Shin'ya
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
OUP-FIRST UNCORRECTED PROOF, August 12, 2014 Particle filters for the geosciences
van Leeuwen, Peter Jan
in the geosciences. To keep the text fluent, I have kept the literature references to a minimum; a more comprehensive, namely that particle filters are completely nonlinear they have no problems with model balances after of the error covariance of the model state. The latter has been overlooked in the past, but is actually a major
A baker's dozen of new particle flows for nonlinear filters, Bayesian decisions and transport
NASA Astrophysics Data System (ADS)
Daum, Fred; Huang, Jim
2015-05-01
We describe a baker's dozen of new particle flows to compute Bayes' rule for nonlinear filters, Bayesian decisions and learning as well as transport. Several of these new flows were inspired by transport theory, but others were inspired by physics or statistics or Markov chain Monte Carlo methods.
Multi-Robot Cooperative Object Tracking Based on Particle Filters Aamir Ahmad Pedro Lima
1 Multi-Robot Cooperative Object Tracking Based on Particle Filters Aamir Ahmad Pedro Lima Institute for Systems and Robotics, Instituto Superior T´ecnico, Lisboa, Portugal Abstract-- This article presents a cooperative approach for tracking a moving object by a team of mobile robots equipped
Particle Filtered Modified-CS (PaFiMoCS) for tracking signal sequences
Vaswani, Namrata
Particle Filtered Modified-CS (PaFiMoCS) for tracking signal sequences Samarjit Das and Namrata,Lu, ISIT'09, IEEE Trans. SP'10] Recon. a sparse signal, x, with support, N, from y := x given partial. w/ partly known support [Vaswani,Lu, ISIT'09, IEEE Trans. SP'
A fast atmospheric turbulent parameters estimation using particle filtering. Application to LIDAR
Baehr, Christophe
on simulated Doppler LIDAR measurements, in tree-dimensional modeling. 1. Introduction In various activitiesA fast atmospheric turbulent parameters estimation using particle filtering. Application to LIDAR cedex 1, France E-mail: florian.suzat@meteo.fr Abstract. Estimating fast turbulence fluctuations
Robust Head Tracking Based on a Multi-State Particle Filter , Haizhou AI1
Ai, Haizhou
Robust Head Tracking Based on a Multi-State Particle Filter Yuan LI1 , Haizhou AI1 , Chang HUANG1 background noise, due to lack of higher level object- specific knowledge. On the other hand, tracking methods@mail.tsinghua.edu.cn Abstract This paper proposes a novel method for robust and auto- matic realtime head tracking by fusing
California at Santa Barbara, University of
appear- ance changes in objects, lighting changes, occlusions, etc. The tracking accuracy of a camera. The main bottleneck for robust object tracking in a cam- era network is the low level visual trackingDISTRIBUTED PARTICLE FILTER TRACKING WITH ONLINE MULTIPLE INSTANCE LEARNING IN A CAMERA SENSOR
Tracking Human Body Parts Using Particle Filters Constrained by Human Biomechanics
Nebel, Jean-Christophe
Tracking Human Body Parts Using Particle Filters Constrained by Human Biomechanics J. Mart´inez1, J of human body parts is introduced. The presented approach demonstrates the feasibility of recover- ing demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail compar
Cluster Based Sensor Scheduling in a Target Tracking Application with Particle Filtering
Bayazit, Ulug
Cluster Based Sensor Scheduling in a Target Tracking Application with Particle Filtering Özgür applications management of sensors is necessary for the classification of data they produce application areas both in military and civil environments. In multi-sensor networks, multiple measurement
Application of Variance Reduction Techniques for Tau-Leaping Systems to Particle Filters
West, Matthew
Application of Variance Reduction Techniques for Tau-Leaping Systems to Particle Filters Peter A are based on vari- ance reduction techniques previously proposed for stochastic simulation of tau-leaping examples pertaining to building population dynamics are presented in both single and multidimensional tau-leaping
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
Little, Jim
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
Indoor occupant positioning system using active RFID deployment and particle filters
for indoor posi- tioning of human-carried active Radio Frequency Identification (RFID) tags basedIndoor occupant positioning system using active RFID deployment and particle filters Kevin Weekly is empirically determined by ground truth RSS measurements while moving the RFID tags along a known trajectory
A Particle Filter for Monocular Vision-Aided Odometry Teddy Yap, Jr
Shelton, Christian R.
such estimates, in most cases a robot fuses measurements from multiple onboard sensors. Typically features in the environment, taken by a camera mounted on the robot. Our key contribution is a novel-- We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot
PARTICLE FILTERS FOR SYSTEM IDENTIFICATION OF STATE-SPACE MODELS LINEAR IN EITHER PARAMETERS
Gustafsson, Fredrik
OR STATES 1 Thomas Sch¨on and Fredrik Gustafsson Division of Automatic Control and Communication Systems formulation in (Gustafsson and Hriljac, 2003). By augmenting the state vector with the parameters, xT t = (z for a part of the state vector. Alternatively, we can apply the particle filter on more complex models
Real-time monitoring of complex industrial processes with particle filters
Poole, David
: an industrial dryer and a level tank. For these appli- cations, we compared three particle filtering variants with a level-tank system that exhibits the dynamic behaviour of these important processes, see Figure 2. A by such as robot navigation or diagnosis of complex systems [1, 2]. This paper considers online monitoring
PARTICLE FILTER BEAMFORMING FOR ACOUSTIC SOURCE LOCALIZATION IN A REVERBERANT ENVIRONMENT
Botea, Adi
PARTICLE FILTER BEAMFORMING FOR ACOUSTIC SOURCE LOCALIZATION IN A REVERBERANT ENVIRONMENT Darren B National University Canberra ACT 0200, Australia ABSTRACT Traditional acoustic source localization uses the source trajectory. 1. INTRODUCTION The ability to localize an acoustic source is critical for the correct
Tagliasacchi, Marco
EFFICIENT LOCALIZATION AND TRACKING OF TWO ACOUSTIC SOURCES USING PARTICLE FILTERS WITH SWARM/driva/sarti/tubaro@.elet.polimi.it ABSTRACT In this paper we consider the problem of localizing mul- tiple acoustic sources in reverberant of localizing and tracking a single acoustic source. A survey on this topic is presented in [2]. The authors pro
Botea, Adi
EXPERIMENTAL COMPARISON OF PARTICLE FILTERING ALGORITHMS FOR ACOUSTIC SOURCE LOCALIZATION source localization techniques attempt to de- termine the current location of an acoustic source from an acoustic source in a reverberant room occurs in several applications, including auto- matic camera steering
Choosing a Starting Configuration for Particle Swarm Optimization
Martinez, Tony R.
with the standard PSO algorithm on several standard test functions. Results suggest that CVT initialization improves PSO performance in high-dimensional spaces. I. INTRODUCTION Particle Swarm Optimization (PSO functions [7][8]. PSO models the search through the problem space as the flight of a swarm of particles
Optimization of Encoded Hydrogel Particles for Nucleic Acid Quantification
Doyle, Patrick S.
Optimization of Encoded Hydrogel Particles for Nucleic Acid Quantification Daniel C. Pregibon analysis using encoded hydrogel particles. Here, we demonstrate tuning of hydrogel poros- ity with semi. With these considerations in mind, hydrogels are proving to be excellent substrates for biomolecule capture
LeGland, François
for Positioning, Navigation, and Tracking Fredrik Gustafsson, Fredrik Gunnarsson, Niclas Bergman, Urban Forssell, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed navigation (as GPS) but with higher integrity. Based on simulations, we also argue how the particle filter
Optimized superficially porous particles for protein separations.
Schuster, Stephanie A; Wagner, Brian M; Boyes, Barry E; Kirkland, Joseph J
2013-11-01
Continuing interest in larger therapeutic molecules by pharmaceutical and biotech companies provides the need for improved tools for examining these molecules both during the discovery phase and later during quality control. To meet this need, larger pore superficially porous particles with appropriate surface properties (Fused-Core(®) particles) have been developed with a pore size of 400 ?, allowing large molecules (<500 kDa) unrestricted access to the bonded phase. In addition, a particle size (3.4 ?m) is employed that allows high-efficiency, low-pressure separations suitable for potentially pressure-sensitive proteins. A study of the shell thickness of the new fused-core particles suggests a compromise between a short diffusion path and high efficiency versus adequate retention and mass load tolerance. In addition, superior performance for the reversed-phase separation of proteins requires that specific design properties for the bonded-phase should be incorporated. As a result, columns of the new particles with unique bonded phases show excellent stability and high compatibility with mass spectrometry-suitable mobile phases. This report includes fast separations of intact protein mixtures, as well as examples of very high-resolution separations of larger monoclonal antibody materials and associated variants. Investigations of protein recovery, sample loading and dynamic range for analysis are shown. The advantages of these new 400 ? fused-core particles, specifically designed for protein analysis, over traditional particles for protein separations are demonstrated. PMID:24094750
NASA Astrophysics Data System (ADS)
Salamon, Peter; Feyen, Luc
2009-10-01
SummarySequential data assimilation techniques offer the possibility to handle different sources of uncertainty explicitly in hydrological models and hence improve their predictive capabilities. Amongst the different techniques, sequential Monte Carlo or particle filter methods offer the capability to handle non-linear/non-Gaussian state-space models while preserving the spatial variability of updated state variables, both desirable features when assimilating data in distributed hydrological models. In this work we apply the residual resampling particle filter to assess parameter, precipitation, and predictive uncertainty in the distributed rainfall-runoff model LISFLOOD. First, we compare estimated posterior parameter distributions with results of the Shuffled Complex Evolution Metropolis global optimization algorithm obtained using identical input data for the Meuse catchment and considering parameter uncertainty only. Both approaches result in well identifiable posterior parameter distributions and provide a reasonable fit to the observed hydrograph. The resulting posterior distributions, however, vary considerably in shape, location, and scale, most likely caused by the different assumptions made in the output error model. An evaluation of the predictive distributions illustrates that predictive uncertainty is significantly underestimated for both approaches when accounting for parameter uncertainty only. A second case study shows that considering additionally precipitation uncertainty not only increases the spread of the posterior parameter distributions but may also result in a completely different location and/or shape of the posterior distributions. Evaluation of the posterior precipitation multiplier distribution reveals that no overall systematic bias exists in the precipitation grids and that particle filtering is a suitable tool to quantify and reduce precipitation uncertainty. Furthermore, considering precipitation and parameter uncertainty leads to an improvement in model predictive capabilities, especially for the high flow periods. However, the remaining underestimation of predictive uncertainty also indicates that model structural uncertainty is equally important, in spite of using a physically-based distributed hydrological model that should theoretically provide an improved description of the hydrological system dynamics.
T. Fortmann; B. Anderson
1973-01-01
The Karhunen-Loève expansion of a random process is used to derive the impulse response of the optimal realizable linear estimator for the process. The expansion is truncated to yield an approximate state-variable model of the process in terms of the firstNeigenvalues and eigenfunctions. The Kalman-Bucy filter for this model provides an approximate realizable linear estimator which approaches the optimal one
Bondugula, Srikant
2010-07-14
Damnjanovic Daren B.H. Cline Head of Department, David V. Rosowsky May 2009 Major Subject: Civil Engineering iii ABSTRACT Optimal Control of Projects Based on Kalman Filter Approach for Tracking & Forecasting the Project... of complex construction process for yielding the optimal control policies. v DEDICATION DEDICATED TO MY FAMILY AND FRIENDS vi ACKNOWLEDGEMENTS I take this opportunity to express my sincere thanks to my thesis...
NASA Astrophysics Data System (ADS)
Erdogan, Eren; Onur Karslioglu, Mahmut; Durmaz, Murat; Aghakarimi, Armin
2014-05-01
In this study, particle filter (PF) which is mainly based on the Monte Carlo simulation technique has been carried out for polynomial modeling of the local ionospheric conditions above the selected ground based stations. Less sensitivity to the errors caused by linearization of models and the effect of unknown or unmodeled components in the system model is one of the advantages of the particle filter as compared to the Kalman filter which is commonly used as a recursive filtering method in VTEC modeling. Besides, probability distribution of the system models is not necessarily required to be Gaussian. In this work third order polynomial function has been incorporated into the particle filter implementation to represent the local VTEC distribution. Coefficients of the polynomial model presenting the ionospheric parameters and the receiver inter frequency biases are the unknowns forming the state vector which has been estimated epoch-wise for each ground station. To consider the time varying characteristics of the regional VTEC distribution, dynamics of the state vector parameters changing permanently have been modeled using the first order Gauss-Markov process. In the processing of the particle filtering, multi-variety probability distribution of the state vector through the time has been approximated by means of randomly selected samples and their associated weights. A known drawback of the particle filtering is that the increasing number of the state vector parameters results in an inefficient filter performance and requires more samples to represent the probability distribution of the state vector. Considering the total number of unknown parameters for all ground stations, estimation of these parameters which were inserted into a single state vector has caused the particle filter to produce inefficient results. To solve this problem, the PF implementation has been carried out separately for each ground station at current time epochs. After estimation of unknown parameters, Ionospheric VTEC map covering the predefined region has been produced by interpolation. VTEC values at a grid node of the map have been computed based on the four closest ground stations by means of inverse distance squared weighted average. The GPS data which is acquired from ground based stations have been made available from the International GNSS Service (IGS) and the Reference Frame Sub-commission for Europe (EUREF). Raw GPS observations have been preprocessed to detect cycle slips and to form geometry-free linear combinations of observables for each continuous arc. Then the obtained pseudoranges have been smoothed with the carrier to code leveling method. Finally, the performance of the particle filter to investigate the local characteristics of the ionospheric Vertical Total Electron Content (VTEC) has been evaluated and the result has been compared with the result of a standard Kalman filter. Keywords: ionosphere, GPS , Particle filer, VTEC modeling
Optimization of magnetic switches for single particle and cell transport
Abedini-Nassab, Roozbeh; Yellen, Benjamin B., E-mail: yellen@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Box 90300 Hudson Hall, Durham, North Carolina 27708 (United States); Joint Institute, University of Michigan—Shanghai Jiao Tong University, Shanghai Jiao Tong University, Shanghai 200240 (China); Murdoch, David M. [Department of Medicine, Duke University, Durham, North Carolina 27708 (United States); Kim, CheolGi [Department of Emerging Materials Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 711-873 (Korea, Republic of)
2014-06-28
The ability to manipulate an ensemble of single particles and cells is a key aim of lab-on-a-chip research; however, the control mechanisms must be optimized for minimal power consumption to enable future large-scale implementation. Recently, we demonstrated a matter transport platform, which uses overlaid patterns of magnetic films and metallic current lines to control magnetic particles and magnetic-nanoparticle-labeled cells; however, we have made no prior attempts to optimize the device geometry and power consumption. Here, we provide an optimization analysis of particle-switching devices based on stochastic variation in the particle's size and magnetic content. These results are immediately applicable to the design of robust, multiplexed platforms capable of transporting, sorting, and storing single cells in large arrays with low power and high efficiency.
Stratified Filtered Sampling in Stochastic Optimization John M. Mulvey
Mitchell, John E.
significant problems dictate the development of strategies for handling sequential decision-making under of a decision strategy returned by the MSO process is crucial to increasing the technology's effectiveness Abstract We develop a methodology for evaluating a decision strategy generated by a stochastic optimization
California at Santa Barbara, University of
Digital Filter Stepsize Control of DASPK and its Effect on Control Optimization Performance.1mod . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Halo Insertion DASPK3.1 . . . . . . . . . . . . . . . . . . . . . . 53 3.4 Halo Insertion DASPK3.1mod . . . . . . . . . . . . . . . . . . . . 57 3.5 Moon DASPK3
Experimental study on optimization of the working conditions of excited state Faraday filter
Liang Zhang; Junxiong Tang
1998-01-01
In this paper the existence of optimal frequency detuning in the pumping process of the excited state Faraday anomalous dispersion optical filter (ESFADOF, also referred as active FADOF) is reported. We measured this detuning and its variation versus cell temperature. Moreover, the dependence of the ESFADOF transmission on the cell temperature and pumping power was also studied experimentally. On the
Optimal filters for detecting cosmic bubble collisions J. D. McEwen,1,
McEwen, Jason
Optimal filters for detecting cosmic bubble collisions J. D. McEwen,1, S. M. Feeney,1, M. C such example is the signature of cosmic bubble collisions which arise in models of eternal inflation. The most of the global parameters defining the theory; however, a direct evaluation is computationally impractical
DATA ANALYSIS FOR THE RESONANT GRAVITATIONAL WAVE DETECTOR AURIGA: OPTIMAL FILTERING, # 2
## # DATA ANALYSIS FOR THE RESONANT GRAVITATIONAL WAVE DETECTOR AURIGA: OPTIMAL FILTERING, # 2 the possibilities in signal processing opened by the new fully numerical data analysis system developed reprocessing. Then we discuss some relevant points of the AURIGA data analysis system such as the data
DMT Bit Rate Maximization With Optimal Time Domain Equalizer Filter Bank Architecture
Evans, Brian L.
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
ANALYTICAL CALCULATION OF GRADIENTS FOR THE OPTIMIZATION OF HPLANE FILTERS WITH THE FEM
Bornemann, Jens
ANALYTICAL CALCULATION OF GRADIENTS FOR THE OPTIMIZATION OF HPLANE FILTERS WITH THE FEM P Abstract This paper introduces a method for the analytical calculation of gradients of a cost functions circumstances, the gradient of a cost function can be calculated analytically without using finite differences
Spectral Filter Optimization for the Recovery of Parameters Which Describe Human Skin
Claridge, Ela
Spectral Filter Optimization for the Recovery of Parameters Which Describe Human Skin Stephen J the error associated with histological parameters characterizing normal skin tissue. These parameters can be recovered from digital images of the skin using a physics-based model of skin coloration. The relationship
NASA Technical Reports Server (NTRS)
Zaychik, Kirill B.; Cardullo, Frank M.
2012-01-01
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.
A Neural Network-Based Optimal Spatial Filter Design Method for Motor Imagery Classification
Yuksel, Ayhan; Olmez, Tamer
2015-01-01
In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy. PMID:25933101
Boundary filters for vector particles passing parity breaking domains
Kolevatov, S. S.; Andrianov, A. A.
2014-07-23
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.
Image quality and dose optimization using novel x-ray source filters tailored to patient size
NASA Astrophysics Data System (ADS)
Toth, Thomas L.; Cesmeli, Erdogan; Ikhlef, Aziz; Horiuchi, Tetsuya
2005-04-01
The expanding set of CT clinical applications demands increased attention to obtaining the maximum image quality at the lowest possible dose. Pre-patient beam shaping filters provide an effective means to improve dose utilization. In this paper we develop and apply characterization methods that lead to a set of filters appropriately matched to the patient. We developed computer models to estimate image noise and a patient size adjusted CTDI dose. The noise model is based on polychromatic X-ray calculations. The dose model is empirically derived by fitting CTDI style dose measurements for a demographically representative set of phantom sizes and shapes with various beam shaping filters. The models were validated and used to determine the optimum IQ vs dose for a range of patient sizes. The models clearly show that an optimum beam shaping filter exists as a function of object diameter. Based on noise and dose alone, overall dose efficiency advantages of 50% were obtained by matching the filter shape to the size of the object. A set of patient matching filters are used in the GE LightSpeed VCT and Pro32 to provide a practical solution for optimum image quality at the lowest possible dose over the range of patient sizes and clinical applications. Moreover, these filters mark the beginning of personalized medicine where CT scanner image quality and radiation dose utilization is truly individualized and optimized to the patient being scanned.
Ultrafine particle emission from incinerators: the role of the fabric filter.
Buonanno, G; Scungio, M; Stabile, L; Tirler, W
2012-01-01
Incinerators are claimed to be responsible of particle and gaseous emissions: to this purpose Best Available Techniques (BAT) are used in the flue-gas treatment sections leading to pollutant emission lower than established threshold limit values. As regard particle emission, only a mass-based threshold limit is required by the regulatory authorities. However; in the last years the attention of medical experts moved from coarse and fine particles towards ultrafine particles (UFPs; diameter less than 0.1 microm), mainly emitted by combustion processes. According to toxicological and epidemiological studies, ultrafine particles could represent a risk for health and environment. Therefore, it is necessary to quantify particle emissions from incinerators also to perform an exposure assessment for the human populations living in their surrounding areas. A further topic to be stressed in the UFP emission from incinerators is the particle filtration efficiency as function of different flue-gas treatment sections. In fact, it could be somehow important to know which particle filtration method is able to assure high abatement efficiency also in terms of UFPs. To this purpose, in the present work experimental results in terms of ultrafine particle emissions from several incineration plants are reported. Experimental campaigns were carried out in the period 2007-2010 by measuring UFP number distributions and total concentrations at the stack of five plants through condensation particle counters and mobility particle sizer spectrometers. Average total particle number concentrations ranging from 0.4 x 10(3) to 6.0 x 10(3) particles cm(-3) were measured at the stack of the analyzed plants. Further experimental campaigns were performed to characterize particle levels before the fabric filters in two of the analyzed plants in order to deepen their particle reduction effect; particle concentrations higher than 1 x 10(7) particles cm(-3) were measured, leading to filtration efficiency greater than 99.99%. PMID:22393815
Constraining clumpy dusty torus models using optimized filter sets
Almeida, A Asensio Ramos \\and C Ramos
2012-01-01
Recent success in explaining several properties of the dusty torus around the central engine of active galactic nuclei has been gathered with the assumption of clumpiness. The properties of such clumpy dusty tori can be inferred by analyzing spectral energy distributions (SEDs), sometimes with scarce sampling given that large aperture telescopes and long integration times are needed to get good spatial resolution and signal. We aim at using the information already present in the data and the assumption of clumpy dusty torus, in particular, the CLUMPY models of Nenkova et al., to evaluate the optimum next observation such that we maximize the constraining power of the new observed photometric point. To this end, we use the existing and barely applied idea of Bayesian adaptive exploration, a mixture of Bayesian inference, prediction and decision theories. The result is that the new photometric filter to use is the one that maximizes the expected utility, which we approximate with the entropy of the predictive d...
Smith, D.H.; Powell, V.; Ibrahim, E.; Ferer, M.; Ahmadi, G.
1996-12-31
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.
Performance Evaluation of Particle Swarm Optimization Based Active Noise Control Algorithm
NASA Astrophysics Data System (ADS)
Rout, Nirmal Kumar; Das, Debi Prasad; Panda, Ganapati
Active noise control (ANC) has been used to control low-frequency acoustic noise. The ANC uses an adaptive filter algorithm and normally uses least mean square (LMS) algorithm. The gradient based LMS algorithm suffers from local minima problem. In this paper, particle swarm optimization (PSO) algorithm, which is a non-gradient but simple evolutionary computing type algorithm, is proposed for the ANC system. Detailed mathematical treatment is made and systematic computer simulation studies are carried out to evaluate the performance of the PSO based ANC algorithm.
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
NASA Astrophysics Data System (ADS)
Basu, Mousumi
2015-07-01
Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.
A hierarchical particle swarm optimizer and its adaptive variant.
Janson, Stefan; Middendorf, Martin
2005-12-01
A hierarchical version of the particle swarm optimization (PSO) metaheuristic is introduced in this paper. In the new method called H-PSO, the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure. Depending on the quality of their so-far best-found solution, the particles move up or down the hierarchy. This gives good particles that move up in the hierarchy a larger influence on the swarm. We introduce a variant of H-PSO, in which the shape of the hierarchy is dynamically adapted during the execution of the algorithm. Another variant is to assign different behavior to the individual particles with respect to their level in the hierarchy. H-PSO and its variants are tested on a commonly used set of optimization functions and are compared to PSO using different standard neighborhood schemes. PMID:16366251
NASA Astrophysics Data System (ADS)
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto optimality of the found solutions can be made. Identification of the leading particle traditionally requires a costly combination of ranking and niching techniques. In our approach, we use a decision rule under uncertainty to identify the currently leading particle of the swarm. In doing so, we consider the different objectives of our optimization problem as competing agents with partially conflicting interests. Analysis of the maximin fitness function allows for robust and cheap identification of the currently leading particle. The final optimization result comprises a set of possible models spread along the Pareto front. For convex Pareto fronts, solution density is expected to be maximal in the region ideally compromising all objectives, i.e. the region of highest curvature.
Support vector machine based on adaptive acceleration particle swarm optimization.
Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali
2014-01-01
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
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization
Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali
2014-01-01
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
Comparison of Kalman filter and optimal smoother estimates of spacecraft attitude
NASA Technical Reports Server (NTRS)
Sedlak, J.
1994-01-01
Given a valid system model and adequate observability, a Kalman filter will converge toward the true system state with error statistics given by the estimated error covariance matrix. The errors generally do not continue to decrease. Rather, a balance is reached between the gain of information from new measurements and the loss of information during propagation. The errors can be further reduced, however, by a second pass through the data with an optimal smoother. This algorithm obtains the optimally weighted average of forward and backward propagating Kalman filters. It roughly halves the error covariance by including future as well as past measurements in each estimate. This paper investigates whether such benefits actually accrue in the application of an optimal smoother to spacecraft attitude determination. Tests are performed both with actual spacecraft data from the Extreme Ultraviolet Explorer (EUVE) and with simulated data for which the true state vector and noise statistics are exactly known.
Optimization-based tuning of LPV fault detection filters for civil transport aircraft
NASA Astrophysics Data System (ADS)
Ossmann, D.; Varga, A.
2013-12-01
In this paper, a two-step optimal synthesis approach of robust fault detection (FD) filters for the model based diagnosis of sensor faults for an augmented civil aircraft is suggested. In the first step, a direct analytic synthesis of a linear parameter varying (LPV) FD filter is performed for the open-loop aircraft using an extension of the nullspace based synthesis method to LPV systems. In the second step, a multiobjective optimization problem is solved for the optimal tuning of the LPV detector parameters to ensure satisfactory FD performance for the augmented nonlinear closed-loop aircraft. Worst-case global search has been employed to assess the robustness of the fault detection system in the presence of aerodynamics uncertainties and estimation errors in the aircraft parameters. An application of the proposed method is presented for the detection of failures in the angle-of-attack sensor.
Decoupled Control Strategy of Grid Interactive Inverter System with Optimal LCL Filter Design
NASA Astrophysics Data System (ADS)
Babu, B. Chitti; Anurag, Anup; Sowmya, Tontepu; Marandi, Debati; Bal, Satarupa
2013-09-01
This article presents a control strategy for a three-phase grid interactive voltage source inverter that links a renewable energy source to the utility grid through a LCL-type filter. An optimized LCL-type filter has been designed and modeled so as to reduce the current harmonics in the grid, considering the conduction and switching losses at constant modulation index (Ma). The control strategy adopted here decouples the active and reactive power loops, thus achieving desirable performance with independent control of active and reactive power injected into the grid. The startup transients can also be controlled by the implementation of this proposed control strategy: in addition to this, optimal LCL filter with lesser conduction and switching copper losses as well as core losses. A trade-off has been made between the total losses in the LCL filter and the Total Harmonic Distortion (THD%) of the grid current, and the filter inductor has been designed accordingly. In order to study the dynamic performance of the system and to confirm the analytical results, the models are simulated in the MATLAB/Simulink environment, and the results are analyzed.
Optimizing spatial filters with kernel methods for BCI applications
NASA Astrophysics Data System (ADS)
Zhang, Jiacai; Tang, Jianjun; Yao, Li
2007-11-01
Brain Computer Interface (BCI) is a communication or control system in which the user's messages or commands do not depend on the brain's normal output channels. The key step of BCI technology is to find a reliable method to detect the particular brain signals, such as the alpha, beta and mu components in EEG/ECOG trials, and then translate it into usable control signals. In this paper, our objective is to introduce a novel approach that is able to extract the discriminative pattern from the non-stationary EEG signals based on the common spatial patterns(CSP) analysis combined with kernel methods. The basic idea of our Kernel CSP method is performing a nonlinear form of CSP by the use of kernel methods that can efficiently compute the common and distinct components in high dimensional feature spaces related to input space by some nonlinear map. The algorithm described here is tested off-line with dataset I from the BCI Competition 2005. Our experiments show that the spatial filters employed with kernel CSP can effectively extract discriminatory information from single-trial EGOG recorded during imagined movements. The high recognition of linear discriminative rates and computational simplicity of "Kernel Trick" make it a promising method for BCI systems.
Plebani, Carmela; Listrani, Stefano; Tranfo, Giovanna; Tombolini, Francesca
2012-01-01
Several studies show the increase of penetration through electrostatic filters during exposure to an aerosol flow, because of particle deposition on filter fibers. We studied the effect of increasing loads of paraffin oil aerosol on the penetration of selected particle sizes through an electrostatic filtering facepiece. FFP2 facepieces were exposed for 8 hr to a flow rate of 95.0 ± 0.5 L/min of polydisperse paraffin aerosol at 20.0 ± 0.5 mg/m(3). The penetration of bis(2-ethylhexyl)sebacate (DEHS) monodisperse neutralized aerosols, with selected particle size in the 0.03-0.40 ?m range, was measured immediately prior to the start of the paraffin aerosol loading and at 1, 4, and 8 hr after the start of paraffin aerosol loading. Penetration through isopropanol-treated facepieces not oil paraffin loaded was also measured to evaluate facepiece behavior when electrostatic capture mechanisms are practically absent. During exposure to paraffin aerosol, DEHS penetration gradually increased for all aerosol sizes, and the most penetrating particle size (0.05 ?m at the beginning of exposure) shifted slightly to larger diameters. After the isopropanol treatment, the higher penetration value was 0.30 ?m. In addition to an increased penetration during paraffin loading at a given particle size, the relative degree of increase was greater as the particle size increased. Penetration value measured after 8 hr for 0.03-?m particles was on average 1.6 times the initial value, whereas it was about 8 times for 0.40-?m particles. This behavior, as well evidenced in the measurements of isopropanol-treated facepieces, can be attributed to the increasing action in particle capture of the electrostatic forces (Coulomb and polarization), which depend strictly on the diameter and electrical charge of neutralized aerosol particles. With reference to electrostatic filtering facepieces as personal protective equipment, results suggest the importance of complying with the manufacturer instructions when it is specified that their use has to be restricted to a single shift. PMID:22862434
Design Optimization of Vena Cava Filters: An application to dual filtration devices
Singer, M A; Wang, S L; Diachin, D P
2009-12-03
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.
NASA Astrophysics Data System (ADS)
Dougherty, Edward R.; Loce, Robert P.
1993-04-01
The hit-or-miss operator is used as the building block of optimal binary restoration filters. Filter design methodologies are given for general-, and maximum-, and minimum-noise environments, the latter two producing optimal thinning and thickening filters, respectively, and for iterative filters. The approach is based on the expression of translation-invariant filters as unions of hit-or-miss transforms. Unions of hit-or-miss transforms are expressed as canonical logical sums of products, and the final hit-or-miss templates are obtained by logic reduction. The net effect is a morphological representation and estimation of the conditional expectation, which is the overall optimal mean-absolute-error filter.
Optimization of detector positioning in the radioactive particle tracking technique.
Dubé, Olivier; Dubé, David; Chaouki, Jamal; Bertrand, François
2014-07-01
The radioactive particle tracking (RPT) technique is a non-intrusive experimental velocimetry and tomography technique extensively applied to the study of hydrodynamics in a great variety of systems. In this technique, arrays of scintillation detector are used to track the motion of a single radioactive tracer particle emitting isotropic ?-rays. This work describes and applies an optimization strategy developed to find an optimal set of positions for the scintillation detectors used in the RPT technique. This strategy employs the overall resolution of the detectors as the objective function and a mesh adaptive direct search (MADS) algorithm to solve the optimization problem. More precisely, NOMAD, a C++ implementation of the MADS algorithm is used. First, the optimization strategy is validated using simple cases with known optimal detector configurations. Next, it is applied to a three-dimensional axisymmetric system (i.e. a vertical cylinder, which could represent a fluidized bed, bubble column, riser or else). The results obtained using the optimization strategy are in agreement with what was previously recommended by Roy et al. (2002) for a similar system. Finally, the optimization strategy is used for a system consisting of a partially filled cylindrical tumbler. The application of insights gained by the optimization strategy is shown to lead to a significant reduction in the error made when reconstructing the position of a tracer particle. The results of this work show that the optimization strategy developed is sensitive to both the type of objective function used and the experimental conditions. The limitations and drawbacks of the optimization strategy are also discussed. PMID:24607536
A thin-film bulk acoustic resonator and filter with optimal edge shapes for mass production.
Hara, Motoaki; Ueda, Masanori; Satoh, Yoshio
2013-01-01
The manufacturing conditions of a thin-film bulk acoustic resonator (FBAR) filter were investigated to obtain a high Q factor which is stable for mass production. The FBAR consist of patterned electrodes and piezoelectric films. In this study, the influence of edge shape of the films on the anti-resonance characteristics was investigated using a numerical method. Optimized shape was applied to a 2.5-GHz band resonator and filter. As a result, significant improvement of the Q factor and the insertion loss was confirmed. PMID:22609327
NASA Astrophysics Data System (ADS)
Baroncini, F.; Castelli, F.
2009-09-01
Data assimilation techniques based on Ensemble Filtering are widely regarded as the best approach in solving forecast and calibration problems in geophysics models. Often the implementation of statistical optimal techniques, like the Ensemble Kalman Filter, is unfeasible because of the large amount of replicas used in each time step of the model for updating the error covariance matrix. Therefore the sub optimal approach seems to be a more suitable choice. Various sub-optimal techniques were tested in atmospheric and oceanographic models, some of them are based on the detection of a "null space". Distributed Hydrologic Models differ from the other geo-fluid-dynamics models in some fundamental aspects that make complex to understanding the relative efficiency of the different suboptimal techniques. Those aspects include threshold processes , preferential trajectories for convection and diffusion, low observability of the main state variables and high parametric uncertainty. This research study is focused on such topics and explore them through some numerical experiments on an continuous hydrologic model, MOBIDIC. This model include both water mass balance and surface energy balance, so it's able to assimilate a wide variety of datasets like traditional hydrometric "on ground" measurements or land surface temperature retrieval from satellite. The experiments that we present concern to a basin of 700 kmq in center Italy, with hourly dataset on a 8 months period that includes both drought and flood events, in this first set of experiment we worked on a low spatial resolution version of the hydrologic model (3.2 km). A new Kalman Filter based algorithm is presented : this filter try to address the main challenges of hydrological modeling uncertainty. The proposed filter use in Forecast step a COFFEE (Complementary Orthogonal Filter For Efficient Ensembles) approach with a propagation of both deterministic and stochastic ensembles to improve robustness and convergence proprieties. After, through a P.O.D. Reduction from control theory, we compute a Reduced Order Forecast Covariance matrix . In analysis step the filter uses a LE (Local Ensemble) Kalman Filter approach. We modify the LE Kalman Filter assimilation scheme and we adapt its formulation to the P.O.D. Reduced sub-space propagated in forecast step. Through this, assimilation of observations is made only in the maximum covariance directions of the model error. Then the efficiency of this technique is weighed in term of hydrometric forecast accuracy in a preliminary convergence test of a synthetic rainfall event toward a real rain fall event.
NASA Technical Reports Server (NTRS)
Mashiku, Alinda; Garrison, James L.; Carpenter, J. Russell
2012-01-01
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.
Preparation and optimization of the laser thin film filter
NASA Astrophysics Data System (ADS)
Su, Jun-hong; Wang, Wei; Xu, Jun-qi; Cheng, Yao-jin; Wang, Tao
2014-08-01
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.
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models
Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237
Kang, B.S-J.; Johnson, E.K.; Rincon, J.
2002-09-19
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.
Multi-information fusion for human motion tracking by particle filter
NASA Astrophysics Data System (ADS)
Du, Ming; Guan, Ling
2005-07-01
Human motion analysis research, especially the human tracking part, remains a challenging task by far. The difficulties lie in several aspects: self-occlusion, high dimensionality of parameter space and the gap between high-level image understanding and low-level image features etc. In our work we use particle filter to track human movement from monocular video sequences with an articulated human body model. We fuse region, color and boundary information to build a robust measurement function. Among them, the boundary information represented by Fourier Descriptors (FD) sets up a new and effective connection between the estimated model parameters and the image likelihoods. Compared with the previously used boundary or contour cue, FD has many noticeable advantages. Moreover, we introduce an adaptive property into the particle filter for more robust state propagation and measurement updating. Our method is shown to work effectively in experiments.
Particle Filters for Real-Time Fault Detection in Planetary Rovers
NASA Technical Reports Server (NTRS)
Dearden, Richard; Clancy, Dan; Koga, Dennis (Technical Monitor)
2001-01-01
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.
Wang, Bo; Xiao, Xuan; Xia, Yuanqing; Fu, Mengyin
2013-01-01
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
NASA Technical Reports Server (NTRS)
Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel
2004-01-01
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.
Wang, Bo; Xiao, Xuan; Xia, Yuanqing; Fu, Mengyin
2013-01-01
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
Highest probability data association and particle filtering for target tracking in clutter
NASA Astrophysics Data System (ADS)
Song, Taek Lyul; Kim, Da Sol
2005-12-01
There proposed a new method of data association called highest probability data association (HPDA) combined with particle filtering and applied to passive sonar tracking in clutter. The HPDA method evaluated the probabilities of one-to-one assignments of measurement-to-track. All of the bearing measurements at the present sampling instance were lined up in the order of signal strength. The measurement with the highest probability was selected to be target-originated and the measurement was used for probabilistic weight update of particle filtering. The proposed HPDA algorithm can be easily extended to multi-target tracking problems. It can be used to avoid track coalescence phenomenon that prevails when several tracks move very close together.
Optimized FPGA Implementation of Multi-Rate FIR Filters Through Thread Decomposition
NASA Technical Reports Server (NTRS)
Zheng, Jason Xin; Nguyen, Kayla; He, Yutao
2010-01-01
Multirate (decimation/interpolation) filters are among the essential signal processing components in spaceborne instruments where Finite Impulse Response (FIR) filters are often used to minimize nonlinear group delay and finite-precision effects. Cascaded (multi-stage) designs of Multi-Rate FIR (MRFIR) filters are further used for large rate change ratio, in order to lower the required throughput while simultaneously achieving comparable or better performance than single-stage designs. Traditional representation and implementation of MRFIR employ polyphase decomposition of the original filter structure, whose main purpose is to compute only the needed output at the lowest possible sampling rate. In this paper, an alternative representation and implementation technique, called TD-MRFIR (Thread Decomposition MRFIR), is presented. The basic idea is to decompose MRFIR into output computational threads, in contrast to a structural decomposition of the original filter as done in the polyphase decomposition. Each thread represents an instance of the finite convolution required to produce a single output of the MRFIR. The filter is thus viewed as a finite collection of concurrent threads. The technical details of TD-MRFIR will be explained, first showing its applicability to the implementation of downsampling, upsampling, and resampling FIR filters, and then describing a general strategy to optimally allocate the number of filter taps. A particular FPGA design of multi-stage TD-MRFIR for the L-band radar of NASA's SMAP (Soil Moisture Active Passive) instrument is demonstrated; and its implementation results in several targeted FPGA devices are summarized in terms of the functional (bit width, fixed-point error) and performance (time closure, resource usage, and power estimation) parameters.
Yamada, Takahiro; Ohshiro, Masahiro; Kawada, Yasushi
2014-05-01
If high-energy beta particles are required for the efficiency determination of surface contamination monitors, wide area filtered (90)Sr/(90)Y sources are commonly used instead of short-lived pure (90)Y sources. In this work we show how the instrument efficiencies are dependent on filtering. Significant difference in the instrument efficiencies between filtered (90)Sr/(90)Y and pure (90)Y source was obtained due to spectral degradation of the beta emission spectrum. Changes in the ?-particle spectra were investigated by the use of a plastic scintillation spectrometer and the anisotropy of ?-particle fluences was examined to clarify the possible reason of such discrepancy. PMID:24369886
Advanced particle filter. Technical progress report No. 19, January 1995--March 1995
NONE
1995-08-01
Tidd advanced particle filtration (APF) test runs 25 through 34 were completed during the first quarter of 1995. All Tidd testing was completed with the conclusion of APF test run 34 on 3/30/95. The Westinghouse activities supporting the APF operation during this quarter included processing of test data and participating in one APF borescope inspection. Data is included on the filter operation.
Visual Tracking in High-Dimensional State Space by Appearance-Guided Particle Filtering
Wen-Yan Chang; Chu-Song Chen; Yong-Dian Jian
2008-01-01
In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adopts some known attractors in the state space and integrates both appearance and motion-transition information for visual tracking. A probability propagation model based on these two types of information is derived from a Bayesian formulation, and a
Jaeschke, B C; Lind, O C; Bradshaw, C; Salbu, B
2015-01-01
Radioactive particles are aggregates of radioactive atoms that may contain significant activity concentrations. They have been released into the environment from nuclear weapons tests, and from accidents and effluents associated with the nuclear fuel cycle. Aquatic filter-feeders can capture and potentially retain radioactive particles, which could then provide concentrated doses to nearby tissues. This study experimentally investigated the retention and effects of radioactive particles in the blue mussel, Mytilus edulis. Spent fuel particles originating from the Dounreay nuclear establishment, and collected in the field, comprised a U and Al alloy containing fission products such as (137)Cs and (90)Sr/(90)Y. Particles were introduced into mussels in suspension with plankton-food or through implantation in the extrapallial cavity. Of the particles introduced with food, 37% were retained for 70 h, and were found on the siphon or gills, with the notable exception of one particle that was ingested and found in the stomach. Particles not retained seemed to have been actively rejected and expelled by the mussels. The largest and most radioactive particle (estimated dose rate 3.18 ± 0.06 Gyh(-1)) induced a significant increase in Comet tail-DNA %. In one case this particle caused a large white mark (suggesting necrosis) in the mantle tissue with a simultaneous increase in micronucleus frequency observed in the haemolymph collected from the muscle, implying that non-targeted effects of radiation were induced by radiation from the retained particle. White marks found in the tissue were attributed to ionising radiation and physical irritation. The results indicate that current methods used for risk assessment, based upon the absorbed dose equivalent limit and estimating the "no-effect dose" are inadequate for radioactive particle exposures. Knowledge is lacking about the ecological implications of radioactive particles released into the environment, for example potential recycling within a population, or trophic transfer in the food chain. PMID:25240099
Robust dead reckoning system for mobile robots based on particle filter and raw range scan.
Duan, Zhuohua; Cai, Zixing; Min, Huaqing
2014-01-01
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
Sound speed estimation and source localization with linearization and particle filtering.
Lin, Tao; Michalopoulou, Zoi-Heleni
2014-03-01
A method is developed for the estimation of source location and sound speed in the water column relying on linearization. The Jacobian matrix, necessary for the proposed linearization approach, includes derivatives with respect to empirical orthogonal function coefficients instead of sound speed directly. First, the inversion technique is tested on synthetic arrival times, using Gaussian distributions for the errors in the considered arrival times. The approach is efficient, requiring a few iterations, and produces accurate results. Probability densities of the estimates are calculated for different levels of noise in the arrival times. Subsequently, particle filtering is employed for the estimation of arrival times from signals recorded during the Shallow Water 06 experiment. It has been shown in the past that particle filtering can be employed for the successful estimation of multipath arrival times from short-range data and, consequently, in geometry, bathymetry, and sound speed inversion. Here probability density functions of arrival times computed via particle filtering are propagated backward through the proposed inversion process. Inversion estimates are consistent with values reported in the literature for the same quantities. Last it is shown that results are consistent with estimates resulting from fast simulated annealing applied to the same data. PMID:24606255
Fitting complex population models by combining particle filters with Markov chain Monte Carlo.
Knape, Jonas; de Valpine, Perry
2012-02-01
We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm. PMID:22624307
Robust Dead Reckoning System for Mobile Robots Based on Particle Filter and Raw Range Scan
Duan, Zhuohua; Cai, Zixing; Min, Huaqing
2014-01-01
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
Particles in swimming pool filters--does pH determine the DBP formation?
Hansen, Kamilla M S; Willach, Sarah; Mosbæk, Hans; Andersen, Henrik R
2012-04-01
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
NASA Astrophysics Data System (ADS)
Dumedah, Gift; Coulibaly, Paulin
2013-10-01
Data assimilation (DA) has facilitated the design and application of hydrological forecasting systems. DA methods such as the ensemble Kalman filter (EnKF) and the particle filter (PF) remain popular in the hydrological literature. But a comparative evaluation of these methods to alternative techniques like the evolutionary based data assimilation (EDA) has not been thoroughly conducted. Evolutionary algorithms have been widely applied in parameter estimation and it appears natural that its application in DA be compared to standard methods, particularly, to evaluate forecasting performance of these methods. This type of evaluation is important for the design of forecasting systems and has implications for real-time forecasting operations. This study has applied the Sacramento Soil Moisture Accounting (SAC-SMA) model in the Spencer Creek catchment in southern Ontario, Canada to evaluate the performance of three DA methods. The methods assimilate streamflow into SAC-SMA, where the updated ensemble members are in turn applied to forecast streamflow for up to 30-day lead time after which they were compared to observation and open-loop estimates. The results showed that the increasing order of performance at assimilation stage and forecasting for short lead times of 10-day is the EnKF, the PF and the EDA. For longer lead times, the PF performs best and is preferable when forecasting for lead times beyond 10-day. The EnKF and the PF evolve members once between assimilation time steps whereas the EDA evolves members multiple times to improve parameter convergence. The high performance of the EDA illustrates that the dynamics of large ensemble members can be encapsulated into a small continuously evolved population and that these members have high assimilation and forecasting capability.
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm
Marco Antonio Montes de Oca; Thomas Stützle; Mauro Birattari; Marco Dorigo
2009-01-01
During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical
UNIFIED PARTICLE SWARM OPTIMIZATION FOR TACKLING OPERATIONS RESEARCH PROBLEMS
Parsopoulos, Konstantinos
of the variables are real, will be considered in future works. Particle Swarm Optimization (PSO) has proved as a unified PSO scheme that com- bines the exploration and exploitation properties of differ- ent PSO variants of UPSO against the standard PSO variants [7, 8]. We investigate the performance of UPSO on minimax
Particle Swarm Optimization Method in Multiobjective K.E. Parsopoulos
Parsopoulos, Konstantinos
(PSO) method in Mu|tiobjective Optimi~.a- tion (MO) problems. The ability of PSO to detect Pareto Algorithms are adapted to the PSO framework in order to develop a multi--swarm PSO that can cope effectively of the search space [20]. The Particle Swarm Optimization (PSO) is a Swarm Intel- ligence method that models
Optimization of Particle-in-Cell Codes on RISC Processors
NASA Technical Reports Server (NTRS)
Decyk, Viktor K.; Karmesin, Steve Roy; Boer, Aeint de; Liewer, Paulette C.
1996-01-01
General strategies are developed to optimize particle-cell-codes written in Fortran for RISC processors which are commonly used on massively parallel computers. These strategies include data reorganization to improve cache utilization and code reorganization to improve efficiency of arithmetic pipelines.
Complex Stock Trading Strategy Based on Particle Swarm Optimization
Cheung, David Wai-lok
Complex Stock Trading Strategy Based on Particle Swarm Optimization Fei Wang, Philip L.H. Yu and David W. Cheung Abstract-- Trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock
Optimization of adenovirus 40 and 41 recovery from tap water using small disk filters.
McMinn, Brian R
2013-11-01
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
Optimizing binary phase and amplitude filters for PCE, SNR, and discrimination
NASA Technical Reports Server (NTRS)
Downie, John D.
1992-01-01
Binary phase-only filters (BPOFs) have generated much study because of their implementation on currently available spatial light modulator devices. On polarization-rotating devices such as the magneto-optic spatial light modulator (SLM), it is also possible to encode binary amplitude information into two SLM transmission states, in addition to the binary phase information. This is done by varying the rotation angle of the polarization analyzer following the SLM in the optical train. Through this parameter, a continuum of filters may be designed that span the space of binary phase and amplitude filters (BPAFs) between BPOFs and binary amplitude filters. In this study, we investigate the design of optimal BPAFs for the key correlation characteristics of peak sharpness (through the peak-to-correlation energy (PCE) metric), signal-to-noise ratio (SNR), and discrimination between in-class and out-of-class images. We present simulation results illustrating improvements obtained over conventional BPOFs, and trade-offs between the different performance criteria in terms of the filter design parameter.
Experimental study on optimization of the working conditions of excited state Faraday filter
NASA Astrophysics Data System (ADS)
Zhang, Liang; Tang, Junxiong
1998-07-01
In this paper the existence of optimal frequency detuning in the pumping process of the excited state Faraday anomalous dispersion optical filter (ESFADOF, also referred as active FADOF) is reported. We measured this detuning and its variation versus cell temperature. Moreover, the dependence of the ESFADOF transmission on the cell temperature and pumping power was also studied experimentally. On the basis of these results, the transmission of rubidium 775.9 nm ESFADOF was raised by more than two orders of magnitude under optimized working conditions.
Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization
NASA Technical Reports Server (NTRS)
Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.
1998-01-01
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.
Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization
NASA Technical Reports Server (NTRS)
Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.
1999-01-01
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.
PCDD/F formation in an iron/potassium-catalyzed diesel particle filter.
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
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
Optimal spectral filtering in soliton self-frequency shift for deep-tissue multiphoton microscopy.
Wang, Ke; Qiu, Ping
2015-05-01
Tunable optical solitons generated by soliton self-frequency shift (SSFS) have become valuable tools for multiphoton microscopy (MPM). Recent progress in MPM using 1700 nm excitation enabled visualizing subcortical structures in mouse brain in vivo for the first time. Such an excitation source can be readily obtained by SSFS in a large effective-mode-area photonic crystal rod with a 1550-nm fiber femtosecond laser. A longpass filter was typically used to isolate the soliton from the residual in order to avoid excessive energy deposit on the sample, which ultimately leads to optical damage. However, since the soliton was not cleanly separated from the residual, the criterion for choosing the optimal filtering wavelength is lacking. Here, we propose maximizing the ratio between the multiphoton signal and the n'th power of the excitation pulse energy as a criterion for optimal spectral filtering in SSFS when the soliton shows dramatic overlapping with the residual. This optimization is based on the most efficient signal generation and entirely depends on physical quantities that can be easily measured experimentally. Its application to MPM may reduce tissue damage, while maintaining high signal levels for efficient deep penetration. PMID:25950644
Asynchronous Digenetic Particle Swarm Optimization for Global and Sustainable Search
NASA Astrophysics Data System (ADS)
Ishii, Yoshinao; Okamoto, Takashi; Aiyoshi, Eitaro
Particle Swarm Optimization (PSO), which has attracted attention as a global optimization method in recent years, has a drawback in that sustainable search cannot be performed until the end of computation due to its strong convergence trend. In this paper, in order to realize a sustainable search in PSO, the improved PSO using concepts of particle ages and digenesis is proposed. In the new PSO, parameters in the update formula are degenerated and a stagnant particle is erased if it loses activity, and then a new search point in which large parameter values are assigned. In addition, information regarding the elite point of all searching points until the current time is reflected to new points in next generation. The effectiveness of the improved method is confirmed through applications to benchmark problems.
Research of spatial high-pass filtering algorithm in particles real-time measurement system
NASA Astrophysics Data System (ADS)
Jin, Xuanhong; Dai, Shuguang; Mu, Pingan
2010-08-01
With the application development of CIMS, enterprises have the more need of the CAQ systems during the process of flexibility and automation. Based the means of computer-based vision technology, Automated Visual Inspection (AVI) is a non-contact measurement mean synthesizing the technologies such as image processing, precision measurement. The particles real-time measurement system is the system which analyzes the target image obtained by the computer vision system and gets the useful measure information. In accordance with existing prior knowledge, the user can timely take some measures to reduce the floating ash. According to the analysis of the particle images, this paper researches the image high-pass filter means, Gradient arithmetic, with characteristics of images. In order to get rid of the interference of background and enhance the edge lines of particles, it uses the two directions kernel to process the images. This Spatial high-pass filtering algorithm also helps to conduct the ensuing image processing to obtain useful information of floating ash particles.
Simulation of particles and gas flow behavior in a riser using a filtered two-fluid model
Wang Shuai; Xu Pengfei; Lu Huilin; Yang Yunchao; Yin Lijie; Wang Jiaxing
2011-01-01
Flow behavior of gas and particles is predicted by a filtered two-fluid model by taking into the effect of particle clustering on the interphase momentum-transfer account. The filtered gas–solid two-fluid model is proposed on the basis of the kinetic theory of granular flow. The subgrid closures for the solid pressure and drag coefficient (Andrews et al., 2005) and the solid
Kornelakis, Aris [Technical University of Crete, Department of Electronic and Computer Engineering, Chania (Greece)
2010-12-15
Particle Swarm Optimization (PSO) is a highly efficient evolutionary optimization algorithm. In this paper a multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic grid-connected systems (PVGCSs) is presented. The proposed methodology intends to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized. The objective function describing the economic benefit of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The second objective function, which corresponds to the environmental benefit, equals to the pollutant gas emissions avoided due to the use of the PVGCS. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. (author)
A study of nonlinear filters with particle flow induced by log-homotopy
NASA Astrophysics Data System (ADS)
Chen, Lingji; Mehra, Raman K.
2010-04-01
In this paper, a study of the particle flow filter proposed by Daum and Huang has been conducted. It is discovered that for certain initial conditions, the desired particle flow that brings one particle from a good location in the prior distribution to a good location in the posterior distribution with an equal value does not exist. This explains the phenomenon of outliers experienced by Daum and Huang. Several ways of dealing with the singularity of the gradient have been discussed, including (1) not moving the particles without a flow solution, (2) stopping the flow entirely when it approaches the singularity, and (3) stopping for one step and starting in the next. In each case the resulting set of particles are examined, and it is doubtful that they form a valid set of samples for the approximation of the desired posterior distribution. In the case of the last method (stop and go), the particles mostly concentrate on the mode of the desired distribution (but they fail to represent the whole distribution), which may explain the "success" reported in the literature so far. An established method of moving particles, the well known Population Monte Carlo method, is briefly presented in this paper for ease of reference.
A particle-inspired Monte Carlo tree estimation method in Bayesian filtering
NASA Astrophysics Data System (ADS)
Wu, Hong; Li, Dehua; Li, Qingguang; Shen, Hui
2013-10-01
A particle-inspired Monte Carlo tree estimation method is proposed to avoid repeating similar simulation and handle the depletion problem in particle filter. Under the inspiration of particles, the method divides the state-space recursively in a top-down manner to form a tree structure that each node in the tree is corresponding to a sub-space. Particles are allocated to the corresponding terminal node during the procedure. Certain size of minimal sub-space or piece is specified to terminate the dividing. Each piece is corresponding to a leaf-node of the tree structure and the prediction probability density in it is approximated by the proportion of its particles in total particles. Instead of importance sampling for each particle, the method takes uniformly random measurements to compute the posterior probability density in each piece. As a result, the method is applied to growth model and has better performance in high SNR environments compared with the Sampling Importance Resampling method.
Particle filtering for arrival time tracking in space and source localization.
Michalopoulou, Zoi-Heleni; Jain, Rashi
2012-11-01
Locating and tracking a source in an ocean environment and estimating environmental parameters of a sound propagation medium are critical tasks in ocean acoustics. Many approaches for both are based on full field calculations which are computationally intensive and sensitive to assumptions on the structure of the environment. Alternative methods that use only select features of the acoustic field for localization and environmental parameter estimation have been proposed. The focus of this paper is the development of a method that extracts arrival times and amplitudes of distinct paths from measured acoustic time-series using sequential Bayesian filtering, namely, particle filtering. These quantities, along with complete posterior probability density functions, also extracted by filtering, are employed in source localization and bathymetry estimation. Aspects of the filtering methodology are presented and studied in terms of their impact on the uncertainty in the arrival time estimates. Using the posterior probability densities of arrival times, source localization and water depth estimation are performed for the Haro Strait Primer experiment; the results are compared to those of conventional methods. The comparison demonstrates a significant advantage in the proposed approach. PMID:23145590
Liu, Yang
2012-07-16
amounts of skews. The implementation of bandpass filtering on forwarded-clock path is able to control the JTB through the controlling of Q. This work introduces a method using bandpass filtering to optimize the JTB in high-speed forwarded...
Condat, Laurent
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. X, NO. XX, 2010 1 A New Color Filter Array with Optimal of the features provided in digital cameras. The heart of a digital still or video camera is its sensor, a 2-D
Sadaghzadeh N, Nargess; Poshtan, Javad; Wagner, Achim; Nordheimer, Eugen; Badreddin, Essameddin
2014-03-01
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
Multi-Swarm Particle Swarm Optimization with an Adaptive Type Selection for Restarting Particles
NASA Astrophysics Data System (ADS)
Tatsumi, Keiji; Yukami, Takashi; Tanino, Tetsuzo
The particle swarm optimization method (PSO) is one of popular metaheuristic methods for global optimization. Although the PSO is simple and shows a good performance of finding a desirable solution, it is reported that almost all particles sometimes converge to an undesirable local minimum for some problems. Thus, many kinds of improved methods have been proposed to keep the diversity of the search process. In this paper, we propose a novel multi-type swarm PSO which uses two kinds of particles and multiple swarms including either kind of particles. All particles in each swarm search for solutions independently where the exchange of information between different swarms is restricted for the extensive exploration. In addition, the proposed model has the restarting system of particles which initializes a particle with a sufficiently small velocity by resetting its velocity and position, and adaptively selects the kind of the particle according to which kind of particles contribute to improvement of the objective function value. Furthermore, through some numerical experiments, we verify the abilities of the proposed model.
Characterization of ultrafine coal fly ash particles by energy-filtered TEM.
Chen, Y; Shah, N; Huggins, F E; Huffman, G P; Dozier, A
2005-03-01
In this study, energy-filtered transmission electron microscopy is demonstrated to be a valuable tool for characterizing ultrafine coal fly ash particles, especially those particles encapsulated in or associated with carbon. By examining a series of elemental maps (K-edge maps of C and O, and L-edge maps of Si, Al, Ti and Fe) recorded using the three-window method, considerable numbers of titanium and iron species with sizes from several nanometres to submicrometre were shown to be present, typically as oxides dispersed in the carbonaceous matrix. Crystalline phases, such as rutile and iron-rich oxide spinel, were also identified from electron diffraction patterns and high-resolution TEM images. Information about these ultrafine coal fly ash particles regarding their size, morphology, elemental composition and distribution, and crystalline phases, which has not been available previously in conventional ash studies, should be useful in toxicological studies and related environmental fields. PMID:15725126
Planar straightness error evaluation based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Mao, Jian; Zheng, Huawen; Cao, Yanlong; Yang, Jiangxin
2006-11-01
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.
Miller, Travis Reed
2010-01-01
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. ...
GREGORY A. STEVENS; ERNEST S. MOYER
1989-01-01
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
Multivariable optimization of liquid rocket engines using particle swarm algorithms
NASA Astrophysics Data System (ADS)
Jones, Daniel Ray
Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.
Schery, Stephen D., Wasiolek, Piotr; Rodgers, John
1999-06-01
Improvement in understanding the deposition of ambient dust particles on ECAM (environmental continuous air monitor) filters, reduction of the alpha-particle interference of radon progeny and other radioactive aerosols in different particle size ranges on filters, and development of ECAMs with increased sensitivity under dusty outdoor conditions.
Jae Hong Park; Ki Young Yoon; Hyungjoo Na; Yang Seon Kim; Jungho Hwang; Jongbaeg Kim; Young Hun Yoon
2011-01-01
We grew multi-walled carbon nanotubes (MWCNTs) on a glass fiber air filter using thermal chemical vapor deposition (CVD) after the filter was catalytically activated with a spark discharge. After the CNT deposition, filtration and antibacterial tests were performed with the filters. Potassium chloride (KCl) particles (<1?m) were used as the test aerosol particles, and their number concentration was measured using
Representation of Probability Density Functions from Orbit Determination using the Particle Filter
NASA Technical Reports Server (NTRS)
Mashiku, Alinda K.; Garrison, James; Carpenter, J. Russell
2012-01-01
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.
NASA Astrophysics Data System (ADS)
Huang, Haibin; Zhuang, Yufei
2015-08-01
This paper proposes a method that plans energy-optimal trajectories for multi-satellite formation reconfiguration in deep space environment. A novel co-evolutionary particle swarm optimization algorithm is stated to solve the nonlinear programming problem, so that the computational complexity of calculating the gradient information could be avoided. One swarm represents one satellite, and through communication with other swarms during the evolution, collisions between satellites can be avoided. In addition, a dynamic depth first search algorithm is proposed to solve the redundant search problem of a co-evolutionary particle swarm optimization method, with which the computation time can be shorten a lot. In order to make the actual trajectories optimal and collision-free with disturbance, a re-planning strategy is deduced for formation reconfiguration maneuver.
Optimal Pid Tuning for Power System Stabilizers Using Adaptive Particle Swarm Optimization Technique
NASA Astrophysics Data System (ADS)
Oonsivilai, Anant; Marungsri, Boonruang
2008-10-01
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.
NASA Astrophysics Data System (ADS)
Banerjee, Pradipta K.; Datta, Asit K.
2013-09-01
An optimized preferential digital-optical correlator is proposed for multi-class face recognition, where false rejection rate and false acceptance rate are improved by incorporating the information preferentially of both intra-class and other class face images for a given database during the synthesis of the correlator filter. Class compactness of both types of classes is made which makes the system more robust to distortion tolerance as well as misclassification. The optimization of trade-off parameters for both constrained and unconstrained type is carried out by particle swarm technique where particle vectors are considered as the correlator parameters to be optimized. Results on standard face databases have established that the performance of proposed correlator is better and more robust than the other existing classes of correlation filters. A digital-optical hybrid hardware architecture is developed and the performance for fast multi-class face recognition is compared with another existing hardware. The main contribution of the paper is related to the selection of optimum filter parameters and hardware testing in the two digital-optical hybrid architectures for multi-class face recognition.
EntropyBased Memetic Particle Swarm Optimization for Computing Periodic Orbits of Nonlinear Mappings
Parsopoulos, Konstantinos
evolutionary) global component. Recently, Memetic Particle Swarm Optimization (MPSO) variants that combine PSOEntropyBased Memetic Particle Swarm Optimization for Computing Periodic Orbits of Nonlinear. Evolutionary algorithms have shown to be an efficient alternative for the computation of periodic orbits
Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing
2015-01-01
An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate. PMID:26064085
State to State and Charged Particle Kinetic Modeling of Time Filtering and Cs Addition
Capitelli, M.; Gorse, C.; Longo, S.; Diomede, P.; Pagano, D.
2007-08-10
We present here an account on the progress of kinetic simulation of non equilibrium plasmas in conditions of interest for negative ion production by using the 1D Bari code for hydrogen plasma simulation. The model includes the state to state kinetics of the vibrational level population of hydrogen molecules, plus a PIC/MCC module for the multispecies dynamics of charged particles. In particular we present new results for the modeling of two issues of great interest: the time filtering and the Cs addition via surface coverage.
Zhuang, Ziqing; Bergman, Michael S; Eimer, Benjamin C; Shaffer, Ronald E
2013-01-01
National Institute for Occupational Safety and Health (NIOSH)-certified N95 filtering facepiece respirators (FFRs) are used for respiratory protection in some workplaces handling engineered nanomaterials. Previous NIOSH research has focused on filtration performance against nanoparticles. This article is the first NIOSH study using human test subjects to compare N95 FFR faceseal leakage (FSL) performance against nanoparticles and "all size" particles. In this study, estimates of FSL were obtained from fit factor (FF) measurements from nine test subjects who participated in previous fit-test studies. These data were analyzed to compare values obtained by: 1) using the PortaCount Plus (8020A, TSI, Inc., MN, USA) alone (measureable particle size range 20 nm to > 1,000 nm, hereby referred to as the "all size particles test"), and 2) using the PortaCount Plus with N95-Companion(TM) accessory (8095, TSI, Inc., Minn.) accessory (negatively charged particles, size range ?40 to 60 nm, hereby referred to as the "nanoparticles test"). Log-transformed FF values were compared for the "all size particles test" and "nanoparticles test" using one-way analysis of variance tests (significant at P < 0.05). For individual FFR models, geometric mean (GM) FF using the "nanoparticles test" was the same or higher than the GM FFs using "all size particles test." For all three FFR models combined, GM FF using the "nanoparticles test" was significantly higher than the GM FF using "all size particles test" (P < 0.05). These data suggest that FSL for negatively charged ?40-60 nm nanoparticles is not greater than the FSL for the larger distribution of charged and uncharged 20 to > 1,000 nm particles. PMID:23927376
Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN
Yangmin Li; Xin Chen
2005-01-01
\\u000a This paper presents a novel design for mobile robot using particle swarm optimization (PSO) and adaptive NN control. The adaptive\\u000a NN control strategy guarantees that robot with nonholonomic constraints can follow smooth trajectories. Based on this property,\\u000a a PSO algorithm for path planning is proposed. The path planning generates smooth path with low computational cost to avoid\\u000a obstacles, so that
Djuriæ, Petar M.
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 56, NO. 6, JUNE 2008 2229 Target Tracking by Particle, and Mónica F. Bugallo, Member, IEEE Abstract--We present particle filtering algorithms for tracking a single for the target movement in the sensor field and estimates the target's trajectory, velocity, and power using
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...
Optimal control of switched linear systems based on Migrant Particle Swarm Optimization algorithm
NASA Astrophysics Data System (ADS)
Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng
2009-10-01
The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
Optimal estimation of diffusion coefficients from single-particle trajectories
NASA Astrophysics Data System (ADS)
Vestergaard, Christian L.; Blainey, Paul C.; Flyvbjerg, Henrik
2014-02-01
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.
Parallel global optimization with the particle swarm algorithm.
Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D
2004-12-01
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
Wenbo Zhang; Xiaoguang Zhang; Qingyue Di; Lixia Xi
2010-01-01
In this paper, we presented a new hybrid particle swarm optimization algorithm which is used to design the control unit of an adaptive polarization mode dispersion compensation. Comparing with the conventional particle swarm optimization algorithm, the new algorithm converges to the global optima much faster than the conventional particle swarm optimization algorithm. Experiments show that the new algorithm is easy
Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine
Chenglin Zhao; Xuebin Sun; Songlin Sun; Ting Jiang
2011-01-01
Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients
Pal, Sankar Kumar
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients June 2007; accepted 23 June 2007 Abstract In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle
PARTICLE SWARM OPTIMIZATION FOR TIME-DIFFERENCE-OF-ARRIVAL BASED LOCALIZATION
So, Hing-Cheung
PARTICLE SWARM OPTIMIZATION FOR TIME-DIFFERENCE-OF-ARRIVAL BASED LOCALIZATION Kenneth W. K. Lui and broadly applied in many fields. In this paper, particle swarm optimization (PSO) is employed localization, particle swarm optimization 1. INTRODUCTION Time-difference-of-arrival (TDOA) based source
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...
Nanodosimetry-Based Plan Optimization for Particle Therapy.
Casiraghi, Margherita; Schulte, Reinhard W
2015-01-01
Treatment planning for particle therapy is currently an active field of research due uncertainty in how to modify physical dose in order to create a uniform biological dose response in the target. A novel treatment plan optimization strategy based on measurable nanodosimetric quantities rather than biophysical models is proposed in this work. Simplified proton and carbon treatment plans were simulated in a water phantom to investigate the optimization feasibility. Track structures of the mixed radiation field produced at different depths in the target volume were simulated with Geant4-DNA and nanodosimetric descriptors were calculated. The fluences of the treatment field pencil beams were optimized in order to create a mixed field with equal nanodosimetric descriptors at each of the multiple positions in spread-out particle Bragg peaks. For both proton and carbon ion plans, a uniform spatial distribution of nanodosimetric descriptors could be obtained by optimizing opposing-field but not single-field plans. The results obtained indicate that uniform nanodosimetrically weighted plans, which may also be radiobiologically uniform, can be obtained with this approach. Future investigations need to demonstrate that this approach is also feasible for more complicated beam arrangements and that it leads to biologically uniform response in tumor cells and tissues. PMID:26167202
3D Head Pose Tracking Using a Particle Filter with Nose Template Matching
NASA Astrophysics Data System (ADS)
Kubota, Hitoshi; Takeshi, Masami; Saito, Hideo
In this paper, we propose a real-time tracking method for driver's head pose in real vehicle environment by using multiple NIR cameras. In order to achieve real-time performance and high-accuracy, 6-DOF of head pose is estimated by a particle filter with restricted state space. Firstly, the 3D position of nose hole is measured by template matching of stereo images. Because nose hole is the most dark area in the captured NIR images, we can robustly detect the position of the nose very precisely. From the 3D position of the nose, we can estimate the head pose as an initial estimate. Then, each hypothesis is updated by prior probability with a constraint of nose position. Thus it can reduce the number of particles with maintained accuracy. The experimental results indicate that this method is effective for head pose tracking.
Han, Shuxin; Yue, Qinyan; Yue, Min; Gao, Baoyu; Li, Qian; Yu, Hui; Zhao, Yaqin; Qi, Yuanfeng
2009-11-15
Novel filter media-sludge-fly ash ceramic particles (SFCP) were prepared using dewatered sludge, fly ash and clay with a mass ratio of 1:1:1. Compared with commercial ceramic particles (CCP), SFCP had higher total porosity, larger total surface area and lower bulk and apparent density. Tests of heavy metal elements in lixivium proved that SFCP were safe for wastewater treatment. A lab-scale upflow anaerobic bioreactor was employed to ascertain the application of SFCP in denitrification process using acetate as carbon source. The results showed that SFCP reactor brought a relative superiority to CCP reactor in terms of total nitrogen (TN) removal at the optimum C/N ratio of 4.03 when volumetric loading rates (VLR) ranged from 0.33 to 3.69 kg TN (m(3)d)(-1). Therefore, SFCP application, as a novel process of treating wastes with wastes, provided a promising way in sludge and fly ash utilization. PMID:19608336
NASA Astrophysics Data System (ADS)
Mao, Jiandong; Li, Jinxuan
2015-08-01
Particle size distribution is essential for describing direct and indirect radiation of aerosols. Because the relationship between the aerosol size distribution and optical thickness (AOT) is an ill-posed Fredholm integral equation of the first type, the traditional techniques for determining such size distributions, such as the Phillips-Twomey regularization method, are often ambiguous. Here, we use an approach based on an improved particle swarm optimization algorithm (IPSO) to retrieve aerosol size distribution. Using AOT data measured by a CE318 sun photometer in Yinchuan, we compared the aerosol size distributions retrieved using a simple genetic algorithm, a basic particle swarm optimization algorithm and the IPSO. Aerosol size distributions for different weather conditions were analyzed, including sunny, dusty and hazy conditions. Our results show that the IPSO-based inversion method retrieved aerosol size distributions under all weather conditions, showing great potential for similar size distribution inversions.
Hybrid Control Method for Optimal Transient Response and Output Filter Minimization for Buck energy transfer converters (buck or forward). In this case, the time-optimal response always results transfer systems, such as a boost, buck-boost, or flyback converters, this is not the case. Here, the time
Nonlinear dynamic analysis of an optimal particle damper
Martín Sánchez; C. Manuel Carlevaro
2011-10-12
We study the dynamical behavior of a single degree of freedom mechanical system with a particle damper. The particle (granular) damping was optimized for the primary system operating condition by using an appropriate gap size for a prismatic enclosure. The particles absorb the kinetic energy of the vibrating structure and convert it into heat through the inelastic collisions and friction. This results in a highly nonlinear mechanical system. Considering linear signal analysis, state space reconstruction, Poincar\\'e sections and the determination of maximal Lyapunov exponents, the motion of the granular system inside the enclosure is characterized for a wide frequency range. With the excitation frequency as control parameter, either regular and chaotic motion of the granular bed are found and their influence on the damping is analyzed.
Heeb, Norbert V; Rey, Maria Dolores; Zennegg, Markus; Haag, Regula; Wichser, Adrian; Schmid, Peter; Seiler, Cornelia; Honegger, Peter; Zeyer, Kerstin; Mohn, Joachim; Bürki, Samuel; Zimmerli, Yan; Czerwinski, Jan; Mayer, Andreas
2015-08-01
Iron-catalyzed diesel particle filters (DPFs) are widely used for particle abatement. Active catalyst particles, so-called fuel-borne catalysts (FBCs), are formed in situ, in the engine, when combusting precursors, which were premixed with the fuel. The obtained iron oxide particles catalyze soot oxidation in filters. Iron-catalyzed DPFs are considered as safe with respect to their potential to form polychlorinated dibenzodioxins/furans (PCDD/Fs). We reported that a bimetallic potassium/iron FBC supported an intense PCDD/F formation in a DPF. Here, we discuss the impact of fatty acid methyl ester (FAME) biofuel on PCDD/F emissions. The iron-catalyzed DPF indeed supported a PCDD/F formation with biofuel but remained inactive with petroleum-derived diesel fuel. PCDD/F emissions (I-TEQ) increased 23-fold when comparing biofuel and diesel data. Emissions of 2,3,7,8-TCDD, the most toxic congener [toxicity equivalence factor (TEF) = 1.0], increased 90-fold, and those of 2,3,7,8-TCDF (TEF = 0.1) increased 170-fold. Congener patterns also changed, indicating a preferential formation of tetra- and penta-chlorodibenzofurans. Thus, an inactive iron-catalyzed DPF becomes active, supporting a PCDD/F formation, when operated with biofuel containing impurities of potassium. Alkali metals are inherent constituents of biofuels. According to the current European Union (EU) legislation, levels of 5 ?g/g are accepted. We conclude that risks for a secondary PCDD/F formation in iron-catalyzed DPFs increase when combusting potassium-containing biofuels. PMID:26176879
Streamflow data assimilation for the mesoscale hydrologic model (mHM) using particle filtering
NASA Astrophysics Data System (ADS)
Noh, Seong Jin; Rakovec, Oldrich; Kumar, Rohini; Samaniego, Luis; Choi, Shin-woo
2015-04-01
Data assimilation has been becoming popular to increase the certainty of the hydrologic prediction considering various sources of uncertainty through the hydrologic modeling chain. In this study, we develop a data assimilation framework for the mesoscale hydrologic model (mHM 5.2, http://www.ufz.de/mhm) using particle filtering, which is a sequential DA method for non-linear and non-Gaussian models. The mHM is a grid based distributed model that is based on numerical approximations of dominant hydrologic processes having similarity with the HBV and VIC models. The developed DA framework for the mHM represents simulation uncertainty by model ensembles and updates spatial distributions of model state variables when new observations are available in each updating time interval. The evaluation of the proposed method is carried out within several large European basins via assimilating multiple streamflow measurements in a daily interval. Dimensional limitations of particle filtering is resolved by effective noise specification methods, which uses spatial and temporal correlation of weather forcing data to represent model structural uncertainty. The presentation will be focused on gains and limitations of streamflow data assimilation in several hindcasting experiments. In addition, impacts of non-Gaussian distributions of state variables on model performance will be discussed.