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.
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.
Incorporating advanced language models into the P300 speller using particle filtering
NASA Astrophysics Data System (ADS)
Speier, W.; Arnold, C. W.; Deshpande, A.; Knall, J.; Pouratian, N.
2015-08-01
Objective. The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject’s electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. Approach. Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. Main result. This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. Significance. These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.
Incorporating advanced language models into the P300 speller using particle filtering
Speier, W; Arnold, CW; Deshpande, A; Knall, J
2015-01-01
Objective The P300 speller is a common brain–computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject’s electroencephalogram (EEG) signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. Approach Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. Main Result This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. Significance These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance. PMID:26061188
NASA Astrophysics Data System (ADS)
Stevens, Mark R.; Gutchess, Dan; Checka, Neal; Snorrason, Magnús
2006-05-01
Image exploitation algorithms for Intelligence, Surveillance and Reconnaissance (ISR) and weapon systems are extremely sensitive to differences between the operating conditions (OCs) under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. As an example, terrain type is an important OC for the problem of tracking hostile vehicles from an airborne camera. A system designed to track cars driving on highways and on major city streets would probably not do well in the EOC of parking lots because of the very different dynamics. In this paper, we present a system we call ALPS for Adaptive Learning in Particle Systems. ALPS takes as input a sequence of video images and produces labeled tracks. The system detects moving targets and tracks those targets across multiple frames using a multiple hypothesis tracker (MHT) tightly coupled with a particle filter. This tracker exploits the strengths of traditional MHT based tracking algorithms by directly incorporating tree-based hypothesis considerations into the particle filter update and resampling steps. We demonstrate results in a parking lot domain tracking objects through occlusions and object interactions.
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
ADVANCED HOT GAS FILTER DEVELOPMENT
E.S. Connolly; G.D. Forsythe
1998-12-22
Advanced, coal-based power plants will require durable and reliable hot gas filtration systems to remove particulate contaminants from the gas streams to protect downstream components such as turbine blades from erosion damage. It is expected that the filter elements in these systems will have to be made of ceramic materials to withstand goal service temperatures of 1600 F or higher. Recent demonstration projects and pilot plant tests have indicated that the current generation of ceramic hot gas filters (cross-flow and candle configurations) are failing prematurely. Two of the most promising materials that have been extensively evaluated are clay-bonded silicon carbide and alumina-mullite porous monoliths. These candidates, however, have been found to suffer progressive thermal shock fatigue damage, as a result of rapid cooling/heating cycles. Such temperature changes occur when the hot filters are back-pulsed with cooler gas to clean them, or in process upset conditions, where even larger gas temperature changes may occur quickly and unpredictably. In addition, the clay-bonded silicon carbide materials are susceptible to chemical attack of the glassy binder phase that holds the SiC particles together, resulting in softening, strength loss, creep, and eventual failure.
Multilevel Ensemble Transform Particle Filtering
NASA Astrophysics Data System (ADS)
Gregory, Alastair; Cotter, Colin; Reich, Sebastian
2016-04-01
This presentation extends the Multilevel Monte Carlo variance reduction technique to nonlinear filtering. In particular, Multilevel Monte Carlo is applied to a certain variant of the particle filter, the Ensemble Transform Particle Filter (ETPF). A key aspect is the use of optimal transport methods to re-establish correlation between coarse and fine ensembles after resampling; this controls the variance of the estimator. Numerical examples present a proof of concept of the effectiveness of the proposed method, demonstrating significant computational cost reductions (relative to the single-level ETPF counterpart) in the propagation of ensembles.
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
Advances in Collaborative Filtering
NASA Astrophysics Data System (ADS)
Koren, Yehuda; Bell, Robert
The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.
Advanced simulation of digital filters
NASA Astrophysics Data System (ADS)
Doyle, G. S.
1980-09-01
An Advanced Simulation of Digital Filters has been implemented on the IBM 360/67 computer utilizing Tektronix hardware and software. The program package is appropriate for use by persons beginning their study of digital signal processing or for filter analysis. The ASDF programs provide the user with an interactive method by which filter pole and zero locations can be manipulated. Graphical output on both the Tektronix graphics screen and the Versatec plotter are provided to observe the effects of pole-zero movement.
ADVANCED HOT GAS FILTER DEVELOPMENT
E.S. Connolly; G.D. Forsythe
2000-09-30
DuPont Lanxide Composites, Inc. undertook a sixty-month program, under DOE Contract DEAC21-94MC31214, in order to develop hot gas candle filters from a patented material technology know as PRD-66. The goal of this program was to extend the development of this material as a filter element and fully assess the capability of this technology to meet the needs of Pressurized Fluidized Bed Combustion (PFBC) and Integrated Gasification Combined Cycle (IGCC) power generation systems at commercial scale. The principal objective of Task 3 was to build on the initial PRD-66 filter development, optimize its structure, and evaluate basic material properties relevant to the hot gas filter application. Initially, this consisted of an evaluation of an advanced filament-wound core structure that had been designed to produce an effective bulk filter underneath the barrier filter formed by the outer membrane. The basic material properties to be evaluated (as established by the DOE/METC materials working group) would include mechanical, thermal, and fracture toughness parameters for both new and used material, for the purpose of building a material database consistent with what is being done for the alternative candle filter systems. Task 3 was later expanded to include analysis of PRD-66 candle filters, which had been exposed to actual PFBC conditions, development of an improved membrane, and installation of equipment necessary for the processing of a modified composition. Task 4 would address essential technical issues involving the scale-up of PRD-66 candle filter manufacturing from prototype production to commercial scale manufacturing. The focus would be on capacity (as it affects the ability to deliver commercial order quantities), process specification (as it affects yields, quality, and costs), and manufacturing systems (e.g. QA/QC, materials handling, parts flow, and cost data acquisition). Any filters fabricated during this task would be used for product qualification tests
Not Available
2007-03-01
Presents the results of a 2,000-hour test of an emissions control system consisting of a nitrogen oxides adsorber catalyst in combination with a diesel particle filter, advanced fuels, and advanced engine controls in an SUV/pick-up truck vehicle platform.
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.
Research on improved mechanism for particle filter
NASA Astrophysics Data System (ADS)
Yu, Jinxia; Xu, Jingmin; Tang, Yongli; Zhao, Qian
2013-03-01
Based on the analysis of particle filter algorithm, two improved mechanism are studied so as to improve the performance of particle filter. Firstly, hybrid proposal distribution with annealing parameter is studied in order to use current information of the latest observed measurement to optimize particle filter. Then, resampling step in particle filter is improved by two methods which are based on partial stratified resampling (PSR). One is that it uses the optimal idea to improve the weights after implementing PSR, and the other is that it uses the optimal idea to improve the weights before implementing PSR and uses adaptive mutation operation for all particles so as to assure the diversity of particle sets after PSR. At last, the simulations based on single object tracking are implemented, and the performances of the improved mechanism for particle filter are estimated.
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.
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.
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.
Advanced Filter Technology For Nuclear Thermal Propulsion
NASA Technical Reports Server (NTRS)
Castillon, Erick
2015-01-01
The Scrubber System focuses on using HEPA filters and carbon filtration to purify the exhaust of a Nuclear Thermal Propulsion engine of its aerosols and radioactive particles; however, new technology may lend itself to alternate filtration options, which may lead to reduction in cost while at the same time have the same filtering, if not greater, filtering capabilities, as its predecessors. Extensive research on various types of filtration methods was conducted with only four showing real promise: ionization, cyclonic separation, classic filtration, and host molecules. With the four methods defined, more research was needed to find the devices suitable for each method. Each filtration option was matched with a device: cyclonic separators for the method of the same name, electrostatic separators for ionization, HEGA filters, and carcerands for the host molecule method. Through many hours of research, the best alternative for aerosol filtration was determined to be the electrostatic precipitator because of its high durability against flow rate and its ability to cleanse up to 99.99% of contaminants as small as 0.001 micron. Carcerands, which are the only alternative to filtering radioactive particles, were found to be non-existent commercially because of their status as a "work in progress" at research institutions. Nevertheless, the conclusions after the research were that HEPA filters is recommended as the best option for filtering aerosols and carbon filtration is best for filtering radioactive particles.
ADVANCED HOT GAS FILTER DEVELOPMENT
RICHARD A. WAGNER
1998-09-04
This report describes the fabrication and testing of continuous fiber ceramic composite (CFCC) based hot gas filters. The fabrication approach utilized a modified filament winding method that combined both continuous and chopped fibers into a novel microstructure. The work was divided into five primary tasks. In the first task, a preliminary set of compositions was fabricated in the form of open end tubes and characterized. The results of this task were used to identify the most promising compositions for sub-scale filter element fabrication and testing. In addition to laboratory measurements of permeability and strength, exposure testing in a coal combustion environment was performed to asses the thermo-chemical stability of the CFCC materials. Four candidate compositions were fabricated into sub-scale filter elements with integral flange and a closed end. Following the 250 hour exposure test in a circulating fluid bed combustor, the retained strength ranged from 70 t 145 percent of the as-fabricated strength. The post-test samples exhibited non-catastrophic failure behavior in contrast to the brittle failure exhibited by monolithic materials. Filter fabrication development continued in a filter improvement and cost reduction task that resulted in an improved fiber architecture, the production of a net shape flange, and an improved low cost bond. These modifications were incorporated into the process and used to fabricate 50 full-sized filter elements for testing in demonstration facilities in Karhula, Finland and at the Power Systems Development Facility (PSDF) in Wilsonville, AL. After 581 hours of testing in the Karhula facility, the elements retained approximately 87 percent of their as-fabricated strength. In addition, mechanical response testing at Virginia Tech provided a further demonstration of the high level of strain tolerance of the vacuum wound filter elements. Additional testing in the M. W. Kellogg unit at the PSDF has accumulated over 1800 hours of
ADVANCED HOT GAS FILTER DEVELOPMENT
Matthew R. June; John L. Hurley; Mark W. Johnson
1999-04-01
Iron aluminide hot gas filters have been developed using powder metallurgy techniques to form seamless cylinders. Three alloys were short-term corrosion tested in simulated IGCC atmospheres with temperatures between 925 F and 1200 F with hydrogen sulfide concentrations ranging from 783 ppm{sub v} to 78,300 ppm{sub v}. Long-term testing was conducted for 1500 hours at 925 F with 78,300 ppm{sub v}. The FAS and FAL alloys were found to be corrosion resistant in the simulated environments. The FAS alloy has been commercialized.
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
Applications of moving granular-bed filters to advanced systems
Wilson, K.W.; Haas, J.C.; Eshelman, M.B.
1993-09-01
The contract is arranged as a base contract with three options. The objective of the base contract is to develop conceptual design(s) of moving granular bed filter and ceramic candle filter technology for control of particles from integrated gasification combined cycle (IGCC) systems, pressurized fluidized-bed combustors (PFBC), and direct coal fueled turbine (DCFT) environments. The conceptual design(s) of these filter technologies are compared, primarily from an economic perspective. The granular bed filter was developed through low pressure, high temperature (1600{degree}F) testing in the late 1970`s and early 1980`s. Collection efficiencies over 99% were obtained. In 1988, high pressure, high temperature testing was completed at New York University, Westbury, N.Y., utilizing a two advanced power generating plants were chosen for developing conceptual designs and cost estimates of the commercial sized filters. One is the 450 MWe, second generation pressurized fluidized bed combustion plant defined by Foster Wheeler. This plant originally included cross-flow filters for hot gas cleanup. The other plant under study is a 100 MWe, KRW air blown gasifier. A cross-flow filter was utilized for gas stream cleanup in this study also. Granular bed and ceramic candle filters were substituted for the cross-flow filters in both these plants, and the resulting costs were compared.
A comparison of EAKF and particle filter: towards a ensemble adjustment Kalman particle filter
NASA Astrophysics Data System (ADS)
Zhang, Xiangming; Shen, Zheqi; Tang, Youmin
2016-04-01
Bayesian estimation theory provides a general approach for the state estimate. In this study, we first explore two Bayesian-based methods: ensemble adjustment Kalman filter (EAKF) and sequential importance resampling particle filter (SIR-PF), using a well-known nonlinear and non-Gaussian model (Lorenz '63 model). The EAKF can be regarded as a deterministic scheme of the ensemble Kalman filter (EnKF), which performs better than the classical (stochastic) EnKF in a general framework. Comparison between the SIR-PF and the EAKF reveals that the former outperforms the latter if ensemble size is very large that can avoid the filter degeneracy, and vice versa. On the basis of comparisons between the SIR-PF and the EAKF, a mixture filter, called ensemble adjustment Kalman particle filter (EAKPF), is proposed to combine their both merits. Similar to the ensemble Kalman particle filter, which combines the stochastic EnKF and SIR-PF analysis schemes with a tuning parameter, the new mixture filter essentially provides a continuous interpolation between the EAKF and SIR-PF. The same Lorenz '63 model is used as a testbed, showing that the EAKPF is able to overcome filter degeneracy while maintaining the non-Gaussian nature, and performs better than the EAKF given limited ensemble size.
ADVANCED SECOND GENERATION CERAMIC CANDLE FILTERS
M.A. Alvin
2002-01-31
Through sponsorship from the Department of Energy's National Energy Technology Laboratory (DOE/NETL), development and manufacture of advanced second generation candle filters was undertaken in the early 1990's. Efforts were primarily focused on the manufacture of fracture toughened, 1.5 m, continuous fiber ceramic composite (CFCC) and filament wound candle filters by 3M, McDermott, DuPont Lanxide Composites, and Techniweave. In order to demonstrate long-term thermal, chemical, and mechanical stability of the advanced second generation candle filter materials, Siemens Westinghouse initiated high temperature, bench-scale, corrosion testing of 3M's CVI-SiC and DuPont's PRD-66 mini-candles, and DuPont's CFCC SiC-SiC and IF&P Fibrosic{sup TM} coupons under simulated, pressurized fluidized-bed combustion (PFBC) conditions. This effort was followed by an evaluation of the mechanical and filtration performance of the advanced second generation filter elements in Siemens Westinghouse's bench-scale PFBC test facility in Pittsburgh, Pennsylvania. Arrays of 1.4-1.5 m 3M CVI-SiC, DuPont PRD-66, DuPont SiC-SiC, and IF&P Fibrosic{sup TM} candles were subjected to steady state process operating conditions, increased severity thermal transients, and accelerated pulse cycling test campaigns which represented {approx}1760 hours of equivalent filter operating life. Siemens Westinghouse subsequently participated in early material surveillance programs which marked entry of the 3M CVI-SiC and DuPont PRD-66 candle filters in Siemens Westinghouse Advanced Particulate Filtration (APF) system at the American Electric Power (AEP) Tidd Demonstration Plant in Brilliant, Ohio. Siemens Westinghouse then conducted an extended, accelerated life, qualification program, evaluating the performance of the 3M, McDermott, and Techniweave oxide-based CFCC filter elements, modified DuPont PRD-66 elements, and the Blasch, Scapa Cerafil{sup TM}, and Specific Surface monolithic candles for use in the APF
Factored interval particle filtering for gait analysis.
Saboune, Jamal; Rose, Cédric; Charpillet, François
2007-01-01
Commercial gait analysis systems rely on wearable sensors. The goal of this study is to develop a low cost marker less human motion capture tool. Our method is based on the estimation of 3d movements using video streams and the projection of a 3d human body model. Dynamic parameters only depend on human body movement constraints. No trained gait model is used which makes this approach generic. The 3d model is characterized by the angular positions of its articulations. The kinematic chain structure allows to factor the state vector representing the configuration of the model. We use a dynamic bayesian network and a modified particle filtering algorithm to estimate the most likely state configuration given an observation sequence. The modified algorithm takes advantage of the factorization of the state vector for efficiently weighting and resampling the particles. PMID:18002684
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.
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
Particle flow for nonlinear filters with log-homotopy
NASA Astrophysics Data System (ADS)
Daum, Fred; Huang, Jim
2008-04-01
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 that are smooth and fully coupled (i.e. dense not sparse). The new filter implements Bayes' rule using particle flow rather than with a pointwise multiplication of two functions; this avoids one of the fundamental and well known problems in particle filters, namely "particle collapse" as a result of Bayes' rule. We use a log-homotopy to derive the ODE that describes particle flow. This paper was written for normal engineers, who do not have homotopy for breakfast.
Online maintaining appearance model using particle filter
NASA Astrophysics Data System (ADS)
Chen, Siying; Lan, Tian; Wang, Jianyu; Ni, Guoqiang
2008-03-01
Tracking by foreground matching heavily depends on the appearance model to establish object correspondences among frames and essentially, the appearance model should encode both the difference part between the object and background to guarantee the robustness and the stable part to ensure tracking consistency. This paper provides a solution for online maintaining appearance models by adjusting features in the model. Object appearance is co-modeled by a subset of Haar features selected from the over-complete feature dictionary which encodes the discriminative part of object appearance and the color histogram which describes the stable appearance. During the particle filtering process, feature values both from background patches and object observations are sampled efficiently by the aid of "foreground" and "background" particles respectively. Based on these sampled values, top-ranked discriminative features are added and invalid features are removed out to ensure the object being distinguishable from current background according to the evolving appearance model. The tracker based on this online appearance model maintaining technique has been tested on people and car tracking tasks and promising experimental results are obtained.
A backtracking algorithm that deals with particle filter degeneracy
NASA Astrophysics Data System (ADS)
Baarsma, Rein; Schmitz, Oliver; Karssenberg, Derek
2016-04-01
Particle filters are an excellent way to deal with stochastic models incorporating Bayesian data assimilation. While they are computationally demanding, the particle filter has no problem with nonlinearity and it accepts non-Gaussian observational data. In the geoscientific field it is this computational demand that creates a problem, since dynamic grid-based models are often already quite computationally demanding. As such it is of the utmost importance to keep the amount of samples in the filter as small as possible. Small sample populations often lead to filter degeneracy however, especially in models with high stochastic forcing. Filter degeneracy renders the sample population useless, as the population is no longer statistically informative. We have created an algorithm in an existing data assimilation framework that reacts to and deals with filter degeneracy based on Spiller et al. [2008]. During the Bayesian updating step of the standard particle filter, the algorithm tests the sample population for filter degeneracy. If filter degeneracy has occurred, the algorithm resets to the last time the filter did work correctly and recalculates the failed timespan of the filter with an increased sample population. The sample population is then reduced to its original size and the particle filter continues as normal. This algorithm was created in the PCRaster Python framework, an open source tool that enables spatio-temporal forward modelling in Python [Karssenberg et al., 2010] . The framework already contains several data assimilation algorithms, including a standard particle filter and a Kalman filter. The backtracking particle filter algorithm has been added to the framework, which will make it easy to implement in other research. The performance of the backtracking particle filter is tested against a standard particle filter using two models. The first is a simple nonlinear point model, and the second is a more complex geophysical model. The main testing
Bayesian auxiliary particle filters for estimating neural tuning parameters.
Mountney, John; Sobel, Marc; Obeid, Iyad
2009-01-01
A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled here as a Poisson process, and the biological driving signal. The Bayesian auxiliary particle filter was evaluated by simultaneously tracking the three parameters of a hippocampal place cell and compared to a stochastic state point process filter. It is shown that Bayesian auxiliary particle filters are substantially more accurate and robust than alternative methods of state parameter estimation. The effects of time-averaging on parameter estimation are also evaluated. PMID:19963911
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
Tractable particle filters for robot fault diagnosis
NASA Astrophysics Data System (ADS)
Verma, Vandi
Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainly or use linear approximations of nonlinear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions. Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Classical Monte Carlo methods, such as particle filters, suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults. The thesis presents a set of complementary algorithms that provide an approach for computationally tractable fault diagnosis. These algorithms leverage probabilistic approaches to decision theory and information theory to efficiently track a large number of faults in a general dynamic system with noisy measurements. The problem of fault diagnosis is represented as hybrid (discrete/continuous) state estimation. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.
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
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
Advanced astronomical interference filters from SCHOTT technology
NASA Astrophysics Data System (ADS)
Hull, Anthony B.; Reichel, Steffen; Brauneck, Ulf; Bourquin, Sebastien; Marin-Franch, Antoni
2016-01-01
Developing precision interference filters for astronomical radiometry often requires simultaneous solutions to very difficult requirements. SCHOTT's 80-year legacy methods with interference filters and 9,200-m2 facility dedicated to filters and optical fabrication bring multiple disciplines together to simultaneously solve requirements that include: narrow-band high-transmission, steep-edge bandpasses, extremely high out-of-band rejection across Si response, sizes accommodating large fields-of-view, precision mechanical filter assemblies and both spectral uniformity and excellent transmitted wavefront across the field. We discuss solutions as satisfied for Spain's state-of-the-art new fast LOCAL UNIVERSE 3° wide-field survey telescope.
Gao, Shuang; Kim, Jinyong; Yermakov, Michael; Elmashae, Yousef; He, Xinjian; Reponen, Tiina; Grinshpun, Sergey A
2015-01-01
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
Simultaneous Eye Tracking and Blink Detection with Interactive Particle Filters
NASA Astrophysics Data System (ADS)
Wu, Junwen; Trivedi, Mohan M.
2007-12-01
We present a system that simultaneously tracks eyes and detects eye blinks. Two interactive particle filters are used for this purpose, one for the closed eyes and the other one for the open eyes. Each particle filter is used to track the eye locations as well as the scales of the eye subjects. The set of particles that gives higher confidence is defined as the primary set and the other one is defined as the secondary set. The eye location is estimated by the primary particle filter, and whether the eye status is open or closed is also decided by the label of the primary particle filter. When a new frame comes, the secondary particle filter is reinitialized according to the estimates from the primary particle filter. We use autoregression models for describing the state transition and a classification-based model for measuring the observation. Tensor subspace analysis is used for feature extraction which is followed by a logistic regression model to give the posterior estimation. The performance is carefully evaluated from two aspects: the blink detection rate and the tracking accuracy. The blink detection rate is evaluated using videos from varying scenarios, and the tracking accuracy is given by comparing with the benchmark data obtained using the Vicon motion capturing system. The setup for obtaining benchmark data for tracking accuracy evaluation is presented and experimental results are shown. Extensive experimental evaluations validate the capability of the algorithm.
Ballistic target tracking algorithm based on improved particle filtering
NASA Astrophysics Data System (ADS)
Ning, Xiao-lei; Chen, Zhan-qi; Li, Xiao-yang
2015-10-01
Tracking ballistic re-entry target is a typical nonlinear filtering problem. In order to track the ballistic re-entry target in the nonlinear and non-Gaussian complex environment, a novel chaos map particle filter (CMPF) is used to estimate the target state. CMPF has better performance in application to estimate the state and parameter of nonlinear and non-Gassuian system. The Monte Carlo simulation results show that, this method can effectively solve particle degeneracy and particle impoverishment problem by improving the efficiency of particle sampling to obtain the better particles to part in estimation. Meanwhile CMPF can improve the state estimation precision and convergence velocity compared with EKF, UKF and the ordinary particle filter.
Method of concurrently filtering particles and collecting gases
Mitchell, Mark A; Meike, Annemarie; Anderson, Brian L
2015-04-28
A system for concurrently filtering particles and collecting gases. Materials are be added (e.g., via coating the ceramic substrate, use of loose powder(s), or other means) to a HEPA filter (ceramic, metal, or otherwise) to collect gases (e.g., radioactive gases such as iodine). The gases could be radioactive, hazardous, or valuable gases.
Resampling Algorithms for Particle Filters: A Computational Complexity Perspective
NASA Astrophysics Data System (ADS)
Bolić, Miodrag; Djurić, Petar M.; Hong, Sangjin
2004-12-01
Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues such as decreasing the number of operations and memory access. Moreover, the algorithms allow for use of higher sampling frequencies by overlapping in time the resampling step with the other particle filtering steps. Since resampling is not dependent on any particular application, the analysis is appropriate for all types of particle filters that use resampling. The performance of the algorithms is evaluated on particle filters applied to bearings-only tracking and joint detection and estimation in wireless communications. We have demonstrated that the proposed algorithms reduce the complexity without performance degradation.
Particle filter-based prognostics: Review, discussion and perspectives
NASA Astrophysics Data System (ADS)
Jouin, Marine; Gouriveau, Rafael; Hissel, Daniel; Péra, Marie-Cécile; Zerhouni, Noureddine
2016-05-01
Particle filters are of great concern in a large variety of engineering fields such as robotics, statistics or automatics. Recently, it has developed among Prognostics and Health Management (PHM) applications for diagnostics and prognostics. According to some authors, it has ever become a state-of-the-art technique for prognostics. Nowadays, around 50 papers dealing with prognostics based on particle filters can be found in the literature. However, no comprehensive review has been proposed on the subject until now. This paper aims at analyzing the way particle filters are used in that context. The development of the tool in the prognostics' field is discussed before entering the details of its practical use and implementation. Current issues are identified, analyzed and some solutions or work trails are proposed. All this aims at highlighting future perspectives as well as helping new users to start with particle filters in the goal of prognostics.
Advanced optical interference filters based on metal and dielectric layers.
Begou, Thomas; Lemarchand, Fabien; Lumeau, Julien
2016-09-01
In this paper, we investigate the design and the fabrication of an advanced optical interference filter based on metal and dielectric layers. This filter respects the specifications of the 2016 OIC manufacturing problem contest. We study and present all the challenges and solutions that allowed achieving a low deviation between the fabricated prototype and the target. PMID:27607695
Cross flow filter development for advanced fossil power generation
Lippert, T.E.; Alvin, M.A.; Bachovchin, D.M.; Haldipur, G.B.; Newby, R.A.; Smeltzer, E.E. )
1990-01-01
The porous ceramic cross flow filter has been under development at Westinghouse in conjunction with the U.S. Department of Energy, Morgantown Energy Technology Center (DOE/METC) for advanced fossil power generation. The ceramic cross flow filter is capable of high temperature operation, and is basically an absolute filter on ash. The cross flow filter can be operated at high flow capacity, while simultaneously exhibiting relatively low pressure drop flow characteristics. This paper describes the cross flow filter development at Westinghouse, and reviews the results of many in-house and field test programs. Testing has included operation of the filter in subpilot pressurized fluidized-bed combustion and coal gasification applications. Testing is also being conducted at Westinghouse to evaluate filter characteristics over long-term operation (3,000 hours) utilizing dedicated test facilities.
Fracture behavior of advanced ceramic hot gas filters: Final report
Singh, J.P.; Majumdar, S.; Sutaria, M.; Bielke, W.
1997-03-01
This report presents the results of mechanical/microstructural evaluation, thermal shock/fatigue testing, and stress analyses of advanced hot-gas filters obtained from different manufacturers. These filters were fabricated from both monolithic ceramics and composites. The composite filters, made of both oxide and nonoxide materials, were in both as-fabricated and exposed conditions, whereas the monolithic filters were made only of nonoxide materials. Mechanical property measurement of composite filters included diametral compression testing with O-ring specimens and burst-testing of short filter segments with rubber plugs. In-situ strength of fibers in the composite filters was evaluated by microscopic technique. Thermal shock/fatigue resistance was estimated by measuring the strengths of filter specimens before and after thermal cycling from an air environment at elevated temperatures to a room temperature oil bath. Filter performance during mechanical and thermal shock/fatigue loadings was correlated with microstructural observations. Micromechanical models were developed to derive properties of composite filter constituents on the basis of measured mechanical properties of the filters. Subsequently, these properties were used to analytically predict the performance of composite filters during thermal shock loading.
An optimization-based parallel particle filter for multitarget tracking
NASA Astrophysics Data System (ADS)
Sutharsan, S.; Sinha, A.; Kirubarajan, T.; Farooq, M.
2005-09-01
Particle filter based estimation is becoming more popular because it has the capability to effectively solve nonlinear and non-Gaussian estimation problems. However, the particle filter has high computational requirements and the problem becomes even more challenging in the case of multitarget tracking. In order to perform data association and estimation jointly, typically an augmented state vector of target dynamics is used. As the number of targets increases, the computation required for each particle increases exponentially. Thus, parallelization is a possibility in order to achieve the real time feasibility in large-scale multitarget tracking applications. In this paper, we present a real-time feasible scheduling algorithm that minimizes the total computation time for the bus connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected processors. Furthermore, we propose a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration is ensured. In this paper, we present the mathematical formulations for scheduling the particles as well as for particle migration via load balancing. Simulation results show the tracking performance of our parallel particle filter and the speedup achieved using parallelization.
Buyel, Johannes F.; Gruchow, Hannah M.; Fischer, Rainer
2015-01-01
The clarification of biological feed stocks during the production of biopharmaceutical proteins is challenging when large quantities of particles must be removed, e.g., when processing crude plant extracts. Single-use depth filters are often preferred for clarification because they are simple to integrate and have a good safety profile. However, the combination of filter layers must be optimized in terms of nominal retention ratings to account for the unique particle size distribution in each feed stock. We have recently shown that predictive models can facilitate filter screening and the selection of appropriate filter layers. Here we expand our previous study by testing several filters with different retention ratings. The filters typically contain diatomite to facilitate the removal of fine particles. However, diatomite can interfere with the recovery of large biopharmaceutical molecules such as virus-like particles and aggregated proteins. Therefore, we also tested filtration devices composed solely of cellulose fibers and cohesive resin. The capacities of both filter types varied from 10 to 50 L m−2 when challenged with tobacco leaf extracts, but the filtrate turbidity was ~500-fold lower (~3.5 NTU) when diatomite filters were used. We also tested pre–coat filtration with dispersed diatomite, which achieved capacities of up to 120 L m−2 with turbidities of ~100 NTU using bulk plant extracts, and in contrast to the other depth filters did not require an upstream bag filter. Single pre-coat filtration devices can thus replace combinations of bag and depth filters to simplify the processing of plant extracts, potentially saving on time, labor and consumables. The protein concentrations of TSP, DsRed and antibody 2G12 were not affected by pre-coat filtration, indicating its general applicability during the manufacture of plant-derived biopharmaceutical proteins. PMID:26734037
Algorithmic and architectural optimizations for computationally efficient particle filtering.
Sankaranarayanan, Aswin C; Srivastava, Ankur; Chellappa, Rama
2008-05-01
In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speed-up of the algorithm using the methodology proposed in the paper. PMID:18390378
Clever particle filters, sequential importance sampling and the optimal proposal
NASA Astrophysics Data System (ADS)
Snyder, Chris
2014-05-01
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. Both these schemes employ proposal distributions at time tk+1 that depend on the state at tk and the observations at time tk+1. I show that, beginning with particles drawn randomly from the conditional distribution of the state at tk given observations through tk, the optimal proposal (the distribution of the state at tk+1 given the state at tk and the observations at tk+1) minimizes the variance of the importance weights for particles at tk overall all possible proposal distributions. This means that bounds on the performance of the optimal proposal, such as those given by Snyder (2011), also bound the performance of the implicit and equivalent-weights particle filters. In particular, in spite of the fact that they may be dramatically more effective than other particle filters in specific instances, those schemes will suffer degeneracy (maximum importance weight approaching unity) unless the ensemble size is exponentially large in a quantity that, in the simplest case that all degrees of freedom in the system are i.i.d., is proportional to the system dimension. I will also discuss the behavior to be expected in more general cases, such as global numerical weather prediction, and how that behavior depends qualitatively on the observing network. Snyder, C., 2012: Particle filters, the "optimal" proposal and high-dimensional systems. Proceedings, ECMWF Seminar on Data Assimilation for Atmosphere and Ocean., 6-9 September 2011.
Particle-filter-based phase estimation in digital holographic interferometry.
Waghmare, Rahul G; Ram Sukumar, P; Subrahmanyam, G R K S; Singh, Rakesh Kumar; Mishra, Deepak
2016-03-01
In this paper, we propose a particle-filter-based technique for the analysis of a reconstructed interference field. The particle filter and its variants are well proven as tracking filters in non-Gaussian and nonlinear situations. We propose to apply the particle filter for direct estimation of phase and its derivatives from digital holographic interferometric fringes via a signal-tracking approach on a Taylor series expanded state model and a polar-to-Cartesian-conversion-based measurement model. Computation of sample weights through non-Gaussian likelihood forms the major contribution of the proposed particle-filter-based approach compared to the existing unscented-Kalman-filter-based approach. It is observed that the proposed approach is highly robust to noise and outperforms the state-of-the-art especially at very low signal-to-noise ratios (i.e., especially in the range of -5 to 20 dB). The proposed approach, to the best of our knowledge, is the only method available for phase estimation from severely noisy fringe patterns even when the underlying phase pattern is rapidly varying and has a larger dynamic range. Simulation results and experimental data demonstrate the fact that the proposed approach is a better choice for direct phase estimation. PMID:26974901
Advanced Techniques for Removal of Retrievable Inferior Vena Cava Filters
Iliescu, Bogdan; Haskal, Ziv J.
2012-08-15
Inferior vena cava (IVC) filters have proven valuable for the prevention of primary or recurrent pulmonary embolism in selected patients with or at high risk for venous thromboembolic disease. Their use has become commonplace, and the numbers implanted increase annually. During the last 3 years, in the United States, the percentage of annually placed optional filters, i.e., filters than can remain as permanent filters or potentially be retrieved, has consistently exceeded that of permanent filters. In parallel, the complications of long- or short-term filtration have become increasingly evident to physicians, regulatory agencies, and the public. Most filter removals are uneventful, with a high degree of success. When routine filter-retrieval techniques prove unsuccessful, progressively more advanced tools and skill sets must be used to enhance filter-retrieval success. These techniques should be used with caution to avoid damage to the filter or cava during IVC retrieval. This review describes the complex techniques for filter retrieval, including use of additional snares, guidewires, angioplasty balloons, and mechanical and thermal approaches as well as illustrates their specific application.
Advanced lightweight ceramic candle filter module
Zievers, J.F.; Eggerstedt, P.
1992-01-01
To determine the economic effect of light weight ceramics, several sizes of filters were cost estimated for operation at 217.5 psi (15 bar) based on the use of all light weight ceramics (Fibro/Fibro) vs. the use of cooled alloy (RA300) tubesheets and silicon carbide candles (Alloy/SiC). A jet pulse delivery system was included in both estimates. The Fibro/Fibro system was estimated with the plenum design while the Alloy/SiC system was based on header/nozzle design. Battery limits were the filters and jet pulse delivery systems, Ex-works, with no main valves or dust removal systems. It was found that the cost of Fibro/Fibro components were consistently lower than the cost of the Alloy/SiC components; this comparison is illustrated in Figure 8.
Advanced lightweight ceramic candle filter module
Zievers, J.F.; Eggerstedt, P.
1992-11-01
To determine the economic effect of light weight ceramics, several sizes of filters were cost estimated for operation at 217.5 psi (15 bar) based on the use of all light weight ceramics (Fibro/Fibro) vs. the use of cooled alloy (RA300) tubesheets and silicon carbide candles (Alloy/SiC). A jet pulse delivery system was included in both estimates. The Fibro/Fibro system was estimated with the plenum design while the Alloy/SiC system was based on header/nozzle design. Battery limits were the filters and jet pulse delivery systems, Ex-works, with no main valves or dust removal systems. It was found that the cost of Fibro/Fibro components were consistently lower than the cost of the Alloy/SiC components; this comparison is illustrated in Figure 8.
Generic Hardware Architectures for Sampling and Resampling in Particle Filters
NASA Astrophysics Data System (ADS)
Athalye, Akshay; Bolić, Miodrag; Hong, Sangjin; Djurić, Petar M.
2005-12-01
Particle filtering is a statistical signal processing methodology that has recently gained popularity in solving several problems in signal processing and communications. Particle filters (PFs) have been shown to outperform traditional filters in important practical scenarios. However their computational complexity and lack of dedicated hardware for real-time processing have adversely affected their use in real-time applications. In this paper, we present generic architectures for the implementation of the most commonly used PF, namely, the sampling importance resampling filter (SIRF). These provide a generic framework for the hardware realization of the SIRF applied to any model. The proposed architectures significantly reduce the memory requirement of the filter in hardware as compared to a straightforward implementation based on the traditional algorithm. We propose two architectures each based on a different resampling mechanism. Further, modifications of these architectures for acceleration of resampling process are presented. We evaluate these schemes based on resource usage and latency. The platform used for the evaluations is the Xilinx Virtex II pro FPGA. The architectures presented here have led to the development of the first hardware (FPGA) prototype for the particle filter applied to the bearings-only tracking problem.
Fish tracking by combining motion based segmentation and particle filtering
NASA Astrophysics Data System (ADS)
Bichot, E.; Mascarilla, L.; Courtellemont, P.
2006-01-01
In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.
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.
Multiple states and joint objects particle filter for eye tracking
NASA Astrophysics Data System (ADS)
Xiong, Jin; Jiang, Zhaohui; Liu, Junwei; Feng, Huanqing
2007-11-01
Recent works have proven that the particle filter is a powerful tracking technique for non-linear and non-Gaussian estimation problem. This paper presents an extension algorithm based on the color-based particle filter framework, which is applicable for complex eye tracking because of two main innovations. Firstly, an employment of an extra discrete-value variable and its associated transition probability matrix (TPM) makes it feasible in tracking multiple states of the eye during blinking. Secondly, the joint-object thought used in state vector eliminates the distraction from eyes to each other. The experimental results illustrate that the proposed algorithm is efficient for eye tracking.
Recent Advances on Endocrine Disrupting Effects of UV Filters
Wang, Jiaying; Pan, Liumeng; Wu, Shenggan; Lu, Liping; Xu, Yiwen; Zhu, Yanye; Guo, Ming; Zhuang, Shulin
2016-01-01
Ultraviolet (UV) filters are used widely in cosmetics, plastics, adhesives and other industrial products to protect human skin or products against direct exposure to deleterious UV radiation. With growing usage and mis-disposition of UV filters, they currently represent a new class of contaminants of emerging concern with increasingly reported adverse effects to humans and other organisms. Exposure to UV filters induce various endocrine disrupting effects, as revealed by increasing number of toxicological studies performed in recent years. It is necessary to compile a systematic review on the current research status on endocrine disrupting effects of UV filters toward different organisms. We therefore summarized the recent advances on the evaluation of the potential endocrine disruptors and the mechanism of toxicity for many kinds of UV filters such as benzophenones, camphor derivatives and cinnamate derivatives. PMID:27527194
Recent Advances on Endocrine Disrupting Effects of UV Filters.
Wang, Jiaying; Pan, Liumeng; Wu, Shenggan; Lu, Liping; Xu, Yiwen; Zhu, Yanye; Guo, Ming; Zhuang, Shulin
2016-01-01
Ultraviolet (UV) filters are used widely in cosmetics, plastics, adhesives and other industrial products to protect human skin or products against direct exposure to deleterious UV radiation. With growing usage and mis-disposition of UV filters, they currently represent a new class of contaminants of emerging concern with increasingly reported adverse effects to humans and other organisms. Exposure to UV filters induce various endocrine disrupting effects, as revealed by increasing number of toxicological studies performed in recent years. It is necessary to compile a systematic review on the current research status on endocrine disrupting effects of UV filters toward different organisms. We therefore summarized the recent advances on the evaluation of the potential endocrine disruptors and the mechanism of toxicity for many kinds of UV filters such as benzophenones, camphor derivatives and cinnamate derivatives. PMID:27527194
Westinghouse hot gas particle filter system
Lippert, T.E.; Bruck, G.J.; Newby, R.A.; Bachovchin, D.M.; Debski, V.L.; Morehead, H.T.
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.
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.
A local particle filter for high dimensional geophysical systems
NASA Astrophysics Data System (ADS)
Penny, S. G.; Miyoshi, T.
2015-12-01
A local particle filter (LPF) is introduced that outperforms traditional ensemble Kalman filters in highly nonlinear/non-Gaussian scenarios, both in accuracy and computational cost. The standard Sampling Importance Resampling (SIR) particle filter is augmented with an observation-space localization approach, for which an independent analysis is computed locally at each gridpoint. The deterministic resampling approach of Kitagawa is adapted for application locally and combined with interpolation of the analysis weights to smooth the transition between neighboring points. Gaussian noise is applied with magnitude equal to the local analysis spread to prevent particle degeneracy while maintaining the estimate of the growing dynamical instabilities. The approach is validated against the Local Ensemble Transform Kalman Filter (LETKF) using the 40-variable Lorenz-96 model. The results show that: (1) the accuracy of LPF surpasses LETKF as the forecast length increases (thus increasing the degree of nonlinearity), (2) the cost of LPF is significantly lower than LETKF as the ensemble size increases, and (3) LPF prevents filter divergence experienced by LETKF in cases with non-Gaussian observation error distributions.
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
Bearings-Only Tracking of Manoeuvring Targets Using Particle Filters
NASA Astrophysics Data System (ADS)
Arulampalam, M. Sanjeev; Ristic, B.; Gordon, N.; Mansell, T.
2004-12-01
We investigate the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs). Three different (PFs) are proposed for this problem which is formulated as a multiple model tracking problem in a jump Markov system (JMS) framework. The proposed filters are (i) multiple model PF (MMPF), (ii) auxiliary MMPF (AUX-MMPF), and (iii) jump Markov system PF (JMS-PF). The performance of these filters is compared with that of standard interacting multiple model (IMM)-based trackers such as IMM-EKF and IMM-UKF for three separate cases: (i) single-sensor case, (ii) multisensor case, and (iii) tracking with hard constraints. A conservative CRLB applicable for this problem is also derived and compared with the RMS error performance of the filters. The results confirm the superiority of the PFs for this difficult nonlinear tracking problem.
Localization using omnivision-based manifold particle filters
NASA Astrophysics Data System (ADS)
Wong, Adelia; Yousefhussien, Mohammed; Ptucha, Raymond
2015-01-01
Developing precise and low-cost spatial localization algorithms is an essential component for autonomous navigation systems. Data collection must be of sufficient detail to distinguish unique locations, yet coarse enough to enable real-time processing. Active proximity sensors such as sonar and rangefinders have been used for interior localization, but sonar sensors are generally coarse and rangefinders are generally expensive. Passive sensors such as video cameras are low cost and feature-rich, but suffer from high dimensions and excessive bandwidth. This paper presents a novel approach to indoor localization using a low cost video camera and spherical mirror. Omnidirectional captured images undergo normalization and unwarping to a canonical representation more suitable for processing. Training images along with indoor maps are fed into a semi-supervised linear extension of graph embedding manifold learning algorithm to learn a low dimensional surface which represents the interior of a building. The manifold surface descriptor is used as a semantic signature for particle filter localization. Test frames are conditioned, mapped to a low dimensional surface, and then localized via an adaptive particle filter algorithm. These particles are temporally filtered for the final localization estimate. The proposed method, termed omnivision-based manifold particle filters, reduces convergence lag and increases overall efficiency.
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.
Fast, parallel implementation of particle filtering on the GPU architecture
NASA Astrophysics Data System (ADS)
Gelencsér-Horváth, Anna; Tornai, Gábor János; Horváth, András; Cserey, György
2013-12-01
In this paper, we introduce a modified cellular particle filter (CPF) which we mapped on a graphics processing unit (GPU) architecture. We developed this filter adaptation using a state-of-the art CPF technique. Mapping this filter realization on a highly parallel architecture entailed a shift in the logical representation of the particles. In this process, the original two-dimensional organization is reordered as a one-dimensional ring topology. We proposed a proof-of-concept measurement on two models with an NVIDIA Fermi architecture GPU. This design achieved a 411- μs kernel time per state and a 77-ms global running time for all states for 16,384 particles with a 256 neighbourhood size on a sequence of 24 states for a bearing-only tracking model. For a commonly used benchmark model at the same configuration, we achieved a 266- μs kernel time per state and a 124-ms global running time for all 100 states. Kernel time includes random number generation on the GPU with curand. These results attest to the effective and fast use of the particle filter in high-dimensional, real-time applications.
ADVANCES IN PARTICLE SAMPLING AND MEASUREMENT
The paper, by five authorities who contributed significantly to the second symposium on advances to particle sampling and measurement (October 1979 in Daytona Beach, FL) summarizes salient developments in the field. Current techniques were described as being expensive, complicate...
Boosting target tracking using particle filter with flow control
NASA Astrophysics Data System (ADS)
Moshtagh, Nima; Chan, Moses W.
2013-05-01
Target detection and tracking with passive infrared (IR) sensors can be challenging due to significant degradation and corruption of target signature by atmospheric transmission and clutter effects. This paper summarizes our efforts in phenomenology modeling of boosting targets with IR sensors, and developing algorithms for tracking targets in the presence of background clutter. On the phenomenology modeling side, the clutter images are generated using a high fidelity end-to-end simulation testbed. It models atmospheric transmission, structured clutter and solar reflections to create realistic background images. The dynamics and intensity of a boosting target are modeled and injected onto the background scene. Pixel level images are then generated with respect to the sensor characteristics. On the tracking analysis side, a particle filter for tracking targets in a sequence of clutter images is developed. The particle filter is augmented with a mechanism to control particle flow. Specifically, velocity feedback is used to constrain and control the particles. The performance of the developed "adaptive" particle filter is verified with tracking of a boosting target in the presence of clutter and occlusion.
Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications.
Ghirmai, Tadesse
2016-01-01
For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms. An important consideration in developing a distributed particle filtering algorithm in wireless sensor networks is reducing the size of data exchanged among the sensors because of power and bandwidth constraints. In this paper, we propose a distributed particle filtering algorithm with the objective of reducing the overhead data that is communicated among the sensors. In our algorithm, the sensors exchange information to collaboratively compute the global likelihood function that encompasses the contribution of the measurements towards building the global posterior density of the unknown location parameters. Each sensor, using its own measurement, computes its local likelihood function and approximates it using a Gaussian function. The sensors then propagate only the mean and the covariance of their approximated likelihood functions to other sensors, reducing the communication overhead. The global likelihood function is computed collaboratively from the parameters of the local likelihood functions using an average consensus filter or a forward-backward propagation information exchange strategy. PMID:27618057
Expected likelihood for tracking in clutter with particle filters
NASA Astrophysics Data System (ADS)
Marrs, Alan; Maskell, Simon; Bar-Shalom, Yaakov
2002-08-01
The standard approach to tracking a single target in clutter, using the Kalman filter or extended Kalman filter, is to gate the measurements using the predicted measurement covariance and then to update the predicted state using probabilistic data association. When tracking with a particle filter, an analog to the predicted measurement covariance is not directly available and could only be constructed as an approximation to the current particle cloud. A common alternative is to use a form of soft gating, based upon a Student's-t likelihood, that is motivated by the concept of score functions in classical statistical hypothesis testing. In this paper, we combine the score function and probabilistic data association approaches to develop a new method for tracking in clutter using a particle filter. This is done by deriving an expected likelihood from known measurement and clutter statistics. The performance of this new approach is assessed on a series of bearings-only tracking scenarios with uncertain sensor location and non-Gaussian clutter.
Particle filter-based track before detect algorithms
NASA Astrophysics Data System (ADS)
Boers, Yvo; Driessen, Hans
2003-12-01
In this paper we will give a general system setup, that allows the formulation of a wide range of Track Before Detect (TBD) problems. A general basic particle filter algorithm for this system is also provided. TBD is a technique, where tracks are produced directly on the basis of raw (radar) measurements, e.g. power or IQ data, without intermediate processing and decision making. The advantage over classical tracking is that the full information is integrated over time, this leads to a better detection and tracking performance, especially for weak targets. In this paper we look at the filtering and the detection aspect of TBD. We will formulate a detection result, that allows the user to implement any optimal detector in terms of the weights of a running particle filter. We will give a theoretical as well as a numerical (experimental) justification for this. Furthermore, we show that the TBD setup, that is chosen in this paper, allows a straightforward extension to the multi-target case. This easy extension is also due to the fact that the implementation of the solution is by means of a particle filter.
Particle filter-based track before detect algorithms
NASA Astrophysics Data System (ADS)
Boers, Yvo; Driessen, Hans
2004-01-01
In this paper we will give a general system setup, that allows the formulation of a wide range of Track Before Detect (TBD) problems. A general basic particle filter algorithm for this system is also provided. TBD is a technique, where tracks are produced directly on the basis of raw (radar) measurements, e.g. power or IQ data, without intermediate processing and decision making. The advantage over classical tracking is that the full information is integrated over time, this leads to a better detection and tracking performance, especially for weak targets. In this paper we look at the filtering and the detection aspect of TBD. We will formulate a detection result, that allows the user to implement any optimal detector in terms of the weights of a running particle filter. We will give a theoretical as well as a numerical (experimental) justification for this. Furthermore, we show that the TBD setup, that is chosen in this paper, allows a straightforward extension to the multi-target case. This easy extension is also due to the fact that the implementation of the solution is by means of a particle filter.
Random set particle filter for bearings-only multitarget tracking
NASA Astrophysics Data System (ADS)
Vihola, Matti
2005-05-01
The random set approach to multitarget tracking is a theoretically sound framework that covers joint estimation of the number of targets and the state of the targets. This paper describes a particle filter implementation of the random set multitarget filter. The contribution of this paper to the random set tracking framework is the formulation of a measurement model where each sensor report is assumed to contain at most one measurement. The implemented filter was tested in synthetic bearings-only tracking scenarios containing up to two targets in the presence of false alarms and missed measurements. The estimated target state consisted of 2D position and velocity components. The filter was capable to track the targets fairly well despite of the missing measurements and the relatively high false alarm rates. In addition, the filter showed robustness against wrong parameter values of false alarm rates. The results that were obtained during the limited tests of the filter show that the random set framework has potential for challenging tracking situations. On the other hand, the computational burden of the described implementation is quite high and increases approximately linearly with respect to the expected number of targets.
Ceramem filters for removal of particles from hot gas streams
Bishop, B.A.; Goldsmith, R.L.
1994-11-01
The need for hot gas cleanup in the power, advanced coal conversion, process and incineration industries is well documented and extensive development is being undertaken to develop and demonstrate suitable filtration technologies. In general, process conditions include (a) oxidizing or reducing atmospheres, (b) temperatures to 1800{degree}F, (c) pressures to 300 psi, and (d) potentially corrosive components in the gas stream. The most developed technologies entail the use of candle or tube filters, which suffer from fragility, lack of oxidation/corrosion resistance, and high cost. The ceramic membrane filter described below offers the potential to eliminate these limitations.
Advances in holographic particle velocimetry
NASA Astrophysics Data System (ADS)
Simmons, Scott; Meng, Hui; Hussain, Fazle; Liu, David
1993-12-01
Holographic particle velocimetry (HPV) is a promising technique for 3D flow velocity and hence vorticity measurements to study turbulence, coherent structures and vortex interactions. We discuss various aspects in the development of this technique ranging from hologram recording configurations such as in-line, off-axis and multibeam to data processing. Difficulties in implementation are analyzed and solutions are discussed. We also present preliminary measurement results in a 3D vortex flow using one of our prototype HPV systems.
NASA Astrophysics Data System (ADS)
Plaza Guingla, D. A.; Pauwels, V. R.; De Lannoy, G. J.; Matgen, P.; Giustarini, L.; De Keyser, R.
2012-12-01
The objective of this work is to analyze the improvement in the performance of the particle filter by including a resample-move step or by using a modified Gaussian particle filter. Specifically, the standard particle filter structure is altered by the inclusion of the Markov chain Monte Carlo move step. The second choice adopted in this study uses the moments of an ensemble Kalman filter analysis to define the importance density function within the Gaussian particle filter structure. Both variants of the standard particle filter are used in the assimilation of densely sampled discharge records into a conceptual rainfall-runoff model. In order to quantify the obtained improvement, discharge root mean square errors are compared for different particle filters, as well as for the ensemble Kalman filter. First, a synthetic experiment is carried out. The results indicate that the performance of the standard particle filter can be improved by the inclusion of the resample-move step, but its effectiveness is limited to situations with limited particle impoverishment. The results also show that the modified Gaussian particle filter outperforms the rest of the filters. Second, a real experiment is carried out in order to validate the findings from the synthetic experiment. The addition of the resample-move step does not show a considerable improvement due to performance limitations in the standard particle filter with real data. On the other hand, when an optimal importance density function is used in the Gaussian particle filter, the results show a considerably improved performance of the particle filter.
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
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
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 μg/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.
Marginalized Particle Filter for Blind Signal Detection with Analog Imperfections
NASA Astrophysics Data System (ADS)
Yoshida, Yuki; Hayashi, Kazunori; Sakai, Hideaki; Bocquet, Wladimir
Recently, the marginalized particle filter (MPF) has been applied to blind symbol detection problems over selective fading channels. The MPF can ease the computational burden of the standard particle filter (PF) while offering better estimates compared with the standard PF. In this paper, we investigate the application of the blind MPF detector to more realistic situations where the systems suffer from analog imperfections which are non-linear signal distortion due to the inaccurate analog circuits in wireless devices. By reformulating the system model using the widely linear representation and employing the auxiliary variable resampling (AVR) technique for estimation of the imperfections, the blind MPF detector is successfully modified to cope with the analog imperfections. The effectiveness of the proposed MPF detector is demonstrated via computer simulations.
Linear multistep methods, particle filtering and sequential Monte Carlo
NASA Astrophysics Data System (ADS)
Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki
2013-08-01
Numerical integration is the main bottleneck in particle filter methodologies for dynamic inverse problems to estimate model parameters, initial values, and non-observable components of an ordinary differential equation (ODE) system from partial, noisy observations, because proposals may result in stiff systems which first slow down or paralyze the time integration process, then end up being discarded. The immediate advantage of formulating the problem in a sequential manner is that the integration is carried out on shorter intervals, thus reducing the risk of long integration processes followed by rejections. We propose to solve the ODE systems within a particle filter framework with higher order numerical integrators which can handle stiffness and to base the choice of the variance of the innovation on estimates of the discretization errors. The application of linear multistep methods to particle filters gives a handle on the stability and accuracy of the propagation, and linking the innovation variance to the accuracy estimate helps keep the variance of the estimate as low as possible. The effectiveness of the methodology is demonstrated with a simple ODE system similar to those arising in biochemical applications.
NASA Astrophysics Data System (ADS)
Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.
2011-12-01
We present results of data assimilation of ground discharge observation and remotely sensed soil moisture observations into Sacramento Soil Moisture Accounting (SACSMA) model in a small watershed (1593 km2) in Minnesota, the Unites States. Specifically, we perform assimilation experiments with Ensemble Kalman Filter (EnKF) and Particle Filter (PF) in order to improve streamflow forecast accuracy at six hourly time step. The EnKF updates the soil moisture states in the SACSMA from the relative errors of the model and observations, while the PF adjust the weights of the state ensemble members based on the likelihood of the forecast. Results of the improvements of each filter over the reference model (without data assimilation) will be presented. Finally, the EnKF and PF are coupled together to further improve the streamflow forecast accuracy.
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
Single layer spectro-polarimetric filter for advanced LWIR FPAs
NASA Astrophysics Data System (ADS)
Jones, A. M.; Kemme, S. A.; Scrymgeour, D. A.; Norwood, R. A.
2012-06-01
We explore the spectral and angular selectivity of near surface normal transmission of grating modified metallic surfaces and their ultimate potential for application as narrow-band spectro-polarimetric planar filter components in the development of advanced infrared focal plane arrays. The developed photonic microstructures exhibit tailored spectral transmission characteristics in the long wavelength infrared, and can be fabricated to preferentially transmit a given linear polarization within the design band. Modification of the material and structural properties of the diffractive optical element enables sub-pixel tuning of the spectro-polarimetric response of the device allowing for intelligent engineering of planar filter components for development of advanced focal plane arrays in the long wavelength infrared. The planar nature of the developed components leaves them immune to fabrication issues that typically plague thin film interference filters used for similar applications in the infrared, namely, deposition of multiple low-stress quarter-wavelength films and modification of the film thicknesses for each pixel. The solution developed here presents the opportunity for subpixel modification of the spectral response leading to an efficient, versatile filter component suitable for direct integration with commercially available focal plane array technologies via standard fabrication techniques. We will discuss the theoretical development and analysis of the described components and compare the results to the current state-of-the-art.
Deinterlacing algorithm with an advanced non-local means filter
NASA Astrophysics Data System (ADS)
Wang, Jin; Jeon, Gwanggil; Jeong, Jechang
2012-04-01
The authors introduce an efficient intra-field deinterlacing algorithm using an advanced non-local means filter. The non-local means (NLM) method has received considerable attention due to its high performance and simplicity. The NLM method adaptively obtains the missing pixel by the weighted average of the gray values of all pixels within the image, and then automatically eliminates unrelated neighborhoods from the weighted average. However, spatial location distance is another important issue for the deinterlacing method. Therefore we introduce an advanced NLM (ANLM) filter while consider neighborhood similarity and patch distance. Moreover, the search region of the conventional NLM is the whole image, while, the ANLM can just utilize the limited search region and achieve good performance and high efficiency. When compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise-ratio while maintaining high efficiency.
Applying a fully nonlinear particle filter on a coupled ocean-atmosphere climate model
NASA Astrophysics Data System (ADS)
Browne, Philip; van Leeuwen, Peter Jan; Wilson, Simon
2014-05-01
It is a widely held assumption that particle filters are not applicable in high-dimensional systems due to filter degeneracy, commonly called the curse of dimensionality. This is only true of naive particle filters, and indeed it has been shown much more advanced methods perform particularly well on systems of dimension up to 216 ≡ 6.5 × 104. In this talk we will present results from using the equivalent weights particle filter in twin experiments with the global climate model HadCM3. These experiments have a number of notable features. Firstly the sheer size of model in use is substantially larger than has been previously achieved. The model has state dimension approximately 4 × 106 and approximately 4 × 104 observations per analysis step. This is 2 orders of magnitude more than has been achieved with a particle filter in the geosciences. Secondly, the use of a fully nonlinear data assimilation technique to initialise a climate model gives us the possibility to find non-Gaussian estimates for the current state of the climate. In doing so we may find that the same model may demonstrate multiple likely scenarios for forecasts on a multi-annular/decadal timescale. The experiments consider to assimilating artificial sea surface temperatures daily for several years. We will discuss how an ensemble based method for assimilation in a coupled system avoids issues faced by variational methods. Practical details of how the experiments were carried out, specifically the use of the EMPIRE data assimilation framework, will be discussed. The results from applying the nonlinear data assimilation method can always be improved through having a better representation of the model error covariance matrix. We will discuss the representation which we have used for this matrix, and in particular, how it was generated from the coupled system.
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.
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
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
Identification of Backlash Type Hysteretic Systems Based on Particle Filter
NASA Astrophysics Data System (ADS)
Masuda, Tetsuya; Sugie, Toshiharu
This paper considers the system identification problem for hysteresis systems. This problem plays an important role in achieving better control performance, because many actuators have hysteresis property. This paper proposes a method to identify linear dynamical systems having input hysteresis property of backlash type. The method is based on particle filter, which is known for its applicability to a wide class of nonlinear systems. Numerical examples are given to demonstrate the effectiveness of the proposed method in detail. Furthermore, experimental validation is performed for a DC servo motor system.
NASA Astrophysics Data System (ADS)
Hirpa, F. A.; Gebremichael, M.; LEE, H.; Hopson, T. M.
2012-12-01
Hydrologic data assimilation techniques provide a means to improve river discharge forecasts through updating hydrologic model states and correcting the atmospheric forcing data via optimally combining model outputs with observations. The performance of the assimilation procedure, however, depends on the data assimilation techniques used and the amount of uncertainty in the data sets. To investigate the effects of these, we comparatively evaluate three data assimilation techniques, including ensemble Kalman filter (EnKF), particle filter (PF) and variational (VAR) technique, which assimilate discharge and synthetic soil moisture data at various uncertainty levels into the Sacramento Soil Moisture accounting (SAC-SMA) model used by the National Weather Service (NWS) for river forecasting in The United States. The study basin is Greens Bayou watershed with area of 178 km2 in eastern Texas. In the presentation, we summarize the results of the comparisons, and discuss the challenges of applying each technique for hydrologic applications.
Particle filter based on thermophoretic deposition from natural convection flow
Sasse, A.G.B.M.; Nazaroff, W.W. ); Gadgil, A.J. )
1994-04-01
We present an analysis of particle migration in a natural convection flow between parallel plates and within the annulus of concentric tubes. The flow channel is vertically oriented with one surface maintained at a higher temperature than the other. Particle migration is dominated by advection in the vertical direction and thermophoresis in the horizontal direction. From scale analysis it is demonstrated that particles are completely removed from air flowing through the channel if its length exceeds L[sub c] = (b[sup 4]g/24K[nu][sup 2]), where b is the width of the channel, g is the acceleration of gravity, K is a thermophoretic coefficient of order 0.5, and [nu] is the kinematic viscosity of air. Precise predictions of particle removal efficiency as a function of system parameters are obtained by numerical solution of the governing equations. Based on the model results, it appears feasible to develop a practical filter for removing smoke particles from a smoldering cigarette in an ashtray by using natural convection in combination with thermophoresis. 22 refs., 8 figs., 1 tab.
Tracking low SNR targets using particle filter with flow control
NASA Astrophysics Data System (ADS)
Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.
2014-06-01
In this work we study the problem of detecting and tracking challenging targets that exhibit low signal-to-noise ratios (SNR). We have developed a particle filter-based track-before-detect (TBD) algorithm for tracking such dim targets. The approach incorporates the most recent state estimates to control the particle flow accounting for target dynamics. The flow control enables accumulation of signal information over time to compensate for target motion. The performance of this approach is evaluated using a sensitivity analysis based on varying target speed and SNR values. This analysis was conducted using high-fidelity sensor and target modeling in realistic scenarios. Our results show that the proposed TBD algorithm is capable of tracking targets in cluttered images with SNR values much less than one.
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.
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
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.
Phase Advance for Wideband Dampers With Notch Filters
McGinnis, Dave; /Fermilab
2000-02-01
Consider a simple damper system shown in Figure 1. The phase advance between pickup and kicker is {phi}{sub k}. Assume a particle passes through the pickup at time t=0 with a complex betatron amplitude: {rvec A}{sub pu} = A{sub o}. When the particle passes through the kicker, the betatron amplitude of the particle will be: {rvec A}{sub kr} = A{sub o}e{sup j{phi}k}. Assume that the damper is timed so that the delay through the electronics matches the time of flight for the particle between pickup to kicker. The difference in phase between the kicker betatron amplitude and the pickup amplitude must be: arg({rvec A}{sub kr})-arg({rvec A}{sub pu}) = (2n + 1) {pi}/2 and {phi}{sub k} = (2n+1) {pi}/2 because the pickup measures the position and the kicker corrects an angle.
Multi-prediction particle filter for efficient parallelized implementation
NASA Astrophysics Data System (ADS)
Chu, Chun-Yuan; Chao, Chih-Hao; Chao, Min-An; Wu, An-Yeu Andy
2011-12-01
Particle filter (PF) is an emerging signal processing methodology, which can effectively deal with nonlinear and non-Gaussian signals by a sample-based approximation of the state probability density function. The particle generation of the PF is a data-independent procedure and can be implemented in parallel. However, the resampling procedure in the PF is a sequential task in natural and difficult to be parallelized. Based on the Amdahl's law, the sequential portion of a task limits the maximum speed-up of the parallelized implementation. Moreover, large particle number is usually required to obtain an accurate estimation, and the complexity of the resampling procedure is highly related to the number of particles. In this article, we propose a multi-prediction (MP) framework with two selection approaches. The proposed MP framework can reduce the required particle number for target estimation accuracy, and the sequential operation of the resampling can be reduced. Besides, the overhead of the MP framework can be easily compensated by parallel implementation. The proposed MP-PF alleviates the global sequential operation by increasing the local parallel computation. In addition, the MP-PF is very suitable for multi-core graphics processing unit (GPU) platform, which is a popular parallel processing architecture. We give prototypical implementations of the MP-PFs on multi-core GPU platform. For the classic bearing-only tracking experiments, the proposed MP-PF can be 25.1 and 15.3 times faster than the sequential importance resampling-PF with 10,000 and 20,000 particles, respectively. Hence, the proposed MP-PF can enhance the efficiency of the parallelization.
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.
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.
International Space Station (ISS) Advanced Recycle Filter Tank Assembly (ARFTA)
NASA Technical Reports Server (NTRS)
Nasrullah, Mohammed K.
2013-01-01
The International Space Station (ISS) Recycle Filter Tank Assembly (RFTA) provides the following three primary functions for the Urine Processor Assembly (UPA): volume for concentrating/filtering pretreated urine, filtration of product distillate, and filtration of the Pressure Control and Pump Assembly (PCPA) effluent. The RFTAs, under nominal operations, are to be replaced every 30 days. This poses a significant logistical resupply problem, as well as cost in upmass and new tanks purchase. In addition, it requires significant amount of crew time. To address and resolve these challenges, NASA required Boeing to develop a design which eliminated the logistics and upmass issues and minimize recurring costs. Boeing developed the Advanced Recycle Filter Tank Assembly (ARFTA) that allowed the tanks to be emptied on-orbit into disposable tanks that eliminated the need for bringing the fully loaded tanks to earth for refurbishment and relaunch, thereby eliminating several hundred pounds of upmass and its associated costs. The ARFTA will replace the RFTA by providing the same functionality, but with reduced resupply requirements
Advanced analysis methods in particle physics
Bhat, Pushpalatha C.; /Fermilab
2010-10-01
Each generation of high energy physics experiments is grander in scale than the previous - more powerful, more complex and more demanding in terms of data handling and analysis. The spectacular performance of the Tevatron and the beginning of operations of the Large Hadron Collider, have placed us at the threshold of a new era in particle physics. The discovery of the Higgs boson or another agent of electroweak symmetry breaking and evidence of new physics may be just around the corner. The greatest challenge in these pursuits is to extract the extremely rare signals, if any, from huge backgrounds arising from known physics processes. The use of advanced analysis techniques is crucial in achieving this goal. In this review, I discuss the concepts of optimal analysis, some important advanced analysis methods and a few examples. The judicious use of these advanced methods should enable new discoveries and produce results with better precision, robustness and clarity.
Analysis of single particle diffusion with transient binding using particle filtering.
Bernstein, Jason; Fricks, John
2016-07-21
Diffusion with transient binding occurs in a variety of biophysical processes, including movement of transmembrane proteins, T cell adhesion, and caging in colloidal fluids. We model diffusion with transient binding as a Brownian particle undergoing Markovian switching between free diffusion when unbound and diffusion in a quadratic potential centered around a binding site when bound. Assuming the binding site is the last position of the particle in the unbound state and Gaussian observational error obscures the true position of the particle, we use particle filtering to predict when the particle is bound and to locate the binding sites. Maximum likelihood estimators of diffusion coefficients, state transition probabilities, and the spring constant in the bound state are computed with a stochastic Expectation-Maximization (EM) algorithm. PMID:27107737
Robust Tracking Using Particle Filter with a Hybrid Feature
NASA Astrophysics Data System (ADS)
Zhao, Xinyue; Satoh, Yutaka; Takauji, Hidenori; Kaneko, Shun'ichi
This paper presents a novel method for robust object tracking in video sequences using a hybrid feature-based observation model in a particle filtering framework. An ideal observation model should have both high ability to accurately distinguish objects from the background and high reliability to identify the detected objects. Traditional features are better at solving the former problem but weak in solving the latter one. To overcome that, we adopt a robust and dynamic feature called Grayscale Arranging Pairs (GAP), which has high discriminative ability even under conditions of severe illumination variation and dynamic background elements. Together with the GAP feature, we also adopt the color histogram feature in order to take advantage of traditional features in resolving the first problem. At the same time, an efficient and simple integration method is used to combine the GAP feature with color information. Comparative experiments demonstrate that object tracking with our integrated features performs well even when objects go across complex backgrounds.
Geoacoustic and source tracking using particle filtering: experimental results.
Yardim, Caglar; Gerstoft, Peter; Hodgkiss, William S
2010-07-01
A particle filtering (PF) approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source. This approach treats both the environmental parameters [e.g., water column sound speed profile (SSP), water depth, sediment and bottom parameters] at the source location and the source parameters (e.g., source depth, range and speed) as unknown random variables that evolve as the source moves. This allows real-time updating of the environment and accurate tracking of the moving source. As a sequential Monte Carlo technique that operates on nonlinear systems with non-Gaussian probability densities, the PF is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities. The approach is demonstrated on both simulated data in a shallow water environment with a sloping bottom and experimental data collected during the SWellEx-96 experiment. PMID:20649203
NASA Technical Reports Server (NTRS)
Rourke, K. H.; Jordan, J. F.
1972-01-01
This paper presents the results of the applications of advanced filtering methods to the determination of the interplanetary orbit of the Mariner '71 spacecraft. The advanced techniques are specific extensions of the Kalman filter. The special problems associated with applying these techniques are discussed and the particular algorithmic implementations are outlined. The advanced methods are compared against the weighted least squares filters of conventional application. The results reveal that relatively simple advanced filter configurations yield solutions superior to those of the conventional methods when applied to the Mariner '71 radio measurements.
PARTICLE REMOVAL AND HEAD LOSS DEVELOPMENT IN BIOLOGICAL FILTERS
The physical performance of granular media filters was studied under pre-chlorinated, backwash-chlorinated, and nonchlorinated conditions. Overall, biological filteration produced a high-quality water. Although effluent turbidities showed littleer difference between the perform...
Particle filter with one-step randomly delayed measurements and unknown latency probability
NASA Astrophysics Data System (ADS)
Zhang, Yonggang; Huang, Yulong; Li, Ning; Zhao, Lin
2016-01-01
In this paper, a new particle filter is proposed to solve the nonlinear and non-Gaussian filtering problem when measurements are randomly delayed by one sampling time and the latency probability of the delay is unknown. In the proposed method, particles and their weights are updated in Bayesian filtering framework by considering the randomly delayed measurement model, and the latency probability is identified by maximum likelihood criterion. The superior performance of the proposed particle filter as compared with existing methods and the effectiveness of the proposed identification method of latency probability are both illustrated in two numerical examples concerning univariate non-stationary growth model and bearing only tracking.
Advanced distortion-invariant minimum average correlation energy (MACE) filters.
Casasent, D; Ravichandran, G
1992-03-10
The original minimum average correlation energy (MACE) filter is addressed by using a new database (strategic relocatable objects, missile launchers) and including noise performance, depression angle, and resolution effects on the number of training set images that are required. Major attention is given to our new MACE filter algorithms for distortion-invariant pattern recognition: shifted-MACE filters (to suppress large false correlation peaks), minimum variance-MACE filters (for improved noise performance), multiple symbolic encoded filters (to reduce the effect of false correlation peaks), and Gaussian-MACE filters (to improve noise performance and intraclass recognition and reduce the training set size). PMID:20720728
Advanced distortion-invariant minimum average correlation energy (MACE) filters
NASA Astrophysics Data System (ADS)
Casasent, David; Ravichandran, Gopalan
1992-03-01
The original minimum average correlation energy (MACE) filter is addressed by using a new database (strategic relocatable objects and missile launchers) and including noise performance, depression angle, and resolution effects on the number of training set images that are required. Major attention is given to new MACE filter algorithms for distortion-invariant pattern recognition: shifted-MACE filters to suppress large false correlation peaks, minimum variance-MACE filters for improved noise performance, multiple symbolic encoded filters to reduce the effect of false correlation peaks, and Gaussian-MACE filters to improve noise performance and intraclass recognition and reduce the training set size.
Sequential bearings-only-tracking initiation with particle filtering method.
Liu, Bin; 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
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
Particle Filtering Equalization Method for a Satellite Communication Channel
NASA Astrophysics Data System (ADS)
Sénécal, Stéphane; Amblard, Pierre-Olivier; Cavazzana, Laurent
2004-12-01
We propose the use of particle filtering techniques and Monte Carlo methods to tackle the in-line and blind equalization of a satellite communication channel. The main difficulties encountered are the nonlinear distortions caused by the amplifier stage in the satellite. Several processing methods manage to take into account these nonlinearities but they require the knowledge of a training input sequence for updating the equalizer parameters. Blind equalization methods also exist but they require a Volterra modelization of the system which is not suited for equalization purpose for the present model. The aim of the method proposed in the paper is also to blindly restore the emitted message. To reach this goal, a Bayesian point of view is adopted. Prior knowledge of the emitted symbols and of the nonlinear amplification model, as well as the information available from the received signal, is jointly used by considering the posterior distribution of the input sequence. Such a probability distribution is very difficult to study and thus motivates the implementation of Monte Carlo simulation methods. The presentation of the equalization method is cut into two parts. The first part solves the problem for a simplified model, focusing on the nonlinearities of the model. The second part deals with the complete model, using sampling approaches previously developed. The algorithms are illustrated and their performance is evaluated using bit error rate versus signal-to-noise ratio curves.
Ultrafine particle removal by residential heating, ventilating, and air-conditioning filters.
Stephens, B; Siegel, J A
2013-12-01
This work uses an in situ filter test method to measure the size-resolved removal efficiency of indoor-generated ultrafine particles (approximately 7-100 nm) for six new commercially available filters installed in a recirculating heating, ventilating, and air-conditioning (HVAC) system in an unoccupied test house. The fibrous HVAC filters were previously rated by the manufacturers according to ASHRAE Standard 52.2 and ranged from shallow (2.5 cm) fiberglass panel filters (MERV 4) to deep-bed (12.7 cm) electrostatically charged synthetic media filters (MERV 16). Measured removal efficiency ranged from 0 to 10% for most ultrafine particles (UFP) sizes with the lowest rated filters (MERV 4 and 6) to 60-80% for most UFP sizes with the highest rated filter (MERV 16). The deeper bed filters generally achieved higher removal efficiencies than the panel filters, while maintaining a low pressure drop and higher airflow rate in the operating HVAC system. Assuming constant efficiency, a modeling effort using these measured values for new filters and other inputs from real buildings shows that MERV 13-16 filters could reduce the indoor proportion of outdoor UFPs (in the absence of indoor sources) by as much as a factor of 2-3 in a typical single-family residence relative to the lowest efficiency filters, depending in part on particle size. PMID:23590456
Alvin, M.A.
1996-12-31
Advanced clay bonded silicon carbide, alumina/mullite and CVI-SiC fiber reinforced composite porous ceramic candle filters have been identified for use in pressurized circulating fluidized-bed combustion (PCFBC) systems where operating temperatures approach 870--900 C. In this paper the author will discuss the performance of these filter elements, and explore the response and stability of the advanced filter materials after 540 hours of operation in Foster Wheeler`s PCFBC system in Karhula, Finland. The potential use of the advanced filter materials for extended operating life in high temperature, pressurized, coal-fired process applications will also be addressed.
Advanced configuration of hybrid passive filter for reactive power and harmonic compensation.
Kececioglu, O Fatih; Acikgoz, Hakan; Sekkeli, Mustafa
2016-01-01
Harmonics is one of the major power quality problems for power systems. The harmonics can be eliminated by power filters such as passive, active, and hybrid. In this study, a new passive filter configuration has been improved in addition to the existing passive filter configurations. Conventional hybrid passive filters are not successful to compensate rapidly changing reactive power demand. The proposed configure are capable of compensating both harmonics and reactive power at the same time. Simulation results show that performance of reactive power and harmonic compensation with advanced hybrid passive filter is better than conventional hybrid passive filters. PMID:27536512
Yang, Juan; Stewart, Marc; Maupin, Gary D.; Herling, Darrell R.; Zelenyuk, Alla
2009-04-15
Diesel offers higher fuel efficiency, but produces higher exhaust particulate matter. Diesel particulate filters are presently the most efficient means to reduce these emissions. These filters typically trap particles in two basic modes: at the beginning of the exposure cycle the particles are captured in the filter holes, and at longer times the particles form a "cake" on which particles are trapped. Eventually the "cake" removed by oxidation and the cycle is repeated. We have investigated the properties and behavior of two commonly used filters: silicon carbide (SiC) and cordierite (DuraTrap® RC) by exposing them to nearly-spherical ammonium sulfate particles. We show that the transition from deep bed filtration to "cake" filtration can easily be identified by recording the change in pressure across the filters as a function of exposure. We investigated performance of these filters as a function of flow rate and particle size. The filters trap small and large particles more efficiently than particles that are ~80 to 200 nm in aerodynamic diameter. A comparison between the experimental data and a simulation using incompressible lattice-Boltzmann model shows very good qualitative agreement, but the model overpredicts the filter’s trapping efficiency.
Improving Hydrologic Data Assimilation by a Multivariate Particle Filter-Markov Chain Monte Carlo
NASA Astrophysics Data System (ADS)
Yan, H.; DeChant, C. M.; Moradkhani, H.
2014-12-01
Data assimilation (DA) is a popular method for merging information from multiple sources (i.e. models and remotely sensing), leading to improved hydrologic prediction. With the increasing availability of satellite observations (such as soil moisture) in recent years, DA is emerging in operational forecast systems. Although these techniques have seen widespread application, developmental research has continued to further refine their effectiveness. This presentation will examine potential improvements to the Particle Filter (PF) through the inclusion of multivariate correlation structures. Applications of the PF typically rely on univariate DA schemes (such as assimilating the outlet observed discharge), and multivariate schemes generally ignore the spatial correlation of the observations. In this study, a multivariate DA scheme is proposed by introducing geostatistics into the newly developed particle filter with Markov chain Monte Carlo (PF-MCMC) method. This new method is assessed by a case study over one of the basin with natural hydrologic process in Model Parameter Estimation Experiment (MOPEX), located in Arizona. The multivariate PF-MCMC method is used to assimilate the Advanced Scatterometer (ASCAT) grid (12.5 km) soil moisture retrievals and the observed streamflow in five gages (four inlet and one outlet gages) into the Sacramento Soil Moisture Accounting (SAC-SMA) model for the same scale (12.5 km), leading to greater skill in hydrologic predictions.
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
Advanced testing method to evaluate the performance of respirator filter media.
Wang, Qiang; Golshahi, Laleh; Chen, Da-Ren
2016-10-01
Filter media for respirator applications are typically exposed to the cyclic flow condition, which is different from the constant flow condition adopted in filter testing standards. To understand the real performance of respirator filter media in the field it is required to investigate the penetration of particles through respirator filters under cyclic flow conditions representing breathing flow patterns of human beings. This article reports a new testing method for studying the individual effect of breathing frequency (BF) and peak inhalation flow rate (PIFR) on the particle penetration through respirator filter media. The new method includes the use of DMA (Differential Mobility Analyzer)-classified particles having the most penetrating particle size, MPPS (at the constant flowrate of equivalent mean inhalation flow rate, MIFR) as test aerosol. Two condensation particle counters (CPCs) are applied to measure the particle concentrations at the upstream and downstream of test filter media at the same time. Given the 10 Hz sampling time of CPCs, close-to-instantaneous particle penetration could be measured. A pilot study was performed to demonstrate the new testing method. It is found that the effect of BF on the particle penetration of test respirator filter media is of importance at all the tested peak inhalation flow rates (PIFRs), which is different from those reported in the previous work. PMID:27104915
NASA Astrophysics Data System (ADS)
Zaugg, David A.; Samuel, Alphonso A.; Waagen, Donald E.; Schmitt, Harry A.
2004-07-01
Bearings-only tracking is widely used in the defense arena. Its value can be exploited in systems using optical sensors and sonar, among others. Non-linearity and non-Gaussian prior statistics are among the complications of bearings-only tracking. Several filters have been used to overcome these obstacles, including particle filters and multiple hypothesis extended Kalman filters (MHEKF). Particle filters can accommodate a wide range of distributions and do not need to be linearized. Because of this they seem ideally suited for this problem. A MHEKF can only approximate the prior distribution of a bearings-only tracking scenario and needs to be linearized. However, the likelihood distribution maintained for each MHEKF hypothesis demonstrates significant memory and lends stability to the algorithm, potentially enhancing tracking convergence. Also, the MHEKF is insensitive to outliers. For the scenarios under investigation, the sensor platform is tracking a moving and a stationary target. The sensor is allowed to maneuver in an attempt to maximize tracking performance. For these scenarios, we compare and contrast the acquisition time and mean-squared tracking error performance characteristics of particle filters and MHEKF via Monte Carlo simulation.
Watson, J.H.P.
1995-02-01
This paper describes the structure and properties of a novel permanently magnetised magnetic filter for fine friable radioactive material. Previously a filter was described and tested. This filter was designed so that the holes in the filter are left open as capture proceeds which means the pressure drop builds up only slowly. This filter is not suitable for friable composite particles which can be broken by mechanical forces. The structure of magnetic part of the second filter has been changed so as to strongly capture particles composed of fine particles weakly bound together which tend to break when captured. This uses a principle of assisted-capture in which coarse particles aid the capture of the fine fragments. The technique has the unfortunate consequence that the pressure drop across the filter rises faster as capture capture proceeds than the filter described previously. These filters have the following characteristics: (1) No external magnet is required. (2) No external power is required. (3) Small is size and portable. (4) Easily interchangeable. (5) Can be cleaned without demagnetising.
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.
Fracture behavior of advanced ceramic hot-gas filters
Singh, J.P.; Majumdar, S.; Sutaria, M.; Bielke, W.
1996-05-01
We have evaluated the microstructural/mechanical, and thermal shock/fatigue behavior and have conducted stress analyses of hot-gas candle filters made by various manufacturers. These filters include both monolithic and composite ceramics. Mechanical-property measurement of the composite filters included diametral compression testing with O-ring specimens and burst testing of short filter segments using rubber plug. In general, strength values obtained by burst testing were lower than those obtained by O-ring compression testing. During single-cycle thermal-shock tests, the composite filters showed little or no strength degradation when quenched from temperatures between 900 and 1000{degrees}C. At higher quenching temperatures, slow strength degradation was observed. The monolithic SiC filters showed no strength degradation when quenched from temperatures of up to {approx}700-900{degrees}C, but displayed decreased strength at a relatively sharp rate when quenched from higher temperatures. On the other hand, a recrystallized monolithic SiC filter showed higher initial strength and retained this strength to higher quenching temperatures than did regular SiC filters. This may be related to the difference in strength of grain boundary phases in the two cases. In thermal cycles between room temperature and 800- 1000{degrees}C, both monolithic and composite filters show a small strength degradation up to three cycles, beyond which the strength remained unchanged. Results of rubber-plug burst testing on composite filters were analyzed to determine the anisotropic elastic constants of the composite in the hoop direction. When these results are combined with axial elastic constants determined from axial tensile tests, the composite can be analyzed for stress due to mechanical (e. g., internal pressure) or thermal loading (thermal shock during pulse cleaning). The stresses can be compared with the strength of the composite to predict filter performance.
Application of extended Kalman particle filter for dynamic interference fringe processing
NASA Astrophysics Data System (ADS)
Ermolaev, Petr A.; Volynsky, Maxim A.
2016-04-01
The application of extended Kalman particle filter for dynamic estimation of interferometric signal parameters is considered. A detail description of the algorithm is given. Proposed algorithm allows obtaining satisfactory estimates of model interferometric signals even in the presence of erroneous information on model signal parameters. It provides twice as high calculation speed in comparison with conventional particle filter by reducing the number of vectors approximating probability density function of signal parameters distribution
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.
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.
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 μm) and large (150-200 μm) 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.
Mountney, John; Silage, Dennis; Obeid, Iyad
2010-01-01
Both linear and nonlinear estimation algorithms have been successfully applied as neural decoding techniques in brain machine interfaces. Nonlinear approaches such as Bayesian auxiliary particle filters offer improved estimates over other methodologies seemingly at the expense of computational complexity. Real-time implementation of particle filtering algorithms for neural signal processing may become prohibitive when the number of neurons in the observed ensemble becomes large. By implementing a parallel hardware architecture, filter performance can be improved in terms of throughput over conventional sequential processing. Such an architecture is presented here and its FPGA resource utilization is reported. PMID:21096196
Alvin, M.A.
1995-07-01
Advancements have been made during the past five years to not only increase the strength of the as-manufactured clay bonded silicon carbide candle filter materials, but also to improve their high temperature creep resistance properties. This report reviews these developments, and describes the results of preliminary qualification testing which has been conducted at Westinghouse prior to utilizing the advanced clay bonded silicon carbide filters in high temperature, pressurized, coal-fired combustion and/or gasification applications.
Assessment of Metal Media Filters for Advanced Coal-Based Power Generation Applications
Alvin, M.A.
2002-09-19
Advanced coal and biomass-based gas turbine power generation technologies (IGCC, PFBC, PCFBC, and Hipps) are currently under development and demonstration. Efforts at Siemens Westinghouse Power Corporation (SWPC) have been focused on the development and demonstration of hot gas filter systems as an enabling technology for power generation. This paper reviews SWPC's material and component assessment efforts, identifying the performance, stability, and life of porous metal, advanced alloy, and intermetallic filters under simulated, pressurized fluidized-bed combustion conditions.
Particle size for greatest penetration of HEPA filters - and their true efficiency
da Roza, R.A.
1982-12-01
The particle size that most greatly penetrates a filter is a function of filter media construction, aerosol density, and air velocity. In this paper the published results of several experiments are compared with a modern filtration theory that predicts single-fiber efficiency and the particle size of maximum penetration. For high-efficiency particulate air (HEPA) filters used under design conditions this size is calculated to be 0.21 ..mu..m diam. This is in good agreement with the experimental data. The penetration at 0.21 ..mu..m is calculated to be seven times greater than at the 0.3 ..mu..m used for testing HEPA filters. Several mechanisms by which filters may have a lower efficiency in use than when tested are discussed.
Advance particle and Doppler measurement methods
NASA Technical Reports Server (NTRS)
Busch, C.
1985-01-01
Particle environments, i.e., rain, ice, and snow particles are discussed. Two types of particles addressed are: (1) the natural environment in which airplanes fly and conduct test flights; and (2) simulation environments that are encountered in ground-test facilities such as wind tunnels, ranges, etc. There are characteristics of the natural environment that one wishes to measure. The liquid water content (LWC) is the one that seems to be of most importance; size distribution may be of importance in some applications. Like snow, the shape of the particle may be an important parameter to measure. As one goes on to environment in simulated tests, additional parameters may be required such as velocity distribution, the velocity lag of the particle relative to the aerodynamic flow, and the trajectory of the particle as it goes through the aerodynamic flow and impacts on the test object.
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)...
Development of a filter regeneration system for advanced spacecraft fluid systems
NASA Technical Reports Server (NTRS)
Behrend, A. F., Jr.; Descamp, V. A.
1974-01-01
The development of a filter regeneration system for efficiently cleaning fluid particulate filters is presented. Based on a backflush/jet impingement technique, the regeneration system demonstrated a cleaning efficiency of 98.7 to 100%. The operating principles and design features are discussed with emphasis on the primary system components that include a regenerable filter, vortex particle separator, and zero-g particle trap. Techniques and equipment used for ground and zero-g performance tests are described. Test results and conclusions, as well as possible areas for commercial application, are included.
Advanced Energy-Efficient Filtration: Fan Filter Unit
Xu, Tengfang
2005-10-01
The objective of this project is to provide assistance in development of a standard test procedure for fan-filter units, which are gaining popularity for use in California cleanrooms. In particular, LBNL carried out collaboration with various stakeholders in the industry and took a lead in developing a draft standard method for testing the energy performance of fan-filter units, and provided assistance to California public utility companies by testing the draft method in PG&E's testing facility. Through testing more units in the future with a robust standard method, baseline performance information can be developed for use in possible energy incentive programs.
Multispectral Filter Arrays: Recent Advances and Practical Implementation
Lapray, Pierre-Jean; Wang, Xingbo; Thomas, Jean-Baptiste; Gouton, Pierre
2014-01-01
Thanks to some technical progress in interferencefilter design based on different technologies, we can finally successfully implement the concept of multispectral filter array-based sensors. This article provides the relevant state-of-the-art for multispectral imaging systems and presents the characteristics of the elements of our multispectral sensor as a case study. The spectral characteristics are based on two different spatial arrangements that distribute eight different bandpass filters in the visible and near-infrared area of the spectrum. We demonstrate that the system is viable and evaluate its performance through sensor spectral simulation. PMID:25407904
Chen, Yong; Zhang, Rong-Hua; Shang, Lei; Hu, Eric
2013-06-01
A method based on motion vectors of feature points and particle filter has been proposed and developed for an active∕moving camera for object detection and tracking purposes. The object is detected by histogram of motion vectors first, and then, on the basis of particle filter algorithm, the weighing factors are obtained via color information. In addition, re-sampling strategy and surf feature points are used to remedy the drawback of particle degeneration. Experimental results demonstrate the practicability and accuracy of the new method and are presented in the paper. PMID:23822380
Forward-looking infrared 3D target tracking via combination of particle filter and SIFT
NASA Astrophysics Data System (ADS)
Li, Xing; Cao, Zhiguo; Yan, Ruicheng; Li, Tuo
2013-10-01
Aiming at the problem of tracking 3D target in forward-looking infrared (FLIR) image, this paper proposes a high-accuracy robust tracking algorithm based on SIFT and particle filter. The main contribution of this paper is the proposal of a new method of estimating the affine transformation matrix parameters based on Monte Carlo methods of particle filter. At first, we extract SIFT features on infrared image, and calculate the initial affine transformation matrix with optimal candidate key points. Then we take affine transformation parameters as particles, and use SIR (Sequential Importance Resampling) particle filter to estimate the best position, thus implementing our algorithm. The experiments demonstrate that our algorithm proves to be robust with high accuracy.
Advanced Trickling Filters. Training Module 2.112.4.77.
ERIC Educational Resources Information Center
Layton, Ronald F.
This document is an instructional module package prepared in objective form for use by an instructor familiar with operation and maintenance of a trickling filter wastewater treatment plant. Included are objectives, instructor guides, student handouts and transparency masters. This is the third level of a three module series and considers…
A genetic resampling particle filter for freeway traffic-state estimation
NASA Astrophysics Data System (ADS)
Bi, Jun; Guan, Wei; Qi, Long-Tao
2012-06-01
On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.
Alvin, M.A.
1993-04-05
(1) After 500 hours of operation in the pressurized fluidized-bed combustion gas environment, the fibrous outer membrane along the clay bonded silicon carbide Schumacher Dia Schumalith candles remained intact. The fibrous outer membrane did not permit penetration of fines through the filter wall. (2) An approximate 10-15% loss of material strength occurred within the intact candle clay bonded silicon carbide matrix after 500 hours of exposure to the PFBC gas environment. A relatively uniform strength change resulted within the intact candles throughout the vessel (i.e., top to bottom plenums), as well as within the various cluster ring positions (i.e., outer versus inner ring candle filters). A somewhat higher loss of material strength, i.e., 25% was detected in fractured candle segments removed from the W-APF ash hopper. (3) Sulfur which is present in the pressurized fluidized-bed combustion gas system induced phase changes along the surface of the binder which coats the silicon carbide grains in the Schumacher Dia Schumalith candle filter matrix.
Analysis of Video-Based Microscopic Particle Trajectories Using Kalman Filtering
Wu, Pei-Hsun; Agarwal, Ashutosh; Hess, Henry; Khargonekar, Pramod P.; Tseng, Yiider
2010-01-01
Abstract The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including in situ single cell particle tracking and in vitro motility assays. However, the image acquisition process is complicated by system noise, which causes positioning error in the trajectories derived from image analysis. Here, we explore the possibility of reducing the positioning error by the application of a Kalman filter, a powerful algorithm to estimate the state of a linear dynamic system from noisy measurements. We show that the optimal Kalman filter parameters can be determined in an appropriate experimental setting, and that the Kalman filter can markedly reduce the positioning error while retaining the intrinsic fluctuations of the dynamic process. We believe the Kalman filter can potentially serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes. PMID:20550894
Lifted linear phase filter banks and the polyphase-with-advance representation
Brislawn, C. M.; Wohlberg, B. E.
2004-01-01
A matrix theory is developed for the noncausal polyphase-with-advance representation that underlies the theory of lifted perfect reconstruction filter banks and wavelet transforms as developed by Sweldens and Daubechies. This theory provides the fundamental lifting methodology employed in the ISO/IEC JPEG-2000 still image coding standard, which the authors helped to develop. Lifting structures for polyphase-with-advance filter banks are depicted in Figure 1. In the analysis bank of Figure 1(a), the first lifting step updates x{sub 0} with a filtered version of x{sub 1} and the second step updates x{sub 1} with a filtered version of x{sub 0}; gain factors 1/K and K normalize the lowpass- and highpass-filtered output subbands. Each of these steps is inverted by the corresponding operations in the synthesis bank shown in Figure 1(b). Lifting steps correspond to upper- or lower-triangular matrices, S{sub i}(z), in a cascade-form decomposition of the polyphase analysis matrix, H{sub a}(z). Lifting structures can also be implemented reversibly (i.e., losslessly in fixed-precision arithmetic) by rounding the lifting updates to integer values. Our treatment of the polyphase-with-advance representation develops an extensive matrix algebra framework that goes far beyond the results of. Specifically, we focus on analyzing and implementing linear phase two-channel filter banks via linear phase lifting cascade schemes. Whole-sample symmetric (WS) and half-sample symmetric (HS) linear phase filter banks are characterized completely in terms of the polyphase-with-advance representation. The theory benefits significantly from a number of new group-theoretic structures arising in the polyphase-with-advance matrix algebra from the lifting factorization of linear phase filter banks.
Filter performance of n99 and n95 facepiece respirators against viruses and ultrafine particles.
Eninger, Robert M; Honda, Takeshi; Adhikari, Atin; Heinonen-Tanski, Helvi; Reponen, Tiina; Grinshpun, Sergey A
2008-07-01
The performance of three filtering facepiece respirators (two models of N99 and one N95) challenged with an inert aerosol (NaCl) and three virus aerosols (enterobacteriophages MS2 and T4 and Bacillus subtilis phage)-all with significant ultrafine components-was examined using a manikin-based protocol with respirators sealed on manikins. Three inhalation flow rates, 30, 85, and 150 l min(-1), were tested. The filter penetration and the quality factor were determined. Between-respirator and within-respirator comparisons of penetration values were performed. At the most penetrating particle size (MPPS), >3% of MS2 virions penetrated through filters of both N99 models at an inhalation flow rate of 85 l min(-1). Inhalation airflow had a significant effect upon particle penetration through the tested respirator filters. The filter quality factor was found suitable for making relative performance comparisons. The MPPS for challenge aerosols was <0.1 mum in electrical mobility diameter for all tested respirators. Mean particle penetration (by count) was significantly increased when the size fraction of <0.1 mum was included as compared to particles >0.1 mum. The filtration performance of the N95 respirator approached that of the two models of N99 over the range of particle sizes tested ( approximately 0.02 to 0.5 mum). Filter penetration of the tested biological aerosols did not exceed that of inert NaCl aerosol. The results suggest that inert NaCl aerosols may generally be appropriate for modeling filter penetration of similarly sized virions. PMID:18477653
NASA Astrophysics Data System (ADS)
Raitoharju, Matti; Nurminen, Henri; Piché, Robert
2015-12-01
Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.
Simplifying Physical Realization of Gaussian Particle Filters with Block-Level Pipeline Control
NASA Astrophysics Data System (ADS)
Hong, Sangjin; Djurić, Petar M.; Bolić, Miodrag
2005-12-01
We present an efficient physical realization method of particle filters for real-time tracking applications. The methodology is based on block-level pipelining where data transfer between processing blocks is effectively controlled by autonomous distributed controllers. Block-level pipelining maintains inherent operational concurrency within the algorithm for high-throughput execution. The proposed use of controllers, via parameters reconfiguration, greatly simplifies the overall controller structure, and alleviates potential speed bottlenecks that may arise due to complexity of the controller. A Gaussian particle filter for bearings-only tracking problem is realized based on the presented methodology. For demonstration, individual coarse grain processing blocks comprising particle filters are synthesized using commercial FPGA. From the execution characteristics obtained from the implementation, the overall controller structure is derived according to the methodology and its temporal correctness verified using Verilog and SystemC.
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.
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
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
A particle-filtering approach to convoy tracking in the midst of civilian traffic
NASA Astrophysics Data System (ADS)
Pollard, Evangeline; Pannetier, Benjamin; Rombaut, Michèle
2008-04-01
In the battlefield surveillance domain, ground target tracking is used to evaluate the threat. Data used for tracking is given by a Ground Moving Target Indicator (GMTI) sensor which only detects moving targets. Multiple target tracking has been widely studied but most of the algorithms have weaknesses when targets are close together, as they are in a convoy. In this work, we propose a filtering approach for convoys in the midst of civilian traffic. Inspired by particle filtering, our specific algorithm cannot be applied to all the targets because of its complexity. That is why well discriminated targets are tracked using an Interacting Multiple Model-Multiple Hypothesis Tracking (IMM-MHT), whereas the convoy targets are tracked with a specific particle filter. We make the assumption that the convoy is detected (position and number of targets). Our approach is based on an Independent Partition Particle Filter (IPPF) incorporating constraint-regions. The originality of our approach is to consider a velocity constraint (all the vehicles belonging to the convoy have the same velocity) and a group constraint. Consequently, the multitarget state vector contains all the positions of the individual targets and a single convoy velocity vector. When another target is detected crossing or overtaking the convoy, a specific algorithm is used and the non-cooperative target is tracked down an adapted particle filter. As demonstrated by our simulations, a high increase in convoy tracking performance is obtained with our approach.
Evaluation of filter media for particle number, surface area and mass penetrations.
Li, Lin; Zuo, Zhili; Japuntich, Daniel A; Pui, David Y H
2012-07-01
The National Institute for Occupational Safety and Health (NIOSH) developed a standard for respirator certification under 42 CFR Part 84, using a TSI 8130 automated filter tester with photometers. A recent study showed that photometric detection methods may not be sensitive for measuring engineered nanoparticles. Present NIOSH standards for penetration measurement are mass-based; however, the threshold limit value/permissible exposure limit for an engineered nanoparticle worker exposure is not yet clear. There is lack of standardized filter test development for engineered nanoparticles, and development of a simple nanoparticle filter test is indicated. To better understand the filter performance against engineered nanoparticles and correlations among different tests, initial penetration levels of one fiberglass and two electret filter media were measured using a series of polydisperse and monodisperse aerosol test methods at two different laboratories (University of Minnesota Particle Technology Laboratory and 3M Company). Monodisperse aerosol penetrations were measured by a TSI 8160 using NaCl particles from 20 to 300 nm. Particle penetration curves and overall penetrations were measured by scanning mobility particle sizer (SMPS), condensation particle counter (CPC), nanoparticle surface area monitor (NSAM), and TSI 8130 at two face velocities and three layer thicknesses. Results showed that reproducible, comparable filtration data were achieved between two laboratories, with proper control of test conditions and calibration procedures. For particle penetration curves, the experimental results of monodisperse testing agreed well with polydisperse SMPS measurements. The most penetrating particle sizes (MPPSs) of electret and fiberglass filter media were ~50 and 160 nm, respectively. For overall penetrations, the CPC and NSAM results of polydisperse aerosols were close to the penetration at the corresponding median particle sizes. For each filter type, power
Individual Particle Analysis of Ambient PM 2.5 Using Advanced Electron Microscopy Techniques
Gerald J. Keeler; Masako Morishita
2006-12-31
The overall goal of this project was to demonstrate a combination of advanced electron microscopy techniques that can be effectively used to identify and characterize individual particles and their sources. Specific techniques to be used include high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM), STEM energy dispersive X-ray spectrometry (EDX), and energy-filtered TEM (EFTEM). A series of ambient PM{sub 2.5} samples were collected in communities in southwestern Detroit, MI (close to multiple combustion sources) and Steubenville, OH (close to several coal fired utility boilers). High-resolution TEM (HRTEM) -imaging showed a series of nano-metal particles including transition metals and elemental composition of individual particles in detail. Submicron and nano-particles with Al, Fe, Ti, Ca, U, V, Cr, Si, Ba, Mn, Ni, K and S were observed and characterized from the samples. Among the identified nano-particles, combinations of Al, Fe, Si, Ca and Ti nano-particles embedded in carbonaceous particles were observed most frequently. These particles showed very similar characteristics of ultrafine coal fly ash particles that were previously reported. By utilizing HAADF-STEM, STEM-EDX, and EF-TEM, this investigation was able to gain information on the size, morphology, structure, and elemental composition of individual nano-particles collected in Detroit and Steubenville. The results showed that the contributions of local combustion sources - including coal fired utilities - to ultrafine particle levels were significant. Although this combination of advanced electron microscopy techniques by itself can not identify source categories, these techniques can be utilized as complementary analytical tools that are capable of providing detailed information on individual particles.
Hybrid three-dimensional variation and particle filtering for nonlinear systems
NASA Astrophysics Data System (ADS)
Leng, Hong-Ze; Song, Jun-Qiang
2013-03-01
This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations. We present a hybrid three-dimensional variation (3DVar) and particle piltering (PF) method, which combines the advantages of 3DVar and particle-based filters. By minimizing the cost function, this approach will produce a better proposal distribution of the state. Afterwards the stochastic resampling step in standard PF can be avoided through a deterministic scheme. The simulation results show that the performance of the new method is superior to the traditional ensemble Kalman filtering (EnKF) and the standard PF, especially in highly nonlinear systems.
NASA Astrophysics Data System (ADS)
Yan, Hongxiang; Moradkhani, Hamid
2016-08-01
Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX). A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that: (1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study.
Workshop on advances in smooth particle hydrodynamics
Wingate, C.A.; Miller, W.A.
1993-12-31
This proceedings contains viewgraphs presented at the 1993 workshop held at Los Alamos National Laboratory. Discussed topics include: negative stress, reactive flow calculations, interface problems, boundaries and interfaces, energy conservation in viscous flows, linked penetration calculations, stability and consistency of the SPH method, instabilities, wall heating and conservative smoothing, tensors, tidal disruption of stars, breaking the 10,000,000 particle limit, modelling relativistic collapse, SPH without H, relativistic KSPH avoidance of velocity based kernels, tidal compression and disruption of stars near a supermassive rotation black hole, and finally relativistic SPH viscosity and energy.
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.
Cardiac fiber tracking using adaptive particle filtering based on tensor rotation invariant in MRI
NASA Astrophysics Data System (ADS)
Kong, Fanhui; Liu, Wanyu; Magnin, Isabelle E.; Zhu, Yuemin
2016-03-01
Diffusion magnetic resonance imaging (dMRI) is a non-invasive method currently available for cardiac fiber tracking. However, accurate and efficient cardiac fiber tracking is still a challenge. This paper presents a probabilistic cardiac fiber tracking method based on particle filtering. In this framework, an adaptive sampling technique is presented to describe the posterior distribution of fiber orientations by adjusting the number and status of particles according to the fractional anisotropy of diffusion. An observation model is then proposed to update the weight of particles by rotating diffusion tensor from the primary eigenvector to a given fiber orientation while keeping the shape of the tensor invariant. The results on human cardiac dMRI show that the proposed method is robust to noise and outperforms conventional streamline and particle filtering techniques.
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
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.
Advanced hot-gas filter development. Topical report, September 30, 1994--May 31, 1996
Lane, J.E.; LeCostaouec, J.F.; Painter, C.J.; Sue, W.A.; Radford, K.C.
1996-12-31
The application of high-performance, high-temperature particulate control devices is considered to be beneficial to advanced fossil fuel processing technology, to selected high-temperature industrial processes, and to waste incineration concepts. Ceramic rigid filters represent the most attractive technology for these applications due to their capability to withstand high-temperature corrosive environments. However, current generation monolithic filters have demonstrated poor resistance to crack propagation and can experience catastrophic failure during use. To address this problem, ceramic fiber-reinforced ceramic matrix composite (CMC) filter materials are needed for reliable damage tolerant candle filters. This program is focused on the development of an oxide-fiber reinforced oxide material composite filter material that is cost competitive with prototype next generation filters. This goal would be achieved through the development of a low cost sol-gel fabrication process and a three-dimensional fiber architecture optimized for high volume filter manufacturing. The 3D continuous fiber reinforcement provides a damage tolerant structure which is not subject to delamination-type failures. This report documents the Phase 1, Filter Material Development and Evaluation, results. Section 2 provides a program summary. Technical results, including experimental procedures, are presented and discussed in Section 3. Section 4 and 5 provide the Phase 1 conclusions and recommendations, respectively. The remaining sections cover acknowledgements and references.
NASA Astrophysics Data System (ADS)
Zhang, Hongjuan; Hendricks-Franssen, Harrie-Jan; Han, Xujun; Vrugt, Jasper A.; Vereecken, Harry
2016-04-01
Land surface models (LSMs) resolve the water and energy balance with different parameters and state variables. Many of the parameters of these models cannot be measured directly in the field, and require calibration against flux and soil moisture data. Two LSMs are used in our work: Variable Infiltration Capacity Hydrologic Model (VIC) and the Community Land Model (CLM). Temporal variations in soil moisture content at 5, 20 and 50 cm depth in the Rollesbroich experimental watershed in Germany are simulated in both LSMs. Data assimilation (DA) provides a good way to jointly estimate soil moisture content and soil properties of the resolved soil domain. Four DA methods combined with the two LSMs are used in our work: the Ensemble Kalman Filter (EnKF) using state augmentation or dual estimation, the Residual Resampling Particle Filter (RRPF) and Markov chain Monte Carlo Particle Filter (MCMCPF). These four DA methods are tuned and calibrated for a five month period, and subsequently evaluated for another five month period. Performances of the two LSMs and the four DA methods are compared. Our results show that all DA methods improve the estimation of soil moisture content of the VIC and CLM models, especially if the soil hydraulic properties (VIC), the maximum baseflow velocity (VIC) and/or soil texture (CLM) are jointly estimated with soil moisture content. The augmentation and dual estimation methods performed slightly better than RRPF and MCMCPF in the evaluation period. The differences in simulated soil moisture content between CLM and VIC were larger than variations among the DA methods. The CLM performed better than the VIC model. The strong underestimation of soil moisture content in the third layer of the VIC model is likely related to an inadequate parameterization of groundwater drainage.
X-RAY FLUORESCENCE ANALYSIS OF FILTER-COLLECTED AEROSOL PARTICLES
X-ray fluorescence (XRF) has become an effective technique for determining the elemental content of aerosol samples. For quantitative analysis, the aerosol particles must be collected as uniform deposits on the surface of Teflon membrane filters. An energy dispersive XRF spectrom...
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 x 30 x 29 cm(3) 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 1.5 to 12 standard m(3)/min. Relative humidity levels are controllable from 5%-90% and the temperature of the aerosol stream is variable from ambient to 150 degrees 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 7 standard m(3)/min, high mass concentrations (approximately 25 mg/m(3)) of dry aerosol streams having count mean diameters centered near the most penetrating particle size for HEPA filters (120-160 nm). 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. PMID
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.
Recent advance in application of acousto-optic tunable filters
NASA Astrophysics Data System (ADS)
Khansuvarov, Ruslan A.; Shakin, Oleg V.; Vaganov, Mikhail A.; Zhdanov, Arseniy Y.; Prokashev, Vadim N.
2014-09-01
This paper aims to inform those interested in the scientific work of a large group of scientists: workers of the Department of Electronics and Optical communications of St. Petersburg State University of Aerospace Instrumentation in collaboration with workers of the Department of Quantum Electronics of St. Petersburg State Technical University in the area of researches and development of acousto-optic tunable filters (AOTF). Paper discusses the important features of the AOTF structure and their parameters that affect its work, such as: spectral range of optical radiation, spectral resolution, active aperture of the optical radiation, optical transmission of the working spectral range, optical radiation polarization (linear, circular or arbitrary) , diffraction efficiency, contrast, distortion of the optical radiation's front, frequency range of elastic waves, switching time, maximum electric control power, impedance. Also the AOTF using is considered: AOTF's implications for control of laser radiation, AOTF's application to determine the counterfeit money. The last part of the report focuses on materials that act as antireflection thin films. Spectral characteristics of "clean" and enlightened substrates of ZnSe and Ge are shown. As seen from the examples in the report, antireflection thin films increase transmittance of optical elements.
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.
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
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
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
Particle Density Using Deposition Filters at the Full Scale RDD Experiments.
Berg, Rodney; Gilhuly, Colleen; Korpach, Ed; Ungar, Kurt
2016-05-01
During the Full-Scale Radiological Dispersal Device (FSRDD) Field Trials carried out in Suffield, Alberta, Canada, several suites of detection equipment and software models were used to measure and characterize the ground deposition. The FSRDD Field Trials were designed to disperse radioactive lanthanum of known activity to better understand such an event. This paper focuses on one means of measuring both concentration and the particle size distribution of the deposition using electrostatic filters placed around the trial site to collect deposited particles for analysis. The measurements made from ground deposition filters provided a basis to guide modeling and validate results by giving insight on how particles are distributed by a plume. PMID:27023034
Recent advances on distributed filtering for stochastic systems over sensor networks
NASA Astrophysics Data System (ADS)
Ding, Derui; Wang, Zidong; Shen, Bo
2014-05-01
Sensor networks comprising of tiny, power-constrained nodes with sensing, computation, and wireless communication capabilities are gaining popularity due to their potential application in a wide variety of environments like monitoring of environmental attributes and various military and civilian applications. Considering the limited power and communication resources of the sensor nodes, the strategy of the distributed information processing is widely exploited. Therefore, it would be interesting to examine how the topology, network-induced phenomena, and power constraints influence the distributed filtering performance and to obtain some suitable schemes in order to solve the addressed distributed filter design problem. In this paper, we aim to survey some recent advances on the distributed filtering and distributed state estimation problems over the sensor networks with various performance requirements and/or randomly occurring network-induced phenomena. First, some practical filter structures are addressed in detail. Then, the developments of the distributed Kalman filtering, distributed state estimation based on the stability or mean-square error analysis, and distributed ? filtering are systematically reviewed. In addition, latest results on the distributed filtering or state estimation over sensor networks are discussed in great detail and some challenges are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out.
Gaussian mixture sigma-point particle filter for optical indoor navigation system
NASA Astrophysics Data System (ADS)
Zhang, Weizhi; Gu, Wenjun; Chen, Chunyi; Chowdhury, M. I. S.; Kavehrad, Mohsen
2013-12-01
With the fast growing and popularization of smart computing devices, there is a rise in demand for accurate and reliable indoor positioning. Recently, systems using visible light communications (VLC) technology have been considered as candidates for indoor positioning applications. A number of researchers have reported that VLC-based positioning systems could achieve position estimation accuracy in the order of centimeter. This paper proposes an Indoors navigation environment, based on visible light communications (VLC) technology. Light-emitting-diodes (LEDs), which are essentially semiconductor devices, can be easily modulated and used as transmitters within the proposed system. Positioning is realized by collecting received-signal-strength (RSS) information on the receiver side, following which least square estimation is performed to obtain the receiver position. To enable tracking of user's trajectory and reduce the effect of wild values in raw measurements, different filters are employed. In this paper, by computer simulations we have shown that Gaussian mixture Sigma-point particle filter (GM-SPPF) outperforms other filters such as basic Kalman filter and sequential importance-resampling particle filter (SIR-PF), at a reasonable computational cost.
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.
Alderman, Steven L; Parsons, Michael S; Hogancamp, Kristina U; Waggoner, Charles A
2008-11-01
High-efficiency particulate air (HEPA) filters are widely used to control particulate matter emissions from processes that involve management or treatment of radioactive materials. Section FC of the American Society of Mechanical Engineers AG-1 Code on Nuclear Air and Gas Treatment currently restricts media velocity to a maximum of 2.5 cm/sec in any application where this standard is invoked. There is some desire to eliminate or increase this media velocity limit. A concern is that increasing media velocity will result in higher emissions of ultrafine particles; thus, it is unlikely that higher media velocities will be allowed without data to demonstrate the effect of media velocity on removal of ultrafine particles. In this study, the performance of nuclear grade HEPA filters, with respect to filter efficiency and most penetrating particle size, was evaluated as a function of media velocity. Deep-pleat nuclear grade HEPA filters (31 cm x 31 cm x 29 cm) were evaluated at media velocities ranging from 2.0 to 4.5 cm/sec using a potassium chloride aerosol challenge having a particle size distribution centered near the HEPA filter most penetrating particle size. Filters were challenged under two distinct mass loading rate regimes through the use of or exclusion of a 3 microm aerodynamic diameter cut point cyclone. Filter efficiency and most penetrating particle size measurements were made throughout the duration of filter testing. Filter efficiency measured at the onset of aerosol challenge was noted to decrease with increasing media velocity, with values ranging from 99.999 to 99.977%. The filter most penetrating particle size recorded at the onset of testing was noted to decrease slightly as media velocity was increased and was typically in the range of 110-130 nm. Although additional testing is needed, these findings indicate that filters operating at media velocities up to 4.5 cm/sec will meet or exceed current filter efficiency requirements. Additionally
NASA Technical Reports Server (NTRS)
Leviton, Douglas B.; Tsevetanov, Zlatan; Woodruff, Bob; Mooney, Thomas A.
1998-01-01
Advanced optical bandpass filters for the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) have been developed on a filter-by-filter basis through detailed studies which take into account the instrument's science goals, available optical filter fabrication technology, and developments in ACS's charge-coupled-device (CCD) detector technology. These filters include a subset of filters for the Sloan Digital Sky Survey (SDSS) which are optimized for astronomical photometry using today's charge-coupled-devices (CCD's). In order for ACS to be truly advanced, these filters must push the state-of-the-art in performance in a number of key areas at the same time. Important requirements for these filters include outstanding transmitted wavefront, high transmittance, uniform transmittance across each filter, spectrally structure-free bandpasses, exceptionally high out of band rejection, a high degree of parfocality, and immunity to environmental degradation. These constitute a very stringent set of requirements indeed, especially for filters which are up to 90 mm in diameter. The highly successful paradigm in which final specifications for flight filters were derived through interaction amongst the ACS Science Team, the instrument designer, the lead optical engineer, and the filter designer and vendor is described. Examples of iterative design trade studies carried out in the context of science needs and budgetary and schedule constraints are presented. An overview of the final design specifications for the ACS bandpass and ramp filters is also presented.
Richardson, R B; Hegyi, G; Starling, S C
2003-01-01
Methods have been developed to assess the size distribution of alpha emitting particles of reactor fuel of known composition captured on air sampler filters. The sizes of uranium oxide and plutonium oxide particles were determined using a system based on CR-39 solid-state nuclear track detectors. The CR-39 plastic was exposed to the deposited particles across a 400 microm airgap. The exposed CR-39 was chemically etched to reveal clusters of tracks radially dispersed from central points. The number and location of the tracks were determined using an optical microscope with an XY motorised table and image analysis software. The sample mounting arrangement allowed individual particles to be simultaneously viewed with their respective track cluster. The predicted diameters correlated with the actual particle diameters, as measured using the optical microscope. The efficacy of the technique was demonstrated with particles of natural uranium oxide (natUO2) of known size, ranging from 4 to 150 microm in diameter. Two personal air sampler (PAS) filters contaminated with actinide particles were placed against CR-39 and estimated to have size distributions of 0.8 and 1.0 microm activity median aerodynamic diameter (AMAD). PMID:14526944
NASA Astrophysics Data System (ADS)
Matgen, P.; Montanari, M.; Hostache, R.; Pfister, L.; Hoffmann, L.; Plaza, D.; Pauwels, V. R. N.; de Lannoy, G. J. M.; de Keyser, R.; Savenije, H. H. G.
2010-03-01
With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multi-mission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filter-based data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydrodynamic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management
NASA Astrophysics Data System (ADS)
Matgen, P.; Montanari, M.; Hostache, R.; Pfister, L.; Hoffmann, L.; Plaza, D.; Pauwels, V. R. N.; de Lannoy, G. J. M.; de Keyser, R.; Savenije, H. H. G.
2010-09-01
With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multi-mission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filter-based data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydraulic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into
Microscopy with spatial filtering for sorting particles and monitoring subcellular morphology
NASA Astrophysics Data System (ADS)
Zheng, Jing-Yi; Qian, Zhen; Pasternack, Robert M.; Boustany, Nada N.
2009-02-01
Optical scatter imaging (OSI) was developed to non-invasively track real-time changes in particle morphology with submicron sensitivity in situ without exogenous labeling, cell fixing, or organelle isolation. For spherical particles, the intensity ratio of wide-to-narrow angle scatter (OSIR, Optical Scatter Image Ratio) was shown to decrease monotonically with diameter and agree with Mie theory. In living cells, we recently reported this technique is able to detect mitochondrial morphological alterations, which were mediated by the Bcl-xL transmembrane domain, and could not be observed by fluorescence or differential interference contrast images. Here we further extend the ability of morphology assessment by adopting a digital micromirror device (DMD) for Fourier filtering. When placed in the Fourier plane the DMD can be used to select scattering intensities at desired combination of scattering angles. We designed an optical filter bank consisting of Gabor-like filters with various scales and rotations based on Gabor filters, which have been widely used for localization of spatial and frequency information in digital images and texture analysis. Using a model system consisting of mixtures of polystyrene spheres and bacteria, we show how this system can be used to sort particles on a microscopic slide based on their size, orientation and aspect ratio. We are currently applying this technique to characterize the morphology of subcellular organelles to help understand fundamental biological processes.
NASA Astrophysics Data System (ADS)
Wu, Jingjing; Hu, Shiqiang; Wang, Yang
2011-09-01
Particle probability hypothesis density (PHD) filter-based visual trackers have achieved considerable success in the visual tracking field. But position measurements based on detection may not have enough ability to discriminate an object from clutter, and accurate state extraction cannot be obtained in the original PHD filtering framework, especially when targets can appear, disappear, merge, or split at any time. To meet the limitations, the proposed algorithm combines a color histogram of a target and the temporal dynamics in a unifying framework and a Gaussian mixture model clustering method for efficient state extraction is designed. The proposed tracker can improve the accuracy of state estimation in tracking a variable number of objects.
Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter
Zhou, Ning; Meng, Da; Lu, Shuai
2013-11-11
In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a non-linear system with non-Gaussian noise. The extended PF modifies a basic PF to improve robustness. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF’s performance is evaluated and compared with the basic PF and an extended Kalman filter (EKF). The extended PF results showed high accuracy and robustness against measurement and model noise.
Continuous collection of soluble atmospheric particles with a wetted hydrophilic filter.
Takeuchi, Masaki; Ullah, S M Rahmat; Dasgupta, Purnendu K; Collins, Donald R; Williams, Allen
2005-12-15
Approximately one-third of the area (14-mm diameter of a 25-mm diameter) of a 5-microm uniform pore size polycarbonate filter is continuously wetted by a 0.25 mL/min water mist. The water forms a continuous thin film on the filter and percolates through it. The flowing water substantially reduces the effective pore size of the filter. At the operational air sampling flow rate of 1.5 standard liters per minute, such a particle collector (PC) efficiently captures particles down to very small size. As determined by fluorescein-tagged NaCl aerosol generated by a vibrating orifice aerosol generator, the capture efficiency was 97.7+% for particle aerodynamic diameters ranging from 0.28 to 3.88 microm. Further, 55.3 and 80.3% of 25- and 100-nm (NH4)2SO4 particles generated by size classification with a differential mobility analyzer were respectively collected by the device. The PC is integrally coupled with a liquid collection reservoir. The liquid effluent from the wetted filter collector, bearing the soluble components of the aerosol, can be continuously collected or periodically withdrawn. The latter strategy permits the use of a robust syringe pump for the purpose. Coupled with a PM2.5 cyclone inlet and a membrane-based parallel plate denuder at the front end and an ion chromatograph at the back end, the PC readily operated for at least 4-week periods without filter replacement or any other maintenance. PMID:16351153
Large particle penetration through N95 respirator filters and facepiece leaks with cyclic flow.
Cho, Kyungmin Jacob; Reponen, Tiina; McKay, Roy; Shukla, Rakesh; Haruta, Hiroki; Sekar, Padmini; Grinshpun, Sergey A
2010-01-01
The aim of this study was to investigate respirator filter and faceseal penetration of particles representing bacterial and fungal spore size ranges (0.7-4 mum). First, field experiments were conducted to determine workplace protection factors (WPFs) for a typical N95 filtering facepiece respirator (FFR). These data (average WPF = 515) were then used to position the FFR on a manikin to simulate realistic donning conditions for laboratory experiments. Filter penetration was also measured after the FFR was fully sealed on the manikin face. This value was deducted from the total penetration (obtained from tests with the partially sealed FFR) to determine the faceseal penetration. All manikin experiments were repeated using three sinusoidal breathing flow patterns corresponding to mean inspiratory flow rates of 15, 30, and 85 l min(-1). The faceseal penetration varied from 0.1 to 1.1% and decreased with increasing particle size (P < 0.001) and breathing rate (P < 0.001). The fractions of aerosols penetrating through the faceseal leakage varied from 0.66 to 0.94. In conclusion, even for a well-fitting FFR respirator, most particle penetration occurs through faceseal leakage, which varies with breathing flow rate and particle size. PMID:19700488
The new approach for infrared target tracking based on the particle filter algorithm
NASA Astrophysics Data System (ADS)
Sun, Hang; Han, Hong-xia
2011-08-01
Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring, precision, and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection, the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure, but in order to capture the change of the state space, it need a certain amount of particles to ensure samples is enough, and this number will increase in accompany with dimension and increase exponentially, this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining", we expand the classic Mean Shift tracking framework .Based on the previous perspective, we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis, Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism, used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy
Particle filtering for sensor-to-sensor self-calibration and motion estimation
NASA Astrophysics Data System (ADS)
Yang, Yafei; Li, Jianguo
2013-01-01
This paper addresses the problem of calibrating the six degrees-of-freedom rigid body transform between a camera and an inertial measurement unit (IMU) while at the same time estimating the 3D motion of a vehicle. A high-fidelity measurement model for the camera and IMU are derived and the estimation algorithm are implemented within the particle filter (PF) framework. Belonging to the class of Monte Carlo sequential methods, the filter uses the unscented Kalman filter (UKF) to generate importance proposal distribution. It can not only avoid the limitation of the UKF which can only apply to Gaussian distribution, but also avoid the limitation of the standard PF which can not include the new measurements. Moreover, the proposed algorithm requires no additional hardware equipment. Simulation results illustrate the ill effects of misalignment on motion estimation and demonstrate accurate estimation of both the calibration parameters and the state of the vehicle.
NASA Astrophysics Data System (ADS)
Liebold, F.; Maas, H.-G.
2016-01-01
The paper shows advanced spatial, temporal and spatio-temporal filtering techniques which may be used to reduce noise effects in photogrammetric image sequence analysis tasks and tools. As a practical example, the techniques are validated in a photogrammetric spatio-temporal crack detection and analysis tool applied in load tests in civil engineering material testing. The load test technique is based on monocular image sequences of a test object under varying load conditions. The first image of a sequence is defined as a reference image under zero load, wherein interest points are determined and connected in a triangular irregular network structure. For each epoch, these triangles are compared to the reference image triangles to search for deformations. The result of the feature point tracking and triangle comparison process is a spatio-temporally resolved strain value field, wherein cracks can be detected, located and measured via local discrepancies. The strains can be visualized as a color-coded map. In order to improve the measuring system and to reduce noise, the strain values of each triangle must be treated in a filtering process. The paper shows the results of various filter techniques in the spatial and in the temporal domain as well as spatio-temporal filtering techniques applied to these data. The best results were obtained by a bilateral filter in the spatial domain and by a spatio-temporal EOF (empirical orthogonal function) filtering technique.
Real-Time Flood Forecasting System Using Channel Flow Routing Model with Updating by Particle Filter
NASA Astrophysics Data System (ADS)
Kudo, R.; Chikamori, H.; Nagai, A.
2008-12-01
A real-time flood forecasting system using channel flow routing model was developed for runoff forecasting at water gauged and ungaged points along river channels. The system is based on a flood runoff model composed of upstream part models, tributary part models and downstream part models. The upstream part models and tributary part models are lumped rainfall-runoff models, and the downstream part models consist of a lumped rainfall-runoff model for hillslopes adjacent to a river channel and a kinematic flow routing model for a river channel. The flow forecast of this model is updated by Particle filtering of the downstream part model as well as by the extended Kalman filtering of the upstream part model and the tributary part models. The Particle filtering is a simple and powerful updating algorithm for non-linear and non-gaussian system, so that it can be easily applied to the downstream part model without complicated linearization. The presented flood runoff model has an advantage in simlecity of updating procedure to the grid-based distributed models, which is because of less number of state variables. This system was applied to the Gono-kawa River Basin in Japan, and flood forecasting accuracy of the system with both Particle filtering and extended Kalman filtering and that of the system with only extended Kalman filtering were compared. In this study, water gauging stations in the objective basin were divided into two types of stations, that is, reference stations and verification stations. Reference stations ware regarded as ordinary water gauging stations and observed data at these stations are used for calibration and updating of the model. Verification stations ware considered as ungaged or arbitrary points and observed data at these stations are used not for calibration nor updating but for only evaluation of forecasting accuracy. The result confirms that Particle filtering of the downstream part model improves forecasting accuracy of runoff at
Nian, H.L.T.; Kuzay, T.M.; Collins, J.; Shu, D.; Benson, C.; Dejus, R.
1996-12-31
This paper reports a thermo-mechanical study of a beamline filter (user filter) for undulator/wiggler operations. It is deployed in conjunction with the current commissioning window assembly on the APS insertion device (ID) front ends. The beamline filter at the Advanced Photon Source (APS) will eventually be used in windowless operations also. Hence survival and reasonable life expectancy of the filters under intense insertion device (ID) heat flu are crucial to the beamline operations. To accommodate various user requirements, the filter is configured to be a multi-choice type and smart to allow only those filter combinations that will be safe to operate with a given ring current and beamline insertion device gap. However, this paper addresses only the thermo-mechanical analysis of individual filter integrity and safety in all combinations possible. The current filter design is configured to have four filter frames in a cascade with each frame holding five filters. This allows a potential 625 total filter combinations. Thermal analysis for all of these combinations becomes a mammoth task considering the desired choices for filter materials (pyrolitic graphite and metallic filters), filter thicknesses, undulator gaps, and the beam currents. The paper addresses how this difficult task has been reduced to a reasonable effort and computational level. Results from thermo-mechanical analyses of the filter combinations are presented both in tabular and graphical format.
Advances in Bayesian Model Based Clustering Using Particle Learning
Merl, D M
2009-11-19
Recent work by Carvalho, Johannes, Lopes and Polson and Carvalho, Lopes, Polson and Taddy introduced a sequential Monte Carlo (SMC) alternative to traditional iterative Monte Carlo strategies (e.g. MCMC and EM) for Bayesian inference for a large class of dynamic models. The basis of SMC techniques involves representing the underlying inference problem as one of state space estimation, thus giving way to inference via particle filtering. The key insight of Carvalho et al was to construct the sequence of filtering distributions so as to make use of the posterior predictive distribution of the observable, a distribution usually only accessible in certain Bayesian settings. Access to this distribution allows a reversal of the usual propagate and resample steps characteristic of many SMC methods, thereby alleviating to a large extent many problems associated with particle degeneration. Furthermore, Carvalho et al point out that for many conjugate models the posterior distribution of the static variables can be parametrized in terms of [recursively defined] sufficient statistics of the previously observed data. For models where such sufficient statistics exist, particle learning as it is being called, is especially well suited for the analysis of streaming data do to the relative invariance of its algorithmic complexity with the number of data observations. Through a particle learning approach, a statistical model can be fit to data as the data is arriving, allowing at any instant during the observation process direct quantification of uncertainty surrounding underlying model parameters. Here we describe the use of a particle learning approach for fitting a standard Bayesian semiparametric mixture model as described in Carvalho, Lopes, Polson and Taddy. In Section 2 we briefly review the previously presented particle learning algorithm for the case of a Dirichlet process mixture of multivariate normals. In Section 3 we describe several novel extensions to the original
NASA Astrophysics Data System (ADS)
Wang, Xuemei; Ni, Wenbo
2016-09-01
For loosely coupled INS/GPS integrated navigation systems with low-cost and low-accuracy microelectromechanical device inertial sensors, in order to obtain enough accuracy, a full-state nonlinear dynamic model rather than a linearized error model is much more preferable. Particle filters are particularly for nonlinear and non-Gaussian situations, but typical bootstrap particle filters (BPFs) and some improved particle filters (IPFs) such as auxiliary particle filters (APFs) and Gaussian particle filters (GPFs) cannot solve the mismatch between the importance function and the likelihood function very well. The predicted particles propagated through inertial navigation equations cannot be scattered with certainty within the effective range of current observation when there are large drift errors of the inertial sensors. Therefore, the current observation cannot play the correction role well and these particle filters are invalid to some extent. The proposed IPF firstly estimates the corresponding state bias errors according to the current observation and then corrects the bias errors of the predicted particles before determining the weights and resampling the particles. Simulations and practical experiments both show that the proposed IPF can effectively solve the mismatch between the importance function and the likelihood function of a BPF and compensate the accumulated errors of INSs very well. It has great robustness in a serious noisy scenario.
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
Advanced visualization technology for terascale particle accelerator simulations
Ma, K-L; Schussman, G.; Wilson, B.; Ko, K.; Qiang, J.; Ryne, R.
2002-11-16
This paper presents two new hardware-assisted rendering techniques developed for interactive visualization of the terascale data generated from numerical modeling of next generation accelerator designs. The first technique, based on a hybrid rendering approach, makes possible interactive exploration of large-scale particle data from particle beam dynamics modeling. The second technique, based on a compact texture-enhanced representation, exploits the advanced features of commodity graphics cards to achieve perceptually effective visualization of the very dense and complex electromagnetic fields produced from the modeling of reflection and transmission properties of open structures in an accelerator design. Because of the collaborative nature of the overall accelerator modeling project, the visualization technology developed is for both desktop and remote visualization settings. We have tested the techniques using both time varying particle data sets containing up to one billion particle s per time step and electromagnetic field data sets with millions of mesh elements.
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.
NASA Astrophysics Data System (ADS)
Aggarwal, Priyanka; Syed, Zainab; El-Sheimy, Naser
2009-05-01
Navigation includes the integration of methodologies and systems for estimating time-varying position, velocity and attitude of moving objects. Navigation incorporating the integrated inertial navigation system (INS) and global positioning system (GPS) generally requires extensive evaluations of nonlinear equations involving double integration. Currently, integrated navigation systems are commonly implemented using the extended Kalman filter (EKF). The EKF assumes a linearized process, measurement models and Gaussian noise distributions. These assumptions are unrealistic for highly nonlinear systems like land vehicle navigation and may cause filter divergence. A particle filter (PF) is developed to enhance integrated INS/GPS system performance as it can easily deal with nonlinearity and non-Gaussian noises. In this paper, a hybrid extended particle filter (HEPF) is developed as an alternative to the well-known EKF to achieve better navigation data accuracy for low-cost microelectromechanical system sensors. The results show that the HEPF performs better than the EKF during GPS outages, especially when simulated outages are located in periods with high vehicle dynamics.
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.
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
Particle filter based visual tracking with multi-cue adaptive fusion
NASA Astrophysics Data System (ADS)
Li, Anping; Jing, Zhongliang; Hu, Shiqiang
2005-06-01
To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.
Auxiliary particle filter-model predictive control of the vacuum arc remelting process
NASA Astrophysics Data System (ADS)
Lopez, F.; Beaman, J.; Williamson, R.
2016-07-01
Solidification control is required for the suppression of segregation defects in vacuum arc remelting of superalloys. In recent years, process controllers for the VAR process have been proposed based on linear models, which are known to be inaccurate in highly-dynamic conditions, e.g. start-up, hot-top and melt rate perturbations. A novel controller is proposed using auxiliary particle filter-model predictive control based on a nonlinear stochastic model. The auxiliary particle filter approximates the probability of the state, which is fed to a model predictive controller that returns an optimal control signal. For simplicity, the estimation and control problems are solved using Sequential Monte Carlo (SMC) methods. The validity of this approach is verified for a 430 mm (17 in) diameter Alloy 718 electrode melted into a 510 mm (20 in) diameter ingot. Simulation shows a more accurate and smoother performance than the one obtained with an earlier version of the controller.
NASA Astrophysics Data System (ADS)
Lin, Xiangdong; Kirubarajan, Thiagalingam; Bar-Shalom, Yaakov; Maskell, Simon
2002-08-01
In this paper we consider a nonlinear bearing-only target tracking problem using three different methods and compare their performances. The study is motivated by a ground surveillance problem where a target is tracked from an airborne sensor at an approximately known altitude using depression angle observations. Two nonlinear suboptimal estimators, namely, the extended Kalman Filter (EKF) and the pseudomeasurement tracking filter are applied in a 2-D bearing-only tracking scenario. The EKF is based on the linearization of the nonlinearities in the dynamic and/or the measurement equations. The pseudomeasurement tracking filter manipulates the original nonlinear measurement algebraically to obtain the linear-like structures measurement. Finally, the particle filter, which is a Monte Carlo integration based optimal nonlinear filter and has been presented in the literature as a better alternative to linearization via EKF, is used on the same problem. The performances of these three different techniques in terms of accuracy and computational load are presented in this paper. The results demonstrate the limitations of these algorithms on this deceptively simple tracking problem.
An approach to measure trace elements in particles collected on fiber filters using EDXRF.
Oztürk, Fatma; Zararsiz, Abdullah; Kirmaz, Ridvan; Tuncel, Gürdal
2011-01-15
A method developed for analyzes of large number of aerosol samples using Energy Dispersive X-Ray Fluorescence (EDXRF) and its performance were discussed in this manuscript. Atmospheric aerosol samples evaluated in this study were collected on cellulose fiber (Whatman-41) filters, employing a Hi-Vol sampler, at a monitoring station located on the Mediterranean coast of Turkey, between 1993 and 2001. Approximately 1700 samples were collected in this period. Six-hundred of these samples were analyzed by instrumental neutron activation (INAA), and the rest were archived. EDXRF was selected as an analytical technique to analyze 1700 aerosol samples because of its speed and non-destructive nature. However, analysis of aerosol samples collected on fiber filters with a surface technique such as EDXRF was a challenge. Penetration depth calculation performed in this study revealed that EDXRF can obtain information from top 150μm of our fiber filter material. Calibration of the instrument with currently available thin film standards caused unsatisfactory results since the actual penetration depth of particles into fiber filters were much deeper than 150μm. A method was developed in this manuscript to analyze fiber filter samples quickly with XRF. Two hundred samples that were analyzed by INAA were divided into two equal batches. One of these batches was used to calibrate the XRF and the second batch was used for verification. The results showed that developed method can be reliably used for routine analysis of fiber samples loaded with ambient aerosol. PMID:21147325
Wey, Ming-Yen; Chen, Ke-Hao; Liu, Kuang-Yu
2005-05-20
The phenomenon of filtering particles by a fluidized bed is complex and the parameters that affect the control efficiency of filtration have not yet been clarified. The major objective of the study focuses on the effect of characteristics of ash and filter media on filtration efficiency in a fluidized bed. The performance of the fluidized bed for removal of particles in flue gas at various fluidized operating conditions, and then the mechanisms of collecting particles were studied. The evaluated parameters included (1) various ashes (coal ash and incinerator ash); (2) bed material size; (3) operating gas velocity; and (4) bed temperature. The results indicate that the removal efficiency of coal ash increases initially with gas velocity, then decreases gradually as velocity exceeds some specific value. Furthermore, the removal of coal ash enhance with silica sand size decreasing. When the fluidized bed is operated at high temperature, diffusion is a more important mechanism than at room temperature especially for small particles. Although the inertial impaction is the main collection mechanism, the "bounce off" effect when the particles collide with the bed material could reduce the removal efficiency significantly. Because of layer inversion in fluidized bed, the removal efficiency of incinerator ash is decreased with increasing of gas velocity. PMID:15885419
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
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
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
NASA Astrophysics Data System (ADS)
Sugano, Hiroki; Ochi, Hiroyuki; Nakamura, Yukihiro; Miyamoto, Ryusuke
Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate object recognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1, 132, 038 transistors and the die area is 2,195µm × 1,985µm under a 0.180µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65MHz operation frequency.
NASA Astrophysics Data System (ADS)
Salazar, Juan P. L. C.; Collins, Lance R.
2012-08-01
In this study, we investigate the effect of "biased sampling," i.e., the clustering of inertial particles in regions of the flow with low vorticity, and "filtering," i.e., the tendency of inertial particles to attenuate the fluid velocity fluctuations, on the probability density function of inertial particle accelerations. In particular, we find that the concept of "biased filtering" introduced by Ayyalasomayajula et al. ["Modeling inertial particle acceleration statistics in isotropic turbulence," Phys. Fluids 20, 0945104 (2008), 10.1063/1.2976174], in which particles filter stronger acceleration events more than weaker ones, is relevant to the higher order moments of acceleration. Flow topology and its connection to acceleration is explored through invariants of the velocity-gradient, strain-rate, and rotation-rate tensors. A semi-quantitative analysis is performed where we assess the contribution of specific flow topologies to acceleration moments. Our findings show that the contributions of regions of high vorticity and low strain decrease significantly with Stokes number, a non-dimensional measure of particle inertia. The contribution from regions of low vorticity and high strain exhibits a peak at a Stokes number of approximately 0.2. Following the methodology of Ooi et al. ["A study of the evolution and characteristics of the invariants of the velocity-gradient tensor in isotropic turbulence," J. Fluid Mech. 381, 141 (1999), 10.1017/S0022112098003681], we compute mean conditional trajectories in planes formed by pairs of tensor invariants in time. Among the interesting findings is the existence of a stable focus in the plane formed by the second invariants of the strain-rate and rotation-rate tensors. Contradicting the results of Ooi et al., we find a stable focus in the plane formed by the second and third invariants of the strain-rate tensor for fluid tracers. We confirm, at an even higher Reynolds number, the conjecture of Collins and Keswani ["Reynolds
Recent advances in the Mercury Monte Carlo particle transport code
Brantley, P. S.; Dawson, S. A.; McKinley, M. S.; O'Brien, M. J.; Stevens, D. E.; Beck, B. R.; Jurgenson, E. D.; Ebbers, C. A.; Hall, J. M.
2013-07-01
We review recent physics and computational science advances in the Mercury Monte Carlo particle transport code under development at Lawrence Livermore National Laboratory. We describe recent efforts to enable a nuclear resonance fluorescence capability in the Mercury photon transport. We also describe recent work to implement a probability of extinction capability into Mercury. We review the results of current parallel scaling and threading efforts that enable the code to run on millions of MPI processes. (authors)
NASA Astrophysics Data System (ADS)
Yu, Zhongbo; Liu, Di; Lü, Haishen; Fu, Xiaolei; Xiang, Long; Zhu, Yonghua
2012-12-01
SummaryHybrid data assimilation (DA) is greatly used in recent hydrology and water resources research. In this study, one newly introduced technique, the ensemble particle filter (EnPF), formed by coupling ensemble Kalman filter (EnKF) with particle filter (PF), is applied for a multi-layer soil moisture prediction in the Meilin watershed based on the support vector machines (SVMs). The data used in this paper includes six-layer soil moisture: 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm and 300 cm and five meteorological parameters: soil temperature at 5 cm and 20 cm, air temperature, relative humidity and solar radiation in the study area. In order to investigate this EnPF approach, another two filters, EnKF and PF are applied as another two data assimilation methods to conduct a comparison. In addition, the SVM model simulated data without updating with data assimilation technique is discussed as well to evaluate the data assimilation technique. Two experimental cases are explored here, one with 200 initial training ensemble members in the SVM training phase while the other with 1000 initial training ensemble members. Three main findings are obtained in this study: (1) the SVMs machine is a statistically sound and robust model for soil moisture prediction in both the surface and root zone layers, and the larger the initial training data ensemble, the more effective the operator derived; (2) data assimilation technique does improve the performance of SVM modeling; (3) EnPF outweighs the performance of other two filters as well as the SVM model; Moreover, the ability of EnPF and PF is not positively related to the resampling ensemble size, when the resampling size exceeds a certain amount, the performance of EnPF and PF would be degraded. Because the EnPF still performs well than EnKF, it can be used as a powerful data assimilation tool in the soil moisture prediction.
Filter feeders and plankton increase particle encounter rates through flow regime control.
Humphries, Stuart
2009-05-12
Collisions between particles or between particles and other objects are fundamental to many processes that we take for granted. They drive the functioning of aquatic ecosystems, the onset of rain and snow precipitation, and the manufacture of pharmaceuticals, powders and crystals. Here, I show that the traditional assumption that viscosity dominates these situations leads to consistent and large-scale underestimation of encounter rates between particles and of deposition rates on surfaces. Numerical simulations reveal that the encounter rate is Reynolds number dependent and that encounter efficiencies are consistent with the sparse experimental data. This extension of aerosol theory has great implications for understanding of selection pressure on the physiology and ecology of organisms, for example filter feeders able to gather food at rates up to 5 times higher than expected. I provide evidence that filter feeders have been strongly selected to take advantage of this flow regime and show that both the predicted peak concentration and the steady-state concentrations of plankton during blooms are approximately 33% of that predicted by the current models of particle encounter. Many ecological and industrial processes may be operating at substantially greater rates than currently assumed. PMID:19416879
The use of an inert, radioactively labeled microsphere as a measure of particle accumulation (filtration activity) by Mulinia lateralis (Say) and Mytilus edulis L. was evaluated. Bottom sediment plus temperature and salinity of the water were varied to induce changes in filtratio...
Achieving sub-nanometre particle mapping with energy-filtered TEM.
Lozano-Perez, S; de Castro Bernal, V; Nicholls, R J
2009-09-01
A combination of state-of-the-art instrumentation and optimized data processing has enabled for the first time the chemical mapping of sub-nanometre particles using energy-filtered transmission electron microscopy (EFTEM). Multivariate statistical analysis (MSA) generated reconstructed datasets where the signal from particles smaller than 1 nm in diameter was successfully isolated from the original noisy background. The technique has been applied to the characterization of oxide dispersion strengthened (ODS) reduced activation FeCr alloys, due to their relevance as structural materials for future fusion reactors. Results revealed that most nanometer-sized particles had a core-shell structure, with an Yttrium-Chromium-Oxygen-rich core and a nano-scaled Chromium-Oxygen-rich shell. This segregation to the nanoparticles caused a decrease of the Chromium dissolved in the matrix, compromising the corrosion resistance of the alloy. PMID:19505762
NASA Astrophysics Data System (ADS)
Shen, Zheqi; Tang, Youmin
2016-04-01
The ensemble Kalman particle filter (EnKPF) is a combination of two Bayesian-based algorithms, namely, the ensemble Kalman filter (EnKF) and the sequential importance resampling particle filter(SIR-PF). It was recently introduced to address non-Gaussian features in data assimilation for highly nonlinear systems, by providing a continuous interpolation between the EnKF and SIR-PF analysis schemes. In this paper, we first extend the EnKPF algorithm by modifying the formula for the computation of the covariancematrix, making it suitable for nonlinear measurement functions (we will call this extended algorithm nEnKPF). Further, a general form of the Kalman gain is introduced to the EnKPF to improve the performance of the nEnKPF when the measurement function is highly nonlinear (this improved algorithm is called mEnKPF). The Lorenz '63 model and Lorenz '96 model are used to test the two modified EnKPF algorithms. The experiments show that the mEnKPF and nEnKPF, given an affordable ensemble size, can perform better than the EnKF for the nonlinear systems with nonlinear observations. These results suggest a promising opportunity to develop a non-Gaussian scheme for realistic numerical models.
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
A particle filter to reconstruct a free-surface flow from a depth camera
NASA Astrophysics Data System (ADS)
Combés, Benoit; Heitz, Dominique; Guibert, Anthony; Mémin, Etienne
2015-10-01
We investigate the combined use of a kinect depth sensor and of a stochastic data assimilation (DA) method to recover free-surface flows. More specifically, we use a weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only. This particle filter accounts for model and observations errors. This DA scheme is enhanced with the use of two observations instead of one classically. We evaluate the developed approach on two numerical test cases: a collapse of a water column as a toy-example and a flow in an suddenly expanding flume as a more realistic flow. The robustness of the method to depth data errors and also to initial and inflow conditions is considered. We illustrate the interest of using two observations instead of one observation into the correction step, especially for unknown inflow boundary conditions. Then, the performance of the Kinect sensor in capturing the temporal sequences of depth observations is investigated. Finally, the efficiency of the algorithm is qualified for a wave in a real rectangular flat bottomed tank. It is shown that for basic initial conditions, the particle filter rapidly and remarkably reconstructs the velocity and height of the free surface flow based on noisy measurements of the elevation alone.
NASA Astrophysics Data System (ADS)
Kirchstetter, T.; Preble, C.; Dallmann, T. R.; DeMartini, S. J.; Tang, N. W.; Kreisberg, N. M.; Hering, S. V.; Harley, R. A.
2013-12-01
Diesel particle filters have become widely used in the United States since the introduction in 2007 of a more stringent exhaust particulate matter emission standard for new heavy-duty diesel vehicle engines. California has instituted additional regulations requiring retrofit or replacement of older in-use engines to accelerate emission reductions and air quality improvements. This presentation summarizes pollutant emission changes measured over several field campaigns at the Port of Oakland in the San Francisco Bay Area associated with diesel particulate filter use and accelerated modernization of the heavy-duty truck fleet. Pollutants in the exhaust plumes of hundreds of heavy-duty trucks en route to the Port were measured in 2009, 2010, 2011, and 2013. Ultrafine particle number, black carbon (BC), nitrogen oxides (NOx), and nitrogen dioxide (NO2) concentrations were measured at a frequency ≤ 1 Hz and normalized to measured carbon dioxide concentrations to quantify fuel-based emission factors (grams of pollutant emitted per kilogram of diesel consumed). The size distribution of particles in truck exhaust plumes was also measured at 1 Hz. In the two most recent campaigns, emissions were linked on a truck-by-truck basis to installed emission control equipment via the matching of transcribed license plates to a Port truck database. Accelerated replacement of older engines with newer engines and retrofit of trucks with diesel particle filters reduced fleet-average emissions of BC and NOx. Preliminary results from the two most recent field campaigns indicate that trucks without diesel particle filters emit 4 times more BC than filter-equipped trucks. Diesel particle filters increase emissions of NO2, however, and filter-equipped trucks have NO2/NOx ratios that are 4 to 7 times greater than trucks without filters. Preliminary findings related to particle size distribution indicate that (a) most trucks emitted particles characterized by a single mode of approximately
A geometry-based particle filtering approach to white matter tractography.
Savadjiev, Peter; Rathi, Yogesh; Malcolm, James G; Shenton, Martha E; Westin, Carl-Fredrik
2010-01-01
We introduce a fibre tractography framework based on a particle filter which estimates a local geometrical model of the underlying white matter tract, formulated as a 'streamline flow' using generalized helicoids. The method is not dependent on the diffusion model, and is applicable to diffusion tensor (DT) data as well as to high angular resolution reconstructions. The geometrical model allows for a robust inference of local tract geometry, which, in the context of the causal filter estimation, guides tractography through regions with partial volume effects. We validate the method on synthetic data and present results on two types in vivo data: diffusion tensors and a spherical harmonic reconstruction of the fibre orientation distribution function (fODF). PMID:20879320
NASA Astrophysics Data System (ADS)
Zhao, X. F.; Huang, S. X.
2012-08-01
This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF) that is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to check up the feasibility of PF-RFC performances.
NASA Astrophysics Data System (ADS)
Zhao, X. F.; Huang, S. X.; Wang, D. X.
2012-11-01
This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF), which is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations, is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to confirm the feasibility of PF-RFC performances.
Modified particle filtering algorithm for single acoustic vector sensor DOA tracking.
Li, Xinbo; Sun, Haixin; Jiang, Liangxu; Shi, Yaowu; Wu, Yue
2015-01-01
The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the "likehood" function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms. PMID:26501280
A localized particle filter for data assimilation in high-dimensional geophysical models.
NASA Astrophysics Data System (ADS)
Poterjoy, Jonathan; Anderon, Jeffrey
2016-04-01
This talk introduces an ensemble data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. PFs make no assumptions regarding prior and posterior error distributions, allowing them to perform well for most applications provided with a sufficiently large number of particles. The proposed method is similar to the PF in that ensemble realizations of the model state are weighted based on the likelihood of observations to approximate posterior probabilities of the system state. The new approach, denoted the local PF, reduces the influence of distant observations on the weight calculations via a localization function. Unlike standard PFs, the local PF provides accurate results using ensemble sizes small enough to be affordable for large models. Comparisons of the local PF and ensemble Kalman filters using a simplified atmospheric general circulation model (with 25 particles) demonstrate that the new method is a viable data assimilation technique for large geophysical systems. The local PF also shows substantial benefits over the EnKF when observation networks consist of measurements that relate nonlinearly to the model state - analogous to remotely sensed data used frequently in atmospheric analyses.
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.
Modified Particle Filtering Algorithm for Single Acoustic Vector Sensor DOA Tracking
Li, Xinbo; Sun, Haixin; Jiang, Liangxu; Shi, Yaowu; Wu, Yue
2015-01-01
The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the “likehood” function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms. PMID:26501280
Particle capture processes and evaporation on a microscopic scale in wet filters.
Mullins, Benjamin J; Braddock, Roger D; Agranovski, Igor E
2004-11-01
This paper details results of an experimental study of the capture of solid and liquid aerosols on fibrous filters wetted with water. A microscopic cell containing a single fibre (made from a variety of materials) was observed via a microscope, with a high speed CCD camera used to dynamically image the interactions between liquid droplets, zeolite and PSL particles and fibres. Variable quantities of liquid irrigation were used, and the possibility for subsequent fibre regeneration after clogging or drying was also studied. It was found that drainage of the wetting liquid (water) from the fibres occurred, even at very low irrigation rates when the droplet consisted almost completely of captured particles. It was also found that the fibre was rapidly loaded with captured particles when the irrigation was not supplied. However, almost complete regeneration (removal of the collected cake) by the liquid droplets occurred shortly after recommencement of the water supply. The study also examined the capture of oily liquid aerosols on fibres wetted with water. A predominance of the barrel shaped droplet on the fibre was observed, with oil droplets displacing water droplets (if the oil and fibre combination created a barrel shaped droplet), creating various compound droplets of oil and water not previously reported in literature. This preferential droplet shape implies that whatever the initial substance wetting a filter, a substance with a greater preferential adherence to the fibre will displace the former one. PMID:15380432
Otsuki, Akira; Bryant, Gary
2015-12-01
This paper aims to summarize recent investigations into the dispersion of fine particles, and the characterization of their interactions, in concentrated suspensions. This summary will provide a better understanding of the current status of this research, and will provide useful feedback for advanced particle processing. Such processes include the fabrication of functional nanostructures and the sustainable beneficiation of complex ores. For example, there has been increasing demand for complex ore utilization due to the noticeable decrease in the accessibility of high grade and easily extractable ores. In order to maintain the sustainable use of mineral resources, the effective beneficiation of complex ores is urgently required. It can be successfully achieved only with selective particle/mineral dispersion/liberation and the assistance of mineralogical and particle characterization. PMID:26298173
Dynamic omni-directional vision localization using a beacon tracker based on particle filter
NASA Astrophysics Data System (ADS)
Cao, Zuoliang; Liu, Shiyu
2007-09-01
Omni-directional vision navigation for AGVs appears definite significant since its advantage of panoramic sight with a single compact visual scene. This unique guidance technique involves target recognition, vision tracking, object positioning, path programming. An algorithm for omni-vision based global localization which utilizes two overhead features as beacon pattern is proposed in this paper. An approach for geometric restoration of omni-vision images has to be considered since an inherent distortion exists. The mapping between image coordinates and physical space parameters of the targets can be obtained by means of the imaging principle on the fisheye lens. The localization of the robot can be achieved by geometric computation. Dynamic localization employs a beacon tracker to follow the landmarks in real time during the arbitrary movement of the vehicle. The coordinate transformation is devised for path programming based on time sequence images analysis. The beacon recognition and tracking are a key procedure for an omni-vision guided mobile unit. The conventional image processing such as shape decomposition, description, matching and other usually employed technique are not directly applicable in omni-vision. Particle filter (PF) has been shown to be successful for several nonlinear estimation problems. A beacon tracker based on Particle Filter which offers a probabilistic framework for dynamic state estimation in visual tracking has been developed. We independently use two Particle Filters to track double landmarks but a composite algorithm on multiple objects tracking conducts for vehicle localization. We have implemented the tracking and localization system and demonstrated the relevant of the algorithm.
Actin filament tracking based on particle filters and stretching open active contour models.
Li, Hongsheng; Shen, Tian; Vavylonis, Dimitrios; Huang, Xiaolei
2009-01-01
We introduce a novel algorithm for actin filament tracking and elongation measurement. Particle Filters (PF) and Stretching Open Active Contours (SOAC) work cooperatively to simplify the modeling of PF in a one-dimensional state space while naturally integrating filament body constraints to tip estimation. Our algorithm reduces the PF state spaces to one-dimensional spaces by tracking filament bodies using SOAC and probabilistically estimating tip locations along the curve length of SOACs. Experimental evaluation on TIRFM image sequences with very low SNRs demonstrates the accuracy and robustness of this approach. PMID:20426170
Indoor patient position estimation using particle filtering and wireless body area networks.
Ren, Hongliang; Meng, Max Q H; Xu, Lisheng
2007-01-01
Wireless Body Area Network (WBAN) has been recently promoted to monitor the physiological parameters of patient in an unobtrusive and natural way. This paper towards to make advantage of those ongoing wireless communication links between the body sensors to provide estimated position information of patients or particular body area networks, which make daily activity surveillance possible for further analysis. The proposed particle filtering based localization algorithm just picks up the received radio signal strength information from beacons or its neighbors to infer its own pose, which do not require additional hardware or instruments. Theoretical analysis and simulation experiments are presented to examine the performance of location estimating method. PMID:18002445
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.
Development of an Advanced Recycle Filter Tank Assembly for the ISS Urine Processor Assembly
NASA Technical Reports Server (NTRS)
Link, Dwight E., Jr.; Carter, Donald Layne; Higbie, Scott
2010-01-01
Recovering water from urine is a process that is critical to supporting larger crews for extended missions aboard the International Space Station. Urine is collected, preserved, and stored for processing into water and a concentrated brine solution that is highly toxic and must be contained to avoid exposure to the crew. The brine solution is collected in an accumulator tank, called a Recycle Filter Tank Assembly (RFTA) that must be replaced monthly and disposed in order to continue urine processing operations. In order to reduce resupply requirements, a new accumulator tank is being developed that can be emptied on orbit into existing ISS waste tanks. The new tank, called the Advanced Recycle Filter Tank Assembly (ARFTA) is a metal bellows tank that is designed to collect concentrated brine solution and empty by applying pressure to the bellows. This paper discusses the requirements and design of the ARFTA as well as integration into the urine processor assembly.
Characterization of exhaled breath particles collected by an electret filter technique.
Tinglev, Åsa Danielsson; Ullah, Shahid; Ljungkvist, Göran; Viklund, Emilia; Olin, Anna-Carin; Beck, Olof
2016-06-01
Aerosol particles that are present in exhaled breath carry nonvolatile components and have gained interest as a specimen for potential biomarkers. Nonvolatile compounds detected in exhaled breath include both endogenous and exogenous compounds. The aim of this study was to study particles collected with a new, simple and convenient filter technique. Samples of breath were collected from healthy volunteers from approximately 30 l of exhaled air. Particles were counted with an optical particle counter and two phosphatidylcholines were measured by liquid chromatography-tandem mass spectrometry. In addition, phosphatidylcholines and methadone was analysed in breath from patients in treatment with methadone and oral fluid was collected with the Quantisal device. The results demonstrated that the majority of particles are <1 μm in size and that the fraction of larger particle contributes most to the total mass. The phosphatidylcholine PC(16 : 0/16 : 0) dominated over PC(16 : 0/18 : 1) and represented a major constituent of the particles. The concentration of the PC(16 : 0/16 : 0) homolog was significantly correlated (p < 0.001) with total mass. From the low concentration of the two phosphatidylcholines and their relative abundance in oral fluid a major contribution from the oral cavity could be ruled out. The concentration of PC(16 : 0/16 : 0) in breath was positively correlated with age (p < 0.01). An attempt to use PC(16 : 0/16 : 0) as a sample size indicator for methadone was not successful, as the large intra-individual variability between samplings even increased after normalization. In conclusion, it was demonstrated that exhaled breath sampled with the filter device represents a specimen corresponding to surfactant. The possible use of PC(16 : 0/16 : 0) as a sample size indicator was supported and deserves further investigations. We propose that the direct and selective collection of the breath aerosol particles is a promising strategy
Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters
Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina
2011-01-01
Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation. PMID:22003741
Segmentation of nerve bundles and ganglia in spine MRI using particle filters.
Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina
2011-01-01
Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation. PMID:22003741
Distributed multi-sensor particle filter for bearings-only tracking
NASA Astrophysics Data System (ADS)
Zhang, Jungen; Ji, Hongbing
2012-02-01
In this article, the classical bearings-only tracking (BOT) problem for a single target is addressed, which belongs to the general class of non-linear filtering problems. Due to the fact that the radial distance observability of the target is poor, the algorithm-based sequential Monte-Carlo (particle filtering, PF) methods generally show instability and filter divergence. A new stable distributed multi-sensor PF method is proposed for BOT. The sensors process their measurements at their sites using a hierarchical PF approach, which transforms the BOT problem from Cartesian coordinate to the logarithmic polar coordinate and separates the observable components from the unobservable components of the target. In the fusion centre, the target state can be estimated by utilising the multi-sensor optimal information fusion rule. Furthermore, the computation of a theoretical Cramer-Rao lower bound is given for the multi-sensor BOT problem. Simulation results illustrate that the proposed tracking method can provide better performances than the traditional PF method.
Sun, Lei; Jia, Yun-xian; Cai, Li-ying; Lin, Guo-yu; Zhao, Jin-song
2013-09-01
The spectrometric oil analysis(SOA) is an important technique for machine state monitoring, fault diagnosis and prognosis, and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system. Because the complexity of machine system, its health state degradation process can't be simply characterized by linear model, while particle filtering(PF) possesses obvious advantages over traditional Kalman filtering for dealing nonlinear and non-Gaussian system, the PF approach was applied to state forecasting by SOA, and the RUL prediction technique based on SOA and PF algorithm is proposed. In the prediction model, according to the estimating result of system's posterior probability, its prior probability distribution is realized, and the multi-step ahead prediction model based on PF algorithm is established. Finally, the practical SOA data of some engine was analyzed and forecasted by the above method, and the forecasting result was compared with that of traditional Kalman filtering method. The result fully shows the superiority and effectivity of the PMID:24369656
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 μg/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
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...
A study on effect of point-of-use filters on defect reduction for advanced 193nm processes
NASA Astrophysics Data System (ADS)
Vitorino, Nelson; Wolfer, Elizabeth; Cao, Yi; Lee, DongKwan; Wu, Aiwen
2009-03-01
Bottom Anti-Reflective Coatings (BARCs) have been widely used in the lithography process for decades. BARCs play important roles in controlling reflections and therefore improving swing ratios, CD variations, reflective notching, and standing waves. The implementation of BARC processes in 193nm dry and immersion lithography has been accompanied by defect reduction challenges on fine patterns. Point-of-Use filters are well known among the most critical components on a track tool ensuring low wafer defects by providing particle-free coatings on wafers. The filters must have very good particle retention to remove defect-causing particulate and gels while not altering the delicate chemical formulation of photochemical materials. This paper describes a comparative study of the efficiency and performance of various Point-of-Use filters in reducing defects observed in BARC materials. Multiple filter types with a variety of pore sizes, membrane materials, and filter designs were installed on an Entegris Intelligent(R) Mini dispense pump which is integrated in the coating module of a clean track. An AZ(R) 193nm organic BARC material was spin-coated on wafers through various filter media. Lithographic performance of filtered BARCs was examined and wafer defect analysis was performed. By this study, the effect of filter properties on BARC process related defects can be learned and optimum filter media and design can be selected for BARC material to yield the lowest defects on a coated wafer.
NASA Astrophysics Data System (ADS)
Ikeda, Takeshi; Kawamoto, Mitsuru; Sashima, Akio; Suzuki, Keiji; Kurumatani, Koichi
In the field of the ubiquitous computing, positioning systems which can provide users' location information have paid attention as an important technical element which can be applied to various services, for example, indoor navigation services, evacuation services, market research services, guidance services, and so on. A lot of researchers have proposed various outdoor and indoor positioning systems. In this paper, we deal with indoor positioning systems. Many conventional indoor positioning systems use expensive infrastructures, because the propagated times of radio waves are used to measure users' positions with high accuracy. In this paper, we propose an indoor autonomous positioning system using radio signal strengths (RSSs) based on ISM band communications. In order to estimate users' positions, the proposed system utilizes a particle filter that is one of the Monte Carlo methods. Because the RSS information is used in the proposed system, the equipments configuring the system are not expensive compared with the conventional indoor positioning systems and it can be installed easily. Moreover, because the particle filter is used to estimate user's position, even if the RSS fluctuates due to, for example, multi-paths, the system can carry out position estimation robustly. We install the proposed system in one floor of a building and carry out some experiments in order to verify the validity of the proposed system. As a result, we confirmed that the average of the estimation errors of the proposed system was about 1.8 m, where the result is enough accuracy for achieving the services mentioned above.
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.
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.
Ma, Huan; Shen, Henggen; Shui, Tiantian; Li, Qing; Zhou, Liuke
2016-01-01
Size- and time-dependent aerodynamic behaviors of indoor particles, including PM1.0, were evaluated in a school office in order to test the performance of air-cleaning devices using different filters. In-situ real-time measurements were taken using an optical particle counter. The filtration characteristics of filter media, including single-pass efficiency, volume and effectiveness, were evaluated and analyzed. The electret filter (EE) medium shows better initial removal efficiency than the high efficiency (HE) medium in the 0.3-3.5 μm particle size range, while under the same face velocity, the filtration resistance of the HE medium is several times higher than that of the EE medium. During service life testing, the efficiency of the EE medium decreased to 60% with a total purifying air flow of 25 × 10⁴ m³/m². The resistance curve rose slightly before the efficiency reached the bottom, and then increased almost exponentially. The single-pass efficiency of portable air cleaner (PAC) with the pre-filter (PR) or the active carbon granule filter (CF) was relatively poor. While PAC with the pre-filter and the high efficiency filter (PR&HE) showed maximum single-pass efficiency for PM1.0 (88.6%), PAC with the HE was the most effective at removing PM1.0. The enhancement of PR with HE and electret filters augmented the single-pass efficiency, but lessened the airflow rate and effectiveness. Combined with PR, the decay constant of large-sized particles could be greater than for PACs without PR. Without regard to the lifetime, the electret filters performed better with respect to resource saving and purification improvement. A most penetrating particle size range (MPPS: 0.4-0.65 μm) exists in both HE and electret filters; the MPPS tends to become larger after HE and electret filters are combined with PR. These results serve to provide a better understanding of the indoor particle removal performance of PACs when combined with different kinds of filters in school
Ma, Huan; Shen, Henggen; Shui, Tiantian; Li, Qing; Zhou, Liuke
2016-01-01
Size- and time-dependent aerodynamic behaviors of indoor particles, including PM1.0, were evaluated in a school office in order to test the performance of air-cleaning devices using different filters. In-situ real-time measurements were taken using an optical particle counter. The filtration characteristics of filter media, including single-pass efficiency, volume and effectiveness, were evaluated and analyzed. The electret filter (EE) medium shows better initial removal efficiency than the high efficiency (HE) medium in the 0.3–3.5 μm particle size range, while under the same face velocity, the filtration resistance of the HE medium is several times higher than that of the EE medium. During service life testing, the efficiency of the EE medium decreased to 60% with a total purifying air flow of 25 × 104 m3/m2. The resistance curve rose slightly before the efficiency reached the bottom, and then increased almost exponentially. The single-pass efficiency of portable air cleaner (PAC) with the pre-filter (PR) or the active carbon granule filter (CF) was relatively poor. While PAC with the pre-filter and the high efficiency filter (PR&HE) showed maximum single-pass efficiency for PM1.0 (88.6%), PAC with the HE was the most effective at removing PM1.0. The enhancement of PR with HE and electret filters augmented the single-pass efficiency, but lessened the airflow rate and effectiveness. Combined with PR, the decay constant of large-sized particles could be greater than for PACs without PR. Without regard to the lifetime, the electret filters performed better with respect to resource saving and purification improvement. A most penetrating particle size range (MPPS: 0.4–0.65 μm) exists in both HE and electret filters; the MPPS tends to become larger after HE and electret filters are combined with PR. These results serve to provide a better understanding of the indoor particle removal performance of PACs when combined with different kinds of filters in school