Multi-parameter studies of environmental aerosols with cascade track filters
International Nuclear Information System (INIS)
Ensinger, W.; Guo, S.-L.; Vater, P.; Brandt, R.
2005-01-01
Aerosols in the air in a factory processing nuclear reactor fuel material were collected by using cascade Kapton track filters with outer pore sizes of 12.8, 4.0 and 1.0μm consecutively and a conventional filter of glass fiber being behind to collect all aerosol particles left-over. The volume of air passed through the filters was measured by a flow meter. The weight of aerosol particles on each filter was obtained by the weight difference of the filter before and after collection of aerosol particles. α-activity on each filter was measured with a methane gas flow proportional counter. The sizes and elemental compositions of aerosol particles on the filters were analyzed by using a scanning electron microscope and an electron microprobe. Special attention was given to uranium aerosol particles. The median sizes of uranium aerosol particles were obtained being 1.97, 1.33 and 0.72μm on the first, second and third filter, respectively. The median size of all the uranium aerosol particles on the three track filters was 1.25μm
Particle Filter Tracking without Dynamics
Directory of Open Access Journals (Sweden)
Jaime Ortegon-Aguilar
2007-01-01
Full Text Available People tracking is an interesting topic in computer vision. It has applications in industrial areas such as surveillance or human-machine interaction. Particle Filters is a common algorithm for people tracking; challenging situations occur when the target's motion is poorly modelled or with unexpected motions. In this paper, an alternative to address people tracking is presented. The proposed algorithm is based in particle filters, but instead of using a dynamical model, it uses background subtraction to predict future locations of particles. The algorithm is able to track people in omnidirectional sequences with a low frame rate (one or two frames per second. Our approach can tackle unexpected discontinuities and changes in the direction of the motion. The main goal of the paper is to track people from laboratories, but it has applications in surveillance, mainly in controlled environments.
Tracking speckle displacement by double Kalman filtering
Institute of Scientific and Technical Information of China (English)
Donghui Li; Li Guo
2006-01-01
@@ A tracking technique using two sequentially-connected Kalman filter for tracking laser speckle displacement is presented. One Kalman filter tracks temporal speckle displacement, while another Kalman filter tracks spatial speckle displacement. The temporal Kalman filter provides a prior for the spatial Kalman filter, and the spatial Kalman filter provides measurements for the temporal Kalman filter. The contribution of a prior to estimations of the spatial Kalman filter is analyzed. An optical analysis system was set up to verify the double-Kalman-filter tracker's ability of tracking laser speckle's constant displacement.
Xu, Zheyao; Qi, Naiming; Chen, Yukun
2015-12-01
Spacecraft simulators are widely used to study the dynamics, guidance, navigation, and control of a spacecraft on the ground. A spacecraft simulator can have three rotational degrees of freedom by using a spherical air-bearing to simulate a frictionless and micro-gravity space environment. The moment of inertia and center of mass are essential for control system design of ground-based three-axis spacecraft simulators. Unfortunately, they cannot be known precisely. This paper presents two approaches, i.e. a recursive least-squares (RLS) approach with tracking differentiator (TD) and Extended Kalman Filter (EKF) method, to estimate inertia parameters. The tracking differentiator (TD) filter the noise coupled with the measured signals and generate derivate of the measured signals. Combination of two TD filters in series obtains the angular accelerations that are required in RLS (TD-TD-RLS). Another method that does not need to estimate the angular accelerations is using the integrated form of dynamics equation. An extended TD (ETD) filter which can also generate the integration of the function of signals is presented for RLS (denoted as ETD-RLS). States and inertia parameters are estimated simultaneously using EKF. The observability is analyzed. All proposed methods are illustrated by simulations and experiments.
Kalman Filter Track Fits and Track Breakpoint Analysis
Astier, Pierre; Cousins, R D; Letessier-Selvon, A A; Popov, B A; Vinogradova, T G; Astier, Pierre; Cardini, Alessandro; Cousins, Robert D.; Letessier-Selvon, Antoine; Popov, Boris A.; Vinogradova, Tatiana
2000-01-01
We give an overview of track fitting using the Kalman filter method in the NOMAD detector at CERN, and emphasize how the wealth of by-product information can be used to analyze track breakpoints (discontinuities in track parameters caused by scattering, decay, etc.). After reviewing how this information has been previously exploited by others, we describe extensions which add power to breakpoint detection and characterization. We show how complete fits to the entire track, with breakpoint parameters added, can be easily obtained from the information from unbroken fits. Tests inspired by the Fisher F-test can then be used to judge breakpoints. Signed quantities (such as change in momentum at the breakpoint) can supplement unsigned quantities such as the various chisquares. We illustrate the method with electrons from real data, and with Monte Carlo simulations of pion decays.
Electron microscope studies on nuclear track filters
International Nuclear Information System (INIS)
Roell, I.; Siegmon, W.
1982-01-01
Nuclear track filters became more and more important in various fields of application. The filtration process can be described by a set of suitable parameters. For some applications it may be necessary to know the structure of the surface and the pores themselves. In most cases the etching process yields surfaces and pore geometries that are quite different from ideal planes and cylinders. In the presented work the production of different filter types will be described. The resulting surfaces and pore structures have been investigated by means of a scanning electron microscope. (author)
Bayesian target tracking based on particle filter
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.
Particle filtering for passive fathometer tracking.
Michalopoulou, Zoi-Heleni; Yardim, Caglar; Gerstoft, Peter
2012-01-01
Seabed interface depths and fathometer amplitudes are tracked for an unknown and changing number of sub-bottom reflectors. This is achieved by incorporating conventional and adaptive fathometer processors into sequential Monte Carlo methods for a moving vertical line array. Sediment layering information and time-varying fathometer response amplitudes are tracked by using a multiple model particle filter with an uncertain number of reflectors. Results are compared to a classical particle filter where the number of reflectors is considered to be known. Reflector tracking is demonstrated for both conventional and adaptive processing applied to the drifting array data from the Boundary 2003 experiment. The layering information is successfully tracked by the multiple model particle filter even for noisy fathometer outputs. © 2012 Acoustical Society of America.
Passive target tracking using marginalized particle filter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A marginalized particle filtering(MPF)approach is proposed for target tracking under the background of passive measurement.Essentially,the MPF is a combination of particle filtering technique and Kalman filter.By making full use of marginalization,the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter,and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter.Simulation studies are performed on an illustrative example,and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation.Real data test results also validate the effectiveness of the presented method.
Random set particle filter for bearings-only multitarget tracking
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.
Some Aspects on Filter Design for Target Tracking
Directory of Open Access Journals (Sweden)
Bertil Ekstrand
2012-01-01
Full Text Available Tracking filter design is discussed. It is argued that the basis of the present stochastic paradigm is questionable. White process noise is not adequate as a model for target manoeuvring, stochastic least-square optimality is not relevant or required in practice, the fact that requirements are necessary for design is ignored, and root mean square (RMS errors are insufficient as performance measure. It is argued that there is no process noise and that the covariance of the assumed process noise contains the design parameters. Focus is on the basic tracking filter, the Kalman filter, which is convenient for clarity and simplicity, but the arguments and conclusions are relevant in general. For design the possibility of an observer transfer function approach is pointed out. The issues can also be considered as a consequence of the fact that there is a difference between estimation and design. The - filter is used for illustration.
Kalman Filter Tracking on Parallel Architectures
International Nuclear Information System (INIS)
Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi
2016-01-01
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment
Ballistic target tracking algorithm based on improved particle filtering
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.
Particle Filtering Applied to Musical Tempo Tracking
Directory of Open Access Journals (Sweden)
Macleod Malcolm D
2004-01-01
Full Text Available This paper explores the use of particle filters for beat tracking in musical audio examples. The aim is to estimate the time-varying tempo process and to find the time locations of beats, as defined by human perception. Two alternative algorithms are presented, one which performs Rao-Blackwellisation to produce an almost deterministic formulation while the second is a formulation which models tempo as a Brownian motion process. The algorithms have been tested on a large and varied database of examples and results are comparable with the current state of the art. The deterministic algorithm gives the better performance of the two algorithms.
Kalman filter tracking on parallel architectures
Cerati, G.; Elmer, P.; Krutelyov, S.; Lantz, S.; Lefebvre, M.; McDermott, K.; Riley, D.; Tadel, M.; Wittich, P.; Wurthwein, F.; Yagil, A.
2017-10-01
We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs.
Institute of Scientific and Technical Information of China (English)
YANGGuo-Sheng; WENCheng-Lin; TANMin
2004-01-01
A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the intersection of the tracking gates formed before and after feedback.Secondly, on the basis of the constructed effective tracking gate, probabilistic data association andstrong tracking Kalman filter are combined to form the new multisensor distributed track fusionalgorithm. At last, simulation is performed on the original algorithm and the algorithm presented.
Target Response Adaptation for Correlation Filter Tracking
Bibi, Adel Aamer
2016-09-16
Most correlation filter (CF) based trackers utilize the circulant structure of the training data to learn a linear filter that best regresses this data to a hand-crafted target response. These circularly shifted patches are only approximations to actual translations in the image, which become unreliable in many realistic tracking scenarios including fast motion, occlusion, etc. In these cases, the traditional use of a single centered Gaussian as the target response impedes tracker performance and can lead to unrecoverable drift. To circumvent this major drawback, we propose a generic framework that can adaptively change the target response from frame to frame, so that the tracker is less sensitive to the cases where circular shifts do not reliably approximate translations. To do that, we reformulate the underlying optimization to solve for both the filter and target response jointly, where the latter is regularized by measurements made using actual translations. This joint problem has a closed form solution and thus allows for multiple templates, kernels, and multi-dimensional features. Extensive experiments on the popular OTB100 benchmark show that our target adaptive framework can be combined with many CF trackers to realize significant overall performance improvement (ranging from 3 %-13.5% in precision and 3.2 %-13% in accuracy), especially in categories where this adaptation is necessary (e.g. fast motion, motion blur, etc.). © Springer International Publishing AG 2016.
Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking
International Nuclear Information System (INIS)
Zu-Tao, Zhang; Jia-Shu, Zhang
2010-01-01
The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n + 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions. (classical areas of phenomenology)
Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations
Directory of Open Access Journals (Sweden)
Pengpeng Chen
2015-01-01
Full Text Available This paper is concerned with the Kalman filtering problem for tracking a single target on the fixed-topology wireless sensor networks (WSNs. Both the insufficient anchor coverage and the packet dropouts have been taken into consideration in the filter design. The resulting tracking system is modeled as a multichannel nonlinear system with multiplicative noise. Noting that the channels may be correlated with each other, we use a general matrix to express the multiplicative noise. Then, a modified extended Kalman filtering algorithm is presented based on the obtained model to achieve high tracking accuracy. In particular, we evaluate the effect of various parameters on the tracking performance through simulation studies.
Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning
Institute of Scientific and Technical Information of China (English)
XIAO Kun; FANG Shao-ji; PANG Yong-jie
2007-01-01
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
Parallel Kalman filter track fit based on vector classes
Energy Technology Data Exchange (ETDEWEB)
Kisel, Ivan [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH (Germany); Kretz, Matthias [Kirchhoff-Institut fuer Physik, Ruprecht-Karls Universitaet, Heidelberg (Germany); Kulakov, Igor [Goethe-Universitaet, Frankfurt am Main (Germany); National Taras Shevchenko University, Kyiv (Ukraine)
2010-07-01
Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of the data reconstruction in high energy physics is the Kalman filter. Therefore, developing the fast Kalman filter algorithm, which uses maximum available power of modern processors, is important, in particular for initial selection of events interesting for the new physics. One of processors features, which can speed up the algorithm, is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. Therefore a flexible and useful interface, which uses the SIMD instruction set on different CPU and GPU processors architectures, has been realized as a vector classes library. The Kalman filter based track fitting algorithm has been implemented with use of the vector classes. Fitting quality tests show good results with the residuals equal to 49 {mu}m and 44 {mu}m for x and y track parameters and relative momentum resolution of 0.7%. The fitting time of 0.053 {mu}s per track has been achieved on Intel Xeon X5550 with 8 cores at 2.6 GHz by using in addition Intel Threading Building Blocks.
Context-Aware Correlation Filter Tracking
Mueller, Matthias; Smith, Neil; Ghanem, Bernard
2017-01-01
Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.
Context-Aware Correlation Filter Tracking
Mueller, Matthias
2017-11-09
Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.
Track filtering by robust neural network
International Nuclear Information System (INIS)
Baginyan, S.A.; Kisel', I.V.; Konotopskaya, E.V.; Ososkov, G.A.
1993-01-01
In the present paper we study the following problems of track information extraction by the artificial neural network (ANN) rotor model: providing initial ANN configuration by an algorithm general enough to be applicable for any discrete detector in- or out of a magnetic field; robustness to heavy contaminated raw data (up to 100% signal-to-noise ratio); stability to the growing event multiplicity. These problems were carried out by corresponding innovations of our model, namely: by a special one-dimensional histogramming, by multiplying weights by a specially designed robust multiplier, and by replacing the simulated annealing schedule by ANN dynamics with an optimally fixed temperature. Our approach is valid for both circular and straight (non-magnetic) tracks and tested on 2D simulated data contaminated by 100% noise points distributed uniformly. To be closer to some reality in our simulation, we keep parameters of the cylindrical spectrometer ARES. 12 refs.; 9 figs
Correlation Filter Learning Toward Peak Strength for Visual Tracking.
Sui, Yao; Wang, Guanghui; Zhang, Li
2018-04-01
This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.
Selection of noise parameters for Kalman filter
Institute of Scientific and Technical Information of China (English)
Ka-Veng Yuen; Ka-In Hoi; Kai-Meng Mok
2007-01-01
The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likklihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation.
Target Response Adaptation for Correlation Filter Tracking
Bibi, Adel Aamer; Mueller, Matthias; Ghanem, Bernard
2016-01-01
Most correlation filter (CF) based trackers utilize the circulant structure of the training data to learn a linear filter that best regresses this data to a hand-crafted target response. These circularly shifted patches are only approximations
Kalman filters for real-time magnetic island phase tracking
International Nuclear Information System (INIS)
Borgers, D.P.; Lauret, M.; Baar, M.R. de
2013-01-01
Highlights: • We propose two Kalman filters for tracking of NTMs on ASDEX Upgrade. • The Kalman filters can track NTMs in a much larger frequency range than PLLs. • The filters are tested on synthetic and experimental data from TEXTOR and TCV. • We conclude that the unscented Kalman filter can be useful for NTM control. -- Abstract: For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX Upgrade however, the desired frequency range in which the islands are to be tracked (100 Hz–10 kHz) is much larger than is possible with a PLL. In this contribution, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are proposed for real-time frequency, phase and amplitude tracking of sinusoidal signals, based on noisy measurements. Compared to PLLs, the EKF and UKF are able to track sinusoidal signals in a much larger frequency range. The filters are applied on synthetic data and on experimental data from the TEXTOR and TCV tokamaks, from which we conclude that the UKF can be useful for real-time control of magnetic islands on ASDEX Upgrade
Kalman filters for real-time magnetic island phase tracking
Energy Technology Data Exchange (ETDEWEB)
Borgers, D.P. [Hybrid and Networked Systems, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Lauret, M., E-mail: M.Lauret@tue.nl [FOM Institute DIFFER – Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, Nieuwegein (Netherlands); Control Systems Technology, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Baar, M.R. de [FOM Institute DIFFER – Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, Nieuwegein (Netherlands); Control Systems Technology, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands)
2013-11-15
Highlights: • We propose two Kalman filters for tracking of NTMs on ASDEX Upgrade. • The Kalman filters can track NTMs in a much larger frequency range than PLLs. • The filters are tested on synthetic and experimental data from TEXTOR and TCV. • We conclude that the unscented Kalman filter can be useful for NTM control. -- Abstract: For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX Upgrade however, the desired frequency range in which the islands are to be tracked (100 Hz–10 kHz) is much larger than is possible with a PLL. In this contribution, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are proposed for real-time frequency, phase and amplitude tracking of sinusoidal signals, based on noisy measurements. Compared to PLLs, the EKF and UKF are able to track sinusoidal signals in a much larger frequency range. The filters are applied on synthetic data and on experimental data from the TEXTOR and TCV tokamaks, from which we conclude that the UKF can be useful for real-time control of magnetic islands on ASDEX Upgrade.
A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking
Institute of Scientific and Technical Information of China (English)
ZHANG ZuTao; ZHANG JiaShu
2009-01-01
Non-Intrusive methods for eye tracking are Important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robust-ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today's interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty In modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and im-prove the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.
Visual object tracking by correlation filters and online learning
Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei
2018-06-01
Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.
Bearings-Only Tracking of Manoeuvring Targets Using Particle Filters
Directory of Open Access Journals (Sweden)
M. Sanjeev Arulampalam
2004-11-01
Full Text Available 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.
Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise
Directory of Open Access Journals (Sweden)
Bowen Hou
2017-11-01
Full Text Available As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α -jerk model.
Unscented Kalman filtering for articulated human tracking
DEFF Research Database (Denmark)
Boesen Lindbo Larsen, Anders; Hauberg, Søren; Pedersen, Kim Steenstrup
2011-01-01
We present an articulated tracking system working with data from a single narrow baseline stereo camera. The use of stereo data allows for some depth disambiguation, a common issue in articulated tracking, which in turn yields likelihoods that are practically unimodal. While current state...... with superior results. Tracking quality is measured by comparing with ground truth data from a marker-based motion capture system....
On tempo tracking: Tempogram representation and Kalman filtering
Cemgil, A.T.; Kappen, H.J.; Desain, P.W.M.; Honing, H.J.
2001-01-01
We formulate tempo tracking in a Bayesian framework where a tempo tracker is modeled as a stochastic dynamical system. The tempo is modeled as a hidden state variable of the system and is estimated by a Kalman filter. The Kalman filter operates on a Tempogram, a wavelet-like multiscale expansion of
Nonlinear Principal Component Analysis Using Strong Tracking Filter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.
Track filter on the basis of a cellular automation
International Nuclear Information System (INIS)
Glazov, A.A.; Kisel', I.V.; Konotopskaya, E.V.; Ososkov, G.A.
1991-01-01
The filtering method for tracks in discrete detectors based on the cellular automation is described. Results of the application of this method to experimental data (the spectrometer ARES) are quite successful: threefold reduction of input information with data grouping according to their belonging to separate tracks. They lift up percentage of useful events, which simplifies and accelerates considerably their next recognition. The described cellular automation for track filtering can be successfully applied in parallel computers and also in on-line mode if hardware implementation is used. 21 refs.; 11 figs
Direct and accelerated parameter mapping using the unscented Kalman filter.
Zhao, Li; Feng, Xue; Meyer, Craig H
2016-05-01
To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.
Robotic fish tracking method based on suboptimal interval Kalman filter
Tong, Xiaohong; Tang, Chao
2017-11-01
Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.
PARTICLE FILTER BASED VEHICLE TRACKING APPROACH WITH IMPROVED RESAMPLING STAGE
Directory of Open Access Journals (Sweden)
Wei Leong Khong
2014-02-01
Full Text Available Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehicle appearance and illumination, besides the occlusion and overlapping incidents. Particle filter has been proven as an approach which can overcome nonlinear and non-Gaussian situations caused by cluttered background and occlusion incidents. Unfortunately, conventional particle filter approach encounters particle degeneracy especially during and after the occlusion. Particle filter with sampling important resampling (SIR is an important step to overcome the drawback of particle filter, but SIR faced the problem of sample impoverishment when heavy particles are statistically selected many times. In this work, genetic algorithm has been proposed to be implemented in the particle filter resampling stage, where the estimated position can converge faster to hit the real position of target vehicle under various occlusion incidents. The experimental results show that the improved particle filter with genetic algorithm resampling method manages to increase the tracking accuracy and meanwhile reduce the particle sample size in the resampling stage.
Parameter tracking with partial forgetting method
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Nagy, Ivan; Kárný, Miroslav
2012-01-01
Roč. 26, č. 1 (2012), s. 1-12 ISSN 0890-6327 R&D Projects: GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : regression models * model * parameter estimation * parameter tracking Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.219, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-0370448.pdf
ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS
W. Nakanishi; T. Fuse; T. Ishikawa
2015-01-01
This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation ...
Track Reconstruction in the ATLAS Experiment The Deterministic Annealing Filter
Fleischmann, S
2006-01-01
The reconstruction of the trajectories of charged particles is essential for experiments at the LHC. The experiments contain precise tracking systems structured in layers around the collision point which measure the positions where particle trajectories intersect those layers. The physics analysis on the other hand mainly needs the momentum and direction of the particle at the estimated creation or reaction point. It is therefore needed to determine these parameters from the initial measurements. At the LHC one has to deal with high backgrounds while even small deficits or artifacts can reduce the signal or may produce additional background after event selection. The track reconstruction does not only contain the estimation of the track parameters, but also a pattern recognition deciding which measurements belong to a track and how many particle tracks can be found. Track reconstruction at the ATLAS experiment suffers from the high event rate at the LHC resulting in a high occupancy of the tracking devices. A...
Robust filtering for uncertain systems a parameter-dependent approach
Gao, Huijun
2014-01-01
This monograph provides the reader with a systematic treatment of robust filter design, a key issue in systems, control and signal processing, because of the fact that the inevitable presence of uncertainty in system and signal models often degrades the filtering performance and may even cause instability. The methods described are therefore not subject to the rigorous assumptions of traditional Kalman filtering. The monograph is concerned with robust filtering for various dynamical systems with parametric uncertainties, and focuses on parameter-dependent approaches to filter design. Classical filtering schemes, like H2 filtering and H¥ filtering, are addressed, and emerging issues such as robust filtering with constraints on communication channels and signal frequency characteristics are discussed. The text features: · design approaches to robust filters arranged according to varying complexity level, and emphasizing robust filtering in the parameter-dependent framework for the first time; ·...
A hand tracking algorithm with particle filter and improved GVF snake model
Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe
2017-07-01
To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.
Fish tracking by combining motion based segmentation and particle filtering
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.
Simultaneous pattern recognition and track fitting by the Kalman filtering method
International Nuclear Information System (INIS)
Billoir, P.
1990-01-01
A progressive pattern recognition algorithm based on the Kalman filtering method has been tested. The algorithm starts from a small track segment or from a fitted track of a neighbouring detector, then extends the candidate tracks by adding measured points one by one. The fitted parameters and weight matrix of the candidate track are updated when adding a point, and give an increasing precision on prediction of the next point. Thus, pattern recognition and track fitting can be accomplished simultaneously. The method has been implemented and tested for track reconstruction for the vertex detector of the ZEUS experiment at DESY. Detailed procedures of the method and its performance are presented. Its flexibility is described as well. (orig.)
Joint polarization tracking and channel equalization based on radius-directed linear Kalman filter
Zhang, Qun; Yang, Yanfu; Zhong, Kangping; Liu, Jie; Wu, Xiong; Yao, Yong
2018-01-01
We propose a joint polarization tracking and channel equalization scheme based on radius-directed linear Kalman filter (RD-LKF) by introducing the butterfly finite-impulse-response (FIR) filter in our previously proposed RD-LKF method. Along with the fast polarization tracking, it can also simultaneously compensate the inter-symbol interference (ISI) effects including residual chromatic dispersion and polarization mode dispersion. Compared with the conventional radius-directed equalizer (RDE) algorithm, it is demonstrated experimentally that three times faster convergence speed, one order of magnitude better tracking capability, and better BER performance is obtained in polarization division multiplexing 16 quadrature amplitude modulation system. Besides, the influences of the algorithm parameters on the convergence and the tracking performance are investigated by numerical simulation.
Detecting Power Voltage Dips using Tracking Filters - A Comparison against Kalman
Directory of Open Access Journals (Sweden)
STANCIU, I.-R.
2012-11-01
Full Text Available Due of its significant economical impact, Power-Quality (PQ analysis is an important domain today. Severe voltage distortions affect the consumers and disturb their activity. They may be caused by short circuits (in this case the voltage drops significantly or by varying loads (with a smaller drop. These two types are the PQ currently issues. Monitoring these phenomena (called dips or sags require powerful techniques. Digital Signal Processing (DSP algorithms are currently employed to fulfill this task. Discrete Wavelet Transforms, (and variants, Kalman filters, and S-Transform are currently proposed by researchers to detect voltage dips. This paper introduces and examines a new tool to detect voltage dips: the so-called tracking filters. Discovered and tested during the cold war, they can estimate a parameter of interest one-step-ahead based on the previously observed values. Two filters are implemented. Their performance is assessed by comparison against the Kalman filter?s results.
Joint Conditional Random Field Filter for Multi-Object Tracking
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Luo Ronghua
2011-03-01
Full Text Available Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF.
Preparation of Track Etch Membrane Filters Using Polystyrene Film
International Nuclear Information System (INIS)
Kaewsaenee, Jerawut; Ratanatongchai, Wichian; Supaphol, Pitt; Visal-athaphand, Pinpan
2007-08-01
Full text: Polystyrene nuclear track etch membrane filters was prepared by exposed 13 .m thin film polystyrene with fission fragment. Nuclear latent track was enlarged to through hole on the film by etching with 80 o C 40% H 2 SO 4 with K 2 Cr 2 O 7 solution for 6-10 hour. The hole size was depend on concentration of etching solution and etching time with 1.3-3.4 .m hole diameter. The flow rate test of water was 0.79-1.56 mm cm-2 min-1 at 109.8-113.7 kPa pressure
Particle filters for object tracking: enhanced algorithm and efficient implementations
International Nuclear Information System (INIS)
Abd El-Halym, H.A.
2010-01-01
Object tracking and recognition is a hot research topic. In spite of the extensive research efforts expended, the development of a robust and efficient object tracking algorithm remains unsolved due to the inherent difficulty of the tracking problem. Particle filters (PFs) were recently introduced as a powerful, post-Kalman filter, estimation tool that provides a general framework for estimation of nonlinear/ non-Gaussian dynamic systems. Particle filters were advanced for building robust object trackers capable of operation under severe conditions (small image size, noisy background, occlusions, fast object maneuvers ..etc.). The heavy computational load of the particle filter remains a major obstacle towards its wide use.In this thesis, an Excitation Particle Filter (EPF) is introduced for object tracking. A new likelihood model is proposed. It depends on multiple functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modified the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. This modification is based on re-exciting the particles if their weights fall below a memorized weight value. The proposed enhanced PF is implemented in software and evaluated. Its results are compared with a single likelihood function PF tracker, Particle Swarm Optimization (PSO) tracker, a correlation tracker, as well as, an edge tracker. The experimental results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness, and occlusion compared with other methods Efficient novel hardware architectures of the Sample Important Re sample Filter (SIRF) and the EPF are implemented. Three novel hardware architectures of the SIRF for object tracking are introduced. The first architecture is a two-step sequential PF machine, where particle generation, weight calculation and normalization are carried out in parallel during the first step followed by a sequential re
Kalman Filter Based Tracking in an Video Surveillance System
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SULIMAN, C.
2010-05-01
Full Text Available In this paper we have developed a Matlab/Simulink based model for monitoring a contact in a video surveillance sequence. For the segmentation process and corect identification of a contact in a surveillance video, we have used the Horn-Schunk optical flow algorithm. The position and the behavior of the correctly detected contact were monitored with the help of the traditional Kalman filter. After that we have compared the results obtained from the optical flow method with the ones obtained from the Kalman filter, and we show the correct functionality of the Kalman filter based tracking. The tests were performed using video data taken with the help of a fix camera. The tested algorithm has shown promising results.
LHCb: Alignment of the LHCb Detector with Kalman Filter Fitted Tracks
Amoraal, J; Hulsbergen, W; Needham, M; Nicolas, L; Pozzi, S; Raven, G; Vecchi, S
2009-01-01
We report on an implementation of a global chisquare algorithm for the simultaneous alignment of all tracking systems in the LHCb detector. Our algorithm uses hit residuals from the standard LHCb track fit which is based on a Kalman filter. The algorithm is implemented in the LHCb reconstruction framework and exploits the fact that all sensitive detector elements have the same geometry interface. A vertex constraint is implemented by fitting tracks to a common point and propagating the change in track parameters to the hit residuals. To remove unconstrained or poorly constrained degrees of freedom (so-called weak modes) the average movements of (subsets of) alignable detector elements can be fixed with Lagrange constraints. Alternatively, weak modes can be removed with a cutoff in the eigenvalue spectrum of the second derivative of the chisquare. As for all LHCb reconstruction and analysis software the configuration of the algorithm is done in python and gives detailed control over the selection of alignable ...
Chaotic secure communication based on strong tracking filtering
International Nuclear Information System (INIS)
Li Xiongjie; Xu Zhengguo; Zhou Donghua
2008-01-01
A scheme for implementing secure communication based on chaotic maps and strong tracking filter (STF) is presented, and a modified STF algorithm with message estimation is developed for the special requirement of chaotic secure communication. At the emitter, the message symbol is modulated by chaotic mapping and is output through a nonlinear function. At the receiver, the driving signal is received and the message symbol is recovered dynamically by the STF with estimation of message symbol. Simulation results of Holmes map demonstrate that when message symbols are binary codes, STF can effectively recover the codes of the message from the noisy chaotic signals. Compared with the extended Kalman filter (EKF), STF has a lower bit error rate
Optimization-based particle filter for state and parameter estimation
Institute of Scientific and Technical Information of China (English)
Li Fu; Qi Fei; Shi Guangming; Zhang Li
2009-01-01
In recent years, the theory of particle filter has been developed and widely used for state and parameter estimation in nonlinear/non-Gaussian systems. Choosing good importance density is a critical issue in particle filter design. In order to improve the approximation of posterior distribution, this paper provides an optimization-based algorithm (the steepest descent method) to generate the proposal distribution and then sample particles from the distribution. This algorithm is applied in 1-D case, and the simulation results show that the proposed particle filter performs better than the extended Kalman filter (EKF), the standard particle filter (PF), the extended Kalman particle filter (PF-EKF) and the unscented particle filter (UPF) both in efficiency and in estimation precision.
Kalman filter estimation of RLC parameters for UMP transmission line
Directory of Open Access Journals (Sweden)
Mohd Amin Siti Nur Aishah
2018-01-01
Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.
International Nuclear Information System (INIS)
Guo Shilun
1993-01-01
Detailed study of the behaviours of radon decay products requires the knowledge of environmental aerosols. A combination of devices and instruments have been tested to be superior in study of aerosol characteristics. Nuclear track filters and cascade track filter impactor are basic devices, which bring the functions of α -spectrometer, scanning electron microscope and electron microprobe into full play. This paper describes how to use these devices and instruments to collect aerosols, to determine aerosol radioactivity, size distribution and elemental compositions and to determine the concentration of aerosols in air as a function of aerosol sizes, which are important parameters in dominating the interactions between aerosol and radon decay products. (author). 15 refs, 7 figs
Directory of Open Access Journals (Sweden)
Jesús García Herrero
2003-07-01
Full Text Available This paper describes the application of evolution strategies to the design of interacting multiple model (IMM tracking filters in order to fulfill a large table of performance specifications. These specifications define the desired filter performance in a thorough set of selected test scenarios, for different figures of merit and input conditions, imposing hundreds of performance goals. The design problem is stated as a numeric search in the filter parameters space to attain all specifications or at least minimize, in a compromise, the excess over some specifications as much as possible, applying global optimization techniques coming from evolutionary computation field. Besides, a new methodology is proposed to integrate specifications in a fitness function able to effectively guide the search to suitable solutions. The method has been applied to the design of an IMM tracker for a real-world civil air traffic control application: the accomplishment of specifications defined for the future European ARTAS system.
Traditional Tracking with Kalman Filter on Parallel Architectures
Cerati, Giuseppe; Elmer, Peter; Lantz, Steven; MacNeill, Ian; McDermott, Kevin; Riley, Dan; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi
2015-05-01
Power density constraints are limiting the performance improvements of modern CPUs. To address this, we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.
Further studies on the filtration of liquids using Kapton nuclear track micro filters
International Nuclear Information System (INIS)
Guo, S.L.; Ganz, M.; Fuest, M.; Vater, P.; Brandt, R.
1990-01-01
The flow rate of some liquids (water, heptane, toluene, xylene, dodecane) through Kapton nuclear track filters has been measured. The results can be interpreted with a modified Poiseuille formula. The influence of the viscosity of the liquids on their throughput through Kapton nuclear track filters has been determined. The purification of liquids with Kapton filters has been investigated. It is possible to measure the density of solid particles in deionized water by using Kapton filters. Consequently, nuclear track filters can remove solid particles from high-purity liquids. The soaking effect of some common liquid chemicals on Kapton filters has also been studied, no such soaking could be observed in most of the cases. Finally, these Kapton nuclear track filters are compared with a filter device from Hamamatsu (Japan). (orig.) [de
Bayesian Parameter Estimation via Filtering and Functional Approximations
Matthies, Hermann G.
2016-11-25
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
Bayesian Parameter Estimation via Filtering and Functional Approximations
Matthies, Hermann G.; Litvinenko, Alexander; Rosic, Bojana V.; Zander, Elmar
2016-01-01
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
Kalman filter data assimilation: targeting observations and parameter estimation.
Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex
2014-06-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Kalman filter data assimilation: Targeting observations and parameter estimation
International Nuclear Information System (INIS)
Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex
2014-01-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation
Adaptive filtering for hidden node detection and tracking in networks.
Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L
2017-07-01
The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.
Robust and Adaptive Block Tracking Method Based on Particle Filter
Directory of Open Access Journals (Sweden)
Bin Sun
2015-10-01
Full Text Available In the field of video analysis and processing, object tracking is attracting more and more attention especially in traffic management, digital surveillance and so on. However problems such as objects’ abrupt motion, occlusion and complex target structures would bring difficulties to academic study and engineering application. In this paper, a fragmentsbased tracking method using the block relationship coefficient is proposed. In this method, we use particle filter algorithm and object region is divided into blocks initially. The contribution of this method is that object features are not extracted just from a single block, the relationship between current block and its neighbor blocks are extracted to describe the variation of the block. Each block is weighted according to the block relationship coefficient when the block is voted on the most matched region in next frame. This method can make full use of the relationship between blocks. The experimental results demonstrate that our method can provide good performance in condition of occlusion and abrupt posture variation.
Computation of nuclear reactor parameters using a stretch Kalman filtering
International Nuclear Information System (INIS)
Zwingelstein, G.; Poujol, A.
1976-01-01
A method of nonlinear stochastic filtering, the stretched Karman filter, is used for the estimation of two basic parameters involved in the control of nuclear reactor start-up. The corresponding algorithm is stored in a small Multi-8 computer and tested with data recorded for the Ulysse reactor (I.N.S.T.N.). The various practical problems involved in using the algorithm are examined: filtering initialization, influence of the model... The quality and time saving obtained in the computation make it possible for a real time operation, the computer being connected with the reactor [fr
Institute of Scientific and Technical Information of China (English)
Changyun Liu; Penglang Shui; Gang Wei; Song Li
2014-01-01
To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneu-vers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is pre-sented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter (UKF) via predicted variance Pk|k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneu-vering target compared with the standard UKF.
Blind Source Parameters for Performance Evaluation of Despeckling Filters
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Nagashettappa Biradar
2016-01-01
Full Text Available The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI, and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink embedded with Stein’s unbiased risk estimation (SURE. The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images.
Blind Source Parameters for Performance Evaluation of Despeckling Filters.
Biradar, Nagashettappa; Dewal, M L; Rohit, ManojKumar; Gowre, Sanjaykumar; Gundge, Yogesh
2016-01-01
The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI), and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink) embedded with Stein's unbiased risk estimation (SURE). The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images.
Directory of Open Access Journals (Sweden)
Yun Wang
2016-01-01
Full Text Available Gamma Gaussian inverse Wishart cardinalized probability hypothesis density (GGIW-CPHD algorithm was always used to track group targets in the presence of cluttered measurements and missing detections. A multiple models GGIW-CPHD algorithm based on best-fitting Gaussian approximation method (BFG and strong tracking filter (STF is proposed aiming at the defect that the tracking error of GGIW-CPHD algorithm will increase when the group targets are maneuvering. The best-fitting Gaussian approximation method is proposed to implement the fusion of multiple models using the strong tracking filter to correct the predicted covariance matrix of the GGIW component. The corresponding likelihood functions are deduced to update the probability of multiple tracking models. From the simulation results we can see that the proposed tracking algorithm MM-GGIW-CPHD can effectively deal with the combination/spawning of groups and the tracking error of group targets in the maneuvering stage is decreased.
An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters
Ait-El-Fquih, Boujemaa
2017-12-11
This work addresses the state-parameter filtering problem for dynamical systems with relatively large-dimensional state and low-dimensional parameters\\' vector. A Bayesian filtering algorithm combining the strengths of the particle filter (PF) and the ensemble Kalman filter (EnKF) is proposed. At each assimilation cycle of the proposed EnKF-PF, the PF is first used to sample the parameters\\' ensemble followed by the EnKF to compute the state ensemble conditional on the resulting parameters\\' ensemble. The proposed scheme is expected to be more efficient than the traditional state augmentation techniques, which suffer from the curse of dimensionality and inconsistency that is particularly pronounced when the state is a strongly nonlinear function of the parameters. In the new scheme, the EnKF and PF interact via their ensembles\\' members, in contrast with the recently introduced two-stage EnKF-PF (TS-EnKF-PF), which exchanges point estimates between EnKF and PF while requiring almost double the computational load. Numerical experiments are conducted with the Lorenz-96 model to assess the behavior of the proposed filter and to evaluate its performances against the joint PF, joint EnKF, and TS-EnKF-PF. Numerical results suggest that the EnKF-PF performs best in all tested scenarios. It was further found to be more robust, successfully estimating both state and parameters in different sensitivity experiments.
An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters
Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2017-01-01
This work addresses the state-parameter filtering problem for dynamical systems with relatively large-dimensional state and low-dimensional parameters' vector. A Bayesian filtering algorithm combining the strengths of the particle filter (PF) and the ensemble Kalman filter (EnKF) is proposed. At each assimilation cycle of the proposed EnKF-PF, the PF is first used to sample the parameters' ensemble followed by the EnKF to compute the state ensemble conditional on the resulting parameters' ensemble. The proposed scheme is expected to be more efficient than the traditional state augmentation techniques, which suffer from the curse of dimensionality and inconsistency that is particularly pronounced when the state is a strongly nonlinear function of the parameters. In the new scheme, the EnKF and PF interact via their ensembles' members, in contrast with the recently introduced two-stage EnKF-PF (TS-EnKF-PF), which exchanges point estimates between EnKF and PF while requiring almost double the computational load. Numerical experiments are conducted with the Lorenz-96 model to assess the behavior of the proposed filter and to evaluate its performances against the joint PF, joint EnKF, and TS-EnKF-PF. Numerical results suggest that the EnKF-PF performs best in all tested scenarios. It was further found to be more robust, successfully estimating both state and parameters in different sensitivity experiments.
UFIR Filtering for GPS-Based Tracking over WSNs with Delayed and Missing Data
Directory of Open Access Journals (Sweden)
Karen Uribe-Murcia
2018-01-01
Full Text Available In smart cities, vehicles tracking is organized to increase safety by localizing cars using the Global Positioning System (GPS. The GPS-based system provides accurate tracking but is also required to be reliable and robust. As a main estimator, we propose using the unbiased finite impulse response (UFIR filter, which meets these needs as being more robust than the Kalman filter (KF. The UFIR filter is developed for vehicle tracking in discrete-time state-space over wireless sensor networks (WSNs with time-stamped data discretely delayed on k-step-lags and missing data. The state-space model is represented in a way such that the UFIR filter, KF, and H∞ filter can be used universally. Applications are given for measurement data, which are cooperatively transferred from a vehicle to a central station through several nodes with k-step-lags. Better tracking performance of the UFIR filter is shown experimentally.
Rapid estimation of high-parameter auditory-filter shapes
Shen, Yi; Sivakumar, Rajeswari; Richards, Virginia M.
2014-01-01
A Bayesian adaptive procedure, the quick-auditory-filter (qAF) procedure, was used to estimate auditory-filter shapes that were asymmetric about their peaks. In three experiments, listeners who were naive to psychoacoustic experiments detected a fixed-level, pure-tone target presented with a spectrally notched noise masker. The qAF procedure adaptively manipulated the masker spectrum level and the position of the masker notch, which was optimized for the efficient estimation of the five parameters of an auditory-filter model. Experiment I demonstrated that the qAF procedure provided a convergent estimate of the auditory-filter shape at 2 kHz within 150 to 200 trials (approximately 15 min to complete) and, for a majority of listeners, excellent test-retest reliability. In experiment II, asymmetric auditory filters were estimated for target frequencies of 1 and 4 kHz and target levels of 30 and 50 dB sound pressure level. The estimated filter shapes were generally consistent with published norms, especially at the low target level. It is known that the auditory-filter estimates are narrower for forward masking than simultaneous masking due to peripheral suppression, a result replicated in experiment III using fewer than 200 qAF trials. PMID:25324086
Tracking single dynamic MEG dipole sources using the projected Extended Kalman Filter.
Yao, Yuchen; Swindlehurst, A Lee
2011-01-01
This paper presents two new algorithms based on the Extended Kalman Filter (EKF) for tracking the parameters of single dynamic magnetoencephalography (MEG) dipole sources. We assume a dynamic MEG dipole source with possibly both time-varying location and dipole orientation. The standard EKF-based tracking algorithm performs well under the assumption that the dipole source components vary in time as a Gauss-Markov process, provided that the background noise is temporally stationary. We propose a Projected-EKF algorithm that is adapted to a more forgiving condition where the background noise is temporally nonstationary, as well as a Projected-GLS-EKF algorithm that works even more universally, when the dipole components vary arbitrarily from one sample to the next.
Energy awareness for supercapacitors using Kalman filter state-of-charge tracking
Nadeau, Andrew; Hassanalieragh, Moeen; Sharma, Gaurav; Soyata, Tolga
2015-11-01
Among energy buffering alternatives, supercapacitors can provide unmatched efficiency and durability. Additionally, the direct relation between a supercapacitor's terminal voltage and stored energy can improve energy awareness. However, a simple capacitive approximation cannot adequately represent the stored energy in a supercapacitor. It is shown that the three branch equivalent circuit model provides more accurate energy awareness. This equivalent circuit uses three capacitances and associated resistances to represent the supercapacitor's internal SOC (state-of-charge). However, the SOC cannot be determined from one observation of the terminal voltage, and must be tracked over time using inexact measurements. We present: 1) a Kalman filtering solution for tracking the SOC; 2) an on-line system identification procedure to efficiently estimate the equivalent circuit's parameters; and 3) experimental validation of both parameter estimation and SOC tracking for 5 F, 10 F, 50 F, and 350 F supercapacitors. Validation is done within the operating range of a solar powered application and the associated power variability due to energy harvesting. The proposed techniques are benchmarked against the simple capacitive model and prior parameter estimation techniques, and provide a 67% reduction in root-mean-square error for predicting usable buffered energy.
Kalman Filter Predictor and Initialization Algorithm for PRI Tracking
National Research Council Canada - National Science Library
Hock, Melinda
1998-01-01
.... The algorithm uses a Kalman filter for prediction combined with a preprocessing routine to determine the period of the stagger sequence and to construct an uncorrupted data set for Kalman filter initialization...
RSSI based indoor tracking in sensor networks using Kalman filters
DEFF Research Database (Denmark)
Tøgersen, Frede Aakmann; Skjøth, Flemming; Munksgaard, Lene
2010-01-01
We propose an algorithm for estimating positions of devices in a sensor network using Kalman filtering techniques. The specific area of application is monitoring the movements of cows in a barn. The algorithm consists of two filters. The first filter enhances the signal-to-noise ratio...
INFLUENCE OF STOCHASTIC NOISE STATISTICS ON KALMAN FILTER PERFORMANCE BASED ON VIDEO TARGET TRACKING
Institute of Scientific and Technical Information of China (English)
Chen Ken; Napolitano; Zhang Yun; Li Dong
2010-01-01
The system stochastic noises involved in Kalman filtering are preconditioned on being ideally white and Gaussian distributed. In this research,efforts are exerted on exploring the influence of the noise statistics on Kalman filtering from the perspective of video target tracking quality. The correlation of tracking precision to both the process and measurement noise covariance is investigated; the signal-to-noise power density ratio is defined; the contribution of predicted states and measured outputs to Kalman filter behavior is discussed; the tracking precision relative sensitivity is derived and applied in this study case. The findings are expected to pave the way for future study on how the actual noise statistics deviating from the assumed ones impacts on the Kalman filter optimality and degradation in the application of video tracking.
COMPARATIVE EVALUATION OF FILTERS USED IN TRACKING AIR TARGETS
Directory of Open Access Journals (Sweden)
Y. I. Strekalovskaya
2015-01-01
Full Text Available Using an imitation model for a flow of heterogeneous air targets the comparative assessment of the αβ, αβγ and the Kalman filters efficiency is evaluated. In the case of slightly maneuvering target the difference in filters’ efficiency is statistically insignificant; in the case of sharp maneuvering the Kalman filter is significantly more precise.
Ion track etching revisited: I. Correlations between track parameters in aged polymers
Fink, D.; Muñoz H., G.; García A., H.; Vacik, J.; Hnatowicz, V.; Kiv, A.; Alfonta, L.
2018-04-01
Some yet poorly understood problems of etching of pristine and swift heavy ion track-irradiated aged polymers were treated, by applying conductometry across the irradiated foils during etching. The onset times of etchant penetration across pristine foils, and the onset times of the different etched track regimes in irradiated foils were determined for polymers of various proveniences, fluences and ages, as well as their corresponding etching speeds. From the results, correlations of the parameters with each other were deduced. The normalization of these parameters enables one to compare irradiated polymer foils of different origin and treatment with one another. In a number of cases, also polymeric gel formation and swelling occur which influence the track etching behaviour. The polymer degradation during aging influences the track etching parameters, which differ from each other on both sides of the foils. With increasing sample age, these differences increase.
Dual linear structured support vector machine tracking method via scale correlation filter
Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen
2018-01-01
Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.
Using Gaussian Process Annealing Particle Filter for 3D Human Tracking
Directory of Open Access Journals (Sweden)
Michael Rudzsky
2008-01-01
Full Text Available We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian process annealing particle filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker's stability and robustness. Comparing with a regular annealed particle filter-based tracker, we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loss.
Tracking of multiple objects with time-adjustable composite correlation filters
Ruchay, Alexey; Kober, Vitaly; Chernoskulov, Ilya
2017-09-01
An algorithm for tracking of multiple objects in video based on time-adjustable adaptive composite correlation filtering is proposed. For each frame a bank of composite correlation filters are designed in such a manner to provide invariance to pose, occlusion, clutter, and illumination changes. The filters are synthesized with the help of an iterative algorithm, which optimizes the discrimination capability for each object. The filters are adapted to the objects changes online using information from the current and past scene frames. Results obtained with the proposed algorithm using real-life scenes are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.
Determination of nuclear tracks parameters on sequentially etched PADC detectors
Horwacik, Tomasz; Bilski, Pawel; Koerner, Christine; Facius, Rainer; Berger, Thomas; Nowak, Tomasz; Reitz, Guenther; Olko, Pawel
Polyallyl Diglycol Carbonate (PADC) detectors find many applications in radiation protection. One of them is the cosmic radiation dosimetry, where PADC detectors measure the linear energy transfer (LET) spectra of charged particles (from protons to heavy ions), supplementing TLD detectors in the role of passive dosemeter. Calibration exposures to ions of known LET are required to establish a relation between parameters of track observed on the detector and LET of particle creating this track. PADC TASTRAK nuclear track detectors were exposed to 12 C and 56 Fe ions of LET in H2 O between 10 and 544 keV/µm. The exposures took place at the Heavy Ion Medical Accelerator (HIMAC) in Chiba, Japan in the frame of the HIMAC research project "Space Radiation Dosimetry-Ground Based Verification of the MATROSHKA Facility" (20P-240). Detectors were etched in water solution of NaOH with three different temperatures and for various etching times to observe the appearance of etched tracks, the evolution of their parameters and the stability of the etching process. The applied etching times (and the solution's concentrations and temperatures) were: 48, 72, 96, 120 hours (6.25 N NaOH, 50 O C), 20, 40, 60, 80 hours (6.25 N NaOH, 60 O C) and 8, 12, 16, 20 hours (7N NaOH, 70 O C). The analysis of the detectors involved planimetric (2D) measurements of tracks' entrance ellipses and mechanical measurements of bulk layer thickness. Further track parameters, like angle of incidence, track length and etch rate ratio were then calculated. For certain tracks, results of planimetric measurements and calculations were also compared with results of optical track profile (3D) measurements, where not only the track's entrance ellipse but also the location of the track's tip could be directly measured. All these measurements have been performed with the 2D/3D measurement system at DLR. The collected data allow to create sets of V(LET in H2 O) calibration curves suitable for short, intermediate and
Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process
Nakanishi, W.; Fuse, T.; Ishikawa, T.
2015-05-01
This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.
Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
Directory of Open Access Journals (Sweden)
Anthony Hoak
2017-03-01
Full Text Available We develop an interactive likelihood (ILH for sequential Monte Carlo (SMC methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL and TUD-Stadtmitte using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA and classification of events, activities and relationships for multi-object trackers (CLEAR MOT. In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.
Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods.
Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J
2017-03-03
We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.
Directory of Open Access Journals (Sweden)
Lubomir Ivanek
2017-01-01
Full Text Available This paper deals with the sensitivity of the input impedance of an open track circuit in the event that the parameters of the track are changed. Weather conditions and the state of pollution are the most common reasons for parameter changes. The results were obtained from the measured values of the parameters R (resistance, G (conductance, L (inductance, and C (capacitance of a rail superstructure depending on the frequency. Measurements were performed on a railway siding in Orlova. The results are used to design a predictor of occupancy of a track section. In particular, we were interested in the frequencies of 75 and 275 Hz for this purpose. Many parameter values of track substructures have already been solved in different works in literature. At first, we had planned to use the parameter values from these sources when we designed the predictor. Deviations between them, however, are large and often differ by three orders of magnitude (see Tab.8. From this perspective, this article presents data that have been updated using modern measurement devices and computer technology. And above all, it shows a transmission (cascade matrix used to determine the parameters.
Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.
Wu, Hao; Chen, Shuxin; Yang, Binfeng; Chen, Kun
2016-05-09
The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers' misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.
Estimation of three-dimensional radar tracking using modified extended kalman filter
Aditya, Prima; Apriliani, Erna; Khusnul Arif, Didik; Baihaqi, Komar
2018-03-01
Kalman filter is an estimation method by combining data and mathematical models then developed be extended Kalman filter to handle nonlinear systems. Three-dimensional radar tracking is one of example of nonlinear system. In this paper developed a modification method of extended Kalman filter from the direct decline of the three-dimensional radar tracking case. The development of this filter algorithm can solve the three-dimensional radar measurements in the case proposed in this case the target measured by radar with distance r, azimuth angle θ, and the elevation angle ϕ. Artificial covariance and mean adjusted directly on the three-dimensional radar system. Simulations result show that the proposed formulation is effective in the calculation of nonlinear measurement compared with extended Kalman filter with the value error at 0.77% until 1.15%.
An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers.
Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan
2017-11-18
Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration-which are the basis of tracking error estimation-are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (-0.25 cycle, 0.25 cycle) to (-0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio
Sensitivity analysis of railpad parameters on vertical railway track dynamics
Oregui Echeverria-Berreyarza, M.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Li, Z.
2016-01-01
This paper presents a sensitivity analysis of railpad parameters on vertical railway track dynamics, incorporating the nonlinear behavior of the fastening (i.e., downward forces compress the railpad whereas upward forces are resisted by the clamps). For this purpose, solid railpads, rail-railpad
The research of radar target tracking observed information linear filter method
Chen, Zheng; Zhao, Xuanzhi; Zhang, Wen
2018-05-01
Aiming at the problems of low precision or even precision divergent is caused by nonlinear observation equation in radar target tracking, a new filtering algorithm is proposed in this paper. In this algorithm, local linearization is carried out on the observed data of the distance and angle respectively. Then the kalman filter is performed on the linearized data. After getting filtered data, a mapping operation will provide the posteriori estimation of target state. A large number of simulation results show that this algorithm can solve above problems effectively, and performance is better than the traditional filtering algorithm for nonlinear dynamic systems.
An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.
Feng, Kaiqiang; Li, Jie; Zhang, Xi; Zhang, Xiaoming; Shen, Chong; Cao, Huiliang; Yang, Yanyu; Liu, Jun
2018-06-12
The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.
Directory of Open Access Journals (Sweden)
Hamza Benzerrouk
2018-03-01
Full Text Available Multi-Unmanned Aerial Vehicle (UAV Doppler-based target tracking has not been widely investigated, specifically when using modern nonlinear information filters. A high-degree Gauss–Hermite information filter, as well as a seventh-degree cubature information filter (CIF, is developed to improve the fifth-degree and third-degree CIFs proposed in the most recent related literature. These algorithms are applied to maneuvering target tracking based on Radar Doppler range/range rate signals. To achieve this purpose, different measurement models such as range-only, range rate, and bearing-only tracking are used in the simulations. In this paper, the mobile sensor target tracking problem is addressed and solved by a higher-degree class of quadrature information filters (HQIFs. A centralized fusion architecture based on distributed information filtering is proposed, and yielded excellent results. Three high dynamic UAVs are simulated with synchronized Doppler measurement broadcasted in parallel channels to the control center for global information fusion. Interesting results are obtained, with the superiority of certain classes of higher-degree quadrature information filters.
Learning based particle filtering object tracking for visible-light systems.
Sun, Wei
2015-10-01
We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.
Gundogdu, Erhan; Ozkan, Huseyin; Alatan, A Aydin
2017-11-01
Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [of complexity O(D) ] based on a large ensemble of CFB trackers. The ensemble [of size O(2 D ) ] is organized over a binary tree (depth D ), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.
TREC2002 Web, Novelty and Filtering Track Experiments Using PIRCS
National Research Council Canada - National Science Library
Kwok, K. L; Deng, P; Dinstl, N; Chan, M
2006-01-01
.... The Web track has two tasks: distillation and named-page retrieval. Distillation is a new utility concept for ranking documents, and needs new design on the output document ranked list after an ad-hoc retrieval from the web (.gov) collection...
Kalman filters for real-time magnetic island phase tracking
Borgers, D. P.; Lauret, M.; M.R. de Baar,
2013-01-01
For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX
Signal Tracking Beyond the Time Resolution of an Atomic Sensor by Kalman Filtering
Jiménez-Martínez, Ricardo; Kołodyński, Jan; Troullinou, Charikleia; Lucivero, Vito Giovanni; Kong, Jia; Mitchell, Morgan W.
2018-01-01
We study causal waveform estimation (tracking) of time-varying signals in a paradigmatic atomic sensor, an alkali vapor monitored by Faraday rotation probing. We use Kalman filtering, which optimally tracks known linear Gaussian stochastic processes, to estimate stochastic input signals that we generate by optical pumping. Comparing the known input to the estimates, we confirm the accuracy of the atomic statistical model and the reliability of the Kalman filter, allowing recovery of waveform details far briefer than the sensor's intrinsic time resolution. With proper filter choice, we obtain similar benefits when tracking partially known and non-Gaussian signal processes, as are found in most practical sensing applications. The method evades the trade-off between sensitivity and time resolution in coherent sensing.
Passive Target Tracking in Non-cooperative Radar System Based on Particle Filtering
Institute of Scientific and Technical Information of China (English)
LI Shuo; TAO Ran
2006-01-01
We propose a target tracking method based on particle filtering(PF) to solve the nonlinear non-Gaussian target-tracking problem in the bistatic radar systems using external radiation sources. Traditional nonlinear state estimation method is extended Kalman filtering (EKF), which is to do the first level Taylor series extension. It will cause an inaccuracy or even a scatter estimation result on condition that there is either a highly nonlinear target or a large noise square-error. Besides, Kalman filtering is the optimal resolution under a Gaussian noise assumption, and is not suitable to the non-Gaussian condition. PF is a sort of statistic filtering based on Monte Carlo simulation that is using some random samples (particles) to simulate the posterior probability density of system random variables. This method can be used in any nonlinear random system. It can be concluded through simulation that PF can achieve higher accuracy than the traditional EKF.
Qin, Yong; Ma, Hong; Chen, Jinfeng; Cheng, Li
2015-12-01
Conventional multitarget tracking systems presume that each target can produce at most one measurement per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments. First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.
Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA
Directory of Open Access Journals (Sweden)
Jing Liu
2012-01-01
Full Text Available The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.
Robust estimation of track parameters in wire chambers
International Nuclear Information System (INIS)
Bogdanova, N.B.; Bourilkov, D.T.
1988-01-01
The aim of this paper is to compare numerically the possibilities of the least square fit (LSF) and robust methods for modelled and real track data to determine the linear regression parameters of charged particles in wire chambers. It is shown, that Tukey robust estimate is superior to more standard (versions of LSF) methods. The efficiency of the method is illustrated by tables and figures for some important physical characteristics
Proposed hardware architectures of particle filter for object tracking
Abd El-Halym, Howida A.; Mahmoud, Imbaby Ismail; Habib, SED
2012-12-01
In this article, efficient hardware architectures for particle filter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.
Identification of lead acid battery parameters using kalman filtering in photovoltaic system
International Nuclear Information System (INIS)
Boutte, Aissa
2006-01-01
The conventional methods of battery identification parameters consist in estimating the state of charge (SOC), and in establishing a command adapted to charge or to discharge the battery, based on electrical model developed with fixed parameters, These methods are inefficient. The causes of this ineffectiveness are different: In the first place model does not adapt itself with the battery (fixed parameters, lack of modulated parameters, a big non-linearity ...).Secondly, the impossibility for the developed algorithms, to adapt itself with the change of the battery's parameters. New models of identification are used by combining the conventional methods with adaptive and dynamic techniques. They already used in other domains where they have proved a good efficiency and a robustness. Taking into consideration the problems mentioned, and trying to resolve them, we have chosen among the various methods of estimation, Kalman filter (KF) known for its efficiency, in the field of tracking parameters. In this work we try tp represent new ideas, to identify battery parameters using KF method and make an experimental analysis of the performance of this method by using Lead Acid Battery, which is a part of a photovoltaic system (PV).(Author)
Simon, M. K.; Alem, W. K.
1978-01-01
Unbalanced quadriphase-shift-keying (QPSK) is an attractive means for transmitting two digital data streams which in general have different average powers, data rates, and data formats. Previous analyses of the tracking performance of Costas loop demodulators of unbalanced QPSK have accounted only for the filtering effect produced by the loop's two arm filters on the equivalent additive noise perturbing the loop. When the bandwidth of these filters is selected on the basis of the order of the data rate, as is typical of optimum Costas loop design, the filtering degradations of the data modulations themselves and the cross-modulation noise produced by their multiplication in the loop often cannot be neglected. The purpose of this paper is to incorporate these additional filtering effects into the analysis. Many of the results obtained herein are in the form of closed-form expressions which can easily be evaluated numerically for design and performance prediction purposes.
Frequency tracking and variable bandwidth for line noise filtering without a reference.
Kelly, John W; Collinger, Jennifer L; Degenhart, Alan D; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei
2011-01-01
This paper presents a method for filtering line noise using an adaptive noise canceling (ANC) technique. This method effectively eliminates the sinusoidal contamination while achieving a narrower bandwidth than typical notch filters and without relying on the availability of a noise reference signal as ANC methods normally do. A sinusoidal reference is instead digitally generated and the filter efficiently tracks the power line frequency, which drifts around a known value. The filter's learning rate is also automatically adjusted to achieve faster and more accurate convergence and to control the filter's bandwidth. In this paper the focus of the discussion and the data will be electrocorticographic (ECoG) neural signals, but the presented technique is applicable to other recordings.
Capturing Revolute Motion and Revolute Joint Parameters with Optical Tracking
Antonya, C.
2017-12-01
Optical tracking of users and various technical systems are becoming more and more popular. It consists of analysing sequence of recorded images using video capturing devices and image processing algorithms. The returned data contains mainly point-clouds, coordinates of markers or coordinates of point of interest. These data can be used for retrieving information related to the geometry of the objects, but also to extract parameters for the analytical model of the system useful in a variety of computer aided engineering simulations. The parameter identification of joints deals with extraction of physical parameters (mainly geometric parameters) for the purpose of constructing accurate kinematic and dynamic models. The input data are the time-series of the marker’s position. The least square method was used for fitting the data into different geometrical shapes (ellipse, circle, plane) and for obtaining the position and orientation of revolute joins.
Test models for improving filtering with model errors through stochastic parameter estimation
International Nuclear Information System (INIS)
Gershgorin, B.; Harlim, J.; Majda, A.J.
2010-01-01
The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.
Carrier tracking by smoothing filter improves symbol SNR
Pomalaza-Raez, Carlos A.; Hurd, William J.
1986-01-01
The potential benefit of using a smoothing filter to estimate carrier phase over use of phase locked loops (PLL) is determined. Numerical results are presented for the performance of three possible configurations of the deep space network advanced receiver. These are residual carrier PLL, sideband aided residual carrier PLL, and finally sideband aiding with a Kalman smoother. The average symbol signal to noise ratio (SNR) after losses due to carrier phase estimation error is computed for different total power SNRs, symbol rates and symbol SNRs. It is found that smoothing is most beneficial for low symbol SNRs and low symbol rates. Smoothing gains up to 0.4 dB over a sideband aided residual carrier PLL, and the combined benefit of smoothing and sideband aiding relative to a residual carrier loop is often in excess of 1 dB.
Carrier tracking by smoothing filter can improve symbol SNR
Hurd, W. J.; Pomalaza-Raez, C. A.
1985-01-01
The potential benefit of using a smoothing filter to estimate carrier phase over use of phase locked loops (PLL) is determined. Numerical results are presented for the performance of three possible configurations of the deep space network advanced receiver. These are residual carrier PLL, sideband aided residual carrier PLL, and finally sideband aiding with a Kalman smoother. The average symbol signal to noise ratio (CNR) after losses due to carrier phase estimation error is computed for different total power SNRs, symbol rates and symbol SNRs. It is found that smoothing is most beneficial for low symbol SNRs and low symbol rates. Smoothing gains up to 0.4 dB over a sideband aided residual carrier PLL, and the combined benefit of smoothing and sideband aiding relative to a residual carrier loop is often in excess of 1 dB.
Ultrasound effects on the electrolytically controlled etching of nuclear track filters (NTFs)
International Nuclear Information System (INIS)
Chakarvarti, S.K.; Mahna, S.K.; Sud, L.V.; Singh, P.
1990-01-01
The mechanical stirring of the etchant creates tremendous changes in the etching properties of SSNTDs. Ultrasound stirring also produces a number of effects in liquids by giving a rapid movement to etchant. Cavitation is the most probable phenomenon caused by ultrasound and responsible for most of the effects observed in chemical reactions. Microbubbles are created in liquid medium and explosion of these microbubbles is responsible for momentarily rise in temperature. The possible effects of ultrasound on etching of particle tracks in plastic track detectors as nuclear track filters has been studied. The ultrasound effects on V t and V b have been studied in this work. (author). 5 re fs
Heavy particle track structure parameters for biophysical modelling
International Nuclear Information System (INIS)
Watt, D.E.
1994-01-01
Averaged values of physical track structure parameters are important in radiobiology and radiological protection for the expression of damage mechanisms and for quantifying radiation effects. To provide a ready reference, tables of relevant quantities have been compiled for heavy charged particles in liquid water. The full tables will be published elsewhere but here illustrative examples are given of the trends for the most important quantities. In the tables, data are given for 74 types of heavy charged particle ranging from protons to uranium ions at specific energies between 0.1 keV/u and 1 GeV/u. Aggregate effects in liquid water are taken into account implicitly in the calculations. Results are presented for instantaneous particle energies and for averages over the charged particle equilibrium spectrum. The latter are of special relevance to radiation dosimetry. Quality parameters calculated are: β 2 ; z 2 /β 2 ; linear primary ionisation and the mean free path between ionisations; LET; track and dose-restricted LET with 100 eV cut-off; relative variances; delta-ray energies and ranges; ion energies and ranges and kerma factors. Here, the procedures used in the calculations are indicated. Representative results are shown in graphical form. The role of the physical track properties is discussed with regard to optimisation of the design of experiments intended to elucidate biological damage mechanisms in mammalian cells and their relevance to radiological protection. ((orig.))
DEFF Research Database (Denmark)
Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar
2016-01-01
, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...
Physichal parameters for wedge filters used in radiotherapy
International Nuclear Information System (INIS)
Strunga, Emil
1995-01-01
Wedge filters using in radiotherapy up two important problems: attenuation of gamma rays introduced by the presence of wedge filters and spinning of isodoses curves plate. Depending of irradiation geometry, characterised by D w , - source filter distance, D c - source dose's estimate point distance, a - side of irradiation field; nature and size filter: α - wedge angle, μ - linear adsorption coefficient, ε - filter cover attenuation w - filter side, and nature of target volume characterised by μ' - linear absorption coefficient of medium has been estimated absorption factor of wedge filter (k w ) for two irradiation geometry: and spinning angle of isodose plate (Θ): 3) tg θ (μD w (μ'D c - 2 Calculated values has been compared with the experimental measured values, for a cobaltotherapy unit Rokus-M, and the result was that between the two series of dates it is a good concordance
Bourque, Alexandra E; Bedwani, Stéphane; Carrier, Jean-François; Ménard, Cynthia; Borman, Pim; Bos, Clemens; Raaymakers, Bas W; Mickevicius, Nikolai; Paulson, Eric; Tijssen, Rob H N
PURPOSE: To assess overall robustness and accuracy of a modified particle filter-based tracking algorithm for magnetic resonance (MR)-guided radiation therapy treatments. METHODS AND MATERIALS: An improved particle filter-based tracking algorithm was implemented, which used a normalized
Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs
Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; Masciovecchio, Mario; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi
2017-08-01
For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.
Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs
Directory of Open Access Journals (Sweden)
Cerati Giuseppe
2017-01-01
Full Text Available For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU, ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC, for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.
Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs
Energy Technology Data Exchange (ETDEWEB)
Cerati, Giuseppe [Fermilab; Elmer, Peter [Princeton U.; Krutelyov, Slava [UC, San Diego; Lantz, Steven [Cornell U.; Lefebvre, Matthieu [Princeton U.; Masciovecchio, Mario [UC, San Diego; McDermott, Kevin [Cornell U.; Riley, Daniel [Cornell U., LNS; Tadel, Matevž [UC, San Diego; Wittich, Peter [Cornell U.; Würthwein, Frank [UC, San Diego; Yagil, Avi [UC, San Diego
2017-01-01
For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.
3D head pose estimation and tracking using particle filtering and ICP algorithm
Ben Ghorbel, Mahdi; Baklouti, Malek; Couvet, Serge
2010-01-01
This paper addresses the issue of 3D head pose estimation and tracking. Existing approaches generally need huge database, training procedure, manual initialization or use face feature extraction manually extracted. We propose a framework for estimating the 3D head pose in its fine level and tracking it continuously across multiple Degrees of Freedom (DOF) based on ICP and particle filtering. We propose to approach the problem, using 3D computational techniques, by aligning a face model to the 3D dense estimation computed by a stereo vision method, and propose a particle filter algorithm to refine and track the posteriori estimate of the position of the face. This work comes with two contributions: the first concerns the alignment part where we propose an extended ICP algorithm using an anisotropic scale transformation. The second contribution concerns the tracking part. We propose the use of the particle filtering algorithm and propose to constrain the search space using ICP algorithm in the propagation step. The results show that the system is able to fit and track the head properly, and keeps accurate the results on new individuals without a manual adaptation or training. © Springer-Verlag Berlin Heidelberg 2010.
GM-PHD Filter Combined with Track-Estimate Association and Numerical Interpolation
Directory of Open Access Journals (Sweden)
Jinguang Chen
2015-01-01
Full Text Available For the standard Gaussian mixture probability hypothesis density (GM-PHD filter, the number of targets can be overestimated if the clutter rate is too high or underestimated if the detection rate is too low. These problems seriously affect the accuracy of multitarget tracking for the number and the value of measurements and clutters cannot be distinguished and recognized. Therefore, we proposed an improved GM-PHD filter to tackle these problems. Firstly, a track-estimate association was implemented in the filtering process to detect and remove false-alarm targets. Secondly, a numerical interpolation technique was used to compensate the missing targets caused by low detection rate. At the end of this paper, simulation results were presented to demonstrate the proposed GM-PHD algorithm is more effective in estimating the number and state of targets than the previous ones.
Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games
Directory of Open Access Journals (Sweden)
Mohammadreza Asghari Oskoei
2017-11-01
Full Text Available This paper proposes an adaptive Kalman filter (AKF to improve the performance of a vision-based human machine interface (HMI applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent.
Event-triggered Kalman-consensus filter for two-target tracking sensor networks.
Su, Housheng; Li, Zhenghao; Ye, Yanyan
2017-11-01
This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
A low-complexity interacting multiple model filter for maneuvering target tracking
Khalid, Syed Safwan
2017-01-22
In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.
A low-complexity interacting multiple model filter for maneuvering target tracking
Khalid, Syed Safwan; Abrar, Shafayat
2017-01-01
In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.
An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking
Raihan A. V, Dilshad; Chakravorty, Suman
2018-03-01
Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.
Arbitrary-step randomly delayed robust filter with application to boost phase tracking
Qin, Wutao; Wang, Xiaogang; Bai, Yuliang; Cui, Naigang
2018-04-01
The conventional filters such as extended Kalman filter, unscented Kalman filter and cubature Kalman filter assume that the measurement is available in real-time and the measurement noise is Gaussian white noise. But in practice, both two assumptions are invalid. To solve this problem, a novel algorithm is proposed by taking the following four steps. At first, the measurement model is modified by the Bernoulli random variables to describe the random delay. Then, the expression of predicted measurement and covariance are reformulated, which could get rid of the restriction that the maximum number of delay must be one or two and the assumption that probabilities of Bernoulli random variables taking the value one are equal. Next, the arbitrary-step randomly delayed high-degree cubature Kalman filter is derived based on the 5th-degree spherical-radial rule and the reformulated expressions. Finally, the arbitrary-step randomly delayed high-degree cubature Kalman filter is modified to the arbitrary-step randomly delayed high-degree cubature Huber-based filter based on the Huber technique, which is essentially an M-estimator. Therefore, the proposed filter is not only robust to the randomly delayed measurements, but robust to the glint noise. The application to the boost phase tracking example demonstrate the superiority of the proposed algorithms.
Nonlinear Vibration Signal Tracking of Large Offshore Bridge Stayed Cable Based on Particle Filter
Directory of Open Access Journals (Sweden)
Ye Qingwei
2015-12-01
Full Text Available The stayed cables are key stress components of large offshore bridge. The fault detection of stayed cable is very important for safe of large offshore bridge. A particle filter model and algorithm of nonlinear vibration signal are used in this paper. Firstly, the particle filter model of stayed cable of large offshore bridge is created. Nonlinear dynamic model of the stayed-cable and beam coupling system is dispersed in temporal dimension by using the finite difference method. The discrete nonlinear vibration equations of any cable element are worked out. Secondly, a state equation of particle filter is fitted by least square algorithm from the discrete nonlinear vibration equations. So the particle filter algorithm can use the accurate state equations. Finally, the particle filter algorithm is used to filter the vibration signal of bridge stayed cable. According to the particle filter, the de-noised vibration signal can be tracked and be predicted for a short time accurately. Many experiments are done at some actual bridges. The simulation experiments and the actual experiments on the bridge stayed cables are all indicating that the particle filter algorithm in this paper has good performance and works stably.
Ma, Jian; Lu, Chen; Liu, Hongmei
2015-01-01
The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.
Directory of Open Access Journals (Sweden)
Jian Ma
Full Text Available The aircraft environmental control system (ECS is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.
Guitarist Fingertip Tracking by Integrating a Bayesian Classifier into Particle Filters
Directory of Open Access Journals (Sweden)
Chutisant Kerdvibulvech
2008-01-01
Full Text Available We propose a vision-based method for tracking guitar fingerings made by guitar players. We present it as a new framework for tracking colored finger markers by integrating a Bayesian classifier into particle filters. This adds the useful abilities of automatic track initialization and recovery from tracking failures in a dynamic background. Furthermore, by using the online adaptation of color probabilities, this method is able to cope with illumination changes. Augmented Reality Tag (ARTag is then utilized to calculate the projection matrix as an online process which allows the guitar to be moved while being played. Representative experimental results are also included. The method presented can be used to develop the application of human-computer interaction (HCI to guitar playing by recognizing the chord being played by a guitarist in virtual spaces. The aforementioned application would assist guitar learners by allowing them to automatically identify if they are using the correct chords required by the musical piece.
An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors.
Li, Jian; Wei, Xinguo; Zhang, Guangjun
2017-08-21
Efficiency and reliability are key issues when a star sensor operates in tracking mode. In the case of high attitude dynamics, the performance of existing attitude tracking algorithms degenerates rapidly. In this paper an extended Kalman filtering-based attitude tracking algorithm is presented. The star sensor is modeled as a nonlinear stochastic system with the state estimate providing the three degree-of-freedom attitude quaternion and angular velocity. The star positions in the star image are predicted and measured to estimate the optimal attitude. Furthermore, all the cataloged stars observed in the sensor field-of-view according the predicted image motion are accessed using a catalog partition table to speed up the tracking, called star mapping. Software simulation and night-sky experiment are performed to validate the efficiency and reliability of the proposed method.
Luciano Cantelli; Samuel Ligama; Giovanni Muscato; Davide Spina
2016-01-01
This paper describes an automatic calibration procedure adopted to improve the localization of an outdoor mobile robot. The proposed algorithm estimates, by using an extended Kalman filter, the main kinematic parameters of the vehicles, such as the wheel radii and the wheelbase as well as the magnetometer offset. Several trials have been performed to validate the proposed strategy on a tracked electrical mobile robot. The mobile robot is aimed to be adopted as a tool to help humanitarian demi...
Directory of Open Access Journals (Sweden)
Luciano Cantelli
2016-11-01
Full Text Available This paper describes an automatic calibration procedure adopted to improve the localization of an outdoor mobile robot. The proposed algorithm estimates, by using an extended Kalman filter, the main kinematic parameters of the vehicles, such as the wheel radii and the wheelbase as well as the magnetometer offset. Several trials have been performed to validate the proposed strategy on a tracked electrical mobile robot. The mobile robot is aimed to be adopted as a tool to help humanitarian demining operations.
Rawicz, Paul Lawrence
In this thesis, the similarities between the structure of the H infinity, H2, and Kalman filters are examined. The filters used in this examination have been derived through duality to the full information controller. In addition, a direct variation of parameters derivation of the Hinfinity filter is presented for both continuous and discrete time (staler case). Direct and controller dual derivations using differential games exist in the literature and also employ variational techniques. Using a variational, rather than a differential games, viewpoint has resulted in a simple relationship between the Riccati equations that arise from the derivation and the results of the Bounded Real Lemma. This same relation has previously been found in the literature and used to relate the Riccati inequality for linear systems to the Hamilton Jacobi inequality for nonlinear systems when implementing the Hinfinity controller. The Hinfinity, H2, and Kalman filters are applied to the two-state target tracking problem. In continuous time, closed form analytic expressions for the trackers and their performance are determined. To evaluate the trackers using a neutral, realistic, criterion, the probability of target escape is developed. That is, the probability that the target position error will be such that the target is outside the radar beam width resulting in a loss of measurement. In discrete time, a numerical example, using the probability of target escape, is presented to illustrate the differences in tracker performance.
Li, Shengbo Eben; Li, Guofa; Yu, Jiaying; Liu, Chang; Cheng, Bo; Wang, Jianqiang; Li, Keqiang
2018-01-01
Detection and tracking of objects in the side-near-field has attracted much attention for the development of advanced driver assistance systems. This paper presents a cost-effective approach to track moving objects around vehicles using linearly arrayed ultrasonic sensors. To understand the detection characteristics of a single sensor, an empirical detection model was developed considering the shapes and surface materials of various detected objects. Eight sensors were arrayed linearly to expand the detection range for further application in traffic environment recognition. Two types of tracking algorithms, including an Extended Kalman filter (EKF) and an Unscented Kalman filter (UKF), for the sensor array were designed for dynamic object tracking. The ultrasonic sensor array was designed to have two types of fire sequences: mutual firing or serial firing. The effectiveness of the designed algorithms were verified in two typical driving scenarios: passing intersections with traffic sign poles or street lights, and overtaking another vehicle. Experimental results showed that both EKF and UKF had more precise tracking position and smaller RMSE (root mean square error) than a traditional triangular positioning method. The effectiveness also encourages the application of cost-effective ultrasonic sensors in the near-field environment perception in autonomous driving systems.
Kalman filter based data fusion for neutral axis tracking in wind turbine towers
DEFF Research Database (Denmark)
Soman, Rohan; Malinowski, Pawel; Ostachowicz, Wieslaw
2015-01-01
downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even...... in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine....
Tracking of nuclear reactor parameters via recursive non linear estimation
International Nuclear Information System (INIS)
Pages Fita, J.; Alengrin, G.; Aguilar Martin, J.; Zwingelstein, M.
1975-01-01
The usefulness of nonlinear estimation in the supervision of nuclear reactors, as well for reactivity determination as for on-line modelisation in order to detect eventual and unwanted changes in working operation is illustrated. It is dealt with the reactivity estimation using an a priori dynamical model under the hypothesis of one group of delayed neutrons (measurements were done with an ionisation chamber). The determination of the reactivity using such measurements appears as a nonlinear estimation procedure derived from a particular form of nonlinear filter. Observed inputs being demand of power and inside temperature, and output being the reactivity balance, a recursive algorithm is derived for the estimation of the parameters that define the actual behavior of the reactor. Example of treatment of real data is given [fr
Jia, Xingyu; Liu, Zhigang; Tao, Long; Deng, Zhongwen
2017-10-16
Frequency scanning interferometry (FSI) with a single external cavity diode laser (ECDL) and time-invariant Kalman filtering is an effective technique for measuring the distance of a dynamic target. However, due to the hysteresis of the piezoelectric ceramic transducer (PZT) actuator in the ECDL, the optical frequency sweeps of the ECDL exhibit different behaviors, depending on whether the frequency is increasing or decreasing. Consequently, the model parameters of Kalman filter appear time varying in each iteration, which produces state estimation errors with time-invariant filtering. To address this, in this paper, a time-varying Kalman filter is proposed to model the instantaneous movement of a target relative to the different optical frequency tuning durations of the ECDL. The combination of the FSI method with the time-varying Kalman filter was theoretically analyzed, and the simulation and experimental results show the proposed method greatly improves the performance of dynamic FSI measurements.
Gotra, A; Doucet, C; Delli Fraine, P; Bessissow, A; Dey, C; Gallix, B; Boucher, L-M; Valenti, D
2018-05-14
To compare changes in inferior vena cava (IVC) filter positional parameters from insertion to removal and examine how they affect retrievability amongst various filter types. A total of 447 patients (260 men, 187 women) with a mean age of 55 years (range: 13-91 years) who underwent IVC filter retrieval between 2007-2014 were retrospectively included. Post-insertion and pre-retrieval angiographic studies were assessed for filter tilt, migration, strut wall penetration and retrieval outcomes. ANCOVA and multiple logistic regression models were used to analyze factors affecting retrieval success. Pairwise comparisons between filter types were performed. Of 488 IVC filter retrieval attempts, 94.1% were ultimately successful. The ALN filter had the highest mean absolute value of tilt (5.6 degrees), the Optease filter demonstrated the largest mean migration (-8.0mm) and the Bard G2 filter showed highest mean penetration (5.2mm). Dwell time of 0-90 days (OR, 11.1; P=0.01) or 90-180 days (OR, 2.6; P=0.02), net tilt of 10-15 degrees (OR 8.9; P=0.05), caudal migration of -10 to 0mm (OR, 3.46; P=0.03) and penetration less than 3mm (OR, 2.6; P=0.01) were positive predictors of successful retrievability. Higher odds of successful retrieval were obtained for the Bard G2X, Bard G2 and Cook Celect when compared to the ALN and Cordis Optease filters. Shorter dwell time, lower mean tilt, caudal migration and less caval wall penetration are positive predictors of successful IVC filter retrieval. Copyright © 2018 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.
Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures
Energy Technology Data Exchange (ETDEWEB)
Cerati, Giuseppe [Fermilab; Elmer, Peter [Princeton U.; Krutelyov, Slava [UC, San Diego; Lantz, Steven [Cornell U., Phys. Dept.; Lefebvre, Matthieu [Princeton U.; Masciovecchio, Mario [UC, San Diego; McDermott, Kevin [Cornell U., Phys. Dept.; Riley, Daniel [Cornell U., Phys. Dept.; Tadel, Matevž [UC, San Diego; Wittich, Peter [Cornell U., Phys. Dept.; Würthwein, Frank [UC, San Diego; Yagil, Avi [UC, San Diego
2017-11-16
Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Examples include the Intel Xeon Phi, GPGPUs, and similar technologies. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, one of the dominant computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction; today, the most common track-finding methods are those based on the Kalman filter. Experience at the LHC, both in the trigger and offline, has shown that these methods are robust and provide high physics performance. Previously we reported the significant parallel speedups that resulted from our efforts to adapt Kalman-filter-based tracking to many-core architectures such as Intel Xeon Phi. Here we report on how effectively those techniques can be applied to more realistic detector configurations and event complexity.
Liu, Hua; Wu, Wen
2017-06-13
For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).
Directory of Open Access Journals (Sweden)
Hua Liu
2017-06-01
Full Text Available For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF. The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF, the interacting multiple model cubature Kalman filter (IMMCKF and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF.
Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks.
Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei
2014-11-11
The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer-Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.
International Nuclear Information System (INIS)
Sanjeev Kumar; Shyam Kumar; Rajesh Kumar; Chakravarti, K.
2004-01-01
Interest in nano/microstructures results from their numerous potential applications in various areas such as materials and biomedical sciences, electronics, optics, magnetism, energy storage and electrochemistry. Materials with micro/nanoscopic dimensions not only have potential technological applications in areas such as device technology and drug delivery, but also are of fundamental interest in that the properties of a material can change in this regime of transition between the bulk and molecular scales. Electrodeposition is a versatile technique combining low processing cost with ambient conditions that can be used to prepare metallic, polymeric and semiconducting microstructures. In the present work ion track membranes of Makrofol (KG) have been used as templates for synthesis of metallic microstructures using the technique of electrodeposition. (author)
Earth orientation parameters from VLBI determined with a Kalman filter
Directory of Open Access Journals (Sweden)
Maria Karbon
2017-11-01
We prove that the Kalman filter is more than on par with the classical least squares method and that it is a valuable alternative, especially on the advent of the VLBI2010 Global Observing System and within the GGOS frame work.
Gralewicz, Grzegorz; Owczarek, Grzegorz; Kubrak, Janusz
2017-03-01
This article presents a comparison of the test results of selected mechanical parameters (hardness, Young's modulus, critical force for delamination) for protective filters intended for eye protection against harmful infrared radiation. Filters with reflective metallic films were studied, as well as interference filters developed at the Central Institute for Labour Protection - National Research Institute (CIOP-PIB). The test results of the selected mechanical parameters were compared with the test results, conducted in accordance with a standardised method, of simulating filter surface destruction that occurs during use.
Directory of Open Access Journals (Sweden)
Hongtao Yang
2018-01-01
Full Text Available This paper proposes a novel strong tracking filter (STF, which is suitable for dealing with the filtering problem of nonlinear systems when the following cases occur: that is, the constructed model does not match the actual system, the measurements have the one-step random delay, and the process and measurement noises are correlated at the same epoch. Firstly, a framework of decoupling filter (DF based on equivalent model transformation is derived. Further, according to the framework of DF, a new extended Kalman filtering (EKF algorithm via using first-order linearization approximation is developed. Secondly, the computational process of the suboptimal fading factor is derived on the basis of the extended orthogonality principle (EOP. Thirdly, the ultimate form of the proposed STF is obtained by introducing the suboptimal fading factor into the above EKF algorithm. The proposed STF can automatically tune the suboptimal fading factor on the basis of the residuals between available and predicted measurements and further the gain matrices of the proposed STF tune online to improve the filtering performance. Finally, the effectiveness of the proposed STF has been proved through numerical simulation experiments.
Directory of Open Access Journals (Sweden)
Mingyue Cui
2016-09-01
Full Text Available A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.
Directory of Open Access Journals (Sweden)
Bizhong Xia
2015-11-01
Full Text Available The estimation of state of charge (SOC is a crucial evaluation index in a battery management system (BMS. The value of SOC indicates the remaining capacity of a battery, which provides a good guarantee of safety and reliability of battery operation. It is difficult to get an accurate value of the SOC, being one of the inner states. In this paper, a strong tracking cubature Kalman filter (STCKF based on the cubature Kalman filter is presented to perform accurate and reliable SOC estimation. The STCKF algorithm can adjust gain matrix online by introducing fading factor to the state estimation covariance matrix. The typical second-order resistor-capacitor model is used as the battery’s equivalent circuit model to dynamically simulate characteristics of the battery. The exponential-function fitting method accomplishes the task of relevant parameters identification. Then, the developed STCKF algorithm has been introduced in detail and verified under different operation current profiles such as Dynamic Stress Test (DST and New European Driving Cycle (NEDC. Making a comparison with extended Kalman filter (EKF and CKF algorithm, the experimental results show the merits of the STCKF algorithm in SOC estimation accuracy and robustness.
Particle Filter with Integrated Voice Activity Detection for Acoustic Source Tracking
Directory of Open Access Journals (Sweden)
Anders M. Johansson
2007-01-01
Full Text Available In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. A natural extension of currently available sound source tracking algorithms is the integration of a voice activity detection (VAD scheme. We describe a new ASLT algorithm based on a particle filtering (PF approach, where VAD measurements are fused within the statistical framework of the PF implementation. Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere. Compared to a previously proposed PF algorithm, the experimental results demonstrate the improved robustness of the method described in this work when tracking sources emitting real-world speech signals, which typically involve significant silence gaps between utterances.
Jan, Shau-Shiun; Kao, Yu-Chun
2013-05-17
The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC) system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM) system that is enabled by global positioning system (GPS) technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF), is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF), is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods.
Directory of Open Access Journals (Sweden)
Shau-Shiun Jan
2013-05-01
Full Text Available The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM system that is enabled by global positioning system (GPS technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF, is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF, is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods.
Szczęsna, Agnieszka; Pruszowski, Przemysław
2016-01-01
Inertial orientation tracking is still an area of active research, especially in the context of out-door, real-time, human motion capture. Existing systems either propose loosely coupled tracking approaches where each segment is considered independently, taking the resulting drawbacks into account, or tightly coupled solutions that are limited to a fixed chain with few segments. Such solutions have no flexibility to change the skeleton structure, are dedicated to a specific set of joints, and have high computational complexity. This paper describes the proposal of a new model-based extended quaternion Kalman filter that allows for estimation of orientation based on outputs from the inertial measurements unit sensors. The filter considers interdependencies resulting from the construction of the kinematic chain so that the orientation estimation is more accurate. The proposed solution is a universal filter that does not predetermine the degree of freedom at the connections between segments of the model. To validation the motion of 3-segments single link pendulum captured by optical motion capture system is used. The next step in the research will be to use this method for inertial motion capture with a human skeleton model.
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-12-09
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.
Gralewicz, Grzegorz; Owczarek, Grzegorz
2016-01-01
The paper analyses the selected optical parameters of protective optic filters used for protection of the eyes against hazardous radiation within the visible (VIS) and near infrared (NIR) spectrum range. The indexes characterizing transmission and reflection of optic radiation incident on the filter are compared. As it follows from the completed analysis, the newly developed interference filters provide more effective blocking of infrared radiation in comparison with the currently used protec...
Sky-Hook Control and Kalman Filtering in Nonlinear Model of Tracked Vehicle Suspension System
Directory of Open Access Journals (Sweden)
Jurkiewicz Andrzej
2017-09-01
Full Text Available The essence of the undertaken topic is application of the continuous sky-hook control strategy and the Extended Kalman Filter as the state observer in the 2S1 tracked vehicle suspension system. The half-car model of this suspension system consists of seven logarithmic spiral springs and two magnetorheological dampers which has been described by the Bingham model. The applied continuous sky-hook control strategy considers nonlinear stiffness characteristic of the logarithmic spiral springs. The control is determined on estimates generated by the Extended Kalman Filter. Improve of ride comfort is verified by comparing simulation results, under the same driving conditions, of controlled and passive vehicle suspension systems.
Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang
2017-11-01
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.
Directory of Open Access Journals (Sweden)
Yazhe Tang
2015-01-01
Full Text Available This paper presents a novel surveillance system named thermal omnidirectional vision (TOV system which can work in total darkness with a wild field of view. Different to the conventional thermal vision sensor, the proposed vision system exhibits serious nonlinear distortion due to the effect of the quadratic mirror. To effectively model the inherent distortion of omnidirectional vision, an equivalent sphere projection is employed to adaptively calculate parameterized distorted neighborhood of an object in the image plane. With the equivalent projection based adaptive neighborhood calculation, a distortion-invariant gradient coding feature is proposed for thermal catadioptric vision. For robust tracking purpose, a rotational kinematic modeled adaptive particle filter is proposed based on the characteristic of omnidirectional vision, which can handle multiple movements effectively, including the rapid motions. Finally, the experiments are given to verify the performance of the proposed algorithm for human tracking in TOV system.
Dual-Channel Particle Filter Based Track-Before-Detect for Monopulse Radar
Directory of Open Access Journals (Sweden)
Fei Cai
2014-01-01
Full Text Available A particle filter based track-before-detect (PF-TBD algorithm is proposed for the monopulse high pulse repetition frequency (PRF pulse Doppler radar. The actual measurement model is adopted, in which the range is highly ambiguous and the sum and difference channels exist in parallel. A quantization method is used to approximate the point spread function to reduce the computation load. The detection decisions of the PF-TBD are fed to a binary integrator to further improve the detection performance. Simulation results show that the proposed algorithm can detect and track the low SNR target efficiently. The detection performance is improved significantly for both the single frame and the multiframe detection compared with the classical detector. A performance comparison with the PF-TBD using sum channel only is also supplied.
Hill, Bryon K.; Walker, Bruce K.
1991-01-01
When using parameter estimation methods based on extended Kalman filter (EKF) theory, it is common practice to assume that the unknown parameter values behave like a random process, such as a random walk, in order to guarantee their identifiability by the filter. The present work is the result of an ongoing effort to quantitatively describe the effect that the assumption of a fictitious noise (called pseudonoise) driving the unknown parameter values has on the parameter estimate convergence rate in filter-based parameter estimators. The initial approach is to examine a first-order system described by one state variable with one parameter to be estimated. The intent is to derive analytical results for this simple system that might offer insight into the effect of the pseudonoise assumption for more complex systems. Such results would make it possible to predict the estimator error convergence behavior as a function of the assumed pseudonoise intensity, and this leads to the natural application of the results to the design of filter-based parameter estimators. The results obtained show that the analytical description of the convergence behavior is very difficult.
Optimizing experimental parameters for tracking of diffusing particles
DEFF Research Database (Denmark)
Vestergaard, Christian L.
2016-01-01
We describe how a single-particle tracking experiment should be designed in order for its recorded trajectories to contain the most information about a tracked particle's diffusion coefficient. The precision of estimators for the diffusion coefficient is affected by motion blur, limited photon st...
Human tracking in thermal images using adaptive particle filters with online random forest learning
Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal
2013-11-01
This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.
DEFF Research Database (Denmark)
Parodi, Yesenia Lacouture
2008-01-01
Several approaches to render binaural signals through loudspeakers have been proposed previously. Some studies had focused on the optimum loudspeaker arrangement while others had proposed efficient filters. However, to our knowledge, the identification of optimal parameters for inverse methods ap...... loudspeaker arrangements. Least square approximations in frequency and time domain are evaluated along with a crosstalk canceler based on minimum-phase approximation. Filter parameters, such as length and regularization, are varied and simulated for different span and elevation angles....
International Nuclear Information System (INIS)
Kostic, D.; Nikezic, D.
1997-01-01
An equation of the etch pit wall is proposed to be used for simulation of the track growth and calculating the major and the minor axis of etch pit opening. Dependence on the following parameters is set up: distance along a track from the point where the particle entered the detector, ratio of the track etch wall to the bulk etch rate, integration constant determined from particle penetration depth and normal distance from the particle trajectory to the etch pit wall. The corresponding computer program was written. The input parameters of this program are: alpha particles energy, incidence angle and removed layer; the output gives track parameters. The results obtained by this method are compared to another approach given by Somogy and Szalay (1973) and a reasonably good agreement is found. (author)
State and parameter estimation of the heat shock response system using Kalman and particle filters.
Liu, Xin; Niranjan, Mahesan
2012-06-01
Traditional models of systems biology describe dynamic biological phenomena as solutions to ordinary differential equations, which, when parameters in them are set to correct values, faithfully mimic observations. Often parameter values are tweaked by hand until desired results are achieved, or computed from biochemical experiments carried out in vitro. Of interest in this article, is the use of probabilistic modelling tools with which parameters and unobserved variables, modelled as hidden states, can be estimated from limited noisy observations of parts of a dynamical system. Here we focus on sequential filtering methods and take a detailed look at the capabilities of three members of this family: (i) extended Kalman filter (EKF), (ii) unscented Kalman filter (UKF) and (iii) the particle filter, in estimating parameters and unobserved states of cellular response to sudden temperature elevation of the bacterium Escherichia coli. While previous literature has studied this system with the EKF, we show that parameter estimation is only possible with this method when the initial guesses are sufficiently close to the true values. The same turns out to be true for the UKF. In this thorough empirical exploration, we show that the non-parametric method of particle filtering is able to reliably estimate parameters and states, converging from initial distributions relatively far away from the underlying true values. Software implementation of the three filters on this problem can be freely downloaded from http://users.ecs.soton.ac.uk/mn/HeatShock
A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer.
Ligorio, Gabriele; Sabatini, Angelo M
2015-08-01
Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.
HCP track calculations in Lif:Mg,Ti: 3D modeling of the ''track – escape'' parameter
International Nuclear Information System (INIS)
Sattinger, D.; Sharon, A.; Horowitz, Y.S.
2011-01-01
The conceptual framework of the track interaction model (TIM) was conceived in the 1970s and mathematically formulated in the 1980s to describe heavy charged particle TL fluence response supralinearity. The extended track interaction model (ETIM) was developed to include saturation effects due to overlapping tracks and has been applied to both proton and alpha particle TL fluence response. One of the parameters of major importance in the TIM is the ''track – escape'' parameter, defined by N e /N w , where N e represents the number of electrons which escape the parent track during heating, and N w is the number of electrons which recombine within the parent track to produce a TL photon. Recently a first attempt was carried out to theoretically model escape parameters calculated in 2D geometry as a function of particle type and energy using trapping center (TC), luminescent center (LC) and competitive center (CC) occupation probabilities calculated from track segment radial dose distributions and optical absorption (OA) dose response. In this study, the calculations are extended to 3D geometry using a Monte Carlo approach which samples the point of creation of the charge carriers according to the TC occupation probabilities and then estimates N w by sampling the chord length to the track exterior. Charge carriers which escape the irradiated track volume contribute to N e . This more sophisticated 3D calculation of N e /N w is expected to increase the reliability of the modeling of heavy charged particle TL fluence response in the framework of the ETIM and enhance our understanding of “track effects” in Heavy Charged Particle (HCP) induced TL.
A NEW METHOD OF CHANNEL FRICTION INVERSION BASED ON KALMAN FILTER WITH UNKNOWN PARAMETER VECTOR
Institute of Scientific and Technical Information of China (English)
CHENG Wei-ping; MAO Gen-hai; LIU Guo-hua
2005-01-01
Channel friction is an important parameter in hydraulic analysis.A channel friction parameter inversion method based on Kalman Filter with unknown parameter vector is proposed.Numerical simulations indicate that when the number of monitoring stations exceeds a critical value, the solution is hardly affected.In addition, Kalman Filter with unknown parameter vector is effective only at unsteady state.For the nonlinear equations, computations of sensitivity matrices are time-costly.Two simplified measures can reduce computing time, but not influence the results.One is to reduce sensitivity matrix analysis time, the other is to substitute for sensitivity matrix.
Cope, Davis; Blakeslee, Barbara; McCourt, Mark E
2013-05-01
The difference-of-Gaussians (DOG) filter is a widely used model for the receptive field of neurons in the retina and lateral geniculate nucleus (LGN) and is a potential model in general for responses modulated by an excitatory center with an inhibitory surrounding region. A DOG filter is defined by three standard parameters: the center and surround sigmas (which define the variance of the radially symmetric Gaussians) and the balance (which defines the linear combination of the two Gaussians). These parameters are not directly observable and are typically determined by nonlinear parameter estimation methods applied to the frequency response function. DOG filters show both low-pass (optimal response at zero frequency) and bandpass (optimal response at a nonzero frequency) behavior. This paper reformulates the DOG filter in terms of a directly observable parameter, the zero-crossing radius, and two new (but not directly observable) parameters. In the two-dimensional parameter space, the exact region corresponding to bandpass behavior is determined. A detailed description of the frequency response characteristics of the DOG filter is obtained. It is also found that the directly observable optimal frequency and optimal gain (the ratio of the response at optimal frequency to the response at zero frequency) provide an alternate coordinate system for the bandpass region. Altogether, the DOG filter and its three standard implicit parameters can be determined by three directly observable values. The two-dimensional bandpass region is a potential tool for the analysis of populations of DOG filters (for example, populations of neurons in the retina or LGN), because the clustering of points in this parameter space may indicate an underlying organizational principle. This paper concentrates on circular Gaussians, but the results generalize to multidimensional radially symmetric Gaussians and are given as an appendix.
International Nuclear Information System (INIS)
Strandlie, A.; Wroldsen, J.
2006-01-01
If any of the probability densities involved in track fitting deviate from the Gaussian assumption, it is plausible that a non-linear estimator which better takes the actual shape of the distribution into account can do better. One such non-linear estimator is the Gaussian-sum filter, which is adequate if the distributions under consideration can be approximated by Gaussian mixtures. The main purpose of this paper is to present a Gaussian-sum filter for track fitting, based on a two-component approximation of the distribution of angular deflections due to multiple scattering. In a simulation study within a linear track model the Gaussian-sum filter is shown to be a competitive alternative to the Kalman filter. Scenarios at various momenta and with various maximum number of components in the Gaussian-sum filter are considered. Particularly at low momenta the Gaussian-sum filter yields a better estimate of the uncertainties than the Kalman filter, and it is also slightly more precise than the latter
Fusing inertial sensor data in an extended Kalman filter for 3D camera tracking.
Erdem, Arif Tanju; Ercan, Ali Özer
2015-02-01
In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-05-13
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
Directory of Open Access Journals (Sweden)
Yibo Feng
2015-05-01
Full Text Available We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF, the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
Observer-based linear parameter varying H∞ tracking control for hypersonic vehicles
Directory of Open Access Journals (Sweden)
Yiqing Huang
2016-11-01
Full Text Available This article aims to develop observer-based linear parameter varying output feedback H∞ tracking controller for hypersonic vehicles. Due to the complexity of an original nonlinear model of the hypersonic vehicle dynamics, a slow–fast loop linear parameter varying polytopic model is introduced for system stability analysis and controller design. Then, a state observer is developed by linear parameter varying technique in order to estimate the unmeasured attitude angular for slow loop system. Also, based on the designed linear parameter varying state observer, a kind of attitude tracking controller is presented to reduce tracking errors for all bounded reference attitude angular inputs. The closed-loop linear parameter varying system is proved to be quadratically stable by Lypapunov function technique. Finally, simulation results show that the developed linear parameter varying H∞ controller has good tracking capability for reference commands.
Zhang, Tao; Gao, Feng; Muhamedsalih, Hussam; Lou, Shan; Martin, Haydn; Jiang, Xiangqian
2018-03-20
The phase slope method which estimates height through fringe pattern frequency and the algorithm which estimates height through the fringe phase are the fringe analysis algorithms widely used in interferometry. Generally they both extract the phase information by filtering the signal in frequency domain after Fourier transform. Among the numerous papers in the literature about these algorithms, it is found that the design of the filter, which plays an important role, has never been discussed in detail. This paper focuses on the filter design in these algorithms for wavelength scanning interferometry (WSI), trying to optimize the parameters to acquire the optimal results. The spectral characteristics of the interference signal are analyzed first. The effective signal is found to be narrow-band (near single frequency), and the central frequency is calculated theoretically. Therefore, the position of the filter pass-band is determined. The width of the filter window is optimized with the simulation to balance the elimination of the noise and the ringing of the filter. Experimental validation of the approach is provided, and the results agree very well with the simulation. The experiment shows that accuracy can be improved by optimizing the filter design, especially when the signal quality, i.e., the signal noise ratio (SNR), is low. The proposed method also shows the potential of improving the immunity to the environmental noise by adapting the signal to acquire the optimal results through designing an adaptive filter once the signal SNR can be estimated accurately.
International Nuclear Information System (INIS)
George, J.L.
1988-04-01
One of the measurement needs of US Department of Energy (DOE) remedial action programs is the estimation of the annual-average indoor radon-daughter concentration (RDC) in structures. The filtered alpha-track method, using a 1-year exposure period, can be used to accomplish RDC estimations for the DOE remedial action programs. This manual describes the procedure used to obtain filtered alpha-track measurements to derive average RDC estimates from the measurrements. Appropriate quality-assurance and quality-control programs are also presented. The ''prompt'' alpha-track method of exposing monitors for 2 to 6 months during specific periods of the year is also briefly discussed in this manual. However, the prompt alpha-track method has been validated only for use in the Mesa County, Colorado, area. 3 refs., 3 figs
Kalman Filter Based Data Fusion for Bi-Axial Neutral Axis Tracking in Wind Turbine Towers
DEFF Research Database (Denmark)
Soman, Rohan; Malinowski, Pawel; Schmidt Paulsen, Uwe
2015-01-01
demonstrates a methodology for the selection of threshold for damage detection based on qualitative data acquired from several damage scenarios of a 10 MW wind turbine. The damage indicator is the change of neutral axis (NA) which is tracked using Kalman Filter (KF). Based on the level of damage to be detected...... in the structure is reflected by a change in this feature. If this change is above a threshold the structure is said to be damaged. In most applications the determination of this threshold is based on engineering judgment and the previous experience of the operator. These practices are highly subjective...... and the probability of occurrence of false positive and false negative detections, a threshold value is selected. This threshold is then applied to strain data from the Nordtank NTK500/41 wind turbine for validation....
Neumann, M; Cuvillon, L; Breton, E; de Matheli, M
2013-01-01
Recently, a workflow for magnetic resonance (MR) image plane alignment based on tracking in real-time MR images was introduced. The workflow is based on a tracking device composed of 2 resonant micro-coils and a passive marker, and allows for tracking of the passive marker in clinical real-time images and automatic (re-)initialization using the microcoils. As the Kalman filter has proven its benefit as an estimator and predictor, it is well suited for use in tracking applications. In this paper, a Kalman filter is integrated in the previously developed workflow in order to predict position and orientation of the tracking device. Measurement noise covariances of the Kalman filter are dynamically changed in order to take into account that, according to the image plane orientation, only a subset of the 3D pose components is available. The improved tracking performance of the Kalman extended workflow could be quantified in simulation results. Also, a first experiment in the MRI scanner was performed but without quantitative results yet.
Physics-based coastal current tomographic tracking using a Kalman filter.
Wang, Tongchen; Zhang, Ying; Yang, T C; Chen, Huifang; Xu, Wen
2018-05-01
Ocean acoustic tomography can be used based on measurements of two-way travel-time differences between the nodes deployed on the perimeter of the surveying area to invert/map the ocean current inside the area. Data at different times can be related using a Kalman filter, and given an ocean circulation model, one can in principle now cast and even forecast current distribution given an initial distribution and/or the travel-time difference data on the boundary. However, an ocean circulation model requires many inputs (many of them often not available) and is unpractical for estimation of the current field. A simplified form of the discretized Navier-Stokes equation is used to show that the future velocity state is just a weighted spatial average of the current state. These weights could be obtained from an ocean circulation model, but here in a data driven approach, auto-regressive methods are used to obtain the time and space dependent weights from the data. It is shown, based on simulated data, that the current field tracked using a Kalman filter (with an arbitrary initial condition) is more accurate than that estimated by the standard methods where data at different times are treated independently. Real data are also examined.
Kalman filter based data fusion for neutral axis tracking in wind turbine towers
Soman, Rohan; Malinowski, Pawel; Ostachowicz, Wieslaw; Paulsen, Uwe S.
2015-03-01
Wind energy is seen as one of the most promising solutions to man's ever increasing demands of a clean source of energy. In particular to reduce the cost of energy (COE) generated, there are efforts to increase the life-time of the wind turbines, to reduce maintenance costs and to ensure high availability. Maintenance costs may be lowered and the high availability and low repair costs ensured through the use of condition monitoring (CM) and structural health monitoring (SHM). SHM allows early detection of damage and allows maintenance planning. Furthermore, it can allow us to avoid unnecessary downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even in the presence of changing ambient conditions. NA is defined as the line or plane in the section of the beam which does not experience any tensile or compressive forces when loaded. The NA is the property of the cross section of the tower and is independent of the applied loads and ambient conditions. Any change in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine.
Directory of Open Access Journals (Sweden)
Zhang Lin Huan
2015-03-01
Full Text Available The aim of this research was to develop a safe human-driven and autonomous leader-follower tracking system for an autonomous tractor. To enable the tracking system, a laser range finder (LRF-based landmark detection system was designed to observe the relative position between a leader and a follower used in agricultural operations. The virtual follower-based formation-tracking algorithm was developed to minimize tracking errors and ensure safety. An extended Kalman filter (EKF was implemented for fusing LRF and odometry position to ensure stability of tracking in noisy farmland conditions. Simulations were conducted for tracking the leader in small and large sinusoidal curved paths. Simulated results verified high accuracy of formation tracking, stable velocity, and regulated steering angle of the follower. The tracking method confirmed the follower could follow the leader with a required formation safely and steadily in noisy conditions. The EKF helped to improve observation accuracy, velocity, and steering angle stability of the follower. As a result of the improved accuracy of observation and motion action, the tracking performance for lateral, longitudinal, and heading were also improved after the EKF was implemented in the tracking system.
State and parameter estimation in a nuclear fuel pin using the extended Kalman filter
International Nuclear Information System (INIS)
Feeley, J.J.
1979-03-01
The Kalman filter is a powerful tool for the design and analysis of stochastic systems. The general nature of the method permits such diverse applications as on-line state estimation in optimal control systems, as well as state and parameter estimation applications in data analysis and system identification. However, while there have been a large number of Kalman filter applications in the aerospace industry, there have been relatively few in the nuclear industry. The report describes some initial efforts made at the Idaho National Engineering Laboratory to gain experience with the methods of Kalman filtering and to test their applicability to nuclear engineering problems. Two specific cases were considered: first, a real-time state estimation problem using a hybrid computer where the process was simulated on the analog portion of the computer, and the Kalman filter was programmed on the digital portion; second, a system identification problem where a digital extended Kalman filter program was used to estimate states and parameters in a nuclear fuel pin using data generated both by actual experiments and computer simulations. The report contains a derivation of the Kalman filter equations, a development of the mathematical model of the nuclear fuel pin, a description of the computer programs used in the analysis, and a discussion of the results obtained
International Nuclear Information System (INIS)
Moraes, M.A.P.V. de; Pugliesi, R.; Khouri, M.T.F.C.
1985-11-01
The solid state nuclear track detectors technique has been used for determination of boron in aqueous solutions, using a filtered neutron beam. The particles tracks from the 10 B(n,α)Li 7 reaction were registered in the CR-39 film, chemically etched in a (30%) KOH solution 70 0 C during 90 minutes. The obtained results showed the usefulness of this technique for boron determination in the ppm range. The inferior detectable limit was 9 ppm. The combined track registration efficiency factor K has been evaluated in the solutions, for the CR-39 detector and its values is K= (4,60 - + 0,06). 10 -4 cm. (Author) [pt
Homogenous polynomially parameter-dependent H∞ filter designs of discrete-time fuzzy systems.
Zhang, Huaguang; Xie, Xiangpeng; Tong, Shaocheng
2011-10-01
This paper proposes a novel H(∞) filtering technique for a class of discrete-time fuzzy systems. First, a novel kind of fuzzy H(∞) filter, which is homogenous polynomially parameter dependent on membership functions with an arbitrary degree, is developed to guarantee the asymptotic stability and a prescribed H(∞) performance of the filtering error system. Second, relaxed conditions for H(∞) performance analysis are proposed by using a new fuzzy Lyapunov function and the Finsler lemma with homogenous polynomial matrix Lagrange multipliers. Then, based on a new kind of slack variable technique, relaxed linear matrix inequality-based H(∞) filtering conditions are proposed. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approach.
A RSSI-based parameter tracking strategy for constrained position localization
Du, Jinze; Diouris, Jean-François; Wang, Yide
2017-12-01
In this paper, a received signal strength indicator (RSSI)-based parameter tracking strategy for constrained position localization is proposed. To estimate channel model parameters, least mean squares method (LMS) is associated with the trilateration method. In the context of applications where the positions are constrained on a grid, a novel tracking strategy is proposed to determine the real position and obtain the actual parameters in the monitored region. Based on practical data acquired from a real localization system, an experimental channel model is constructed to provide RSSI values and verify the proposed tracking strategy. Quantitative criteria are given to guarantee the efficiency of the proposed tracking strategy by providing a trade-off between the grid resolution and parameter variation. The simulation results show a good behavior of the proposed tracking strategy in the presence of space-time variation of the propagation channel. Compared with the existing RSSI-based algorithms, the proposed tracking strategy exhibits better localization accuracy but consumes more calculation time. In addition, a tracking test is performed to validate the effectiveness of the proposed tracking strategy.
Estimating Stellar Parameters and Interstellar Extinction from Evolutionary Tracks
Directory of Open Access Journals (Sweden)
Sichevsky S.
2016-03-01
Full Text Available Developing methods for analyzing and extracting information from modern sky surveys is a challenging task in astrophysical studies. We study possibilities of parameterizing stars and interstellar medium from multicolor photometry performed in three modern photometric surveys: GALEX, SDSS, and 2MASS. For this purpose, we have developed a method to estimate stellar radius from effective temperature and gravity with the help of evolutionary tracks and model stellar atmospheres. In accordance with the evolution rate at every point of the evolutionary track, star formation rate, and initial mass function, a weight is assigned to the resulting value of radius that allows us to estimate the radius more accurately. The method is verified for the most populated areas of the Hertzsprung-Russell diagram: main-sequence stars and red giants, and it was found to be rather precise (for main-sequence stars, the average relative error of radius and its standard deviation are 0.03% and 3.87%, respectively.
Bachmann-Machnik, Anna; Meyer, Daniel; Waldhoff, Axel; Fuchs, Stephan; Dittmer, Ulrich
2018-04-01
Retention Soil Filters (RSFs), a form of vertical flow constructed wetlands specifically designed for combined sewer overflow (CSO) treatment, have proven to be an effective tool to mitigate negative impacts of CSOs on receiving water bodies. Long-term hydrologic simulations are used to predict the emissions from urban drainage systems during planning of stormwater management measures. So far no universally accepted model for RSF simulation exists. When simulating hydraulics and water quality in RSFs, an appropriate level of detail must be chosen for reasonable balancing between model complexity and model handling, considering the model input's level of uncertainty. The most crucial parameters determining the resultant uncertainties of the integrated sewer system and filter bed model were identified by evaluating a virtual drainage system with a Retention Soil Filter for CSO treatment. To determine reasonable parameter ranges for RSF simulations, data of 207 events from six full-scale RSF plants in Germany were analyzed. Data evaluation shows that even though different plants with varying loading and operation modes were examined, a simple model is sufficient to assess relevant suspended solids (SS), chemical oxygen demand (COD) and NH4 emissions from RSFs. Two conceptual RSF models with different degrees of complexity were assessed. These models were developed based on evaluation of data from full scale RSF plants and column experiments. Incorporated model processes are ammonium adsorption in the filter layer and degradation during subsequent dry weather period, filtration of SS and particulate COD (XCOD) to a constant background concentration and removal of solute COD (SCOD) by a constant removal rate during filter passage as well as sedimentation of SS and XCOD in the filter overflow. XCOD, SS and ammonium loads as well as ammonium concentration peaks are discharged primarily via RSF overflow not passing through the filter bed. Uncertainties of the integrated
International Nuclear Information System (INIS)
Souza, Anderson S.; Rostelato, Maria Elisa C.M.; Zeituni, Carlos A.; Moura, Eduardo S.; Rodrigues, Bruna T.; Souza, Daiane C.; Tiezzi, Rodrigo; Souza, Carla D.; Melo, Emerson R.; Camargo, Anderson R.; Batista, Talita Q.
2015-01-01
This paper discusses the main features associated with the dosimetric parameters between FFF and FF Linacs. A set of Varian TrueBeam Linac and Varian 23EX dosimetric measurements was acquired to perform the experimental measurements. The dose measurements were carried out in a water Blue phantom, with a waterproof ionization chambers: farmer ionization chamber (0.6 cm 3 ) and Exradin A1SL(0.053 cm 3 ) , for fields 5 x 5, 8 x 8, 10 x 10, 15 x 15, 30 x 30 cm 2 . The 6 MV FFF and FF was the energy used in this work. Percent Depth Dose (PDD) was the dosimetric parameters evaluated using a fixed Source Surface Distance of 100 cm. One depth were applied for the measurements, 10 cm (central axis) from the water surface. The 6 MV FFF showed less penetrating than the 6 MV FF. This is due to the removal flattening filter causes more lower energy photons on the central axis. The field sizes were equivalent for both FFF and FF. The main advantage in operate linear accelerators without flattening filter is due to the high doses rates delivered during the treatment. High doses rates could reduce the patient treatment time and may be beneficial for some treatment techniques such as IMRT and SRT. (author)
Lubineau, Gilles
2009-05-16
The post-processing of experiments with nonuniform fields is still a challenge: the information is often much richer, but its interpretation for identification purposes is not straightforward. However, this is a very promising field of development because it would pave the way for the robust identification of multiple material parameters using only a small number of experiments. This paper presents a goal-oriented filtering technique in which data are combined into new output fields which are strongly correlated with specific quantities of interest (the material parameters to be identified). Thus, this combination, which is nonuniform in space, constitutes a filter of the experimental outputs, whose relevance is quantified by a quality function based on global variance analysis. Then, this filter is optimized using genetic algorithms. © 2009 Springer-Verlag.
Ion track etching revisited: I. Correlations between track parameters in aged polymers
Czech Academy of Sciences Publication Activity Database
Fink, Dietmar; Munoz, G. H.; García Arellano, H.; Vacík, Jiří; Hnatowicz, Vladimír; Kiv, A.; Alfonta, L.
2018-01-01
Roč. 420, č. 4 (2018), s. 57-68 ISSN 0168-583X R&D Projects: GA ČR(CZ) GBP108/12/G108 Institutional support: RVO:61389005 Keywords : ion track * polymer * etching Subject RIV: BG - Nuclear, Atomic and Molecular Physics , Colliders OBOR OECD: Nuclear physics Impact factor: 1.109, year: 2016
PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.
Xia, Jing; Wang, Michelle Yongmei
Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.
An Automatic Parameter Identification Method for a PMSM Drive with LC-Filter
DEFF Research Database (Denmark)
Bech, Michael Møller; Christensen, Jeppe Haals; Weber, Magnus L.
2016-01-01
of the PMSM fed through an LC-filter. Based on the measured current response, model parameters for both the filter (L, R, C) and the PMSM (L and R) are estimated: First, the frequency response of the system is estimated using Welch Modified Periodogram method and then an optimization algorithm is used to find...... the parameters in an analytical reference model that minimize the model error. To demonstrate the practical feasibility of the method, a fully functional drive including an embedded real-time controller has been built. In addition to modulation, data acquisition and control the whole parameter identification...... method is also implemented on the real-time controller. Based on laboratory experiments on a 22 kW drive, it is concluded that the embedded identification method can estimate the five parameters in less than ten seconds....
Aihara, ShinIchi; Bagchi, Arunabha; Saha, S.
Despite the success of particle filter, there are two factors which cause difficulties in its implementation. The first one is the choice of importance functions commonly used in the literature which are far from being optimal. The second one is the combined state and parameter estimation problem.
International Nuclear Information System (INIS)
Gershgorin, B.; Harlim, J.; Majda, A.J.
2010-01-01
The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates
Cheung, Shao-Yong; Lee, Chieh-Han; Yu, Hwa-Lung
2017-04-01
Due to the limited hydrogeological observation data and high levels of uncertainty within, parameter estimation of the groundwater model has been an important issue. There are many methods of parameter estimation, for example, Kalman filter provides a real-time calibration of parameters through measurement of groundwater monitoring wells, related methods such as Extended Kalman Filter and Ensemble Kalman Filter are widely applied in groundwater research. However, Kalman Filter method is limited to linearity. This study propose a novel method, Bayesian Maximum Entropy Filtering, which provides a method that can considers the uncertainty of data in parameter estimation. With this two methods, we can estimate parameter by given hard data (certain) and soft data (uncertain) in the same time. In this study, we use Python and QGIS in groundwater model (MODFLOW) and development of Extended Kalman Filter and Bayesian Maximum Entropy Filtering in Python in parameter estimation. This method may provide a conventional filtering method and also consider the uncertainty of data. This study was conducted through numerical model experiment to explore, combine Bayesian maximum entropy filter and a hypothesis for the architecture of MODFLOW groundwater model numerical estimation. Through the virtual observation wells to simulate and observe the groundwater model periodically. The result showed that considering the uncertainty of data, the Bayesian maximum entropy filter will provide an ideal result of real-time parameters estimation.
Tracking magma volume recovery at okmok volcano using GPS and an unscented kalman filter
Fournier, T.; Freymueller, Jeffrey T.; Cervelli, Peter
2009-01-01
Changes beneath a volcano can be observed through position changes in a GPS network, but distinguishing the source of site motion is not always straightforward. The records of continuous GPS sites provide a favorable data set for tracking magma migration. Dense campaign observations usually provide a better spatial picture of the overall deformation field, at the expense of an episodic temporal record. Combining these observations provides the best of both worlds. A Kalman filter provides a means for integrating discrete and continuous measurements and for interpreting subtle signals. The unscented Kalman filter (UKF) is a nonlinear method for time-dependent observations. We demonstrate the application of this technique to deformation data by applying it to GPS data collected at Okmok volcano. Seven years of GPS observations at Okmok are analyzed using a Mogi source model and the UKF. The deformation source at Okmok is relatively stable at 2.5 km depth below sea level, located beneath the center of the caldera, which means the surface deformation is caused by changes in the strength of the source. During the 7 years of GPS observations more than 0.5 m of uplift has occurred, a majority of that during the time period January 2003 to July 2004. The total volume recovery at Okmok since the last eruption in 1997 is ??60-80%. The UKF allows us to solve simultaneously for the time-dependence of the source strength and for the location without a priori information about the source. ?? 2009 by the American Geophysical Union.
Improved Particle Filter for Passive Target Tracking%改进粒子滤波在被动目标跟踪中的应用
Institute of Scientific and Technical Information of China (English)
邓小龙; 谢剑英; 杨煜普
2005-01-01
As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved extended Kalman filter (EKF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for target tracking. Performances of the EKF, basic PF and the improved PF are compared in target tracking examples. The simulation results confirm that the improved particle filter outperforms the others.
Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter.
Kim, Seongwan; Ban, Yuseok; Lee, Sangyoun
2017-01-17
The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor), usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor's stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.
Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter
Directory of Open Access Journals (Sweden)
Seongwan Kim
2017-01-01
Full Text Available The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor, usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor’s stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.
Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim
2016-01-01
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.
Ait-El-Fquih, Boujemaa
2016-08-12
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.
Autonomous sensor particle for parameter tracking in large vessels
International Nuclear Information System (INIS)
Thiele, Sebastian; Da Silva, Marco Jose; Hampel, Uwe
2010-01-01
A self-powered and neutrally buoyant sensor particle has been developed for the long-term measurement of spatially distributed process parameters in the chemically harsh environments of large vessels. One intended application is the measurement of flow parameters in stirred fermentation biogas reactors. The prototype sensor particle is a robust and neutrally buoyant capsule, which allows free movement with the flow. It contains measurement devices that log the temperature, absolute pressure (immersion depth) and 3D-acceleration data. A careful calibration including an uncertainty analysis has been performed. Furthermore, autonomous operation of the developed prototype was successfully proven in a flow experiment in a stirred reactor model. It showed that the sensor particle is feasible for future application in fermentation reactors and other industrial processes
Institute of Scientific and Technical Information of China (English)
Xiaogu ZHENG
2009-01-01
An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.
Directory of Open Access Journals (Sweden)
Yajun Li
2015-01-01
Full Text Available This paper deals with the robust H∞ filter design problem for a class of uncertain neutral stochastic systems with Markovian jumping parameters and time delay. Based on the Lyapunov-Krasovskii theory and generalized Finsler Lemma, a delay-dependent stability condition is proposed to ensure not only that the filter error system is robustly stochastically stable but also that a prescribed H∞ performance level is satisfied for all admissible uncertainties. All obtained results are expressed in terms of linear matrix inequalities which can be easily solved by MATLAB LMI toolbox. Numerical examples are given to show that the results obtained are both less conservative and less complicated in computation.
Tracking time-varying parameters with local regression
DEFF Research Database (Denmark)
Joensen, Alfred Karsten; Nielsen, Henrik Aalborg; Nielsen, Torben Skov
2000-01-01
This paper shows that the recursive least-squares (RLS) algorithm with forgetting factor is a special case of a varying-coe\\$cient model, and a model which can easily be estimated via simple local regression. This observation allows us to formulate a new method which retains the RLS algorithm, bu......, but extends the algorithm by including polynomial approximations. Simulation results are provided, which indicates that this new method is superior to the classical RLS method, if the parameter variations are smooth....
Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter
International Nuclear Information System (INIS)
Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki
2014-01-01
A commonly encountered problem in numerous areas of applications is to estimate the unknown coefficients of a dynamical system from direct or indirect observations at discrete times of some of the components of the state vector. A related problem is to estimate unobserved components of the state. An egregious example of such a problem is provided by metabolic models, in which the numerous model parameters and the concentrations of the metabolites in tissue are to be estimated from concentration data in the blood. A popular method for addressing similar questions in stochastic and turbulent dynamics is the ensemble Kalman filter (EnKF), a particle-based filtering method that generalizes classical Kalman filtering. In this work, we adapt the EnKF algorithm for deterministic systems in which the numerical approximation error is interpreted as a stochastic drift with variance based on classical error estimates of numerical integrators. This approach, which is particularly suitable for stiff systems where the stiffness may depend on the parameters, allows us to effectively exploit the parallel nature of particle methods. Moreover, we demonstrate how spatial prior information about the state vector, which helps the stability of the computed solution, can be incorporated into the filter. The viability of the approach is shown by computed examples, including a metabolic system modeling an ischemic episode in skeletal muscle, with a high number of unknown parameters. (paper)
Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin
Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.
International Nuclear Information System (INIS)
Tsunesada, Yoshiki; Tatsumi, Daisuke; Kanda, Nobuyuki; Nakano, Hiroyuki; Ando, Masaki; Sasaki, Misao; Tagoshi, Hideyuki; Takahashi, Hirotaka
2005-01-01
Gravitational radiation from a slightly distorted black hole with ringdown waveform is well understood in general relativity. It provides a probe for direct observation of black holes and determination of their physical parameters, masses and angular momenta (Kerr parameters). For ringdown searches using data of gravitational wave detectors, matched filtering technique is useful. In this paper, we describe studies on problems in matched filtering analysis in realistic gravitational wave searches using observational data. Above all, we focus on template constructions, matches or signal-to-noise ratios (SNRs), detection probabilities for Galactic events, and accuracies in evaluation of waveform parameters or black hole hairs. In template design for matched filtering, search parameter ranges and template separations are determined by requirements from acceptable maximum loss of SNRs, detection efficiencies, and computational costs. In realistic searches using observational data, however, effects of nonstationary noises cause decreases of SNRs, and increases of errors in waveform parameter determinations. These problems will potentially arise in any matched filtering searches for any kind of waveforms. To investigate them, we have performed matched filtering analysis for artificial ringdown signals which are generated with Monte-Carlo technique and injected into the TAMA300 observational data. We employed an efficient method to construct a bank of ringdown filters recently proposed by Nakano et al., and use a template bank generated from a criterion such that losses of SNRs of any signals do not exceed 2%. We found that this criterion is fulfilled in ringdown searches using TAMA300 data, by examining distribution of SNRs of simulated signals. It is also shown that with TAMA300 sensitivity, the detection probability for Galactic ringdown events is about 50% for black holes of masses greater than 20M · with SNR>10. The accuracies in waveform parameter estimations are
On the use of particle filters for electromagnetic tracking in high dose rate brachytherapy
Götz, Th I.; Lahmer, G.; Brandt, T.; Kallis, K.; Strnad, V.; Bert, Ch; Hensel, B.; Tomé, A. M.; Lang, E. W.
2017-10-01
Modern radiotherapy of female breast cancers often employs high dose rate brachytherapy, where a radioactive source is moved inside catheters, implanted in the female breast, according to a prescribed treatment plan. Source localization relative to the patient’s anatomy is determined with solenoid sensors whose spatial positions are measured with an electromagnetic tracking system. Precise sensor dwell position determination is of utmost importance to assure irradiation of the cancerous tissue according to the treatment plan. We present a hybrid data analysis system which combines multi-dimensional scaling with particle filters to precisely determine sensor dwell positions in the catheters during subsequent radiation treatment sessions. Both techniques are complemented with empirical mode decomposition for the removal of superimposed breathing artifacts. We show that the hybrid model robustly and reliably determines the spatial positions of all catheters used during the treatment and precisely determines any deviations of actual sensor dwell positions from the treatment plan. The hybrid system only relies on sensor positions measured with an EMT system and relates them to the spatial positions of the implanted catheters as initially determined with a computed x-ray tomography.
A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers
Liu, Zhuowei; Chen, Shuxin; Wu, Hao; He, Renke; Hao, Lin
2018-01-01
In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers. PMID:29617348
Halyo, N.
1976-01-01
A digital automatic control law to capture a steep glideslope and track the glideslope to a specified altitude is developed for the longitudinal/vertical dynamics of a CTOL aircraft using modern estimation and control techniques. The control law uses a constant gain Kalman filter to process guidance information from the microwave landing system, and acceleration from body mounted accelerometer data. The filter outputs navigation data and wind velocity estimates which are used in controlling the aircraft. Results from a digital simulation of the aircraft dynamics and the control law are presented for various wind conditions.
Directory of Open Access Journals (Sweden)
P. A. Ermolaev
2014-03-01
Full Text Available Data processing in the interferometer systems requires high-resolution and high-speed algorithms. Recurrence algorithms based on parametric representation of signals execute consequent processing of signal samples. In some cases recurrence algorithms make it possible to increase speed and quality of data processing as compared with classic processing methods. Dependence of the measured interferometer signal on parameters of its model and stochastic nature of noise formation in the system is, in general, nonlinear. The usage of nonlinear stochastic filtering algorithms is expedient for such signals processing. Extended Kalman filter with linearization of state and output equations by the first vector parameters derivatives is an example of these algorithms. To decrease approximation error of this method the second order extended Kalman filtering is suggested with additionally usage of the second vector parameters derivatives of model equations. Examples of algorithm implementation with the different sets of estimated parameters are described. The proposed algorithm gives the possibility to increase the quality of data processing in interferometer systems in which signals are forming according to considered models. Obtained standard deviation of estimated amplitude envelope does not exceed 4% of the maximum. It is shown that signal-to-noise ratio of reconstructed signal is increased by 60%.
El Gharamti, Mohamad; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2015-01-01
The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation
Study of heat treatment parameters for large-scale hydraulic steel gate track
Directory of Open Access Journals (Sweden)
Ping-zhou Cao
2013-10-01
Full Text Available In order to enhance external hardness and strength, a large-scale hydraulic gate track should go through heat treatment. The current design method of hydraulic gate wheels and tracks is based on Hertz contact linear elastic theory, and does not take into account the changes in mechanical properties of materials caused by heat treatment. In this study, the heat treatment parameters were designed and analyzed according to the bearing mechanisms of the wheel and track. The quenching process of the track was simulated by the ANSYS program, and the temperature variation, residual stress, and deformation were obtained and analyzed. The metallurgical structure field after heat treatment was predicted by the method based on time-temperature-transformation (TTT curves. The results show that the analysis method and designed track heat treatment process are feasible, and can provide a reference for practical projects.
Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach
Miran, Sina; Akram, Sahar; Sheikhattar, Alireza; Simon, Jonathan Z.; Zhang, Tao; Babadi, Behtash
2018-01-01
Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed
Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach
Directory of Open Access Journals (Sweden)
Sina Miran
2018-05-01
Full Text Available Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG and electroencephalography (EEG. To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach or vice versa (the encoding approach. To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1 Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2 Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3 Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our
Determination of Nuclear Track Parameters for LR-115 Detector by Using of MATLAB Software Technique
International Nuclear Information System (INIS)
AL-Jomaily, F.M.; AL-joburi, H.A.; Mheemeed, A.K.
2013-01-01
The nuclear track detector parameters, such as nuclear track diameter D(μm), number of track N T and area of track A T were determined by using MATLAB software technique for IR-115 detector irradiated by alpha particle from 241 Am source under 1.5, 2.5 and 3.5 MeV at etching time T B of 90, 120, 150 and 180 min.By using the image analysis of MATLAB software for nuclear track, the full width at half maximum FWHM and relative resolution R% were calculated for each energy of alpha particles.In this study, it was shown that increasing the alpha energy on the IR-115 detector leads to increased etching time T B and the dropping of R% to minimum value, and then reach a stable value before dropping at values 1.5, 2.5 MeV and unstable at 3.5 MeV. Imaging analysis by MATLAB technique which used in this study reflect good and accurate results for nuclear track detector parameters and we recommend using this technique for determination of these parameters
DEFF Research Database (Denmark)
Adoamnei, Evdochia; Mendiola, Jaime; Moñino-García, Miriam
2018-01-01
positively associated with T/E2 (β = 0.04, 95%CI: 0.002; 0.07) and negatively with inhibin b/FSH (β = -0.11, 95%CI: -0.21; -0.006) ratio. No significant associations were found between other urinary BP-type UV filters and other reproductive hormone levels or between any semen parameters and any...
Computer Vision Tracking Using Particle Filters for 3D Position Estimation
2014-03-27
List of Acronyms Acronym Definition AFIT Air Force Institute of Technology ASIR Auxiliary Sampling Importance Re-sampling BPF Bootstrap Particle Filter...Auxiliary Sampling Importance Re-sampling ( ASIR ) filter, and Regularized Particle Filter (RPF), also seek to eliminate weight collapse through a variety
KALMAN FILTER BASED FEATURE ANALYSIS FOR TRACKING PEOPLE FROM AIRBORNE IMAGES
Directory of Open Access Journals (Sweden)
B. Sirmacek
2012-09-01
Full Text Available Recently, analysis of man events in real-time using computer vision techniques became a very important research field. Especially, understanding motion of people can be helpful to prevent unpleasant conditions. Understanding behavioral dynamics of people can also help to estimate future states of underground passages, shopping center like public entrances, or streets. In order to bring an automated solution to this problem, we propose a novel approach using airborne image sequences. Although airborne image resolutions are not enough to see each person in detail, we can still notice a change of color components in the place where a person exists. Therefore, we propose a color feature detection based probabilistic framework in order to detect people automatically. Extracted local features behave as observations of the probability density function (pdf of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf. First, we use estimated pdf to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in non-crowd regions automatically for each image in the sequence. We benefit from Kalman filtering to track motion of detected people. To test our algorithm, we use a stadium entrance image data set taken from airborne camera system. Our experimental results indicate possible usage of the algorithm in real-life man events. We believe that the proposed approach can also provide crucial information to police departments and crisis management teams to achieve more detailed observations of people in large open area events to prevent possible accidents or unpleasant conditions.
Optimization of band-pass filtering parameters of a Raman lidar detecting atmospheric water vapor
International Nuclear Information System (INIS)
Cao, Kai-Fa; Hu, Shun-Xing; Wang, Ying-jian
2012-01-01
It is very important for daytime Raman lidar measurement of water vapor to determine the parameters of a band-pass filter, which are pertinent to the lidar signal to noise ratio (SNR). The simulated annealing (SA) algorithm method has an advantage in finding the extremum of a certain cost function. In this paper, the Raman spectrum of water vapor is simulated and then a first realization of a simulated annealing algorithm in the optimization of a band-pass filter of a Raman lidar system designed to detect daytime water vapor is presented. The simulated results indicate that the narrow band-pass filter has higher SNR than the wide filter does but there would be an increase in the temperature sensitivity of a narrowband Raman water vapor lidar in the upper troposphere. The numerical simulation indicates that the magnitude of the temperature dependent effect can reach 3.5% or more for narrow band-pass Raman water vapor measurements so it is necessary to consider a new water vapor Raman lidar equation that permits the temperature sensitivity of these equations to be confined to a single term. (paper)
Joint Multi-Fiber NODDI Parameter Estimation and Tractography using the Unscented Information Filter
Directory of Open Access Journals (Sweden)
Yogesh eRathi
2016-04-01
Full Text Available Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF. Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters, which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.
Directory of Open Access Journals (Sweden)
Shaharudin Nuraida
2017-01-01
Full Text Available An adequate supply of safe drinking water is one of major ways to obtain healthy life. Water filter system is one way to improve the water quality. However, to maintain the performance of the system, it need to undergo the maintenance service. This study evaluate the requirement of maintenance service in water filter system. Water quality was measured before and after maintenance service. Parameters measured were pH, turbidity, residual chlorine, nitrate and heavy metals and these parameters were compared with National Drinking Water Quality Standards. Collection of data were involved three housing areas in Johor. The quality of drinking water from water filter system were analysed using pH meter, turbidity meter, DR6000 and Inductively Coupled Plasma-Mass Spectrometer. pH value was increased from 16.4% for before maintenance services to 30.7% for after maintenance service. Increment of removal percentage for turbidity, residual chlorine and nitrate after maintenance were 21.5, 13.6 and 26.7, respectively. This result shows that maintenance service enhance the performance of the system. However, less significant of maintenance service for enhance the removal of heavy metals which the increment of removal percentage in range 0.3 to 9.8. Only aluminium shows percentage removal for after maintenance with 92.8% lower compared to before maintenance service with 95.5%.
International Nuclear Information System (INIS)
Fogliata, A.; Garcia, R.; Knöös, T.; Nicolini, G.; Clivio, A.; Vanetti, E.; Khamphan, C.; Cozzi, L.
2012-01-01
Purpose: Flattening filter free (FFF) beams generated by medical linear accelerators have recently started to be used in radiotherapy clinical practice. Such beams present fundamental differences with respect to the standard filter flattened (FF) beams, making the generally used dosimetric parameters and definitions not always viable. The present study will propose possible definitions and suggestions for some dosimetric parameters for use in quality assurance of FFF beams generated by medical linacs in radiotherapy. Methods: The main characteristics of the photon beams have been analyzed using specific data generated by a Varian TrueBeam linac having both FFF and FF beams of 6 and 10 MV energy, respectively. Results: Definitions for dose profile parameters are suggested starting from the renormalization of the FFF with respect to the corresponding FF beam. From this point the flatness concept has been translated into one of “unflatness” and other definitions have been proposed, maintaining a strict parallelism between FFF and FF parameter concepts. Conclusions: Ideas for quality controls used in establishing a quality assurance program when introducing FFF beams into the clinical environment are given here, keeping them similar to those used for standard FF beams. By following the suggestions in this report, the authors foresee that the introduction of FFF beams into a clinical radiotherapy environment will be as safe and well controlled as standard beam modalities using the existing guidelines.
Wang, Stephen L; Cha, Hsien-Hwa A; Lin, James R; Francis, Bolanos; Elizabeth, Wakley; Martin, Porras; Rajan, Sudhir
2016-05-01
To evaluate the effects of physician familiarity with current evidence and guidelines on inferior vena cava (IVC) filter use and the availability of IVC filter tracking infrastructure on retrieval rates. Fourteen continuing medical education-approved in-hospital grand rounds covering evidence-based review of the literature on IVC filter efficacy, patient-centered outcomes, guidelines for IVC filter indications, and complications were performed across a large United States (US) health care region serving more than 3.5 million members. A computer-based IVC filter tracking system was deployed simultaneously. IVC filter use, rates of attempted retrieval, and fulfillment of guidelines for IVC filter indications were retrospectively evaluated at each facility for 12 months before intervention (n = 427) and for 12 months after intervention (n = 347). After education, IVC filter use decreased 18.7%, with a member enrollment-adjusted decrease of 22.2%, despite an increasing IVC filter use trend for 4 years. Reduction in IVC filter use at each facility strongly correlated with physician attendance at grand rounds (r = -0.69; P = .007). Rates of attempted retrieval increased from 38.9% to 54.0% (P = .0006), with similar rates of successful retrieval (82.3% before education and 85.8% after education on first attempt). Improvement in IVC filter retrieval attempts correlated with physician attendance at grand rounds (r = 0.51; P = .051). IVC filter dwell times at first retrieval attempt were similar (10.2 wk before and 10.8 wk after). Physician education dramatically reduced IVC filter use across a large US health care region, and represents a learning opportunity for physicians who request and place them. Education and a novel tracking system improved rates of retrieval for IVC filter devices. Copyright © 2016 SIR. Published by Elsevier Inc. All rights reserved.
Ali, Rubbiya A.; Landsberg, Michael J.; Knauth, Emily; Morgan, Garry P.; Marsh, Brad J.; Hankamer, Ben
2012-01-01
3D image reconstruction of large cellular volumes by electron tomography (ET) at high (≤5 nm) resolution can now routinely resolve organellar and compartmental membrane structures, protein coats, cytoskeletal filaments, and macromolecules. However, current image analysis methods for identifying in situ macromolecular structures within the crowded 3D ultrastructural landscape of a cell remain labor-intensive, time-consuming, and prone to user-bias and/or error. This paper demonstrates the development and application of a parameter-free, 3D implementation of the bilateral edge-detection (BLE) algorithm for the rapid and accurate segmentation of cellular tomograms. The performance of the 3D BLE filter has been tested on a range of synthetic and real biological data sets and validated against current leading filters—the pseudo 3D recursive and Canny filters. The performance of the 3D BLE filter was found to be comparable to or better than that of both the 3D recursive and Canny filters while offering the significant advantage that it requires no parameter input or optimisation. Edge widths as little as 2 pixels are reproducibly detected with signal intensity and grey scale values as low as 0.72% above the mean of the background noise. The 3D BLE thus provides an efficient method for the automated segmentation of complex cellular structures across multiple scales for further downstream processing, such as cellular annotation and sub-tomogram averaging, and provides a valuable tool for the accurate and high-throughput identification and annotation of 3D structural complexity at the subcellular level, as well as for mapping the spatial and temporal rearrangement of macromolecular assemblies in situ within cellular tomograms. PMID:22479430
Study of the effects of atmospheric parameters on ground radon concentration by track technique
International Nuclear Information System (INIS)
Tidjani, Adams
1988-01-01
Radon emanation was continuously monitored for 24 months, accompanied by measurements of atmospheric parameters. Integrated measurments of radon concentrations have been performed with LR-115 cellulose nitrate track detectors. The monitoring was conducted at 16 sites distributed around the Dakar University area. Observed changes in radon concentration are interpreted as being caused by changes in meteorological conditions and ocean tides. (author)
DEFF Research Database (Denmark)
Mu, Xiaobin; Wang, Jiuhe; Wu, Weimin
2018-01-01
The passivity-based control (PBC) has a better control performance using an accurate mathematical model of the control object. It can offer an alternative tracking control scheme for the shunt active power filter (SAPF). However, the conventional PBC-based SAPF cannot achieve zero steady...
Autonomous underwater vehicle motion tracking using a Kalman filter for sensor fusion
CSIR Research Space (South Africa)
Holtzhausen, S
2008-11-01
Full Text Available it will be shown how a Kalman Filter is used to estimate the position of an autonomous vehicle in a three dimensional space. The Kalman filter is used to estimate movement and position using measurements from multiple sensors...
Directory of Open Access Journals (Sweden)
Daniel H Rapoport
Full Text Available Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters
Parameter Deduction and Accuracy Analysis of Track Beam Curves in Straddle-type Monorail Systems
Directory of Open Access Journals (Sweden)
Xiaobo Zhao
2015-12-01
Full Text Available The accuracy of the bottom curve of a PC track beam is strongly related to the production quality of the entire beam. Many factors may affect the parameters of the bottom curve, such as the superelevation of the curve and the deformation of a PC track beam. At present, no effective method has been developed to determine the bottom curve of a PC track beam; therefore, a new technique is presented in this paper to deduce the parameters of such a curve and to control the accuracy of the computation results. First, the domain of the bottom curve of a PC track beam is assumed to be a spindle plane. Then, the corresponding supposed top curve domain is determined based on a geometrical relationship that is the opposite of that identified by the conventional method. Second, several optimal points are selected from the supposed top curve domain according to the dichotomy algorithm; the supposed top curve is thus generated by connecting these points. Finally, one rigorous criterion is established in the fractal dimension to assess the accuracy of the assumed top curve deduced in the previous step. If this supposed curve coincides completely with the known top curve, then the assumed bottom curve corresponding to the assumed top curve is considered to be the real bottom curve. This technique of determining the bottom curve of a PC track beam is thus proven to be efficient and accurate.
Energy Technology Data Exchange (ETDEWEB)
Bajla, Ivan [Austrian Research Centres Sibersdorf, Department of High Performance Image Processing and Video-Technology, A-2444 Seibersdorf (Austria); Hollander, Igor [Institute of information Processing, Austrian Academy of Sciences, Sonnenfelsgasse 19/2, 1010 Wien (Austria)
1999-12-31
A novel method of local adapting of the conductance using a pixel dissimilarity measure is developed. An alternative processing methodology is proposed, which is based on intensity gradient histogram calculated for region interiors and boundaries of a phantom which models real MR brain scans. It involves a specific cost function suitable for the calculation of the optimum relaxation parameter Kopt and for the selection of the optimal exponential conductance. Computer experiments for locally adaptive geometry-driven-diffusion filtering of an MR brain phantom have been performed and evaluated. (authors) 6 refs., 3 figs.2 tabs.
International Nuclear Information System (INIS)
Bajla, Ivan; Hollander, Igor
1998-01-01
A novel method of local adapting of the conductance using a pixel dissimilarity measure is developed. An alternative processing methodology is proposed, which is based on intensity gradient histogram calculated for region interiors and boundaries of a phantom which models real MR brain scans. It involves a specific cost function suitable for the calculation of the optimum relaxation parameter Kopt and for the selection of the optimal exponential conductance. Computer experiments for locally adaptive geometry-driven-diffusion filtering of an MR brain phantom have been performed and evaluated. (authors)
Modified temporal approach to meta-optimizing an extended Kalman filter's parameters
CSIR Research Space (South Africa)
Salmon
2014-07-01
Full Text Available stream_source_info Salmon_2014.pdf.txt stream_content_type text/plain stream_size 1233 Content-Encoding UTF-8 stream_name Salmon_2014.pdf.txt Content-Type text/plain; charset=UTF-8 2014 IEEE International Geoscience... and Remote Sensing Symposium, Québec, Canada, 13-18 July 2014 A modified temporal approach to meta-optimizing an Extended Kalman Filter's parameters B. P. Salmon ; W. Kleynhans ; J. C. Olivier ; W. C. Olding ; K. J. Wessels ; F. van den Bergh...
Parameter estimation in an atmospheric GCM using the Ensemble Kalman Filter
Directory of Open Access Journals (Sweden)
J. D. Annan
2005-01-01
Full Text Available We demonstrate the application of an efficient multivariate probabilistic parameter estimation method to a spectral primitive equation atmospheric GCM. The method, which is based on the Ensemble Kalman Filter, is effective at tuning the surface air temperature climatology of the model to both identical twin data and reanalysis data. When 5 parameters were simultaneously tuned to fit the model to reanalysis data, the model errors were reduced by around 35% compared to those given by the default parameter values. However, the precipitation field proved to be insensitive to these parameters and remains rather poor. The model is computationally cheap but chaotic and otherwise realistic, and the success of these experiments suggests that this method should be capable of tuning more sophisticated models, in particular for the purposes of climate hindcasting and prediction. Furthermore, the method is shown to be useful in determining structural deficiencies in the model which can not be improved by tuning, and so can be a useful tool to guide model development. The work presented here is for a limited set of parameters and data, but the scalability of the method is such that it could easily be extended to a more comprehensive parameter set given sufficient observational data to constrain them.
Development of etched nuclear tracks
International Nuclear Information System (INIS)
Somogyi, G.
1980-01-01
The theoretical description of the evolution of etched tracks in solid state nuclear track detectors is considered for different initial conditions, for the cases of constant and varying track etch rates, isotropic and anisotropic bulk etching as well as for thick and thin detectors. It is summarized how one can calculate the main parameters of etch-pit geometry, the track length, the axes of a surface track opening, track profile and track contour. The application of the theory of etch-track evolution is demonstrated with selected practical problems. Attention is paid to certain questions related to the determination of unknown track parameters and calculation of surface track sizes. Finally, the theory is extended to the description of the perforation and etch-hole evolution process in thin detectors, which is of particular interest for track radiography and nuclear filter production. (orig.)
Development of etched nuclear tracks
International Nuclear Information System (INIS)
Somogyi, G.
1979-01-01
The theoretical description of the evolution of etched tracks in solid state nuclear track detectors is considered for different initial conditions, for the cases of constant and varying track etch rates, isotopic and unisotropic bulk etching as well as for thick and thin detectors. It is summarized how the main parameters of etch-pit geometry, the track length, the axes of a surface track opening, the track profile and the track contour can be calculated. The application of the theory of etch-track evolution is demonstrated with selected practical problems. Attention is paid to certain questions related to the determination of unknown track parameters and calculation of surface track sizes. Finally, the theory is extended to the description of the perforation and etch-hole evolution process in thin detectors, which is of particular interest for track radiography and nuclear filter production. (author)
International Nuclear Information System (INIS)
Hermsdorf, D.; Reichelt, U.
2010-01-01
Solid State Nuclear Track Detectors (SSNTD) irradiated in realistic radiation fields exhibits after chemical etching very complex track images resulting from different species of particles and their energy spectra and randomly distributed angles of incidence or emission. Reading out such an etched detector surface with a light microscope, quite different track opening contours are observed. Beside the number of tracks, typically their major and minor axes are measured. In this work following problems arising from such experimental situations will be investigated: ·the measurement of track contour parameters for oblique incident 4 He and 7 Li-ions of different energies and angles in CR-39 detectors ·the theoretical description of the angular variation of both axes. ·the possibility to extract physical and spectroscopic information from major and minor track axes. This analysis is based on an intensive experimental program and the comprehensive study of theoretical models available for description of track revealing processes in CR-39.
Real-time tracking for virtual environments using scaat kalman filtering and unsynchronised cameras
DEFF Research Database (Denmark)
Rasmussen, Niels Tjørnly; Störring, Morritz; Moeslund, Thomas B.
2006-01-01
This paper presents a real-time outside-in camera-based tracking system for wireless 3D pose tracking of a user’s head and hand in a virtual environment. The system uses four unsynchronised cameras as sensors and passive retroreflective markers arranged in rigid bodies as targets. In order to ach...
Multi-agent target tracking using particle filters enhanced with context data
CSIR Research Space (South Africa)
Claessens, R
2015-05-01
Full Text Available The proposed framework for Multi-Agent Target Tracking supports i) tracking of objects and ii) search and rescue based on the fusion of very heterogeneous data. The system is based on a novel approach to fusing sensory observations, intelligence...
Marković, D.; Koch, M.
2005-09-01
The influence of the periodic signals in time series on the Hurst parameter estimate is investigated with temporal, spectral and time-scale methods. The Hurst parameter estimates of the simulated periodic time series with a white noise background show a high sensitivity on the signal to noise ratio and for some methods, also on the data length used. The analysis is then carried on to the investigation of extreme monthly river flows of the Elbe River (Dresden) and of the Rhine River (Kaub). Effects of removing the periodic components employing different filtering approaches are discussed and it is shown that such procedures are a prerequisite for an unbiased estimation of H. In summary, our results imply that the first step in a time series long-correlation study should be the separation of the deterministic components from the stochastic ones. Otherwise wrong conclusions concerning possible memory effects may be drawn.
HMM filtering and parameter estimation of an electricity spot price model
International Nuclear Information System (INIS)
Erlwein, Christina; Benth, Fred Espen; Mamon, Rogemar
2010-01-01
In this paper we develop a model for electricity spot price dynamics. The spot price is assumed to follow an exponential Ornstein-Uhlenbeck (OU) process with an added compound Poisson process. In this way, the model allows for mean-reversion and possible jumps. All parameters are modulated by a hidden Markov chain in discrete time. They are able to switch between different economic regimes representing the interaction of various factors. Through the application of reference probability technique, adaptive filters are derived, which in turn, provide optimal estimates for the state of the Markov chain and related quantities of the observation process. The EM algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters. We implement this self-calibrating model on a deseasonalised series of daily spot electricity prices from the Nordic exchange Nord Pool. On the basis of one-step ahead forecasts, we found that the model is able to capture the empirical characteristics of Nord Pool spot prices. (author)
An efficient incremental learning mechanism for tracking concept drift in spam filtering.
Directory of Open Access Journals (Sweden)
Jyh-Jian Sheu
Full Text Available This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email's header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1 Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email's content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2 Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3 We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment.
El Gharamti, Mohamad; Hoteit, Ibrahim
2014-01-01
The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.
El Gharamti, Mohamad
2014-02-01
The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.
Directory of Open Access Journals (Sweden)
Shousong Han
2017-01-01
Full Text Available Ride safety of a tracked vehicle is the key focus of this research. The factors that affect the ride safety of a vehicle are analyzed and evaluation parameters with their criteria are proposed. A multibody cosimulation approach is used to investigate the effects of hydropneumatic parameters on the ride safety and aid with design optimization and tuning of the suspension system. Based on the cosimulation environment, the vehicle multibody dynamics (MBD model and the road model are developed using RecurDyn, which is linked to the hydropneumatic suspension model developed in Lab AMESim. Test verification of a single suspension unit is accomplished and the suspension parameters are implemented within the hydropneumatic model. Virtual tests on a G class road at different speeds are conducted. Effects of the accumulator charge pressure, damping diameter, and the track tensioning pressure on the ride safety are analyzed and quantified. This research shows that low accumulator charge pressure, improper damping diameter, and insufficient track tensioning pressure will deteriorate the ride safety. The results provide useful references for the optimal design and control of the parameters of a hydropneumatic suspension.
International Nuclear Information System (INIS)
Docimo, D.J.; Ghanaatpishe, M.; Mamun, A.
2017-01-01
This paper develops an algorithm for estimating photovoltaic (PV) module temperature and effective irradiation level. The power output of a PV system depends directly on both of these states. Estimating the temperature and irradiation allows for improved state-based control methods while eliminating the need of additional sensors. Thermal models and irradiation estimators have been developed in the literature, but none incorporate feedback for estimation. This paper outlines an Extended Kalman Filter for temperature and irradiation estimation. These estimates are, in turn, used within a novel state-based controller that tracks the maximum power point of the PV system. Simulation results indicate this state-based controller provides up to an 8.5% increase in energy produced per day as compared to an impedance matching controller. A sensitivity analysis is provided to examine the impact state estimate errors have on the ability to find the optimal operating point of the PV system. - Highlights: • Developed a temperature and irradiation estimator for photovoltaic systems. • Designed an Extended Kalman Filter to handle model and measurement uncertainty. • Developed a state-based controller for maximum power point tracking (MPPT). • Validated combined estimator/controller algorithm for different weather conditions. • Algorithm increases energy captured up to 8.5% over traditional MPPT algorithms.
Off-line tracking of series parameters in distribution systems using AMI data
Energy Technology Data Exchange (ETDEWEB)
Williams, Tess L.; Sun, Yannan; Schneider, Kevin
2016-05-01
Electric distribution systems have historically lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors at the distribution level is enabling observability. This paper presents an off-line parameter value tracking procedure that takes advantage of the increasing number of measurement devices being deployed at the distribution level to estimate changes in series impedance parameter values over time. The tracking of parameter values enables non-diurnal and non-seasonal change to be flagged for investigation. The presented method uses an unbalanced Distribution System State Estimation (DSSE) and a measurement residual-based parameter estimation procedure. Measurement residuals from multiple measurement snapshots are combined in order to increase the effective local redundancy and improve the robustness of the calculations in the presence of measurement noise. Data from devices on the primary distribution system and from customer meters, via an AMI system, form the input data set. Results of simulations on the IEEE 13-Node Test Feeder are presented to illustrate the proposed approach applied to changes in series impedance parameters. A 5% change in series resistance elements can be detected in the presence of 2% measurement error when combining less than 1 day of measurement snapshots into a single estimate.
Medina, H.; Romano, N.; Chirico, G. B.
2012-12-01
We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.
Determination of spatially dependent diffusion parameters in bovine bone using Kalman filter.
Shokry, Abdallah; Ståhle, Per; Svensson, Ingrid
2015-11-07
Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samples, saturated with potassium chloride (KCl), were put into distilled water and the conductivity of the water was followed. Chloride ions in the bone samples escaped out in the water through diffusion and the increase of the conductivity was measured. A one-dimensional, spatially dependent mathematical model describing the diffusion process is used. The diffusion parameters in the model are determined using a Kalman filter technique. The parameters for spatially dependent at endosteal and periosteal surfaces are found to be (12.8 ± 4.7) × 10(-11) and (5 ± 3.5) × 10(-11)m(2)/s respectively. The mathematical model function using the obtained diffusion parameters fits very well with the experimental data with mean square error varies from 0.06 × 10(-6) to 0.183 × 10(-6) (μS/m)(2). Copyright © 2015 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Boronina Lyudmila Vladimirovna
2012-12-01
Full Text Available Improvement of water intake technologies are of great importance. These technologies are required to provide high quality water intake and treatment; they must be sufficiently simple and reliable, and they must be easily adjustable to particular local conditions. A mathematical model of a water supply area near the filtering water intake is proposed. On its basis, a software package designated for the calculation of parameters of the supply area along with its graphical representation is developed. To improve the efficiency of water treatment plants, the authors propose a new method of their integration into the landscape by taking account of velocity distributions in the water supply area within the water reservoir where the plant installation is planned. In the proposed relationship, the filtration rate and the scattering rate at the outlet of the supply area are taken into account, and they assure more precise projections of the inlet velocity. In the present study, assessment of accuracy of the mathematical model involving the scattering of a turbulent flow has been done. The assessment procedure is based on verification of the mean values equality hypothesis and on comparison with the experimental data. The results and conclusions obtained by means of the method developed by the authors have been verified through comparison of deviations of specific values calculated through the employment of similar algorithms in MathCAD, Maple and PLUMBING. The method of the water supply area analysis, with the turbulent scattering area having been taken into account, and the software package enable to numerically estimate the efficiency of the pre-purification process by tailoring a number of parameters of the filtering component of the water intake to the river hydrodynamic properties. Therefore, the method and the software package provide a new tool for better design, installation and operation of water treatment plants with respect to filtration and
Directory of Open Access Journals (Sweden)
Hongxiao Yu
2015-05-01
Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.
Convolutional Neural Network for Track Seed Filtering at the CMS HLT
CERN. Geneva
2018-01-01
Collider will constantly bring nominal luminosity increase, with the ultimate goal of reaching a peak luminosity of $5 · 10^{34} cm^{−2} s^{−1}$ for ATLAS and CMS experiments planned for the High Luminosity LHC (HL-LHC) upgrade. This rise in luminosity will directly result in an increased number of simultaneous proton collisions (pileup), up to 200, that will pose new challenges for the CMS detector and, specifically, for track reconstruction in the Silicon Pixel Tracker. One of the first steps of the track finding workflow is the creation of track seeds, i.e. compatible pairs of hits from different detector layers, that are subsequently fed to to higher level pattern recognition steps. However the set of compatible hit pairs is highly affected by combinatorial background resulting in the next steps of the tracking algorithm to process a significant fraction of fake doublets. A possible way of reducing this effect is taking into account the shape of the hit pixel cluster to check the compatibility bet...
Huang, Yi-Chih; Lin, Yuh-Lang
2017-05-01
Essential parameters for making a looping track when a westward-moving tropical cyclone (TC) approaches a mesoscale mountain are investigated by examining several key nondimensional control parameters with a series of systematic, idealized numerical experiments, such as U/Nh, V max/Nh, U/fL x , V max/fR, h/L x , and R/L y . Here U is the uniform zonal wind velocity, N the Brunt-Vaisala frequency, h the mountain height, f the Coriolis parameter, V max the maximum tangential velocity at a radius of R from the cyclone center and L x is the halfwidth of the mountain in the east-west direction. It is found that looping tracks (a) tend to occur under small U/Nh and U/fL x , moderate h/L x , and large V max/Nh, which correspond to slow movement (leading to subgeostrophic flow associated with strong orographic blocking), moderate steepness, and strong tangential wind associated with TC vortex; (b) are often accompanied by an area of perturbation high pressure to the northeast of the mountain, which lasts for only a short period; and (c) do not require the existence of a northerly jet. The nondimensional control parameters are consolidated into a TC looping index (LI), {U2 R2 }/{V_{max}2 hLy }} , which is tested by several historical looping and non-looping typhoons approaching Taiwan's Central Mountain Range (CMR) from east or southeast. It is found that LI < 0.0125 may serve as a criterion for looping track to occur.
Huang, Yi-Chih; Lin, Yuh-Lang
2018-06-01
Essential parameters for making a looping track when a westward-moving tropical cyclone (TC) approaches a mesoscale mountain are investigated by examining several key nondimensional control parameters with a series of systematic, idealized numerical experiments, such as U/ Nh, V max/ Nh, U/ fL x , V max/ fR, h/ L x , and R/ L y . Here U is the uniform zonal wind velocity, N the Brunt-Vaisala frequency, h the mountain height, f the Coriolis parameter, V max the maximum tangential velocity at a radius of R from the cyclone center and L x is the halfwidth of the mountain in the east-west direction. It is found that looping tracks (a) tend to occur under small U/ Nh and U/ fL x , moderate h/ L x , and large V max/ Nh, which correspond to slow movement (leading to subgeostrophic flow associated with strong orographic blocking), moderate steepness, and strong tangential wind associated with TC vortex; (b) are often accompanied by an area of perturbation high pressure to the northeast of the mountain, which lasts for only a short period; and (c) do not require the existence of a northerly jet. The nondimensional control parameters are consolidated into a TC looping index (LI), {U2 R2 }/{V_{max 2 hLy }} , which is tested by several historical looping and non-looping typhoons approaching Taiwan's Central Mountain Range (CMR) from east or southeast. It is found that LI < 0.0125 may serve as a criterion for looping track to occur.
International Nuclear Information System (INIS)
Abo-Elmagd, M.; Mansy, M.; Eissa, H.M.; El-Fiki, M.A.
2006-01-01
The equilibrium factor F between radon and its daughters as a function of the track density ratio D/D 0 between bare and in can track detectors is solved graphically and gave more accurate solution than that solved mathematically elsewhere. The advantages of the graphical solution come from its simplicity and does not need any tedious mathematical formula or a computer program. The simplicity of this solution makes us study many parameters that affect the equilibrium factor determination such as the detector type, the diffusion chamber dimensions, the membrane specifications and the behavior of α-emitters around the detector. The results show that the equilibrium factor as a function of D/D 0 takes different form according to the facility used. The range of this study covers two widely used detectors (CR-39 and LR-115) equipped in two widely used diffusion chambers (small and medium chambers)
Production parameters for the formation of metallic nanotubules in etched tracks
International Nuclear Information System (INIS)
Fink, D.; Petrov, A.V.; Rao, V.; Wilhelm, M.; Demyanov, S.; Szimkowiak, P.; Behar, M.; Alegaonkar, P.S.; Chadderton, L.T.
2003-01-01
The formation of conducting nanotubules in etched tracks is reported in literature since about a decade. However, up to now precise production recipes are scarce. For this sake we present here a systematic study on some important factors that influence the formation of metallic nanotubules. In the case of chemical deposition, the first question to be answered is the choice of the activation technique to produce the required activation centers. Both the time of activation and the time of subsequent chemical deposition are crucial parameters in this connection. Finally, the maximum temperature is determined up to which thermal stability of the etched tracks and of the tubules therein is given. This study should allow one to predict better the efficiency of conducting nanotubule formation
Directory of Open Access Journals (Sweden)
Farid Choirul Akbar
2016-04-01
Full Text Available Gerakan lateral quadcopter dapat dilakukan apabila quadcopter dapat menjaga kestabilan pada saat hover, sehingga quadcopter dapat melakukan gerak rotasi. Perubahan sudut roll akan mengakibatkan gerak translasi pada sumbu Y, sedangkan perubahan sudut pitch akan mengakibatkan gerak translasi pada sumbu X. Disisi lain, quadcopter merupakan suatu sistem non-linear dan memiliki kestabilan yang rendah sehingga rentan terhadap gangguan. Pada penelitian Tugas Akhir ini dirancang pengendalian gerak rotasi quadcopter menggunakan Linear Quadratic Regulator (LQR dan Linear Quadratic Tracking (LQT untuk pengendalian gerak translasi. Untuk mendapatkan parameter dari LQT digunakan Algoritma Genetika (GA. Hasil tuning GA yang digunakan pada LQT memiliki nilai Qx 700,1884, nilai Qy 700,6315, nilai Rx 0,1568, dan nilai Ry 0,1579. Respon LQT tersebut memiliki RMSE pada sumbu X dan sumbu Y sebesar 1,99 % serta memiliki time lagging 0,35 detik. Dengan hasil tersebut quadcopter mampu men-tracking trajectory berbentuk segitigaTekni
Directory of Open Access Journals (Sweden)
Kenshi Saho
2017-07-01
Full Text Available We present a strategy for designing an α - β - η - θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured α - β filter. Moreover, we apply an α - β - η - θ filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an α - β - η - θ filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target’s acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated.
Simon, Ehouarn; Samuelsen, Annette; Bertino, Laurent; Mouysset, Sandrine
2015-12-01
A sequence of one-year combined state-parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical-biogeochemical model HYCOM-NORWECOM over the period 2007-2010. The aim is to evaluate the ability of an ensemble-based data assimilation method to calibrate ecosystem model parameters in a pre-operational setting, namely the production of the MyOcean pilot reanalysis of the Arctic biology. For that purpose, four biological parameters (two phyto- and two zooplankton mortality rates) are estimated by assimilating weekly data such as, satellite-derived Sea Surface Temperature, along-track Sea Level Anomalies, ice concentrations and chlorophyll-a concentrations with an Ensemble Kalman Filter. The set of optimized parameters locally exhibits seasonal variations suggesting that time-dependent parameters should be used in ocean ecosystem models. A clustering analysis of the optimized parameters is performed in order to identify consistent ecosystem regions. In the north part of the domain, where the ecosystem model is the most reliable, most of them can be associated with Longhurst provinces and new provinces emerge in the Arctic Ocean. However, the clusters do not coincide anymore with the Longhurst provinces in the Tropics due to large model errors. Regarding the ecosystem state variables, the assimilation of satellite-derived chlorophyll concentration leads to significant reduction of the RMS errors in the observed variables during the first year, i.e. 2008, compared to a free run simulation. However, local filter divergences of the parameter component occur in 2009 and result in an increase in the RMS error at the time of the spring bloom.
State and parameter estimation in nonlinear systems as an optimal tracking problem
International Nuclear Information System (INIS)
Creveling, Daniel R.; Gill, Philip E.; Abarbanel, Henry D.I.
2008-01-01
In verifying and validating models of nonlinear processes it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, we present a framework for connecting a data signal with a model in a way that minimizes the required coupling yet allows the estimation of unknown parameters in the model. The need to evaluate unknown parameters in models of nonlinear physical, biophysical, and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. Our approach builds on existing work that uses synchronization as a tool for parameter estimation. We address some of the critical issues in that work and provide a practical framework for finding an accurate solution. In particular, we show the equivalence of this problem to that of tracking within an optimal control framework. This equivalence allows the application of powerful numerical methods that provide robust practical tools for model development and validation
International Nuclear Information System (INIS)
Kim, Joo Yeon; Ryu, Hyung Joon; Jung, Gyu Hwan; Lee, Jai Ki
2011-01-01
The dependency within the sequential realizations in the generated Markov chains and their reliabilities are monitored by introducing the autocorrelation and the potential scale reduction factor (PSRF) by model parameters in the atmospheric dispersion. These two diagnostics have been applied for the posterior quantities of the release point and the release rate inferred through the inverse tracking of unknown model parameters for the Yonggwang atmospheric tracer experiment in Korea. The autocorrelations of model parameters are decreasing to low values approaching to zero with increase of lag, resulted in decrease of the dependencies within the two sequential realizations. Their PSRFs are reduced to within 1.2 and the adequate simulation number recognized from these results. From these two convergence diagnostics, the validation of Markov chains generated have been ensured and PSRF then is especially suggested as the efficient tool for convergence monitoring for the source reconstruction in atmospheric dispersion. (author)
Medina, H.; Romano, N.; Chirico, G. B.
2014-07-01
This study presents a dual Kalman filter (DSUKF - dual standard-unscented Kalman filter) for retrieving states and parameters controlling the soil water dynamics in a homogeneous soil column, by assimilating near-surface state observations. The DSUKF couples a standard Kalman filter for retrieving the states of a linear solver of the Richards equation, and an unscented Kalman filter for retrieving the parameters of the soil hydraulic functions, which are defined according to the van Genuchten-Mualem closed-form model. The accuracy and the computational expense of the DSUKF are compared with those of the dual ensemble Kalman filter (DEnKF) implemented with a nonlinear solver of the Richards equation. Both the DSUKF and the DEnKF are applied with two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil water matric pressure head (h). The comparison analyses are conducted with reference to synthetic time series of the true states, noise corrupted observations, and synthetic time series of the meteorological forcing. The performance of the retrieval algorithms are examined accounting for the effects exerted on the output by the input parameters, the observation depth and assimilation frequency, as well as by the relationship between retrieved states and assimilated variables. The uncertainty of the states retrieved with DSUKF is considerably reduced, for any initial wrong parameterization, with similar accuracy but less computational effort than the DEnKF, when this is implemented with ensembles of 25 members. For ensemble sizes of the same order of those involved in the DSUKF, the DEnKF fails to provide reliable posterior estimates of states and parameters. The retrieval performance of the soil hydraulic parameters is strongly affected by several factors, such as the initial guess of the unknown parameters, the wet or dry
Shrivastava, Akash; Mohanty, A. R.
2018-03-01
This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise.
Soppe, A I A; Heijman, S G J; Gensburger, I; Shantz, A; van Halem, D; Kroesbergen, J; Wubbels, G H; Smeets, P W M H
2015-06-01
The need to improve the access to safe water is generally recognized for the benefit of public health in developing countries. This study's objective was to identify critical parameters which are essential for improving the performance of ceramic pot filters (CPFs) as a point-of-use water treatment system. Defining critical production parameters was also relevant to confirm that CPFs with high-flow rates may have the same disinfection capacity as pots with normal flow rates. A pilot unit was built in Cambodia to produce CPFs under controlled and constant conditions. Pots were manufactured from a mixture of clay, laterite and rice husk in a small-scale, gas-fired, temperature-controlled kiln and tested for flow rate, removal efficiency of bacteria and material strength. Flow rate can be increased by increasing pore sizes and by increasing porosity. Pore sizes were increased by using larger rice husk particles and porosity was increased with larger proportions of rice husk in the clay mixture. The main conclusions: larger pore size decreases the removal efficiency of bacteria; higher porosity does not affect the removal efficiency of bacteria, but does influence the strength of pots; flow rates of CPFs can be raised to 10-20 L/hour without a significant decrease in bacterial removal efficiency.
Kalman滤波在井下人员跟踪定位中的应用%Application of Kalman filter in underground personnel tracking and positioning
Institute of Scientific and Technical Information of China (English)
罗宇锋; 刘勇; 李芳
2013-01-01
In view of problem that RSSI location algorithm does not have continuity in positioning process,the paper proposed an underground personnel positioning method with continuity based on Kalman filter.The method adopts Kalman filter to filter processing position coordinates of underground personnel estimated by RSSI location algorithm,and established a Kalman filter model on the basis of this coordinate in order to real-timely track underground personnel.The experimental results show that the positioning method based on Kalman filter has accurate tracking effect,and can improve real-time performance and tracking accuracy of underground personnel tracking and positioning system.%针对基于RSSI定位算法在定位过程中不具备连续性问题,提出了一种基于Kalman滤波的连续性并下人员定位方法.采用Kalman滤波对基于RSSI定位算法估算出的井下人员位置坐标进行滤波处理,在此坐标的基础上,建立Kalman滤波模型,利用Kalman滤波实现对井下人员的实时跟踪.实验结果表明,基于Kalman滤波的定位方法对井下人员的跟踪效果较好,提高了系统的实时性和跟踪精度.
Hadwin, Paul J; Peterson, Sean D
2017-04-01
The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.
Natural uranium impurities in fission track detectors and associated geocronological parameters
International Nuclear Information System (INIS)
Ricabarra, G.H.; Bovisio de Ricabarra, M.D.; Waisman, Dina; Faradjie de Turjanski, Rosa
1981-01-01
A technique, based in counting neutron induced fission tracks, has been developed for the measurement of uranium impurities in mica. Uranium concentrations of 10 -10 and 10 -9 (U atom/mica atom) have been measured. As a part of the development of this technique, the mica geological age was also measured, by fossil and induced track detection. The agreement obtained by this method, T = (472+-52) x 10 6 years with that of (450+-15) x 10 6 years obtained by the Ar-K technique is satisfactory and is an indirect test of the fission track technique used. A careful analysis of the neutron field parameters and nuclear data used in the age determination was made. This analysis is useful for applications in geocronology. According to this analysis a value of lambdasub(f)=(7.1+-0.1) x 10 -17 years -1 is recommended for the spontaneous fission of U238. However, in order to compare the results, the quoted age, T=(472+-52) x 10 6 years, was obtained with the generally accepted value of lambdasub(f)=(6.85-0.20) x 10 -17 years -1 (Fleischer and Price 1964). (author) [es
Chaganti, Shikha; Nelson, Katrina; Mundy, Kevin; Luo, Yifu; Harrigan, Robert L.; Damon, Steve; Fabbri, Daniel; Mawn, Louise; Landman, Bennett
2016-03-01
Pathologies of the optic nerve and orbit impact millions of Americans and quantitative assessment of the orbital structures on 3-D imaging would provide objective markers to enhance diagnostic accuracy, improve timely intervention, and eventually preserve visual function. Recent studies have shown that the multi-atlas methodology is suitable for identifying orbital structures, but challenges arise in the identification of the individual extraocular rectus muscles that control eye movement. This is increasingly problematic in diseased eyes, where these muscles often appear to fuse at the back of the orbit (at the resolution of clinical computed tomography imaging) due to inflammation or crowding. We propose the use of Kalman filters to track the muscles in three-dimensions to refine multi-atlas segmentation and resolve ambiguity due to imaging resolution, noise, and artifacts. The purpose of our study is to investigate a method of automatically generating orbital metrics from CT imaging and demonstrate the utility of the approach by correlating structural metrics of the eye orbit with clinical data and visual function measures in subjects with thyroid eye disease. The pilot study demonstrates that automatically calculated orbital metrics are strongly correlated with several clinical characteristics. Moreover, it is shown that the superior, inferior, medial and lateral rectus muscles obtained using Kalman filters are each correlated with different categories of functional deficit. These findings serve as foundation for further investigation in the use of CT imaging in the study, analysis and diagnosis of ocular diseases, specifically thyroid eye disease.
DEFF Research Database (Denmark)
Drews, Martin; Lauritzen, Bent; Madsen, Henrik
2005-01-01
A Kalman filter method is discussed for on-line estimation of radioactive release and atmospheric dispersion from a time series of off-site radiation monitoring data. The method is based on a state space approach, where a stochastic system equation describes the dynamics of the plume model...... parameters, and the observables are linked to the state variables through a static measurement equation. The method is analysed for three simple state space models using experimental data obtained at a nuclear research reactor. Compared to direct measurements of the atmospheric dispersion, the Kalman filter...... estimates are found to agree well with the measured parameters, provided that the radiation measurements are spread out in the cross-wind direction. For less optimal detector placement it proves difficult to distinguish variations in the source term and plume height; yet the Kalman filter yields consistent...
We developed a sequential Monte Carlo filter to estimate the states and the parameters in a stochastic model of Japanese Encephalitis (JE) spread in the Philippines. This method is particularly important for its adaptability to the availability of new incidence data. This method can also capture the...
CSIR Research Space (South Africa)
Salmon, BP
2011-07-01
Full Text Available . This paper proposes a meta-optimization approach for setting the parameters of the non-linear Extended Kalman Filter to rapidly and efficiently estimate the features for the pair of triply modulated cosine functions. The approach is based on a unsupervised...
van Gent, P. L.; Schrijer, F. F. J.; van Oudheusden, B. W.
2018-04-01
The present study characterises the spatio-temporal filtering associated with pseudo-tracking. A combined theoretical and numerical assessment is performed that uses the relatively simple flow case of a two-dimensional Taylor vortex as analytical test case. An additional experimental assessment considers the more complex flow of a low-speed axisymmetric base flow, for which time-resolved tomographic PIV measurements and microphone measurements were obtained. The results of these assessments show how filtering along Lagrangian tracks leads to amplitude modulation of flow structures. A cut-off track length and spatial resolution are specified to support future applications of the pseudo-tracking approach. The experimental results show a fair agreement between PIV and microphone pressure data in terms of fluctuation levels and pressure frequency spectra. The coherence and correlation between microphone and PIV pressure measurements were found to be substantial and almost independent of the track length, indicating that the low-frequency behaviour of the flow could be reproduced regardless of the track length. It is suggested that a spectral analysis can be used inform the selection of a suitable track length and to estimate the local error margin of reconstructed pressure values.
Danielson, E. F.; Hipskind, R. S.; Gaines, S. E.
1980-01-01
Results are presented from computer processing and digital filtering of radiosonde and radar tracking data obtained during the ITCZ experiment when coordinated measurements were taken daily over a 16 day period across the Panama Canal Zone. The temperature relative humidity and wind velocity profiles are discussed.
Amanda L. Fox; Dean E. Eisenhauer; Michael G. Dosskey
2005-01-01
Vegetated filters (buffers) are used to intercept overland runoff and reduce sediment and other contaminant loads to streams (Dosskey, 2001). Filters function by reducing runoff velocity and volume, thus enhancing sedimentation and infiltration. lnfiltration is the main mechanism for soluble contaminant removal, but it also plays a role in suspended particle removal....
Gharamti, M. E.
2015-05-11
The ensemble Kalman filter (EnKF) is a popular method for state-parameters estimation of subsurface flow and transport models based on field measurements. The common filtering procedure is to directly update the state and parameters as one single vector, which is known as the Joint-EnKF. In this study, we follow the one-step-ahead smoothing formulation of the filtering problem, to derive a new joint-based EnKF which involves a smoothing step of the state between two successive analysis steps. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. This new algorithm bears strong resemblance with the Dual-EnKF, but unlike the latter which first propagates the state with the model then updates it with the new observation, the proposed scheme starts by an update step, followed by a model integration step. We exploit this new formulation of the joint filtering problem and propose an efficient model-integration-free iterative procedure on the update step of the parameters only for further improved performances. Numerical experiments are conducted with a two-dimensional synthetic subsurface transport model simulating the migration of a contaminant plume in a heterogenous aquifer domain. Contaminant concentration data are assimilated to estimate both the contaminant state and the hydraulic conductivity field. Assimilation runs are performed under imperfect modeling conditions and various observational scenarios. Simulation results suggest that the proposed scheme efficiently recovers both the contaminant state and the aquifer conductivity, providing more accurate estimates than the standard Joint and Dual EnKFs in all tested scenarios. Iterating on the update step of the new scheme further enhances the proposed filter’s behavior. In term of computational cost, the new Joint-EnKF is almost equivalent to that of the Dual-EnKF, but requires twice more model
Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter
Massonnet, F.; Goosse, H.; Fichefet, T.; Counillon, F.
2014-07-01
The choice of parameter values is crucial in the course of sea ice model development, since parameters largely affect the modeled mean sea ice state. Manual tuning of parameters will soon become impractical, as sea ice models will likely include more parameters to calibrate, leading to an exponential increase of the number of possible combinations to test. Objective and automatic methods for parameter calibration are thus progressively called on to replace the traditional heuristic, "trial-and-error" recipes. Here a method for calibration of parameters based on the ensemble Kalman filter is implemented, tested and validated in the ocean-sea ice model NEMO-LIM3. Three dynamic parameters are calibrated: the ice strength parameter P*, the ocean-sea ice drag parameter Cw, and the atmosphere-sea ice drag parameter Ca. In twin, perfect-model experiments, the default parameter values are retrieved within 1 year of simulation. Using 2007-2012 real sea ice drift data, the calibration of the ice strength parameter P* and the oceanic drag parameter Cw improves clearly the Arctic sea ice drift properties. It is found that the estimation of the atmospheric drag Ca is not necessary if P* and Cw are already estimated. The large reduction in the sea ice speed bias with calibrated parameters comes with a slight overestimation of the winter sea ice areal export through Fram Strait and a slight improvement in the sea ice thickness distribution. Overall, the estimation of parameters with the ensemble Kalman filter represents an encouraging alternative to manual tuning for ocean-sea ice models.
Developing a software for tracking the memory states of the machines in the LHCb Filter Farm
Jain, Harshit
2017-01-01
The LHCb Event Filter Farm consists of more than 1500 server nodes with a total amount of roughly 65 TB operating memory .The memory is crucial for the success of the LHCb experiment, since the proton-proton collisions are temporarily stored on these memory modules. Unfortunately, the aging nodes of the server farm occasionally suffer losses of their memory modules. The lower the available memory, the lower performance we can get out of it. Inducing the users or administrators to pay attention to this matter is inefficient. One needs to upgrade it to an acceptable way. The aim of this project was to develop a software to monitor a set of test machines. The software stores the data of the memory sticks in advance in a database which will be used for future reference. Then it checks the memory sticks at a future time instant to find any failures. In the case of any such losses the software looks up in the database to find out which memory sticks have lost and displays all information of those sticks in a log fi...
Briseño, Jessica; Herrera, Graciela S.
2010-05-01
Herrera (1998) proposed a method for the optimal design of groundwater quality monitoring networks that involves space and time in a combined form. The method was applied later by Herrera et al (2001) and by Herrera and Pinder (2005). To get the estimates of the contaminant concentration being analyzed, this method uses a space-time ensemble Kalman filter, based on a stochastic flow and transport model. When the method is applied, it is important that the characteristics of the stochastic model be congruent with field data, but, in general, it is laborious to manually achieve a good match between them. For this reason, the main objective of this work is to extend the space-time ensemble Kalman filter proposed by Herrera, to estimate the hydraulic conductivity, together with hydraulic head and contaminant concentration, and its application in a synthetic example. The method has three steps: 1) Given the mean and the semivariogram of the natural logarithm of hydraulic conductivity (ln K), random realizations of this parameter are obtained through two alternatives: Gaussian simulation (SGSim) and Latin Hypercube Sampling method (LHC). 2) The stochastic model is used to produce hydraulic head (h) and contaminant (C) realizations, for each one of the conductivity realizations. With these realization the mean of ln K, h and C are obtained, for h and C, the mean is calculated in space and time, and also the cross covariance matrix h-ln K-C in space and time. The covariance matrix is obtained averaging products of the ln K, h and C realizations on the estimation points and times, and the positions and times with data of the analyzed variables. The estimation points are the positions at which estimates of ln K, h or C are gathered. In an analogous way, the estimation times are those at which estimates of any of the three variables are gathered. 3) Finally the ln K, h and C estimate are obtained using the space-time ensemble Kalman filter. The realization mean for each one
BJUT at TREC 2015 Microblog Track: Real Time Filtering Using Knowledge Base
2015-11-20
all non- English language tweeter are supposed to be junk, thus we reduce that once we detect a non- English Unicode character in it. • Corpus Generation...new query. According to the new query, we use it to generate Lemur Query Parameter File. Noted that we simply select the unigram Language Model with...learning to rank of tweets. In Proceedings of the 23rd International Conference on Computational Linguistics , pages 295–303. Association for Computational
Directory of Open Access Journals (Sweden)
H. Zhang
2017-09-01
Full Text Available Land surface models (LSMs use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L and the Community Land Model (CLM using a 5-month calibration (assimilation period (March–July 2012 of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012. As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC or fractions of sand, clay, and organic matter of each layer (CLM are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the
Directory of Open Access Journals (Sweden)
Chuanfeng Li
2017-01-01
Full Text Available Hypersonic vehicle is a typical parameter uncertain system with significant characteristics of strong coupling, nonlinearity, and external disturbance. In this paper, a combined system modeling approach is proposed to approximate the actual vehicle system. The state feedback control strategy is adopted based on the robust guaranteed cost control (RGCC theory, where the Lyapunov function is applied to get control law for nonlinear system and the problem is transformed into a feasible solution by linear matrix inequalities (LMI method. In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. Simulation results show the stability and well tracking performance for the proposed strategy in controlling the vehicle system.
El Gharamti, Mohamad
2015-11-26
The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.
Czech Academy of Sciences Publication Activity Database
Ökzan, E.; Šmídl, Václav; Saha, S.; Lundquist, C.; Gustafsson, F.
2013-01-01
Roč. 49, č. 6 (2013), s. 1566-1575 ISSN 0005-1098 R&D Projects: GA ČR(CZ) GAP102/11/0437 Keywords : Unknown Noise Statistics * Adaptive Filtering * Marginalized Particle Filter * Bayesian Conjugate prior Subject RIV: BC - Control Systems Theory Impact factor: 3.132, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf
International Nuclear Information System (INIS)
Miyamoto, Naoki; Ishikawa, Masayori; Sutherland, Kenneth
2015-01-01
In the real-time tumor-tracking radiotherapy system, a surrogate fiducial marker inserted in or near the tumor is detected by fluoroscopy to realize respiratory-gated radiotherapy. The imaging dose caused by fluoroscopy should be minimized. In this work, an image processing technique is proposed for tracing a moving marker in low-dose imaging. The proposed tracking technique is a combination of a motion-compensated recursive filter and template pattern matching. The proposed image filter can reduce motion artifacts resulting from the recursive process based on the determination of the region of interest for the next frame according to the current marker position in the fluoroscopic images. The effectiveness of the proposed technique and the expected clinical benefit were examined by phantom experimental studies with actual tumor trajectories generated from clinical patient data. It was demonstrated that the marker motion could be traced in low-dose imaging by applying the proposed algorithm with acceptable registration error and high pattern recognition score in all trajectories, although some trajectories were not able to be tracked with the conventional spatial filters or without image filters. The positional accuracy is expected to be kept within ±2 mm. The total computation time required to determine the marker position is a few milliseconds. The proposed image processing technique is applicable for imaging dose reduction. (author)
Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter
Directory of Open Access Journals (Sweden)
Li Sun
2014-01-01
Full Text Available Fluidized bed combustion (FBC boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD. The simulation results demonstrate that the approach is feasible and effective.
Monteiro, Bruna Moraes; Nobrega Filho, Denys Silveira; Lopes, Patrícia de Medeiros Loureiro; de Sales, Marcelo Augusto Oliveira
2012-01-01
The aim of this study was to analyze the influence of filters (algorithms) to improve the image of Cone Beam Computed Tomography (CBCT) in diagnosis of osteolytic lesions of the mandible, in order to establish the protocols for viewing images more suitable for CBCT diagnostics. 15 dry mandibles in which perforations were performed, simulating lesions, were submitted to CBCT examination. Two examiners analyzed the images, using filters to improve image Hard, Normal, and Very Sharp, contained in the iCAT Vision software, and protocols for assessment: axial; sagittal and coronal; and axial, sagittal and coronal planes simultaneously (MPR), on two occasions. The sensitivity and specificity (validity) of the cone beam computed tomography (CBCT) have been demonstrated as the values achieved were above 75% for sensitivity and above 85% for specificity, reaching around 95.5% of sensitivity and 99% of specificity when we used the appropriate observation protocol. It was concluded that the use of filters (algorithms) to improve the CBCT image influences the diagnosis, due to the fact that all measured values were correspondingly higher when it was used the filter Very Sharp, which justifies its use for clinical activities, followed by Hard and Normal filters, in order of decreasing values.
Directory of Open Access Journals (Sweden)
Bruna Moraes Monteiro
2012-01-01
Full Text Available The aim of this study was to analyze the influence of filters (algorithms to improve the image of Cone Beam Computed Tomography (CBCT in diagnosis of osteolytic lesions of the mandible, in order to establish the protocols for viewing images more suitable for CBCT diagnostics. 15 dry mandibles in which perforations were performed, simulating lesions, were submitted to CBCT examination. Two examiners analyzed the images, using filters to improve image Hard, Normal, and Very Sharp, contained in the iCAT Vision software, and protocols for assessment: axial; sagittal and coronal; and axial, sagittal and coronal planes simultaneously (MPR, on two occasions. The sensitivity and specificity (validity of the cone beam computed tomography (CBCT have been demonstrated as the values achieved were above 75% for sensitivity and above 85% for specificity, reaching around 95.5% of sensitivity and 99% of specificity when we used the appropriate observation protocol. It was concluded that the use of filters (algorithms to improve the CBCT image influences the diagnosis, due to the fact that all measured values were correspondingly higher when it was used the filter Very Sharp, which justifies its use for clinical activities, followed by Hard and Normal filters, in order of decreasing values.
Scheme of adaptive polarization filtering based on Kalman model
Institute of Scientific and Technical Information of China (English)
Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande
2006-01-01
A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.
Haberka, Maciej; Liszka, Jerzy; Kozyra, Andrzej; Finik, Maciej; Gąsior, Zbigniew
2015-03-01
The aim of the study was to evaluate the left ventricle (LV) function with speckle tracking echocardiography (STE) and to assess its relation to prognosis in patients after acute myocardial infarction (AMI). Sixty-three patients (F/M = 16/47 pts; 62.33 ± 11.85 years old) with AMI (NSTEMI/STEMI 24/39 pts) and successful percutaneous coronary intervention (PCI) with stent implantation (thrombolysis in myocardial infarction; TIMI 3 flow) were enrolled in this study. All patients underwent baseline two-dimensional conventional echocardiography and STE 3 days (baseline) and 30 days after PCI. All patients were followed up for cardiovascular clinical endpoints, major adverse cardiovascular endpoint (MACE), and functional status (Canadian Cardiovascular Society and New York Heart Association). During the follow-up (31.9 ± 5.1 months), there were 3 cardiovascular deaths, 15 patients had AMI, 2 patients had cerebral infarction, 24 patients reached the MACE. Baseline LV torsion (P = 0.035), but none of the other strain parameters were associated with the time to first unplanned cardiovascular hospitalization. Univariate analysis showed that baseline longitudinal two-chamber and four-chamber strain (sLa2 0 and sLa4 0) and the same parameters obtained 30 days after the AMI together with transverse four-chamber strain (sLa2 30, sLa4 30, and sTa4 30) were significantly associated with combined endpoint (MACE). The strongest association in the univariate analysis was found for the baseline sLa2. However, in multivariable analysis only a left ventricular remodeling (LVR - 27% pts) was significantly associated with MACE and strain parameters were not associated with the combined endpoint. The assessment of LV function with STE may improve cardiovascular risk prediction in postmyocardial infarction patients. © 2014, Wiley Periodicals, Inc.
Tracking the business cycle of the Euro area: A multivariate model-based band-pass filter
Azevedo, J.M.; Koopman, S.J.; Rua, A.
2006-01-01
This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties.
Superimposed chirped pulse parameter estimation based on the extended Kalman filter (EKF)
CSIR Research Space (South Africa)
Olivier, JC
2009-05-01
Full Text Available An extended Kalman filter (EKF) is proposed to estimate the frequencies and chirp rate of multiple superimposed chirped pulses. The estimation problem is a difficult one, where maximum likelyhood methods are very complex especially if more than two...
Institute of Scientific and Technical Information of China (English)
王铁成; 李伟力; 孙建伟
2003-01-01
A mathematical model has been built up for compound cage rotor induction machine with the rotor re-sistance and leakage inductance in the model identified through Kalman filtering method. Using the identifiedparameters, simulation studies are performed, and simulation results are compared with testing results.
A spatial track formation model and its use for calculating etch-pit parameters of light nuclei
International Nuclear Information System (INIS)
Somogyi, G.; Scherzer, R.; Grabisch, K.; Enge, W.
1976-01-01
A generalized geometrical model of etch-pit formation in three dimensions is presented for nuclear particles entering isotropic solids at arbitrary angles of incidence. With this model one can calculate the relations between any particle parameter /Z = charge, M = mass, R = range, theta = angle of incidence/ and etching or track parameter /h = removed detector layer, L = track length, d = track diameter, etch-pit profile and contour/ for track etching rates varying monotonically along the trajectory of particles. Using a computer algorithm, calculations have been performed to study identification problems of nuclei of Z = 1-8 registered in a stack of polycarbonate sheets. For these calculations the etching rate ratio vs residual range curves were parametrized with a form of V -1 (R) = 1-Σasub(i) exp (- bsub(i)R) which does not involve the existence of a threshold for track registration. Particular attention was paid to the study of the evolution of etch-pit sizes for relatively high values of h. For this case, data are presented for the charge and isotope resolving power of the identification methods based on the relations L(R) of d(R). Calculations were also made to show the effect of the relative /parallel and opposite/ orientations between the directions of track etching and particle speed on etch-pit evolution. These studies offered new identification methods based on the determination of the curves L(parallel) vs L(opposite) and d(parallel) vs d(opposite), respectively. (orig.) [de
Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang
2017-05-18
This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.
Institute of Scientific and Technical Information of China (English)
Zaiyu; Chen; Minghui; Yin; Lianjun; Zhou; Yaping; Xia; Jiankun; Liu; Yun; Zou
2017-01-01
Since mechanical loads exert a significant influence on the life span of wind turbines, the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking(MPPT) controller.Moreover, a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue. However, for the existing control strategies based on nonlinear model of wind turbines, the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft. Hence, in this paper, a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously. Then,simulations on FAST(Fatigue, Aerodynamics, Structures, and Turbulence) code and experiments on the wind turbine simulator(WTS) based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load.
Institute of Scientific and Technical Information of China (English)
Zaiyu Chen; Minghui Yin; Lianjun Zhou; Yaping Xia; Jiankun Liu; Yun Zou
2017-01-01
Since mechanical loads exert a significant influence on the life span of wind turbines,the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking (MPPT) controller.Moreover,a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue.However,for the existing control strategies based on nonlinear model of wind turbines,the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft.Hence,in this paper,a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously.Then,simulations on FAST (Fatigue,Aerodynamics,Structures,and Turbulence) code and experiments on the wind turbine simulator (WTS) based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load.
Zhang, Xi; Miao, Lingjuan; Shao, Haijun
2016-05-02
If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper.
Directory of Open Access Journals (Sweden)
Erik Cuevas J.
2006-01-01
Full Text Available El filtro de Kalman ha sido usado exitosamente en diferentes aplicaciones de predicción o determinación del estado de un sistema. Un campo importante en la visión por computadora es el seguimiento de objetos. Diferentes tipos de movimiento así como el ocultamiento de objetos a seguir pueden obstaculizar la labor de seguimiento. En este trabajo, se presenta el uso del filtro de Kalman para el seguimiento de objetos. Además se considera tanto la capacidad del filtro para tolerar pequeños ocultamientos del objeto así como el uso del filtro de Kalman extendido para el modelaje de movimientos complejos
2013-02-01
ventilation and air conditioning (HVAC) filters made of polyethylene –polypropylene and of a synthetic polymer were compared with a glass microfiber...airborne pathogens. 2.2.2. Viral Aerosols Bioaerosols are airborne particles with biological origins, such as nonviable pollen, and viable fungi ...heterogeneous airborne bacteria and fungi at 600 W for four periods of 2.5 min, each separated by 5 min from the next. Elhafi et al. (2004) demonstrated
Pudovkin, A. P.; Panasyuk, Yu N.; Danilov, S. N.; Moskvitin, S. P.
2018-05-01
The problem of improving automated air traffic control systems is considered through the example of the operation algorithm synthesis for a range measurement channel to track the aircraft, using its kinematic and dynamic parameters. The choice of the state and observation models has been justified, the computer simulations have been performed and the results of the investigated algorithms have been obtained.
Track etch parameters and annealing kinetics assessment of protons of low energy in CR-39 detector
International Nuclear Information System (INIS)
Jain, R.K.; Kumar, Ashok; Singh, B.K.
2012-01-01
Highlights: ► We calibrate CR-39 detector with very low energy protons. ► We establish linear relationship between track diameter and time/energy up to 200 keV. ► We determine activation energy of annealing using different models. ► We justify concept of single annealing activation energy in CR-39. - Abstract: In this paper threshold of the registration sensitivity of very low energy proton in CR-39 is investigated. Irradiation of CR-39 (poly-allyl-diglycol carbonate) was carried out with very low energy mono energetic protons of 20–60 keV from a mini proton accelerator. Nearly 10 4 /cm 2 fluence of protons was used. The variation of track diameter with etching time as well as proton energy response curve was carefully calibrated. The bulk and track etch rates were measured by using proton track diameters. Bulk etch rate was also measured by the thickness of removed surface layer. The thermal annealing of proton track at temperatures ranging from 100 to 200 °C in CR-39 was studied by several models. Activation energy of annealed CR-39 detectors was calculated by slope of track etch rate and temperature plot. The data of proton tracks of 200, 250 and 300 keV from 400 kV Van-de-Graaff accelerator was also used and compared with the track diameters of different energies of proton.
Parameter importance and uncertainty in predicting runoff pesticide reduction with filter strips.
Muñoz-Carpena, Rafael; Fox, Garey A; Sabbagh, George J
2010-01-01
Vegetative filter strips (VFS) are an environmental management tool used to reduce sediment and pesticide transport from surface runoff. Numerical models of VFS such as the Vegetative Filter Strip Modeling System (VFSMOD-W) are capable of predicting runoff, sediment, and pesticide reduction and can be useful tools to understand the effectiveness of VFS and environmental conditions under which they may be ineffective. However, as part of the modeling process, it is critical to identify input factor importance and quantify uncertainty in predicted runoff, sediment, and pesticide reductions. This research used state-of-the-art global sensitivity and uncertainty analysis tools, a screening method (Morris) and a variance-based method (extended Fourier Analysis Sensitivity Test), to evaluate VFSMOD-W under a range of field scenarios. The three VFS studies analyzed were conducted on silty clay loam and silt loam soils under uniform, sheet flow conditions and included atrazine, chlorpyrifos, cyanazine, metolachlor, pendimethalin, and terbuthylazine data. Saturated hydraulic conductivity was the most important input factor for predicting infiltration and runoff, explaining >75% of the total output variance for studies with smaller hydraulic loading rates ( approximately 100-150 mm equivalent depths) and approximately 50% for the higher loading rate ( approximately 280-mm equivalent depth). Important input factors for predicting sedimentation included hydraulic conductivity, average particle size, and the filter's Manning's roughness coefficient. Input factor importance for pesticide trapping was controlled by infiltration and, therefore, hydraulic conductivity. Global uncertainty analyses suggested a wide range of reductions for runoff (95% confidence intervals of 7-93%), sediment (84-100%), and pesticide (43-100%) . Pesticide trapping probability distributions fell between runoff and sediment reduction distributions as a function of the pesticides' sorption. Seemingly
DEFF Research Database (Denmark)
Lacouture Parodi, Yesenia; Rubak, Per
2011-01-01
for crosstalk cancellation filters applied to different loudspeaker configurations has not yet been addressed systematically. A study of three different inversion techniques applied to several loudspeaker arrangements is documented. Least-squares approximations in the frequency and time domains are evaluated...... along with a crosstalk canceler based on minimum-phase approximation with a frequency-independent delay. The three methods were applied to loudspeaker configurations with two channels and the least-squares approaches to configurations with four channels. Several different span angles and elevations were...
Caiazzo, A; Caforio, Federica; Montecinos, Gino; Muller, Lucas O; Blanco, Pablo J; Toro, Eluterio F
2016-10-25
This work presents a detailed investigation of a parameter estimation approach on the basis of the reduced-order unscented Kalman filter (ROUKF) in the context of 1-dimensional blood flow models. In particular, the main aims of this study are (1) to investigate the effects of using real measurements versus synthetic data for the estimation procedure (i.e., numerical results of the same in silico model, perturbed with noise) and (2) to identify potential difficulties and limitations of the approach in clinically realistic applications to assess the applicability of the filter to such setups. For these purposes, the present numerical study is based on a recently published in vitro model of the arterial network, for which experimental flow and pressure measurements are available at few selected locations. To mimic clinically relevant situations, we focus on the estimation of terminal resistances and arterial wall parameters related to vessel mechanics (Young's modulus and wall thickness) using few experimental observations (at most a single pressure or flow measurement per vessel). In all cases, we first perform a theoretical identifiability analysis on the basis of the generalized sensitivity function, comparing then the results owith the ROUKF, using either synthetic or experimental data, to results obtained using reference parameters and to available measurements. Copyright © 2016 John Wiley & Sons, Ltd.
Directory of Open Access Journals (Sweden)
David C Stoner
Full Text Available The effect of climatically-driven plant phenology on mammalian reproduction is one key to predicting species-specific demographic responses to climate change. Large ungulates face their greatest energetic demands from the later stages of pregnancy through weaning, and so in seasonal environments parturition dates should match periods of high primary productivity. Interannual variation in weather influences the quality and timing of forage availability, which can influence neonatal survival. Here, we evaluated macro-scale patterns in reproductive performance of a widely distributed ungulate (mule deer, Odocoileus hemionus across contrasting climatological regimes using satellite-derived indices of primary productivity and plant phenology over eight degrees of latitude (890 km in the American Southwest. The dataset comprised > 180,000 animal observations taken from 54 populations over eight years (2004-2011. Regionally, both the start and peak of growing season ("Start" and "Peak", respectively are negatively and significantly correlated with latitude, an unusual pattern stemming from a change in the dominance of spring snowmelt in the north to the influence of the North American Monsoon in the south. Corresponding to the timing and variation in both the Start and Peak, mule deer reproduction was latest, lowest, and most variable at lower latitudes where plant phenology is timed to the onset of monsoonal moisture. Parturition dates closely tracked the growing season across space, lagging behind the Start and preceding the Peak by 27 and 23 days, respectively. Mean juvenile production increased, and variation decreased, with increasing latitude. Temporally, juvenile production was best predicted by primary productivity during summer, which encompassed late pregnancy, parturition, and early lactation. Our findings offer a parsimonious explanation of two key reproductive parameters in ungulate demography, timing of parturition and mean annual
Directory of Open Access Journals (Sweden)
Zvonko M. Radosavljević
2003-07-01
Full Text Available U radu je dat primer primene jedne vrste niskofrekventnog filtriranja sa usrednjavanjem, koje se primenjuje u sistemima za detekciju i praćenje ciljeva u vazdušnom prostoru primenom termovizije. Date su dve metode filtriranja slike. Prva metoda koristi niskofrekventno konvoluciono filtriranje a druga usrednjavajući filtar na osnovu srednje vrednosti nivoa sivog. Ovi filtri su primenjeni u sistemima za praćenje uz pomoć infracrvenih senzora. Određivanje nivoa praga filtriranja vrši se uz pomoć statističkih osobina slike. Veoma važan korak u procesu praćenja je određivanje prozora praćenja, koji maze biti, po dimenzijama, fiksan ili adaptibilan. Pogrešna procena o postojanju cilja u prozoru može se doneti u slučaju prisustva šuma pozadine, predpojačavača, detektora, itd. Filtriranje je neophodan korak u ovim sistemima, kao značajan činilac U povećanju brzine i tačnosti praćenja. / A case of image filtering in air target detecting and tracking systems is described in this paper. Two image filtering methods are given. The first method is performed using a low pass convolving filter and the second one uses the mean value of gray level filter. The main goal of the cited filtering is implementation in IR (infra red systems. Some statistical features of the images were used for selecting the threshold level. The next step in the algorithm is the determination of a 'tracking window' that can be fixed or adaptive in size. A false estimation of a target existing in the window may be influenced by the background noise, low noise amplifier detector, etc.
Directory of Open Access Journals (Sweden)
Yasser Diab
2017-07-01
Full Text Available A real-time determination of battery parameters is challenging because batteries are non-linear, time-varying systems. The transient behaviour of lithium-ion batteries is modelled by a Thevenin-equivalent circuit with two time constants characterising activation and concentration polarization. An experimental approach is proposed for directly determining battery parameters as a function of physical quantities. The model’s parameters are a function of the state of charge and of the discharge rate. These can be expressed by regression equations in the model to derive a continuous-discrete extended Kalman estimator of the state of charge and of other parameters. This technique is based on numerical integration of the ordinary differential equations to predict the state of the stochastic dynamic system and the corresponding error covariance matrix. Then a standard correction step of the extended Kalman filter (EKF is applied to increase the accuracy of estimated parameters. Simulations resulting from this proposed estimator model were compared with experimental results under a variety of operating scenarios—analysis of the results demonstrate the accuracy of the estimator for correctly identifying battery parameters.
Breebaart, D.J.; Nather, F.; Kohlrausch, A.G.
2010-01-01
The audibility of HRTF information reduction was investigated using a parametric analysis and synthesis approach. Nonindividualized HRTFs were characterized by magnitude and interaural phase properties computed for warped critical bands. The minimum number of parameters was established as a function
Energy Technology Data Exchange (ETDEWEB)
Guignard, P.A.; Chan, W. (Royal Melbourne Hospital, Parkville (Australia). Dept. of Nuclear Medicine)
1984-09-01
Several techniques for the processing of a series of curves derived from two left ventricular time-activity curves acquired at rest and during exercise with a nuclear stethoscope were evaluated. They were three and five point time smoothing. Fourier filtering preserving one to four harmonics (H), truncated curve Fourier filtering, and third degree polynomial curve fitting. Each filter's ability to recover, with fidelity, systolic and diastolic function parameters was evaluated under increasingly 'noisy' conditions and at several sampling rates. Third degree polynomial curve fittings and truncated Fourier filters exhibited very high sensitivity to noise. Three and five point time smoothing had moderate sensitivity to noise, but were highly affected by sampling rate. Fourier filtering preserving 2H or 3H produced the best compromise with high resilience to noise and independence of sampling rate as far as the recovery of these functional parameters is concerned.
International Nuclear Information System (INIS)
Guignard, P.A.; Chan, W.
1984-01-01
Several techniques for the processing of a series of curves derived from two left ventricular time-activity curves acquired at rest and during exercise with a nuclear stethoscope were evaluated. They were three and five point time smoothing. Fourier filtering preserving one to four harmonics (H), truncated curve Fourier filtering, and third degree polynomial curve fitting. Each filter's ability to recover, with fidelity, systolic and diastolic function parameters was evaluated under increasingly 'noisy' conditions and at several sampling rates. Third degree polynomial curve fittings and truncated Fourier filters exhibited very high sensitivity to noise. Three and five point time smoothing had moderate sensitivity to noise, but were highly affected by sampling rate. Fourier filtering preserving 2H or 3H produced the best compromise with high resilience to noise and independence of sampling rate as far as the recovery of these functional parameters is concerned. (author)
Directory of Open Access Journals (Sweden)
Niancheng Zhou
2014-08-01
Full Text Available The influence of electric vehicle charging stations on power grid harmonics is becoming increasingly significant as their presence continues to grow. This paper studies the operational principles of the charging current in the continuous and discontinuous modes for a three-phase uncontrolled rectification charger with a passive power factor correction link, which is affected by the charging power. A parameter estimation method is proposed for the equivalent circuit of the charger by using the measured characteristic AC (Alternating Current voltage and current data combined with the charging circuit constraints in the conduction process, and this method is verified using an experimental platform. The sensitivity of the current harmonics to the changes in the parameters is analyzed. An analytical harmonic model of the charging station is created by separating the chargers into groups by type. Then, the harmonic current amplification caused by the shunt active power filter is researched, and the analytical formula for the overload factor is derived to further correct the capacity of the shunt active power filter. Finally, this method is validated through a field test of a charging station.
Energy Technology Data Exchange (ETDEWEB)
Fogliata, Antonella, E-mail: antonella.fogliata@humanitas.it; Reggiori, Giacomo; Stravato, Antonella; Scorsetti, Marta; Cozzi, Luca [Radiotherapy and Radiosurgery Department, Humanitas Research Hospital, Milan-Rozzano I-20098 (Italy); Fleckenstein, Jens; Schneider, Frank; Lohr, Frank [Department of Radiation Oncology, University Medical Center Mannheim, University of Heidelberg, Mannheim D-68167 (Germany); Pachoud, Marc; Ghandour, Sarah [Radiation Oncology Department, Hôpital Riviera Chablais, Vevey CH-1800 (Switzerland); Krauss, Harald [Radio-Oncology Department, Kaiser Franz Josef Spital, Vienna A-1100 (Austria)
2016-01-15
Purpose: Flattening filter free (FFF) beams generated by medical linear accelerators are today clinically used for stereotactical and non-stereotactical radiotherapy treatments. Such beams differ from the standard flattened beams (FF) in the high dose rate and the profile shape peaked on the beam central axis. Definition of new parameters as unflatness and slope for FFF beams has been proposed based on a renormalization factor for FFF profiles. The present study aims to assess the dosimetric differences between FFF beams generated by linear accelerators from different vendors, and to provide renormalization and parameter data of the two kinds of units. Methods: Dosimetric data from two Varian TrueBeam and two Elekta Versa HD linear accelerators, all with 6 and 10 MV nominal accelerating potentials, FF and FFF modes have been collected. Renormalization factors and related fit parameters according to Fogliata et al. [“Definition of parameters for quality assurance of flattening filter free (FFF) photon beams in radiation therapy,” Med. Phys. 39, 6455–6464 (2012)] have been evaluated for FFF beams of both units and energies. Unflatness and slope parameters from profile curves were evaluated. Dosimetric differences in terms of beam penetration and near-the-surface dose were also assessed. Results: FFF profile parameters have been updated; renormalization factors and unflatness from the Varian units are consistent with the published data. Elekta FFF beam qualities, different from the Varian generated beams, tend to express similar behaviour as the FF beam of the corresponding nominal energy. TPR{sub 20,10} for 6 and 10 MV FF and FFF TrueBeam beams are 0.665, 0.629 (6 MV) and 0.738, 0.703 (10 MV). The same figures for Versa HD units are 0.684, 0.678 (6 MV) and 0.734, 0.721 (10 MV). Conclusions: Renormalization factor and unflatness parameters evaluated from Varian and Elekta FFF beams are provided, in particular renormalization factors table and fit parameters.
Response-based estimation of sea state parameters - Influence of filtering
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam
2007-01-01
Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The wave spectrum can be estimated from procedures based on measured ship responses. The paper deals with two procedures—Bayesian Modelling and Parametric Modelling...
Track reconstruction at the ILC: the ILD tracking software
International Nuclear Information System (INIS)
Gaede, Frank; Aplin, Steven; Rosemann, Christoph; Voutsinas, Georgios; Glattauer, Robin
2014-01-01
One of the key requirements for Higgs physics at the International Linear Collider ILC is excellent track reconstruction with very good momentum and impact parameter resolution. ILD is one of the two detector concepts at the ILC. Its central tracking system comprises of an outer Si-tracker, a highly granular TPC, an intermediate silicon tracker and a pixel vertex detector, and it is complemented by silicon tracking disks in the forward direction. Large hit densities from beam induced coherent electron-positron pairs at the ILC pose an additional challenge to the pattern recognition algorithms. We present the recently developed new ILD tracking software, the pattern recognition algorithms that are using clustering techniques, Cellular Automatons and Kalman filter based track extrapolation. The performance of the ILD tracking system is evaluated using a detailed simulation including dead material, gaps and imperfections.
Battery State-of-Charge and Parameter Estimation Algorithm Based on Kalman Filter
DEFF Research Database (Denmark)
Dragicevic, Tomislav; Sucic, Stjepan; Guerrero, Josep M.
2013-01-01
Electrochemical battery is the most widely used energy storage technology, finding its application in various devices ranging from low power consumer electronics to utility back-up power. All types of batteries show highly non-linear behaviour in terms of dependence of internal parameters...... on operating conditions, momentary replenishment and a number of past charge/discharge cycles. A good indicator for the quality of overall customer service in any battery based application is the availability and reliability of these informations, as they point out important runtime variables...
International Nuclear Information System (INIS)
Muto, S.; Tatsumi, K.; Rusz, J.
2013-01-01
We present a parameter-free method of extraction of the electron magnetic circular dichroism spectra from energy-filtered diffraction patterns measured on a crystalline specimen. The method is based on a multivariate curve resolution technique. The main advantage of the proposed method is that it allows extraction of the magnetic signal regardless of the symmetry and orientation of the crystal, as long as there is a sufficiently strong magnetic component of the signal in the diffraction plane. This method essentially overcomes difficulties in extraction of the EMCD signal caused by complexity of dynamical diffraction effects. - Highlights: ► New method of extraction of EMCD signal using statistical methods (multivariate curve resolution). ► EMCD can be extracted quantitatively regardless of symmetry of crystal or its orientation. ► First principles simulation of EFDIF datacube, including dynamical diffraction effects
DEFF Research Database (Denmark)
Li, Zipeng; Jiang, Aiting; Shen, Pan
2016-01-01
, this paper presents a systematic design method for the LCL-LC filtered grid-connected photovoltaic (PV) system. With this method, controller parameters and the active damping feedback coefficient are easily obtained by specifying the system stability and dynamic performance indices, and it is more convenient......-frequency harmonics attenuation ability, but the resonant problem affects the system stability remarkably. In this paper, active damping based on the capacitor voltage feedback is proposed using the concept of the equivalent virtual impedance in parallel with the capacitor. With the consideration of system delay...... to optimize the system performance according to the predefined satisfactory region. Finally, the simulation results are presented to validate the proposed design method and control scheme....
Directory of Open Access Journals (Sweden)
Junhua Wu
2017-01-01
Full Text Available Carbon fibre composites have a promising application future of the vehicle, due to its excellent physical properties. Debonding is a major defect of the material. Analyses of wave packets are critical for identification of the defect on ultrasonic nondestructive evaluation and testing. In order to isolate different components of ultrasonic guided waves (GWs, a signal decomposition algorithm combining Smoothed Pseudo Wigner-Ville distribution and Vold–Kalman filter order tracking is presented. In the algorithm, the time-frequency distribution of GW is first obtained by using Smoothed Pseudo Wigner-Ville distribution. The frequencies of different modes are computed based on summation of the time-frequency coefficients in the frequency direction. On the basis of these frequencies, isolation of different modes is done by Vold–Kalman filter order tracking. The results of the simulation signal and the experimental signal reveal that the presented algorithm succeeds in decomposing the multicomponent signal into monocomponents. Even though components overlap in corresponding Fourier spectrum, they can be isolated by using the presented algorithm. So the frequency resolution of the presented method is promising. Based on this, we can do research about defect identification, calculation of the defect size, and locating the position of the defect.
Pande-Chhetri, Roshan
High resolution hyperspectral imagery (airborne or ground-based) is gaining momentum as a useful analytical tool in various fields including agriculture and aquatic systems. These images are often contaminated with stripes and noise resulting in lower signal-to-noise ratio, especially in aquatic regions where signal is naturally low. This research investigates effective methods for filtering high spatial resolution hyperspectral imagery and use of the imagery in water quality parameter estimation and aquatic vegetation classification. The striping pattern of the hyperspectral imagery is non-parametric and difficult to filter. In this research, a de-striping algorithm based on wavelet analysis and adaptive Fourier domain normalization was examined. The result of this algorithm was found superior to other available algorithms and yielded highest Peak Signal to Noise Ratio improvement. The algorithm was implemented on individual image bands and on selected bands of the Maximum Noise Fraction (MNF) transformed images. The results showed that image filtering in the MNF domain was efficient and produced best results. The study investigated methods of analyzing hyperspectral imagery to estimate water quality parameters and to map aquatic vegetation in case-2 waters. Ground-based hyperspectral imagery was analyzed to determine chlorophyll-a (Chl-a) concentrations in aquaculture ponds. Two-band and three-band indices were implemented and the effect of using submerged reflectance targets was evaluated. Laboratory measured values were found to be in strong correlation with two-band and three-band spectral indices computed from the hyperspectral image. Coefficients of determination (R2) values were found to be 0.833 and 0.862 without submerged targets and stronger values of 0.975 and 0.982 were obtained using submerged targets. Airborne hyperspectral images were used to detect and classify aquatic vegetation in a black river estuarine system. Image normalization for water
Mathematical modeling of a biogenous filter cake and identification of oilseed material parameters
Directory of Open Access Journals (Sweden)
Očenášek J.
2009-12-01
Full Text Available Mathematical modeling of the filtration and extrusion process inside a linear compression chamber has gained a lot of attention during several past decades. This subject was originally related to mechanical and hydraulic properties of soils (in particular work of Terzaghi and later was this approach adopted for the modeling of various technological processes in the chemical industry (work of Shirato. Developed mathematical models of continuum mechanics of porous materials with interstitial fluid were then applied also to the problem of an oilseed expression. In this case, various simplifications and partial linearizations are introduced in models for the reason of an analytical or numerical solubility; or it is not possible to generalize the model formulation into the fully 3D problem of an oil expression extrusion with a complex geometry such as it has a screw press extruder.We proposed a modified model for the oil seeds expression process in a linear compression chamber. The model accounts for the rheological properties of the deformable solid matrix of compressed seed, where the permeability of the porous solid is described by the Darcy's law. A methodology of the experimental work necessary for a material parameters identification is presented together with numerical simulation examples.
Directory of Open Access Journals (Sweden)
Li Ke
2014-12-01
Full Text Available A large-scale high altitude environment simulation test cabin was developed to accurately control temperatures and pressures encountered at high altitudes. The system was developed to provide slope-tracking dynamic control of the temperature–pressure two-parameter and overcome the control difficulties inherent to a large inertia lag link with a complex control system which is composed of turbine refrigeration device, vacuum device and liquid nitrogen cooling device. The system includes multi-parameter decoupling of the cabin itself to avoid equipment damage of air refrigeration turbine caused by improper operation. Based on analysis of the dynamic characteristics and modeling for variations in temperature, pressure and rotation speed, an intelligent controller was implemented that includes decoupling and fuzzy arithmetic combined with an expert PID controller to control test parameters by decoupling and slope tracking control strategy. The control system employed centralized management in an open industrial ethernet architecture with an industrial computer at the core. The simulation and field debugging and running results show that this method can solve the problems of a poor anti-interference performance typical for a conventional PID and overshooting that can readily damage equipment. The steady-state characteristics meet the system requirements.
SU-F-T-521: Flattening-Filter-Free Beam Parameters Comparison From Different Linac Machine Types
Energy Technology Data Exchange (ETDEWEB)
Hussain, A [King Faisal Specialist Hospital, Riyadh, Saudi Arabia, Arkansas Cancer Institute, Pine Bluff, AR (Saudi Arabia)
2016-06-15
Purpose: Novel linac machines, TrueBeam (TB) and Elekta Versa have updated head designing and software control system, include flattening-filter-free (FFF) photon and electron beams. Later on FFF beams were also introduced on C-Series machines. In this work FFF beams for same energy 6MV but from different machine versions were studied with reference to beam data parameters. Methods: The 6MV-FFF percent depth doses, profile symmetry and flatness, dose rate tables, and multi-leaf collimator (MLC) transmission factors were measured during commissioning process of both C-series and Truebeam machines. The scanning and dosimetric data for 6MV-FFF beam from Truebeam and C-Series linacs was compared. A correlation of 6MV-FFF beam from Elekta Versa with that of Varian linacs was also found. Results: The scanning files were plotted for both qualitative and quantitative analysis. The dosimetric leaf gap (DLG) for C-Series 6MV-FFF beam is 1.1 mm. Published values for Truebeam dosimetric leaf gap is 1.16 mm. 6MV MLC transmission factor varies between 1.3 % and 1.4 % in two separate measurements and measured DLG values vary between 1.32 mm and 1.33 mm on C-Series machine. MLC transmission factor from C-Series machine varies between 1.5 % and 1.6 %. Some of the measured data values from C-Series FFF beam are compared with Truebeam representative data. 6MV-FFF beam parameter values like dmax, OP factors, beam symmetry and flatness and additional parameters for C-Series and Truebeam liancs will be presented and compared in graphical form and tabular data form if selected. Conclusion: The 6MV flattening filter (FF) beam data from C-Series & Truebeam and 6MV-FFF beam data from Truebeam has already presented. This particular analysis to compare 6MV-FFF beam from C-Series and Truebeam provides opportunity to better elaborate FFF mode on novel machines. It was found that C-Series and Truebeam 6MV-FFF dosimetric and beam data was quite similar.
Gardezi, A.; Umer, T.; Butt, F.; Young, R. C. D.; Chatwin, C. R.
2016-04-01
A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. The main concern for using the SPOT-MACH is its computationally intensive nature. However in the past enhancements techniques were proposed for the SPOT-MACH to make its execution time comparable to its frequency domain counterpart. In this paper a novel approach is discussed which uses VANET parameters coupled with the SPOT-MACH in order to minimise the extensive processing of the large video dataset acquired from the Pakistan motorways surveillance system. The use of VANET parameters gives us an estimation criterion of the flow of traffic on the Pakistan motorway network and acts as a precursor to the training algorithm. The use of VANET in this scenario would contribute heavily towards the computational complexity minimization of the proposed monitoring system.
Boada, Beatriz L.; Boada, Maria Jesus L.; Vargas-Melendez, Leandro; Diaz, Vicente
2018-01-01
Nowadays, one of the main objectives in road transport is to decrease the number of accident victims. Rollover accidents caused nearly 33% of all deaths from passenger vehicle crashes. Roll Stability Control (RSC) systems prevent vehicles from untripped rollover accidents. The lateral load transfer is the main parameter which is taken into account in the RSC systems. This parameter is related to the roll angle, which can be directly measured from a dual-antenna GPS. Nevertheless, this is a costly technique. For this reason, roll angle has to be estimated. In this paper, a novel observer based on H∞ filtering in combination with a neural network (NN) for the vehicle roll angle estimation is proposed. The design of this observer is based on four main criteria: to use a simplified vehicle model, to use signals of sensors which are installed onboard in current vehicles, to consider the inaccuracy in the system model and to attenuate the effect of the external disturbances. Experimental results show the effectiveness of the proposed observer.
Ultra-Fast Tracking Power Supply with 4th order Output Filter and Fixed-Frequency Hysteretic Control
DEFF Research Database (Denmark)
Høyerby, Mikkel Christian Wendelboe; Andersen, Michael Andreas E.
2008-01-01
A practical solution is presented for the design of a non-isolated DC/DC power converter with very low output ripple voltage and very fast output voltage step response. The converter is intended for use as an envelope tracking power supply for an RFPA (Radio Frequency Power Amplifier) in a Tetra2...
Measurement of exhalation and diffusion parameters of radon in solids by plastic track detectors
Energy Technology Data Exchange (ETDEWEB)
Somogyi, G.; Haffez, A.-F.; Hunyadi, I.; Toth-Szilagyi, M.
1986-01-01
There are large discrepancies in data available in the literature for the exhalation and diffusion behaviour of radon in various materials. Therefore there is a need for more studies in this field. For this purpose we have developed and used track methods to measure mass and areal exhalation rates of radon from different fly ashes and sand. In addition, methods were also developed to determine the diffusion length of radon and the porosity of materials. For getting the radon emanation coefficient we have applied the autoradiographic method and the ''can-technique'' for determining the real and effective radium contents. The disturbing effect expected from the geometry of measuring cans and samples is discussed. Relations are derived for the correction of such effect.
International Nuclear Information System (INIS)
Sookpeng, Supawitoo; Martin, Colin J.; Gentle, David J.
2015-01-01
Variation in the user selected CT scanning parameters under automatic tube current modulation (ATCM) between hospitals has a substantial influence on the radiation doses and image quality for patients. The aim of this study was to investigate the effect of changing image reconstruction filter and scan parameter settings on tube current, dose and image quality for various CT scanners operating under ATCM. The scan parameters varied were pitch factor, rotation time, collimator configuration, kVp, image thickness and image filter convolution (FC) used for reconstruction. The Toshiba scanner varies the tube current to achieve a set target noise. Changes in the FC setting and image thickness for the first reconstruction were the major factors affecting patient dose. A two-step change in FC from smoother to sharper filters doubles the dose, but is counterbalanced by an improvement in spatial resolution. In contrast, Philips and Siemens scanners maintained tube current values similar to those for a reference image and patient, and the tube current only varied slightly for changes in individual CT scan parameters. The selection of a sharp filter increased the image noise, while use of iDose iterative reconstruction reduced the noise. Since the principles used by CT manufacturers for ATCM vary, it is important that parameters which affect patient dose and image quality for each scanner are made clear to operator to aid in optimisation. (authors)
Basu, Amar S
2013-05-21
Emerging assays in droplet microfluidics require the measurement of parameters such as drop size, velocity, trajectory, shape deformation, fluorescence intensity, and others. While micro particle image velocimetry (μPIV) and related techniques are suitable for measuring flow using tracer particles, no tool exists for tracking droplets at the granularity of a single entity. This paper presents droplet morphometry and velocimetry (DMV), a digital video processing software for time-resolved droplet analysis. Droplets are identified through a series of image processing steps which operate on transparent, translucent, fluorescent, or opaque droplets. The steps include background image generation, background subtraction, edge detection, small object removal, morphological close and fill, and shape discrimination. A frame correlation step then links droplets spanning multiple frames via a nearest neighbor search with user-defined matching criteria. Each step can be individually tuned for maximum compatibility. For each droplet found, DMV provides a time-history of 20 different parameters, including trajectory, velocity, area, dimensions, shape deformation, orientation, nearest neighbour spacing, and pixel statistics. The data can be reported via scatter plots, histograms, and tables at the granularity of individual droplets or by statistics accrued over the population. We present several case studies from industry and academic labs, including the measurement of 1) size distributions and flow perturbations in a drop generator, 2) size distributions and mixing rates in drop splitting/merging devices, 3) efficiency of single cell encapsulation devices, 4) position tracking in electrowetting operations, 5) chemical concentrations in a serial drop dilutor, 6) drop sorting efficiency of a tensiophoresis device, 7) plug length and orientation of nonspherical plugs in a serpentine channel, and 8) high throughput tracking of >250 drops in a reinjection system. Performance metrics
International Nuclear Information System (INIS)
Vanin, V.R.
1990-01-01
The multidetector systems for high resolution gamma spectroscopy are presented. The observable parameters for identifying nuclides produced simultaneously in the reaction are analysed discussing the efficiency of filter systems. (M.C.K.)
Energy Technology Data Exchange (ETDEWEB)
Guenzel, T F
1989-01-01
The ep-collider HERA at DESY in Hamburg will probe unexplored kinematical regions of the deep-inelastic electron-proton-scattering. The ZEUS-Collaboration develops a detector to study the physical interactions of the incoming particles. Among the requirements of the ZEUS-detector a good momentum resolution of the particle tracks in the inner detector resp. forward tracking detector (FTD) is necessary. The behaviour of the tracks in the forward tracking detector is determined by the lorentzboost ({beta}=0.93) in forward direction and by the inhomogeneous magnetic field in the FTD, which is mainly parallel to the beampipe-axis. In this thesis all studies regard only the FTD (not including VXD or CTD) for the momentum determination. Two scenarios are considered to determine the limits of momentum measurability in the FTD. The results show that the determination of the momentum in the FTD is only possible with the (exact) knowledge of the vertex position, where the tracks come from. The functional dependence of the track parameters at the vertex on the track parameters at the first FTD-module was intensively studied. Disconnected ambiguities are not found. A track model for tracks in the FTD is developed without regarding multiple scattering. The momentum determination of a track is mainly based on the track part between the first FTD-module and the vertex. A momentum resolution of {sigma}p/P=0.008 P-0.0025 P can be achieved depending on the polar angle (relative to the beampipe-axis) of the track at the vertex. Finally the influence of the multiple scattering between the first FTD-module and the vertex on the momentum resolution is estimated. The increase of the dead material for small polar angles restricts the area of acceptable momentum resolution to polar angles of {lambda}>180 mrad. (orig.).
Jannati, Mojtaba; Valadan Zoej, Mohammad Javad; Mokhtarzade, Mehdi
2018-03-01
This paper presents a novel approach to epipolar resampling of cross-track linear pushbroom imagery using orbital parameters model (OPM). The backbone of the proposed method relies on modification of attitude parameters of linear array stereo imagery in such a way to parallelize the approximate conjugate epipolar lines (ACELs) with the instantaneous base line (IBL) of the conjugate image points (CIPs). Afterward, a complementary rotation is applied in order to parallelize all the ACELs throughout the stereo imagery. The new estimated attitude parameters are evaluated based on the direction of the IBL and the ACELs. Due to the spatial and temporal variability of the IBL (respectively changes in column and row numbers of the CIPs) and nonparallel nature of the epipolar lines in the stereo linear images, some polynomials in the both column and row numbers of the CIPs are used to model new attitude parameters. As the instantaneous position of sensors remains fix, the digital elevation model (DEM) of the area of interest is not required in the resampling process. According to the experimental results obtained from two pairs of SPOT and RapidEye stereo imagery with a high elevation relief, the average absolute values of remained vertical parallaxes of CIPs in the normalized images were obtained 0.19 and 0.28 pixels respectively, which confirm the high accuracy and applicability of the proposed method.
Lumped parameter modeling of a two-phase thermal-hydraulic channel with interface tracking
International Nuclear Information System (INIS)
Jo, J.H.; Kaufman, J.M.; Ruger, C.J.; Stein, S.
1978-01-01
A nonhomogenous, thermal nonequilibrium model for one-dimensional two-phase flow in a heated channel has been formulated in lumped parameter form. The channel is divided into a variable number of flow regimes separated by moving interfaces. The model can be used to predict the behavior of a LWR core and both primary and secondary sides of a steam generator under transient conditions. (author)
He, L.; Chen, J. M.; Liu, J.; Mo, G.; Zhen, T.; Chen, B.; Wang, R.; Arain, M.
2013-12-01
Terrestrial ecosystem models have been widely used to simulate carbon, water and energy fluxes and climate-ecosystem interactions. In these models, some vegetation and soil parameters are determined based on limited studies from literatures without consideration of their seasonal variations. Data assimilation (DA) provides an effective way to optimize these parameters at different time scales . In this study, an ensemble Kalman filter (EnKF) is developed and applied to optimize two key parameters of an ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS): (1) the maximum photosynthetic carboxylation rate (Vcmax) at 25 °C, and (2) the soil water stress factor (fw) for stomatal conductance formulation. These parameters are optimized through assimilating observations of gross primary productivity (GPP) and latent heat (LE) fluxes measured in a 74 year-old pine forest, which is part of the Turkey Point Flux Station's age-sequence sites. Vcmax is related to leaf nitrogen concentration and varies slowly over the season and from year to year. In contrast, fw varies rapidly in response to soil moisture dynamics in the root-zone. Earlier studies suggested that DA of vegetation parameters at daily time steps leads to Vcmax values that are unrealistic. To overcome the problem, we developed a three-step scheme to optimize Vcmax and fw. First, the EnKF is applied daily to obtain precursor estimates of Vcmax and fw. Then Vcmax is optimized at different time scales assuming fw is unchanged from first step. The best temporal period or window size is then determined by analyzing the magnitude of the minimized cost-function, and the coefficient of determination (R2) and Root-mean-square deviation (RMSE) of GPP and LE between simulation and observation. Finally, the daily fw value is optimized for rain free days corresponding to the Vcmax curve from the best window size. The optimized fw is then used to model its relationship with soil moisture. We found that
Radiation doses in mammography as planning parameters for premature breast cancer tracking programs
International Nuclear Information System (INIS)
Souza Ferreira, Rubemar de.
1994-01-01
Radiation doses are the main parameters applied to the evaluation of mammographic radiological impact. This study, for a sample of 407 women, were analyzed, through the thermoluminescent dosimetry, radiation doses in the surface of skin and glandular absorbed doses for cranio-caudal view. The results show the presence of a large dose range to the same mammographic procedure, which, analyzed enclosed with 585 facilities, suggest be necessary the standardization of the mammographic technique. From that results, with the additive model, the excess of breast cancer (radioinduced) and lifetime loss risk, for age groups between 30 and 70 years were estimated. Is demonstrated that the benefits from dedicated mammography, overcome the relationship among the epidemiological aspects of breast cancer and ionizing radiation as an harmful agent, which may show an important correlation for large exposed populations, point out the importance of the continuous risk and benefit evaluation to the new technologies introduced. (author). 86 refs., 40 figs., 14 tabs
Discovery of a new ECE parameter affecting the response of polymer track detectors
International Nuclear Information System (INIS)
Sohrabi, M.; Katouzi, M.
1993-01-01
The pressure applied to the electrochemical etching (ECE) chamber system and in turn to the rubber washers holding a detector tight in place was discovered to be a new parameter in ECE having a direct effect on internal heating and thus on the detector's response. The type, material, shape and size of the washers showed significant effects on the detector's response. Special pressure ECE (PECE) chambers with measurable and reproducible pressure were designed, constructed and used in this study. The effects observed seem to be due to forced vibrations of the detector in an electric field the degree of which depends on the pressure applied and stretching the detectors, like winding the strings of a musical instrument. The results of the above studies are presented and discussed. (author)
CATS: a cellular automaton for tracking in silicon for the HERA-B vertex detector
International Nuclear Information System (INIS)
Abt, I.; Emeliyanov, D.; Kisel, I.; Masciocchi, S.
2002-01-01
The new track reconstruction program CATS developed for the Vertex Detector System of the HERA-B experiment at DESY is presented. It employs a cellular automaton for track searching and the Kalman filter for track fitting. This results in a very fast algorithm that combines highly efficient track recognition with accurate and reliable track parameter estimation. To reduce the computational cost of the fit an optimized numerical implementation of the Kalman filter is used. Alternative approaches to the track reconstruction in the VDS are also discussed. Since 1999, after extensive tests on simulated data, CATS has been employed to reconstruct experimental data collected in HERA-B. Results regarding tracking performance, the accuracy of track parameter estimates and CPU time consumption are presented
A new parameter in the electrochemical etching of polymer track detectors
International Nuclear Information System (INIS)
Sohrabi, M.; Katouzi, M.
1993-01-01
It was discovered that the pressure applied to the electrochemical etching (ECE) chamber system and in turn to washers holding the detector tight in place between two semi-chambers has a direct effect on the internal heating and time to breakdown of the polymer detector. The effect was found to be dependent on the type, material, shape and size of the washers holding the detector in place under pressure. To verify such parameters, a pressure ECE chamber (PECE) with measurable and reproducible pressure was designed and constructed. Three types of rubber washers, such as ''O'' rings, flat rings and sheets as well as polycarbonate (PC) detectors glued directly between two semi-syringes, were used. Flat rubber sheets were shown to have relatively minor effects on the internal heating rate and are recommended. The effect seems to be due to forced vibrations of the detector under an electric field, the frequency of which depends on the degree to which the detector is stretched under pressure, like winding the strings of a musical instrument. The results of the above studies are presented and discussed. (orig.)
International Nuclear Information System (INIS)
Kao, Jim; Flicker, Dawn; Ide, Kayo; Ghil, Michael
2006-01-01
This paper builds upon our recent data assimilation work with the extended Kalman filter (EKF) method [J. Kao, D. Flicker, R. Henninger, S. Frey, M. Ghil, K. Ide, Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys. 196 (2004) 705-723.]. The purpose is to test the capability of EKF in optimizing a model's physical parameters. The problem is to simulate the evolution of a shock produced through a high-speed flyer plate. In the earlier work, we have showed that the EKF allows one to estimate the evolving state of the shock wave from a single pressure measurement, assuming that all model parameters are known. In the present paper, we show that imperfectly known model parameters can also be estimated accordingly, along with the evolving model state, from the same single measurement. The model parameter optimization using the EKF can be achieved through a simple modification of the original EKF formalism by including the model parameters into an augmented state variable vector. While the regular state variables are governed by both deterministic and stochastic forcing mechanisms, the parameters are only subject to the latter. The optimally estimated model parameters are thus obtained through a unified assimilation operation. We show that improving the accuracy of the model parameters also improves the state estimate. The time variation of the optimized model parameters results from blending the data and the corresponding values generated from the model and lies within a small range, of less than 2%, from the parameter values of the original model. The solution computed with the optimized parameters performs considerably better and has a smaller total variance than its counterpart using the original time-constant parameters. These results indicate that the model parameters play a dominant role in the performance of the shock-wave hydrodynamic code at hand
Gulel, Okan; Akcay, Murat; Soylu, Korhan; Aksan, Gokhan; Yuksel, Serkan; Zengin, Halit; Meric, Murat; Sahin, Mahmut
2016-05-01
The coronary slow flow phenomenon (CSFP) is defined as a delayed distal vessel contrast opacification in the absence of obstructive epicardial coronary artery disease during coronary angiography. There is conflicting data in medical literature regarding the effects of CSFP on the left ventricular functions assessed by conventional echocardiography or tissue Doppler imaging. Therefore, we aimed to evaluate whether there is any abnormality in the myocardial deformation parameters (strain, strain rate (SR), rotation, twist) of the left ventricle obtained by speckle tracking echocardiography (STE) in patients with CSFP. Twenty patients with CSFP were included prospectively in the study. Another 20 patients with similar demographics and cardiovascular risk factors as well as normal coronary angiography were used as the control group. Two-dimensional echocardiographic images of the left ventricle from the apical long-axis, two-chamber, four-chamber, and parasternal short-axis views were used for STE analysis. The analysis of left ventricular circumferential deformation parameters showed that the averaged peak systolic strain, systolic SR, and early diastolic SR values were significantly lower in patients with CSFP (P = 0.009, P = 0.02, and P = 0.02, respectively). Among the left ventricular rotation and twist values, apical rotation was significantly lower in patients with CSFP (P = 0.02). Further, the mean thrombolysis in myocardial infarction frame count value was found to be negatively correlated with the averaged peak circumferential early diastolic SR (r = -0.35, P = 0.03). It was positively correlated with the averaged peak circumferential systolic strain (r = 0.47, P = 0.003) and circumferential systolic SR (r = 0.46, P = 0.005). Coronary slow flow phenomenon leads to significant alterations in the myocardial deformation parameters of the left ventricle as assessed by STE. Specifically, circumferential deformation parameters are affected in CSFP patients. © 2015
NONLINEAR ESTIMATION METHODS FOR AUTONOMOUS TRACKED VEHICLE WITH SLIP
Institute of Scientific and Technical Information of China (English)
ZHOU Bo; HAN Jianda
2007-01-01
In order to achieve precise, robust autonomous guidance and control of a tracked vehicle, a kinematic model with longitudinal and lateral slip is established. Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly. The first filter is the well-known extended Kalman filter. The second filter is an unscented version of the Kalman filter. The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution. The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies. The four different approaches have different complexities, behavior and advantages that are surveyed and compared.
International Nuclear Information System (INIS)
Goodhead, D.T.
1989-01-01
The Katz track-model of cell inactivation has been more successful than any other biophysical model in fitting and predicting inactivation of mammalian cells exposed to a wide variety of ionising radiations. Although the model was developed as a parameterised phenomenological description, without necessarily implying any particular mechanistic processes, the present analysis attempts to interpret it and thereby benefit further from its success to date. A literal interpretation of the parameters leads to contradictions with other experimental and theoretical information, especially since the fitted parameters imply very large (> ∼ 4 μm) subcellular sensitive sites which each require very large amounts (> ∼ 100 keV) of energy deposition in order to be inactivated. Comparisons of these fits with those for cell mutation suggest a re-interpretation in terms of (1) very much smaller sites and (2) a clearer distinction between the ion-kill and γ-kill modes of inactivation. It is suggested that this re-interpretation may be able to guide future development of the phenomenological Katz model and also parameterisation of mechanistic biophysical models. (author)
Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan
2015-01-01
Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.
International Nuclear Information System (INIS)
Zhang, Xu; Wang, Yujie; Yang, Duo; Chen, Zonghai
2016-01-01
Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and single cell is put forward to analyze the energy efficiency influenced by battery inconsistency, then a lumped parameter battery model is built up to describe the dynamic behavior of battery pack. Furthermore, the extend Kalman filter-unscented Kalman filter algorithm is developed to identify the parameters of battery pack and forecast state-of-charge concurrently. The extend Kalman filter is applied to update the battery pack parameters by real-time measured data, while the unscented Kalman filter is employed to estimate the battery pack state-of-charge. Finally, the proposed approach is verified by experiments operated on the lithium-ion battery under constant current condition and the dynamic stress test profiles. Experimental results indicate that the proposed method can estimate the battery pack state-of-charge with high accuracy. - Highlights: • A novel space state equation is built to describe the pack dynamic behavior. • The dual filters method is used to estimate the pack state-of-charge. • Battery inconsistency is considered to analyze the pack usage efficiency. • The accuracy of the proposed method is verified under different conditions.
Wang, Licheng; Wang, Zidong; Han, Qing-Long; Wei, Guoliang
2018-03-01
This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.
Directory of Open Access Journals (Sweden)
Xixiang Liu
2014-01-01
Full Text Available In the initial alignment process of strapdown inertial navigation system (SINS, large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles.
Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.
2017-08-01
The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.
Amoraal, Jan Mennis; Hulsbergen, W D
2011-01-01
The LHC at CERN provides a new testing ground for the Standard Model of elementary particles as well an opportunity to further explore the mysteries and expand our knowledge of Nature. The Standard Model is a theory based on experimental observations of particle interactions at collider experiments and at cosmic ray experiments. Despite the successes of the Standard Model it does not provide a complete picture of the beautiful intricately woven tapestry called Nature. To further explore the finest threads of this tapestry and to validate or exclude the Standard Model or extensions thereof, so-called New Physics Models, particle physicists at the LHC have built precision instruments to measure fundamental parameters, which may reveal New Physics, with the highest possible precision. A case in point is the weak mixing phase s, a key measurement of the LHCb experiment, which can be accessed via $B^{0}_{s} \\to J/\\psi\\phi$ decays. According to the Standard Model this phase is expected to small, approximately $\\phi...
Willemink, Martin J.; Leiner, Tim; Budde, Ricardo P. J.; de Kort, Freek P. L.; Vliegenthart, Rozemarijn; van Ooijen, Peter M. A.; Oudkerk, Matthijs; de Jong, Pim A.
2012-01-01
OBJECTIVE. Iterative reconstruction potentially can reduce radiation dose compared with filtered back projection (FBP) for chest CT. This is especially important for repeated CT scanning, as is the case in patients with indeterminate lung nodules. It is currently unknown whether absolute nodule
Directory of Open Access Journals (Sweden)
Bizhong Xia
2017-12-01
Full Text Available State of charge (SOC estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.
Effect of cross-correlation on track-to-track fusion
Saha, Rajat K.
1994-07-01
Since the advent of target tracking systems employing a diverse mixture of sensors, there has been increasing recognition by air defense system planners and other military system analysts of the need to integrate these tracks so that a clear air picture can be obtained in a command center. A popular methodology to achieve this goal is to perform track-to-track fusion, which performs track-to-track association as well as kinematic state vector fusion. This paper seeks to answer analytically the extent of improvement achievable by means of kinetic state vector fusion when the tracks are obtained from dissimilar sensors (e.g., Radar/ESM/IRST/IFF). It is well known that evaluation of the performance of state vector fusion algorithms at steady state must take into account the effects of cross-correlation between eligible tracks introduced by the input noise which, unfortunately, is often neglected because of added computational complexity. In this paper, an expression for the steady-state cross-covariance matrix for a 2D state vector track-to-track fusion is obtained. This matrix is shown to be a function of the parameters of the Kalman filters associated with the candidate tracks being fused. Conditions for positive definiteness of the cross-covariance matrix have been derived and the effect of positive definiteness on performance of track-to-track fusion is also discussed.
Directory of Open Access Journals (Sweden)
Ignacio Pérez Abril
2012-07-01
Full Text Available Los filtros de armónicos cumplen la función de evitar la circulación de las corrientes de armónico por el sistema y reducir la distorsión de la tensión. Estos pueden ser pasivos (compuestos por arreglos de impedancias o activos(basados en electrónica de potencia. Las características de los filtros pasivos pueden encontrarse en la bibliografía especializada. Sin embargo, las ecuaciones para el diseño de los mismos no se muestran en todos los casos, lo que dificulta el cálculo de sus componentes y de su estrés en condiciones de operación. El objetivo fundamental de este trabajo es desarrollar el procedimiento general para el cálculo de los filtros pasivos de armónicos y determinar las ecuaciones correspondientes a los distintos tipos de filtro. Además, se describe una aplicación en Matlab que calcula los parámetros R, L y C de los distintos tipos de filtro y evalúa el estrés a que se someten los componentes de los mismos. The purpose of the harmonic filters in the electrical power systems is the avoiding the harmonic currents circulation in the network and the reduction of the voltages waveform distortion. The harmonic filters can be of passive type (composite of impedances or active type (based on power electronic. The characteristics of passive filters can be found in the specialized bibliography. However, the equations for the filter design are not showed in all cases, which difficult the filter’s components calculation and the evaluation of its stress in operation conditions. The objective of thepresented work is the developing of a general procedure for the harmonic passive filters parameters calculation and the determination of the needed equations for each type of filter. Besides, a Matlab application that calculates the R, L and C parameters, and the stress of all the treated filters is showed.
Directory of Open Access Journals (Sweden)
Karl Friston
2010-01-01
Full Text Available We describe a Bayesian filtering scheme for nonlinear state-space models in continuous time. This scheme is called Generalised Filtering and furnishes posterior (conditional densities on hidden states and unknown parameters generating observed data. Crucially, the scheme operates online, assimilating data to optimize the conditional density on time-varying states and time-invariant parameters. In contrast to Kalman and Particle smoothing, Generalised Filtering does not require a backwards pass. In contrast to variational schemes, it does not assume conditional independence between the states and parameters. Generalised Filtering optimises the conditional density with respect to a free-energy bound on the model's log-evidence. This optimisation uses the generalised motion of hidden states and parameters, under the prior assumption that the motion of the parameters is small. We describe the scheme, present comparative evaluations with a fixed-form variational version, and conclude with an illustrative application to a nonlinear state-space model of brain imaging time-series.
International Nuclear Information System (INIS)
Souza, Anderson Sorgatti de
2017-01-01
Teletherapy, radiation therapy with linear accelerators, for cancer treatment has being used for years with good clinical results.Since the 90's the removal of the flattening filter, item placed at the gantry of the machine, has shown better results for the treatment of some cancers thus being extensively studied. Treatments with Intensity Modulated Radiotherapy (IMRT) and Stereotaxic Radiotherapy (SRT) were more efficient without the flattening filter. Varian Oncology released the TrueBeam in 2012, a accelerator capable of operating with or without the flattening filter. The aim of this work is to access homogeneity of the percentage depth dose (PDP) and beam quality index (TPR20/10), two important parameters used in patient dose calculations. The data used for analysis were obtained with the Israelita Albert Einstein Hospital (HIAE), Real Portugues Hospital (RHP) and 3 more institutions located in the United States. The statistical data analysis allowed to observe the parameters behaviors. In general, they were very homogeneous, with errors smaller than 1% confirming the conformance of the TrueBeam accelerators. (author)
Directory of Open Access Journals (Sweden)
Martha C. Guzmán-Zapata
2013-11-01
Full Text Available This paper considers the edges and contrasts obtained with high-pass filters used in the estimation of daytime atmospheric visibility from digital images, and the behavior of these edges and contrasts is characterized by varying the parameters of high-pass filters such as the Ideal, Gaussian, and Homomorphic-Gaussian. A synthetic image of regions with different contrasts is used to apply different filters, then, we define an index to measure the quality of the edges obtained in the filtered image and it is used to analyze the results. The results show that both, the filter selection and the selection of its parameters: affects the characteristics and quality of the detected edges in the filtered image, also determine the amount of noise that the filter added to the image (artifacts that were not present in the original image, and also establish if achieved, or not, the edge detection. The results also show that the edge quality index reaches maximum values at certain combinations of the filters parameters, which means that some combinations of parameters reduce situations distorting the edges and distorting atmospheric visibility measures based on the Fourier transform. So these parameters which provide maximum quality edges are established as suitable for use in visibility measurement.
DEFF Research Database (Denmark)
Sørensen, Jacob Viborg Tornfeldt; Madsen, Henrik; Madsen, H.
2006-01-01
In applications of data assimilation algorithms, a number of poorly known assimilation parameters usually need to be specified. Hence, the documented success of data assimilation methodologies must rely on a moderate sensitivity to these parameters. This contribution presents a parameter sensitiv...
Track reconstruction principle in ALICE for LHC run I and run II
Maire, Antonin
2011-01-01
Principles of tracking for an ALICE event, showing the three successive paths allowing to build a track and refine its parameters. Numbers ranging from 1 to 10 mention the bits that are activated in case of success during the propgation of the Kalman filter at the considered stage.
DEFF Research Database (Denmark)
Grzanka, Leszek; Waligórski, M. P. R.; Bassler, Niels
different sets of data obtained for the same cell line and different ions, measured at different laboratories, we have fitted model parameters to a set of carbon-irradiated V79 cells, published by Furusawa et al. (2), and to a set of proton-irradiated V79 cells, published by Wouters et al. (3), separately....... We found that values of model parameters best fitted to the carbon data of Furusawa et al. yielded predictions of V79 survival after proton irradiation which did not match the V79 proton data of Wouters et al. Fitting parameters to both sets combined did not improve the accuracy of model predictions...... carbon irradiation. 1. Katz, R., Track structure in radiobiology and in radiation detection. Nuclear Track Detection 2: 1-28 (1978). 2. Furusawa Y. et al. Inactivation of aerobic and hypoxic cells from three different cell lines by accelerated 3He-, 12C- and 20Ne beams. Radiat Res. 2012 Jan; 177...
Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior
Popovici, Alexandru; Zaal, Peter M. T.; Pool, Daan M.
2017-01-01
A Dual Extended Kalman Filter was implemented for the identification of time-varying human manual control behavior. Two filters that run concurrently were used, a state filter that estimates the equalization dynamics, and a parameter filter that estimates the neuromuscular parameters and time delay. Time-varying parameters were modeled as a random walk. The filter successfully estimated time-varying human control behavior in both simulated and experimental data. Simple guidelines are proposed for the tuning of the process and measurement covariance matrices and the initial parameter estimates. The tuning was performed on simulation data, and when applied on experimental data, only an increase in measurement process noise power was required in order for the filter to converge and estimate all parameters. A sensitivity analysis to initial parameter estimates showed that the filter is more sensitive to poor initial choices of neuromuscular parameters than equalization parameters, and bad choices for initial parameters can result in divergence, slow convergence, or parameter estimates that do not have a real physical interpretation. The promising results when applied to experimental data, together with its simple tuning and low dimension of the state-space, make the use of the Dual Extended Kalman Filter a viable option for identifying time-varying human control parameters in manual tracking tasks, which could be used in real-time human state monitoring and adaptive human-vehicle haptic interfaces.
Shea, M.; Alin, S. R.; Evans, W.; Sutton, A.; Hales, B. R.; Newton, J.; Feely, R. A.
2016-12-01
In 2007 to 2008, U.S. Pacific Northwest shellfish hatcheries experienced unprecedented larval mortality, attributed to upwelling along the Washington-Oregon coast that brought seawater enriched in anthropogenic CO2 and undersaturated with respect to aragonite to the surface. In response, several hatcheries have been outfitted with land-based analyzers to measure CO2 partial pressure (pCO2) and total dissolved CO2 (TCO2) through U.S. IOOS and NOAA OAP funding. This analyzer, developed at Oregon State University and known as the `Burke-O-Lator,' allows users to track CO2 system parameters in real-time. The data are available in near real-time on the IOOS Pacific Region Ocean Acidification (IPACOA) data portal, which feeds to the Global Ocean Acidification Observing Network (GOA-ON). Here, we explore the broader use of this system as an environmental monitoring tool. Most of the high-quality OA time-series locations in GOA-ON are in the open and coastal ocean, yet many areas of biological interest—such as shellfish hatcheries, shellfish farms, and coastal laboratories—are in the nearshore area of the coastal zone. A truly globally integrated assessment of OA must include nearshore conditions, which have been shown to be quite different in terms of variability, drivers, and range. We evaluated two pCO2 time-series from the coastal nearshore: the Taylor Shellfish Hatchery Burke-O-Lator system on the shore of Dabob Bay in Puget Sound, WA, and the nearby but offshore Dabob ORCA buoy MAPCO2 system within the bay. Preliminary comparison of three years of data reveals similar patterns despite differences in location and seawater intake depth, highlighting the opportunity for the addition of coupled nearshore biology and biogeochemistry measurements in GOA-ON. In addition, the well-calibrated, dual-parameter nature of the system is important for constraining nearshore chemistry, as biology, groundwater, and river inputs can lead to strong variability in carbonate
Particle filter in vision tracking
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Erik Cuevas J.
2007-01-01
Full Text Available El filtro de Kalman ha sido usado exitosamente en diferentes aplicaciones de predicción o determinación del estado de un sistema. Un campo importante en la visión por computadora es el seguimiento de objetos. Diferentes tipos de movimiento así como el ocultamiento de objetos a seguir pueden obstaculizar la labor de seguimiento. En este trabajo, se presenta el uso del filtro de Kalman para el seguimiento de objetos. Además se considera tanto la capacidad del filtro para tolerar pequeños ocultamientos del objeto así como el uso del filtro de Kalman extendido para el modelaje de movimientos complejos.
Vachálek, Ján
2011-12-01
The paper compares the abilities of forgetting methods to track time varying parameters of two different simulated models with different types of excitation. The observed parameters in the simulations are the integral sum of the Euclidean norm, deviation of the parameter estimates from their true values and a selected band prediction error count. As supplementary information, we observe the eigenvalues of the covariance matrix. In the paper we used a modified method of Regularized Exponential Forgetting with Alternative Covariance Matrix (REFACM) along with Directional Forgetting (DF) and three standard regularized methods.
Akbarnejad, Shahin; Saffari Pour, Mohsen; Jonsson, Lage Tord Ingemar; Jönsson, Pӓr Göran
2017-02-01
Ceramic foam filters (CFFs) are used to remove solid particles and inclusions from molten metal. In general, molten metal which is poured on the top of a CFF needs to reach a certain height to build the required pressure (metal head) to prime the filter. To estimate the required metal head, it is necessary to obtain permeability coefficients using permeametry experiments. It has been mentioned in the literature that to avoid fluid bypassing, during permeametry, samples need to be sealed. However, the effect of fluid bypassing on the experimentally obtained pressure gradients seems not to be explored. Therefore, in this research, the focus was on studying the effect of fluid bypassing on the experimentally obtained pressure gradients as well as the empirically obtained Darcy and non-Darcy permeability coefficients. Specifically, the aim of the research was to investigate the effect of fluid bypassing on the liquid permeability of 30, 50, and 80 pores per inch (PPI) commercial alumina CFFs. In addition, the experimental data were compared to the numerically modeled findings. Both studies showed that no sealing results in extremely poor estimates of the pressure gradients and Darcy and non-Darcy permeability coefficients for all studied filters. The average deviations between the pressure gradients of the sealed and unsealed 30, 50, and 80 PPI samples were calculated to be 57.2, 56.8, and 61.3 pct. The deviations between the Darcy coefficients of the sealed and unsealed 30, 50, and 80 PPI samples found to be 9, 20, and 31 pct. The deviations between the non-Darcy coefficients of the sealed and unsealed 30, 50, and 80 PPI samples were calculated to be 59, 58, and 63 pct.
International Nuclear Information System (INIS)
Kharrati, Hedi; Zarrad, Boubaker
2003-01-01
A computer program was implemented to predict mammography x-ray beam parameters in the range 20-40 kV for Mo/Mo, Mo/Rh, Rh/Rh, and W/Al target/filter combinations. The computation method used to simulate mammography x-ray spectra is based on the Boone et al. model. The beam quality parameters such as the half-value layer (HVL), the homogeneity coefficient (HC), and the average photon energy were computed by simulating the interaction of the spectrum photons with matter. The checking of this computation was done using a comparison of the results with published data and measured values obtained at the Netherlands Metrology Institute Van Swinden Laboratorium, National Institute of Standards and Technology, and International Atomic Energy Agency. The predicted values with a mean deviation of 3.3% of HVL, 3.7% of HC, and 1.5% of average photon energy show acceptable agreement with published data and measurements for all target/filter combinations in the 23-40 kV range. The accuracy of this computation can be considered clinically acceptable and can allow an appreciable estimation for the beam quality parameters
Directory of Open Access Journals (Sweden)
Ngoc-Tham Tran
2017-01-01
Full Text Available State of charge (SOC and state of health (SOH are key issues for the application of batteries, especially the absorbent glass mat valve regulated lead-acid (AGM VRLA type batteries used in the idle stop start systems (ISSs that are popularly integrated into conventional engine-based vehicles. This is due to the fact that SOC and SOH estimation accuracy is crucial for optimizing battery energy utilization, ensuring safety and extending battery life cycles. The dual extended Kalman filter (DEKF, which provides an elegant and powerful solution, is widely applied in SOC and SOH estimation based on a battery parameter model. However, the battery parameters are strongly dependent on operation conditions such as the SOC, current rate and temperature. In addition, battery parameters change significantly over the life cycle of a battery. As a result, many experimental pretests investigating the effects of the internal and external conditions of a battery on its parameters are required, since the accuracy of state estimation depends on the quality of the information regarding battery parameter changes. In this paper, a novel method for SOC and SOH estimation that combines a DEKF algorithm, which considers hysteresis and diffusion effects, and an auto regressive exogenous (ARX model for online parameters estimation is proposed. The DEKF provides precise information concerning the battery open circuit voltage (OCV to the ARX model. Meanwhile, the ARX model continues monitoring parameter variations and supplies information on them to the DEKF. In this way, the estimation accuracy can be maintained despite the changing parameters of a battery. Moreover, online parameter estimation from the ARX model can save the time and effort used for parameter pretests. The validation of the proposed algorithm is given by simulation and experimental results.
Selection vector filter framework
Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.
2003-10-01
We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.
CSIR Research Space (South Africa)
Salmon
2012-07-01
Full Text Available stream_source_info Salmon1_2012_ABSTRACT ONLY.pdf.txt stream_content_type text/plain stream_size 1654 Content-Encoding ISO-8859-1 stream_name Salmon1_2012_ABSTRACT ONLY.pdf.txt Content-Type text/plain; charset=ISO-8859...-1 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012 A search algorithm to meta-optimize the parameters for an extended Kalman filter to improve classification on hyper-temporal images yzB.P. Salmon, yz...
International Nuclear Information System (INIS)
Abu-Jarad, F.; Islam, M.A.; Que, A.
1991-01-01
An Automated Interactive Built Analysis System (IBAS) is being used to analyze the alpha and fission fragment tracks in CR-39 and Makrofol E detectors. It primarily consists of a control computer (IBAS1) and an image processor (IBAS2). The system is used for counting the nuclear tracks, measuring their areas, diameters, statistics and displaying the frequency distribution of their sizes. All information can be printed out or stored on floppy disks. (author)
Directory of Open Access Journals (Sweden)
Uwe Schneider
εgeo and ε are 0.10 and 0.71. For the linker-DNA εgeo and ε for randomly distributed hits are 0.010 and 0.073, and for hits on rays 0.0058 and 0.041, respectively. The calculated ε fits the experimentally obtained ε = 0.64±0.32 best for hits on the tetranucleosome when they are close to each other both, for high and low energy electrons.The parameter εgeo of the track event model was obtained by pure geometrical considerations of the chromatin structure and is 0.095 ± 0.022. It can be used as a fixed parameter in the track-event theory.
Barber, Jared; Tanase, Roxana; Yotov, Ivan
2016-06-01
Several Kalman filter algorithms are presented for data assimilation and parameter estimation for a nonlinear diffusion model of epithelial cell migration. These include the ensemble Kalman filter with Monte Carlo sampling and a stochastic collocation (SC) Kalman filter with structured sampling. Further, two types of noise are considered -uncorrelated noise resulting in one stochastic dimension for each element of the spatial grid and correlated noise parameterized by the Karhunen-Loeve (KL) expansion resulting in one stochastic dimension for each KL term. The efficiency and accuracy of the four methods are investigated for two cases with synthetic data with and without noise, as well as data from a laboratory experiment. While it is observed that all algorithms perform reasonably well in matching the target solution and estimating the diffusion coefficient and the growth rate, it is illustrated that the algorithms that employ SC and KL expansion are computationally more efficient, as they require fewer ensemble members for comparable accuracy. In the case of SC methods, this is due to improved approximation in stochastic space compared to Monte Carlo sampling. In the case of KL methods, the parameterization of the noise results in a stochastic space of smaller dimension. The most efficient method is the one combining SC and KL expansion. Copyright © 2016 Elsevier Inc. All rights reserved.
Track reconstruction algorithms for the CBM experiment at FAIR
International Nuclear Information System (INIS)
Lebedev, Andrey; Hoehne, Claudia; Kisel, Ivan; Ososkov, Gennady
2010-01-01
The Compressed Baryonic Matter (CBM) experiment at the future FAIR accelerator complex at Darmstadt is being designed for a comprehensive measurement of hadron and lepton production in heavy-ion collisions from 8-45 AGeV beam energy, producing events with large track multiplicity and high hit density. The setup consists of several detectors including as tracking detectors the silicon tracking system (STS), the muon detector (MUCH) or alternatively a set of Transition Radiation Detectors (TRD). In this contribution, the status of the track reconstruction software including track finding, fitting and propagation is presented for the MUCH and TRD detectors. The track propagation algorithm takes into account an inhomogeneous magnetic field and includes accurate calculation of multiple scattering and energy losses in the detector material. Track parameters and covariance matrices are estimated using the Kalman filter method and a Kalman filter modification by assigning weights to hits and using simulated annealing. Three different track finder algorithms based on track following have been developed which either allow for track branches, just select nearest hits or use the mentioned weighting method. The track reconstruction efficiency for central Au+Au collisions at 25 AGeV beam energy using events from the UrQMD model is at the level of 93-95% for both detectors.
A comparative study of track reconstruction methods in the context of CMS physics
International Nuclear Information System (INIS)
Winkler, M.
2002-05-01
This thesis deals mainly with the problem of reconstructing the tracks of charged particles from the measurements recorded by the CMS Tracker, in particular with track fitting. The task of the track fit is to use the available information (the subsets of hits belonging to the track of a particle found by a track finder) in a statistically optimal way for the estimation of the track parameters and the associated errors. Due to the high track multiplicity at LHC and the large background of noise hits, combinatorial methods such as the Kalman filter may have problems in the association of hits to tracks, and their results will be biased in the case of wrong hits assigned to a track. Recently developped non-linear, adaptive methods such as the deterministic annealing filter and the multi track filter try to solve the assignment problem during the track fit, by determining an assignment probability of a hit to a track. The methods are studied in the CMS Tracker using various event topologies. (author)
A Novel Generic Framework for Track Fitting in Complex Detector Systems
Höppner, C.; Neubert, S.; Ketzer, B.; Paul, S.
2009-01-01
This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement. This goal is achieved through a completely modular design. Fitting algorithms are implemented as interchangeable modules. At present, the framework contains a validated Kalman filter. Track parameterizations and the routines required to extrapolate the track parameters and their covariance matrices th...
López-Cruz, I.L.; Beveren, Van P.J.M.; Mourik, Van S.; Henten, Van E.J.
2017-01-01
In dynamic modeling of the greenhouse climate, prediction errors are a significant issue due to uncertainties in initial state values, input variables, model parameters and model structure, all propagating in time in a nonlinear way. We investigated a data assimilation approach using two non-linear
Júnez-Ferreira, H E; Herrera, G S; González-Hita, L; Cardona, A; Mora-Rodríguez, J
2016-01-01
A new method for the optimal design of groundwater quality monitoring networks is introduced in this paper. Various indicator parameters were considered simultaneously and tested for the Irapuato-Valle aquifer in Mexico. The steps followed in the design were (1) establishment of the monitoring network objectives, (2) definition of a groundwater quality conceptual model for the study area, (3) selection of the parameters to be sampled, and (4) selection of a monitoring network by choosing the well positions that minimize the estimate error variance of the selected indicator parameters. Equal weight for each parameter was given to most of the aquifer positions and a higher weight to priority zones. The objective for the monitoring network in the specific application was to obtain a general reconnaissance of the water quality, including water types, water origin, and first indications of contamination. Water quality indicator parameters were chosen in accordance with this objective, and for the selection of the optimal monitoring sites, it was sought to obtain a low-uncertainty estimate of these parameters for the entire aquifer and with more certainty in priority zones. The optimal monitoring network was selected using a combination of geostatistical methods, a Kalman filter and a heuristic optimization method. Results show that when monitoring the 69 locations with higher priority order (the optimal monitoring network), the joint average standard error in the study area for all the groundwater quality parameters was approximately 90 % of the obtained with the 140 available sampling locations (the set of pilot wells). This demonstrates that an optimal design can help to reduce monitoring costs, by avoiding redundancy in data acquisition.
Vargas-Melendez, Leandro; Boada, Beatriz L; Boada, Maria Jesus L; Gauchia, Antonio; Diaz, Vicente
2017-04-29
Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33 % of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle's parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle's roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle's states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.
Target Tracking of a Linear Time Invariant System under Irregular Sampling
Directory of Open Access Journals (Sweden)
Jin Xue-Bo
2012-11-01
Full Text Available Due to event-triggered sampling in a system, or maybe with the aim of reducing data storage, tracking many applications will encounter irregular sampling time. By calculating the matrix exponential using an inverse Laplace transform, this paper transforms the irregular sampling tracking problem to the problem of tracking with time-varying parameters of a system. Using the common Kalman filter, the developed method is used to track a target for the simulated trajectory and video tracking. The results of simulation experiments have shown that it can obtain good estimation performance even at a very high irregular rate of measurement sampling time.
Directory of Open Access Journals (Sweden)
Meleiro L.A.C.
2000-01-01
Full Text Available Most advanced computer-aided control applications rely on good dynamics process models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-line identification experiments on the process. These experiments for identification often result in input-output data with small output signal-to-noise ratio, and using these data results in inaccurate model parameter estimates [1]. In this work, a multivariable adaptive self-tuning controller (STC was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN. A second approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge about the process being modeled with artificial neural networks. In this case, kinetic parameters - which are very hard to be accurately determined in real time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varying and nonlinear models. This advanced control strategy was applied to a fermentation process to produce ethyl alcohol (ethanol in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the proposed procedure in this work has a great potential for application.
Particle tracking from image sequences of complex plasma crystals
International Nuclear Information System (INIS)
Hadziavdic, Vedad; Melandsoe, Frank; Hanssen, Alfred
2006-01-01
In order to gather information about the physics of the complex plasma crystals from the experimental data, particles have to be tracked through a sequence of images. An application of the Kalman filter for that purpose is presented, using a one-dimensional approximation of the particle dynamics as a model for the filter. It is shown that Kalman filter is capable of tracking dust particles even with high levels of measurement noise. An inherent part of the Kalman filter, the innovation process, can be used to estimate values of the physical system parameters from the experimental data. The method is shown to be able to estimate the characteristic oscillation frequency from noisy data
Track-before-detect procedures for detection of extended object
Fan, Ling; Zhang, Xiaoling; Shi, Jun
2011-12-01
In this article, we present a particle filter (PF)-based track-before-detect (PF TBD) procedure for detection of extended objects whose shape is modeled by an ellipse. By incorporating of an existence variable and the target shape parameters into the state vector, the proposed algorithm performs joint estimation of the target presence/absence, trajectory and shape parameters under unknown nuisance parameters (target power and noise variance). Simulation results show that the proposed algorithm has good detection and tracking capabilities for extended objects.
Track-before-detect procedures for detection of extended object
Directory of Open Access Journals (Sweden)
Fan Ling
2011-01-01
Full Text Available Abstract In this article, we present a particle filter (PF-based track-before-detect (PF TBD procedure for detection of extended objects whose shape is modeled by an ellipse. By incorporating of an existence variable and the target shape parameters into the state vector, the proposed algorithm performs joint estimation of the target presence/absence, trajectory and shape parameters under unknown nuisance parameters (target power and noise variance. Simulation results show that the proposed algorithm has good detection and tracking capabilities for extended objects.
Directory of Open Access Journals (Sweden)
W. Ju
2010-03-01
Full Text Available Soil and atmospheric water deficits have significant influences on CO_{2} and energy exchanges between the atmosphere and terrestrial ecosystems. Model parameterization significantly affects the ability of a model to simulate carbon, water, and energy fluxes. In this study, an ensemble Kalman filter (EnKF and observations of gross primary productivity (GPP and latent heat (LE fluxes were used to optimize model parameters significantly affecting the calculation of these fluxes for a subtropical coniferous plantation in southeastern China. The optimized parameters include the maximum carboxylation rate (Vc_{max}, the slope in the modified Ball-Berry model (M and the coefficient determining the sensitivity of stomatal conductance to atmospheric water vapor deficit (D_{0}. Optimized Vc_{max} and M showed larger variations than D_{0}. Seasonal variations of Vc_{max} and M were more pronounced than the variations between the two years. Vc_{max} and M were associated with soil water content (SWC. During dry periods, SWC at the 20 cm depth explained 61% and 64% of variations of Vc_{max} and M, respectively. EnKF parameter optimization improved the simulations of GPP, LE and SH, mainly during dry periods. After parameter optimization using EnKF, the variations of GPP, LE and SH explained by the model increased by 1% to 4% at half-hourly steps and by 3% to 5% at daily time steps. Further efforts are needed to differentiate the real causes of parameter variations and improve the ability of models to describe the change of stomatal conductance with net photosynthesis rate and the sensitivity of photosynthesis capacity to soil water stress under different environmental conditions.
Energy Technology Data Exchange (ETDEWEB)
Schmidt, Björn, E-mail: bjoernschmidt1989@gmx.de [Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne (Germany); Dick, Anastasia, E-mail: anastasia-dick@web.de [Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne (Germany); Treutlein, Melanie, E-mail: melanie-treutlein@web.de [Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne (Germany); Schiller, Petra, E-mail: petra.schiller@uni-koeln.de [Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne, Kerpener Str. 62, D-50937, Cologne (Germany); Bunck, Alexander C., E-mail: alexander.bunck@uk-koeln.de [Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne (Germany); Maintz, David, E-mail: david.maintz@uk-koeln.de [Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne (Germany); Baeßler, Bettina, E-mail: bettina.baessler@uk-koeln.de [Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne (Germany)
2017-04-15
Highlights: • Left and right ventricular CMR feature tracking is highly reproducible. • The only exception is radial strain and strain rate. • Sample size estimations are presented as a practical reference for future studies. - Abstract: Objectives: To investigate the reproducibility of regional and global strain and strain rate (SR) parameters of both ventricles and to determine sample sizes for all investigated strain and SR parameters in order to generate a practical reference for future studies. Materials and methods: The study population consisted of 20 healthy individuals and 20 patients with acute myocarditis. Cine sequences in three horizontal long axis views and a stack of short axis views covering the entire left and right ventricle (LV, RV) were retrospectively analysed using a dedicated feature tracking (FT) software algorithm (TOMTEC). For intra-observer analysis, one observer analysed CMR images of all patients and volunteers twice. For inter-observer analysis, three additional blinded observers analysed the same datasets once. Intra- and inter-observer reproducibility were tested in all patients and controls using Bland-Altman analyses, intra-class correlation coefficients (ICCs) and coefficients of variation. Results: Intra-observer reproducibility of global LV strain and SR parameters was excellent (range of ICCs: 0.81–1.00), the only exception being global radial SR with a poor reproducibility (ICC 0.23). On a regional level, basal and midventricular strain and SR parameters were more reproducible when compared to apical parameters. Inter-observer reproducibility of all LV parameters was slightly lower than intra-observer reproducibility, yet still good to excellent for all global and regional longitudinal and circumferential strain and SR parameters (range of ICCs: 0.66–0.93). Similar to the LV, all global RV longitudinal and circumferential strain and SR parameters showed an excellent reproducibility, (range of ICCs: 0.75–0
International Nuclear Information System (INIS)
Schmidt, Björn; Dick, Anastasia; Treutlein, Melanie; Schiller, Petra; Bunck, Alexander C.; Maintz, David; Baeßler, Bettina
2017-01-01
Highlights: • Left and right ventricular CMR feature tracking is highly reproducible. • The only exception is radial strain and strain rate. • Sample size estimations are presented as a practical reference for future studies. - Abstract: Objectives: To investigate the reproducibility of regional and global strain and strain rate (SR) parameters of both ventricles and to determine sample sizes for all investigated strain and SR parameters in order to generate a practical reference for future studies. Materials and methods: The study population consisted of 20 healthy individuals and 20 patients with acute myocarditis. Cine sequences in three horizontal long axis views and a stack of short axis views covering the entire left and right ventricle (LV, RV) were retrospectively analysed using a dedicated feature tracking (FT) software algorithm (TOMTEC). For intra-observer analysis, one observer analysed CMR images of all patients and volunteers twice. For inter-observer analysis, three additional blinded observers analysed the same datasets once. Intra- and inter-observer reproducibility were tested in all patients and controls using Bland-Altman analyses, intra-class correlation coefficients (ICCs) and coefficients of variation. Results: Intra-observer reproducibility of global LV strain and SR parameters was excellent (range of ICCs: 0.81–1.00), the only exception being global radial SR with a poor reproducibility (ICC 0.23). On a regional level, basal and midventricular strain and SR parameters were more reproducible when compared to apical parameters. Inter-observer reproducibility of all LV parameters was slightly lower than intra-observer reproducibility, yet still good to excellent for all global and regional longitudinal and circumferential strain and SR parameters (range of ICCs: 0.66–0.93). Similar to the LV, all global RV longitudinal and circumferential strain and SR parameters showed an excellent reproducibility, (range of ICCs: 0.75–0
Exploration and extension of an improved Riemann track fitting algorithm
Strandlie, A.; Frühwirth, R.
2017-09-01
Recently, a new Riemann track fit which operates on translated and scaled measurements has been proposed. This study shows that the new Riemann fit is virtually as precise as popular approaches such as the Kalman filter or an iterative non-linear track fitting procedure, and significantly more precise than other, non-iterative circular track fitting approaches over a large range of measurement uncertainties. The fit is then extended in two directions: first, the measurements are allowed to lie on plane sensors of arbitrary orientation; second, the full error propagation from the measurements to the estimated circle parameters is computed. The covariance matrix of the estimated track parameters can therefore be computed without recourse to asymptotic properties, and is consequently valid for any number of observation. It does, however, assume normally distributed measurement errors. The calculations are validated on a simulated track sample and show excellent agreement with the theoretical expectations.
Etched track radiometers in radon measurements: a review
Nikolaev, V A
1999-01-01
Passive radon radiometers, based on alpha particle etched track detectors, are very attractive for the assessment of radon exposure. The present review considers various devices used for measurement of the volume activity of radon isotopes and their daughters and determination of equilibrium coefficients. Such devices can be classified into 8 groups: (i) open or 'bare' detectors, (ii) open chambers, (iii) sup 2 sup 2 sup 2 Rn chambers with an inlet filter, (iv) advanced sup 2 sup 2 sup 2 Rn radiometers, (v) multipurpose radiometers, (vi) radiometers based on a combination of etched track detectors and an electrostatic field, (vii) radiometers based on etched track detectors and activated charcoal and (viii) devices for the measurement of radon isotopes and/or radon daughters by means of track parameter measurements. Some of them such as the open detector and the chamber with an inlet filter have a variety of modifications and are applied widely both in geophysical research and radon dosimetric surveys. At the...
Urbano-Moral, Jose Angel; Gangadharamurthy, Dakshin; Comenzo, Raymond L; Pandian, Natesa G; Patel, Ayan R
2015-08-01
The study of myocardial mechanics has a potential role in the detection of cardiac involvement in patients with amyloidosis. This study aimed to characterize 3-dimensional-speckle tracking echocardiography-derived left and right ventricular myocardial mechanics in light chain amyloidosis and examine their relationship with brain natriuretic peptide. In patients with light chain amyloidosis, left ventricular longitudinal and circumferential strain (n=40), and right ventricular longitudinal strain and radial displacement (n=26) were obtained by 3-dimensional-speckle tracking echocardiography. Brain natriuretic peptide levels were determined. All myocardial mechanics measurements showed differences when compared by brain natriuretic peptide level tertiles. Left and right ventricular longitudinal strain were highly correlated (r=0.95, P<.001). Left ventricular longitudinal and circumferential strain were reduced in patients with cardiac involvement (-9±4 vs -16±2; P<.001, and -24±6 vs -29±4; P=.01, respectively), with the most prominent impairment at the basal segments. Right ventricular longitudinal strain and radial displacement were diminished in patients with cardiac involvement (-9±3 vs -17±3; P<.001, and 2.7±0.8 vs 3.8±0.3; P=.002). On multivariate analysis, left ventricular longitudinal strain was associated with the presence of cardiac involvement (odds ratio = 1.6; 95% confidence interval, 1.04 to 2.37; P=.03) independent of the presence of brain natriuretic peptide and troponin I criteria for cardiac amyloidosis. Three-dimensional-speckle tracking echocardiography-derived left and right ventricular myocardial mechanics are increasingly altered as brain natriuretic peptide increases in light chain amyloidosis. There appears to be a strong association between left ventricular longitudinal strain and cardiac involvement, beyond biomarkers such as brain natriuretic peptide and troponin I. Copyright © 2015 Sociedad Española de Cardiología. Published by
International Nuclear Information System (INIS)
Bhagwat, A.M.; Naik, G.R.; Thampan, S.; Rudran, K.; Joshi, V.B.; Iyer, M.R.
1992-01-01
High sensitivity of CR-39 film in turn leads to higher and variable background track-densities. A two-step etching process, each consisting of CE and ECE, is therefore suggested which permits not only partial freezing of the background but also allows to know its level. The procedure identifies bad pieces with scratches and determines the minimum detection limit (MDL) of each film individually. Activities as low as 0.2 mBq (∼ 5 x 10 -15 curies) can thus be measured with low background films for exposure periods of 7-10 days (exposure is carried out after first processing). (author)
Perspectives on Nonlinear Filtering
Law, Kody
2015-01-01
The solution to the problem of nonlinear filtering may be given either as an estimate of the signal (and ideally some measure of concentration), or as a full posterior distribution. Similarly, one may evaluate the fidelity of the filter either by its ability to track the signal or its proximity to the posterior filtering distribution. Hence, the field enjoys a lively symbiosis between probability and control theory, and there are plenty of applications which benefit from algorithmic advances, from signal processing, to econometrics, to large-scale ocean, atmosphere, and climate modeling. This talk will survey some recent theoretical results involving accurate signal tracking with noise-free (degenerate) dynamics in high-dimensions (infinite, in principle, but say d between 103 and 108 , depending on the size of your application and your computer), and high-fidelity approximations of the filtering distribution in low dimensions (say d between 1 and several 10s).
Perspectives on Nonlinear Filtering
Law, Kody
2015-01-07
The solution to the problem of nonlinear filtering may be given either as an estimate of the signal (and ideally some measure of concentration), or as a full posterior distribution. Similarly, one may evaluate the fidelity of the filter either by its ability to track the signal or its proximity to the posterior filtering distribution. Hence, the field enjoys a lively symbiosis between probability and control theory, and there are plenty of applications which benefit from algorithmic advances, from signal processing, to econometrics, to large-scale ocean, atmosphere, and climate modeling. This talk will survey some recent theoretical results involving accurate signal tracking with noise-free (degenerate) dynamics in high-dimensions (infinite, in principle, but say d between 103 and 108 , depending on the size of your application and your computer), and high-fidelity approximations of the filtering distribution in low dimensions (say d between 1 and several 10s).
Kalman Orbit Optimized Loop Tracking
Young, Lawrence E.; Meehan, Thomas K.
2011-01-01
Under certain conditions of low signal power and/or high noise, there is insufficient signal to noise ratio (SNR) to close tracking loops with individual signals on orbiting Global Navigation Satellite System (GNSS) receivers. In addition, the processing power available from flight computers is not great enough to implement a conventional ultra-tight coupling tracking loop. This work provides a method to track GNSS signals at very low SNR without the penalty of requiring very high processor throughput to calculate the loop parameters. The Kalman Orbit-Optimized Loop (KOOL) tracking approach constitutes a filter with a dynamic model and using the aggregate of information from all tracked GNSS signals to close the tracking loop for each signal. For applications where there is not a good dynamic model, such as very low orbits where atmospheric drag models may not be adequate to achieve the required accuracy, aiding from an IMU (inertial measurement unit) or other sensor will be added. The KOOL approach is based on research JPL has done to allow signal recovery from weak and scintillating signals observed during the use of GPS signals for limb sounding of the Earth s atmosphere. That approach uses the onboard PVT (position, velocity, time) solution to generate predictions for the range, range rate, and acceleration of the low-SNR signal. The low- SNR signal data are captured by a directed open loop. KOOL builds on the previous open loop tracking by including feedback and observable generation from the weak-signal channels so that the MSR receiver will continue to track and provide PVT, range, and Doppler data, even when all channels have low SNR.
Random Scenario Generation for a Multiple Target Tracking Environment Evaluation
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar
2006-01-01
, which were normally crossing targets, was to test the efficiency of the track splitting algorithm for different situations. However this approach only gives a measure of performance for a specific, possibly unrealistic, scenario and it was felt appropriate to develop procedures that would enable a more...... general performance assessment. Therefore, a random target motion scenario is adopted. Its implementation in particular for testing the track splitting algorithm using Kalman filters is used and a couple of tracking performance parameters are computed to investigate such random scenarios....
Directory of Open Access Journals (Sweden)
Hao Jin
2015-01-01
Full Text Available Steel-spring floating slab tracks are one of the most effective methods to reduce vibrations from underground railways, which has drawn more and more attention in scientific communities. In this paper, the steel-spring floating slab track located in Track Vibration Abatement and Control Laboratory was modeled with four-pole parameter method. The influences of the fastener damping ratio, the fastener stiffness, the steel-spring damping ratio, and the steel-spring stiffness were researched for the rail displacement and the foundation acceleration. Results show that the rail displacement and the foundation acceleration will decrease with the increase of the fastener stiffness or the steel-spring damping ratio. However, the rail displacement and the foundation acceleration have the opposite variation tendency for the fastener damping ratio and the steel-spring stiffness. In order to optimize the rail displacement and the foundation acceleration affected by the fastener damping ratio and the steel-spring stiffness at the same time, a multiobjective ant colony optimization (ACO was employed. Eventually, Pareto optimal frontier of the rail displacement and the foundation acceleration was derived. Furthermore, the desirable values of the fastener damping ratio and the steel-spring stiffness can be obtained according to the corresponding Pareto optimal solution set.
Vergara, H. J.; Kirstetter, P.; Hong, Y.; Gourley, J. J.; Wang, X.
2013-12-01
The Ensemble Kalman Filter (EnKF) is arguably the assimilation approach that has found the widest application in hydrologic modeling. Its relatively easy implementation and computational efficiency makes it an attractive method for research and operational purposes. However, the scientific literature featuring this approach lacks guidance on how the errors in the forecast need to be characterized so as to get the required corrections from the assimilation process. Moreover, several studies have indicated that the performance of the EnKF is 'sub-optimal' when assimilating certain hydrologic observations. Likewise, some authors have suggested that the underlying assumptions of the Kalman Filter and its dependence on linear dynamics make the EnKF unsuitable for hydrologic modeling. Such assertions are often based on ineffectiveness and poor robustness of EnKF implementations resulting from restrictive specification of error characteristics and the absence of a-priori information of error magnitudes. Therefore, understanding the capabilities and limitations of the EnKF to improve hydrologic forecasts require studying its sensitivity to the manner in which errors in the hydrologic modeling system are represented through ensembles. This study presents a methodology that explores various uncertainty representation configurations to characterize the errors in the hydrologic forecasts in a data assimilation context. The uncertainty in rainfall inputs is represented through a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which provides information about second-order statistics of quantitative precipitation estimates (QPE) error. The uncertainty in model parameters is described adding perturbations based on parameters covariance information. The method allows for the identification of rainfall and parameter perturbation combinations for which the performance of the EnKF is 'optimal' given a set of objective functions. In this process, information about
Directory of Open Access Journals (Sweden)
Yu J
2014-12-01
Full Text Available Cigarette testing regulations based on more intensive puffing conditions than standard Federal Trade Commission/International Organisation for Standardization (FTC/ISO conditions, together with intentional filter vent-blocking of cigarettes during testing, are currently required in some countries. Recently, an initial recommendation under the auspices of the Framework Convention on Tobacco Control, has called for international machine-testing of cigarettes with a 55 cc/30 s/2 s puffing regimen after 100% filter vent-blocking. While much is currently known regarding changes in smoke yields with different machine smoking parameters, a more limited understanding of potential changes in smoke composition exists. In the present work, the influence of smoking conditions on nicotine fate in a burning cigarette was studied by gas chromatography with atomic emission detection (GC-AED using core-injected nicotine-d4. Tobacco rods were injected via a syringe to a fixed length with a constant volume of a methanol solution of known concentration of deuterated nicotine. Four different puffing conditions and two different vent-blocking conditions were studied. GC with mass spectrometric detection was used to identify the deuterium-labeled compounds that gave an enhanced deuterium AED-response. A comparison of the distribution of compounds containing deuterium in the mainstream smoke, sidestream smoke, and cigarette remains (butt and ash of a full flavor cigarette brand under the four smoking conditions studied indicated that a greater percentage of labeled nicotine remained intact during the smoking process as the intensity of the puffing regimen increased. As smoking regimen intensity increased, the amounts of nicotine pyrolysis and oxidation products detected in sidestream smoke decreased, while marginal increases in these compounds were observed in mainstream smoke and in the cigarette butt. The sidestream/mainstream nicotine ratio decreased significantly
Energy Technology Data Exchange (ETDEWEB)
Blago, Michele Piero; Kar, Tamasi Rameshchandra; Schoening, Andre [Physikalisches Institut, Universitaet Heidelberg (Germany)
2016-07-01
Recent progress in pixel detector technology, for example using High Voltage-Monolithic Pixel Sensors (HV-MAPS), makes it feasible to construct an all-silicon pixel detector for large scale particle experiments like ATLAS and CMS or other future collider experiments. Preliminary studies have shown that nine layers of pixel sensors are sufficient to reliably reconstruct particle trajectories. The performance of such an all-pixel detector is studied based on a full GEANT simulation for high luminosity conditions at the upgraded LHC. Furthermore, the ability of an all-pixel detector to form trigger decisions using a special triplet pixel layer design is studied. Such a design could be used to reconstruct all tracks originating from the proton-proton interaction at the first hardware level at 40 MHz collision frequency.
Directory of Open Access Journals (Sweden)
Anindya Prawita Sari
2014-09-01
Full Text Available Air sumur merupakan air tanah yang sering kali digunakan masyarakat untuk aktivitas sehari-hari. Air sumur dengan kadar organik dan deterjen tinggi tidak layak dikonsumsi masyarakat karena dapat menyebabkan berbagai macam penyakit. Selain itu, adanya zat organik dan deterjen mempengaruhi warna dan bau air sumur sehingga tidak layak konsumsi. Slow sand filter merupakan unit pengolahan yang mampu meremoval zat organik pada air. Slow sand filter dan rapid sand filter tidak menggunakan bahan kimia dalam proses pengolahan sehingga lebih ekonomis dan efektif. Sedangkan ozon, efektif digunakan untuk meremoval zat organik yang ada dalam air dengan mengubah rantai zat organik menjadi lebih sederhana. Tujuan penelitian ini adalah untuk mengetahui keefektifan penggunaan slow sand filter, ozon generator dan rapid sand filter dalam menyisihkan beban deterjen dan zat organik pada air sumur. Hasil penelitian menunjukkan bahwa efisiensi removal pada unit slow sand filter untuk beban organik dan deterjen sebesar 57,6% dan 60,5 %, pada unit ozonasi sebesar 47,4% dan 17,5%, dan pada unit rapid sand filter sebesar 50,0% dan 50,9 %.
Application of Knowledge-Based Techniques to Tracking Function
National Research Council Canada - National Science Library
Farina, A
2006-01-01
...: historical survey of stochastic filtering theory; overview of tracking systems with some details on mono-sensor and multi-sensor tracking, evolution of filtering logics, evolution of correlation logics, and presentation of recent findings on non...
Thermal Tracking of Sports Players
DEFF Research Database (Denmark)
Gade, Rikke; Moeslund, Thomas B.
2014-01-01
We present here a real-time tracking algorithm for thermal video from a sports game. Robust detection of people includes routines for handling occlusions and noise before tracking each detected person with a Kalman filter. This online tracking algorithm is compared with a state-of-the-art offline...
Energy Technology Data Exchange (ETDEWEB)
Bowman, Wesley; Sattarivand, Mike [Department of Radiation Oncology, Dalhousie University at Nova Scotia Health Authority, Department of Radiation Oncology, Dalhousie University at Nova Scotia Health Authority (Canada)
2016-08-15
Objective: To optimize dual-energy parameters of ExacTrac stereoscopic x-ray imaging system for lung SBRT patients Methods: Simulated spectra and a lung phantom were used to optimize filter material, thickness, kVps, and weighting factors to obtain bone subtracted dual-energy images. Spektr simulations were used to identify material in the atomic number (Z) range [3–83] based on a metric defined to separate spectrums of high and low energies. Both energies used the same filter due to time constraints of image acquisition in lung SBRT imaging. A lung phantom containing bone, soft tissue, and a tumor mimicking material was imaged with filter thicknesses range [0–1] mm and kVp range [60–140]. A cost function based on contrast-to-noise-ratio of bone, soft tissue, and tumor, as well as image noise content, was defined to optimize filter thickness and kVp. Using the optimized parameters, dual-energy images of anthropomorphic Rando phantom were acquired and evaluated for bone subtraction. Imaging dose was measured with dual-energy technique using tin filtering. Results: Tin was the material of choice providing the best energy separation, non-toxicity, and non-reactiveness. The best soft-tissue-only image in the lung phantom was obtained using 0.3 mm tin and [140, 80] kVp pair. Dual-energy images of the Rando phantom had noticeable bone elimination when compared to no filtration. Dose was lower with tin filtering compared to no filtration. Conclusions: Dual-energy soft-tissue imaging is feasible using ExacTrac stereoscopic imaging system utilizing a single tin filter for both high and low energies and optimized acquisition parameters.
International Nuclear Information System (INIS)
Bowman, Wesley; Sattarivand, Mike
2016-01-01
Objective: To optimize dual-energy parameters of ExacTrac stereoscopic x-ray imaging system for lung SBRT patients Methods: Simulated spectra and a lung phantom were used to optimize filter material, thickness, kVps, and weighting factors to obtain bone subtracted dual-energy images. Spektr simulations were used to identify material in the atomic number (Z) range [3–83] based on a metric defined to separate spectrums of high and low energies. Both energies used the same filter due to time constraints of image acquisition in lung SBRT imaging. A lung phantom containing bone, soft tissue, and a tumor mimicking material was imaged with filter thicknesses range [0–1] mm and kVp range [60–140]. A cost function based on contrast-to-noise-ratio of bone, soft tissue, and tumor, as well as image noise content, was defined to optimize filter thickness and kVp. Using the optimized parameters, dual-energy images of anthropomorphic Rando phantom were acquired and evaluated for bone subtraction. Imaging dose was measured with dual-energy technique using tin filtering. Results: Tin was the material of choice providing the best energy separation, non-toxicity, and non-reactiveness. The best soft-tissue-only image in the lung phantom was obtained using 0.3 mm tin and [140, 80] kVp pair. Dual-energy images of the Rando phantom had noticeable bone elimination when compared to no filtration. Dose was lower with tin filtering compared to no filtration. Conclusions: Dual-energy soft-tissue imaging is feasible using ExacTrac stereoscopic imaging system utilizing a single tin filter for both high and low energies and optimized acquisition parameters.
Schikora, Marek; Gning, Amadou; Mihaylova, Lyudmila; Cremers, Daniel; Koch, Wofgang; Streit, Roy
2012-01-01
This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables thi...
A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.
Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd
2017-08-01
The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.
Development of the code for filter calculation
International Nuclear Information System (INIS)
Gritzay, O.O.; Vakulenko, M.M.
2012-01-01
This paper describes a calculation method, which commonly used in the Neutron Physics Department to develop a new neutron filter or to improve the existing neutron filter. This calculation is the first step of the traditional filter development procedure. It allows easy selection of the qualitative and quantitative contents of a composite filter in order to receive the filtered neutron beam with given parameters
Page, Ralph H.; Doty, Patrick F.
2017-08-01
The various technologies presented herein relate to a tiled filter array that can be used in connection with performance of spatial sampling of optical signals. The filter array comprises filter tiles, wherein a first plurality of filter tiles are formed from a first material, the first material being configured such that only photons having wavelengths in a first wavelength band pass therethrough. A second plurality of filter tiles is formed from a second material, the second material being configured such that only photons having wavelengths in a second wavelength band pass therethrough. The first plurality of filter tiles and the second plurality of filter tiles can be interspersed to form the filter array comprising an alternating arrangement of first filter tiles and second filter tiles.
Full-field particle velocimetry with a photorefractive optical novelty filter
International Nuclear Information System (INIS)
Woerdemann, Mike; Holtmann, Frank; Denz, Cornelia
2008-01-01
We utilize the finite time constant of a photorefractive optical novelty filter microscope to access full-field velocity information of fluid flows on microscopic scales. In contrast to conventional methods such as particle image velocimetry and particle tracking velocimetry, not only image acquisition of the tracer particle field but also evaluation of tracer particle velocities is done all-optically by the novelty filter. We investigate the velocity dependent parameters of two-beam coupling based optical novelty filters and demonstrate calibration and application of a photorefractive velocimetry system. Theoretical and practical limits to the range of accessible velocities are discussed
Faouzi, B.; Washaya, P.
2017-09-01
This paper is based on using DMSP-OLS data from satellites nighttime light observations to detect both sources of light emissions in Algeria from human settlement areas and gas flaring from oil-extraction and natural gas production. We used the time series of data from DMSP-OLS images to examine the spatial and temporal characteristics of urban development in 48 Algerian provinces from 1993 to 2012. A systematic nighttime light calibration method was used to improve the consistency and comparability of the DSMPOSL images and then a separation is made between light detected from human settlements and light detected from gas flaring in order to allow us to study human settlements without other light emissions and then assess the suitability of using DMSP data in southern Algeria and its ability to monitor gas flaring. Linear regression methods were developed to identify the dynamic change of nighttime light and estimated its growth directions at pixel level. This work is the first to use nighttime light observations to detect and monitor the growth of human settlements in North Africa. In this study, we made use of DMSP-OLS data as a return ticket to the years of crises and we found the most affected provinces during that period. The DMSP-OLS data proved to be an index of growth in the economy during the period of stability in Algeria expressed by positive dynamic changes in the lighted area in all Algerian provinces. We used NTL data as an alternative to annual growth indexes for each province, which are unavailable, and its help as a monitoring system for socioeconomic parameters to fill the gap of data availability. We also proposed nighttime light remote sensing data as a useful tool to control and reduce CO2 emissions in Algeria's petroleum sector.
Experimental study of filter cake formation on different filter media
International Nuclear Information System (INIS)
Saleem, M.
2009-01-01
Removal of particulate matter from gases generated in the process industry is important for product recovery as well as emission control. Dynamics of filtration plant depend on operating conditions. The models, that predict filter plant behaviour, involve empirical resistance parameters which are usually derived from limited experimental data and are characteristics of the filter media and filter cake (dust deposited on filter medium). Filter cake characteristics are affected by the nature of filter media, process parameters and mode of filter regeneration. Removal of dust particles from air is studied in a pilot scale jet pulsed bag filter facility resembling closely to the industrial filters. Limestone dust and ambient air are used in this study with two widely different filter media. All important parameters like pressure drop, gas flow rate, dust settling, are recorded continuously at 1s interval. The data is processed for estimation of the resistance parameters. The pressure drop rise on test filter media is compared. Results reveal that the surface of filter media has an influence on pressure drop rise (concave pressure drop rise). Similar effect is produced by partially jet pulsed filter surface. Filter behaviour is also simulated using estimated parameters and a simplified model and compared with the experimental results. Distribution of cake area load is therefore an important aspect of jet pulse cleaned bag filter modeling. Mean specific cake resistance remains nearly constant on thoroughly jet pulse cleaned membrane coated filter bags. However, the trend can not be confirmed without independent cake height and density measurements. Thus the results reveal the importance of independent measurements of cake resistance. (author)
Box-particle probability hypothesis density filtering
Schikora, M.; Gning, A.; Mihaylova, L.; Cremers, D.; Koch, W.
2014-01-01
This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small number of box-p...
Directory of Open Access Journals (Sweden)
Haibo Zou
2018-01-01
Full Text Available After a tropical cyclone (TC making landfall, the numerical model output sea level pressure (SLP presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85% the DDs are small (between −30 km and 30 km, while in a few cases (about 15% the DDs are large (greater than 30 km even 70 km. This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter.
Thermal Tracking of Sports Players
Directory of Open Access Journals (Sweden)
Rikke Gade
2014-07-01
Full Text Available We present here a real-time tracking algorithm for thermal video from a sports game. Robust detection of people includes routines for handling occlusions and noise before tracking each detected person with a Kalman filter. This online tracking algorithm is compared with a state-of-the-art offline multi-target tracking algorithm. Experiments are performed on a manually annotated 2-minutes video sequence of a real soccer game. The Kalman filter shows a very promising result on this rather challenging sequence with a tracking accuracy above 70% and is superior compared with the offline tracking approach. Furthermore, the combined detection and tracking algorithm runs in real time at 33 fps, even with large image sizes of 1920 × 480 pixels.
Thermal tracking of sports players.
Gade, Rikke; Moeslund, Thomas B
2014-07-29
We present here a real-time tracking algorithm for thermal video from a sports game. Robust detection of people includes routines for handling occlusions and noise before tracking each detected person with a Kalman filter. This online tracking algorithm is compared with a state-of-the-art offline multi-target tracking algorithm. Experiments are performed on a manually annotated 2-minutes video sequence of a real soccer game. The Kalman filter shows a very promising result on this rather challenging sequence with a tracking accuracy above 70% and is superior compared with the offline tracking approach. Furthermore, the combined detection and tracking algorithm runs in real time at 33 fps, even with large image sizes of 1920 × 480 pixels.
Robust track fitting in the Belle II inner tracking detector
International Nuclear Information System (INIS)
Nadler, Moritz; Frühwirth, Rudolf
2012-01-01
Track fitting in the new inner tracker of the Belle II experiment uses the GENFIT package. In the latter both a standard Kalman filter and a robust extension, the deterministic annealing filter (DAF), are implemented. This contribution presents the results of a simulation experiment which examines the performance of the DAF in the inner tracker, in terms of outlier detection ability and of the impact of different kinds of background on the quality of the fitted tracks.
International Nuclear Information System (INIS)
Dikusar, N.D.
1993-01-01
The new approach to solving of the finding problem is proposed. The method is based on Discrete Projective Transformations (DPT), the List Square Fitting (LSF) and uses the information feedback in tracing for linear or quadratic track segments (TS). The fast and stable with respect to measurement errors and background points recurrent algorithm is suggested. The algorithm realizes the family of digital adaptive projective filters (APF) with known nonlinear weight functions-projective invariants. APF can be used in adequate control systems for collection, processing and compression of data, including tracking problems for the wide class of detectors. 10 refs.; 9 figs
Generalized Selection Weighted Vector Filters
Directory of Open Access Journals (Sweden)
Rastislav Lukac
2004-09-01
Full Text Available This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03 in Grado, Italy.
Directory of Open Access Journals (Sweden)
Y. A. Bladyko
2010-01-01
Full Text Available The paper contains definition of a smoothing factor which is suitable for any rectifier filter. The formulae of complex smoothing factors have been developed for simple and complex passive filters. The paper shows conditions for application of calculation formulae and filters.
SU-F-T-458: Tracking Trends of TG-142 Parameters Via Analysis of Data Recorded by 2D Chamber Array
Energy Technology Data Exchange (ETDEWEB)
Alexandrian, A; Kabat, C; Defoor, D; Saenz, D; Rasmussen, K; Kirby, N; Gutierrez, A; Papanikolaou, N; Stathakis, S [University of Texas HSC SA, San Antonio, TX (United States)
2016-06-15
Purpose: With increasing QA demands of medical physicists in clinical radiation oncology, the need for an effective method of tracking clinical data has become paramount. A tool was produced which scans through data automatically recorded by a 2D chamber array and extracts relevant information recommended by TG-142. Using this extracted information a timely and comprehensive analysis of QA parameters can be easily performed enabling efficient monthly checks on multiple linear accelerators simultaneously. Methods: A PTW STARCHECK chamber array was used to record several months of beam outputs from two Varian 2100 series linear accelerators and a Varian NovalisTx−. In conjunction with the chamber array, a beam quality phantom was used to simultaneously to determine beam quality. A minimalist GUI was created in MatLab that allows a user to set the file path of the data for each modality to be analyzed. These file paths are recorded to a MatLab structure and then subsequently accessed by a script written in Python (version 3.5.1) which then extracts values required to perform monthly checks as outlined by recommendations from TG-142. The script incorporates calculations to determine if the values recorded by the chamber array fall within an acceptable threshold. Results: Values obtained by the script are written to a spreadsheet where results can be easily viewed and annotated with a “pass” or “fail” and saved for further analysis. In addition to creating a new scheme for reviewing monthly checks, this application allows for able to succinctly store data for follow up analysis. Conclusion: By utilizing this tool, parameters recommended by TG-142 for multiple linear accelerators can be rapidly obtained and analyzed which can be used for evaluation of monthly checks.
International Nuclear Information System (INIS)
Zhang Zhihui; Zhou Hong; Ren Luquan; Tong Xin; Shan Hongyu; Li Xianzhou
2008-01-01
Aiming to form the high quality of non-smooth biomimetic unit, the influence of laser processing parameters (pulse energy, pulse duration, frequency and scanning speed in the present work) on the surface morphology of scanned tracks was studied based on the 3Cr2W8V die steel. The evolution of the surface morphology was explained according to the degree of melting and vaporization of surface material, and the trend of mean surface roughness and maximum peak-to-valley height. Cross-section morphology revealed the significant microstructural characteristic of the laser-treated zone used for forming the functional zone on the biomimetic surface. Results showed that the combination of pulse energy and pulse duration plays a major role in determining the local height difference on the irradiated surface and the occurrence of melting or vaporization. While frequency and scanning speed have a minor effect on the change of the surface morphology, acting mainly by the different overlapping amount and overlapping mode. The mechanisms behind these influences were discussed, and schematic drawings were introduced to describe the mechanisms
Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing
Lee, James S. J.; Nguyen, Dziem D.; Lin, C.
1989-03-01
A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.
Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat
2017-05-01
Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.
International Nuclear Information System (INIS)
McNabb, J.
2001-01-01
The analysis of data from CLAS is a multi-step process. After the detectors for a given running period have been calibrated, the data is processed in the so called pass-1 cooking. During the pass-1 cooking each event is reconstructed by the program a1c which finds particle tracks and computes momenta from the raw data. The results are then passed on to several data monitoring and filtering utilities. In CLAS software, a filter is a parameterless function which returns an integer indicating whether an event should be kept by that filter or not. There is a main filter program called g1-filter which controls several specific filters and outputs several files, one for each filter. These files may then be analyzed separately, allowing individuals interested in one reaction channel to work from smaller files than using the whole data set would require. There are several constraints on what the filter functions should do. Obviously, the filtered files should be as small as possible, however the filter should also not reject any events that might be used in the later analysis for which the filter was intended
Energy Technology Data Exchange (ETDEWEB)
Zhao, L.; Landi, E. [Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, MI 48105 (United States); Gibson, S. E., E-mail: lzh@umich.edu [NCAR/HAO, P.O. Box 3000, Boulder, CO 80307-3000 (United States)
2013-08-20
Since the unusually prolonged and weak solar minimum between solar cycles 23 and 24 (2008-2010), the sunspot number is smaller and the overall morphology of the Sun's magnetic field is more complicated (i.e., less of a dipole component and more of a tilted current sheet) compared with the same minimum and ascending phases of the previous cycle. Nearly 13 yr after the last solar maximum ({approx}2000), the monthly sunspot number is currently only at half the highest value of the past cycle's maximum, whereas the polar magnetic field of the Sun is reversing (north pole first). These circumstances make it timely to consider alternatives to the sunspot number for tracking the Sun's magnetic cycle and measuring its complexity. In this study, we introduce two novel parameters, the standard deviation (SD) of the latitude of the heliospheric current sheet (HCS) and the integrated slope (SL) of the HCS, to evaluate the complexity of the Sun's magnetic field and track the solar cycle. SD and SL are obtained from the magnetic synoptic maps calculated by a potential field source surface model. We find that SD and SL are sensitive to the complexity of the HCS: (1) they have low values when the HCS is flat at solar minimum, and high values when the HCS is highly tilted at solar maximum; (2) they respond to the topology of the HCS differently, as a higher SD value indicates that a larger part of the HCS extends to higher latitude, while a higher SL value implies that the HCS is wavier; (3) they are good indicators of magnetically anomalous cycles. Based on the comparison between SD and SL with the normalized sunspot number in the most recent four solar cycles, we find that in 2011 the solar magnetic field had attained a similar complexity as compared to the previous maxima. In addition, in the ascending phase of cycle 24, SD and SL in the northern hemisphere were on the average much greater than in the southern hemisphere, indicating a more tilted and wavier
Track Detection in Railway Sidings Based on MEMS Gyroscope Sensors
Broquetas, Antoni; Comerón, Adolf; Gelonch, Antoni; Fuertes, Josep M.; Castro, J. Antonio; Felip, Damià; López, Miguel A.; Pulido, José A.
2012-01-01
The paper presents a two-step technique for real-time track detection in single-track railway sidings using low-cost MEMS gyroscopes. The objective is to reliably know the path the train has taken in a switch, diverted or main road, immediately after the train head leaves the switch. The signal delivered by the gyroscope is first processed by an adaptive low-pass filter that rejects noise and converts the temporal turn rate data in degree/second units into spatial turn rate data in degree/meter. The conversion is based on the travelled distance taken from odometer data. The filter is implemented to achieve a speed-dependent cut-off frequency to maximize the signal-to-noise ratio. Although direct comparison of the filtered turn rate signal with a predetermined threshold is possible, the paper shows that better detection performance can be achieved by processing the turn rate signal with a filter matched to the rail switch curvature parameters. Implementation aspects of the track detector have been optimized for real-time operation. The detector has been tested with both simulated data and real data acquired in railway campaigns. PMID:23443376
Calculation of track and vertex errors for detector design studies
International Nuclear Information System (INIS)
Harr, R.
1995-01-01
The Kalman Filter technique has come into wide use for charged track reconstruction in high-energy physics experiments. It is also well suited for detector design studies, allowing for the efficient estimation of optimal track covariance matrices without the need of a hit level Monte Carlo simulation. Although much has been published about the Kalman filter equations, there is a lack of previous literature explaining how to implement the equations. In this paper, the operators necessary to implement the Kalman filter equations for two common detector configurations are worked out: a central detector in a uniform solenoidal magnetic field, and a fixed-target detector with no magnetic field in the region of the interactions. With the track covariance matrices in hand, vertex and invariant mass errors are readily calculable. These quantities are particularly interesting for evaluating experiments designed to study weakly decaying particles which give rise to displaced vertices. The optimal vertex errors are obtained via a constrained vertex fit. Solutions are presented to the constrained vertex problem with and without kinematic constraints. Invariant mass errors are obtained via propagation of errors; the use of vertex constrained track parameters is discussed. Many of the derivations are new or previously unpublished
Iio, Chiharuko; Inoue, Katsuji; Nishimura, Kazuhisa; Fujii, Akira; Nagai, Takayuki; Suzuki, Jun; Okura, Takafumi; Higaki, Jitsuo; Ogimoto, Akiyoshi
2015-12-01
The pathological process of left ventricular (LV) hypertrophy is associated with left atrial (LA) remodeling. This study was aimed to evaluate the prognostic value of LA strain parameters in patients with pathological LV hypertrophy. This study included 95 patients with hypertensive heart disease (HHD: n = 24), hypertrophic cardiomyopathy (HCM: n = 56), cardiac amyloidosis (CA: n = 15), and control subjects (n = 20). We used two-dimensional speckle tracking echocardiography (STE) to analyze LA global strain. LA electromechanical conduction time (EMT) at the septal (EMT-septal) and lateral wall (EMT-lateral), and their time difference (EMT-diff) were calculated. The incidence of cardiac death and heart failure hospitalization was defined as major cardiac events and that of atrial fibrillation as secondary outcome. Left atrial volume index was increased and LA booster strain was decreased in the HCM and CA groups compared with the HHD group. EMT-lateral was increased in the diseased groups compared with the control. EMT-diff was prolonged in the CA group compared with the HCM group. During the follow-up period (mean 3.4 years), major cardiac events and atrial fibrillation occurred in 17 and 13 patients, respectively. The occurrence of atrial fibrillation was associated with CA etiology, E/e', LA volume index, LAa, and EMT-lateral. The incidence of major cardiac events was independently correlated with LA volume index and EMT-diff in multivariate analysis. This study suggested that the EMT-diff could discriminate patients with a high risk of cardiac events among patients with pathological LV hypertrophy. © 2015, Wiley Periodicals, Inc.
Lee, Jun Chang; Nam, Kyoung Won; Jang, Dong Pyo; Paik, Nam Jong; Ryu, Ju Seok; Kim, In Young
2017-04-01
Conventional kinematic analysis of videofluoroscopic (VF) swallowing image, most popular for dysphagia diagnosis, requires time-consuming and repetitive manual extraction of diagnostic information from multiple images representing one swallowing period, which results in a heavy work load for clinicians and excessive hospital visits for patients to receive counseling and prescriptions. In this study, a software platform was developed that can assist in the VF diagnosis of dysphagia by automatically extracting a two-dimensional moving trajectory of the hyoid bone as well as 11 temporal and kinematic parameters. Fifty VF swallowing videos containing both non-mandible-overlapped and mandible-overlapped cases from eight patients with dysphagia of various etiologies and 19 videos from ten healthy controls were utilized for performance verification. Percent errors of hyoid bone tracking were 1.7 ± 2.1% for non-overlapped images and 4.2 ± 4.8% for overlapped images. Correlation coefficients between manually extracted and automatically extracted moving trajectories of the hyoid bone were 0.986 ± 0.017 (X-axis) and 0.992 ± 0.006 (Y-axis) for non-overlapped images, and 0.988 ± 0.009 (X-axis) and 0.991 ± 0.006 (Y-axis) for overlapped images. Based on the experimental results, we believe that the proposed platform has the potential to improve the satisfaction of both clinicians and patients with dysphagia.
A New Filtering Algorithm Utilizing Radial Velocity Measurement
Institute of Scientific and Technical Information of China (English)
LIU Yan-feng; DU Zi-cheng; PAN Quan
2005-01-01
Pulse Doppler radar measurements consist of range, azimuth, elevation and radial velocity. Most of the radar tracking algorithms in engineering only utilize position measurement. The extended Kalman filter with radial velocity measureneut is presented, then a new filtering algorithm utilizing radial velocity measurement is proposed to improve tracking results and the theoretical analysis is also given. Simulation results of the new algorithm, converted measurement Kalman filter, extended Kalman filter are compared. The effectiveness of the new algorithm is verified by simulation results.
Kalman Filtering with Real-Time Applications
Chui, Charles K
2009-01-01
Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.
Numerical study of canister filters with alternatives filter cap configurations
Mohammed, A. N.; Daud, A. R.; Abdullah, K.; Seri, S. M.; Razali, M. A.; Hushim, M. F.; Khalid, A.
2017-09-01
Air filtration system and filter play an important role in getting a good quality air into turbo machinery such as gas turbine. The filtration system and filter has improved the quality of air and protect the gas turbine part from contaminants which could bring damage. During separation of contaminants from the air, pressure drop cannot be avoided but it can be minimized thus helps to reduce the intake losses of the engine [1]. This study is focused on the configuration of the filter in order to obtain the minimal pressure drop along the filter. The configuration used is the basic filter geometry provided by Salutary Avenue Manufacturing Sdn Bhd. and two modified canister filter cap which is designed based on the basic filter model. The geometries of the filter are generated by using SOLIDWORKS software and Computational Fluid Dynamics (CFD) software is used to analyse and simulates the flow through the filter. In this study, the parameters of the inlet velocity are 0.032 m/s, 0.063 m/s, 0.094 m/s and 0.126 m/s. The total pressure drop produce by basic, modified filter 1 and 2 is 292.3 Pa, 251.11 Pa and 274.7 Pa. The pressure drop reduction for the modified filter 1 is 41.19 Pa and 14.1% lower compared to basic filter and the pressure drop reduction for modified filter 2 is 17.6 Pa and 6.02% lower compared to the basic filter. The pressure drops for the basic filter are slightly different with the Salutary Avenue filter due to limited data and experiment details. CFD software are very reliable in running a simulation rather than produces the prototypes and conduct the experiment thus reducing overall time and cost in this study.
International Nuclear Information System (INIS)
Butterworth, D.J.
1980-01-01
This invention relates to liquid filters, precoated by replaceable powders, which are used in the production of ultra pure water required for steam generation of electricity. The filter elements are capable of being installed and removed by remote control so that they can be used in nuclear power reactors. (UK)
Hydrodynamics of microbial filter feeding
DEFF Research Database (Denmark)
Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia
2017-01-01
Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate......-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude......; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet...
Multiplier-free filters for wideband SAR
DEFF Research Database (Denmark)
Dall, Jørgen; Christensen, Erik Lintz
2001-01-01
This paper derives a set of parameters to be optimized when designing filters for digital demodulation and range prefiltering in SAR systems. Aiming at an implementation in field programmable gate arrays (FPGAs), an approach for the design of multiplier-free filters is outlined. Design results...... are presented in terms of filter complexity and performance. One filter has been coded in VHDL and preliminary results indicate that the filter can meet a 2 GHz input sample rate....
Hydrodynamics of microbial filter feeding.
Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia; Walther, Jens H; Kiørboe, Thomas; Andersen, Anders
2017-08-29
Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate amounts of water. Also, the trade-off in the filter spacing remains unexplored, despite its simple formulation: A filter too coarse will allow suitably sized prey to pass unintercepted, whereas a filter too fine will cause strong flow resistance. We quantify the feeding flow of the filter-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet), something notoriously difficult to visualize but sporadically observed in the related choanocytes (sponges). A CFD model with a flagellar vane correctly predicts the filtration rate of D. grandis , and using a simple model we can account for the filtration rates of other microbial filter feeders. We finally predict how optimum filter mesh size increases with cell size in microbial filter feeders, a prediction that accords very well with observations. We expect our results to be of significance for small-scale biophysics and trait-based ecological modeling.
Kalman filtering with real-time applications
Chui, Charles K
2017-01-01
This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. Over 100 exercises and problems with solutions help de...
Mixed labelling in multitarget particle filtering
Boers, Y.; Sviestins, Egils; Driessen, Hans
2010-01-01
The so-called mixed labelling problem inherent to a joint state multitarget particle filter implementation is treated. The mixed labelling problem would be prohibitive for track extraction from a joint state multitarget particle filter. It is shown, using the theory of Markov chains, that the mixed
Robust visual tracking via multi-task sparse learning
Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra
2012-01-01
In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates
Directory of Open Access Journals (Sweden)
Tolkachev E.N.
2016-12-01
Full Text Available Using the basic design of the conveyor with suspended belt and distributed drive, a series of numerical calcu-lations was performed. As a result, the influence of design parameters of vertical loop route on the main tech-nical parameters of the conveyor was established. Recommendations on the choice of rational parameters were formulated.
Reconfigurable Mixed Mode Universal Filter
Directory of Open Access Journals (Sweden)
Neelofer Afzal
2014-01-01
Full Text Available This paper presents a novel mixed mode universal filter configuration capable of working in voltage and transimpedance mode. The proposed single filter configuration can be reconfigured digitally to realize all the five second order filter functions (types at single output port. Other salient features of proposed configuration include independently programmable filter parameters, full cascadability, and low sensitivity figure. However, all these features are provided at the cost of quite large number of active elements. It needs three digitally programmable current feedback amplifiers and three digitally programmable current conveyors. Use of six active elements is justified by introducing three additional reduced hardware mixed mode universal filter configurations and its comparison with reported filters.
Processing of plastic track detectors
International Nuclear Information System (INIS)
Somogyi, G.
1977-01-01
A survey of some actual problems of the track processing methods available at this time for plastics is presented. In the case of the conventional chemical track-etching technique, mainly the etching situations related to detector geometry, and the relationship between registration sensitivity and the etching parameters are considered. Special attention is paid to the behaviour of track-revealing by means of electrochemical etching. Finally, some properties of a promising new track processing method based on graft polymerization are discussed. (author)
Processing of plastic track detectors
International Nuclear Information System (INIS)
Somogyi, G.
1976-01-01
A survey of some actual problems of the track processing methods available at this time for plastics is presented. In the case of the conventional chemical track etching technique mainly the etching situations related to detector geometry and the relationship of registration sensitivity and the etching parameters are considered. A special attention is paid to the behaviour of track revealing by means of electrochemical etching. Finally, some properties of a promising new track processing method based on graft polymerization is discussed. (orig.) [de
Bottom loaded filter for radioactive liquid
International Nuclear Information System (INIS)
Wendland, W.G.
1980-01-01
A specification is given for a bottom loaded filter assembly for filtering radioactive liquids through a replaceable cartridge filter, which includes a lead-filled jacket enveloping a housing having a chamber therein for the filter cartridge. A track arrangement carries a hatch for sealing the chamber. A spacer plug supports the cartridge within guide means associated with the inlet conduit in the chamber. The plug and cartridge drop out of the chamber when the hatch is unbolted and moved laterally of the chamber along the track. During cartridge replacement a new plug and cartridge are supported in the guide means by a spacer bar inserted across the track means under the chamber. The hatch is then slid under the chamber and bolted to a flange on the housing, engaging an O-ring to seal the chamber. (author)
Energy Technology Data Exchange (ETDEWEB)
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory
2009-01-01
Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.
Manifold Regularized Correlation Object Tracking.
Hu, Hongwei; Ma, Bo; Shen, Jianbing; Shao, Ling
2018-05-01
In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.
Application and Optimization of Kalman Filter for Baseband Signal Processing of GPS Receivers
Directory of Open Access Journals (Sweden)
He Yanpin
2016-01-01
Full Text Available High sensitivity tracking in GPS receiver is required in many weak signal circumstances. The key of improving sensitivity is the optimization of the loop filter in tracking. As Kalman filter is the most optimized linear filter, it is used in many engineering fields. This article introduced the application of Kalman filter as the loop filter of the carrier tracking loop in GPS receiver, to improve tracking sensitivity. The traditional loop filter is replaced. Simulation results show that the new structure improves the tracking sensitivity by 6dB and can make the tracking loop more robust when the navigation signal is languishing. The optimization of theKalman filter is also analysed, which further improves the sensitivity by 4dB.
Bottom loaded filter for radioactive liquids
International Nuclear Information System (INIS)
Wendland, W.G.
1980-01-01
A bottom loaded filter assembly for filtering radioactive liquids through a replaceable cartridge filter is disclosed. The filter assembly includes a lead-filled jacket enveloping a housing having a chamber therein for the filter cartridge. A track arrangement carries a hatch for sealing the chamber. A spacer plug supports the cartridge within guide means associated with the inlet conduit in the chamber. The plug and cartridge drop out of the chamber when the hatch is unbolted and move laterally of the chamber. During cartridge replacement, a new plug and cartridge are supported in the guide means by a spacer bar inserted across the track means under the chamber. The hatch is then slid under the chamber and bolted to the vessel, engaging an o-ring to seal the chamber
The intractable cigarette 'filter problem'.
Harris, Bradford
2011-05-01
When lung cancer fears emerged in the 1950s, cigarette companies initiated a shift in cigarette design from unfiltered to filtered cigarettes. Both the ineffectiveness of cigarette filters and the tobacco industry's misleading marketing of the benefits of filtered cigarettes have been well documented. However, during the 1950s and 1960s, American cigarette companies spent millions of dollars to solve what the industry identified as the 'filter problem'. These extensive filter research and development efforts suggest a phase of genuine optimism among cigarette designers that cigarette filters could be engineered to mitigate the health hazards of smoking. This paper explores the early history of cigarette filter research and development in order to elucidate why and when seemingly sincere filter engineering efforts devolved into manipulations in cigarette design to sustain cigarette marketing and mitigate consumers' concerns about the health consequences of smoking. Relevant word and phrase searches were conducted in the Legacy Tobacco Documents Library online database, Google Patents, and media and medical databases including ProQuest, JSTOR, Medline and PubMed. 13 tobacco industry documents were identified that track prominent developments involved in what the industry referred to as the 'filter problem'. These reveal a period of intense focus on the 'filter problem' that persisted from the mid-1950s to the mid-1960s, featuring collaborations between cigarette producers and large American chemical and textile companies to develop effective filters. In addition, the documents reveal how cigarette filter researchers' growing scientific knowledge of smoke chemistry led to increasing recognition that filters were unlikely to offer significant health protection. One of the primary concerns of cigarette producers was to design cigarette filters that could be economically incorporated into the massive scale of cigarette production. The synthetic plastic cellulose acetate
Fast Filter Central Drift Chamber Program. MAC Note 404
International Nuclear Information System (INIS)
Ford, W.T.
1979-01-01
A fast filter of the central detector tracking program was developed and tested. The search for tracks is conducted as in the full linking program - from the outermost layer hit to the next layer in. Any hit is accepted within a road centered which is large enough to allow for stero displacement and any curvature. With the fast filter, this is continued to the innermost layer, with five or more hits accepted as a track
Design and construction of electronic filters
International Nuclear Information System (INIS)
Becerril Z, E.R.; Moreno P, C.; Salinas B, E.
1979-01-01
The design and construction of very low frequencies electronic filters which will be used for carrying out analysis of pile noise at Mexico's Nuclear Center Triga Mark III Reactor, in order to realize measurements of its parameters is presented. NIM norms and active filters with lineal integrated circuits were used: a. Band pass filter from 10 to 500 hertz, band width 50. b. Low pass filter from 0.001 to 10 hertz in 3 steps. c. Kalman Bucy filter, an analogical simulation of this filter was undertaken, obtained from a mathematical model of a Zero power experimental reactor, with the purpose to establish a control searching. (author)
Directory of Open Access Journals (Sweden)
Audrey Barbakoff
2011-03-01
Full Text Available In the Library with the Lead Pipe welcomes Audrey Barbakoff, a librarian at the Milwaukee Public Library, and Ahniwa Ferrari, Virtual Experience Manager at the Pierce County Library System in Washington, for a point-counterpoint piece on filtering in libraries. The opinions expressed here are those of the authors, and are not endorsed by their employers. [...
Aerosol filtration with metallic fibrous filters
International Nuclear Information System (INIS)
Klein, M.; Goossens, W.R.A.
1983-01-01
The filtration efficiency of stainless steel fibrous filters (BEKIPOR porous mats and sintered webs) is determined using submicronic monodisperse polystyrene aerosols. Lasers spectrometers are used for the aerosol measurements. The parameters varied are the fiber diameter, the number of layers, the aerosol diameter and the superficial velocity. Two selected types of filters are tested with polydisperse methylene blue aerosols to determine the effect of bed loading on the filter performance and to test washing techniques for the regeneration of the filter
International Nuclear Information System (INIS)
Abe, K.; Abt, I.; Ahn, C.J.; Akagi, T.; Ash, W.W.; Aston, D.; Bacchetta, N.; Baird, K.G.; Baltay, C.; Band, H.R.; Barakat, M.B.; Baranko, G.; Bardon, O.; Barklow, T.; Bazarko, A.O.; Ben-David, R.; Benvenuti, A.C.; Bienz, T.; Bilei, G.M.; Bisello, D.; Blaylock, G.; Bogart, J.R.; Bolton, T.; Bower, G.R.; Brau, J.E.; Breidenbach, M.; Bugg, W.M.; Burke, D.; Burnett, T.H.; Burrows, P.N.; Busza, W.; Calcaterra, A.; Caldwell, D.O.; Calloway, D.; Camanzi, B.; Carpinelli, M.; Cassell, R.; Castaldi, R.; Castro, A.; Cavalli-Sforza, M.; Church, E.; Cohn, H.O.; Coller, J.A.; Cook, V.; Cotton, R.; Cowan, R.F.; Coyne, D.G.; D'Oliveira, A.; Damerell, C.J.S.; Dasu, S.; De Sangro, R.; De Simone, P.; Dell'Orso, R.; Dima, M.; Du, P.Y.C.; Dubois, R.; Eisenstein, B.I.; Elia, R.; Falciai, D.; Fan, C.; Fero, M.J.; Frey, R.; Furuno, K.; Gillman, T.; Gladding, G.; Gonzalez, S.; Hallewell, G.D.; Hart, E.L.; Hasegawa, Y.; Hedges, S.; Hertzbach, S.S.; Hildreth, M.D.; Huber, J.; Huffer, M.E.; Hughes, E.W.; Hwang, H.; Iwasaki, Y.; Jacques, P.; Jaros, J.; Johnson, A.S.; Johnson, J.R.; Johnson, R.A.; Junk, T.; Kajikawa, R.; Kalelkar, M.; Karliner, I.; Kawahara, H.; Kendall, H.W.; Kim, Y.; King, M.E.; King, R.; Kofler, R.R.; Krishna, N.M.; Kroeger, R.S.; Labs, J.F.; Langston, M.; Lath, A.; Lauber, J.A.; Leith, D.W.G.; Liu, X.; Loreti, M.; Lu, A.; Lynch, H.L.; Ma, J.; Mancinelli, G.; Manly, S.; Mantovani, G.; Markiewicz, T.W.; Maruyama, T.; Massetti, R.; Masuda, H.; Mazzucato, E.; McKemey, A.K.; Meadows, B.T.; Messner, R.; Mockett, P.M.; Moffeit, K.C.; Mours, B.; Mueller, G.; Muller, D.; Nagamine, T.; Nauenberg, U.; Neal, H.; Nussbaum, M.; Ohnishi, Y.; Osborne, L.S.; Panvini, R.S.; Park, H.; Pavel, T.J.; Peruzzi, I.; Pescara, L.; Piccolo, M.; Piemontese, L.; Pieroni, E.; Pitts, K.T.; Plano, R.J.; Prepost, R.; Prescott, C.Y.; Punkar, G.D.; Quigley, J.; Ratcliff, B.N.; Reeves, T.W.; Rensing, P.E.; Rochester, L.S.; Rothberg, J.E.; Rowson, P.C.; Russell, J.J.; Saxton, O.H.; Schalk, T.
1995-01-01
Using an impact parameter tag to select an enriched sample of Z 0 →bbbar events, and the net momentum-weighted track charge to identify the sign of the charge of the underlying b quark, we have measured the left-right forward-backward asymmetry for b quark production as a function of polar angle. Based on 1.8pb -1 of Z 0 decay data produced with a mean electron beam polarization of P e =63%, this yields a direct measurement of the extent of parity violation in the Zbb coupling of A b =0.87±0.11(stat)±0.09(syst)
Factors Influencing HEPA Filter Performance
International Nuclear Information System (INIS)
Parsons, M.S.; Waggoner, Ch.A.
2009-01-01
Properly functioning HEPA air filtration systems depend on a variety of factors that start with the use of fully characterized challenge conditions for system design and then process control during operation. This paper addresses factors that should be considered during the design phase as well as operating parameters that can be monitored to ensure filter function and lifetime. HEPA filters used in nuclear applications are expected to meet design, fabrication, and performance requirements set forth in the ASME AG-1 standard. The DOE publication Nuclear Air Cleaning Handbook (NACH) is an additional guidance document for design and operation HEPA filter systems in DOE facilities. These two guidelines establish basic maximum operating parameters for temperature, maximum aerosol particle size, maximum particulate matter mass concentration, acceptable differential pressure range, and filter media velocity. Each of these parameters is discussed along with data linking variability of each parameter with filter function and lifetime. Temporal uncertainty associated with gas composition, temperature, and absolute pressure of the air flow can have a direct impact on the volumetric flow rate of the system with a corresponding impact on filter media velocity. Correlations between standard units of flow rate (standard meters per minute or cubic feet per minute) versus actual units of volumetric flow rate are shown for variations in relative humidity for a 70 deg. C to 200 deg. C temperature range as an example of gas composition that, uncorrected, will influence media velocity. The AG-1 standard establishes a 2.5 cm/s (5 feet per minute) ceiling for media velocities of nuclear grade HEPA filters. Data are presented that show the impact of media velocities from 2.0 to 4.0 cm/s media velocities (4 to 8 fpm) on differential pressure, filter efficiency, and filter lifetime. Data will also be presented correlating media velocity effects with two different particle size
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction
Li, Zhencai; Wang, Yang; Liu, Zhen
2016-01-01
The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703
An efficient central DOA tracking algorithm for multiple incoherently distributed sources
Hassen, Sonia Ben; Samet, Abdelaziz
2015-12-01
In this paper, we develop a new tracking method for the direction of arrival (DOA) parameters assuming multiple incoherently distributed (ID) sources. The new approach is based on a simple covariance fitting optimization technique exploiting the central and noncentral moments of the source angular power densities to estimate the central DOAs. The current estimates are treated as measurements provided to the Kalman filter that model the dynamic property of directional changes for the moving sources. Then, the covariance-fitting-based algorithm and the Kalman filtering theory are combined to formulate an adaptive tracking algorithm. Our algorithm is compared to the fast approximated power iteration-total least square-estimation of signal parameters via rotational invariance technique (FAPI-TLS-ESPRIT) algorithm using the TLS-ESPRIT method and the subspace updating via FAPI-algorithm. It will be shown that the proposed algorithm offers an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method especially at low signal-to-noise ratio (SNR) values. Moreover, the performances of the two methods increase as the SNR values increase. This increase is more prominent with the FAPI-TLS-ESPRIT method. However, their performances degrade when the number of sources increases. It will be also proved that our method depends on the form of the angular distribution function when tracking the central DOAs. Finally, it will be shown that the more the sources are spaced, the more the proposed method can exactly track the DOAs.
[Study on method of tracking the active cells in image sequences based on EKF-PF].
Tang, Chunming; Liu, Ying
2013-02-01
In cell image sequences, due to the nonlinear and nonGaussian motion characteristics of active cells, the accurate prediction and tracking is still an unsolved problem. We applied extended Kalman particle filter (EKF-PF) here in our study, attempting to solve the problem. Firstly we confirmed the existence and positions of the active cells. Then we established a motion model and improved it via adding motion angle estimation. Next we predicted motion parameters, such as displacement, velocity, accelerated velocity and motion angle, in region centers of the cells being tracked. Finally we obtained the motion traces of active cells. There were fourteen active cells in three image sequences which have been tracked. The errors were less than 2.5 pixels when the prediction values were compared with actual values. It showed that the presented algorithm may basically reach the solution of accurate predition and tracking of the active cells.
Impact of the genfit2 Kalman-filter-based algorithms on physics simulations performed with PandaRoot
Energy Technology Data Exchange (ETDEWEB)
Prencipe, Elisabetta; Ritman, James [Forschungszentrum Juelich, IKP1, Juelich (Germany); Collaboration: PANDA-Collaboration
2016-07-01
PANDA is a planned experiment at FAIR (Darmstadt) with a cooled antiproton beam in a range [1.5;15] GeV/c, allowing a wide physics program in nuclear and particle physics. It is the only experiment worldwide, which combines a solenoid field (B=2 T) and a dipole field (B=2 Tm) in an experiment with a fixed target topology, in that energy regime. The tracking system of PANDA involves the presence of a high performance silicon vertex detector, a GEM detector, a Straw-Tubes central tracker, a forward tracking system, and a luminosity monitor. The offline tracking algorithm is developed within the PandaRoot framework, which is a part of the FAIRRoot project. The algorithm here presented is based on a tool containing the Kalman Filter equations and a deterministic annealing filter (genfit). The Kalman-Filter-based algorithms have a wide range of applications; among those in particle physics they can perform extrapolations of track parameters and covariance matrices. The impact on physics simulations performed for the PANDA experiment is shown for the first time, with the PandaRoot framework: improvement is shown for those channels where a good low momentum tracking is required (p{sub T}<400 MeV/c), i.e. D mesons and Λ reconstruction, of about a factor 2.
Model Adaptation for Prognostics in a Particle Filtering Framework
Directory of Open Access Journals (Sweden)
Bhaskar Saha
2011-01-01
Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Model Adaptation for Prognostics in a Particle Filtering Framework
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.
Kalman Filter Application to Symmetrical Fault Detection during Power Swing
DEFF Research Database (Denmark)
Khodaparast, Jalal; Silva, Filipe Miguel Faria da; Khederzadeh, M.
2016-01-01
capability of Kalman Filter. The proposed index is calculated by assessing the difference between predicted and actual samples of impedance. The predicted impedance samples are obtained using Kalman filter and Taylor expansion, which is used in this paper to track the phasor precisely. Second order of Taylor...... expansion is used to decrease corrugation effect of impedance estimation and increase the reliability of proposed method. The instantaneous estimation and prediction capability of Kalman filter are two reasons for proposing utilizing Kalman filter....
PolarTrack: Optical Outside-In Device Tracking that Exploits Display Polarization
DEFF Research Database (Denmark)
Rädle, Roman; Jetter, Hans-Christian; Fischer, Jonathan
2018-01-01
PolarTrack is a novel camera-based approach to detecting and tracking mobile devices inside the capture volume. In PolarTrack, a polarization filter continuously rotates in front of an off-the-shelf color camera, which causes the displays of observed devices to periodically blink in the camera feed....... The periodic blinking results from the physical characteristics of current displays, which shine polarized light either through an LC overlay to produce images or through a polarizer to reduce light reflections on OLED displays. PolarTrack runs a simple detection algorithm on the camera feed to segment...... displays and track their locations and orientations, which makes PolarTrack particularly suitable as a tracking system for cross-device interaction with mobile devices. Our evaluation of PolarTrack's tracking quality and comparison with state-of-the-art camera-based multi-device tracking showed a better...
Directory of Open Access Journals (Sweden)
Jorge Francisco Madrigal Díaz
2013-03-01
Full Text Available This paper describes an efficient implementation of multiple-target multiple-view tracking in video-surveillance sequences. It takes advantage of the capabilities of multiple core Central Processing Units (CPUs and of graphical processing units under the Compute Unifie Device Arquitecture (CUDA framework. The principle of our algorithm is 1 in each video sequence, to perform tracking on all persons to track by independent particle filters and 2 to fuse the tracking results of all sequences. Particle filters belong to the category of recursive Bayesian filters. They update a Monte-Carlo representation of the posterior distribution over the target position and velocity. For this purpose, they combine a probabilistic motion model, i.e. prior knowledge about how targets move (e.g. constant velocity and a likelihood model associated to the observations on targets. At this first level of single video sequences, the multi-threading library Threading Buildings Blocks (TBB has been used to parallelize the processing of the per-target independent particle filters. Afterwards at the higher level, we rely on General Purpose Programming on Graphical Processing Units (generally termed as GPGPU through CUDA in order to fuse target-tracking data collected on multiple video sequences, by solving the data association problem. Tracking results are presented on various challenging tracking datasets.Este artículo describe una implementación eficiente de un algoritmo de seguimiento de múltiples objetivos en múltiples vistas en secuencias de video vigilancia. Aprovecha las capacidades de las Unidades Centrales de Procesamiento (CPUs, por sus siglas en inglés de múltiples núcleos y de las unidades de procesamiento gráfico, bajo el entorno de desarrollo de Arquitectura Unificada de Dispositivos de Cómputo (CUDA, por sus siglas en inglés. El principio de nuestro algoritmo es: 1 aplicar el seguimiento visual en cada secuencia de video sobre todas las
Track reconstruction in CMS high luminosity environment
AUTHOR|(CDS)2067159
2016-01-01
The CMS tracker is the largest silicon detector ever built, covering 200 square meters and providing an average of 14 high-precision measurements per track. Tracking is essential for the reconstruction of objects like jets, muons, electrons and tau leptons starting from the raw data from the silicon pixel and strip detectors. Track reconstruction is widely used also at trigger level as it improves objects tagging and resolution.The CMS tracking code is organized in several levels, known as iterative steps, each optimized to reconstruct a class of particle trajectories, as the ones of particles originating from the primary vertex or displaced tracks from particles resulting from secondary vertices. Each iterative step consists of seeding, pattern recognition and fitting by a kalman filter, and a final filtering and cleaning. Each subsequent step works on hits not yet associated to a reconstructed particle trajectory.The CMS tracking code is continuously evolving to make the reconstruction computing load compat...
Track reconstruction in CMS high luminosity environment
Goetzmann, Christophe
2014-01-01
The CMS tracker is the largest silicon detector ever built, covering 200 square meters and providing an average of 14 high-precision measurements per track. Tracking is essential for the reconstruction of objects like jets, muons, electrons and tau leptons starting from the raw data from the silicon pixel and strip detectors. Track reconstruction is widely used also at trigger level as it improves objects tagging and resolution.The CMS tracking code is organized in several levels, known as iterative steps, each optimized to reconstruct a class of particle trajectories, as the ones of particles originating from the primary vertex or displaced tracks from particles resulting from secondary vertices. Each iterative step consists of seeding, pattern recognition and fitting by a kalman filter, and a final filtering and cleaning. Each subsequent step works on hits not yet associated to a reconstructed particle trajectory.The CMS tracking code is continuously evolving to make the reconstruction computing load compat...
Automatic detection, tracking and sensor integration
Trunk, G. V.
1988-06-01
This report surveys the state of the art of automatic detection, tracking, and sensor integration. In the area of detection, various noncoherent integrators such as the moving window integrator, feedback integrator, two-pole filter, binary integrator, and batch processor are discussed. Next, the three techniques for controlling false alarms, adapting thresholds, nonparametric detectors, and clutter maps are presented. In the area of tracking, a general outline is given of a track-while-scan system, and then a discussion is presented of the file system, contact-entry logic, coordinate systems, tracking filters, maneuver-following logic, tracking initiating, track-drop logic, and correlation procedures. Finally, in the area of multisensor integration the problems of colocated-radar integration, multisite-radar integration, radar-IFF integration, and radar-DF bearing strobe integration are treated.
A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics
Directory of Open Access Journals (Sweden)
Joaquín Míguez
2004-11-01
Full Text Available In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a 2-dimensional space.
Wang, Minlin; Ren, Xuemei; Chen, Qiang
2018-01-01
The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Yoshida, M; Komeda, I; Takizaki, K
1982-01-01
Bag filters are widely used throughout the cement industry for recovering raw materials and products and for improving the environment. Their general mechanism, performance and advantages are shown in a classification table, and there are comparisons and explanations. The outer and inner sectional construction of the Shinto ultra-jet collector for pulverized coal is illustrated and there are detailed descriptions of dust cloud prevention, of measures used against possible sources of ignition, of oxygen supply and of other topics. Finally, explanations are given of matters that require careful and comprehensive study when selecting equipment.
Hamming, Richard W
1997-01-01
Digital signals occur in an increasing number of applications: in telephone communications; in radio, television, and stereo sound systems; and in spacecraft transmissions, to name just a few. This introductory text examines digital filtering, the processes of smoothing, predicting, differentiating, integrating, and separating signals, as well as the removal of noise from a signal. The processes bear particular relevance to computer applications, one of the focuses of this book.Readers will find Hamming's analysis accessible and engaging, in recognition of the fact that many people with the s
International Nuclear Information System (INIS)
Boukhal, H.
1993-01-01
This study investigates the quantity variations of radon emanating from soil in accordance with time. It aims to verify the possibility of the radon sign use in earthquake prediction. Regular measures of radon concentration in soil have been carried out over the two years 1991 and 1992 in five towns of Morocco: Rabat, Tetouan, Ifrane and Khouribga, and in geophysic observatory of Ibn Rochd (Berchid region). The measuring method is based on the solid state nuclear track detectors technique. The obtained results have shown an influence of the atmospheric effects on the radon emanation. The experiment proved that, on one hand, the variations of the aforesaid influence are correlated to variations of the pluviometry and the atmospheric temperature and, on the other hand, there is no notable effect of atmospheric pressure or atmospheric humidity. The good correlations between the different seismic activities and the variations of radon emanation rate in the five measurement stations, have shown the interest of radon use in the earthquake prediction field. 81 refs., 100 figs., 17 tabs.(F. M.)
Low Complexity Track Initialization from a Small Set of Non-Invertible Measurements
Directory of Open Access Journals (Sweden)
Wolfgang Koch
2008-02-01
Full Text Available Target tracking from non-invertible measurement sets, for example, incomplete spherical coordinates measured by asynchronous sensors in a sensor network, is a task of data fusion present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by poor initialization. Instead of using high complexity numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations. This directly pertains to multi-hypothesis tracking (MHT, where in the presence of clutter and/or multiple targets (i low complexity algorithms are desirable and (ii using a small set of measurements avoids the combinatorial explosion. Our approach uses no numerical optimization, simply evaluating several equations to find the state estimates. This is possible since we avoid an over-determined setup by initializing only from the minimum necessary subset of measurements. Loss in accuracy is minimized by choosing the best subset using an optimality criterion and incorporating the leftover measurements afterwards. Additionally, we provide the possibility to estimate only sub-sets of parameters, and to reliably model the resulting added uncertainties by the covariance matrix. We compare two different implementations, differing in the approximation of the posterior: linearizing the measurement equation as in the extended Kalman filter (EKF or employing the unscented transform (UT. The approach will be studied in two practical examples: 3D track initialization using bearingsonly measurements or using slant-range and azimuth only.
Farroni, Flavio; Lamberti, Raffaele; Mancinelli, Nicolò; Timpone, Francesco
2018-03-01
Tyres play a key role in ground vehicles' dynamics because they are responsible for traction, braking and cornering. A proper tyre-road interaction model is essential for a useful and reliable vehicle dynamics model. In the last two decades Pacejka's Magic Formula (MF) has become a standard in simulation field. This paper presents a Tool, called TRIP-ID (Tyre Road Interaction Parameters IDentification), developed to characterize and to identify with a high grade of accuracy and reliability MF micro-parameters from experimental data deriving from telemetry or from test rig. The tool guides interactively the user through the identification process on the basis of strong diagnostic considerations about the experimental data made evident by the tool itself. A motorsport application of the tool is shown as a case study.
Tracking Multiple Topics for Finding Interesting Articles
Energy Technology Data Exchange (ETDEWEB)
Pon, R K; Cardenas, A F; Buttler, D J; Critchlow, T J
2008-01-03
We introduce multiple topic tracking (MTT) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time. As an extension of the basic Rocchio algorithm, traditional topic detection and tracking, and single-pass clustering, MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback. Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy. iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm. We identify several operating parameters that work well for MTT. Using the same parameters, we show that MTT alone yields high quality results for recommending interesting articles from several corpora. The inclusion of MTT improves iScore's performance by 25% in recommending news articles from the Yahoo! News RSS feeds and the TREC11 adaptive filter article collection. And through a small user study, we show that iScore can still perform well when only provided with little user feedback.
An improved particle filtering algorithm for aircraft engine gas-path fault diagnosis
Directory of Open Access Journals (Sweden)
Qihang Wang
2016-07-01
Full Text Available In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure, which adjusts the position of the particles through simulating attraction–repulsion mechanism between charged particles of the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the diversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement information. Comparison of the proposed method with three different filter algorithms is carried out on a univariate nonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal particle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period and the root mean square error of parameters estimation is reduced.
Adaptable Iterative and Recursive Kalman Filter Schemes
Zanetti, Renato
2014-01-01
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.
Law, Kody
2016-01-06
This talk will pertain to the filtering of partially observed diffusions, with discrete-time observations. It is assumed that only biased approximations of the diffusion can be obtained, for choice of an accuracy parameter indexed by l. A multilevel estimator is proposed, consisting of a telescopic sum of increment estimators associated to the successive levels. The work associated to O( 2) mean-square error between the multilevel estimator and average with respect to the filtering distribution is shown to scale optimally, for example as O( 2) for optimal rates of convergence of the underlying diffusion approximation. The method is illustrated on some toy examples as well as estimation of interest rate based on real S&P 500 stock price data.
International Nuclear Information System (INIS)
Mais, H.; Ripken, G.; Wrulich, A.; Schmidt, F.
1986-02-01
After a brief description of typical applications of particle tracking in storage rings and after a short discussion of some limitations and problems related with tracking we summarize some concepts and methods developed in the qualitative theory of dynamical systems. We show how these concepts can be applied to the proton ring HERA. (orig.)
DEFF Research Database (Denmark)
Düdder, Boris; Ross, Omry
2017-01-01
Managing and verifying forest products in a value chain is often reliant on easily manipulated document or digital tracking methods - Chain of Custody Systems. We aim to create a new means of tracking timber by developing a tamper proof digital system based on Blockchain technology. Blockchain...
Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor
Directory of Open Access Journals (Sweden)
Wenjie Lou
2016-02-01
Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.
High Pass Filtering of Satellite Altimeter Data,
1982-10-01
bathymetry [7] and filtered data tracks (N = 3, X = 200 km) near the Clipperton Fracture Zone just East of the Christmas Island Ridge. Along the multiple...We also notice a negative signature associated with the Clipperton Fracture Zone and extending over all the tracks. It may indicate a trough covered...in Mid-Pacific Seamount Province..Mid-Iat tic and near the Western Clipperton Fracture Zone respectively. These charts arc to he overlaid by Figures
Syeda, Bonni; Höfer, Peter; Pichler, Philipp; Vertesich, Markus; Bergler-Klein, Jutta; Roedler, Susanne; Mahr, Stephane; Goliasch, Georg; Zuckermann, Andreas; Binder, Thomas
2011-07-01
Longitudinal strain determined by speckle tracking is a sensitive parameter to detect systolic left ventricular dysfunction. In this study, we assessed regional and global longitudinal strain values in long-term heart transplants and compared deformation indices with ejection fraction as determined by transthoracic echocardiography (TTE) and multislice computed tomographic coronary angiography (MSCTA). TTE and MSCTA were prospectively performed in 31 transplant patients (10.6 years post-transplantation) and in 42 control subjects. Grey-scale apical views were recorded for speckle tracking (EchoPAC 7.0, GE) of the 16 segments of the left ventricle. The presence of coronary artery disease (CAD) was assessed by MSCTA. Strain analysis was performed in 1168 segments [496 in transplant patients (42.5%), 672 in control subjects (57.7%)]. Global longitudinal peak systolic strain was significantly lower in the transplant recipients than in the healthy population (-13.9 ± 4.2 vs. -17.4 ± 5.8%, PSimpsons method) was 60.7 ± 10.1%/60.2 ± 6.7% in transplant recipients vs. 64.7 ± 6.4%/63.0 ± 6.2% in the healthy population, P=ns. Even though 'healthy' heart transplants without CAD exhibit normal ejection fraction, deformation indices are reduced in this population when compared with control subjects. Our findings suggests that strain analysis is more sensitive than assessment of ejection fraction for the detection of abnormalities of systolic function.
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Tracking in Object Action Space
DEFF Research Database (Denmark)
Krüger, Volker; Herzog, Dennis
2013-01-01
the space of the object affordances, i.e., the space of possible actions that are applied on a given object. This way, 3D body tracking reduces to action tracking in the object (and context) primed parameter space of the object affordances. This reduces the high-dimensional joint-space to a low...
Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing
Directory of Open Access Journals (Sweden)
Yang Jun
2016-02-01
Full Text Available A multiple targets cognitive radar tracking method based on Compressed Sensing (CS is proposed. In this method, the theory of CS is introduced to the case of cognitive radar tracking process in multiple targets scenario. The echo signal is sparsely expressed. The designs of sparse matrix and measurement matrix are accomplished by expressing the echo signal sparsely, and subsequently, the restruction of measurement signal under the down-sampling condition is realized. On the receiving end, after considering that the problems that traditional particle filter suffers from degeneracy, and require a large number of particles, the particle swarm optimization particle filter is used to track the targets. On the transmitting end, the Posterior Cramér-Rao Bounds (PCRB of the tracking accuracy is deduced, and the radar waveform parameters are further cognitively designed using PCRB. Simulation results show that the proposed method can not only reduce the data quantity, but also provide a better tracking performance compared with traditional method.
Radionuclide release rate inversion of nuclear accidents in nuclear facility based on Kalman filter
International Nuclear Information System (INIS)
Tang Xiuhuan; Bao Lihong; Li Hua; Wan Junsheng
2014-01-01
The rapidly and continually back-calculating source term is important for nuclear emergency response. The Gaussian multi-puff atmospheric dispersion model was used to produce regional environment monitoring data virtually, and then a Kalman filter was designed to inverse radionuclide release rate of nuclear accidents in nuclear facility and the release rate tracking in real time was achieved. The results show that the Kalman filter combined with Gaussian multi-puff atmospheric dispersion model can successfully track the virtually stable, linear or nonlinear release rate after being iterated about 10 times. The standard error of inversion results increases with the true value. Meanwhile extended Kalman filter cannot inverse the height parameter of accident release as interceptive error is too large to converge. Kalman filter constructed from environment monitoring data and Gaussian multi-puff atmospheric dispersion model can be applied to source inversion in nuclear accident which is characterized by static height and position, short and continual release in nuclear facility. Hence it turns out to be an alternative source inversion method in nuclear emergency response. (authors)
Learning Rotation for Kernel Correlation Filter
Hamdi, Abdullah
2017-08-11
Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.
Energy Technology Data Exchange (ETDEWEB)
Anon.
1986-10-15
In many modern tracking chambers, the sense wires, rather than being lined up uniformly, are grouped into clusters to facilitate the pattern recognition process. However, with higher energy machines providing collisions richer in secondary particles, event reconstruction becomes more complicated. A Caltech / Illinois / SLAC / Washington group developed an ingenious track finding and fitting approach for the Mark III detector used at the SPEAR electron-positron ring at SLAC (Stanford). This capitalizes on the detector's triggering, which uses programmable logic circuits operating in parallel, each 'knowing' the cell patterns for all tracks passing through a specific portion of the tracker (drift chamber)
Large Radius Tracking at the ATLAS Experiment
Lutz, Margaret Susan; The ATLAS collaboration
2017-01-01
Many exotics and SUSY models include particles which are long lived resulting in decays which are highly displaced from the proton-proton interaction point (IP). The standard track reconstruction algorithm used by the ATLAS collaboration is optimized for tracks from “primary” particles, which originate close to the IP. Thus, tight restrictions on the transverse and longitudinal impact parameters, as well as on several other tracking variables, are applied to improve the track reconstruction performance and to reduce the fake rate. This track reconstruction is very efficient for primary particles, but not for the non-prompt particles mentioned above. In order to reconstruct tracks with large impact parameters due to displaced decays, a tracking algorithm has been optimized to re-run with loosened requirements over the hits left over after standard track reconstruction has finished. Enabling this “retracking” has significantly increased the efficiency of reconstructing tracks from displaced decays, wh...
A novel generic framework for track fitting in complex detector systems
International Nuclear Information System (INIS)
Hoeppner, C.; Neubert, S.; Ketzer, B.; Paul, S.
2010-01-01
This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement. This goal is achieved through a completely modular design. Fitting algorithms are implemented as interchangeable modules. At present, the framework contains a validated Kalman filter. Track parameterizations and the routines required to extrapolate the track parameters and their covariance matrices through the experiment are also implemented as interchangeable modules. Different track parameterizations and extrapolation routines can be used simultaneously for fitting of the same physical track. Representations of detector hits are the third modular ingredient to the framework. The hit dimensionality and orientation of planar tracking detectors are not restricted. Tracking information from detectors which do not measure the passage of particles in a fixed physical detector plane, e.g. drift chambers or TPCs, is used without any simplification. The concept is implemented in a light-weight C++ library called GENFIT, which is available as free software.
A novel generic framework for track fitting in complex detector systems
Energy Technology Data Exchange (ETDEWEB)
Hoeppner, C., E-mail: christian.hoeppner@cern.c [Technische Universitaet Muenchen, Physik Department, 85748 Garching (Germany); Neubert, S.; Ketzer, B.; Paul, S. [Technische Universitaet Muenchen, Physik Department, 85748 Garching (Germany)
2010-08-21
This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement. This goal is achieved through a completely modular design. Fitting algorithms are implemented as interchangeable modules. At present, the framework contains a validated Kalman filter. Track parameterizations and the routines required to extrapolate the track parameters and their covariance matrices through the experiment are also implemented as interchangeable modules. Different track parameterizations and extrapolation routines can be used simultaneously for fitting of the same physical track. Representations of detector hits are the third modular ingredient to the framework. The hit dimensionality and orientation of planar tracking detectors are not restricted. Tracking information from detectors which do not measure the passage of particles in a fixed physical detector plane, e.g. drift chambers or TPCs, is used without any simplification. The concept is implemented in a light-weight C++ library called GENFIT, which is available as free software.
Chen, Wentao; Zhang, Weidong
2009-10-01
In an optical disk drive servo system, to attenuate the external periodic disturbances induced by inevitable disk eccentricity, repetitive control has been used successfully. The performance of a repetitive controller greatly depends on the bandwidth of the low-pass filter included in the repetitive controller. However, owing to the plant uncertainty and system stability, it is difficult to maximize the bandwidth of the low-pass filter. In this paper, we propose an optimality based repetitive controller design method for the track-following servo system with norm-bounded uncertainties. By embedding a lead compensator in the repetitive controller, both the system gain at periodic signal's harmonics and the bandwidth of the low-pass filter are greatly increased. The optimal values of the repetitive controller's parameters are obtained by solving two optimization problems. Simulation and experimental results are provided to illustrate the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Coghetto Roland
2015-09-01
Full Text Available We are inspired by the work of Henri Cartan [16], Bourbaki [10] (TG. I Filtres and Claude Wagschal [34]. We define the base of filter, image filter, convergent filter bases, limit filter and the filter base of tails (fr: filtre des sections.
Virtual experiment of optical spatial filtering in Matlab environment
Ji, Yunjing; Wang, Chunyong; Song, Yang; Lai, Jiancheng; Wang, Qinghua; Qi, Jing; Shen, Zhonghua
2017-08-01
The principle of spatial filtering experiment has been introduced, and the computer simulation platform with graphical user interface (GUI) has been made out in Matlab environment. Using it various filtering processes for different input image or different filtering purpose will be completed accurately, and filtering effect can be observed clearly with adjusting experimental parameters. The physical nature of the optical spatial filtering can be showed vividly, and so experimental teaching effect will be promoted.
International Nuclear Information System (INIS)
Burchart, J.; Kral, J.
1979-01-01
A comparison is made of two methods of determining the age of rocks, ie., the krypton-argon method and the fission tracks method. The former method is more accurate but is dependent on the temperature and on the grain size of the investigated rocks (apatites, biotites, muscovites). As for the method of fission tracks, the determination is not dependent on grain size. This method allows dating and the determination of uranium concentration and distribution in rocks. (H.S.)
International Nuclear Information System (INIS)
Khayat, O.; Ghergherehchi, M.; Afarideh, H.; Durrani, S.A.; Pouyan, Ali A.; Kim, Y.S.
2013-01-01
A computer program named ATMS written in MATLAB and running with a friendly interface has been developed for recognition and parametric measurements of etched tracks in images captured from the surface of Solid State Nuclear Track Detectors. The program, using image analysis tools, counts the number of etched tracks and depending on the current working mode classifies them according to their radii (small object removal) or their axis (non-perpendicular or non-circular etched tracks), their mean intensity value and their orientation through the minor and major axes. Images of the detectors' surfaces are input to the code, which generates text and figure files as output, including the number of counted etched tracks with the associated track parameters, histograms and a figure showing edge and center of detected etched tracks. ATMS code is running hierarchically as calibration, testing and measurement modes to demonstrate the reliability, repeatability and adaptability. Fuzzy Hough Transform is used for the estimation of the number of etched tracks and their parameters, providing results even in cases that overlapping and orientation occur. ATMS code is finally converted to a standalone file which makes it able to run out of MATLAB environment. - Highlights: ► Presenting a novel code named ATMS for nuclear track measurements. ► Execution in three modes for generality, adaptability and reliability. ► Using Fuzzy Hough Transform for overlapping detection and orientation recognition. ► Using DFT as a filter for noise removal process in track images. ► Processing the noisy track images and demonstration of the presented code
International Nuclear Information System (INIS)
Bella, H.; Stiehl, H.H.; Sinhuber, D.
1977-01-01
The knowledge of the parameters of HEPA filters used at present in nuclear plants allows optimization of such filters with respect to flow rate, pressure drop and service life. The application of optimizing new types of HEPA filters of improved performance is reported. The calculated results were checked experimentally. The use of HEPA filters optimized with respect to dust capacity and service life, and the effects of this new type of filter on the reduction of operating and maintenance costs are discussed