A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling
Bekaert, D. P.; Segall, P.; Wright, T. J.; Hooper, A. J.
2016-12-01
Time-dependent slip modeling can be a powerful tool to improve our understanding of the interaction of earthquake cycle processes such as interseismic, coseismic, postseismic, and aseismic slip. Interferometric Synthetic Aperture Radar (InSAR) observations allow us to model slip at depth with a higher spatial resolution than when using GNSS alone. Typically the temporal resolution of InSAR has been limited. However, the recent generation of SAR satellites including Sentinel-1, COSMO-SkyMED, and RADARSAT-2 permits the use of InSAR for time-dependent slip modeling, at intervals of a few days when combined. The increasing amount of SAR data makes a simultaneous data inversion of all epochs challenging. Here, we expanded the original Network Inversion Filter (Segall and Matthews, 1997) to include InSAR observations of surface displacements in addition to GNSS. In the NIF framework, geodetic observations are limited to those of a given epoch, where a physical model describes the slip evolution over time. The combination of the Kalman forward filtering and backward smoothing allows all geodetic observations to constrain the complete observation period. Combining GNSS and InSAR allows us to model time-dependent slip at an unprecedented spatial resolution. We validate the approach with a simulation of the 2006 Guerrero slow slip event. In our study, we emphasize the importance of including the InSAR covariance information, and demonstrate that InSAR provides an additional constraint on the spatial extent of the slow slip. References: Segall, P., and M. Matthews (1997), Time dependent inversion of geodetic data, J. Geophys. Res., 102 (B10), 22,391 - 22,409, doi:10.1029/97JB01795. Bekaert, D., P. Segall, T.J. Wright, and A. Hooper (2016), A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling, JGR, doi:10.1002/2015JB012638 (open access).
INVERSE FILTERING TECHNIQUES IN SPEECH ANALYSIS
African Journals Online (AJOL)
Dr Obe
ABSTRACT. This paper reviews certain speech analytical techniques to which the label 'inverse filtering' has been applied. The unifying features of these techniques are presented, namely: 1. a basis in the source-filter theory of speech production,. 2. the use of a network whose transfer function is the inverse of the transfer ...
INVERSE FILTERING TECHNIQUES IN SPEECH ANALYSIS
African Journals Online (AJOL)
Dr Obe
particular system filter being inverted and in the manner of realisation. provide a basis for the classification adopted in the paper which is as follows: (1) inverse vocal tract analogue filtering. (2) inverse vocal tract digital filtering. (3) direct inverse glottal filtering. (4) linear predictive coding. An assessment of the comparative ...
Alternating minimisation for glottal inverse filtering
Rodrigo Bleyer, Ismael; Lybeck, Lasse; Auvinen, Harri; Airaksinen, Manu; Alku, Paavo; Siltanen, Samuli
2017-06-01
A new method is proposed for solving the glottal inverse filtering (GIF) problem. The goal of GIF is to separate an acoustical speech signal into two parts: the glottal airflow excitation and the vocal tract filter. To recover such information one has to deal with a blind deconvolution problem. This ill-posed inverse problem is solved under a deterministic setting, considering unknowns on both sides of the underlying operator equation. A stable reconstruction is obtained using a double regularization strategy, alternating between fixing either the glottal source signal or the vocal tract filter. This enables not only splitting the nonlinear and nonconvex problem into two linear and convex problems, but also allows the use of the best parameters and constraints to recover each variable at a time. This new technique, called alternating minimization glottal inverse filtering (AM-GIF), is compared with two other approaches: Markov chain Monte Carlo glottal inverse filtering (MCMC-GIF), and iterative adaptive inverse filtering (IAIF), using synthetic speech signals. The recent MCMC-GIF has good reconstruction quality but high computational cost. The state-of-the-art IAIF method is computationally fast but its accuracy deteriorates, particularly for speech signals of high fundamental frequency (F0). The results show the competitive performance of the new method: With high F0, the reconstruction quality is better than that of IAIF and close to MCMC-GIF while reducing the computational complexity by two orders of magnitude.
Sequential Geoacoustic Filtering and Geoacoustic Inversion
2015-09-30
directions of the angular spectrum . Grid refinement alleviates basis mismatch at the expense of increased computational complexity, especially in large...two-dimensional or three-dimensional geoacoustic inversion problems such as seismic imaging. Importantly, grid refinement causes increased coherence
A passive inverse filter for Green's function retrieval.
Gallot, Thomas; Catheline, Stefan; Roux, Philippe; Campillo, Michel
2012-01-01
Passive methods for the recovery of Green's functions from ambient noise require strong hypotheses, including isotropic distribution of the noise sources. Very often, this distribution is nonisotropic, which introduces bias in the Green's function reconstruction. To minimize this bias, a spatiotemporal inverse filter is proposed. The method is tested on a directive noise field computed from an experimental active seismic data set. The results indicate that the passive inverse filter allows the manipulation of the spatiotemporal degrees of freedom of a complex wave field, and it can efficiently compensate for the noise wavefield directivity. © 2012 Acoustical Society of America.
Scattering angle base filtering of the inversion gradients
Alkhalifah, Tariq Ali
2014-01-01
Full waveform inversion (FWI) requires a hierarchical approach based on the availability of low frequencies to maneuver the complex nonlinearity associated with the problem of velocity inversion. I develop a model gradient filter to help us access the parts of the gradient more suitable to combat this potential nonlinearity. The filter is based on representing the gradient in the time-lag normalized domain, in which low scattering angles of the gradient update are initially muted. The result are long-wavelength updates controlled by the ray component of the wavefield. In this case, even 10 Hz data can produce near zero wavelength updates suitable for a background correction of the model. Allowing smaller scattering angle to contribute provides higher resolution information to the model.
Efficient scattering angle filtering for Full waveform inversion
Alkhalifah, Tariq Ali
2015-08-19
Controlling the scattering angles between the state and the adjoint variables for the energy admitted into an inversion gradient or an image can help improve these functions for objectives in full waveform inversion (FWI) or seismic imaging. However, the access of the scattering angle information usually requires an axis extension that could be costly, especially in 3D. For the purpose of a scattering angle filter, I develop techniques that utilize the mapping nature (no domain extension) of the filter for constant-velocity background models to interpolate between such filtered gradients using the actual velocity. The concept has well known roots in the application of phase-shift-plus-interpolation utilized commonly in the downward continuation process. If the difference between the minimum and maximum velocity of the background medium is large, we obtain filtered gradients corresponding to more constant velocity backgrounds and use linear interpolation between such velocities. The accuracy of this approximation for the Marmousi model gradient demonstrates the e ectiveness of the approach.
Filtering and control of wireless networked systems
Zhang, Dan; Yu, Li
2017-01-01
This self-contained book, written by leading experts, offers a cutting-edge, in-depth overview of the filtering and control of wireless networked systems. It addresses the energy constraint and filter/controller gain variation problems, and presents both the centralized and the distributed solutions. The first two chapters provide an introduction to networked control systems and basic information on system analysis. Chapters (3–6) then discuss the centralized filtering of wireless networked systems, presenting different approaches to deal with energy efficiency and filter/controller gain variation problems. The next part (chapters 7–10) explores the distributed filtering of wireless networked systems, addressing the main problems of energy constraint and filter gain variation. The final part (chapters 11–14) focuses on the distributed control of wireless networked systems.
Deep Convolutional Neural Network for Inverse Problems in Imaging.
Jin, Kyong Hwan; McCann, Michael T; Froustey, Emmanuel; Unser, Michael
2017-06-15
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on the GPU.
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, Kyong Hwan; McCann, Michael T.; Froustey, Emmanuel; Unser, Michael
2017-09-01
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on GPU.
Scattering-angle based filtering of the waveform inversion gradients
Alkhalifah, Tariq Ali
2014-11-22
Full waveform inversion (FWI) requires a hierarchical approach to maneuver the complex non-linearity associated with the problem of velocity update. In anisotropic media, the non-linearity becomes far more complex with the potential trade-off between the multiparameter description of the model. A gradient filter helps us in accessing the parts of the gradient that are suitable to combat the potential non-linearity and parameter trade-off. The filter is based on representing the gradient in the time-lag normalized domain, in which the low scattering angle of the gradient update is initially muted out in the FWI implementation, in what we may refer to as a scattering angle continuation process. The result is a low wavelength update dominated by the transmission part of the update gradient. In this case, even 10 Hz data can produce vertically near-zero wavenumber updates suitable for a background correction of the model. Relaxing the filtering at a later stage in the FWI implementation allows for smaller scattering angles to contribute higher-resolution information to the model. The benefits of the extended domain based filtering of the gradient is not only it\\'s ability in providing low wavenumber gradients guided by the scattering angle, but also in its potential to provide gradients free of unphysical energy that may correspond to unrealistic scattering angles.
Chen, Xiangdong; He, Liwen; Jeon, Gwanggil; Jeong, Jechang
2014-05-01
In this paper, we present a novel color image demosaicking algorithm based on a directional weighted interpolation method and gradient inverse-weighted filter-based refinement method. By applying a directional weighted interpolation method, the missing center pixel is interpolated, and then using the nearest neighboring pixels of the pre-interpolated pixel within the same color channel, the accuracy of interpolation is refined using a five-point gradient inverse weighted filtering method we proposed. The refined interpolated pixel values can be used to estimate the other missing pixel values successively according to the correlation inter-channels. Experimental analysis of images revealed that our proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional state-of-the-art demosaicking algorithms. Our implementation has very low complexity and is therefore well suited for real-time applications.
Jeong, Jinsoo
2011-01-01
This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure. PMID:22163987
Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion
Directory of Open Access Journals (Sweden)
Takehiko Ogawa
2010-01-01
Full Text Available In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.
National Research Council Canada - National Science Library
Jeong, Jinsoo
2011-01-01
...) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal...
Decoupled deblurring filter and its application to elastic migration and inversion
Feng, Zongcai
2017-08-17
We present a decoupled deblurring filter that approximates the multiparameter Hessian inverse by using local filters to approximate its submatrices for the same and different parameter classes. Numerical tests show that the filter not only reduces the footprint noise, balances the amplitudes and increases the resolution of the elastic migration images, but also mitigates the crosstalk artifacts. When used as a preconditioner, it accelerates the convergence rate for elastic inversion.
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological ...
National Research Council Canada - National Science Library
Haji-saeed, Bahareh; Khoury, Jed; Woods, Charles L; Kierstead, John
2008-01-01
...) for facial recognition is proposed. In order to avoid spectral overlap and nonlinear crosstalk, superposition of rotationally variant sets of inverse filter Fourier-transformed Radon-processed templates is used to generate the SDF...
Glottal inverse filtering analysis of human voice production—A ...
Indian Academy of Sciences (India)
Glottal inverse ﬁltering (GIF) refers to methods of estimating the source of voiced speech, the glottal volume velocity waveform. GIF is based on the idea of inversion, in which the effects of the vocal tract and lip radiation are cancelled from the output of the voice production mechanism, the speech signal. This article provides ...
Inverse kinematics problem in robotics using neural networks
Choi, Benjamin B.; Lawrence, Charles
1992-01-01
In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
MICHAEL
modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data obtained from an inverse fluidized bed reactor treating the starch industry wastewater.
Image Filtering with Neural Networks: applications and performance evaluation
Spreeuwers, Lieuwe Jan
1992-01-01
A simple and elegant method to design image filters with neural networks is proposed: using small networks that scan the image and perform position invariant filtering. In the theses examples of image filtering with error backpropagation networks for edge detection, image deblurring and noise
A Novel Local Transform Inverse S-Transform Algorithm for Statistical Filter
Yin, Baiqiang; Sun, Zhanfeng; Yi, Zhong; He, Yigang
2017-09-01
S-transform (ST) is a useful tool for time-frequency filter. However, the conventional inverse S-transform (IST) algorithm suffers from time or frequency leakage. In this paper, we proposed a novel local transform inverse S-Transform (LTIST) algorithm for statistical filter. First, the matrix S-transform (MST) and MIST are derived. Then the proposed LTIST approach applies to denoising. The statistical property of stochastic noise in the MIST is discussed. The results show that the proposed MIST algorithm has better time-frequency localization in statistical filtering than the conventional methods. Illustrative examples verify the effectiveness of the proposed algorithm.
Energy Technology Data Exchange (ETDEWEB)
Khan, T.; Ramuhalli, Pradeep; Dass, Sarat
2011-06-30
Flaw profile characterization from NDE measurements is a typical inverse problem. A novel transformation of this inverse problem into a tracking problem, and subsequent application of a sequential Monte Carlo method called particle filtering, has been proposed by the authors in an earlier publication [1]. In this study, the problem of flaw characterization from multi-sensor data is considered. The NDE inverse problem is posed as a statistical inverse problem and particle filtering is modified to handle data from multiple measurement modes. The measurement modes are assumed to be independent of each other with principal component analysis (PCA) used to legitimize the assumption of independence. The proposed particle filter based data fusion algorithm is applied to experimental NDE data to investigate its feasibility.
Filtering in hybrid dynamic Bayesian networks (left)
DEFF Research Database (Denmark)
Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used...... that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...
Filtering in hybrid dynamic Bayesian networks
DEFF Research Database (Denmark)
Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin
2004-01-01
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used...... that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...
Filtering in hybrid dynamic Bayesian networks (center)
DEFF Research Database (Denmark)
Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used...... that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...
Improving information filtering via network manipulation
Zhang, Fuguo
2012-01-01
Recommender system is a very promising way to address the problem of overabundant information for online users. Though the information filtering for the online commercial systems received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e. low recommendation accuracy for the small degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improve the recommendation performance. Specifically, it not only improve the recommendations accur...
A family of quantization based piecewise linear filter networks
DEFF Research Database (Denmark)
Sørensen, John Aasted
1992-01-01
A family of quantization-based piecewise linear filter networks is proposed. For stationary signals, a filter network from this family is a generalization of the classical Wiener filter with an input signal and a desired response. The construction of the filter network is based on quantization...... of the input signal x(n) into quantization classes. With each quantization class is associated a linear filter. The filtering at time n is carried out by the filter belonging to the actual quantization class of x(n ) and the filters belonging to the neighbor quantization classes of x(n) (regularization......). This construction leads to a three-layer filter network. The first layer consists of the quantization class filters for the input signal. The second layer carries out the regularization between neighbor quantization classes, and the third layer constitutes a decision of quantization class from where the resulting...
PP and PS joint inversion with a posterior constraint and with particle filtering
Tang, Jing; Wang, Yanfei
2017-12-01
The Bayesian framework works well in amplitude versus offset (AVO) inversion, which merges multi-information together to generate posterior distributions of P-wave velocity, S-wave velocity and density. Most existing AVO inversion methods utilize PP reflection seismic data to predict the three elastic parameters. These methods are not usually sensitive to S-wave velocity and density, which make the inversion methods inaccurate and unstable. One way of solving these problems is to perform PP and PS joint inversion by incorporating PS seismic data. Another way is to provide a relatively accurate prior model. In this paper, we apply a particle filtering technique to produce a prior model for the PP and PS joint inversion. In the Bayesian inversion setting, the prior model works as the regularization term. Particle filtering is a Bayesian recursive method that combines prior information with observed data to provide a posterior constraint to reduce the joint inversion’s uncertainty. We generate synthetic models with different signal-to-noise ratios to validate our new method. Comparisons are provided with the traditional joint inversion, which adopts the Gaussian prior model. The inversion results show that the three elastic parameters are retrieved well when the signal-to-noise ratios are high. As the signal-to-noise ratio reduces, our new method can depict more detailed changes than the traditional inversion method, and improves the inversion accuracy apparent in the target layers.
Kalman Filter Inversion of Regional NOx Emissions based on OMI NO2 Observations
Cohan, D. S.; Tang, W.
2012-12-01
Nitrogen oxides (NOx) are crucial precursors of tropospheric ozone and particulate matter. Uncertain emissions inventories for NOx are among the leading causes of uncertainty in photochemical models used to inform air quality management. Emission inventories derived from bottom-up approaches typically serve as the basis for state implementation plans and other regulatory modeling. However, inverse modeling can be used to create top-down estimates of emissions based on observed pollutant levels in order to evaluate or supplement traditional inventories. Here, we apply NO2 column densities observed by the OMI instrument aboard the Aura satellite (OMI Standard Product version 2.0) to estimate top-down NOx emission rates for seven urban and rural regions of east Texas. The CAMx photochemical model with Decoupled Direct Method (DDM) sensitivity analysis is applied to simulate 3-dimensional fields of NO2 concentrations and their sensitivities to NOx emissions from each region, starting from an emissions inventory used in recent Texas ozone attainment planning. Averaging kernels from the OMI retrievals are used to adjust CAMx results to corresponding column densities. Lightning NO emissions are added to the a priori inventory based on National Lightning Detection Network data, which rectifies a portion of the underprediction of NO2 in rural regions. A Kalman Filter inversion is applied to estimate regional emissions scaling factors that yield best agreement between CAMx and OMI results, using an iterative approach until convergence is achieved. Pseudo-data testing demonstrates that the Kalman Filter can rectify known perturbations to a base field within four iterations. Ambient observations of NOx from regulatory monitors and from the Texas Air Quality Study 2006 field campaign are used to evaluate the original and top-down emissions inventories. Both inventories are applied in the CAMx simulations of Texas ozone attainment modeling episodes to evaluate differences in
Gaussian filters and filter synthesis using a Hermite/Laguerre neural network.
Mackenzie, Mark; Tieu, Kiet
2004-01-01
A neural network for calculating the correlation of a signal with a Gaussian function is described. The network behaves as a Gaussian filter and has two outputs: the first approximates the noisy signal and the second represents the filtered signal. The filtered output provides improvement by a factor of ten in the signal-to-noise ratio. A higher order Gaussian filter was synthesized by combining several Hermite functions together.
Resolution enhancement of pump-probe microscopy with an inverse-annular spatial filter
Kobayashi, T.; Kawasumi, K.; Miyazaki, J.; Nakata, K.
2016-12-01
We have introduced a pupil filter, an inverse-annular pupil filter in a pump-probe photothermal microscope, which provides resolution enhancement in three dimensions. The resolution is probed to be improved in lateral and axial resolution by imaging experiment using 20 nm gold nanoparticles. The improvement in X (perpendicular to the common pump and probe polarization direction), Y (parallel to the polarization direction), and Z (axial direction) are by 15±6, 8±8, and 21±2 % from the resolution without a pupil filter. The resolution enhancement is even better than the calculation using vector field, which predicts the corresponding enhancement of 11, 8, and 6 %. The discussion is made to explain the unexpected results. We also demonstrate the photothermal imaging of thick biological samples (cells from rabbit intestine and kidney) stained with hematoxylin and eosin dye with the inverse-annular filter.
Capmany, José; Pastor, Daniel; Martinez, Alfonso; Ortega, Beatriz; Sales, Salvador
2003-08-15
We report on a novel technical approach to the implementation of photonic rf filters that is based on the pi phase inversion that a rf modulating signal suffers in an electro-optic Mach-Zehnder modulator, which depends on whether the positive or the negative linear slope of the signal's modulation transfer function is employed. Experimental evidence is provided of the implementation of filters with negative coefficients that shows excellent agreement with results predicted by the theory.
Comparison of filter with Prairie and European Network data
Energy Technology Data Exchange (ETDEWEB)
Canavan, G.H.
1997-10-01
Earlier notes derived a model for the hydrodynamics, ablation, and radiation of meteor impacts at the level needed to infer meteor parameters from observations and extended it to objects that fragment during entry, using models based on related cometary studies. This note completes the comparison of the resulting filter model to European and Prairie Network (EN and PN) data and models of meteor impact. In cases of mutual applicability, US and European models give broadly consistent results. The quantitative analysis of the EN and PN data is best discussed in conjunction with the Russian program of its analysis, because the Russian program has bypassed the large reported photometrically based masses to derive more plausible estimates of sizes, masses, and radiation efficiencies, which are the primary quantities of concern here. This note completes the discussion of the PN and EN data begun earlier, uses the data to produce filter predictions, and compares it with observations and the predictions of the Russian analytic effort. The overall agreement is useful in that the Russian efforts have employed more complex models that use observational data directly, while the filter model is at a level of simplification much better suited to data inversion.
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 of the obse......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...
Improving information filtering via network manipulation
Zhang, Fuguo; Zeng, An
2012-12-01
The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.
Two-dimensional unwrapped phase inversion with damping and a Gaussian filter
Choi, Yun Seok
2014-01-01
Phase wrapping is one of main causes of the local minima problem in waveform inversion. However, the unwrapping process for 2D phase maps that includes singular points (residues) is complicated and does not guarantee unique solutions. We employ an exponential damping to eliminate the residues in the 2D phase maps, which makes the 2D phase unwrapping process easy and produce a unique solution. A recursive inversion process using the damped unwrapped phase provides an opportunity to invert for smooth background updates first, and higher resolution updates later as we reduce the damping. We also apply a Gaussian filter to the gradient to mitigate the edge artifacts resulting from the narrow shape of the sensitivity kernels at high damping. Numerical examples demonstrate that our unwrapped phase inversion with damping and a Gaussian filter produces good convergent results even for a 3Hz single frequency of Marmousi dataset and with a starting model far from the true model.
A Decoupling Control Method for Shunt Hybrid Active Power Filter Based on Generalized Inverse System
Directory of Open Access Journals (Sweden)
Xin Li
2017-01-01
Full Text Available In this paper, a novel decoupling control method based on generalized inverse system is presented to solve the problem of SHAPF (Shunt Hybrid Active Power Filter possessing the characteristics of 2-input-2-output nonlinearity and strong coupling. Based on the analysis of operation principle, the mathematical model of SHAPF is firstly built, which is verified to be invertible using interactor algorithm; then the generalized inverse system of SHAPF is obtained to connect in series with the original system so that the composite system is decoupled under the generalized inverse system theory. The PI additional controller is finally designed to control the decoupled 1-order pseudolinear system to make it possible to adjust the performance of the subsystem. The simulation results demonstrated by MATLAB show that the presented generalized inverse system strategy can realise the dynamic decoupling of SHAPF. And the control system has fine dynamic and static performance.
Robust Filtering for Networked Stochastic Systems Subject to Sensor Nonlinearity
Directory of Open Access Journals (Sweden)
Guoqiang Wu
2013-01-01
Full Text Available The problem of network-based robust filtering for stochastic systems with sensor nonlinearity is investigated in this paper. In the network environment, the effects of the sensor saturation, output quantization, and network-induced delay are taken into simultaneous consideration, and the output measurements received in the filter side are incomplete. The random delays are modeled as a linear function of the stochastic variable described by a Bernoulli random binary distribution. The derived criteria for performance analysis of the filtering-error system and filter design are proposed which can be solved by using convex optimization method. Numerical examples show the effectiveness of the design method.
Directory of Open Access Journals (Sweden)
G. Arul Elango
2015-01-01
Full Text Available The lower visibility of the satellite in the acquisition stage of a GPS receiver under worst noisy situation leads to reacquisition of the data and thereby takes a longer time to obtain the first position fix. If the impulse noise affects the GPS signal, the conventional ways of acquiring the satellites do not guarantee to meet the minimum requirement of four satellites to find the user position. The performance of GPS receiver acquisition can be improved in the low SNR level using inverse spiking filtering technique. In the proposed method, the estimate of the desired GPS L1 signal corrupted by impulse noise (gn is obtained by the prediction error filter (hopt, which is the optimum inverse filter that reshapes the noisy signal (yn into a desired GPS signal (xn. In the proposed method, to detect the visible satellites under weak signal conditions the traditional differential coherent approach is combined with the inverse spiking filter method to increase the number of visible satellites and to avoid the reacquisition process. Montecarlo simulation is carried out to assess the performance of the proposed method for C/N0 of 20 dB-Hz and results indicate that the modified differential coherent method effectively excises the noise with 90% probability of detection. Subsequently tracking operation is also tested to confirm the acquisition performance by demodulating the navigation data successfully.
Realization of Broadband Matched Filter Structures Based on Dual Networks
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M. Gerding
2005-01-01
Full Text Available This paper deals with the basic electrical properties of dual networks and with their application in broadband matched filter structures. Starting with the main characteristics and different realization methods of dual networks, a filter structure is presented, which is based on a combination of dual networks and which provides a broadband matched input and two decoupled output ports. This filter synthesis focuses on the design of high pass filters, which are suitable to be used as differentiating stages in electrical pulse generators as a part of the so-called pulse shaping network. In order to achieve a proper pulse shape and for the prevention of multiple reflections between the switching circuit and the differentiating network, a broadband matched filter is a basic requirement.
Information filtering in evolving online networks
Chen, Bo-Lun; Li, Fen-Fen; Zhang, Yong-Jun; Ma, Jia-Lin
2018-02-01
Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.
Improved neural network modeling of inverse lens distortion
CSIR Research Space (South Africa)
De Villiers, JP
2011-04-01
Full Text Available Inverse lens distortion modelling allows one to find the pixel in a distorted image which corresponds to a known point in object space, such as may be produced by a RADAR. This paper extends recent work using neural networks as a compromise between...
Inverse Problem of Air Filtration of Nanoparticles: Optimal Quality Factors of Fibrous Filters
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Dahua Shou
2015-01-01
Full Text Available Application of nanofibers has become an emerging approach to enhance filtration efficiency, but questions arise about the decrease in Quality factor (QF for certain particles due to the rapidly increasing pressure drop. In this paper, we theoretically investigate the QF of dual-layer filters for filtration of monodisperse and polydisperse nanoparticles. The inverse problem of air filtration, as defined in this work, consists in determining the optimal construction of the two-layer fibrous filter with the maximum QF. In comparison to a single-layer substrate, improved QF values for dual-layer filters are found when a second layer with proper structural parameters is added. The influences of solidity, fiber diameter, filter thickness, face velocity, and particle size on the optimization of QF are studied. The maximum QF values for realistic polydisperse particles with a lognormal size distribution are also found. Furthermore, we propose a modified QF (MQF accounting for the effects of energy cost and flow velocity, which are significant in certain operations. The optimal MQF of the dual-layer filter is found to be over twice that of the first layer. This work provides a quick tool for designing and optimizing fibrous structures with better performance for the air filtration of specific nanoparticles.
Improving Artificial Neural Network Forecasts with Kalman Filtering ...
African Journals Online (AJOL)
... used to compare the two models over different set of data from different companies over a period of 750 trading days. In all the cases we find that the Kalman filter algorithm significantly adds value to the forecasting process. Keywords: Artificial Neural Networks, Kalman filter, Stock prices, Forecasting, Back propagation ...
A Novel Modulation Classification Approach Using Gabor Filter Network
Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed
2014-01-01
A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603
A Novel Modulation Classification Approach Using Gabor Filter Network
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Sajjad Ahmed Ghauri
2014-01-01
Full Text Available A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN. The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR on AWGN channel.
Recursive inverse kinematics for robot arms via Kalman filtering and Bryson-Frazier smoothing
Rodriguez, G.; Scheid, R. E., Jr.
1987-01-01
This paper applies linear filtering and smoothing theory to solve recursively the inverse kinematics problem for serial multilink manipulators. This problem is to find a set of joint angles that achieve a prescribed tip position and/or orientation. A widely applicable numerical search solution is presented. The approach finds the minimum of a generalized distance between the desired and the actual manipulator tip position and/or orientation. Both a first-order steepest-descent gradient search and a second-order Newton-Raphson search are developed. The optimal relaxation factor required for the steepest descent method is computed recursively using an outward/inward procedure similar to those used typically for recursive inverse dynamics calculations. The second-order search requires evaluation of a gradient and an approximate Hessian. A Gauss-Markov approach is used to approximate the Hessian matrix in terms of products of first-order derivatives. This matrix is inverted recursively using a two-stage process of inward Kalman filtering followed by outward smoothing. This two-stage process is analogous to that recently developed by the author to solve by means of spatial filtering and smoothing the forward dynamics problem for serial manipulators.
Network selection, Information filtering and Scalable computation
Ye, Changqing
This dissertation explores two application scenarios of sparsity pursuit method on large scale data sets. The first scenario is classification and regression in analyzing high dimensional structured data, where predictors corresponds to nodes of a given directed graph. This arises in, for instance, identification of disease genes for the Parkinson's diseases from a network of candidate genes. In such a situation, directed graph describes dependencies among the genes, where direction of edges represent certain causal effects. Key to high-dimensional structured classification and regression is how to utilize dependencies among predictors as specified by directions of the graph. In this dissertation, we develop a novel method that fully takes into account such dependencies formulated through certain nonlinear constraints. We apply the proposed method to two applications, feature selection in large margin binary classification and in linear regression. We implement the proposed method through difference convex programming for the cost function and constraints. Finally, theoretical and numerical analyses suggest that the proposed method achieves the desired objectives. An application to disease gene identification is presented. The second application scenario is personalized information filtering which extracts the information specifically relevant to a user, predicting his/her preference over a large number of items, based on the opinions of users who think alike or its content. This problem is cast into the framework of regression and classification, where we introduce novel partial latent models to integrate additional user-specific and content-specific predictors, for higher predictive accuracy. In particular, we factorize a user-over-item preference matrix into a product of two matrices, each representing a user's preference and an item preference by users. Then we propose a likelihood method to seek a sparsest latent factorization, from a class of over
Dynamic Inverse Problem Solution Using a Kalman Filter Smoother for Neuronal Activity Estimation
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Eduardo Giraldo-Suárez
2011-12-01
Full Text Available This article presents an estimation method of neuronal activity into the brain using a Kalman smoother approach that takes into account in the solution of the inverse problem the dynamic variability of the time series. This method is applied over a realistic head model calculated with the boundary element method. A comparative analysis for the dynamic estimation methods is made up from simulated EEG signals for several noise conditions. The solution of the inverse problem is achieved by using high performance computing techniques and an evaluation of the computational cost is performed for each method. As a result, the Kalman smoother approach presents better performance in the estimation task than the regularized static solution, and the direct Kalman filter.
Directory of Open Access Journals (Sweden)
Wang Wei
2016-01-01
Full Text Available The related theory and algorithm of adaptive inverse control were presented through the research which pointed out the adaptive inverse control strategy could effectively eliminate the noise influence on the system control. Proposed using a frequency domain filter-X LMS adaptive inverse control algorithm, and the control algorithm was applied to the two-exciter hydraulic vibration test system of random shock vibration control process and summarized the process of the adaptive inverse control strategies in the realization of the random shock vibration test. The self-closed-loop and field test show that using the frequency-domain filter-X LMS adaptive inverse control algorithm can realize high precision control of random shock vibration test.
Adaptive training of feedforward neural networks by Kalman filtering
Energy Technology Data Exchange (ETDEWEB)
Ciftcioglu, Oe. [Istanbul Technical Univ. (Turkey). Dept. of Electrical Engineering; Tuerkcan, E. [Netherlands Energy Research Foundation (ECN), Petten (Netherlands)
1995-02-01
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.).
Energy Technology Data Exchange (ETDEWEB)
Manoli, Gabriele, E-mail: manoli@dmsa.unipd.it [Department of Mathematics, University of Padova, Via Trieste 63, 35121 Padova (Italy); Nicholas School of the Environment, Duke University, Durham, NC 27708 (United States); Rossi, Matteo [Department of Geosciences, University of Padova, Via Gradenigo 6, 35131 Padova (Italy); Pasetto, Damiano [Department of Mathematics, University of Padova, Via Trieste 63, 35121 Padova (Italy); Deiana, Rita [Dipartimento dei Beni Culturali, University of Padova, Piazza Capitaniato 7, 35139 Padova (Italy); Ferraris, Stefano [Interuniversity Department of Regional and Urban Studies and Planning, Politecnico and University of Torino, Viale Mattioli 39, 10125 Torino (Italy); Cassiani, Giorgio [Department of Geosciences, University of Padova, Via Gradenigo 6, 35131 Padova (Italy); Putti, Mario [Department of Mathematics, University of Padova, Via Trieste 63, 35121 Padova (Italy)
2015-02-15
The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.
Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.
Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J
2016-11-01
This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.
Convolutional Neural Networks for Inverse Problems in Imaging: A Review
McCann, Michael T.; Jin, Kyong Hwan; Unser, Michael
2017-11-01
In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, super-resolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions: Where does the training data come from? What is the architecture of the CNN? and How is the learning problem formulated and solved? We also bring together a few key theoretical papers that offer perspective on why CNNs are appropriate for inverse problems and point to some next steps in the field.
Photonic hyperuniform networks by silicon double inversion of polymer templates
Muller, Nicolas; Marichy, Catherine; Scheffold, Frank
2016-01-01
Hyperuniform disordered networks belong to a peculiar class of structured materials predicted to possess partial and complete photonic bandgaps for relatively moderate refractive index contrasts. The practical realization of such photonic designer materials is challenging however, as it requires control over a multi-step fabcrication process on optical length scales. Here we report the direct-laser writing of hyperuniform polymeric templates followed by a silicon double inversion procedure leading to high quality network structures made of polycrystalline silicon. We observe a pronounced gap in the shortwave infrared centered at a wavelength of $\\lambda_{\\text{Gap}}\\simeq $ 2.5 $\\mu$m, in nearly quantitative agreement with numerical simulations. In the experiments the typical structural length scale of the seed pattern can be varied between 2 $\\mu$m and 1.54 $\\mu$m leading to a blue-shift of the gap accompanied by an increase of the silicon volume filling fraction.
Shear wavelength estimation based on inverse filtering and multiple-point shear wave generation
Kitazaki, Tomoaki; Kondo, Kengo; Yamakawa, Makoto; Shiina, Tsuyoshi
2016-07-01
Elastography provides important diagnostic information because tissue elasticity is related to pathological conditions. For example, in a mammary gland, higher grade malignancies yield harder tumors. Estimating shear wave speed enables the quantification of tissue elasticity imaging using time-of-flight. However, time-of-flight measurement is based on an assumption about the propagation direction of a shear wave which is highly affected by reflection and refraction, and thus might cause an artifact. An alternative elasticity estimation approach based on shear wavelength was proposed and applied to passive configurations. To determine the elasticity of tissue more quickly and more accurately, we proposed a new method for shear wave elasticity imaging that combines the shear wavelength approach and inverse filtering with multiple shear wave sources induced by acoustic radiation force (ARF). The feasibility of the proposed method was verified using an elasticity phantom with a hard inclusion.
Inverse class-f power amplifier using slot resonators as a harmonic filter
Directory of Open Access Journals (Sweden)
Rassokhina Yu. V.
2014-06-01
Full Text Available The authors proposed and experimentally verified the power amplifier circuit of inverse class F (F–1 based on GaN transistor NPTB00004, operating at 1,7 GHz. The novelty of this scheme is the application of a three-layer structure based on slot rectangular shaped resonators in the ground plane of the microstrip transmission line as a filter of higher harmonics. To control the levels of the second and third harmonics in the output signal spectrum and simultaneously to match the 50 ohm load at the operating frequency of the amplifier, a planar periodic structure is used, consisting of two slot resonators of different lengths. Power added efficiency for experimental model of the amplifier is 60% at an output power of 3.9 W and a gain factor of 13 dB.
Noise Filtering and Prediction in Biological Signaling Networks
Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael
2016-01-01
Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Borgerding, Mark; Schniter, Philip; Rangan, Sundeep
2017-08-01
Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction. First, we propose a "learned AMP" network that significantly improves upon Gregor and LeCun's "learned ISTA." Second, inspired by the recently proposed "vector AMP" (VAMP) algorithm, we propose a "learned VAMP" network that offers increased robustness to deviations in the measurement matrix from i.i.d. Gaussian. In both cases, we jointly learn the linear transforms and scalar nonlinearities of the network. Interestingly, with i.i.d. signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP. Finally, we apply our methods to two problems from 5G wireless communications: compressive random access and massive-MIMO channel estimation.
Improving Artificial Neural Network Forecasts with Kalman Filtering ...
African Journals Online (AJOL)
In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies ...
Solving ill-posed inverse problems using iterative deep neural networks
Adler, Jonas; Öktem, Ozan
2017-12-01
We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the ‘gradient’ component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 × 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).
Gravity Effects on Information Filtering and Network Evolving
Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi
2014-01-01
In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model. PMID:24622162
Brown, Malcolm
2009-01-01
Inversions are fascinating phenomena. They are reversals of the normal or expected order. They occur across a wide variety of contexts. What do inversions have to do with learning spaces? The author suggests that they are a useful metaphor for the process that is unfolding in higher education with respect to education. On the basis of…
A Topological Criterion for Filtering Information in Complex Brain Networks.
Directory of Open Access Journals (Sweden)
Fabrizio De Vico Fallani
2017-01-01
Full Text Available In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO, to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.
An extended Kalman-filter for regional scale inverse emission estimation
Directory of Open Access Journals (Sweden)
D. Brunner
2012-04-01
Full Text Available A Kalman-filter based inverse emission estimation method for long-lived trace gases is presented for use in conjunction with a Lagrangian particle dispersion model like FLEXPART. The sequential nature of the approach allows tracing slow seasonal and interannual changes rather than estimating a single period-mean emission field. Other important features include the estimation of a slowly varying concentration background at each measurement station, the possibility to constrain the solution to non-negative emissions, the quantification of uncertainties, the consideration of temporal correlations in the residuals, and the applicability to potentially large inversion problems. The method is first demonstrated for a set of synthetic observations created from a prescribed emission field with different levels of (correlated noise, which closely mimics true observations. It is then applied to real observations of the three halocarbons HFC-125, HFC-152a and HCFC-141b at the remote research stations Jungfraujoch and Mace Head for the quantification of emissions in Western European countries from 2006 to 2010. Estimated HFC-125 emissions are mostly consistent with national totals reported to UNFCCC in the framework of the Kyoto Protocol and show a generally increasing trend over the considered period. Results for HFC-152a are much more variable with estimated emissions being both higher and lower than reported emissions in different countries. The highest emissions of the order of 700–800 Mg yr^{−1} are estimated for Italy, which so far does not report HFC-152a emissions. Emissions of HCFC-141b show a continuing strong decrease as expected due to its controls in developed countries under the Montreal Protocol. Emissions from France, however, were still rather large, in the range of 700–1000 Mg yr^{−1} in the years 2006 and 2007 but strongly declined thereafter.
Camporese, Matteo; Cassiani, Giorgio; Deiana, Rita; Salandin, Paolo; Binley, Andrew
2015-05-01
Recent advances in geophysical methods have been increasingly exploited as inverse modeling tools in groundwater hydrology. In particular, several attempts to constrain the hydrogeophysical inverse problem to reduce inversion errors have been made using time-lapse geophysical measurements through both coupled and uncoupled (also known as sequential) inversion approaches. Despite the appeal and popularity of coupled inversion approaches, their superiority over uncoupled methods has not been proved conclusively; the goal of this work is to provide an objective comparison between the two approaches within a specific inversion modeling framework based on the ensemble Kalman filter (EnKF). Using EnKF and a model of Lagrangian transport, we compare the performance of a fully coupled and uncoupled inversion method for the reconstruction of heterogeneous saturated hydraulic conductivity fields through the assimilation of ERT-monitored tracer test data. The two inversion approaches are tested in a number of different scenarios, including isotropic and anisotropic synthetic aquifers, where we change the geostatistical parameters used to generate the prior ensemble of hydraulic conductivity fields. Our results show that the coupled approach outperforms the uncoupled when the prior statistics are close to the ones used to generate the true field. Otherwise, the coupled approach is heavily affected by "filter inbreeding" (an undesired effect of variance underestimation typical of EnKF), while the uncoupled approach is more robust, being able to correct biased prior information, thanks to its capability of capturing the solute travel times even in presence of inversion artifacts such as the violation of mass balance. Furthermore, the coupled approach is more computationally intensive than the uncoupled, due to the much larger number of forward runs required by the electrical model. Overall, we conclude that the relative merit of the coupled versus the uncoupled approach cannot
Järvinen, Kati; Laukkanen, Anne-Maria; Geneid, Ahmed
2017-03-01
Language shift from native (L1) to foreign language (L2) may affect speaker's voice production and induce vocal fatigue. This study investigates the effects of language shift on voice source and perceptual voice quality. This is a comparative experimental study. Twenty-four subjects were recorded in L1 and L2. Twelve of the subjects were native Finnish speakers and 12 were native English speakers, and the foreign languages were English and Finnish. Two groups were created based on reports of fatigability. Group 1 had the subjects who did not report more vocal fatigue in L2 than in L1, and in group 2 those who reported more vocal fatigue in L2 than in L1. Acoustic analyses by inverse filtering were conducted in L1 and L2. Also, the subjects' voices were perceptually evaluated in both languages. Results show that language shift from L1 to L2 increased perceived pressedness of voice. Acoustic analyses correlated with the perceptual evaluations. Also, the subjects who reported more vocal loading had poorer voice quality, more strenuous voice production, more pressed phonation, and a higher pitch. Voice production was less optimal in L2 than in L1. Speech training given in L2 could be beneficial for people who need to use L2 extensively. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
Spam email filtering using network-level properties
Cortez, Paulo; Correia, André; Sousa, Pedro; Rocha, Miguel; Rio, Miguel
2010-01-01
Spam is serious problem that affects email users (e.g. phishing attacks, viruses and time spent reading unwanted messages). We propose a novel spam email filtering approach based on network-level attributes (e.g. the IP sender geographic coordinates) that are more persistent in time when compared to message content. This approach was tested using two classifiers, Naive Bayes (NB) and Support Vector Machines (SVM), and compared against bag-of-words models and eight blacklists. Several experime...
Whitelists Based Multiple Filtering Techniques in SCADA Sensor Networks
Directory of Open Access Journals (Sweden)
DongHo Kang
2014-01-01
Full Text Available Internet of Things (IoT consists of several tiny devices connected together to form a collaborative computing environment. Recently IoT technologies begin to merge with supervisory control and data acquisition (SCADA sensor networks to more efficiently gather and analyze real-time data from sensors in industrial environments. But SCADA sensor networks are becoming more and more vulnerable to cyber-attacks due to increased connectivity. To safely adopt IoT technologies in the SCADA environments, it is important to improve the security of SCADA sensor networks. In this paper we propose a multiple filtering technique based on whitelists to detect illegitimate packets. Our proposed system detects the traffic of network and application protocol attacks with a set of whitelists collected from normal traffic.
Rocadenbosch, Francesc; Soriano, Cecilia; Comerón, Adolfo; Baldasano, José-María
1999-05-01
A first inversion of the backscatter profile and extinction-to-backscatter ratio from pulsed elastic-backscatter lidar returns is treated by means of an extended Kalman filter (EKF). The EKF approach enables one to overcome the intrinsic limitations of standard straightforward nonmemory procedures such as the slope method, exponential curve fitting, and the backward inversion algorithm. Whereas those procedures are inherently not adaptable because independent inversions are performed for each return signal and neither the statistics of the signals nor a priori uncertainties (e.g., boundary calibrations) are taken into account, in the case of the Kalman filter the filter updates itself because it is weighted by the imbalance between the a priori estimates of the optical parameters (i.e., past inversions) and the new estimates based on a minimum-variance criterion, as long as there are different lidar returns. Calibration errors and initialization uncertainties can be assimilated also. The study begins with the formulation of the inversion problem and an appropriate atmospheric stochastic model. Based on extensive simulation and realistic conditions, it is shown that the EKF approach enables one to retrieve the optical parameters as time-range-dependent functions and hence to track the atmospheric evolution; the performance of this approach is limited only by the quality and availability of the a priori information and the accuracy of the atmospheric model used. The study ends with an encouraging practical inversion of a live scene measured at the Nd:YAG elastic-backscatter lidar station at our premises at the Polytechnic University of Catalonia, Barcelona.
Camporese, Matteo; Binley, Andrew; Cassiani, Giorgio; Deiana, Rita; Salandin, Paolo
2013-04-01
Recent advances in geophysical methods have been increasingly exploited as inverse modeling tools in groundwater hydrology. In particular, several attempts to constrain the hydrogeophysical inverse problem to reduce inversion error have been made using time-lapse geophysical measurements through both coupled and uncoupled inversion approaches. The main advantage of coupled approaches is that the numerical models for the geophysical and hydrological processes are linked together such that the geophysical data are inverted directly for the hydrological properties of interest. On the other hand, uncoupled approaches allow assessing in advance the reliability of the data, thanks to the geophysical inversion that is carried out before estimating the hydrological variable of interest. In spite of the recent popularity of fully coupled inversion approaches, we argue that their superiority over uncoupled methods still needs to be proven. The objective of this work is to shed some light on this debate. An approach based on the Lagrangian formulation of transport and the ensemble Kalman filter (EnKF) is here applied to assess the spatial distribution of hydraulic conductivity (K) by assimilating ERT data generated for a synthetic tracer test in a heterogeneous aquifer. In the coupled version of our inverse modeling tool, the K distribution is retrieved by assimilating raw ERT voltage data without the need for a preliminary electrical inversion. In the uncoupled version, K is estimated by assimilating time-lapse cross-hole electrical resistivity tomography (ERT) images derived by an electrical inversion. We compare the performance of the two approaches in a number of simulation scenarios and assess the impact on the inversions of the choice of the prior statistics of K.
Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks
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Myong-Soon Park
2011-07-01
Full Text Available RFID (Radio frequency identification and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes.
Energy efficient in-network RFID data filtering scheme in wireless sensor networks.
Bashir, Ali Kashif; Lim, Se-Jung; Hussain, Chauhdary Sajjad; Park, Myong-Soon
2011-01-01
RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes' energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes.
Interviewer Effects on a Network-Size Filter Question
Directory of Open Access Journals (Sweden)
Josten Michael
2016-06-01
Full Text Available There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey “Panel Study ‘Labour Market and Social Security’” (PASS, where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.
Optimization of operating conditions for compressor performance by means of neural network inverse
Energy Technology Data Exchange (ETDEWEB)
Cortes, O.; Urquiza, G.; Hernandez, J.A. [Centro de Investigacion en Ingenieria y Ciencias Aplicadas, Universidad Autonoma del Estado de Morelos. Av. Universidad 1001, Col. Chamilpa, CP 62209, Cuernavaca (Mexico)
2009-11-15
A way to optimize the parameters (i.e. operating conditions), related to compressor performance, based on artificial neural network and the Nelder-Mead simplex optimization method is proposed. It inverts the neural network to find the optimum parameter value under given conditions (artificial neural network inverse, ANNi). In order to do so, first an artificial neural network (ANN) was developed to predict: compressor pressure ratio, isentropic compressor efficiency, corrected speed, and finally corrected air mass flow rate. Input variables for this ANN include: ambient pressure, ambient temperature, wet bulb temperature, cooler temperature drop, filter pressure drop, outlet compressor temperature, outlet compressor pressure, gas turbine net power, exhaust gas temperature, and finally fuel flow mass rate. For the network, a feed-forward with one hidden layer, a Levenberg-Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer-function and a linear transfer-function were used. The best fitting with the training database was obtained with 12 neurons in the hidden layer. For the validation of present database, simulation and experimental database were in good agreement (R{sup 2}>0.99). Thus, the obtained ANN model can be used to predict the operating conditions when input parameters are well-known. Second, results from the ANNi that was developed also show good agreement with experimental and target data (error <0.1%), in this case, cooler temperature was found for a required efficiency. Therefore, the proposed methodology of ANNi can be applied to optimize the performance of the compressor with an elapsed time minor to 0.5 s. (author)
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)
Ahmed R. J. Almusawi; L. Canan Dülger; Sadettin Kapucu
2016-01-01
This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional...
Small Universal Accepting Networks of Evolutionary Processors with Filtered Connections
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Remco Loos
2009-07-01
Full Text Available In this paper, we present some results regarding the size complexity of Accepting Networks of Evolutionary Processors with Filtered Connections (ANEPFCs. We show that there are universal ANEPFCs of size 10, by devising a method for simulating 2-Tag Systems. This result significantly improves the known upper bound for the size of universal ANEPFCs which is 18. We also propose a new, computationally and descriptionally efficient simulation of nondeterministic Turing machines by ANEPFCs. More precisely, we describe (informally, due to space limitations how ANEPFCs with 16 nodes can simulate in O(f(n time any nondeterministic Turing machine of time complexity f(n. Thus the known upper bound for the number of nodes in a network simulating an arbitrary Turing machine is decreased from 26 to 16.
Hand-Eye Calibration and Inverse Kinematics of Robot Arm using Neural Network
DEFF Research Database (Denmark)
Wu, Haiyan; Tizzano, Walter; Andersen, Thomas Timm
2013-01-01
tasks. This paper describes the theory and implementation of neural networks for hand-eye calibration and inverse kinematics of a six degrees of freedom robot arm equipped with a stereo vision system. The feedforward neural network and the network training with error propagation algorithm are applied...
Steep optical filtering for next generation optical access networks
Korček, Dušan; Müllerová, Jarmila
2012-01-01
Future development of optical access technologies expects increasing traffic and bandwidth. The first candidates to improve Gigabit-capable passive optical networks (GPON) are 10-Gigabit-PON (XG-PON) and wavelength-division multiplexing PON (WDM PON). Another possibility for increasing penetration of current PON branch is to extend number of channels provided on one optical fiber for one PON technology. Coexistence of GPON, XG-PON and WDM-PON in the same infrastructure is a most discussed issue concerning passive optical networks nowadays. Therefore, extensive studies are necessary to design proper and low-cost candidates. International Telecommunication Union (ITU) allocates specific wavelength bands for the present status quo and the future development of access technologies. However, within coexistence, it is necessary to protect signals from various PON technologies from interference. A potential barrier to deploying XG-GPONs and WDM PONs with current GPONs is the usage of broadband light sources and sophisticated optical methods of slicing the light source emission into specific wavelength channels. Protective measures comprise the exact allocation of upstream and downstream signal bands for each technology; the so-called guard bands within the wavelength allocation scheme to protect signals; and optionally the usage of wavelength blocking filters. In this contribution, bandpass thin-film filters are numerically presented for hybrid time division/wavelength division multiplexing TDM/WDM (TWDM) and for simple operation. They have been designed to be tunable and as steep as possible to reject the wavelength bands outside those allocated to TWDM-PON. The TWDM-PON filters are proposed to guarantee steep transmission curves in the vicinity of cut-on/cut-off wavelengths of the specific allocated wavelength bands and facilitate migration from legacy GPON and XG-PON to TWDM-PON. Their deployment protects the allocated wavelength bands from the undesirable interference.
Distributed Bandpass Filtering and Signal Demodulation in Cortical Network Models
McDonnell, Mark D.
Experimental recordings of cortical activity often exhibit narrowband oscillations, at various center frequencies ranging in the order of 1-200 Hz. Many neuronal mechanisms are known to give rise to oscillations, but here we focus on a population effect known as sparsely synchronised oscillations. In this effect, individual neurons in a cortical network fire irregularly at slow average spike rates (1-10 Hz), but the population spike rate oscillates at gamma frequencies (greater than 40 Hz) in response to spike bombardment from the thalamus. These cortical networks form recurrent (feedback) synapses. Here we describe a model of sparsely synchronized population oscillations using the language of feedback control engineering, where we treat spiking as noisy feedback. We show, using a biologically realistic model of synaptic current that includes a delayed response to inputs, that the collective behavior of the neurons in the network is like a distributed bandpass filter acting on the network inputs. Consequently, the population response has the character of narrowband random noise, and therefore has an envelope and instantaneous frequency with lowpass characteristics. Given that there exist biologically plausible neuronal mechanisms for demodulating the envelope and instantaneous frequency, we suggest there is potential for similar effects to be exploited in nanoscale electronics implementations of engineered communications receivers.
Energy Technology Data Exchange (ETDEWEB)
Sakurai, K.; Shima, H. [OYO Corp., Tokyo (Japan)
1996-10-01
This paper proposes a modeling method of one-dimensional complex resistivity using linear filter technique which has been extended to the complex resistivity. In addition, a numerical test of inversion was conducted using the monitoring results, to discuss the measured frequency band. Linear filter technique is a method by which theoretical potential can be calculated for stratified structures, and it is widely used for the one-dimensional analysis of dc electrical exploration. The modeling can be carried out only using values of complex resistivity without using values of potential. In this study, a bipolar method was employed as a configuration of electrodes. The numerical test of one-dimensional complex resistivity inversion was conducted using the formulated modeling. A three-layered structure model was used as a numerical model. A multi-layer structure with a thickness of 5 m was analyzed on the basis of apparent complex resistivity calculated from the model. From the results of numerical test, it was found that both the chargeability and the time constant agreed well with those of the original model. A trade-off was observed between the chargeability and the time constant at the stage of convergence. 3 refs., 9 figs., 1 tab.
Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks
Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li
2016-06-01
Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.
Energy Technology Data Exchange (ETDEWEB)
Melo, Paulo Espinheira Menezes de; Porsani, Milton Jose [Universidade Federal da Bahia (UFBA), Salvador, BA (Brazil). Centro de Pesquisa em Geofisica e Geologia
2004-07-01
In the oil industry, the method of seismic filtering called Wiener-Levinson (WL) deconvolution is frequently used with the objective to compress the seismic pulse, allowing improvement of the definition and resolution of the seismic sections recovering, thus, the reflectivity function. Such technique is based on some basic premises that encompass, among others, the fact that the seismic pulse should be of minimum phase and stationary, and that the reflectivity function should be random. In last the 40 years innumerable works on seismic filtering have appeared in literature and in the majority of the times they focus on and they try to solve the problem related with the characteristics of the phase of the seismic pulse. The method of iterative deconvolution considered by Melo (2002) does not require that the pulse satisfies the premise of minimum phase. Also it is of easy computational and numerically-stable implementation. Although the method supplies results of excellent quality and it does not possess the restrictions of the WL method, it is computationally slow, since it acts several times on all the seismic section. In this work, we present a new procedure of deconvolution based on the method considered by Melo (2002) and Porsani, et al (2003). A set of seismic traces of the original section is used to estimate the non-causal filter as well as the seismic pulse associated to it. Once the inverse filter is obtained all the volume of seismic data is deconvolved. This procedure is sufficiently efficient with excellent results. The achieved results are very promising using synthetic and real seismic data, showing how the impulsive response of the earth can be recovered with high fidelity, showing a better seismic pulse compression resolution and lateral continuity of the reflections. (author)
Optimizing Single Sweep Range and Doppler Processing for FMCW Radar using Inverse Filtering
Jong, A.J. de; Dorp, Ph. van
2005-01-01
We discuss range and Doppler processing for FMCW radar using only a single pulse or frequency sweep. The first step is correlation processing, for which the range and Doppler resolution are limited by the ambiguity function. We show that this resolution can be optimized with an additional inverse
Energy Technology Data Exchange (ETDEWEB)
Wojciechowski, Kenneth E; Olsson, III, Roy H; Ziaei-Moayyed, Maryam
2013-07-30
A microelectromechanical (MEM) filter is disclosed which has a plurality of lattice networks formed on a substrate and electrically connected together in parallel. Each lattice network has a series resonant frequency and a shunt resonant frequency provided by one or more contour-mode resonators in the lattice network. Different types of contour-mode resonators including single input, single output resonators, differential resonators, balun resonators, and ring resonators can be used in MEM filter. The MEM filter can have a center frequency in the range of 10 MHz-10 GHz, with a filter bandwidth of up to about 1% when all of the lattice networks have the same series resonant frequency and the same shunt resonant frequency. The filter bandwidth can be increased up to about 5% by using unique series and shunt resonant frequencies for the lattice networks.
Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches
Lehký, David; Slowik, Ondřej; Novák, Drahomír
2014-01-01
Part 7: Genetic Algorithms; International audience; The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation. Efficie...
Active-Varying Sampling-Based Fault Detection Filter Design for Networked Control Systems
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Yu-Long Wang
2014-01-01
Full Text Available This paper is concerned with fault detection filter design for continuous-time networked control systems considering packet dropouts and network-induced delays. The active-varying sampling period method is introduced to establish a new discretized model for the considered networked control systems. The mutually exclusive distribution characteristic of packet dropouts and network-induced delays is made full use of to derive less conservative fault detection filter design criteria. Compared with the fault detection filter design adopting a constant sampling period, the proposed active-varying sampling-based fault detection filter design can improve the sensitivity of the residual signal to faults and shorten the needed time for fault detection. The simulation results illustrate the merits and effectiveness of the proposed fault detection filter design.
Inverse parameter identification for a branching 1D arterial network
CSIR Research Space (South Africa)
Bogaers, Alfred EJ
2012-07-01
Full Text Available In this paper we investigate the invertability of a branching 1D arterial blood flow network. We limit our investigation to a single bifurcating vessel, where the material properties, unloaded areas and variables characterizing the input and output...
Directory of Open Access Journals (Sweden)
Ali T. Hasan
2012-01-01
Full Text Available This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.
Porosity Estimation By Artificial Neural Networks Inversion . Application to Algerian South Field
Eladj, Said; Aliouane, Leila; Ouadfeul, Sid-Ali
2017-04-01
One of the main geophysicist's current challenge is the discovery and the study of stratigraphic traps, this last is a difficult task and requires a very fine analysis of the seismic data. The seismic data inversion allows obtaining lithological and stratigraphic information for the reservoir characterization . However, when solving the inverse problem we encounter difficult problems such as: Non-existence and non-uniqueness of the solution add to this the instability of the processing algorithm. Therefore, uncertainties in the data and the non-linearity of the relationship between the data and the parameters must be taken seriously. In this case, the artificial intelligence techniques such as Artificial Neural Networks(ANN) is used to resolve this ambiguity, this can be done by integrating different physical properties data which requires a supervised learning methods. In this work, we invert the acoustic impedance 3D seismic cube using the colored inversion method, then, the introduction of the acoustic impedance volume resulting from the first step as an input of based model inversion method allows to calculate the Porosity volume using the Multilayer Perceptron Artificial Neural Network. Application to an Algerian South hydrocarbon field clearly demonstrate the power of the proposed processing technique to predict the porosity for seismic data, obtained results can be used for reserves estimation, permeability prediction, recovery factor and reservoir monitoring. Keywords: Artificial Neural Networks, inversion, non-uniqueness , nonlinear, 3D porosity volume, reservoir characterization .
DEFF Research Database (Denmark)
Bhowmik, Subrata; Weber, Felix; Høgsberg, Jan Becker
2013-01-01
This paper presents a systematic design and training procedure for the feed-forward backpropagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output...... an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper...... force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where...
Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective.
Qian, Xiaoning; Dougherty, Edward R
2016-12-01
The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models.
A Survey on Distributed Filtering and Fault Detection for Sensor Networks
Directory of Open Access Journals (Sweden)
Hongli Dong
2014-01-01
Full Text Available In recent years, theoretical and practical research on large-scale networked systems has gained an increasing attention from multiple disciplines including engineering, computer science, and mathematics. Lying in the core part of the area are the distributed estimation and fault detection problems that have recently been attracting growing research interests. In particular, an urgent need has arisen to understand the effects of distributed information structures on filtering and fault detection in sensor networks. In this paper, a bibliographical review is provided on distributed filtering and fault detection problems over sensor networks. The algorithms employed to study the distributed filtering and detection problems are categorised and then discussed. In addition, some recent advances on distributed detection problems for faulty sensors and fault events are also summarized in great detail. Finally, we conclude the paper by outlining future research challenges for distributed filtering and fault detection for sensor networks.
Hu, Jun; Gao, Huijun
2014-01-01
This monograph introduces methods for handling filtering and control problems in nonlinear stochastic systems arising from network-induced phenomena consequent on limited communication capacity. Such phenomena include communication delay, packet dropout, signal quantization or saturation, randomly occurring nonlinearities and randomly occurring uncertainties.The text is self-contained, beginning with an introduction to nonlinear stochastic systems, network-induced phenomena and filtering and control, moving through a collection of the latest research results which focuses on the three aspects
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
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Wangyan Li
2015-01-01
Full Text Available Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development of multisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.
Multi-target Particle Filter Tracking Algorithm Based on Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Liu Hong-Xia
2014-05-01
Full Text Available In order to improve the multi-target tracking efficiency for wireless sensor networks and solve the problem of data transmission, analyzed existing particle filter tracking algorithm, ensure that one of the core technology for wireless sensor network performance. In this paper, from the basic theory of target tracking, in-depth analysis on the basis of the principle of particle filter, based on dynamic clustering, proposed the multi-target Kalman particle filter (MEPF algorithm, through the expansion of Calman filter (EKF to generate the proposal distribution, a reduction in the required number of particles to improve the particle filter accuracy at the same time, reduce the computational complexity of target tracking algorithm, thus reducing the energy consumption. Application results show that the MEPF in the proposed algorithm can achieve better tracking of target tracking and forecasting, in a small number of particles still has good tracking accuracy.
Krippner, Wolfgang; Wagner, Felix; Bauer, Sebastian; Puente León, Fernando
2017-06-01
Using appropriately designed spectral filters allows to optically determine material abundances. While an infinite number of possibilities exist for determining spectral filters, we take advantage of using neural networks to derive spectral filters leading to precise estimations. To overcome some drawbacks that regularly influence the determination of material abundances using hyperspectral data, we incorporate the spectral variability of the raw materials into the training of the considered neural networks. As a main result, we successfully classify quantized material abundances optically. Thus, the main part of the high computational load, which belongs to the use of neural networks, is avoided. In addition, the derived material abundances become invariant against spatially varying illumination intensity as a remarkable benefit in comparison with spectral filters based on the Moore-Penrose pseudoinverse, for instance.
H∞ Filtering for Networked Markovian Jump Systems with Multiple Stochastic Communication Delays
Directory of Open Access Journals (Sweden)
Hui Dong
2015-01-01
Full Text Available This paper is concerned with the H∞ filtering for a class of networked Markovian jump systems with multiple communication delays. Due to the existence of communication constraints, the measurement signal cannot arrive at the filter completely on time, and the stochastic communication delays are considered in the filter design. Firstly, a set of stochastic variables is introduced to model the occurrence probabilities of the delays. Then based on the stochastic system approach, a sufficient condition is obtained such that the filtering error system is stable in the mean-square sense and with a prescribed H∞ disturbance attenuation level. The optimal filter gain parameters can be determined by solving a convex optimization problem. Finally, a simulation example is given to show the effectiveness of the proposed filter design method.
Directory of Open Access Journals (Sweden)
Asis Mazumder
2009-06-01
Full Text Available The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF by using Weglin's design criteria based on 1/3–2/3 filter theory. The main objective of the present study is to validate HRF developed in the laboratory with Slow Sand Filter (SSF as a pretreatment unit with the help of Weglin's design criteria for HRF with respect to raw water condition and neuro-genetic model developed based on the filter dataset. The results achieved from the three different models were compared to find whether the performance of the experimental HRF with SSF output conforms to the other two models which will verify the validity of the former. According to the results, the experimental setup was coherent with the neural model but incoherent with the results from Weglin's formula as lowest mean square error was observed in case of the neuro-genetic model while comparing with the values found from the experimental SSF-HRF unit. As neural models are known to learn a problem with utmost efficiency, the model verification result was taken as positive.
Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models
Plant, Nathaniel G.; Holland, K. Todd
2011-01-01
A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.
Privman, Vladimir; Zavalov, Oleksandr; Halámková, Lenka; Moseley, Fiona; Halámek, Jan; Katz, Evgeny
2013-12-05
We report the first study of a network of connected enzyme-catalyzed reactions, with added chemical and enzymatic processes that incorporate the recently developed biochemical filtering steps into the functioning of this biocatalytic cascade. New theoretical expressions are derived to allow simple, few-parameter modeling of network components concatenated in such cascades, both with and without filtering. The derived expressions are tested against experimental data obtained for the realized network's responses, measured optically, to variations of its input chemicals' concentrations with and without filtering processes. We also describe how the present modeling approach captures and explains several observations and features identified in earlier studies of enzymatic processes when they were considered as potential network components for multistep information/signal processing systems.
Directory of Open Access Journals (Sweden)
Heryanto M Ary
2015-01-01
Full Text Available UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.
Quantum exciton-polariton networks through inverse four-wave mixing
Liew, T. C. H.; Rubo, Y. G.
2018-01-01
We demonstrate the potential of quantum operation using lattices of exciton-polaritons in patterned semiconductor microcavities. By introducing an inverse four-wave mixing scheme acting on localized modes, we show that it is possible to develop nonclassical correlations between individual condensates. This allows a concept of quantum exciton-polariton networks, characterized by the appearance of multimode entanglement even in the presence of realistic levels of dissipation.
Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai
2016-09-01
The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.
Directory of Open Access Journals (Sweden)
Shujie Yang
2016-01-01
Full Text Available Network virtualization has become pervasive and is used in many applications. Through the combination of network virtualization and wireless sensor networks, it can greatly improve the multiple applications of traditional wireless sensor networks. However, because of the dynamic reconfiguration of topologies in the physical layer of virtualized sensor networks (VSNs, it requires a mechanism to guarantee the accuracy of estimate values by sensors. In this paper, we focus on the distributed Kalman filter algorithm with dynamic topologies to support this requirement. As one strategy of distributed Kalman filter algorithms, diffusion Kalman filter algorithm has a better performance on the state estimation. However, the existing diffusion Kalman filter algorithms all focus on the fixed topologies. Considering the dynamic topologies in the physical layer of VSNs mentioned above, we present a diffusion Kalman filter algorithm with dynamic topologies (DKFdt. Then, we emphatically derive the theoretical expressions of the mean and mean-square performance. From the expressions, the feasibility of the algorithm is verified. Finally, simulations confirm that the proposed algorithm achieves a greatly improved performance as compared with a noncooperative manner.
Camporese, M.; Cassiani, G.; Deiana, R.; Salandin, P.; Binley, A. M.
2013-12-01
Recent advances in geophysical methods have been increasingly exploited as inverse modeling tools in groundwater hydrology. In particular, several attempts to constrain the hydrogeophysical inverse problem to reduce inversion error have been made using time-lapse geophysical measurements through both coupled and uncoupled inversion approaches. On one hand, the main advantage of coupled approaches is that the numerical models for the geophysical and hydrological processes are linked together such that the geophysical data are inverted directly for the hydrological properties of interest, avoiding artifacts related to the classical geophysical inversions. On the other hand, uncoupled approaches, relying upon a geophysical inversion that is carried out before estimating the hydrological variable of interest, could reveal something about the process that is not accounted for in a model, i.e., they are not constrained by the conceptualization of the hydrological model. In spite of the appeal and popularity of fully coupled inversion approaches, their superiority over more traditional uncoupled methods still needs to be objectively proven; the aim of this work is to shed some light on this debate. An approach based on the Lagrangian formulation of transport and the ensemble Kalman filter (EnKF) is here applied to assess the spatial distribution of hydraulic conductivity (K) by assimilating time-lapse cross-hole electrical resistivity tomography (ERT) data generated for a synthetic tracer test in a heterogeneous aquifer. In the coupled version of the proposed inverse modeling approach, the K distribution is retrieved by assimilating raw ERT resistance data without the need for a preliminary geoelectrical inversion. In the uncoupled version, K is estimated by assimilating electrical conductivity data derived from a previously performed classical geophysical inversion of the same resistance dataset. We compare the performance of the two approaches in a number of simulation
Wutsqa, D. U.; Marwah, M.
2017-06-01
In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.
DEFF Research Database (Denmark)
Pandey, Bishwajeet; Das, Bhagwan; Kaur, Amanpreet
2017-01-01
There are many areas of communication and network, which have open scope to use FIR filter. Therefore, energy efficient FIR filter will increase lifetime of network and FIR filter with less delay and latency will increase performance of network. In this work, we are going to design an FIR filter ...... provide by Xilinx. We transform that C code into HDL using Vivado HLS 2016.2 before power analysis on Vivado 2016.2. Ultrascale FPGA is generally used for packet processing in 100G networking and heterogeneous wireless infrastructure....
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)
Dülger, L. Canan; Kapucu, Sadettin
2016-01-01
This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129
Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin
2016-01-01
This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242
Directory of Open Access Journals (Sweden)
Ahmed R. J. Almusawi
2016-01-01
Full Text Available This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.
Radar Target Classification Using Neural Network and Median Filter
J. Kurty; Matousek, Z.
2001-01-01
The paper deals with Radar Target Classification based on the use of a neural network. A radar signal was acquired from the output of a J frequency band noncoherent radar. We applied the three layer feed forward neural network using the backpropagation learning algorithm. We defined classes of radar targets and designated each of them by its number. Our classification process resulted in the number of a radar target class, which the radar target belongs to.
Radar Target Classification Using Neural Network and Median Filter
Directory of Open Access Journals (Sweden)
J. Kurty
2001-09-01
Full Text Available The paper deals with Radar Target Classification based on the use ofa neural network. A radar signal was acquired from the output of a Jfrequency band noncoherent radar. We applied the three layer feedforward neural network using the backpropagation learning algorithm. Wedefined classes of radar targets and designated each of them by itsnumber. Our classification process resulted in the number of a radartarget class, which the radar target belongs to.
An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models
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Alex Alexandridis
2018-01-01
Full Text Available This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.
Hansen, T. M.; Cordua, K. S.
2017-12-01
Probabilistically formulated inverse problems can be solved using Monte Carlo-based sampling methods. In principle, both advanced prior information, based on for example, complex geostatistical models and non-linear forward models can be considered using such methods. However, Monte Carlo methods may be associated with huge computational costs that, in practice, limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical forward response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival traveltime inversion of crosshole ground penetrating radar data. An accurate forward model, based on 2-D full-waveform modeling followed by automatic traveltime picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the accurate and computationally expensive forward model, and also considerably faster and more accurate (i.e. with better resolution), than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of non-linear and non-Gaussian inverse problems that have to be solved using Monte Carlo sampling techniques.
Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.
Xia, Youshen; Wang, Jun
2015-07-01
This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.
Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels
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Du Yong Kim
2012-01-01
Full Text Available We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.
Wada, Daichi; Sugimoto, Yohei
2017-04-01
Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.
Data-Filtering System to Avoid Total Data Distortion in IoT Networking
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Dae-Young Kim
2017-01-01
Full Text Available In the Internet of Things (IoT networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed data from the objects. Then, the servers analyze the collected data and provide proper intelligent services to users through the results of the analysis. When the server analyzes the collected data, if there exists malfunctioning data, distortional results of the analysis will be generated. The distortional results lead to misdirection of the intelligent services, leading to poor user experience. In the analysis for intelligent services in IoT, malfunctioning data should be avoided because integrity of the collected data is crucial. Therefore, this paper proposes a data-filtering system for the server in the cloud. The proposed data-filtering system is placed in front of the server and firstly receives the sensed data from the objects. It employs the naïve Bayesian classifier and, by learning, classifies the malfunctioning data from among the collected data. Data with integrity is delivered to the server for analysis. Because the proposed system filters the malfunctioning data, the server can obtain accurate analysis results and reduce computing load. The performance of the proposed data-filtering system is evaluated through computer simulation. Through the simulation results, the efficiency of the proposed data-filtering system is shown.
Distributed H∞ Sampled-Data Filtering over Sensor Networks with Markovian Switching Topologies
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Bin Yang
2014-01-01
Full Text Available This paper considers a distributed H∞ sampled-data filtering problem in sensor networks with stochastically switching topologies. It is assumed that the topology switching is triggered by a Markov chain. The output measurement at each sensor is first sampled and then transmitted to the corresponding filters via a communication network. Considering the effect of a transmission delay, a distributed filter structure for each sensor is given based on the sampled data from itself and its neighbor sensor nodes. As a consequence, the distributed H∞ sampled-data filtering in sensor networks under Markovian switching topologies is transformed into H∞ mean-square stability problem of a Markovian jump error system with an interval time-varying delay. By using Lyapunov Krasovskii functional and reciprocally convex approach, a new bounded real lemma (BRL is derived, which guarantees the mean-square stability of the error system with a desired H∞ performance. Based on this BRL, the topology-dependent H∞ sampled-data filters are obtained. An illustrative example is given to demonstrate the effectiveness of the proposed method.
Neural Network-Based Passive Filtering for Delayed Neutral-Type Semi-Markovian Jump Systems.
Shi, Peng; Li, Fanbiao; Wu, Ligang; Lim, Cheng-Chew
2017-09-01
This paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.
Wang, Rui; Li, Yanxiao; Sun, Hui; Chen, Zengqiang
2017-11-01
The modern civil aircrafts use air ventilation pressurized cabins subject to the limited space. In order to monitor multiple contaminants and overcome the hypersensitivity of the single sensor, the paper constructs an output correction integrated sensor configuration using sensors with different measurement theories after comparing to other two different configurations. This proposed configuration works as a node in the contaminant distributed wireless sensor monitoring network. The corresponding measurement error models of integrated sensors are also proposed by using the Kalman consensus filter to estimate states and conduct data fusion in order to regulate the single sensor measurement results. The paper develops the sufficient proof of the Kalman consensus filter stability when considering the system and the observation noises and compares the mean estimation and the mean consensus errors between Kalman consensus filter and local Kalman filter. The numerical example analyses show the effectiveness of the algorithm. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Inverse design of high-Q wave filters in two-dimensional phononic crystals by topology optimization.
Dong, Hao-Wen; Wang, Yue-Sheng; Zhang, Chuanzeng
2017-04-01
Topology optimization of a waveguide-cavity structure in phononic crystals for designing narrow band filters under the given operating frequencies is presented in this paper. We show that it is possible to obtain an ultra-high-Q filter by only optimizing the cavity topology without introducing any other coupling medium. The optimized cavity with highly symmetric resonance can be utilized as the multi-channel filter, raising filter and T-splitter. In addition, most optimized high-Q filters have the Fano resonances near the resonant frequencies. Furthermore, our filter optimization based on the waveguide and cavity, and our simple illustration of a computational approach to wave control in phononic crystals can be extended and applied to design other acoustic devices or even opto-mechanical devices. Copyright Â© 2016 Elsevier B.V. All rights reserved.
Context discovery using attenuated Bloom filters in ad-hoc networks
Liu, F.; Heijenk, Geert; Braun, Torsten; Carle, Georg; Fahmy, Sonia; Koucheryavy, Yevgeni
A novel approach to performing context discovery in ad-hoc networks based on the use of attenuated Bloom filters is proposed in this paper. In order to investigate the performance of this approach, a model has been developed. This document describes the model and its validation. The model has been
RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances.
Li, Juan; Li, Shihua; Chen, Xisong; Yang, Jun
2014-07-01
An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Neural Network modeling of forward and inverse behavior of rotary MR damper
DEFF Research Database (Denmark)
Bhowmik, Subrata; Høgsberg, Jan Becker; Weber, Felix
2010-01-01
Magneto-rheological (MR) dampers have received considerable attention within the last decades, mainly because of their design simplicity, low power requirements, large force range and robustness. The most common models to describe the dynamic MR damper behavior are the Bouc-Wen model, the Lu...... of nonlinear problems. The present paper concerns the nonparametric neural network modeling of the dynamic behavior of a rotary MR damper. A rotary type MR damper consists of a rotating disk which is enclosed in a metallic housing filled with the MR fluid which is operated in shear mode. The dissipative torque...... produced is transformed into a translational force through the crank shaft mechanism. A feed-forward back propagation neural network is used to model both the forward and the inverse dynamics of the MR damper. The forward model output is the estimated force and therefore can be used later as observer...
Satellite image classification by narrowband Gabor filters and artificial neural networks
Nezamoddini-Kachouie, Nezamoddin; Alirezaie, Javad
2004-05-01
Satellite image segmentation is an important task to generate classification maps. Land areas are classified and clustered into groups of similar land cover or land use by segmentation of satellite images. It may be broad classification such as urban, forested, open fields and water or may be more specific such as differentiating corn, soybean, beet and wheat fields. One of the most important among them is partitioning the urban area to different regions. On the other hand Multi-Channel filtering is used widely for texture segmentation by many researchers. This paper describes a texture segmentation algorithm to segment satellite images using Gabor filter bank and neural networks. In the proposed method feature vectors are extracted by multi-channel decomposition. The spatial/spatial-frequency features of the input satellite image are extracted by optimized Gabor filter bank. Some important considerations about filter parameters, filter bank coverage in frequency domain and the reduction of feature dimensions are discussed. A competitive network is trained to extract the best features and to reduce the feature dimension. Eventually a Multi-Layer Perceptron (MLP) is employed to accomplish the segmentation task. Our MLP uses the sigmoid transfer function in all layers and during the training, random selected feature vectors are assigned to proper classes. After MLP is trained the optimized extracted features are classified into sections according to the textured land cover regions.
Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices
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Andrea Baiocchi
2015-01-01
Full Text Available Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.
He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang
2017-03-01
Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.
Park, Kihong
2013-02-01
In this paper, we study a two-hop relaying network consisting of one source, one destination, and three amplify-and-forward (AF) relays with multiple antennas. To compensate for the capacity prelog factor loss of 1/2$ due to the half-duplex relaying, alternate transmission is performed among three relays, and the inter-relay interference due to the alternate relaying is aligned to make additional degrees of freedom. In addition, suboptimal linear filter designs at the nodes are proposed to maximize the achievable sum rate for different fading scenarios when the destination utilizes a minimum mean-square error filter. © 1967-2012 IEEE.
An R implementation of a Recurrent Neural Network Trained by Extended Kalman Filter
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Bogdan Oancea
2016-06-01
Full Text Available Nowadays there are several techniques used for forecasting with different performances and accuracies. One of the most performant techniques for time series prediction is neural networks. The accuracy of the predictions greatly depends on the network architecture and training method. In this paper we describe an R implementation of a recurrent neural network trained by the Extended Kalman Filter. For the implementation of the network we used the Matrix package that allows efficient vector-matrix and matrix-matrix operations. We tested the performance of our R implementation comparing it with a pure C++ implementation and we showed that R can achieve about 75% of the C++ programs. Considering the other advantages of R, our results recommend R as a serious alternative to classical programming languages for high performance implementations of neural networks.
A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling
Bekaert, D.; Segall, P; Wright, TJ; Hooper, A.
2016-01-01
Studies of the earthquake cycle benefit from long-term time-dependent slip modeling, as it can be a powerful means to improve our understanding on the interaction of earthquake cycle processes such as interseismic, coseismic, postseismic, and aseismic slip. Observations from Interferometric Synthetic Aperture Radar (InSAR) allow us to model slip at depth with a higher spatial resolution than when using GNSS alone. While the temporal resolution of InSAR has typically been limited, the recent f...
An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network
Directory of Open Access Journals (Sweden)
Kai Hu
2013-01-01
Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.
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.
Zhang, Yin; Wei, Zhiyuan; Zhang, Yinping; Wang, Xin
2017-12-01
Urban heating in northern China accounts for 40% of total building energy usage. In central heating systems, heat is often transferred from heat source to users by the heat network where several heat exchangers are installed at heat source, substations and terminals respectively. For given overall heating capacity and heat source temperature, increasing the terminal fluid temperature is an effective way to improve the thermal performance of such cascade heat exchange network for energy saving. In this paper, the mathematical optimization model of the cascade heat exchange network with three-stage heat exchangers in series is established. Aim at maximizing the cold fluid temperature for given hot fluid temperature and overall heating capacity, the optimal heat exchange area distribution and the medium fluids' flow rates are determined through inverse problem and variation method. The preliminary results show that the heat exchange areas should be distributed equally for each heat exchanger. It also indicates that in order to improve the thermal performance of the whole system, more heat exchange areas should be allocated to the heat exchanger where flow rate difference between two fluids is relatively small. This work is important for guiding the optimization design of practical cascade heating systems.
Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.
Chen, Chen; Tong, Hanghang; Xie, Lei; Ying, Lei; He, Qing
2017-08-01
The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.
Enhanced Effective Filtering Approach (eEFA for Improving HSR Network Performance in Smart Grids
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Nguyen Xuan Tien
2018-01-01
Full Text Available The effective filtering approach (EFA is one of the most effective approaches for improving the network traffic performance of high-availability seamless redundancy (HSR networks. However, because EFA uses port locking (PL for detecting nondestination doubly-attached nodes with HSR protocol (DANH rings in HSR networks, it forwards the first sent frame to all DANH rings in the network. In addition, it uses a control message for discovering passive QuadBox rings in both unidirectional and bidirectional communications. In this study, we propose an enhanced version of EFA called enhanced-EFA (eEFA that does not forward unicast frames to nondestination DANH rings. eEFA does not use any control message to discover passive QuadBox rings in bidirectional communications. eEFA thus reduces the network traffic in HSR networks compared with EFA. Analytical and simulation results for a sample network show that the traffic reduction of eEFA was 4–26% and 2–20% for unidirectional and bidirectional communications, respectively, compared to EFA. eEFA, thus, clearly saves network bandwidth and improves the network performance.
Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System
Directory of Open Access Journals (Sweden)
Xin Zhang
2014-01-01
Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.
Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games
Directory of Open Access Journals (Sweden)
Emmanuel García
2014-01-01
Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.
Quantum neural network-based EEG filtering for a brain-computer interface.
Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin
2014-02-01
A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.
A simple structure wavelet transform circuit employing function link neural networks and SI filters
Mu, Li; Yigang, He
2016-12-01
Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.
Laloy, Eric; Hérault, Romain; Lee, John; Jacques, Diederik; Linde, Niklas
2017-12-01
Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200-500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.
Towards Effective Trust-Based Packet Filtering in Collaborative Network Environments
DEFF Research Database (Denmark)
Meng, Weizhi; Li, Wenjuan; Kwok, Lam-For
2017-01-01
Overhead network packets are a big challenge for intrusion detection systems (IDSs), which may increase system burden, degrade system performance, and even cause the whole system collapse, when the number of incoming packets exceeds the maximum handling capability. To address this issue, packet...... filtration is considered as a promising solution, and our previous research efforts have proven that designing a trust-based packet filter was able to refine unwanted network packets and reduce the workload of a local IDS. With the development of Internet cooperation, collaborative intrusion detection...
Authors’ Reply to Comments on “The Inverse S-Transform in Filters With Time-Frequency Localization”
Schimmel, Martin; Gallart Muset, Josep
2007-01-01
Abstract—Pinnegar points in his comment to a mistake in our paper on the inverse S-transform. It seems, in fact, that the error should mainly be attributed to the discretization of the S-transform, not to our inverse S-transform. Pinnegar bases his approach on the discrete S-transform by using its representation in the frequency domain. It is shown in an accompanying paper by Simon et al. that there are differences whether one discretizes the S-transform using the time-domai...
Zhang, Haiwei; Lu, Ying; Duan, Liangcheng; Zhao, Zhiqiang; Shi, Wei; Yao, Jianquan
2014-10-06
We report the system design and experimental verification of an intracavity absorption multiplexed sensor network with hollow core photonic crystal fiber (HCPCF) sensors and dense wavelength division multiplexing (DWDM) filters. Compared with fiber Bragg grating (FBG), it is easier for the DWDM to accomplish a stable output. We realize the concentration detection of three gas cells filled with acetylene. The sensitivity is up to 100 ppmV at 1536.71 nm. Voltage gradient is firstly used to optimize the intracavity sensor network enhancing the detection efficiency up to 6.5 times. To the best of our knowledge, DWDM is firstly used as a wavelength division multiplexing device to realize intracavity absorption multiplexed sensor network. It make it possible to realize high capacity intracavity sensor network via multiplexed technique.
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
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Juntao Fei
2013-05-01
Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.
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Raquel Caballero-Águila
2015-01-01
Full Text Available The distributed fusion state estimation problem is addressed for sensor network systems with random state transition matrix and random measurement matrices, which provide a unified framework to consider some network-induced random phenomena. The process noise and all the sensor measurement noises are assumed to be one-step autocorrelated and different sensor noises are one-step cross-correlated; also, the process noise and each sensor measurement noise are two-step cross-correlated. These correlation assumptions cover many practical situations, where the classical independence hypothesis is not realistic. Using an innovation methodology, local least-squares linear filtering estimators are recursively obtained at each sensor. The distributed fusion method is then used to form the optimal matrix-weighted sum of these local filters according to the mean squared error criterion. A numerical simulation example shows the accuracy of the proposed distributed fusion filtering algorithm and illustrates some of the network-induced stochastic uncertainties that can be dealt with in the current system model, such as sensor gain degradation, missing measurements, and multiplicative noise.
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Lijun Song
2018-01-01
Full Text Available The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA. But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.
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M. Kumar
2016-01-01
Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.
A Bloom Filter-Powered Technique Supporting Scalable Semantic Discovery in Data Service Networks
Zhang, J.; Shi, R.; Bao, Q.; Lee, T. J.; Ramachandran, R.
2016-12-01
More and more Earth data analytics software products are published onto the Internet as a service, in the format of either heavyweight WSDL service or lightweight RESTful API. Such reusable data analytics services form a data service network, which allows Earth scientists to compose (mashup) services into value-added ones. Therefore, it is important to have a technique that is capable of helping Earth scientists quickly identify appropriate candidate datasets and services in the global data service network. Most existing services discovery techniques, however, mainly rely on syntax or semantics-based service matchmaking between service requests and available services. Since the scale of the data service network is increasing rapidly, the run-time computational cost will soon become a bottleneck. To address this issue, this project presents a way of applying network routing mechanism to facilitate data service discovery in a service network, featuring scalability and performance. Earth data services are automatically annotated in Web Ontology Language for Services (OWL-S) based on their metadata, semantic information, and usage history. Deterministic Annealing (DA) technique is applied to dynamically organize annotated data services into a hierarchical network, where virtual routers are created to represent semantic local network featuring leading terms. Afterwards Bloom Filters are generated over virtual routers. A data service search request is transformed into a network routing problem in order to quickly locate candidate services through network hierarchy. A neural network-powered technique is applied to assure network address encoding and routing performance. A series of empirical study has been conducted to evaluate the applicability and effectiveness of the proposed approach.
On Seismic Ground Roll Filtering Using the Wavelet Transform and Neural Network
Benaissa, Zahia; Benaissa, Abdelkader; Ouadfeul, Sid-Ali; Aliouane, Leila; Boudella, Amar
2013-04-01
Here, we present an adapted filtering technique for the non-stationary signals. It is based on the wavelet transform and its rebuilding formula. This technique is used generally to detect and extract locally in the time-scale field particular events from seismic data. We show the efficiency of this technique to filter the ground roll from reflection seismic vibroseis recording (shot gather). The results for two different filtering processes are presented, one of these results is based on the annulment of the transform coefficients in the selected zone relating to the ground roll, and the other one is based on their attenuation (roll-off). Obtained results shows the efficiency of the first process especially when the wavelet transform is calculated only on the noisy zone and when the ground roll is made up of two or more pseudo-Rayleigh waves, in this case iterations are mandatory to improve the signal to noise ratio using the second process. The current work shows also the use of the artificial neural network on the prediction of the mute parameters in the F-K domain to be used on the Ground Roll attenuation. The proposed idea is very robust and useful in case of 3D seismic data. A set of 3D seismic Inlines are used for the training of the Multilayer Perceptron (MLP) neural network machine. Application to real data shows clearly the robustness of the proposed technique. Keywords: Filtering - Ground roll - Wavelet transform - Seismic - Reflection - Signal to noise ratio - Artificial neuronal network -3D-MLP- Training.
A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.
Cai, Binghuang; Jiang, Xia
2014-04-01
Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.
Ceylan, Halil; Gopalakrishnan, Kasthurirangan; Birkan Bayrak, Mustafa; Guclu, Alper
2013-09-01
The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement
Miao, Zhiyong; Shi, Hongyang; Zhang, Yi; Xu, Fan
2017-10-01
In this paper, a new variational Bayesian adaptive cubature Kalman filter (VBACKF) is proposed for nonlinear state estimation. Although the conventional VBACKF performs better than cubature Kalman filtering (CKF) in solving nonlinear systems with time-varying measurement noise, its performance may degrade due to the uncertainty of the system model. To overcome this drawback, a multilayer feed-forward neural network (MFNN) is used to aid the conventional VBACKF, generalizing it to attain higher estimation accuracy and robustness. In the proposed neural-network-aided variational Bayesian adaptive cubature Kalman filter (NN-VBACKF), the MFNN is used to turn the state estimation of the VBACKF adaptively, and it is used for both state estimation and in the online training paradigm simultaneously. To evaluate the performance of the proposed method, it is compared with CKF and VBACKF via target tracking problems. The simulation results demonstrate that the estimation accuracy and robustness of the proposed method are better than those of the CKF and VBACKF.
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L. Li
2012-02-01
Full Text Available The normal-score ensemble Kalman filter (NS-EnKF is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i the localized NS-EnKF can characterize the non-multiGaussian underlying hydraulic distribution even when an erroneous prior random function model is used, (ii localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii the NS-EnKF works equally well under very different flow configurations.
Reconstruction of two-dimensional fracture network geometry by transdimensional inversion
Somogyvári, Márk; Jalali, Mohammadreza; Jimenez Parras, Santos; Bayer, Peter
2017-04-01
Transport processes in a fractured aquifer are mainly controlled by the geometry of the fracture network. Such a network is ideally modelled as discrete fracture network (DFN), which is composed by a skeleton of hydraulically conductive fractures that intersect the impermeable rock matrix. The orientation and connectivity of the fractures are highly case-specific, and mapping especially the hydraulically active parts of a fracture network requires insight from hydraulic or transport related experiments, such as tracer tests. Single tracer tests, however, offer only an integral picture of an aquifeŕs transport properties. Here, multiple tracer tests are proposed and evaluated together in a tracer tomography framework to obtain spatially distributed data. The interpretation of the data obtained from these experiments is challenging, since there exists no common recipe for reconstructing the fracture network in a DFN model. A crucial point is that the number of fractures (and thus the number of model parameters) is unknown. We propose the use of a transdimensional inversion method, which can be applied to calibrate fracture properties and number. In this study, the reversible jump Markov Chain Monte Carlo algorithm is selected and conservative tracer tomography experiments are interpreted with two-dimensional DFN models. In our approach, a randomly generated initial DFN solution is evolved through a Markov sequence. In each iteration the DFN model is updated by a random manipulation of the geometry (fracture addition, fracture deletion or fracture shift). The tracer tomography experiment is simulated with the updated model, and the simulated tracer breakthroughs curves are compared to the original observations. Each updated DFN realization is evaluated using the Metropolis-Hastings-Green acceptance criteria. This evaluation is based on probabilistic properties of the updates and the improvement of the fit of the breakthrough curves. The transdimensional algorithm
Energy-Efficient Distributed Filtering in Sensor Networks: A Unified Switched System Approach.
Zhang, Dan; Shi, Peng; Zhang, Wen-An; Yu, Li
2016-04-21
This paper is concerned with the energy-efficient distributed filtering in sensor networks, and a unified switched system approach is proposed to achieve this goal. For the system under study, the measurement is first sampled under nonuniform sampling periods, then the local measurement elements are selected and quantized for transmission. Then, the transmission rate is further reduced to save constrained power in sensors. Based on the switched system approach, a unified model is presented to capture the nonuniform sampling, the measurement size reduction, the transmission rate reduction, the signal quantization, and the measurement missing phenomena. Sufficient conditions are obtained such that the filtering error system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. Both simulation and experiment studies are given to show the effectiveness of the proposed new design technique.
Kumar, M; Mishra, S K
2017-01-01
The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive. There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images. In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented. The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters. The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.
Information Filtering via Clustering Coefficients of User-Object Bipartite Networks
Guo, Qiang; Leng, Rui; Shi, Kerui; Liu, Jian-Guo
The clustering coefficient of user-object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user-object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user-object bipartite networks should be investigated to estimate users' tastes.
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Amina Noor
2013-01-01
Full Text Available This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF and Kalman filter (KF techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.
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Eline Janssens
2016-11-01
Full Text Available Speed is an important parameter of an inspection system. Inline computed tomography systems exist but are generally expensive. Moreover, their throughput is limited by the speed of the reconstruction algorithm. In this work, we propose a Neural Network-based Hilbert transform Filtered Backprojection (NN-hFBP method to reconstruct objects in an inline scanning environment in a fast and accurate way. Experiments based on apple X-ray scans show that the NN-hFBP method allows to reconstruct images with a substantially better tradeoff between image quality and reconstruction time.
Active power filter for medium voltage networks with predictive current control
Energy Technology Data Exchange (ETDEWEB)
Verne, Santiago A.; Valla, Maria I. [Laboratorio de Electronica Industrial, Control e Instrumentacion (LEICI), Facultad de Ingenieria, Universidad Nacional de La Plata and CONICET, La Plata (Argentina)
2010-12-15
A transformer less Shunt Active Power Filter (SAPF) for medium voltage distribution networks based on Multilevel Diode Clamped Inverter is presented in this paper. Converter current control is based on a Model Predictive strategy, which gives very fast current response. Also, the algorithm includes voltage balancing capability which is essential for proper converter operation. The presented current control algorithm is naturally applicable to converters with an arbitrary number of levels with reduced computational effort by virtue of the incorporation of switching restrictions which are necessary for reliable converter operation. The performance of the proposed algorithm is evaluated by means of computer simulations. (author)
Inverse and direct problems of optics: usage of artificial neural networks
Abrukov, Victor S.; Pavlov, Roman I.; Malinin, Gennadiy I.
2004-08-01
We describe an application of artificial neural networks (ANN) for solving of inverse and direct problems of optics. Using the ANN we calculate local and integral characteristics of object by means of incomplete set of data that characterize optical images. Possibilities of usage the only one value of a function of signal intensity distributionn in a plane of a registration for full determination of distribution of local characteristics in an object are shown. It is very important for optical fiber sensors, smart sensors and MEMS. Examples of ANN usage for a case of object with a cylindrical symmetry in a field of interferometry are presented. Results obtained show that determination of object local and integral characteristics can be perform very much simpler than by means of standard procedures and numerical approaches for signal processing, reduction and analysis. The ANN can allow also to solve number of tasks that could not be solved by means of usual approaches. In prospects, this method can be used for creation of automated systems for diagnostics, testing and control in various fields of scientific and applied research as well as in industry.
Cerebellum-inspired neural network solution of the inverse kinematics problem.
Asadi-Eydivand, Mitra; Ebadzadeh, Mohammad Mehdi; Solati-Hashjin, Mehran; Darlot, Christian; Abu Osman, Noor Azuan
2015-12-01
The demand today for more complex robots that have manipulators with higher degrees of freedom is increasing because of technological advances. Obtaining the precise movement for a desired trajectory or a sequence of arm and positions requires the computation of the inverse kinematic (IK) function, which is a major problem in robotics. The solution of the IK problem leads robots to the precise position and orientation of their end-effector. We developed a bioinspired solution comparable with the cerebellar anatomy and function to solve the said problem. The proposed model is stable under all conditions merely by parameter determination, in contrast to recursive model-based solutions, which remain stable only under certain conditions. We modified the proposed model for the simple two-segmented arm to prove the feasibility of the model under a basic condition. A fuzzy neural network through its learning method was used to compute the parameters of the system. Simulation results show the practical feasibility and efficiency of the proposed model in robotics. The main advantage of the proposed model is its generalizability and potential use in any robot.
Kamalakar, M. Venkata; Dankert, André; Kelly, Paul J.; Dash, Saroj P.
2016-01-01
Two dimensional atomically thin crystals of graphene and its insulating isomorph hexagonal boron nitride (h-BN) are promising materials for spintronic applications. While graphene is an ideal medium for long distance spin transport, h-BN is an insulating tunnel barrier that has potential for efficient spin polarized tunneling from ferromagnets. Here, we demonstrate the spin filtering effect in cobalt|few layer h-BN|graphene junctions leading to a large negative spin polarization in graphene at room temperature. Through nonlocal pure spin transport and Hanle precession measurements performed on devices with different interface barrier conditions, we associate the negative spin polarization with high resistance few layer h-BN|ferromagnet contacts. Detailed bias and gate dependent measurements reinforce the robustness of the effect in our devices. These spintronic effects in two-dimensional van der Waals heterostructures hold promise for future spin based logic and memory applications. PMID:26883717
Gu, Yamei; You, Shanhong
2016-07-01
With the rapid growth of data rate, the optical network is evolving from fixed-grid to flexible-grid to provide spectrum-efficient and scalable transport of 100 Gb/s services and beyond. Also, the deployment of wavelength converter in the existing network can increase the flexibility of routing and wavelength allocation (RWA) and improve blocking performance of the optical networks. In this paper, we present a methodology for computing approximate blocking probabilities of the provision of multiclass services in the flexible-grid optical networks with sub-band spectrum conversion and inverse multiplexing respectively. Numerical calculation results based on the model are compared to the simulation results for the different cases. It is shown that the calculation results match well with the simulation results for the flexible-grid optical networks at different scenarios.
A Multipath Routing Protocol Based on Bloom Filter for Multihop Wireless Networks
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Junwei Jin
2016-01-01
Full Text Available On-demand multipath routing in a wireless ad hoc network is effective in achieving load balancing over the network and in improving the degree of resilience to mobility. In this paper, the salvage capable opportunistic node-disjoint multipath routing (SNMR protocol is proposed, which forms multiple routes for data transmission and supports packet salvaging with minimum overhead. The proposed mechanism constructs a primary path and a node-disjoint backup path together with alternative paths for the intermediate nodes in the primary path. It can be achieved by considering the reverse route back to the source stored in the route cache and the primary path information compressed by a Bloom filter. Our protocol presents higher capability in packet salvaging and lower overhead in forming multiple routes. Simulation results show that SNMR outperforms the compared protocols in terms of packet delivery ratio, normalized routing load, and throughput.
Oblique Projection Polarization Filtering-Based Interference Suppressions for Radar Sensor Networks
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Cao Bin
2010-01-01
Full Text Available The interferences coming from the radar members degrade the detection and recognition performance of the radar sensor networks (RSNs if the waveforms of the radar members are nonorthogonal. In this paper, we analyze the interferences by exploring the polarization information of the electromagnetic (EM waves. Then, we propose the oblique projection polarization filtering- (OPPF- based scheme to suppress the interferences while keeping the amplitude and phase of its own return in RSNs, even if the polarized states of the radar members are not orthogonal. We consider the cooperative RSNs environment where the polarization information of each radar member is known to all. The proposed method uses all radar members' polarization information to establish the corresponding filtering operator. The Doppler-shift and its uncertainty are independent of the polarization information, which contributes that the interferences can be suppressed without the utilization of the spatial, the temporal, the frequency, the time-delay and the Doppler-shift information. Theoretical analysis and the mathematical deduction show that the proposed scheme is a valid and simple implementation. Simulation results also demonstrate that this method can obtain a good filtering performance when dealing with the problem of interference suppressions for RSNs.
Guided filter and convolutional network based tracking for infrared dim moving target
Qian, Kun; Zhou, Huixin; Qin, Hanlin; Rong, Shenghui; Zhao, Dong; Du, Juan
2017-09-01
The dim moving target usually submerges in strong noise, and its motion observability is debased by numerous false alarms for low signal-to-noise ratio. A tracking algorithm that integrates the Guided Image Filter (GIF) and the Convolutional neural network (CNN) into the particle filter framework is presented to cope with the uncertainty of dim targets. First, the initial target template is treated as a guidance to filter incoming templates depending on similarities between the guidance and candidate templates. The GIF algorithm utilizes the structure in the guidance and performs as an edge-preserving smoothing operator. Therefore, the guidance helps to preserve the detail of valuable templates and makes inaccurate ones blurry, alleviating the tracking deviation effectively. Besides, the two-layer CNN method is adopted to obtain a powerful appearance representation. Subsequently, a Bayesian classifier is trained with these discriminative yet strong features. Moreover, an adaptive learning factor is introduced to prevent the update of classifier's parameters when a target undergoes sever background. At last, classifier responses of particles are utilized to generate particle importance weights and a re-sample procedure preserves samples according to the weight. In the predication stage, a 2-order transition model considers the target velocity to estimate current position. Experimental results demonstrate that the presented algorithm outperforms several relative algorithms in the accuracy.
Fischer, P.; Jardani, A.; Lecoq, N.
2018-02-01
In this paper, we present a novel inverse modeling method called Discrete Network Deterministic Inversion (DNDI) for mapping the geometry and property of the discrete network of conduits and fractures in the karstified aquifers. The DNDI algorithm is based on a coupled discrete-continuum concept to simulate numerically water flows in a model and a deterministic optimization algorithm to invert a set of observed piezometric data recorded during multiple pumping tests. In this method, the model is partioned in subspaces piloted by a set of parameters (matrix transmissivity, and geometry and equivalent transmissivity of the conduits) that are considered as unknown. In this way, the deterministic optimization process can iteratively correct the geometry of the network and the values of the properties, until it converges to a global network geometry in a solution model able to reproduce the set of data. An uncertainty analysis of this result can be performed from the maps of posterior uncertainties on the network geometry or on the property values. This method has been successfully tested for three different theoretical and simplified study cases with hydraulic responses data generated from hypothetical karstic models with an increasing complexity of the network geometry, and of the matrix heterogeneity.
Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images
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Anselm Brachmann
2016-12-01
Full Text Available We propose a method for measuring symmetry in images by using filter responses from Convolutional Neural Networks (CNNs. The aim of the method is to model human perception of left/right symmetry as closely as possible. Using the Convolutional Neural Network (CNN approach has two main advantages: First, CNN filter responses closely match the responses of neurons in the human visual system; they take information on color, edges and texture into account simultaneously. Second, we can measure higher-order symmetry, which relies not only on color, edges and texture, but also on the shapes and objects that are depicted in images. We validated our algorithm on a dataset of 300 music album covers, which were rated according to their symmetry by 20 human observers, and compared results with those from a previously proposed method. With our method, human perception of symmetry can be predicted with high accuracy. Moreover, we demonstrate that the inclusion of features from higher CNN layers, which encode more abstract image content, increases the performance further. In conclusion, we introduce a model of left/right symmetry that closely models human perception of symmetry in CD album covers.
Josso, Nicolas F; Ioana, Cornel; Mars, Jérôme I; Gervaise, Cédric
2010-12-01
Acoustic channel properties in a shallow water environment with moving source and receiver are difficult to investigate. In fact, when the source-receiver relative position changes, the underwater environment causes multipath and Doppler scale changes on the transmitted signal over low-to-medium frequencies (300 Hz-20 kHz). This is the result of a combination of multiple paths propagation, source and receiver motions, as well as sea surface motion or water column fast changes. This paper investigates underwater acoustic channel properties in a shallow water (up to 150 m depth) and moving source-receiver conditions using extracted time-scale features of the propagation channel model for low-to-medium frequencies. An average impulse response of one transmission is estimated using the physical characteristics of propagation and the wideband ambiguity plane. Since a different Doppler scale should be considered for each propagating signal, a time-warping filtering method is proposed to estimate the channel time delay and Doppler scale attributes for each propagating path. The proposed method enables the estimation of motion-compensated impulse responses, where different Doppler scaling factors are considered for the different time delays. It was validated for channel profiles using real data from the BASE'07 experiment conducted by the North Atlantic Treaty Organization Undersea Research Center in the shallow water environment of the Malta Plateau, South Sicily. This paper provides a contribution to many field applications including passive ocean tomography with unknown natural sources position and movement. Another example is active ocean tomography where sources motion enables to rapidly cover one operational area for rapid environmental assessment and hydrophones may be drifting in order to avoid additional flow noise.
DEFF Research Database (Denmark)
Kouchaki, Alireza; Nymand, Morten
2016-01-01
This paper presents LCL filter design method for three-phase two-level power factor correction (PFC) using line impedance stabilization network (LISN). A straightforward LCL filter design along with variation in grid impedance is not simply achievable and inevitably lead to an iterative solution...... is derived using the current ripple behavior of converter-side inductor. The grid-side inductor is achieved as a function of LISN impedance to fulfill the grid regulation. To verify the analyses, an LCL filter is designed for a 5 kW SiC-based PFC. The simulation and experimental results support the validity...
A Kalman-filter based approach to identification of time-varying gene regulatory networks.
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Jie Xiong
Full Text Available MOTIVATION: Conventional identification methods for gene regulatory networks (GRNs have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs. RESULTS: It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.
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Руслан Володимирович Власенко
2016-07-01
Full Text Available Electricity quality improving is extremely relevant nowadays. With such industrial loads as induction motors, induction furnaces, welding machines, controlled or uncontrolled rectifiers, frequency converters and others reactive power, harmonics and unbalance are generated in power grid. Reactive power, higher harmonic currents and asymmetry loads influence the functioning of electric devices and electrical mains. An effective technical solution is the use of new compensating devices, that is active power filters. The emergence of consumers with a unit capacity of four wire networks requires a new approach to building system control active power filter. When designing the active power filter control system the current flowing in the neutral wire must be taken into account. To assess the power balance in the four wire active power filter, scientists have proposed to apply pqr theory of power based on the Clarke transformation. There are different topologies of three-phase four wire active power filters. A visual simulation of Matlab / Simulink model with an active power filter based on pqr theory of power has been created. A method of pulse width modulation with four control channels was used as pulses forming systems with transistor keys. Operating conditions of three-phase four wire active power filter with asymmetry, non-sinosoidal voltage source and asymmetric load have been studied. The correction taking into account the means improving the active power filter has been offered as pqr theory of power does not take into account non-sinosoidal voltage
Hardware design of the median filter based on window structure and batcher′s oddeven sort network
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SUN Kaimin
2013-06-01
Full Text Available Area and speed are two important factors to be considered in designing Median Filter with digital circuits.Area consideration requires the use of logical resources as little as possible,while speed consideration requires the system capable of working on higher clock frequencies,with as few clock cycles as possible to complete a frame filtering or real time filtering.This paper gives a new design of Median Filter,the hardware structure of which is a 3×3 window structure with two buffers.The filter function module is based on Batcher′s Odd-Even Sort network theory.Structural design is implemented in FPGA,verified by ModelSim software and realizes video image filtering.The experimental analysis shows that this new structure of Median Filter effectively decreases logical resources (merely using 741 Logic Elements,and accelerates the pixel processing speed up to 27MHz.This filter achieves realtime processing of video images of 30 frames/s.This design not only has a certain practicality,but also provides a reference for the hardware structure design ideas in digital image processing.
Bayesian filtering in spiking neural networks: noise, adaptation, and multisensory integration.
Bobrowski, Omer; Meir, Ron; Eldar, Yonina C
2009-05-01
A key requirement facing organisms acting in uncertain dynamic environments is the real-time estimation and prediction of environmental states, based on which effective actions can be selected. While it is becoming evident that organisms employ exact or approximate Bayesian statistical calculations for these purposes, it is far less clear how these putative computations are implemented by neural networks in a strictly dynamic setting. In this work, we make use of rigorous mathematical results from the theory of continuous time point process filtering and show how optimal real-time state estimation and prediction may be implemented in a general setting using simple recurrent neural networks. The framework is applicable to many situations of common interest, including noisy observations, non-Poisson spike trains (incorporating adaptation), multisensory integration, and state prediction. The optimal network properties are shown to relate to the statistical structure of the environment, and the benefits of adaptation are studied and explicitly demonstrated. Finally, we recover several existing results as appropriate limits of our general setting.
Xiong, Jieqing; Huang, Zhitong; Zhuang, Kaiyu; Ji, Yuefeng
2016-08-01
We propose a novel handover scheme for indoor microcellular visible light communication (VLC) network. With such a scheme, the room, which is fully coverage by light, is divided into several microcells according to the layout of light-emitting diodes (LEDs). However, the directionality of light arises new challenges in keeping the connectivity between the mobile devices and light source under the mobile circumstances. The simplest solution is that all LEDs broadcast data of every user simultaneously, but it wastes too much bandwidth resource, especially when the amount of users increases. To solve this key problem, we utilize the optical positioning assisting handover procedure in this paper. In the positioning stage, the network manager obtains the location information of user device via downlink and uplink signal strength information, which is white light and infrared, respectively. After that, a Kalman filter is utilized for improving the tracking performance of a mobile device. Then, the network manager decides how to initiate the handover process by the previous information. Results show that the proposed scheme can achieve low-cost, seamless data communication, and a high probability of successful handover.
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Chiung-An Chen
2012-11-01
Full Text Available In this paper, a low-cost, low-power and high performance micro control unit (MCU core is proposed for wireless body sensor networks (WBSNs. It consists of an asynchronous interface, a register bank, a reconfigurable filter, a slop-feature forecast, a lossless data encoder, an error correct coding (ECC encoder, a UART interface, a power management (PWM, and a multi-sensor controller. To improve the system performance and expansion abilities, the asynchronous interface is added for handling signal exchanges between different clock domains. To eliminate the noise of various bio-signals, the reconfigurable filter is created to provide the functions of average, binomial and sharpen filters. The slop-feature forecast and the lossless data encoder is proposed to reduce the data of various biomedical signals for transmission. Furthermore, the ECC encoder is added to improve the reliability for the wireless transmission and the UART interface is employed the proposed design to be compatible with wireless devices. For long-term healthcare monitoring application, a power management technique is developed for reducing the power consumption of the WBSN system. In addition, the proposed design can be operated with four different bio-sensors simultaneously. The proposed design was successfully tested with a FPGA verification board. The VLSI architecture of this work contains 7.67-K gate counts and consumes the power of 5.8 mW or 1.9 mW at 100 MHz or 133 MHz processing rate using a TSMC 0.18 μm or 0.13 μm CMOS process. Compared with previous techniques, this design achieves higher performance, more functions, more flexibility and higher compatibility than other micro controller designs.
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.
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Zitong Zhang
Full Text Available Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT, wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families, and wavelet length (2 to 24-each essential parameters in wavelet-based methods-on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.
Color filtering localization for three-dimensional underwater acoustic sensor networks.
Liu, Zhihua; Gao, Han; Wang, Wuling; Chang, Shuai; Chen, Jiaxing
2015-03-12
Accurate localization of mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. The proportion factor of distance is also proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. The normalized nearness degrees are considered as the weighted standards to calculate the coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can localize the mobile node in a timely way. The average localization error of PCFL is decreased by about 30.4% compared to the AFLA method.
Kim, Keonwook
2013-08-23
The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving vehicle and reject the signal from motionless automobiles around the WSN node. A cascade structure between analog envelope detector and digital exponential smoothing filter presents the velocity vector-sensitive output with low analog circuit and digital computation complexity. The optimal parameters in the exponential smoothing filter are obtained by analytical and mathematical methods for maximum variation over the vehicle speed. For stationary targets, the derived simulation based on the acoustic field parameters demonstrates that the system significantly reduces the communication requirements with low complexity and can be expected to extend the operation time considerably.
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Zhang, Y.; Dalguer, L. A.; Song, S.; Clinton, J. F.
2013-12-01
Detailed source imaging of the spatial and temporal slip distribution of earthquakes is a main research goal for seismology. In this study we investigate how the number and geometrical distribution of seismic stations affect finite kinematic source inversion results by inverting ground motions derived from a known synthetic dynamic earthquake rupture model, which is governed by the slip weakening friction law with heterogeneous stress distribution. Our target dynamic rupture model is a buried strike-slip event (Mw 6.5) in a layered half space (Dalguer & Mai, 2011) with broadband synthetic ground motions created at 168 near-field stations. In the inversion, we modeled low frequency (under 1Hz) waveforms using a genetic algorithm in a Bayesian framework (Moneli et al. 2008) to retrieve peak slip velocity, rupture time, and rise time of the source. The dynamic consistent regularized Yoffe function (Tinti et al. 2005) was applied as a single window slip velocity function. Tikhonov regularization was used to smooth final slip. We tested three station network geometry cases: (a) single station, in which we inverted 3 component waveforms from a single station varying azimuth and epicentral distance; (b) multi-station configurations with similar numbers of stations all at similar distances from, but regularly spaced around the fault; (c) irregular multi-station configurations using different numbers of stations. For analysis, waveform misfits are calculated using all 168 stations. Our results show: 1) single station tests suggest that it may be possible to obtain a relatively good source model even using one station, with a waveform misfit comparable to that obtained with the best source model. The best single station performance occurs with stations in which amplitude ratios between the three components are not large, indicating that P & S waves are all present. We infer that both body wave radiation pattern and distance play an important role in selection of optimal
Use of Gabor filters and deep networks in the segmentation of retinal vessel morphology
Leopold, Henry A.; Orchard, Jeff; Zelek, John; Lakshminarayanan, Vasudevan
2017-02-01
The segmentation of retinal morphology has numerous applications in assessing ophthalmologic and cardiovascular disease pathologies. The early detection of many such conditions is often the most effective method for reducing patient risk. Computer aided segmentation of the vasculature has proven to be a challenge, mainly due to inconsistencies such as noise, variations in hue and brightness that can greatly reduce the quality of fundus images. Accurate fundus and/or retinal vessel maps give rise to longitudinal studies able to utilize multimodal image registration and disease/condition status measurements, as well as applications in surgery preparation and biometrics. This paper further investigates the use of a Convolutional Neural Network as a multi-channel classifier of retinal vessels using the Digital Retinal Images for Vessel Extraction database, a standardized set of fundus images used to gauge the effectiveness of classification algorithms. The CNN has a feed-forward architecture and varies from other published architectures in its combination of: max-pooling, zero-padding, ReLU layers, batch normalization, two dense layers and finally a Softmax activation function. Notably, the use of Adam to optimize training the CNN on retinal fundus images has not been found in prior review. This work builds on prior work of the authors, exploring the use of Gabor filters to boost the accuracy of the system to 0.9478 during post processing. The mean of a series of Gabor filters with varying frequencies and sigma values are applied to the output of the network and used to determine whether a pixel represents a vessel or non-vessel.
Blind Identification of Graph Filters
Segarra, Santiago; Mateos, Gonzalo; Marques, Antonio G.; Ribeiro, Alejandro
2017-03-01
Network processes are often represented as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of linear graph filters modeling underlying network dynamics. This paper deals with the problem of joint identification of a graph filter and its input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to the less-structured graph domain. Given a graph signal $\\mathbf{y}$ modeled as the output of a graph filter, the goal is to recover the vector of filter coefficients $\\mathbf{h}$, and the input signal $\\mathbf{x}$ which is assumed to be sparse. While $\\mathbf{y}$ is a bilinear function of $\\mathbf{x}$ and $\\mathbf{h}$, the filtered graph signal is also a linear combination of the entries of the lifted rank-one, row-sparse matrix $\\mathbf{x} \\mathbf{h}^T$. The blind graph-filter identification problem can thus be tackled via rank and sparsity minimization subject to linear constraints, an inverse problem amenable to convex relaxations offering provable recovery guarantees under simplifying assumptions. Numerical tests using both synthetic and real-world networks illustrate the merits of the proposed algorithms, as well as the benefits of leveraging multiple signals to aid the blind identification task.
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Lubomir Scholtz
2016-01-01
Full Text Available In the design of new components for passive optical networks (PONs, the non-ideal properties are worth considering. In this paper the influence of interface roughness and temperature changes on final transmittance of downstream channels blocking filters for next generation dense wavelength division multiplexing passive optical networks(DWDM-PONs is shown. The transmittance as the filter transfer characteristicswas calculated with the transfer matrix method. The roughness was expressed by root mean square deviations from an ideally smooth surface and was taken into account in the modified Fresnel coefficients. It is demonstrated how the interfacial roughness may increase the insertion loss and decrease the channel bandwidth which results in reduction of transmitted light energy through the filter.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-08
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
Spatiotemporal filtering for regional GPS network in China using independent component analysis
Ming, Feng; Yang, Yuanxi; Zeng, Anmin; Zhao, Bin
2017-04-01
Removal of the common mode error (CME) is a routine procedure in postprocessing regional GPS network observations, which is commonly performed using principal component analysis (PCA). PCA decomposes a network time series into a group of modes, where each mode comprises a common temporal function and corresponding spatial response based on second-order statistics (variance and covariance). However, the probability distribution function of a GPS time series is non-Gaussian; therefore, the largest variances do not correspond to the meaningful axes, and the PCA-derived components may not have an obvious physical meaning. In this study, the CME was assumed statistically independent of other errors, and it was extracted using independent component analysis (ICA), which involves higher-order statistics. First, the ICA performance was tested using a simulated example and compared with PCA and stacking methods. The existence of strong local effects on some stations causes significant large spatial responses and, therefore, a strategy based on median and interquartile range statistics was proposed to identify abnormal sites. After discarding abnormal sites, two indices based on the analysis of the spatial responses of all sites in each independent component (east, north, and vertical) were used to define the CME quantitatively. Continuous GPS coordinate time series spanning ˜ 4.5 years obtained from 259 stations of the Tectonic and Environmental Observation Network of Mainland China (CMONOC II) were analyzed using both PCA and ICA methods and their results compared. The results suggest that PCA is susceptible to deriving an artificial spatial structure, whereas ICA separates the CME from other errors reliably. Our results demonstrate that the spatial characteristics of the CME for CMONOC II are not uniform for the east, north, and vertical components, but have an obvious north-south or east-west distribution. After discarding 84 abnormal sites and performing spatiotemporal
Lin, Bo; Tjin, Swee Chuan; Zhang, Han; Tang, Dingyuan; Hao, Jianzhong; Dong, Bo; Liang, Sheng
2010-12-20
We present a stable and switchable dual-wavelength erbium-doped fiber laser. In the ring cavity, an inverse-Gaussian apodized fiber Bragg grating serves as an ultranarrow dual-wavelength passband filter, a semiconductor optical amplifier biased in the low-gain regime reduces the gain competition of the two wavelengths, and a feedback fiber loop acts as a mode filter to guarantee a stable single-longitudinal-mode operation. Two lasing lines with a wavelength separation of approximately 0.1 nm are obtained experimentally. A microwave signal at 12.51 GHz is demonstrated by beating the dual wavelengths at a photodetector.
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Li Zeng
2015-11-01
Full Text Available This article puts forward inductive magnetic suspension spherical active joints and has researched on its mechanism. The expression of motor’s electromagnetic torque is derived from the point of power balance of three-dimensional electromagnetic model, and on the basis of the air gap magnetic flux density distribution, we establish the joint’s mathematical model of electromagnetic levitation force. The relationship between the two of displacement, angle, and current and the transfer function expression of motor system are derived by the state equation and the inverse system theory We established the inverse system of joint’s original system using fuzzy neural network theory and simplified coupling relationship of the motor’s complex multivariable to establish ANFIS model of joint’s inverse system. An internal model controller with high robustness and stability was designed, and an internal model control joint pseudo linear system was built. According to the simulation analysis and experimental verification of the joint control system, the conclusion indicates that the rotor has quick dynamic response and high robustness.
Piatyszek, E.; Voignier, P.; Graillot, D.
2000-05-01
One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.
Real-time environmental inversion using a network of light receiving systems
Soares, C.; Jesus, S.M.
2007-01-01
This paper reports preliminary environmental inversion results of acoustic data collected simultaneously at two receiving systems during the RADAR’07 sea trial. These receiving systems have communication capabilities that allow for transfering acoustic and telemetric data to a base station with processing capabilities in order to produce environmental estimates during the acoustic experiment. During a large part of the experiment estimates on the temperature field appear to agree with c...
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Xingang Fu
2016-04-01
Full Text Available This paper investigates a novel recurrent neural network (NN-based vector control approach for single-phase grid-connected converters (GCCs with L (inductor, LC (inductor-capacitor and LCL (inductor-capacitor-inductor filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies.
Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network
Liu, Derong; Pang, Zhongyu; Wang, Zhuo
2009-12-01
None of the current epileptic seizure prediction methods can widely be accepted, due to their poor consistency in performance. In this work, we have developed a novel approach to analyze intracranial EEG data. The energy of the frequency band of 4-12 Hz is obtained by wavelet transform. A dynamic model is introduced to describe the process and a hidden variable is included. The hidden variable can be considered as indicator of seizure activities. The method of particle filter associated with a neural network is used to calculate the hidden variable. Six patients' intracranial EEG data are used to test our algorithm including 39 hours of ictal EEG with 22 seizures and 70 hours of normal EEG recordings. The minimum least square error algorithm is applied to determine optimal parameters in the model adaptively. The results show that our algorithm can successfully predict 15 out of 16 seizures and the average prediction time is 38.5 minutes before seizure onset. The sensitivity is about 93.75% and the specificity (false prediction rate) is approximately 0.09 FP/h. A random predictor is used to calculate the sensitivity under significance level of 5%. Compared to the random predictor, our method achieved much better performance.
Scalable high-throughput identification of genetic targets by network filtering.
Bevilacqua, Vitoantonio; Pannarale, Paolo
2013-01-01
Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. One of the most common approaches to this problem is finding sets of differentially expressed, and more recently differentially co-expressed, genes. Other approaches require libraries of genetic mutants or require to perform a large number of assays. Another elegant approach is the filtering of mRNA expression profiles using reverse-engineered gene network models of the target cell. This approach has the advantage of not needing control samples, libraries or numerous assays. Nevertheless, the impementations of this strategy proposed so far are computationally demanding. Moreover the user has to arbitrarily choose a threshold on the number of potentially relevant genes from the algorithm output. Our solution, while performing comparably to state of the art algorithms in terms of discovered targets, is more efficient in terms of memory and time consumption. The proposed algorithm computes the likelihood associated to each gene and outputs to the user only the list of likely perturbed genes. The proposed algorithm is a valid alternative to existing algorithms and is particularly suited to contemporary gene expression microarrays, given the number of probe sets in each chip, also when executed on common desktop computers.
He, Jian; Bai, Shuang; Wang, Xiaoyi
2017-06-16
Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.
Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network
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Derong Liu
2009-01-01
Full Text Available None of the current epileptic seizure prediction methods can widely be accepted, due to their poor consistency in performance. In this work, we have developed a novel approach to analyze intracranial EEG data. The energy of the frequency band of 4–12 Hz is obtained by wavelet transform. A dynamic model is introduced to describe the process and a hidden variable is included. The hidden variable can be considered as indicator of seizure activities. The method of particle filter associated with a neural network is used to calculate the hidden variable. Six patients' intracranial EEG data are used to test our algorithm including 39 hours of ictal EEG with 22 seizures and 70 hours of normal EEG recordings. The minimum least square error algorithm is applied to determine optimal parameters in the model adaptively. The results show that our algorithm can successfully predict 15 out of 16 seizures and the average prediction time is 38.5 minutes before seizure onset. The sensitivity is about 93.75% and the specificity (false prediction rate is approximately 0.09 FP/h. A random predictor is used to calculate the sensitivity under significance level of 5%. Compared to the random predictor, our method achieved much better performance.
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Muhammad Ammirrul Atiqi Mohd Zainuri
2016-05-01
Full Text Available This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC adaptive linear element (ADALINE neural network with the integration of photovoltaic (PV to shunt active power filters (SAPFs as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S, and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP. From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD, time response and reduction of source power from grid have successfully been verified and achieved.
Mushkin, I.; Solomon, S.
2017-10-01
We study the inverse contagion problem (ICP). As opposed to the direct contagion problem, in which the network structure is known and the question is when each node will be contaminated, in the inverse problem the links of the network are unknown but a sequence of contagion histories (the times when each node was contaminated) is observed. We consider two versions of the ICP: The strong problem (SICP), which is the reconstruction of the network and has been studied before, and the weak problem (WICP), which requires "only" the prediction (at each time step) of the nodes that will be contaminated at the next time step (this is often the real life situation in which a contagion is observed and predictions are made in real time). Moreover, our focus is on analyzing the increasing accuracy of the solution, as a function of the number of contagion histories already observed. For simplicity, we discuss the simplest (deterministic and synchronous) contagion dynamics and the simplest solution algorithm, which we have applied to different network types. The main result of this paper is that the complex problem of the convergence of the ICP for a network can be reduced to an individual property of pairs of nodes: the "false link difficulty". By definition, given a pair of unlinked nodes i and j, the difficulty of the false link (i,j) is the probability that in a random contagion history, the nodes i and j are not contaminated at the same time step (or at consecutive time steps). In other words, the "false link difficulty" of a non-existing network link is the probability that the observations during a random contagion history would not rule out that link. This probability is relatively straightforward to calculate, and in most instances relies only on the relative positions of the two nodes (i,j) and not on the entire network structure. We have observed the distribution of false link difficulty for various network types, estimated it theoretically and confronted it
Zhang, Yunong; Guo, Dongsheng; Li, Zhan
2013-04-01
In this paper, two simple-structure neural networks based on the error back-propagation (BP) algorithm (i.e., BP-type neural networks, BPNNs) are proposed, developed, and investigated for online generalized matrix inversion. Specifically, the BPNN-L and BPNN-R models are proposed and investigated for the left and right generalized matrix inversion, respectively. In addition, for the same problem-solving task, two discrete-time Hopfield-type neural networks (HNNs) are developed and investigated in this paper. Similar to the classification of the presented BPNN-L and BPNN-R models, the presented HNN-L and HNN-R models correspond to the left and right generalized matrix inversion, respectively. Comparing the BPNN weight-updating formula with the HNN state-transition equation for the specific (i.e., left or right) generalized matrix inversion, we show that such two derived learning-expressions turn out to be the same (in mathematics), although the BP and Hopfield-type neural networks are evidently different from each other a great deal, in terms of network architecture, physical meaning, and training patterns. Numerical results with different illustrative examples further demonstrate the efficacy of the presented BPNNs and HNNs for online generalized matrix inversion and, more importantly, their common natures of learning.
DEFF Research Database (Denmark)
Alzola, Rafael Pena; Liserre, Marco; Blaabjerg, Frede
2014-01-01
) nor its rationale has been explained. Thus, in this paper a straightforward procedure is developed to tune the lead-lag network with the help of software tools. The rationale of this procedure, based on the capacitor current feedback, is elucidated. Stability is studied by means of the root locus......Three-phase active rectifiers guarantee sinusoidal input currents and unity power factor at the price of a high switching frequency ripple. To adopt an LCL-filter, instead of an L-filter, allows using reduced values for the inductances and so preserving dynamics. However, stability problems can...... without using dissipative elements but, sometimes, needing additional sensors. This solution has been addressed in many publications. The lead-lag network method is one of the first reported procedures and continues being in use. However, neither there is a direct tuning procedure (without trial and error...
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Jatin Narula
2010-05-01
Full Text Available Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad-a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models.
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S. N. Naikwad
2009-01-01
Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.
Cortical Network Modeling for Inverse Kinematic Computation of an Anthropomorphic Finger
Gentili, Rodolphe J.; Oh, Hyuk; Molina, Javier; Contreras-Vidal, José L.
2014-01-01
The performance of reaching movements to visual targets requires complex kinematic mechanisms such as redundant, multijointed, anthropomorphic actuators and thus is a difficult problem since the relationship between sensory and motor coordinates is highly nonlinear. In this article, we present a neural model able to learn the inverse kinematics of a simulated anthropomorphic robot finger (ShadowHand™ finger) having four degrees of freedom while performing 3D reaching movements. The results revealed that this neural model was able to control accurately and robustly the finger when performing single 3D reaching movements as well as more complex patterns of motion while generating kinematics comparable to those observed in human. The long term goal of this research is to design a bio-mimetic controller providing adaptive, robust and flexible control of dexterous robotic/prosthetics hands. PMID:22256258
Karanovic, Marinko; Muffels, Christopher T.; Tonkin, Matthew J.; Hunt, Randall J.
2012-01-01
Models of environmental systems have become increasingly complex, incorporating increasingly large numbers of parameters in an effort to represent physical processes on a scale approaching that at which they occur in nature. Consequently, the inverse problem of parameter estimation (specifically, model calibration) and subsequent uncertainty analysis have become increasingly computation-intensive endeavors. Fortunately, advances in computing have made computational power equivalent to that of dozens to hundreds of desktop computers accessible through a variety of alternate means: modelers have various possibilities, ranging from traditional Local Area Networks (LANs) to cloud computing. Commonly used parameter estimation software is well suited to take advantage of the availability of such increased computing power. Unfortunately, logistical issues become increasingly important as an increasing number and variety of computers are brought to bear on the inverse problem. To facilitate efficient access to disparate computer resources, the PESTCommander program documented herein has been developed to provide a Graphical User Interface (GUI) that facilitates the management of model files ("file management") and remote launching and termination of "slave" computers across a distributed network of computers ("run management"). In version 1.0 described here, PESTCommander can access and ascertain resources across traditional Windows LANs: however, the architecture of PESTCommander has been developed with the intent that future releases will be able to access computing resources (1) via trusted domains established in Wide Area Networks (WANs) in multiple remote locations and (2) via heterogeneous networks of Windows- and Unix-based operating systems. The design of PESTCommander also makes it suitable for extension to other computational resources, such as those that are available via cloud computing. Version 1.0 of PESTCommander was developed primarily to work with the
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Prima Dewi Purnamasari
2017-05-01
Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction
A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting
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Youzhu Li
2014-01-01
Full Text Available This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.
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Xia-an Bi
2015-01-01
Full Text Available With the development of wireless mesh networks and distributed computing, lots of new P2P services have been deployed and enrich the Internet contents and applications. The rapid growth of P2P flows brings great pressure to the regular network operation. So the effective flow identification and management of P2P applications become increasingly urgent. In this paper, we build a multilevel bloom filters data structure to identify the P2P flows through researches on the locality characteristics of P2P flows. Different level structure stores different numbers of P2P flow rules. According to the characteristics values of the P2P flows, we adjust the parameters of the data structure of bloom filters. The searching steps of the scheme traverse from the first level to the last level. Compared with the traditional algorithms, our method solves the drawbacks of previous schemes. The simulation results demonstrate that our algorithm effectively enhances the performance of P2P flows identification. Then we deploy our flow identification algorithm in the traffic monitoring sensors which belong to the network traffic monitoring system at the export link in the campus network. In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.
Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente
2016-08-31
This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.
An inverse method was developed to integrate satellite observations of atmospheric pollutant column concentrations and direct sensitivities predicted by a regional air quality model in order to discern biases in the emissions of the pollutant precursors.
Lary, David J.; Mussa, Yussuf
2004-01-01
In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.
Jin, Long; Zhang, Yunong; Li, Shuai
2016-12-01
Matrix inversion often arises in the fields of science and engineering. Many models for matrix inversion usually assume that the solving process is free of noises or that the denoising has been conducted before the computation. However, time is precious for the real-time-varying matrix inversion in practice, and any preprocessing for noise reduction may consume extra time, possibly violating the requirement of real-time computation. Therefore, a new model for time-varying matrix inversion that is able to handle simultaneously the noises is urgently needed. In this paper, an integration-enhanced Zhang neural network (IEZNN) model is first proposed and investigated for real-time-varying matrix inversion. Then, the conventional ZNN model and the gradient neural network model are presented and employed for comparison. In addition, theoretical analyses show that the proposed IEZNN model has the global exponential convergence property. Moreover, in the presence of various kinds of noises, the proposed IEZNN model is proven to have an improved performance. That is, the proposed IEZNN model converges to the theoretical solution of the time-varying matrix inversion problem no matter how large the matrix-form constant noise is, and the residual errors of the proposed IEZNN model can be arbitrarily small for time-varying noises and random noises. Finally, three illustrative simulation examples, including an application to the inverse kinematic motion planning of a robot manipulator, are provided and analyzed to substantiate the efficacy and superiority of the proposed IEZNN model for real-time-varying matrix inversion.
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KEYVAN ASEFPOUR VAKILIAN
2016-04-01
Full Text Available With the advent of applications of machine learning methods in food engineering in recent decades, several intelligent methods have been introduced in fruit grading technology. In this study, an apple grading system is presented using image’s textural features extraction and artificial intelligence. The objective of this study was to simplify the use of Gabor filter in classification of two varieties of apple fruits (Golden Delicious and Red Delicious in four categories according to the European fruit quality standards. Using this filter, neural network classifier was trained for four category grading of the fruits. Two textural parameters were extracted from each obtained image: mean and variance of energy values of obtained image representing image’s luminous intensity and contrast, respectively. Experimental results indicated that the training of extracted features of about 350 fruits enabled the network to classify the test samples with appropriate accuracy. Compared to the state-of-the-art, the proposed grading categories (‘Extra’, ‘Type 1’, ‘Type 2’ and ‘Rejected’ classes achieved acceptable recognition rates of about 89 % and 92 % overall accuracy for Golden Delicious and Red Delicious varieties, respectively. These experimental results show the appropriate application of proposed method in fast grading of apple fruits. Furthermore, proposed feature extraction and network training methods can be used efficiently in online applications.
Chen, Huayan; Zhang, Senlin; Liu, Meiqin; Zhang, Qunfei
2017-04-27
We study the problem of energy-efficient target tracking in underwater wireless sensor networks (UWSNs). Since sensors of UWSNs are battery-powered, it is impracticable to replace the batteries when exhausted. This means that the battery life affects the lifetime of the whole network. In order to extend the network lifetime, it is worth reducing the energy consumption on the premise of sufficient tracking accuracy. This paper proposes an energy-efficient filter that implements the tradeoff between communication cost and tracking accuracy. Under the distributed fusion framework, local sensors should not send their weak information to the fusion center if their measurement residuals are smaller than the pre-given threshold. In order to guarantee the target tracking accuracy, artificial measurements are generated to compensate for those unsent real measurements. Then, an adaptive scheme is derived to take full advantages of the artificial measurements-based filter in terms of energy-efficiency. Furthermore, a computationally efficient optimal sensor selection scheme is proposed to improve tracking accuracy on the premise of employing the same number of sensors. Simulation demonstrates that our scheme has superior advantages in the tradeoff between communication cost and tracking accuracy. It saves much energy while loosing little tracking accuracy or improves tracking performance with less additional energy cost.
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Huayan Chen
2017-04-01
Full Text Available We study the problem of energy-efficient target tracking in underwater wireless sensor networks (UWSNs. Since sensors of UWSNs are battery-powered, it is impracticable to replace the batteries when exhausted. This means that the battery life affects the lifetime of the whole network. In order to extend the network lifetime, it is worth reducing the energy consumption on the premise of sufficient tracking accuracy. This paper proposes an energy-efficient filter that implements the tradeoff between communication cost and tracking accuracy. Under the distributed fusion framework, local sensors should not send their weak information to the fusion center if their measurement residuals are smaller than the pre-given threshold. In order to guarantee the target tracking accuracy, artificial measurements are generated to compensate for those unsent real measurements. Then, an adaptive scheme is derived to take full advantages of the artificial measurements-based filter in terms of energy-efficiency. Furthermore, a computationally efficient optimal sensor selection scheme is proposed to improve tracking accuracy on the premise of employing the same number of sensors. Simulation demonstrates that our scheme has superior advantages in the tradeoff between communication cost and tracking accuracy. It saves much energy while loosing little tracking accuracy or improves tracking performance with less additional energy cost.
Korcyl, K; Dobinson, Robert W; Ivanovici, M; Losada-Maia, Marcia; Meirosu, C; Sladowski, G
2004-01-01
We present a system for measuring network performance as part of the feasibility studies for locating the ATLAS third level trigger, the event filter (EF), in remote locations. Part of the processing power required to run the EF algorithms, the current estimate is 2000 state off the art processors, can be provided in remote, CERN-affiliated institutes, if a suitable network connection between CERN and the remote site could be achieved. The system is composed of two PCs equipped with GPS systems, CERN-designed clock cards and Alteon gigabit programmable network interface cards. In the first set of measurements we plan to quantify connection in terms of end-to-end latency, throughput, jitter and packet loss. Running streaming tests and study throughput, IP QoS, routing testing and traffic shaping follows this. Finally, we plan to install the event filter software in a remote location and feed it with data from test beams at CERN. Each of these tests should be preformed with the test traffic treated in the netwo...
Performance study on the effect of filter curve in CWDM System for the access network
Ali, N.; Rahman, N. A.; Hambali, N. A. M. Ahmad; Rashidi, C. B. M.
2017-11-01
This paper presents the study on the effect of filter variation on the coarse wavelength division multiplexing (CWDM) system. The filter curve will affect the performance of the CWDM system due to changes of received power lever and isolation of the signal. The significant impact on the received power level and isolation can be found when the required signal is isolated from unwanted signal by the steep curve of filter. As a result, BER of 1.0x 10-12 was obtained corresponding to receive power level of -24.27 dBm with isolation of 23.22 dB. When the wavelength spacing is reduced to 1nm, the isolation is only 11.30 dB and BER increased to 5.49x10-7 with a received power of -15.39 dBm.
Shi, Peng; Zhang, Yingqi; Chadli, Mohammed; Agarwal, Ramesh K
2016-04-01
In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.
Eckstein, Monika; Markett, Sebastian; Kendrick, Keith M; Ditzen, Beate; Liu, Fang; Hurlemann, Rene; Becker, Benjamin
2017-04-01
The hypothalamic neuropeptide oxytocin (OT) has received increasing attention for its role in modulating social-emotional processes across species. Previous studies on using intranasal-OT in humans point to a crucial engagement of the amygdala in the observed neuromodulatory effects of OT under task and rest conditions. However, the amygdala is not a single homogenous structure, but rather a set of structurally and functionally heterogeneous nuclei that show distinct patterns of connectivity with limbic and frontal emotion-processing regions. To determine potential differential effects of OT on functional connectivity of the amygdala subregions, 79 male participants underwent resting-state fMRI following randomized intranasal-OT or placebo administration. In line with previous studies OT increased the connectivity of the total amygdala with dorso-medial prefrontal regions engaged in emotion regulation. In addition, OT enhanced coupling of the total amygdala with cerebellar regions. Importantly, OT differentially altered the connectivity of amygdala subregions with distinct up-stream cortical nodes, particularly prefrontal/parietal, and cerebellar down-stream regions. OT-induced increased connectivity with cerebellar regions were largely driven by effects on the centromedial and basolateral subregions, whereas increased connectivity with prefrontal regions were largely mediated by right superficial and basolateral subregions. OT decreased connectivity of the centromedial subregions with core hubs of the emotional face processing network in temporal, occipital and parietal regions. Preliminary findings suggest that effects on the superficial amygdala-prefrontal pathway were inversely associated with levels of subclinical depression, possibly indicating that OT modulation may be blunted in the context of increased pathological load. Together, the present findings suggest a subregional-specific modulatory role of OT on amygdala-centered emotion processing networks in
Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zechang
2015-10-01
The accurate monitoring of battery cell temperature is indispensible to the design of battery thermal management system. To obtain the internal temperature of a battery cell online, an adaptive temperature estimation method based on Kalman filtering and an equivalent time-variant electrical network thermal (EENT) model is proposed. The EENT model uses electrical components to simulate the battery thermodynamics, and the model parameters are obtained with a least square algorithm. With a discrete state-space description of the EENT model, a Kalman filtering (KF) based internal temperature estimator is developed. Moreover, considering the possible time-varying external heat exchange coefficient, a joint Kalman filtering (JKF) based estimator is designed to simultaneously estimate the internal temperature and the external thermal resistance. Several experiments using the hard-cased LiFePO4 cells with embedded temperature sensors have been conducted to validate the proposed method. Validation results show that, the EENT model expresses the battery thermodynamics well, the KF based temperature estimator tracks the real central temperature accurately even with a poor initialization, and the JKF based estimator can simultaneously estimate both central temperature and external thermal resistance precisely. The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
Zhou, Xingyu; Zhuge, Qunbi; Qiu, Meng; Xiang, Meng; Zhang, Fangyuan; Wu, Baojian; Qiu, Kun; Plant, David V.
2018-02-01
We investigate the capacity improvement achieved by bandwidth variable transceivers (BVT) in meshed optical networks with cascaded ROADM filtering at fixed channel spacing, and then propose an artificial neural network (ANN)-aided provisioning scheme to select optimal symbol rate and modulation format for the BVTs in this scenario. Compared with a fixed symbol rate transceiver with standard QAMs, it is shown by both experiments and simulations that BVTs can increase the average capacity by more than 17%. The ANN-aided BVT provisioning method uses parameters monitored from a coherent receiver and then employs a trained ANN to transform these parameters into the desired configuration. It is verified by simulation that the BVT with the proposed provisioning method can approach the upper limit of the system capacity obtained by brute-force search under various degrees of flexibilities.
Dehghan, E.; Sanavi Khoshnoud, D.; Naeimi, A. S.
2018-01-01
The spin-resolved electron transport through a triangular network of quantum nanorings is studied in the presence of Rashba spin-orbit interaction (RSOI) and a magnetic flux using quantum waveguide theory. This study illustrates that, by tuning Rashba constant, magnetic flux and incoming electron energy, the triangular network of quantum rings can act as a perfect logical spin-filtering with high efficiency. By changing in the energy of incoming electron, at a proper value of the Rashba constant and magnetic flux, a reverse in the direction of spin can take place in the triangular network of quantum nanorings. Furthermore, the triangular network of quantum nanorings can be designed as a device and shows several simultaneous spintronic properties such as spin-splitter and spin-inverter. This spin-splitting is dependent on the energy of the incoming electron. Additionally, different polarizations can be achieved in the two outgoing leads from an originally incoming spin state that simulates a Stern-Gerlach apparatus.
Round trip time estimation in communication networks using adaptive Kalman filtering
Jacobsson, Krister; Hjalmarsson, Håkan; Möller, Niels; Johansson, Karl Henrik
2004-01-01
Heterogeneous communication networks with their variety of application demands, uncertain time-varying traffic load, and mixture of wired and wireless links pose several challenging problem in modeling and control. In this paper we focus on the roundtrip time (RTT), which is a particularly important variable for efficient end-to-end congestion control. Based on a simple aggregated model of the network, an algorithm combining a Kalmanfilter and a change detection algorithm is proposed for RTT ...
Real-Time Data Filtering and Compression in Wide Area Simulation Networks
1992-10-02
transmission or at reception). 14 F100 so 60 °I 00 60 I 40 * 20 1.I I I , I a 1.0 1.5 2.0 2.5 3.0 Saftey time (hours) Fig. 6. Filtering rate vs. safety...based on a novel idea of mapping the decoding/encoding tree of any variable length binary code on to a memory device that corresponds to simultaneous...increasing. Communication and display technologies allow the use of pictorial3 information and photographic images in various scientific, industrial, medical
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Xinbin Li
2017-12-01
Full Text Available Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs. However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.
Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping
2017-12-21
Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.
Kalman Filter-Based Hybrid Indoor Position Estimation Technique in Bluetooth Networks
Directory of Open Access Journals (Sweden)
Fazli Subhan
2013-01-01
Full Text Available This paper presents an extended Kalman filter-based hybrid indoor position estimation technique which is based on integration of fingerprinting and trilateration approach. In this paper, Euclidian distance formula is used for the first time instead of radio propagation model to convert the received signal to distance estimates. This technique combines the features of fingerprinting and trilateration approach in a more simple and robust way. The proposed hybrid technique works in two stages. In the first stage, it uses an online phase of fingerprinting and calculates nearest neighbors (NN of the target node, while in the second stage it uses trilateration approach to estimate the coordinate without the use of radio propagation model. The distance between calculated NN and detective access points (AP is estimated using Euclidian distance formula. Thus, distance between NN and APs provides radii for trilateration approach. Therefore, the position estimation accuracy compared to the lateration approach is better. Kalman filter is used to further enhance the accuracy of the estimated position. Simulation and experimental results validate the performance of proposed hybrid technique and improve the accuracy up to 53.64% and 25.58% compared to lateration and fingerprinting approaches, respectively.
Directory of Open Access Journals (Sweden)
Jongpil Lee
2018-01-01
Full Text Available Convolutional Neural Networks (CNN have been applied to diverse machine learning tasks for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw waveform-based approach has been explored to directly learn hierarchical characteristics of audio. However, the majority of previous studies have limited their model capacity by taking a frame-level structure similar to short-time Fourier transforms. We previously proposed a CNN architecture which learns representations using sample-level filters beyond typical frame-level input representations. The architecture showed comparable performance to the spectrogram-based CNN model in music auto-tagging. In this paper, we extend the previous work in three ways. First, considering the sample-level model requires much longer training time, we progressively downsample the input signals and examine how it affects the performance. Second, we extend the model using multi-level and multi-scale feature aggregation technique and subsequently conduct transfer learning for several music classification tasks. Finally, we visualize filters learned by the sample-level CNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency.
Sbarufatti, Claudio; Corbetta, Matteo; Giglio, Marco; Cadini, Francesco
2017-03-01
Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.
Streaming Parallel GPU Acceleration of Large-Scale filter-based Spiking Neural Networks
L.P. Slazynski (Leszek); S.M. Bohte (Sander)
2012-01-01
htmlabstractThe arrival of graphics processing (GPU) cards suitable for massively parallel computing promises a↵ordable large-scale neural network simulation previously only available at supercomputing facil- ities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of
Attractor switching in neuron networks and Spatiotemporal filters for motion processing
Subramanian, Easwara Naga
2008-01-01
From a broader perspective, we address two important questions, viz., (a) what kind of mechanism would enable a neuronal network to switch between various tasks or stored patterns? (b) what are the properties of neurons that are used by the visual system in early motion detection? To address (a) we
van Vugt, Pieter Karel Anton; Bijman, Rob; Timens, R.B.; Leferink, Frank Bernardus Johannes
2013-01-01
Insulated terrestrial power networks are used for reliable systems such as large production plants, hospital operating rooms and naval ships. The system is isolated from ground and a first fault, such as a short circuit between a phase and ground, will not result in disconnection of the power via
Suzuki, Kenji
2009-09-01
Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved. First presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11-13 December 2008.
Kim, Keonwook
2013-01-01
The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving v...
DEFF Research Database (Denmark)
May, Tobias
2018-01-01
This study presents an algorithm for binaural speech dereverberation based on the supervised learning of short-term binaural cues. The proposed system combined a delay-and-sum beamformer (DSB) with a neural network-based post-filter that attenuated reverberant components in individual time...
Cheng, Rui; Xia, Li; Sima, Chaotan; Ran, Yanli; Rohollahnejad, Jalal; Zhou, Jiaao; Wen, Yongqiang; Yu, Can
2016-02-08
Ultrashort fiber Bragg gratings (US-FBGs) have significant potential as weak grating sensors for distributed sensing, but the exploitation have been limited by their inherent broad spectra that are undesirable for most traditional wavelength measurements. To address this, we have recently introduced a new interrogation concept using shifted optical Gaussian filters (SOGF) which is well suitable for US-FBG measurements. Here, we apply it to demonstrate, for the first time, an US-FBG-based self-referencing distributed optical sensing technique, with the advantages of adjustable sensitivity and range, high-speed and wide-range (potentially >14000 με) intensity-based detection, and resistance to disturbance by nonuniform parameter distribution. The entire system is essentially based on a microwave network, which incorporates the SOGF with a fiber delay-line between the two arms. Differential detections of the cascaded US-FBGs are performed individually in the network time-domain response which can be obtained by analyzing its complex frequency response. Experimental results are presented and discussed using eight cascaded US-FBGs. A comprehensive numerical analysis is also conducted to assess the system performance, which shows that the use of US-FBGs instead of conventional weak FBGs could significantly improve the power budget and capacity of the distributed sensing system while maintaining the crosstalk level and intensity decay rate, providing a promising route for future sensing applications.
Quang Truong, Dinh; Ahn, Kyoung Kwan
2014-07-01
An ion polymer metal composite (IPMC) is an electroactive polymer that bends in response to a small applied electric field as a result of mobility of cations in the polymer network and vice versa. This paper presents an innovative and accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The model is constructed via a general multilayer perceptron neural network (GMLPNN) integrated with a smart learning mechanism (SLM) that is based on an extended Kalman filter with self-decoupling ability (SDEKF). Here the GMLPNN is built with an ability to autoadjust its structure based on its characteristic vector. Furthermore, by using the SLM based on the SDEKF, the GMLPNN parameters are optimized with small computational effort, and the modeling accuracy is improved. An apparatus employing an IPMC actuator is first set up to investigate the IPMC characteristics and to generate the data for training and validating the model. The advanced NBBM model for the IPMC system is then created with the proper inputs to estimate IPMC tip displacement. Next, the model is optimized using the SLM mechanism with the training data. Finally, the optimized NBBM model is verified with the validating data. A comparison between this model and the previously developed model is also carried out to prove the effectiveness of the proposed modeling technique.
Sakurai, Gen; Yonemura, Seiichiro; Kishimoto-Mo, Ayaka W; Murayama, Shohei; Ohtsuka, Toshiyuki; Yokozawa, Masayuki
2015-01-01
Carbon dioxide (CO2) efflux from the soil surface, which is a major source of CO2 from terrestrial ecosystems, represents the total CO2 production at all soil depths. Although many studies have estimated the vertical profile of the CO2 production rate, one of the difficulties in estimating the vertical profile is measuring diffusion coefficients of CO2 at all soil depths in a nondestructive manner. In this study, we estimated the temporal variation in the vertical profile of the CO2 production rate using a data assimilation method, the particle filtering method, in which the diffusion coefficients of CO2 were simultaneously estimated. The CO2 concentrations at several soil depths and CO2 efflux from the soil surface (only during the snow-free period) were measured at two points in a broadleaf forest in Japan, and the data were assimilated into a simple model including a diffusion equation. We found that there were large variations in the pattern of the vertical profile of the CO2 production rate between experiment sites: the peak CO2 production rate was at soil depths around 10 cm during the snow-free period at one site, but the peak was at the soil surface at the other site. Using this method to estimate the CO2 production rate during snow-cover periods allowed us to estimate CO2 efflux during that period as well. We estimated that the CO2 efflux during the snow-cover period (about half the year) accounted for around 13% of the annual CO2 efflux at this site. Although the method proposed in this study does not ensure the validity of the estimated diffusion coefficients and CO2 production rates, the method enables us to more closely approach the "actual" values by decreasing the variance of the posterior distribution of the values.
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.
An X-band 22.5°/45° digital phase shifter based on switched filter networks
Sun, Pengpeng; Liu, Hui; Geng, Miao; Zhang, Rong; Wang, Qi; Luo, Weijun
2017-06-01
The design approach and performance of a 22.5°/45° digital phase shifter based on a switched filter network for X-band phased arrays are described. Both the MMIC phase shifters are fabricated employing a 0.25 μm gate GaAs pHEMT process and share in the same chip size of 0.82 × 1.06 mm2. The measurement results of the proposed phase shifters over the whole operating frequency range show that the phase shift error is less than 22.5°± 2.5°, 45°± 3.5°, which shows an excellent agreement with the simulated performance, the insertion loss is within the range of 0.9-1.2 dB for the 22.5° phase shifter and 0.9-1.4 dB for the 45° phase shifter, and the input/output return loss is better than -12.5 and -11 dB respectively. They also achieve the similar {P}1{{dB}} continuous wave power handing capability of 24.8 dBm at 10 GHz. The phase shifters show a good phase shift error, insertion loss and return loss in the X-band (40%), which can be employed into the wide bandwidth multi-bit digital phase shifter.
Directory of Open Access Journals (Sweden)
Jingbo Chen
2018-02-01
Full Text Available Semantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs, have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable.
Additive Feed Forward Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1999-01-01
This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...
Particle Filtering for Model-Based Anomaly Detection in Sensor Networks
Solano, Wanda; Banerjee, Bikramjit; Kraemer, Landon
2012-01-01
A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The
Dolenko, T. A.; Burikov, S. A.; Vervald, E. N.; Efitorov, A. O.; Laptinskiy, K. A.; Sarmanova, O. E.; Dolenko, S. A.
2017-02-01
Elaboration of methods for the control of biochemical reactions with deoxyribonucleic acid (DNA) strands is necessary for the solution of one of the basic problems in the creation of biocomputers—improvement in the reliability of molecular DNA computing. In this paper, the results of the solution of the four-parameter inverse problem of laser Raman spectroscopy—the determination of the type and concentration of each of the DNA nitrogenous bases in multi-component solutions—are presented.
Directory of Open Access Journals (Sweden)
S. Maiti
2011-03-01
Full Text Available Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M=6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN theory using the concept of Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC simulation scheme. The new method is applied to invert one and two-dimensional Direct Current (DC vertical electrical sounding (VES data acquired around the Koyna region in India. Prior to apply the method on actual resistivity data, the new method was tested for simulating synthetic signal. In this approach the objective/cost function is optimized following the Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC sampling based algorithm and each trajectory was updated by approximating the Hamiltonian differential equations through a leapfrog discretization scheme. The stability of the new inversion technique was tested in presence of correlated red noise and uncertainty of the result was estimated using the BNN code. The estimated true resistivity distribution was compared with the results of singular value decomposition (SVD-based conventional resistivity inversion results. Comparative results based on the HMC-based Bayesian Neural Network are in good agreement with the existing model results, however in some cases, it also provides more detail and precise results, which appears to be justified with local geological and structural details. The new BNN approach based on HMC is faster and proved to be a promising inversion scheme to interpret complex and non-linear resistivity problems. The HMC-based BNN results
Directory of Open Access Journals (Sweden)
Yu Jingyuan
2011-08-01
Full Text Available In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template (F, centrifugal acceleration (v and sintering temperature (T, while the only output was the ultimate compressive strength (σ. According to the registered BP model, the effects of F, v, T on σ were analyzed. The predicted results agree with the actual data within reasonable experimental error, indicating that the BP model is practically a very useful tool in property prediction and process parameter design of the Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting.
Energy Technology Data Exchange (ETDEWEB)
McWilliams, T.; Widdoes, Jr., L. C.; Wood, L.
1976-09-30
The design of an extremely high performance programmable digital filter of novel architecture, the LLL Programmable Digital Filter, is described. The digital filter is a high-performance multiprocessor having general purpose applicability and high programmability; it is extremely cost effective either in a uniprocessor or a multiprocessor configuration. The architecture and instruction set of the individual processor was optimized with regard to the multiple processor configuration. The optimal structure of a parallel processing system was determined for addressing the specific Navy application centering on the advanced digital filtering of passive acoustic ASW data of the type obtained from the SOSUS net. 148 figures. (RWR)
Ghausi, M. S.
1984-01-01
The evolution of active filters during the time from 1920 to 1980 is considered, taking into account the hardware used to implement a filtering network for voice frequency over 60 years. From 1920 to 1960 the majority of voice-frequency filters was realized as discrete RLC networks. After the development of transistors, it was realized that size and cost reductions could be achieved by replacing the inductors with active networks. In the early 1970's, batch-processed thin-film hybrid integrated circuits began to be employed. The synthesis of transfer functions which are predominantly input/output types is considered. Attention is given to direct realizations, synthesis using component simulation, cascade synthesis, multiple-loop feedback design, active-R and active-C filters, aspects of sensitivity, and switched-capacitor filters.
Kollat, J. B.; Reed, P. M.
2009-12-01
This study contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The ASSIST framework combines contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF) and many-objective evolutionary optimization. Our goal in this work is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design. Our many-objective analysis considers up to 6 design objectives simultaneously and consequently synthesizes prior monitoring network design methodologies into a single, flexible framework. This study demonstrates the ASSIST framework using a tracer study conducted within a physical aquifer transport experimental tank located at the University of Vermont. The tank tracer experiment was extensively sampled to provide high resolution estimates of tracer plume behavior. The simulation component of the ASSIST framework consists of stochastic ensemble flow-and-transport predictions using ParFlow coupled with the Lagrangian SLIM transport model. The ParFlow and SLIM ensemble predictions are conditioned with tracer observations using a bias-aware EnKF. The EnKF allows decision makers to enhance plume transport predictions in space and time in the presence of uncertain and biased model predictions by conditioning them on uncertain measurement data. In this initial demonstration, the position and frequency of sampling were optimized to: (i) minimize monitoring cost, (ii) maximize information provided to the EnKF, (iii) minimize failure to detect the tracer, (iv) maximize the detection of tracer flux, (v) minimize error in quantifying tracer mass, and (vi) minimize error in quantifying the moment of the tracer plume. The results demonstrate that the many-objective problem
Gamboa, O L; Tagliazucchi, E; von Wegner, F; Jurcoane, A; Wahl, M; Laufs, H; Ziemann, U
2014-07-01
Multiple sclerosis (MS) is an autoimmune inflammatory demyelinating and neurodegenerative disorder of the central nervous system characterized by multifocal white matter brain lesions leading to alterations in connectivity at the subcortical and cortical level. Graph theory, in combination with neuroimaging techniques, has been recently developed into a powerful tool to assess the large-scale structure of brain functional connectivity. Considering the structural damage present in the brain of MS patients, we hypothesized that the topological properties of resting-state functional networks of early MS patients would be re-arranged in order to limit the impact of disease expression. A standardized dual task (Paced Auditory Serial Addition Task simultaneously performed with a paper and pencil task) was administered to study the interactions between behavioral performance and functional network re-organization. We studied a group of 16 early MS patients (35.3±8.3 years, 11 females) and 20 healthy controls (29.9±7.0 years, 10 females) and found that brain resting-state networks of the MS patients displayed increased network modularity, i.e. diminished functional integration between separate functional modules. Modularity correlated negatively with dual task performance in the MS patients. Our results shed light on how localized anatomical connectivity damage can globally impact brain functional connectivity and how these alterations can impair behavioral performance. Finally, given the early stage of the MS patients included in this study, network modularity could be considered a promising biomarker for detection of earliest-stage brain network reorganization, and possibly of disease progression. Copyright © 2013 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
X. Yang
2009-07-01
Full Text Available A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF, is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated with the latter ensemble and have infinite variance. The assumption of infinite variance corresponds to the limit of "complete lack of knowledge" and differs dramatically from the implicit assumption made in most other ensemble filters, which is that the forecast errors orthogonal to the first guess ensemble have vanishing errors. The DEnF is independent of the detailed covariances assumed in the space orthogonal to the ensemble space, and reduces to conventional ensemble square root filters when the number of ensembles exceeds the model dimension. The DEnF is well defined only in data rich regimes and involves the inversion of relatively large matrices, although this barrier might be circumvented by variational methods. Two algorithms for solving the DEnF, namely the Diffuse Ensemble Kalman Filter (DEnKF and the Diffuse Ensemble Transform Kalman Filter (DETKF, are proposed and found to give comparable results. These filters generally converge to the traditional EnKF and ETKF, respectively, when the ensemble size exceeds the model dimension. Numerical experiments demonstrate that the DEnF eliminates filter collapse, which occurs in ensemble Kalman filters for small ensemble sizes. Also, the use of the DEnF to initialize a conventional square root filter dramatically accelerates the spin-up time for convergence. However, in a perfect model scenario, the DEnF produces larger errors than ensemble square root filters that have covariance localization and inflation. For imperfect forecast models, the DEnF produces smaller errors than the ensemble square root filter with inflation. These experiments suggest that the DEnF has some advantages relative to the ensemble square root filters in the regime of small ensemble size, imperfect model, and copious
Ingram, WT
2012-01-01
Inverse limits provide a powerful tool for constructing complicated spaces from simple ones. They also turn the study of a dynamical system consisting of a space and a self-map into a study of a (likely more complicated) space and a self-homeomorphism. In four chapters along with an appendix containing background material the authors develop the theory of inverse limits. The book begins with an introduction through inverse limits on [0,1] before moving to a general treatment of the subject. Special topics in continuum theory complete the book. Although it is not a book on dynamics, the influen
Directory of Open Access Journals (Sweden)
Carlos López-Franco
2015-01-01
Full Text Available We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.
Directory of Open Access Journals (Sweden)
R. A. Bahari
2014-10-01
Full Text Available Today, air pollutant is a big challenge for busy and big cities due to its direct effect on both human health and the environment. Tehran, as the capital city of Iran, concludes 12 million people and is one of the most polluted cities in Iran. According to the reports, the main cause of Tehran's pollution is particle matters. The main factors affecting the density and distribution of pollution in Tehran are topography, traffic, and meteorological parameters including wind speed and direction, environment temperature, cloud cover, relative humidity, the sunshine overs a day, the rainfall, pressure, and temperature inversion. To help the urban management of Tehran, in this paper, a novel method is proposed to predicted PM2.5 concentration for upcoming 72 hours. The results show that the proposed model has high capability in predicting PM2.5 concentration and the achieved statistic coefficient of determination (R2 was equal to 0.61–0.79, which indicates the goodness of fit of our proposed model supports the prediction of PM2.5 concentration.
Tangborn, Andrew; Cooper, Robert; Pawson, Steven; Sun, Zhibin
2009-01-01
We present a source inversion technique for chemical constituents that uses assimilated constituent observations rather than directly using the observations. The method is tested with a simple model problem, which is a two-dimensional Fourier-Galerkin transport model combined with a Kalman filter for data assimilation. Inversion is carried out using a Green's function method and observations are simulated from a true state with added Gaussian noise. The forecast state uses the same spectral spectral model, but differs by an unbiased Gaussian model error, and emissions models with constant errors. The numerical experiments employ both simulated in situ and satellite observation networks. Source inversion was carried out by either direct use of synthetically generated observations with added noise, or by first assimilating the observations and using the analyses to extract observations. We have conducted 20 identical twin experiments for each set of source and observation configurations, and find that in the limiting cases of a very few localized observations, or an extremely large observation network there is little advantage to carrying out assimilation first. However, in intermediate observation densities, there decreases in source inversion error standard deviation using the Kalman filter algorithm followed by Green's function inversion by 50% to 95%.
Monolithic integrated switched-capacitor filters. I
Rienecker, W.
1980-06-01
The historical background of switched-capacitor filters is reviewed, and advanced design techniques for such filters are considered with reference to filter implementation in integrated systems. It is shown that basic equivalences between the components of time-invariant and time-variant networks make it possible to construct switched-capacitor filters that imitate classic filter types. Some examples of commercial switched-capacitor filters show the advantages of this technology.
Onizuka, Miho; Hoang, Huu; Kawato, Mitsuo; Tokuda, Isao T; Schweighofer, Nicolas; Katori, Yuichi; Aihara, Kazuyuki; Lang, Eric J; Toyama, Keisuke
2013-11-01
The inferior olive (IO) possesses synaptic glomeruli, which contain dendritic spines from neighboring neurons and presynaptic terminals, many of which are inhibitory and GABAergic. Gap junctions between the spines electrically couple neighboring neurons whereas the GABAergic synaptic terminals are thought to act to decrease the effectiveness of this coupling. Thus, the glomeruli are thought to be important for determining the oscillatory and synchronized activity displayed by IO neurons. Indeed, the tendency to display such activity patterns is enhanced or reduced by the local administration of the GABA-A receptor blocker picrotoxin (PIX) or the gap junction blocker carbenoxolone (CBX), respectively. We studied the functional roles of the glomeruli by solving the inverse problem of estimating the inhibitory (gi) and gap-junctional conductance (gc) using an IO network model. This model was built upon a prior IO network model, in which the individual neurons consisted of soma and dendritic compartments, by adding a glomerular compartment comprising electrically coupled spines that received inhibitory synapses. The model was used in the forward mode to simulate spike data under PIX and CBX conditions for comparison with experimental data consisting of multi-electrode recordings of complex spikes from arrays of Purkinje cells (complex spikes are generated in a one-to-one manner by IO spikes and thus can substitute for directly measuring IO spike activity). The spatiotemporal firing dynamics of the experimental and simulation spike data were evaluated as feature vectors, including firing rates, local variation, auto-correlogram, cross-correlogram, and minimal distance, and were contracted onto two-dimensional principal component analysis (PCA) space. gc and gi were determined as the solution to the inverse problem such that the simulation and experimental spike data were closely matched in the PCA space. The goodness of the match was confirmed by an analysis of variance
Zhou, Jiafeng
2010-01-01
The general theory of microwave filter design based on lumped-element circuit is described in this chapter. The lowpass prototype filters with Butterworth, Chebyshev and quasielliptic characteristics are synthesized, and the prototype filters are then transformed to bandpass filters by lowpass to bandpass frequency mapping. By using immitance inverters ( J - or K -inverters), the bandpass filters can be realized by the same type of resonators. One design example is given to verify the theory ...
Directory of Open Access Journals (Sweden)
Li Qiang
2013-07-01
Full Text Available BP neural network was used in this study to model the porosity and the compressive strength of a gradient Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The influences of the load applied on the epispastic polystyrene template (F, the centrifugal acceleration (v and sintering temperature (T on the porosity (P and compressive strength (σ of the sintered products were studied by using the registered three-layer BP model. The accuracy of the model was verified by comparing the BP model predicted results with the experimental ones. Results show that the model prediction agrees with the experimental data within a reasonable experimental error, indicating that the three-layer BP network based modeling is effective in predicting both the properties and processing parameters in designing the gradient Al2O3-ZrO2 ceramic foam filter. The prediction results show that the porosity percentage increases and compressive strength decreases with an increase in the applied load on epispastic polystyrene template. As for the influence of sintering temperature, the porosity percentage decreases monotonically with an increase in sintering temperature, yet the compressive strength first increases and then decreases slightly in a given temperature range. Furthermore, the porosity percentage changes little but the compressive strength first increases and then decreases when the centrifugal acceleration increases.
Lapierre, J. L.; Sonnenfeld, R. G.; Hager, W. W.; Morris, K.
2011-12-01
Researchers have long studied the copious and complex electric field waveforms caused by lightning. By combining electric-field measurements taken at many different locations on the ground simultaneously [Krehbiel et al., 1979], we hope to learn more about charge sources for lightning flashes. The Langmuir Electric Field Array (LEFA) is a network of nine field-change measurement stations (slow-antennas) arranged around Langmuir Laboratory near Magdalena, New Mexico. Using a mathematical method called the Levenberg-Marquardt (LM) method, we can invert the electric field data to determine the magnitude and position of the charge centroid removed from the cloud. We analyzed three return strokes (RS) following a dart-leader from a storm occurring on October 21st 2011. RS 'A' occurred at 07:17:00.63 UT. The altitude of the charge centroid was estimated to be 5 km via LMA data. Because the LM method requires a prediction, the code was run with a wide range of values to verify the robustness of the method. Predictions varied from ±3 C for the charge magnitude and ±20 km N-S and E-W for the position (with the coordinate origin being the Langmuir Laboratory Annex). The LM method converged to a charge magnitude of -5.5 C and a centroid position of 3.3 km E-W and 12 km, N-S for that RS. RS 'B' occurred at 07:20:05.9 UT. With an altitude of 4 km, the predictions were again varied; ±3 C, ±15 km N-S and E-W. Most runs converged to -27.5 C, 4 km E-W, and 10.9 km N-S. Finally, while results seem best for events right over the array, success was had locating more distant events. RS 'C' occurred at 02:42:46.8 UT. Assuming an altitude of 5 km and varying the predictions as with RS 'A', the results converged to -9.2 C, 35.5 km E-W, and 9 km N-S. All of these results are broadly consistent with the LMA and the NLDN. By continuing this type of analysis, we hope to learn more about how lightning channels propagate and how the charges in the cloud respond to the sudden change in
Foolad, Foad; Franz, Trenton E.; Wang, Tiejun; Gibson, Justin; Kilic, Ayse; Allen, Richard G.; Suyker, Andrew
2017-03-01
In this study, the feasibility of using inverse vadose zone modeling for estimating field-scale actual evapotranspiration (ETa) was explored at a long-term agricultural monitoring site in eastern Nebraska. Data from both point-scale soil water content (SWC) sensors and the area-average technique of cosmic-ray neutron probes were evaluated against independent ETa estimates from a co-located eddy covariance tower. While this methodology has been successfully used for estimates of groundwater recharge, it was essential to assess the performance of other components of the water balance such as ETa. In light of recent evaluations of land surface models (LSMs), independent estimates of hydrologic state variables and fluxes are critically needed benchmarks. The results here indicate reasonable estimates of daily and annual ETa from the point sensors, but with highly varied soil hydraulic function parameterizations due to local soil texture variability. The results of multiple soil hydraulic parameterizations leading to equally good ETa estimates is consistent with the hydrological principle of equifinality. While this study focused on one particular site, the framework can be easily applied to other SWC monitoring networks across the globe. The value-added products of groundwater recharge and ETa flux from the SWC monitoring networks will provide additional and more robust benchmarks for the validation of LSM that continues to improve their forecast skill. In addition, the value-added products of groundwater recharge and ETa often have more direct impacts on societal decision-making than SWC alone. Water flux impacts human decision-making from policies on the long-term management of groundwater resources (recharge), to yield forecasts (ETa), and to optimal irrigation scheduling (ETa). Illustrating the societal benefits of SWC monitoring is critical to insure the continued operation and expansion of these public datasets.
Directory of Open Access Journals (Sweden)
Germán Buitrago Salazar
2015-05-01
Full Text Available En este trabajo se presentan los resultados de un sistema servocontrol visual de un brazo robótico de seis grados de libertad. Para esto, se utiliza una red neuronal de tipo feed forward, entrenada por back propagation, para determinar la distancia entre el brazo robótico y un objeto de referencia, que permite ubicarlo en un espacio de trabajo. Las entradas de la red corresponden a la información obtenida de las imágenes capturadas por el Kinect, utilizando un filtro que discrimina la posición de los elementos, en el espacio de color CIELAB (Commission Internationale de l'Eclairage L*a*b components. El resultado de esta investigación demostró que la distancia estimada por la red tiene un margen de error menor, que el algoritmo propuesto en otros trabajos. Igualmente, se probó que el sistema de procesamiento de imágenes es más robusto a ruidos digitales, en comparación con los sistemas que utilizan filtros en el dominio RGB (Red-Green-Blue.Palabras claves: sistema de servocontrol visual, CIELAB, redes neuronales, filtrado de imágenes.______________________________________________________________________________AbstractIn this paper the results of visual servo-control system for a robotic arm with six degrees of freedom are presented. For this purpose, a feed fordward neural network, which was trained by back propagation, is used to determine the distance between the robot arm and a reference object and sitting the robot in the workspace. The inputs of neural network correspond to the information obtained from the images captured by the Kinect, using a filter that discriminates the position of the elements in the CIELAB (Commission Internationale de l'Eclairage L*a*bcomponents color space. The result of this research showed that the estimated distance with the network has an errorless than the algorithm proposed in other works. Similarly, it was proved that the image processing system is more robust to digital noise, compared to
1993-01-01
The Aquaspace H2OME Guardian Water Filter, available through Western Water International, Inc., reduces lead in water supplies. The filter is mounted on the faucet and the filter cartridge is placed in the "dead space" between sink and wall. This filter is one of several new filtration devices using the Aquaspace compound filter media, which combines company developed and NASA technology. Aquaspace filters are used in industrial, commercial, residential, and recreational environments as well as by developing nations where water is highly contaminated.
1987-01-01
A compact, lightweight electrolytic water filter generates silver ions in concentrations of 50 to 100 parts per billion in the water flow system. Silver ions serve as effective bactericide/deodorizers. Ray Ward requested and received from NASA a technical information package on the Shuttle filter, and used it as basis for his own initial development, a home use filter.
Deconvolution using a neural network
Energy Technology Data Exchange (ETDEWEB)
Lehman, S.K.
1990-11-15
Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.
Anderson, Brian D O
1979-01-01
This graduate-level text augments and extends beyond undergraduate studies of signal processing, particularly in regard to communication systems and digital filtering theory. Vital for students in the fields of control and communications, its contents are also relevant to students in such diverse areas as statistics, economics, bioengineering, and operations research.Topics include filtering, linear systems, and estimation; the discrete-time Kalman filter; time-invariant filters; properties of Kalman filters; computational aspects; and smoothing of discrete-time signals. Additional subjects e
Evolving matched filter transform pairs for satellite image processing
Peterson, Michael R.; Horner, Toby; Moore, Frank
2011-06-01
Wavelets provide an attractive method for efficient image compression. For transmission across noisy or bandwidth limited channels, a signal may be subjected to quantization in which the signal is transcribed onto a reduced alphabet in order to save bandwidth. Unfortunately, the performance of the discrete wavelet transform (DWT) degrades at increasing levels of quantization. In recent years, evolutionary algorithms (EAs) have been employed to optimize wavelet-inspired transform filters to improve compression performance in the presence of quantization. Wavelet filters consist of a pair of real-valued coefficient sets; one set represents the compression filter while the other set defines the image reconstruction filter. The reconstruction filter is defined as the biorthogonal inverse of the compression filter. Previous research focused upon two approaches to filter optimization. In one approach, the original wavelet filter is used for image compression while the reconstruction filter is evolved by an EA. In the second approach, both the compression and reconstruction filters are evolved. In both cases, the filters are not biorthogonally related to one another. We propose a novel approach to filter evolution. The EA optimizes a compression filter. Rather than using a wavelet filter or evolving a second filter for reconstruction, the reconstruction filter is computed as the biorthogonal inverse of the evolved compression filter. The resulting filter pair retains some of the mathematical properties of wavelets. This paper compares this new approach to existing filter optimization approaches to determine its suitability for the optimization of image filters appropriate for defense applications of image processing.
Cacciani, Alessandro; Rosati, P.; Ricci, D.; Marquedant, R.; Smith, E.
1988-01-01
The magneto-optical filter (MOF) was used to get high and intermediate l-modes of solar oscillations. For very low l-modes the imaging capability of the MOF is still attractive since it allows a pixel by pixel intensity normalization. However, a crude attempt to get very low l power spectra from Dopplergrams obtained at Mt. Wilson gave noisy results. This means that a careful analysis of all the factors potentially affecting high resolution Dopplergrams should be accomplished. In order to better investigate this problem, a nonimaging channel using the lock-in amplifier technique was considered. Two systems are now operational, one at JPL and the other at University of Rome. Observations in progress are used to discuss the MOF stability, the noise level, and the possible application in asteroseismology.
Bayesian ISOLA: new tool for automated centroid moment tensor inversion
Vackář, Jiří; Burjánek, Jan; Gallovič, František; Zahradník, Jiří; Clinton, John
2017-08-01
We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances are rejected and full-waveform inversion in a space-time grid around a provided hypocentre. A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequency ranges. The method is tested on synthetic and observed data. It is applied on a data set from the Swiss seismic network and the results are compared with the existing high-quality MT catalogue. The software package programmed in Python is designed to be as versatile as possible in order to be applicable in various networks ranging from local to regional. The method can be applied either to the everyday network data flow, or to process large pre-existing earthquake catalogues and data sets.
Inverse feasibility problems of the inverse maximum flow problems
Indian Academy of Sciences (India)
A strongly polynomial time algorithm to solve the inverse maximum flow problem under l1 norm (denoted ... IMF can not be solved using weakly polynomial algorithms (although sometimes they can be preferred) because ..... in the network ˜G. We shall sort descending the arcs of ˜G by their capacities˜c1. After sorting, the.
Microwave filters and circuits contributions from Japan
Matsumoto, Akio
1970-01-01
Microwave Filters and Circuits: Contributions from Japan covers ideas and novel circuits used to design microwave filter that have been developed in Japan, as well as network theory into the field of microwave transmission networks. The book discusses the general properties and synthesis of transmission-line networks; transmission-line filters on the image-parameter basis; and experimental results on a class of transmission-line filter constructed only with commensurate TEM lossless transmission lines. The text describes lines constants, approximation problems in transmission-line networks, as
Yao, J. G.; Lagrosas, N.; Ampil, L. J. Y.; Lorenzo, G. R. H.; Simpas, J.
2016-12-01
A hybrid piecewise rainfall value interpolation algorithm was formulated using the commonly known Inverse Distance Weighting (IDW) and Gauss-Seidel variant Successive Over Relaxation (SOR) to interpolate rainfall values over Metro Manila, Philippines. Due to the fact that the SOR requires boundary values for its algorithm to work, the IDW method has been used to estimate rainfall values at the boundary. Iterations using SOR were then done on the defined boundaries to obtain the desired results corresponding to the lowest RMSE value. The hybrid method was applied to rainfall datasets obtained from a dense network of 30 stations in Metro Manila which has been collecting meteorological data every 5 minutes since 2012. Implementing the Davis Vantage Pro 2 Plus weather monitoring system, each station sends data to a central server which could be accessed through the website metroweather.com.ph. The stations are spread over approximately 625 sq km of area such that each station is approximately within 25 sq km from each other. The locations of the stations determined by the Metro Manila Development Authority (MMDA) are in critical sections of Metro Manila such as watersheds and flood-prone areas. Three cases have been investigated in this study, one for each type of rainfall present in Metro Manila: monsoon-induced (8/20/13), typhoon (6/29/13), and thunderstorm (7/3/15 & 7/4/15). The area where the rainfall stations are located is divided such that large measured rainfall values are used as part of the boundaries for the SOR. Measured station values found inside the area where SOR is implemented are compared with results from interpolated values. Root mean square error (RMSE) and correlation trends between measured and interpolated results are quantified. Results from typhoon, thunderstorm and monsoon cases show RMSE values ranged from 0.25 to 2.46 mm for typhoons, 1.55 to 10.69 mm for monsoon-induced rain and 0.01 to 6.27 mm for thunderstorms. R2 values, on the other
DEFF Research Database (Denmark)
Meng, Weizhi; Li, Wenjuan; Kwok, Lam For
2017-01-01
Network intrusion detection systems (NIDSs) which aim to identify various attacks, have become an essential part of current security infrastructure. In particular, signature-based NIDSs are being widely implemented in industry due to their low rate of false alarms. However, the signature matching...... process is a big challenge for these systems, in which the cost is at least linear to the size of an input string. As a result, overhead packets will be a major issue for practical usage, where the incoming packets exceed the maximum capability of an intrusion detection system (IDS). To mitigate...
Monolithic integrated filters - An overview
Entenmann, W.
1981-04-01
An overview of the state of the art in monolithic integrated filter design is given. The close mutual influence of technology and network theory and the continuing development of filter designs with higher integration, higher reliability, lower costs and lower space demands are examined. The fundamental concepts of circuit theory and MOS technology are described and the principal construction of the components of the three major classes of MOS filter circuits examined, namely the change-transfer filter, the switched-capacitor filter and the digital filter. The most important properties, such as the periodicity of the spectra, the impulse response, as well as recursive, nonrecursive, linear and minimal phase filters are covered. Some methods for calculating filter circuits by using classical reactance filter synthesis with the aid of suitable transformations from analog time-continuous reference circuits are discussed. The obtainable signal frequency ranges and filter grades are shown in order to compare the efficiency and operating range of monolithic integrated filter circuits with each other and with other concepts.
Rodigues, Jose Eduardo; Santosdealmeida, Wagner
1987-12-01
Some of the main aspects related to photographic filters are examined and prepared as a reference for researchers and students of remote sensing. A large range of information about the filters including their basic fundamentals, classification, and main types is presented. The theme cannot be exhausted in this or any other individual publication because of its great complexity, profound theoretical publication, and dynmaic technological development. The subject does not deal only with filter applications in remote sensing. As much as possible, additional information about the utilization of these products in other professional areas, as pictorial photography, photographic processing, and optical engineering, were included.
Recursive Robot-Arm Dynamics via Filtering and Smoothing
Rodriguez, Guillermo
1987-01-01
Forward and inverse dynamics solved using Kalman filtering and Bryson-Frazier smoothing. Dynamics of serial-link robot arm solved by using recursive techniques from linear filtering and smoothing theory. Solutions of dynamical equations give forces, moments, and accelerations at joints between links, and multilink inertia matrix and its inverse. Theoretical developments lay foundation for use of filtering and smoothing techniques in design of robot controls.
Xu, Qimin; Li, Xu; Chan, Ching-Yao
2017-06-18
In this paper, we propose a cost-effective localization solution for land vehicles, which can simultaneously adapt to the uncertain noise of inertial sensors and bridge Global Positioning System (GPS) outages. First, three Unscented Kalman filters (UKFs) with different noise covariances are introduced into the framework of Interacting Multiple Model (IMM) algorithm to form the proposed IMM-based UKF, termed as IMM-UKF. The IMM algorithm can provide a soft switching among the three UKFs and therefore adapt to different noise characteristics. Further, two IMM-UKFs are executed in parallel when GPS is available. One fuses the information of low-cost GPS, in-vehicle sensors, and micro electromechanical system (MEMS)-based reduced inertial sensor systems (RISS), while the other fuses only in-vehicle sensors and MEMS-RISS. The differences between the state vectors of the two IMM-UKFs are considered as training data of a Grey Neural Network (GNN) module, which is known for its high prediction accuracy with a limited amount of samples. The GNN module can predict and compensate position errors when GPS signals are blocked. To verify the feasibility and effectiveness of the proposed solution, road-test experiments with various driving scenarios were performed. The experimental results indicate that the proposed solution outperforms all the compared methods.
Introduction to neural networks
James, Frederick E
1994-02-02
1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.
Seismic processing in the inverse data space
Berkhout, A.J.
2006-01-01
Until now, seismic processing has been carried out by applying inverse filters in the forward data space. Because the acquired data of a seismic survey is always discrete, seismic measurements in the forward data space can be arranged conveniently in a data matrix (P). Each column in the data matrix
Internet Censorship in China: Where Does the Filtering Occur?
Xu, Xueyang; Mao, Z. Morley; Halderman, J. Alex
China filters Internet traffic in and out of the country. In order to circumvent the firewall, it is helpful to know where the filtering occurs. In this work, we explore the AS-level topology of China's network, and probe the firewall to find the locations of filtering devices. We find that even though most filtering occurs in border ASes, choke points also exist in many provincial networks. The result suggests that two major ISPs in China have different approaches placing filtering devices.
Inverse imbalance reconstruction in rotordynamics
Energy Technology Data Exchange (ETDEWEB)
Ramlau, R. [Austrian Academy of Sciences, Linz (Austria). Johann Radon Inst. for Computational and Applied Mathematics; Dicken, V. [MeVis GmbH, Bremen (Germany); Maass, P. [Bremen Univ. (Germany). Zentrum fuer Technomathematik; Streller, C. [Rolls-Royce Germany GmbH, Dahlewitz (Germany); Rienaecker, A. [MTU Aero Engines GmbH, Muenchen (Germany)
2006-05-15
The goal of this work is to establish and compare algorithms for inverse imbalance reconstruction in aircraft turbines. Such algorithms are based on a validated whole engine model of a turbo engine under consideration. Base on the model, the impact of an imbalance distribution on the vibration behaviour of the turbine can be described as a matrix-vector multiplication Af = g, where f is the imbalance distribution and g the vibration response. It turns out that the matrix A is very ill-conditioned. As the measured data is highly affected with noise, we have to use regularization methods in order to stabilize the inversion. Our main interest was in the use of nonlinear regularization methods, in particular nonlinear filtered singular value decomposition and conjugate gradient regularization. (orig.)
Bayesian ISOLA: new tool for automated centroid moment tensor inversion
Vackář, Jiří; Burjánek, Jan; Gallovič, František; Zahradník, Jiří; Clinton, John
2017-04-01
Focal mechanisms are important for understanding seismotectonics of a region, and they serve as a basic input for seismic hazard assessment. Usually, the point source approximation and the moment tensor (MT) are used. We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances and high signal-to-noise are rejected, and full-waveform inversion in a space-time grid around a provided hypocenter. The method is innovative in the following aspects: (i) The CMT inversion is fully automated, no user interaction is required, although the details of the process can be visually inspected latter on many figures which are automatically plotted.(ii) The automated process includes detection of disturbances based on MouseTrap code, so disturbed recordings do not affect inversion.(iii) A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequencies.(iv) Bayesian approach is used, so not only the best solution is obtained, but also the posterior probability density function.(v) A space-time grid search effectively combined with the least-squares inversion of moment tensor components speeds up the inversion and allows to obtain more accurate results compared to stochastic methods. The method has been tested on synthetic and observed data. It has been tested by comparison with manually processed moment tensors of all events greater than M≥3 in the Swiss catalogue over 16 years using data available at the Swiss data center (http://arclink.ethz.ch). The quality of the results of the presented automated process is comparable with careful manual processing of data. The software package programmed in Python has been designed to be as versatile as possible in
1988-01-01
Seeking to find a more effective method of filtering potable water that was highly contaminated, Mike Pedersen, founder of Western Water International, learned that NASA had conducted extensive research in methods of purifying water on board manned spacecraft. The key is Aquaspace Compound, a proprietary WWI formula that scientifically blends various types of glandular activated charcoal with other active and inert ingredients. Aquaspace systems remove some substances; chlorine, by atomic adsorption, other types of organic chemicals by mechanical filtration and still others by catalytic reaction. Aquaspace filters are finding wide acceptance in industrial, commercial, residential and recreational applications in the U.S. and abroad.
Kuban, D.P.; Singletary, B.H.; Evans, J.H.
A plurality of holding tubes are respectively mounted in apertures in a partition plate fixed in a housing receiving gas contaminated with particulate material. A filter cartridge is removably held in each holding tube, and the cartridges and holding tubes are arranged so that gas passes through apertures therein and across the the partition plate while particulate material is collected in the cartridges. Replacement filter cartridges are respectively held in holding canisters mounted on a support plate which can be secured to the aforesaid housing, and screws mounted on said canisters are arranged to push replacement cartridges into the cartridge holding tubes and thereby eject used cartridges therefrom.
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. [...
Spatial filters on demand based on aperiodic Photonic Crystals
Energy Technology Data Exchange (ETDEWEB)
Gailevicius, Darius; Purlys, Vytautas; Peckus, Martynas; Gadonas, Roaldas [Laser Research Center, Department of Quantum Electronics, Vilnius University (Lithuania); Staliunas, Kestutis [DONLL, Departament de Fisica, Universitat Politecnica de Catalunya (UPC), Terrassa (Spain); Institucio Catalana de Recerca i Estudis Avancats (ICREA), Barcelona (Spain)
2017-08-15
Photonic Crystal spatial filters, apart from stand-alone spatial filtering function, can also suppress multi-transverse-mode operation in laser resonators. Here it is shown that such photonic crystals can be designed by solving the inverse problem: for a given spatial filtering profile. Optimized Photonic Crystal filters were fabricated in photosensitive glass. Experiments have shown that such filters provide a more pronounced filtering effect for total and partial transmissivity conditions. (copyright 2017 by WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)
A direct inversion scheme for deep resistivity sounding data using ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 113; Issue 1. A direct inversion scheme for deep resistivity ... In the present work, we investigate the performance of neural networks in the direct inversion of DC sounding data, without the need of a priori information. We introduce a two-step network approach ...
1982-01-01
A compact, lightweight electrolytic water sterilizer available through Ambassador Marketing, generates silver ions in concentrations of 50 to 100 parts per billion in water flow system. The silver ions serve as an effective bactericide/deodorizer. Tap water passes through filtering element of silver that has been chemically plated onto activated carbon. The silver inhibits bacterial growth and the activated carbon removes objectionable tastes and odors caused by addition of chlorine and other chemicals in municipal water supply. The three models available are a kitchen unit, a "Tourister" unit for portable use while traveling and a refrigerator unit that attaches to the ice cube water line. A filter will treat 5,000 to 10,000 gallons of water.
Directory of Open Access Journals (Sweden)
Kyle Holzer
2015-05-01
Full Text Available Integration of a class-E power amplifier (PA and a thin-film bulk acoustic wave resonator (FBAR filter is shown to provide high power added efficiency in addition to superior out-of-band spectrum suppression. A discrete gallium arsenide pseudomorphic high-electron-mobility transistor is implemented to operate as a class-E amplifier from 2496 to 2690 MHz. The ACPF7041 compact bandpass FBAR filter is incorporated to replace the resonant LC tank in a traditional class-E PA. To reduce drain voltage stress, the supply choke is replaced by a finite inductance. The fabricated PA provides up to 1 W of output power with a peak power added efficiency (PAE of 58%. The improved out-of-band spectrum filtering is compared to a traditional class-E with discrete LC resonant filtering. Such PAs can be combined with linearisation techniques to reduce out-of-band emissions.
Rosso, Edoardo G. F.
2015-01-01
Sport players' likelihood to fulfil their career expectations is influenced by both technical and non-technical aspects, including self-drive, self-confidence and access to high-quality coaching and to positive learning environments. Among other factors, belonging in the "right" social networks may help players to gain access to critical…
Bukhari, W; Hong, S-M
2016-03-07
The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient's breathing cycle. The algorithm, named EKF-GPRN(+) , first employs an extended Kalman filter (EKF) independently along each coordinate to predict the respiratory motion and then uses a Gaussian process regression network (GPRN) to correct the prediction error of the EKF in 3D space. The GPRN is a nonparametric Bayesian algorithm for modeling input-dependent correlations between the output variables in multi-output regression. Inference in GPRN is intractable and we employ variational inference with mean field approximation to compute an approximate predictive mean and predictive covariance matrix. The approximate predictive mean is used to correct the prediction error of the EKF. The trace of the approximate predictive covariance matrix is utilized to capture the uncertainty in EKF-GPRN(+) prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification enables us to pause the treatment beam over such instances. EKF-GPRN(+) implements a gating function by using simple calculations based on the trace of the predictive covariance matrix. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPRN(+) . The experimental results show that the EKF-GPRN(+) algorithm reduces the patient-wise prediction error to 38%, 40% and 40% in root-mean-square, compared to no prediction, at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The EKF-GPRN(+) algorithm can further reduce the prediction error by employing the gating
Sun, Xiaodong; Su, Bokai; Chen, Long; Yang, Zebin; Xu, Xing; Shi, Zhou
2017-05-01
The capacity of improving the control accuracy and dynamic performance of a four degree-of-freedom (DOF) permanent magnet biased active magnetic bearing (PMBAMB) system is critical to developing and maintaining a high precision application in a magnetically suspended direct-driven spindle system. The 4-DOF PMBAMB system, however, is a multivariable, strong coupled and nonlinear system with unavoidable and unmeasured external disturbances, in addition to having parameter variations. The satisfactory control performance cannot be obtained by using traditional strategies. Therefore, it is important to present a novel control scheme to construct a robust controller with good closed-loop capability. This paper proposes a new decoupling control scheme for a 4-DOF PMBAMB in a direct-driven spindle system based on the neural network inverse (NNI) and 2- degree-of-freedom (DOF) internal model control method. By combining the inversion of the 4-DOF PMBAMB system with its original system, a new pseudolinear system can be developed. In addition, by introducing the 2-DOF internal model controller into the pseudolinear system to design extra closed-loop controllers, we can effectively eliminate the influence of the unmodeled dynamics to the decoupling control accuracy, as well as adjust the properties of tracking and disturbance rejection independently. The experimental results demonstrate the effectiveness of the proposed control scheme.
Directory of Open Access Journals (Sweden)
Carlos Villaseñor
2017-12-01
Full Text Available Nowadays, there are several meta-heuristics algorithms which offer solutions for multi-variate optimization problems. These algorithms use a population of candidate solutions which explore the search space, where the leadership plays a big role in the exploration-exploitation equilibrium. In this work, we propose to use a Germinal Center Optimization algorithm (GCO which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection. GCO is used to find an optimal set of parameters for a neural inverse optimal control applied to all-terrain tracked robot. In the Neural Inverse Optimal Control (NIOC scheme, a neural identifier, based on Recurrent High Orden Neural Network (RHONN trained with an extended kalman filter algorithm, is used to obtain a model of the system, then, a control law is design using such model with the inverse optimal control approach. The RHONN identifier is developed without knowledge of the plant model or its parameters, on the other hand, the inverse optimal control is designed for tracking velocity references. Applicability of the proposed scheme is illustrated using simulations results as well as real-time experimental results with an all-terrain tracked robot.
Moment tensor inversions using strong motion waveforms of Taiwan TSMIP data, 1993–2009
Chang, Kaiwen; Chi, Wu-Cheng; Gung, Yuancheng; Dreger, Douglas; Lee, William H K.; Chiu, Hung-Chie
2011-01-01
Earthquake source parameters are important for earthquake studies and seismic hazard assessment. Moment tensors are among the most important earthquake source parameters, and are now routinely derived using modern broadband seismic networks around the world. Similar waveform inversion techniques can also apply to other available data, including strong-motion seismograms. Strong-motion waveforms are also broadband, and recorded in many regions since the 1980s. Thus, strong-motion data can be used to augment moment tensor catalogs with a much larger dataset than that available from the high-gain, broadband seismic networks. However, a systematic comparison between the moment tensors derived from strong motion waveforms and high-gain broadband waveforms has not been available. In this study, we inverted the source mechanisms of Taiwan earthquakes between 1993 and 2009 by using the regional moment tensor inversion method using digital data from several hundred stations in the Taiwan Strong Motion Instrumentation Program (TSMIP). By testing different velocity models and filter passbands, we were able to successfully derive moment tensor solutions for 107 earthquakes of Mw >= 4.8. The solutions for large events agree well with other available moment tensor catalogs derived from local and global broadband networks. However, for Mw = 5.0 or smaller events, we consistently over estimated the moment magnitudes by 0.5 to 1.0. We have tested accelerograms, and velocity waveforms integrated from accelerograms for the inversions, and found the results are similar. In addition, we used part of the catalogs to study important seismogenic structures in the area near Meishan Taiwan which was the site of a very damaging earthquake a century ago, and found that the structures were dominated by events with complex right-lateral strike-slip faulting during the recent decade. The procedures developed from this study may be applied to other strong-motion datasets to compliment or fill
A regional high-resolution carbon flux inversion of North America for 2004
Directory of Open Access Journals (Sweden)
A. E. Schuh
2010-05-01
Full Text Available Resolving the discrepancies between NEE estimates based upon (1 ground studies and (2 atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a high-resolution inversion based upon a regional meteorology model (RAMS and an underlying biosphere (SiB3 model, both running on an identical 40 km grid over most of North America. Current operational systems like CarbonTracker as well as many previous global inversions including the Transcom suite of inversions have utilized inversion regions formed by collapsing biome-similar grid cells into larger aggregated regions. An extreme example of this might be where corrections to NEE imposed on forested regions on the east coast of the United States might be the same as that imposed on forests on the west coast of the United States while, in reality, there likely exist subtle differences in the two areas, both natural and anthropogenic. Our current inversion framework utilizes a combination of previously employed inversion techniques while allowing carbon flux corrections to be biome independent. Temporally and spatially high-resolution results utilizing biome-independent corrections provide insight into carbon dynamics in North America. In particular, we analyze hourly CO_{2} mixing ratio data from a sparse network of eight towers in North America for 2004. A prior estimate of carbon fluxes due to Gross Primary Productivity (GPP and Ecosystem Respiration (ER is constructed from the SiB3 biosphere model on a 40 km grid. A combination of transport from the RAMS and the Parameterized Chemical Transport Model (PCTM models is used to forge a connection between upwind biosphere fluxes and downwind observed CO_{2} mixing ratio data. A Kalman filter procedure is used to estimate weekly corrections to biosphere fluxes based upon observed CO_{2}. RMSE-weighted annual NEE estimates, over an ensemble of potential inversion parameter sets, show a
A regional high-resolution carbon flux inversion of North America for 2004
Schuh, A. E.; Denning, A. S.; Corbin, K. D.; Baker, I. T.; Uliasz, M.; Parazoo, N.; Andrews, A. E.; Worthy, D. E. J.
2010-05-01
Resolving the discrepancies between NEE estimates based upon (1) ground studies and (2) atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a high-resolution inversion based upon a regional meteorology model (RAMS) and an underlying biosphere (SiB3) model, both running on an identical 40 km grid over most of North America. Current operational systems like CarbonTracker as well as many previous global inversions including the Transcom suite of inversions have utilized inversion regions formed by collapsing biome-similar grid cells into larger aggregated regions. An extreme example of this might be where corrections to NEE imposed on forested regions on the east coast of the United States might be the same as that imposed on forests on the west coast of the United States while, in reality, there likely exist subtle differences in the two areas, both natural and anthropogenic. Our current inversion framework utilizes a combination of previously employed inversion techniques while allowing carbon flux corrections to be biome independent. Temporally and spatially high-resolution results utilizing biome-independent corrections provide insight into carbon dynamics in North America. In particular, we analyze hourly CO2 mixing ratio data from a sparse network of eight towers in North America for 2004. A prior estimate of carbon fluxes due to Gross Primary Productivity (GPP) and Ecosystem Respiration (ER) is constructed from the SiB3 biosphere model on a 40 km grid. A combination of transport from the RAMS and the Parameterized Chemical Transport Model (PCTM) models is used to forge a connection between upwind biosphere fluxes and downwind observed CO2 mixing ratio data. A Kalman filter procedure is used to estimate weekly corrections to biosphere fluxes based upon observed CO2. RMSE-weighted annual NEE estimates, over an ensemble of potential inversion parameter sets, show a mean estimate 0.57 Pg/yr sink in North America
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
Energy Technology Data Exchange (ETDEWEB)
Yao, Jie, E-mail: yjie2@uh.edu [Department of Mechanical Engineering, University of Houston, Houston, Texas 77204 (United States); Lesage, Anne-Cécile; Hussain, Fazle [Department of Mechanical Engineering, Texas Tech University, Lubbock, Texas 79409 (United States); Bodmann, Bernhard G. [Department of Mathematics, University of Houston, Houston, Texas 77204 (United States); Kouri, Donald J. [Department of Physics, University of Houston, Houston, Texas 77204 (United States)
2014-12-15
The reversion of the Born-Neumann series of the Lippmann-Schwinger equation is one of the standard ways to solve the inverse acoustic scattering problem. One limitation of the current inversion methods based on the reversion of the Born-Neumann series is that the velocity potential should have compact support. However, this assumption cannot be satisfied in certain cases, especially in seismic inversion. Based on the idea of distorted wave scattering, we explore an inverse scattering method for velocity potentials without compact support. The strategy is to decompose the actual medium as a known single interface reference medium, which has the same asymptotic form as the actual medium and a perturbative scattering potential with compact support. After introducing the method to calculate the Green’s function for the known reference potential, the inverse scattering series and Volterra inverse scattering series are derived for the perturbative potential. Analytical and numerical examples demonstrate the feasibility and effectiveness of this method. Besides, to ensure stability of the numerical computation, the Lanczos averaging method is employed as a filter to reduce the Gibbs oscillations for the truncated discrete inverse Fourier transform of each order. Our method provides a rigorous mathematical framework for inverse acoustic scattering with a non-compact support velocity potential.
Curry, Ann; Haycock, Ken
2001-01-01
Discusses results of a survey questionnaire of public and school libraries that investigated the use of Internet filtering software. Considers filter alternatives; reasons for filtering or not filtering; brand names; satisfaction with site blocking; satisfaction with the decision to install filter software; and guidelines for considering filters.…
Theory and design of microwave filters
Hunter, Ian
2000-01-01
This is a thorough, graduate-level text which provides a single source for filter design including basic circuit theory, network synthesis and the design of a variety of microwave filter structures. The aim is to present design theories followed by specific examples with numerical simulations of the designs, with pictures of real devices wherever possible. The book is aimed at designers, engineers and researchers working in microwave electronics who need to design or specify filters.
Implementation of Kalman Filter with Python Language
Laaraiedh, Mohamed
2009-01-01
International audience; In this paper, we investigate the implementation of a Python code for a Kalman Filter using the Numpy package. A Kalman Filtering is carried out in two steps: Prediction and Update. Each step is investigated and coded as a function with matrix input and output. These different functions are explained and an example of a Kalman Filter application for the localization of mobile in wireless networks is given.
Finessing filter scarcity problem in face recognition via multi-fold filter convolution
Low, Cheng-Yaw; Teoh, Andrew Beng-Jin
2017-06-01
The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (ℳ-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by ℳ folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).
Meerschaert, Mark M; Straka, Peter
2013-01-01
The inverse stable subordinator provides a probability model for time-fractional differential equations, and leads to explicit solution formulae. This paper reviews properties of the inverse stable subordinator, and applications to a variety of problems in mathematics and physics. Several different governing equations for the inverse stable subordinator have been proposed in the literature. This paper also shows how these equations can be reconciled.
MEERSCHAERT, MARK M.; STRAKA, PETER
2013-01-01
The inverse stable subordinator provides a probability model for time-fractional differential equations, and leads to explicit solution formulae. This paper reviews properties of the inverse stable subordinator, and applications to a variety of problems in mathematics and physics. Several different governing equations for the inverse stable subordinator have been proposed in the literature. This paper also shows how these equations can be reconciled. PMID:25045216
Granade, Christopher; Wiebe, Nathan
2017-08-01
A major challenge facing existing sequential Monte Carlo methods for parameter estimation in physics stems from the inability of existing approaches to robustly deal with experiments that have different mechanisms that yield the results with equivalent probability. We address this problem here by proposing a form of particle filtering that clusters the particles that comprise the sequential Monte Carlo approximation to the posterior before applying a resampler. Through a new graphical approach to thinking about such models, we are able to devise an artificial-intelligence based strategy that automatically learns the shape and number of the clusters in the support of the posterior. We demonstrate the power of our approach by applying it to randomized gap estimation and a form of low circuit-depth phase estimation where existing methods from the physics literature either exhibit much worse performance or even fail completely.
Sampling and handling artifacts can bias filter-based measurements of particulate organic carbon (OC). Several measurement-based methods for OC artifact reduction and/or estimation are currently used in research-grade field studies. OC frequently is not artifact-corrected in larg...
DEFF Research Database (Denmark)
Guo, Xiaoqiang; Wu, Weiyang; Chen, Zhe
2011-01-01
) and synchronization techniques have been presented in the past decades. Most of them make a tradeoff between the accuracy and dynamic response under severe distorted and unbalanced conditions. In this paper, a multiple-complex coefficient-filter-based PLL is presented, and its unique feature lies in the accurate...
Inverse boundary spectral problems
Kachalov, Alexander; Lassas, Matti
2001-01-01
Inverse boundary problems are a rapidly developing area of applied mathematics with applications throughout physics and the engineering sciences. However, the mathematical theory of inverse problems remains incomplete and needs further development to aid in the solution of many important practical problems.Inverse Boundary Spectral Problems develop a rigorous theory for solving several types of inverse problems exactly. In it, the authors consider the following: ""Can the unknown coefficients of an elliptic partial differential equation be determined from the eigenvalues and the boundary value
An OTA-C filter for ECG acquisition systems with highly linear range and less passband attenuation
Jihai, Duan; Chuang, Lan; Weilin, Xu; Baolin, Wei
2015-05-01
A fifth order operational transconductance amplifier-C (OTA-C) Butterworth type low-pass filter with highly linear range and less passband attenuation is presented for wearable bio-telemetry monitoring applications in a UWB wireless body area network. The source degeneration structure applied in typical small transconductance circuit is improved to provide a highly linear range for the OTA-C filter. Moreover, to reduce the passband attenuation of the filter, a cascode structure is employed as the output stage of the OTA. The OTA-based circuit is operated in weak inversion due to strict power limitation in the biomedical chip. The filter is fabricated in a SMIC 0.18-μm CMOS process. The measured results for the filter have shown a passband gain of -6.2 dB, while the -3-dB frequency is around 276 Hz. For the 0.8 VPP sinusoidal input at 100 Hz, a total harmonic distortion (THD) of -56.8 dB is obtained. An electrocardiogram signal with noise interference is fed into this chip to validate the function of the designed filter. Project supported by the National Natural Science Foundation of China (Nos. 61161003, 61264001, 61166004) and the Guangxi Natural Science Foundation (No. 2013GXNSFAA019333).
Pulvirenti, Luca; Pierdicca, Nazzareno; Marzano, Frank S
2008-12-03
A simulation study to assess the potentiality of sea surface wind vector estimation based on the approximation of the forward model through Neural Networks and on the Bayesian theory of parameter estimation is presented. A polarimetric microwave radiometer has been considered and its observations have been simulated by means of the two scale model. To perform the simulations, the atmospheric and surface parameters have been derived from ECMWF analysis fields. To retrieve wind speed, Minimum Variance (MV) and Maximum Posterior Probability (MAP) criteria have been used while, for wind direction, a Maximum Likelihood (ML) criterion has been exploited. To minimize the cost function of MAP and ML, conventional Gradient Descent method, as well as Simulated Annealing optimization technique, have been employed. Results have shown that the standard deviation of the wind speed retrieval error is approximately 1.1 m/s for the best estimator. As for the wind direction, the standard deviation of the estimation error is less than 13° for wind speeds larger than 6 m/s. For lower wind velocities, the wind direction signal is too weak to ensure reliable retrievals. A method to deal with the non-uniqueness of the wind direction solution has been also developed. A test on a case study has yielded encouraging results.
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.
Inverse Kinematics using Quaternions
DEFF Research Database (Denmark)
Henriksen, Knud; Erleben, Kenny; Engell-Nørregård, Morten
In this project I describe the status of inverse kinematics research, with the focus firmly on the methods that solve the core problem. An overview of the different methods are presented Three common methods used in inverse kinematics computation have been chosen as subject for closer inspection....
Inverse logarithmic potential problem
Cherednichenko, V G
1996-01-01
The Inverse and Ill-Posed Problems Series is a series of monographs publishing postgraduate level information on inverse and ill-posed problems for an international readership of professional scientists and researchers. The series aims to publish works which involve both theory and applications in, e.g., physics, medicine, geophysics, acoustics, electrodynamics, tomography, and ecology.
Glottal inverse filtering analysis of human voice production — A ...
Indian Academy of Sciences (India)
. Acad. Sci. U.S.A.. 100(9): 5567–5572. Childers D 1995 Glottal source modeling for voice conversion, Speech Commun. 16: 127–138. Childers D, Ahn C 1995 Modeling the glottal volume-velocity waveform for three voice types, J. Acoust.
Linear and Nonlinear Filtering and Related Inverse Scattering Problems.
1983-05-01
that this necessitates taking a Bayesian point of view. Stochastic Calculus of Variations could be considered as a special case of Stochastic Control...The question of smoothness of the Zakai equation as a function of y (the observations) and the use of the Malliavin Calculus to obtain bounds on...5. M.G. Krein, "On a Fundamental APproximation Problem in the Theory of Extrapolation and Filtration of Stationary Random Processes," Dokl. Akad
Tunable photonic filters: a digital signal processing design approach.
Binh, Le Nguyen
2009-05-20
Digital signal processing techniques are used for synthesizing tunable optical filters with variable bandwidth and centered reference frequency including the tunability of the low-pass, high-pass, bandpass, and bandstop optical filters. Potential applications of such filters are discussed, and the design techniques and properties of recursive digital filters are outlined. The basic filter structures, namely, the first-order all-pole optical filter (FOAPOF) and the first-order all-zero optical filter (FOAZOF), are described, and finally the design process of tunable optical filters and the designs of the second-order Butterworth low-pass, high-pass, bandpass, and bandstop tunable optical filters are presented. Indeed, we identify that the all-zero and all-pole networks are equivalent with well known principles of optics of interference and resonance, respectively. It is thus very straightforward to implement tunable optical filters, which is a unique feature.
Multiscattering inversion for low-model wavenumbers
Alkhalifah, Tariq Ali
2016-09-21
A successful full-waveform inversion implementation updates the low-wavenumber model components first for a proper description of the wavefield propagation and slowly adds the high wavenumber potentially scattering parts of the model. The low-wavenumber components can be extracted from the transmission parts of the recorded wavefield emanating directly from the source or the transmission parts from the single- or double-scattered wavefield computed from a predicted scatter field acting as secondary sources.We use a combined inversion of data modeled from the source and those corresponding to single and double scattering to update the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data. A scattering-angle filter is used to divide the gradient of the combined inversion, so initially the high-wavenumber (low-scattering-angle) components of the gradient are directed to the perturbation model and the low-wavenumber (highscattering- angle) components are directed to the velocity model. As our background velocity matures, the scatteringangle divide is slowly lowered to allow for more of the higher wavenumbers to contribute the velocity model. Synthetic examples including the Marmousi model are used to demonstrate the additional illumination and improved velocity inversion obtained when including multiscattered energy. © 2016 Society of Exploration Geophysicists.
Airborne Network Optimization with Dynamic Network Update
2015-03-26
Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University...require small amounts of network bandwidth to perform routing. This thesis advocates the use of Kalman filters to predict network congestion in...airborne networks. Intelligent agents can make use of Kalman filter predictions to make informed decisions to manage communication in airborne networks. The
Optimal Gaussian Filter for Effective Noise Filtering
Kopparapu, Sunil; Satish, M
2014-01-01
In this paper we show that the knowledge of noise statistics contaminating a signal can be effectively used to choose an optimal Gaussian filter to eliminate noise. Very specifically, we show that the additive white Gaussian noise (AWGN) contaminating a signal can be filtered best by using a Gaussian filter of specific characteristics. The design of the Gaussian filter bears relationship with the noise statistics and also some basic information about the signal. We first derive a relationship...
Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling
Directory of Open Access Journals (Sweden)
S. Henne
2016-03-01
Full Text Available Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH4 from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH4 emissions to be 196 ± 18 Gg yr−1 for the year 2013 (1σ uncertainty. This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr−1 as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter, and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH4 source categories in Switzerland are agriculture (78 %, waste handling (15 % and natural gas distribution and combustion (6 %. The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH4 emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH4 in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr−1 reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr−1 implied by the
CSIR Research Space (South Africa)
Du Plessis, WP
2009-10-01
Full Text Available flexible, and allows design tradeoffs to be evaluated in an intuitive way. Keywords: Cavity resonator filters, microwave filters, coupled transmission lines. 1 Introduction Interdigital filters are popular at the higher microwave frequencies for a... number of reasons. Ideal interdigital filters have perfect symmetry which means that they have better phase and delay characteristics than combline filters [1]. The couplings between the resonators of interdigital filters are also lower than those...
Sander, K F
1964-01-01
Linear Network Theory covers the significant algebraic aspect of network theory, with minimal reference to practical circuits. The book begins the presentation of network analysis with the exposition of networks containing resistances only, and follows it up with a discussion of networks involving inductance and capacity by way of the differential equations. Classification and description of certain networks, equivalent networks, filter circuits, and network functions are also covered. Electrical engineers, technicians, electronics engineers, electricians, and students learning the intricacies
A distributed Kalman filter with global covariance
Sijs, J.; Lazar, M.
2011-01-01
Most distributed Kalman filtering (DKF) algorithms for sensor networks calculate a local estimate of the global state-vector in each node. An important challenge within distributed estimation is that all sensors in the network contribute to the local estimate in each node. In this paper, a novel DKF
Sharp spatially constrained inversion
DEFF Research Database (Denmark)
Vignoli, Giulio G.; Fiandaca, Gianluca G.; Christiansen, Anders Vest C A.V.C.
2013-01-01
We present sharp reconstruction of multi-layer models using a spatially constrained inversion with minimum gradient support regularization. In particular, its application to airborne electromagnetic data is discussed. Airborne surveys produce extremely large datasets, traditionally inverted...... by using smoothly varying 1D models. Smoothness is a result of the regularization constraints applied to address the inversion ill-posedness. The standard Occam-type regularized multi-layer inversion produces results where boundaries between layers are smeared. The sharp regularization overcomes...... inversions are compared against classical smooth results and available boreholes. With the focusing approach, the obtained blocky results agree with the underlying geology and allow for easier interpretation by the end-user....
Dura-Bernal, Salvador; Li, Kan; Neymotin, Samuel A; Francis, Joseph T; Principe, Jose C; Lytton, William W
2016-01-01
Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.
Directory of Open Access Journals (Sweden)
O. Rakovec
2012-09-01
Full Text Available This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property.
Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km^{2}, a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1 various sets of the spatially distributed discharge gauges and (2 the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.
Active Optical Lattice Filters
Directory of Open Access Journals (Sweden)
Gary Evans
2005-06-01
Full Text Available Optical lattice filter structures including gains are introduced and analyzed. The photonic realization of the active, adaptive lattice filter is described. The algorithms which map between gains space and filter coefficients space are presented and studied. The sensitivities of filter parameters with respect to gains are derived and calculated. An example which is relevant to adaptive signal processing is also provided.
Optimized Multichannel Filter Bank with Flat Frequency Response for Texture Segmentation
Kachouie Nezamoddin N; Alirezaie Javad
2005-01-01
Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank ...
A family of inversion formulas in thermoacoustic tomography
Nguyen, Linh
2009-10-01
We present a family of closed form inversion formulas in thermoacoustic tomography in the case of a constant sound speed. The formulas are presented in both time-domain and frequency-domain versions. As special cases, they imply most of the previously known filtered backprojection type formulas. © 2009 AMERICAN INSTITUTE OF MATHEMATICAL SCIENCES.
A comparative analysis of algorithms for the magnetoencephalography inverse problem
Sorrentino, A.; Pascarella, A.; Campi, C.; Piana, M.
2008-11-01
We present a comparison of three methods for the solution of the magnetoencephalography inverse problem. The methods are: an eigenspace projected beamformer, an algorithm implementing multiple signal classification with recursively applied projection and a particle filter for Bayesian tracking. Synthetic data with neurophysiological significance are analyzed by the three methods to recover position and amplitude time course of the active sources.
Filter replacement lifetime prediction
Hamann, Hendrik F.; Klein, Levente I.; Manzer, Dennis G.; Marianno, Fernando J.
2017-10-25
Methods and systems for predicting a filter lifetime include building a filter effectiveness history based on contaminant sensor information associated with a filter; determining a rate of filter consumption with a processor based on the filter effectiveness history; and determining a remaining filter lifetime based on the determined rate of filter consumption. Methods and systems for increasing filter economy include measuring contaminants in an internal and an external environment; determining a cost of a corrosion rate increase if unfiltered external air intake is increased for cooling; determining a cost of increased air pressure to filter external air; and if the cost of filtering external air exceeds the cost of the corrosion rate increase, increasing an intake of unfiltered external air.
Directory of Open Access Journals (Sweden)
Elena Adomaitienė
2017-01-01
Full Text Available We suggest employing the first-order stable RC filters, based on a single capacitor, for control of unstable fixed points in an array of oscillators. A single capacitor is sufficient to stabilize an entire array, if the oscillators are coupled strongly enough. An array, composed of 24 to 30 mean-field coupled FitzHugh–Nagumo (FHN type asymmetric oscillators, is considered as a case study. The investigation has been performed using analytical, numerical, and experimental methods. The analytical study is based on the mean-field approach, characteristic equation for finding the eigenvalue spectrum, and the Routh–Hurwitz stability criteria using low-rank Hurwitz matrix to calculate the threshold value of the coupling coefficient. Experiments have been performed with a hardware electronic analog, imitating dynamical behavior of an array of the FHN oscillators.
Bessel smoothing filter for spectral-element mesh
Trinh, P. T.; Brossier, R.; Métivier, L.; Virieux, J.; Wellington, P.
2017-06-01
Smoothing filters are extremely important tools in seismic imaging and inversion, such as for traveltime tomography, migration and waveform inversion. For efficiency, and as they can be used a number of times during inversion, it is important that these filters can easily incorporate prior information on the geological structure of the investigated medium, through variable coherent lengths and orientation. In this study, we promote the use of the Bessel filter to achieve these purposes. Instead of considering the direct application of the filter, we demonstrate that we can rely on the equation associated with its inverse filter, which amounts to the solution of an elliptic partial differential equation. This enhances the efficiency of the filter application, and also its flexibility. We apply this strategy within a spectral-element-based elastic full waveform inversion framework. Taking advantage of this formulation, we apply the Bessel filter by solving the associated partial differential equation directly on the spectral-element mesh through the standard weak formulation. This avoids cumbersome projection operators between the spectral-element mesh and a regular Cartesian grid, or expensive explicit windowed convolution on the finite-element mesh, which is often used for applying smoothing operators. The associated linear system is solved efficiently through a parallel conjugate gradient algorithm, in which the matrix vector product is factorized and highly optimized with vectorized computation. Significant scaling behaviour is obtained when comparing this strategy with the explicit convolution method. The theoretical numerical complexity of this approach increases linearly with the coherent length, whereas a sublinear relationship is observed practically. Numerical illustrations are provided here for schematic examples, and for a more realistic elastic full waveform inversion gradient smoothing on the SEAM II benchmark model. These examples illustrate well the
Analog fault diagnosis by inverse problem technique
Ahmed, Rania F.
2011-12-01
A novel algorithm for detecting soft faults in linear analog circuits based on the inverse problem concept is proposed. The proposed approach utilizes optimization techniques with the aid of sensitivity analysis. The main contribution of this work is to apply the inverse problem technique to estimate the actual parameter values of the tested circuit and so, to detect and diagnose single fault in analog circuits. The validation of the algorithm is illustrated through applying it to Sallen-Key second order band pass filter and the results show that the detecting percentage efficiency was 100% and also, the maximum error percentage of estimating the parameter values is 0.7%. This technique can be applied to any other linear circuit and it also can be extended to be applied to non-linear circuits. © 2011 IEEE.
Multiscale Phase Inversion of Seismic Data
Fu, Lei
2017-12-02
We present a scheme for multiscale phase inversion (MPI) of seismic data that is less sensitive to the unmodeled physics of wave propagation and a poor starting model than standard full waveform inversion (FWI). To avoid cycle-skipping, the multiscale strategy temporally integrates the traces several times, i.e. high-order integration, to produce low-boost seismograms that are used as input data for the initial iterations of MPI. As the iterations proceed, higher frequencies in the data are boosted by using integrated traces of lower order as the input data. The input data are also filtered into different narrow frequency bands for the MPI implementation. At low frequencies, we show that MPI with windowed reflections approximates wave equation inversion of the reflection traveltimes, except no traveltime picking is needed. Numerical results with synthetic acoustic data show that MPI is more robust than conventional multiscale FWI when the initial model is far from the true model. Results from synthetic viscoacoustic and elastic data show that MPI is less sensitive than FWI to some of the unmodeled physics. Inversion of marine data shows that MPI is more robust and produces modestly more accurate results than FWI for this data set.
Substrate Integrated Evanescent Filters Employing Coaxial Stubs
DEFF Research Database (Denmark)
Zhurbenko, Vitaliy
2015-01-01
is designed, fabricated, and tested. The filter exhibits a transmission zero due to the implemented stubs. The problem of evanescent mode filter analysis is formulated in terms of conventional network concepts. This formulation is then used for modelling of the filters. Strategies to further miniaturization...... and small height of the waveguide. In this work, one of the realization methods of evanescent mode waveguides using a single layer substrate is considered. The method is based on the use of coaxial stubs as capacitive susceptances externally connected to a SIW. A microwave filter based on these principles...... of the microwave filter are discussed. The approach is useful in applications where a sharp roll-off at the upper stop-band is required....
Design Procedure for Compact Folded Waveguide Filters
DEFF Research Database (Denmark)
Dong, Yunfeng; Johansen, Tom Keinicke; Zhurbenko, Vitaliy
-dimensional full-wave electromagnetic simulations. The proposed structure and the fabricated folded waveguide filter are shown in Fig. 1. A network analyzer (HP8720D) was used to test the fabricated folded waveguide filter. The measurement results are shown in Fig. 2 in comparison with the simulation results......Waveguide filters are widely used in communication systems due to low losses and high power handling capabilities. One drawback of the conventional waveguide filters is their large size, especially for low-frequency and high-order realizations. It has been shown that the footprint of conventional...... waveguide resonators can be reduced to one quarter by folding the electric and magnetic fields inside the cavity (J. S. Hong, Microwave Symposium Digest, 2004, Vol. 1, pp. 213-216). This paper presents a novel systematic procedure for designing compact low-loss bandpass filters by using folded waveguide...
Low-Pass Parabolic FFT Filter for Airborne and Satellite Lidar Signal Processing
Zhongke Jiao; Bo Liu; Enhai Liu; Yongjian Yue
2015-01-01
In order to reduce random errors of the lidar signal inversion, a low-pass parabolic fast Fourier transform filter (PFFTF) was introduced for noise elimination. A compact airborne Raman lidar system was studied, which applied PFFTF to process lidar signals. Mathematics and simulations of PFFTF along with low pass filters, sliding mean filter (SMF), median filter (MF), empirical mode decomposition (EMD) and wavelet transform (WT) were studied, and the practical engineering value of PFFTF for l...
Cancellation of neutral current harmonics by using a four-branch star hybrid filter
DEFF Research Database (Denmark)
Blaabjerg, Frede; Rodriguez, Pedro; Candela, I.
2008-01-01
This paper presents a new technique for filtering current harmonics in three-phase four-wire networks based on the usage of a four-branch star (FBS) filter topology. Based on single-phase inductors and capacitors, the specific layout of the FBS filter topology allows achieving a power filter with...
Sensory Pollution from Bag Filters, Carbon Filters and Combinations
DEFF Research Database (Denmark)
Bekö, Gabriel; Clausen, Geo; Weschler, Charles J.
2008-01-01
by an upstream pre-filter (changed monthly), an EU7 filter protected by an upstream activated carbon (AC) filter, and EU7 filters with an AC filter either downstream or both upstream and downstream. In addition, two types of stand-alone combination filters were evaluated: a bag-type fiberglass filter...
Filter for interpretation of fragmentation during entry
Energy Technology Data Exchange (ETDEWEB)
Canavan, G.H.
1997-10-01
Objects that fragment cascade and decelerate abruptly, producing short, bright, signatures which can be used to estimate object diameter and speed. Other objects can be incorporated into a generalized fragmentation filter. This note summarizes the results of previous reports on the prediction and inversion of signatures from objects that radiate, ablate, and fragment during entry and uses them to produce models for the parameters of entering objects.
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.
Inversion assuming weak scattering
DEFF Research Database (Denmark)
Xenaki, Angeliki; Gerstoft, Peter; Mosegaard, Klaus
2013-01-01
due to the complex nature of the field. A method based on linear inversion is employed to infer information about the statistical properties of the scattering field from the obtained cross-spectral matrix. A synthetic example based on an active high-frequency sonar demonstrates that the proposed...
DEFF Research Database (Denmark)
Mosegaard, Klaus
2012-01-01
For non-linear inverse problems, the mathematical structure of the mapping from model parameters to data is usually unknown or partly unknown. Absence of information about the mathematical structure of this function prevents us from presenting an analytical solution, so our solution depends on ou...
Calculation of the inverse data space via sparse inversion
Saragiotis, Christos
2011-01-01
The inverse data space provides a natural separation of primaries and surface-related multiples, as the surface multiples map onto the area around the origin while the primaries map elsewhere. However, the calculation of the inverse data is far from trivial as theory requires infinite time and offset recording. Furthermore regularization issues arise during inversion. We perform the inversion by minimizing the least-squares norm of the misfit function by constraining the $ell_1$ norm of the solution, being the inverse data space. In this way a sparse inversion approach is obtained. We show results on field data with an application to surface multiple removal.
Burt, David
1997-01-01
Presents responses to 10 common arguments against the use of Internet filters in libraries. Highlights include keyword blocking; selection of materials; liability of libraries using filters; users' judgments; Constitutional issues, including First Amendment rights; and censorship. (LRW)
HEPA filter monitoring program
Kirchner, K. N.; Johnson, C. M.; Aiken, W. F.; Lucerna, J. J.; Barnett, R. L.; Jensen, R. T.
1986-07-01
The testing and replacement of HEPA filters, widely used in the nuclear industry to purify process air, are costly and labor-intensive. Current methods of testing filter performance, such as differential pressure measurement and scanning air monitoring, allow determination of overall filter performance but preclude detection of incipient filter failure such as small holes in the filters. Using current technology, a continual in-situ monitoring system was designed which provides three major improvements over current methods of filter testing and replacement. The improvements include: cost savings by reducing the number of intact filters which are currently being replaced unnecessarily; more accurate and quantitative measurement of filter performance; and reduced personnel exposure to a radioactive environment by automatically performing most testing operations.
Novel Backup Filter Device for Candle Filters
Energy Technology Data Exchange (ETDEWEB)
Bishop, B.; Goldsmith, R.; Dunham, G.; Henderson, A.
2002-09-18
The currently preferred means of particulate removal from process or combustion gas generated by advanced coal-based power production processes is filtration with candle filters. However, candle filters have not shown the requisite reliability to be commercially viable for hot gas clean up for either integrated gasifier combined cycle (IGCC) or pressurized fluid bed combustion (PFBC) processes. Even a single candle failure can lead to unacceptable ash breakthrough, which can result in (a) damage to highly sensitive and expensive downstream equipment, (b) unacceptably low system on-stream factor, and (c) unplanned outages. The U.S. Department of Energy (DOE) has recognized the need to have fail-safe devices installed within or downstream from candle filters. In addition to CeraMem, DOE has contracted with Siemens-Westinghouse, the Energy & Environmental Research Center (EERC) at the University of North Dakota, and the Southern Research Institute (SRI) to develop novel fail-safe devices. Siemens-Westinghouse is evaluating honeycomb-based filter devices on the clean-side of the candle filter that can operate up to 870 C. The EERC is developing a highly porous ceramic disk with a sticky yet temperature-stable coating that will trap dust in the event of filter failure. SRI is developing the Full-Flow Mechanical Safeguard Device that provides a positive seal for the candle filter. Operation of the SRI device is triggered by the higher-than-normal gas flow from a broken candle. The CeraMem approach is similar to that of Siemens-Westinghouse and involves the development of honeycomb-based filters that operate on the clean-side of a candle filter. The overall objective of this project is to fabricate and test silicon carbide-based honeycomb failsafe filters for protection of downstream equipment in advanced coal conversion processes. The fail-safe filter, installed directly downstream of a candle filter, should have the capability for stopping essentially all particulate
Energy Technology Data Exchange (ETDEWEB)
Poirier, M. R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Burket, P. R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Duignan, M. R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)
2015-03-12
The Savannah River Site (SRS) is currently treating radioactive liquid waste with the Actinide Removal Process (ARP) and the Modular Caustic Side Solvent Extraction Unit (MCU). The low filter flux through the ARP has limited the rate at which radioactive liquid waste can be treated. Recent filter flux has averaged approximately 5 gallons per minute (gpm). Salt Batch 6 has had a lower processing rate and required frequent filter cleaning. Savannah River Remediation (SRR) has a desire to understand the causes of the low filter flux and to increase ARP/MCU throughput. In addition, at the time the testing started, SRR was assessing the impact of replacing the 0.1 micron filter with a 0.5 micron filter. This report describes testing of MST filterability to investigate the impact of filter pore size and MST particle size on filter flux and testing of filter enhancers to attempt to increase filter flux. The authors constructed a laboratory-scale crossflow filter apparatus with two crossflow filters operating in parallel. One filter was a 0.1 micron Mott sintered SS filter and the other was a 0.5 micron Mott sintered SS filter. The authors also constructed a dead-end filtration apparatus to conduct screening tests with potential filter aids and body feeds, referred to as filter enhancers. The original baseline for ARP was 5.6 M sodium salt solution with a free hydroxide concentration of approximately 1.7 M.3 ARP has been operating with a sodium concentration of approximately 6.4 M and a free hydroxide concentration of approximately 2.5 M. SRNL conducted tests varying the concentration of sodium and free hydroxide to determine whether those changes had a significant effect on filter flux. The feed slurries for the MST filterability tests were composed of simple salts (NaOH, NaNO_{2}, and NaNO_{3}) and MST (0.2 – 4.8 g/L). The feed slurry for the filter enhancer tests contained simulated salt batch 6 supernate, MST, and filter enhancers.
R. Bharadwaj; A. Patel, S. Chokdeepanich, Ph.D.; G.G. Chase, Ph.D.
2008-01-01
Coalescing filters are widely used throughout industry and improved performance will reduce droplet emissions and operating costs. Experimental observations show orientation of micro fibers in filter media effect the permeability and the separation efficiency of the filter media. In this work two methods are used to align the fibers to alter the filter structure. The results show that axially aligned fiber media improve quality factor on the order of 20% and cutting media on an angle from a t...
Track inspection data filtering based on EMD
Wang, YiJun; Liang, Guangzhu
2017-04-01
In order to reduce the influence of the coarse error noise in the original data acquired by railway inspection instrument, we propose that filtering the original data by Empirical Mode Decomposition combine with ROR criterion. The ROR criterion is used to identify and eliminate the coarse error in the first layer of original data which is IMF1 obtained by empirical mode decomposition, and then we can get the signal after removal of noise by inverse operation of empirical mode decomposition. The mean square error and the signal-to-noise ratio are used to analyze and evaluate the effect of recursive median method and proposed method on filtering noise, the advantage of proposed method in dealing with nonlinear nonstationary signals is verified. The example shows that the method proposed in this paper can effectively identify the coarse error in the signal and eliminate the noise, and get the ideal filtering result.
Sellers, Cheryl L [Peoria, IL; Nordyke, Daniel S [Arlington Heights, IL; Crandell, Richard A [Morton, IL; Tomlins, Gregory [Peoria, IL; Fei, Dong [Peoria, IL; Panov, Alexander [Dunlap, IL; Lane, William H [Chillicothe, IL; Habeger, Craig F [Chillicothe, IL
2008-12-09
According to an exemplary embodiment of the present disclosure, a system for removing matter from a filtering device includes a gas pressurization assembly. An element of the assembly is removably attachable to a first orifice of the filtering device. The system also includes a vacuum source fluidly connected to a second orifice of the filtering device.
DEFF Research Database (Denmark)
Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan
2006-01-01
This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model sta...
Gates-Anderson, Dianne D.; Kidd, Scott D.; Bowers, John S.; Attebery, Ronald W.
2003-01-01
A low viscosity resin is delivered into a spent HEPA filter or other waste. The resin is introduced into the filter or other waste using a vacuum to assist in the mass transfer of the resin through the filter media or other waste.
Conditioning the full waveform inversion gradient to welcome anisotropy
Alkhalifah, Tariq Ali
2014-08-05
Multi-parameter full waveform inversion (FWI) suffers from the complex nonlinearity in the objective function, compounded by the eventual tradeoff between the model parameters. A hierarchical approach based on frequency and arrival time data decimation to maneuver the complex nonlinearity associated with this problem usually falls short in anisotropic media. In place of data decimation, I use a model gradient filter approach to access the parts of the gradient more suitable to combat the potential nonlinearity and parameter trade off. The filter is based on representing the gradient in the time-lag normalized domain in which the small scattering angles of the gradient update is initially muted out. A model update hierarchical filtering strategy includes applying varying degree of filtering to the different parameter updates. A feature not easily accessible to simple data decimation. Using both FWI and reection based FWI (RFWI), two strategies to combat the tradeoff between anisotropic parameters are outlined.
Particulate matter sample deposit geometry and effective filter face velocities.
McDade, Charles E; Dillner, Ann M; Indresand, Hege
2009-09-01
Aerosol filter face velocities can be underestimated when the sample deposit area does not cover the entire face of the filter. In many aerosol samplers, Teflon filters are backed with a metal support screen. In these samplers, air flows through the filter only in the small area upstream of each hole in the screen, resulting in a sample deposit that is an array of tiny dots that mimics the array of holes. Thus, the filter deposit area is smaller than the total filter area and the effective face velocity is greater than that calculated from the sample deposit envelope. The Interagency Monitoring of Protected Visual Environments (IMPROVE) network has used filter holders with two different screen hole arrays. The U.S. Environmental Protection Agency's Chemical Speciation Network (CSN) and the Federal Reference Method samplers also use a metal support screen, but with much smaller screen holes than IMPROVE. These networks also use larger filters and lower flow rates than those used in IMPROVE. Filter face velocities for all of these networks that are calculated using the actual deposit array area range from 1.6 to 3.5 times larger than those calculated incorrectly using the entire sample deposit envelope.
DEFF Research Database (Denmark)
Gale, A.S.; Surlyk, Finn; Anderskouv, Kresten
2013-01-01
Evidence from regional stratigraphical patterns in Santonian−Campanian chalk is used to infer the presence of a very broad channel system (5 km across) with a depth of at least 50 m, running NNW−SSE across the eastern Isle of Wight; only the western part of the channel wall and fill is exposed. W......−Campanian chalks in the eastern Isle of Wight, involving penecontemporaneous tectonic inversion of the underlying basement structure, are rejected....
Regenerative particulate filter development
Descamp, V. A.; Boex, M. W.; Hussey, M. W.; Larson, T. P.
1972-01-01
Development, design, and fabrication of a prototype filter regeneration unit for regenerating clean fluid particle filter elements by using a backflush/jet impingement technique are reported. Development tests were also conducted on a vortex particle separator designed for use in zero gravity environment. A maintainable filter was designed, fabricated and tested that allows filter element replacement without any leakage or spillage of system fluid. Also described are spacecraft fluid system design and filter maintenance techniques with respect to inflight maintenance for the space shuttle and space station.
Ceramic fiber filter technology
Energy Technology Data Exchange (ETDEWEB)
Holmes, B.L.; Janney, M.A.
1996-06-01
Fibrous filters have been used for centuries to protect individuals from dust, disease, smoke, and other gases or particulates. In the 1970s and 1980s ceramic filters were developed for filtration of hot exhaust gases from diesel engines. Tubular, or candle, filters have been made to remove particles from gases in pressurized fluidized-bed combustion and gasification-combined-cycle power plants. Very efficient filtration is necessary in power plants to protect the turbine blades. The limited lifespan of ceramic candle filters has been a major obstacle in their development. The present work is focused on forming fibrous ceramic filters using a papermaking technique. These filters are highly porous and therefore very lightweight. The papermaking process consists of filtering a slurry of ceramic fibers through a steel screen to form paper. Papermaking and the selection of materials will be discussed, as well as preliminary results describing the geometry of papers and relative strengths.
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.
2016-02-13
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
Compact planar microwave blocking filters
U-Yen, Kongpop (Inventor); Wollack, Edward J. (Inventor)
2012-01-01
A compact planar microwave blocking filter includes a dielectric substrate and a plurality of filter unit elements disposed on the substrate. The filter unit elements are interconnected in a symmetrical series cascade with filter unit elements being organized in the series based on physical size. In the filter, a first filter unit element of the plurality of filter unit elements includes a low impedance open-ended line configured to reduce the shunt capacitance of the filter.
Gibbons, Steven J.; Näsholm, Sven Peter; Kværna, Tormod
2014-05-01
Correlation detectors facilitate seismic monitoring in the near vicinity of previously observed events at far lower detection thresholds than are possible using the methods applied in most existing processing pipelines. The use of seismic arrays has been demonstrated to be highly beneficial in pressing down the detection threshold, due to superior noise suppression, and also in eliminating vast numbers of false alarms by performing array processing on the multi-channel output of the correlation detectors. This last property means that it is highly desirable to run continuous detectors for sites of repeating seismic events on a single-array basis for many arrays across a global network. Spurious detections for a given signal template on a single array can however still occur when an unrelated wavefront crosses the array from a very similar direction to that of the master event wavefront. We present an algorithm which scans automatically the output from multiple stations - both array and 3-component - for coherence between the individual station correlator outputs that is consistent with a disturbance in the vicinity of the master event. The procedure results in a categorical rejection of an event hypothesis in the absence of support from stations other than the one generating the trigger and provides a fully automatic relative event location estimate when patterns in the correlation detector outputs are found to be consistent with a common event. This coherence-based approach removes the need to make explicit measurements of the time-differences for single stations and this eliminates a potential source of error. The method is demonstrated for the North Korea nuclear test site and the relative event location estimates obtained for the 2006, 2009, and 2013 events are compared with previous estimates from different station configurations.
Application of a Kalman Filter with Augmented Measurement Model in Non-Invasive Cardiac Imaging
Elies Henar, Francesc
2011-01-01
This work will focus on improving the Kalman filter by use of an extended measurement model [Kaipio et Somersalo, 1999] which introduces spatial regularization terms into the filter. This model has been applied in solvers of the inverse problem of electrical impedance tomography [Hiltunen et al., 2010], though this tomography inverse problem is non-linear, it is mathematically very similar to the imaging of electric sources in the heart. Trabajo trata sobre la mejora de un filtro de Kalman...
GARCH modelling of covariance in dynamical estimation of inverse solutions
Energy Technology Data Exchange (ETDEWEB)
Galka, Andreas [Institute of Experimental and Applied Physics, University of Kiel, 24098 Kiel (Germany) and Institute of Statistical Mathematics (ISM), Minami-Azabu 4-6-7, Tokyo 106-8569 (Japan)]. E-mail: galka@physik.uni-kiel.de; Yamashita, Okito [ATR Computational Neuroscience Laboratories, Hikaridai 2-2-2, Kyoto 619-0288 (Japan); Ozaki, Tohru [Institute of Statistical Mathematics (ISM), Minami-Azabu 4-6-7, Tokyo 106-8569 (Japan)
2004-12-06
The problem of estimating unobserved states of spatially extended dynamical systems poses an inverse problem, which can be solved approximately by a recently developed variant of Kalman filtering; in order to provide the model of the dynamics with more flexibility with respect to space and time, we suggest to combine the concept of GARCH modelling of covariance, well known in econometrics, with Kalman filtering. We formulate this algorithm for spatiotemporal systems governed by stochastic diffusion equations and demonstrate its feasibility by presenting a numerical simulation designed to imitate the situation of the generation of electroencephalographic recordings by the human cortex.
New LMS adaptive filter for GPR processing
Dube, F. N.; Devlin, John C.
2000-04-01
they do not require feedback, the input signal is used to change the parameters of the filter. An adaptive algorithm used by an ALP in closed loop filters changes the FIR filter coefficients such that the transfer function of the adaptive filter become the inverse of that possessed by the input process. Once adapted the frequency response of the FIR filter will be unity at the frequency of the input signal and the ALP's output. The error will be at a minimal value. By comparison the frequency response of the FIR filter in an open loop configuration will be zero at the frequency of the input signal. Open loop filters are more stable than closed loops adaptive filters. A novel frequency domain open loop adaptive filter is also introduced. This filter offers more stability, does not need feed back and has increased convergence speed.
Detecting Defects in Textile Fabrics with Optimal Gabor Filters
K. L. Mak; P. Peng
2008-01-01
This paper investigates the problem of automated defect detection for textile fabrics and proposes a new optimal filter design method to solve this problem. Gabor Wavelet Network (GWN) is chosen as the major technique to extract the texture features from textile fabrics. Based on the features extracted, an optimal Gabor filter can be designed. In view of this optimal filter, a new semi-supervised defect detection scheme is proposed, which consists of one real-valued Gabor...
Filter frequency response of time dependent signal using Laplace transform
Energy Technology Data Exchange (ETDEWEB)
Shestakov, Aleksei I. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2018-01-16
We analyze the effect a filter has on a time dependent signal x(t). If X(s) is the Laplace transform of x and H (s) is the filter Transfer function, the response in frequency space is X (s) H (s). Consequently, in real space, the response is the convolution (x*h) (t), where hi is the Laplace inverse of H. Effects are analyzed and analytically for functions such as (t/t_{c})^{2} e^{-t/t$_c$}, where t_{c} = const. We consider lowpass, highpass and bandpass filters.
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.
Computer-Aided Numerical Inversion of Laplace Transform
Umesh Kumar
2000-01-01
This paper explores the technique for the computer aided numerical inversion of Laplace transform. The inversion technique is based on the properties of a family of three parameter exponential probability density functions. The only limitation in the technique is the word length of the computer being used. The Laplace transform has been used extensively in the frequency domain solution of linear, lumped time invariant networks but its application to the time domain has been limited, mainly be...
Inverse folding of RNA pseudoknot structures.
Gao, James Zm; Li, Linda Ym; Reidys, Christian M
2010-06-23
RNA exhibits a variety of structural configurations. Here we consider a structure to be tantamount to the noncrossing Watson-Crick and G-U-base pairings (secondary structure) and additional cross-serial base pairs. These interactions are called pseudoknots and are observed across the whole spectrum of RNA functionalities. In the context of studying natural RNA structures, searching for new ribozymes and designing artificial RNA, it is of interest to find RNA sequences folding into a specific structure and to analyze their induced neutral networks. Since the established inverse folding algorithms, RNAinverse, RNA-SSD as well as INFO-RNA are limited to RNA secondary structures, we present in this paper the inverse folding algorithm Inv which can deal with 3-noncrossing, canonical pseudoknot structures. In this paper we present the inverse folding algorithm Inv. We give a detailed analysis of Inv, including pseudocodes. We show that Inv allows to design in particular 3-noncrossing nonplanar RNA pseudoknot 3-noncrossing RNA structures-a class which is difficult to construct via dynamic programming routines. Inv is freely available at http://www.combinatorics.cn/cbpc/inv.html. The algorithm Inv extends inverse folding capabilities to RNA pseudoknot structures. In comparison with RNAinverse it uses new ideas, for instance by considering sets of competing structures. As a result, Inv is not only able to find novel sequences even for RNA secondary structures, it does so in the context of competing structures that potentially exhibit cross-serial interactions.
Towards self-organizing Kalman filters
Sijs, J.; Papp, Z.
2012-01-01
Distributed Kalman filtering is an important signal processing method for state estimation in large-scale sensor networks. However, existing solutions do not account for unforeseen events that are likely to occur and thus dramatically changing the operational conditions (e.g. node failure,
MR fingerprinting reconstruction with Kalman filter.
Zhang, Xiaodi; Zhou, Zechen; Chen, Shiyang; Chen, Shuo; Li, Rui; Hu, Xiaoping
2017-09-01
Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching. In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm. The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.
Generic Kalman Filter Software
Lisano, Michael E., II; Crues, Edwin Z.
2005-01-01
The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on
Design of dual ring wavelength filters for WDM applications
Sathyadevaki, R.; Shanmuga sundar, D.; Sivanantha Raja, A.
2016-12-01
Wavelength division multiplexing plays a prime role in an optical communication due to its advantages such as easy network expansion, longer span lengths etc. In this work, photonic crystal based filters with the dual rings are proposed which act as band pass filters (BPF) and channel drop filter (CDF) that has found a massive applications in C and L-bands used for wavelength selection and noise filtering at erbium doped fiber amplifiers and dense wavelength division multiplexing operation. These filters are formulated on the square lattice with crystal rods of silicon material of refractive index 3.4 which are perforated on an air of refractive index 1. Dual ring double filters (band pass filter and channel drop filter) on single layout possess passing and dropping band of wavelengths in two distinct arrangements with entire band quality factors of 92.09523 & 505.263 and 124.85019 & 456.8633 for the pass and drop filters of initial setup and amended setup respectively. These filters have the high-quality factor with broad and narrow bandwidths of 16.8 nm & 3.04 nm and 12.85 nm & 3.3927 nm. Transmission spectra and band gap of the desired filters is analyzed using Optiwave software suite. Two dual ring filters incorporated on a single layout comprises the size of 15×11 μm which can also be used in the integrated photonic chips for the ultra-compact unification of devices.
Planar high temperature superconductor filters with backside coupling
Shen, Zhi-Yuan (Inventor)
1998-01-01
An improved high temperature superconducting planar filter wherein the coupling circuit or connecting network is located, in whole or in part, on the side of the substrate opposite the resonators and enables higher power handling capability.
Generalized Impedance Converter (GIC) Filter Utilizing Composite Amplifier
National Research Council Canada - National Science Library
Cheong, Heng W
2005-01-01
.... Various classroom software aids such as MATLAB, P-SPICE and MAPLE are utilized to simulate varies circuit parameter changes in ideal and non-ideal GIC filter, such as network sensitivity, effects...
Arathy Rajagopal; B. Geethanjali; Arulprakash P
2015-01-01
A major security challenge on the Internet is the existence of the large number of compromised machines. Such machines have been increasingly used to launch various security attacks including spamming and spreading malware, DDoS, and identity theft. These compromised machines are called "Zombies". In general E-mail applications and providers uses spam filters to filter the spam messages. Spam filtering is a technique for discriminating the genuine message from the spam messages. The attackers...
Laicer, Castro; Rasimick, Brian; Green, Zachary
2012-01-01
Cabin environmental control is an important issue for a successful Moon mission. Due to the unique environment of the Moon, lunar dust control is one of the main problems that significantly diminishes the air quality inside spacecraft cabins. Therefore, this innovation was motivated by NASA s need to minimize the negative health impact that air-suspended lunar dust particles have on astronauts in spacecraft cabins. It is based on fabrication of a hybrid filter comprising nanofiber nonwoven layers coated on porous polymer membranes with uniform cylindrical pores. This design results in a high-efficiency gas particulate filter with low pressure drop and the ability to be easily regenerated to restore filtration performance. A hybrid filter was developed consisting of a porous membrane with uniform, micron-sized, cylindrical pore channels coated with a thin nanofiber layer. Compared to conventional filter media such as a high-efficiency particulate air (HEPA) filter, this filter is designed to provide high particle efficiency, low pressure drop, and the ability to be regenerated. These membranes have well-defined micron-sized pores and can be used independently as air filters with discreet particle size cut-off, or coated with nanofiber layers for filtration of ultrafine nanoscale particles. The filter consists of a thin design intended to facilitate filter regeneration by localized air pulsing. The two main features of this invention are the concept of combining a micro-engineered straight-pore membrane with nanofibers. The micro-engineered straight pore membrane can be prepared with extremely high precision. Because the resulting membrane pores are straight and not tortuous like those found in conventional filters, the pressure drop across the filter is significantly reduced. The nanofiber layer is applied as a very thin coating to enhance filtration efficiency for fine nanoscale particles. Additionally, the thin nanofiber coating is designed to promote capture of
Social Collaborative Filtering by Trust.
Yang, Bo; Lei, Yu; Liu, Jiming; Li, Wenjie
2017-08-01
Recommender systems are used to accurately and actively provide users with potentially interesting information or services. Collaborative filtering is a widely adopted approach to recommendation, but sparse data and cold-start users are often barriers to providing high quality recommendations. To address such issues, we propose a novel method that works to improve the performance of collaborative filtering recommendations by integrating sparse rating data given by users and sparse social trust network among these same users. This is a model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations. We use four large-scale datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation algorithms for social collaborative filtering based on trust.
Fundamentals of Stochastic Filtering
Crisan, Dan
2008-01-01
The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient
Zakharov, Alexander V.; Ilchenko, Mykhailo Ye.; Trubarov, Igor V.; Pinchuk, Ludmila S.
2016-01-01
There are considered constructions of microsized stripe delay filters, which are realized on a basis of ceramic materials with high dielectric permittivity. Delay time of non-minimal phase filters is 7–12 ns at frequencies of 1900 MHz with relative bandwidth of 3.6–3.85%. Filters dimensions are comparable with ones used in portable communication devices. Dimensions of researched three-resonator filter at frequency of 1900 MHz are 8.4×5×2 mm with material dielectric permittivity εr = 92, and 5...
Nanofiber Filters Eliminate Contaminants
2009-01-01
With support from Phase I and II SBIR funding from Johnson Space Center, Argonide Corporation of Sanford, Florida tested and developed its proprietary nanofiber water filter media. Capable of removing more than 99.99 percent of dangerous particles like bacteria, viruses, and parasites, the media was incorporated into the company's commercial NanoCeram water filter, an inductee into the Space Foundation's Space Technology Hall of Fame. In addition to its drinking water filters, Argonide now produces large-scale nanofiber filters used as part of the reverse osmosis process for industrial water purification.
Rugate filter design: An analytical approach using uniform WKB solutions
Perelman, N.; Averbukh, I.
1996-03-01
An analytical approach to the design of rugate filters with a smooth amplitude modulation of the sine-wave index is developed. The approach is based on the uniform WKB solutions (asymptotic expansions) of the coupled-wave equations. A closed-form solution for the inverse problem (finding the refractive index profile for a given reflectance shape inside the stop band) is found.
Optimization based inversion method for the inverse heat conduction problems
Mu, Huaiping; Li, Jingtao; Wang, Xueyao; Liu, Shi
2017-05-01
Precise estimation of the thermal physical properties of materials, boundary conditions, heat flux distributions, heat sources and initial conditions is highly desired for real-world applications. The inverse heat conduction problem (IHCP) analysis method provides an alternative approach for acquiring such parameters. The effectiveness of the inversion algorithm plays an important role in practical applications of the IHCP method. Different from traditional inversion models, in this paper a new inversion model that simultaneously highlights the measurement errors and the inaccurate properties of the forward problem is proposed to improve the inversion accuracy and robustness. A generalized cost function is constructed to convert the original IHCP into an optimization problem. An iterative scheme that splits a complicated optimization problem into several simpler sub-problems and integrates the superiorities of the alternative optimization method and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is developed for solving the proposed cost function. Numerical experiment results validate the effectiveness of the proposed inversion method.
Song, Yujiang; Shelnutt, John A.
2012-11-06
A metallic nanowire network synthesized using chemical reduction of a metal ion source by a reducing agent in the presence of a soft template comprising a tubular inverse micellar network. The network of interconnected polycrystalline nanowires has a very high surface-area/volume ratio, which makes it highly suitable for use in catalytic applications.
A Bayesian approach to linear inverse problems in seismic tomography
Tian, Y.; Zhou, Y.; Chung, J.; Chung, M.; Ning, J.
2014-12-01
Seismic tomography is often an ill-posed linear inverse problem and regularization such as damping and smoothing has been widely applied to find an approximate solution to the inverse problem. The "optimal" solution is chosen based on the tradeoff between model norm (or model roughness) and data misfit. The main difficulty associated with this deterministic approach is in determining a balance between model uncertainty and data fit. This can make interpretation of tomographic structures subjective because models at the "corner" of the tradeoff curve often show large variability. In this study, we investigate a Bayesian approach to the linear inverse problem by minimizing an empirical Bayes risk function based on training dataset generated for the tomographic problem. We show that sample average approximation can be used to find optimal spectral filters to solve the linear tomographic problem based on singular value decomposition (SVD). We compare optimal truncated SVD, optimal Tikhonov filtering as well as independent optimal spectral filtering in finite-frequency tomography and ray theoretical tomography using a global dataset of surface-wave dispersion measurements.
Directory of Open Access Journals (Sweden)
Markus Spiliotis
Full Text Available Inverse fusion PCR cloning (IFPC is an easy, PCR based three-step cloning method that allows the seamless and directional insertion of PCR products into virtually all plasmids, this with a free choice of the insertion site. The PCR-derived inserts contain a vector-complementary 5'-end that allows a fusion with the vector by an overlap extension PCR, and the resulting amplified insert-vector fusions are then circularized by ligation prior transformation. A minimal amount of starting material is needed and experimental steps are reduced. Untreated circular plasmid, or alternatively bacteria containing the plasmid, can be used as templates for the insertion, and clean-up of the insert fragment is not urgently required. The whole cloning procedure can be performed within a minimal hands-on time and results in the generation of hundreds to ten-thousands of positive colonies, with a minimal background.
Convolutional sketch inversion
Güçlütürk, Y.; Güçlü, U.; Lier, R.J. van; Gerven, M.A.J. van
2016-01-01
In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large number of computer-generated face sketches with different styles and corresponding face images by expanding existing
Estimation of Swiss methane emissions by near surface observations and inverse modeling
Henne, Stephan; Brian, Oney; Leuenberger, Markus; Bamberger, Ines; Eugster, Werner; Steinbacher, Martin; Meinhardt, Frank; Brunner, Dominik
2015-04-01
On a global scale methane (CH4) is the second most important long-lived greenhouse gas. It is released from both natural and anthropogenic processes and its atmospheric burden has more than doubled since preindustrial times. Current CH4 emission estimates are associated with comparatively large uncertainties both globally and regionally. For example, the Swiss national greenhouse gas inventory assigns an uncertainty of 18% to the country total anthropogenic CH4 emissions as compared to only 3% for anthropogenic CO2 emissions. In Switzerland, CH4 is thought to be mainly released by agricultural activities (ruminants and manure management >80%), while natural emissions from wetlands and wild animals represent a minor source (~3 %). The country total and especially the spatial distribution of CH4 emission within Switzerland strongly differs between the national and different European scale inventories. To validate the 'bottom-up' Swiss CH4 emission estimate and to reduce its uncertainty both in total and spatially, 'top-down' methods combining atmospheric CH4 observations and regional scale transport simulations can be used. Here, we analyse continuous, near surface observations of CH4 concentrations as collected within the newly established CarboCountCH measurement network (http://www.carbocount.ch). The network consists of 4 sites situated on the Swiss Plateau, comprising a tall tower site (217 m), two elevated (mountaintop) sites and a small tower site (32 m) in flat terrain. In addition, continuous CH4 observations from the nearby high-altitude site Jungfraujoch (Alps) and the mountaintop site Schauinsland (Germany) were used. Two inversion frameworks were applied to the CH4 observations in combination with source sensitivities (footprints) calculated with the regional scale version of the Lagrangian Particle Dispersion Model FLEXPART. One inversion system was based on a Bayesian framework, while the other utilized an extended Kalman filter approach. The transport
Randomized Filtering Algorithms
DEFF Research Database (Denmark)
Katriel, Irit; Van Hentenryck, Pascal
2008-01-01
of AllDifferent and is generalization, the Global Cardinality Constraint. The first delayed filtering scheme is a Monte Carlo algorithm: its running time is superior, in the worst case, to that of enforcing are consistency after every domain event, while its filtering effectiveness is analyzed...
Multilevel ensemble Kalman filter
Chernov, Alexey
2016-01-06
This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.
DEFF Research Database (Denmark)
Wells, George; Beaton, Dorcas E; Tugwell, Peter
2014-01-01
The "Discrimination" part of the OMERACT Filter asks whether a measure discriminates between situations that are of interest. "Feasibility" in the OMERACT Filter encompasses the practical considerations of using an instrument, including its ease of use, time to complete, monetary costs, and inter...
Fast Anisotropic Gauss Filtering
Geusebroek, J.M.; Smeulders, A.W.M.; van de Weijer, J.; Heyden, A.; Sparr, G.; Nielsen, M.; Johansen, P.
2002-01-01
We derive the decomposition of the anisotropic Gaussian in a one dimensional Gauss filter in the x-direction followed by a one dimensional filter in a non-orthogonal direction phi. So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a
Vena cava filter; Vena-cava-Filter
Energy Technology Data Exchange (ETDEWEB)
Helmberger, T. [Klinikum Bogenhausen, Institut fuer Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Muenchen (Germany)
2007-05-15
Fulminant pulmonary embolism is one of the major causes of death in the Western World. In most cases, deep leg and pelvic venous thrombosis are the cause. If an anticoagulant/thrombotic therapy is no longer possible or ineffective, a vena cava filter implant may be indicated if an embolism is threatening. Implantation of the filter is a simple and safe intervention. Nevertheless, it is necessary to take into consideration that the data base for determining the indications for this treatment are very limited. Currently, a reduction in the risk of thromboembolism with the use of filters of about 30%, of recurrences of almost 5% and fatal pulmonary embolism of 1% has been reported, with a risk of up to 20% of filter induced vena cava thrombosis. (orig.) [German] Die fulminante Lungenembolie zaehlt zu den Haupttodesursachen in der westlichen Welt. In der Mehrzahl der Faelle sind tiefe Bein- und Beckenvenenthrombosen ursaechlich verantwortlich. Ist eine antikoagulative/-thrombotische Therapie nicht (mehr) moeglich oder unwirksam, kann bei drohender Emboliegefahr die Vena-cava-Filterimplantation indiziert sein. Die Filterimplantation ist eine einfache und sehr sichere Intervention. Dennoch muss bei der Indikationsstellung beruecksichtigt werden, dass die Datenlage zur Wirksamkeit sehr limitiert ist. So wird aktuell ueber eine Reduktion des Thrombembolierisikos um 30% bei Embolierezidiven von knapp 5% und fatalen Lungenembolien von 1% unter Filterprophylaxe berichtet, bei einem Risiko von bis zu 20% fuer die filterinduzierte Vena-cava-Thrombose. (orig.)
Inversion-based data-driven time-space domain random noise attenuation method
Zhao, Yu-Min; Li, Guo-Fa; Wang, Wei; Zhou, Zhen-Xiao; Tang, Bo-Wen; Zhang, Wen-Bo
2017-12-01
Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data's space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.
Sironi, Amos; Tekin, Bugra; Rigamonti, Roberto; Lepetit, Vincent; Fua, Pascal
2015-01-01
Learning filters to produce sparse image representations in terms of over-complete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, making their use computationally expensive. In this paper, we show that such filters can be computed as linear combinations of a smaller number of separable ones, thus greatly reducing the computational complexity at no cost in terms of performance. This makes filter learning approaches practical even for large images or 3D volumes, and we show that we significantly outperform state-of-the-art methods on the curvilinear structure extraction task, in terms of both accuracy and speed. Moreover, our approach is general and can be used on generic convolutional filter banks to reduce the complexity of the feature extraction step.
Distributed Kalman-Consensus Filtering for Sparse Signal Estimation
Directory of Open Access Journals (Sweden)
Yisha Liu
2014-01-01
Full Text Available A Kalman filtering-based distributed algorithm is proposed to deal with the sparse signal estimation problem. The pseudomeasurement-embedded Kalman filter is rebuilt in the information form, and an improved parameter selection approach is discussed. By introducing the pseudomeasurement technology into Kalman-consensus filter, a distributed estimation algorithm is developed to fuse the measurements from different nodes in the network, such that all filters can reach a consensus on the estimate of sparse signals. Some numerical examples are provided to demonstrate the effectiveness of the proposed approach.
Design of Microwave Multibandpass Filters with Quasilumped Resonators
Directory of Open Access Journals (Sweden)
Dejan Miljanović
2015-01-01
Full Text Available Design of RF and microwave filters has always been the challenging engineering field. Modern filter design techniques involve the use of the three-dimensional electromagnetic (3D EM solvers for predicting filter behavior, yielding the most accurate filter characteristics. However, the 3D EM simulations are time consuming. In this paper, we propose electric-circuit models, instead of 3D EM models, suitable for design of RF and microwave filters with quasilumped coupled resonators. Using the diakoptic approach, the 3D filter structure is decomposed into domains that are modeled by electric networks. The coupling between these domains is modeled by capacitors and coupled inductors. Furthermore, we relate the circuit-element values to the physical dimensions of the 3D filter structure. We propose the filter design procedure that is based on the circuit models and fast circuit-level simulations, yielding the element values from which the physical dimensions can be obtained. The obtained dimensions should be slightly refined for achieving the desired filter characteristics. The mathematical problems encountered in the procedure are solved by numerical and symbolic computations. The procedure is exemplified by designing a triple-bandpass filter and validated by measurements on the fabricated filter. The simulation and experimental results are in good agreement.
Takam Takougang, E. M.; Bouzidi, Y.
2016-12-01
Multi-offset Vertical Seismic Profile (walkaway VSP) data were collected in an oil field located in a shallow water environment dominated by carbonate rocks, offshore the United Arab Emirates. The purpose of the survey was to provide structural information of the reservoir, around and away from the borehole. Five parallel lines were collected using an air gun at 25 m shot interval and 4 m source depth. A typical recording tool with 20 receivers spaced every 15.1 m, and located in a deviated borehole with an angle varying between 0 and 24 degree from the vertical direction, was used to record the data. The recording tool was deployed at different depths for each line, from 521 m to 2742 m depth. Smaller offsets were used for shallow receivers and larger offsets for deeper receivers. The lines merged to form the input dataset for waveform tomography. The total length of the combined lines was 9 km, containing 1344 shots and 100 receivers in the borehole located half-way down. Acoustic full waveform inversion was applied in the frequency domain to derive a high resolution velocity model. The final velocity model derived after the inversion using the frequencies 5-40 Hz, showed good correlation with velocities estimated from vertical incidence VSP and sonic log, confirming the success of the inversion. The velocity model showed anomalous low values in areas that correlate with known location of hydrocarbon reservoir. Pre-stack depth Reverse time migration was then applied using the final velocity model from waveform inversion and the up-going wavefield from the input data. The final estimated source signature from waveform inversion was used as input source for reverse time migration. To save computational memory and time, every 3 shots were used during reverse time migration and the data were low-pass filtered to 30 Hz. Migration artifacts were attenuated using a second order derivative filter. The final migration image shows a good correlation with the waveform
Comparison of weighting techniques for acoustic full waveform inversion
Jeong, Gangwon; Hwang, Jongha; Min, Dong-Joo
2017-12-01
To reconstruct long-wavelength structures in full waveform inversion (FWI), the wavefield-damping and weighting techniques have been used to synthesize and emphasize low-frequency data components in frequency-domain FWI. However, these methods have some weak points. The application of wavefield-damping method on filtered data fails to synthesize reliable low-frequency data; the optimization formula obtained introducing the weighting technique is not theoretically complete, because it is not directly derived from the objective function. In this study, we address these weak points and present how to overcome them. We demonstrate that the source estimation in FWI using damped wavefields fails when the data used in the FWI process does not satisfy the causality condition. This phenomenon occurs when a non-causal filter is applied to data. We overcome this limitation by designing a causal filter. Also we modify the conventional weighting technique so that its optimization formula is directly derived from the objective function, retaining its original characteristic of emphasizing the low-frequency data components. Numerical results show that the newly designed causal filter enables to recover long-wavelength structures using low-frequency data components synthesized by damping wavefields in frequency-domain FWI, and the proposed weighting technique enhances the inversion results.
Molecular circuits for dynamic noise filtering.
Zechner, Christoph; Seelig, Georg; Rullan, Marc; Khammash, Mustafa
2016-04-26
The invention of the Kalman filter is a crowning achievement of filtering theory-one that has revolutionized technology in countless ways. By dealing effectively with noise, the Kalman filter has enabled various applications in positioning, navigation, control, and telecommunications. In the emerging field of synthetic biology, noise and context dependency are among the key challenges facing the successful implementation of reliable, complex, and scalable synthetic circuits. Although substantial further advancement in the field may very well rely on effectively addressing these issues, a principled protocol to deal with noise-as provided by the Kalman filter-remains completely missing. Here we develop an optimal filtering theory that is suitable for noisy biochemical networks. We show how the resulting filters can be implemented at the molecular level and provide various simulations related to estimation, system identification, and noise cancellation problems. We demonstrate our approach in vitro using DNA strand displacement cascades as well as in vivo using flow cytometry measurements of a light-inducible circuit in Escherichia coli.
Bank of Weight Filters for Deep CNNs
2016-11-22
with 4096 inputs and 1000 outputs. Unlike the traditional neural nets , the Alexnet uses the Rectified Linear Unit(ReLU) as the non-linearity which is...Durrant and Kee-Eung Kim Abstract Convolutional neural networks (CNNs) are seen to be extremely effective in many large object recognition tasks. One...the dependency among the filters and the layers of the CNN is not strict. One can choose any pre-trained filter instead of a fixed pre-trained net
Directory of Open Access Journals (Sweden)
Juan Carlos García Infante
2011-01-01
Full Text Available Multivariate identifier filters (multiple inputs and multiple outputs - MIMO are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948. This paper considers the MIMO digital filter model using neuro fuzzy digital filtering to find an adaptive parameter matrix which is integrated into the Kalman filter by the transition matrix. The filter uses the neural network as back-propagation into the fuzzy mechanism to do this, interpreting its variables and its respective levels and selecting the best values for automatically adjusting transition matrix values. The Matlab simulation describes the neural fuzzy digital filter giving an approximation of exponential convergence seen in functional error.
Inverse halftoning based on the bayesian theorem.
Liu, Yun-Fu; Guo, Jing-Ming; Lee, Jiann-Der
2011-04-01
This study proposes a method which can generate high quality inverse halftone images from halftone images. This method can be employed prior to any signal processing over a halftone image or the inverse halftoning used in JBIG2. The proposed method utilizes the least-mean-square (LMS) algorithm to establish a relationship between the current processing position and its corresponding neighboring positions in each type of halftone image, including direct binary search, error diffusion, dot diffusion, and ordered dithering. After which, a referenced region called a support region (SR) is used to extract features. The SR can be obtained by relabeling the LMS-trained filters with the order of importance. Moreover, the probability of black pixel occurrence is considered as a feature in this work. According to this feature, the probabilities of all possible grayscale values at the current processing position can be obtained by the Bayesian theorem. Consequently, the final output at this position is the grayscale value with the highest probability. Experimental results show that the proposed method offers better visual quality than that of Mese-Vaidyanathan's and Chang et al's methods in terms of human-visual peak signal-to-noise ratio (HPSNR). In addition, the memory consumption is also superior to Mese-Vaidyanathan's method.
Ceramic fiber reinforced filter
Stinton, David P.; McLaughlin, Jerry C.; Lowden, Richard A.
1991-01-01
A filter for removing particulate matter from high temperature flowing fluids, and in particular gases, that is reinforced with ceramic fibers. The filter has a ceramic base fiber material in the form of a fabric, felt, paper of the like, with the refractory fibers thereof coated with a thin layer of a protective and bonding refractory applied by chemical vapor deposition techniques. This coating causes each fiber to be physically joined to adjoining fibers so as to prevent movement of the fibers during use and to increase the strength and toughness of the composite filter. Further, the coating can be selected to minimize any reactions between the constituents of the fluids and the fibers. A description is given of the formation of a composite filter using a felt preform of commercial silicon carbide fibers together with the coating of these fibers with pure silicon carbide. Filter efficiency approaching 100% has been demonstrated with these filters. The fiber base material is alternately made from aluminosilicate fibers, zirconia fibers and alumina fibers. Coating with Al.sub.2 O.sub.3 is also described. Advanced configurations for the composite filter are suggested.
Four-branch Star Hybrid Power Filter for Three-phase Four-wire Systems
DEFF Research Database (Denmark)
Blaabjerg, Frede; Teodorescu, Remus; Rodriguez, Pedro
2008-01-01
This paper presents a new concept for filtering current harmonics in three-phase four-wire networks. The four-branch star (FBS) filtering topology presented in this work is characterized by a particular layout consisting of single-phase inductances and capacitors. Via this layout, a power filter,...
Dip filters; Filtros de echado recursivos
Energy Technology Data Exchange (ETDEWEB)
Cabrales Vargas, A.; Chavez Perez, S. [Facultad de Ingenieria, UNAM, Mexico, D.F. (Mexico)
2002-09-01
In exploration seismology, dip filters are used to enhance subsoil images by attenuating coherent noise and other signals. They can be applied in frequency-wavenumber (f-k), frequency-distance (f-x), time-wavenumber (t-k) or time distance (t-k) domains. Fourier domain assumes constant dips. Recursive dip filters are applied in t-x domain, as they do not have this limitation. However, we have to determine their optimal parameters by trial and error. Recursive dip filters are based on single order Butterworth filters, by adding the wavenumber. Their amplitude spectrum is a surface. We perform a bilinear transform to digitize the filter and pass from the f-k to the t-k domain. We obtain the t-x domain filter by inverse transforming through wavenumber and by using a three-coefficient approximation (leading to a tridiagonal matrix). For the sake of illustration in geophysical engineering, we apply these filters to a shallow field record, to attenuate the air wave and random noise, and to a marine seismic section to enhance a fault zone. Both examples show that these filters are useful and practical to enhance seismic data. Their use is easier and more economical than median filters, utilized nowadays in commercial software for the oil industry. [Spanish] En sismologia de exploracion, los filtros de echado se utilizan para enfatizar imagenes del subsuelo, atenuado ruido coherente y otras senales. Pueden aplicarse en los dominios de frecuencia y numero de onda (f-k), frecuencia y distancia (f-x), tiempo y numero de onda (t-k) o tiempo y distancia (t-x). En el dominio de Fourier suponemos echados constantes. Los filtros de echado recursivos se aplican en el dominio t-x, careciendo de esta limitante. Sin embargo, tenemos que recurrir al ensayo y error para determinar sus parametros optimos. Los filtros de hecho recursivos se basan en filtros de Butterworth de orden uno, anadiendo el numero de onda. Su espectro de amplitud es una superficie. Utilizamos la trasformada
Inverse problem in hydrogeology
Carrera, Jesús; Alcolea, Andrés; Medina, Agustín; Hidalgo, Juan; Slooten, Luit J.
2005-03-01
The state of the groundwater inverse problem is synthesized. Emphasis is placed on aquifer characterization, where modelers have to deal with conceptual model uncertainty (notably spatial and temporal variability), scale dependence, many types of unknown parameters (transmissivity, recharge, boundary conditions, etc.), nonlinearity, and often low sensitivity of state variables (typically heads and concentrations) to aquifer properties. Because of these difficulties, calibration cannot be separated from the modeling process, as it is sometimes done in other fields. Instead, it should be viewed as one step in the process of understanding aquifer behavior. In fact, it is shown that actual parameter estimation methods do not differ from each other in the essence, though they may differ in the computational details. It is argued that there is ample room for improvement in groundwater inversion: development of user-friendly codes, accommodation of variability through geostatistics, incorporation of geological information and different types of data (temperature, occurrence and concentration of isotopes, age, etc.), proper accounting of uncertainty, etc. Despite this, even with existing codes, automatic calibration facilitates enormously the task of modeling. Therefore, it is contended that its use should become standard practice. L'état du problème inverse des eaux souterraines est synthétisé. L'accent est placé sur la caractérisation de l'aquifère, où les modélisateurs doivent jouer avec l'incertitude des modèles conceptuels (notamment la variabilité spatiale et temporelle), les facteurs d'échelle, plusieurs inconnues sur différents paramètres (transmissivité, recharge, conditions aux limites, etc.), la non linéarité, et souvent la sensibilité de plusieurs variables d'état (charges hydrauliques, concentrations) des propriétés de l'aquifère. A cause de ces difficultés, le calibrage ne peut êtreséparé du processus de modélisation, comme c'est le
Zhang, Dongliang
2013-01-01
To increase the illumination of the subsurface and to eliminate the dependency of FWI on the source wavelet, we propose multiples waveform inversion (MWI) that transforms each hydrophone into a virtual point source with a time history equal to that of the recorded data. These virtual sources are used to numerically generate downgoing wavefields that are correlated with the backprojected surface-related multiples to give the migration image. Since the recorded data are treated as the virtual sources, knowledge of the source wavelet is not required, and the subsurface illumination is greatly enhanced because the entire free surface acts as an extended source compared to the radiation pattern of a traditional point source. Numerical tests on the Marmousi2 model show that the convergence rate and the spatial resolution of MWI is, respectively, faster and more accurate then FWI. The potential pitfall with this method is that the multiples undergo more than one roundtrip to the surface, which increases attenuation and reduces spatial resolution. This can lead to less resolved tomograms compared to conventional FWI. The possible solution is to combine both FWI and MWI in inverting for the subsurface velocity distribution.
Far infrared interference filters.
Varma, S P; Möller, K D
1969-08-01
Capacitive meshes for far ir, low pass filters are prepared from Cu layers on 2.5 micro plastic film. The properties of these meshes of different mesh constants g with their different combinations in two-mesh, fourmesh, and eight-mesh filters are studied in the spectral region 160 cm(-1) to 10 cm(-1) by the use of a grating spectrometer. The applications of these meshes as low pass filters in the far ir spectral region in a grating spectrometer are described.
Chen, Wai-Kai
2003-01-01
A bestseller in its first edition, The Circuits and Filters Handbook has been thoroughly updated to provide the most current, most comprehensive information available in both the classical and emerging fields of circuits and filters, both analog and digital. This edition contains 29 new chapters, with significant additions in the areas of computer-aided design, circuit simulation, VLSI circuits, design automation, and active and digital filters. It will undoubtedly take its place as the engineer's first choice in looking for solutions to problems encountered in the design, analysis, and behavi
Ozenbaugh, Richard Lee
2011-01-01
With today's electrical and electronics systems requiring increased levels of performance and reliability, the design of robust EMI filters plays a critical role in EMC compliance. Using a mix of practical methods and theoretical analysis, EMI Filter Design, Third Edition presents both a hands-on and academic approach to the design of EMI filters and the selection of components values. The design approaches covered include matrix methods using table data and the use of Fourier analysis, Laplace transforms, and transfer function realization of LC structures. This edition has been fully revised
Modular theory of inverse systems
1979-01-01
The relationship between multivariable zeros and inverse systems was explored. A definition of zero module is given in such a way that it is basis independent. The existence of essential right and left inverses were established. The way in which the abstract zero module captured previous definitions of multivariable zeros is explained and examples are presented.
Inverse problems for Maxwell's equations
Romanov, V G
1994-01-01
The Inverse and Ill-Posed Problems Series is a series of monographs publishing postgraduate level information on inverse and ill-posed problems for an international readership of professional scientists and researchers. The series aims to publish works which involve both theory and applications in, e.g., physics, medicine, geophysics, acoustics, electrodynamics, tomography, and ecology.
Inverse comorbidity in multiple sclerosis
DEFF Research Database (Denmark)
Thormann, Anja; Koch-Henriksen, Nils; Laursen, Bjarne
2016-01-01
Background Inverse comorbidity is disease occurring at lower rates than expected among persons with a given index disease. The objective was to identify inverse comorbidity in MS. Methods We performed a combined case-control and cohort study in a total nationwide cohort of cases with clinical ons...
Novel Control Strategy for VSI and CSI Active Filters and Comparing These Two Types of Filters
Directory of Open Access Journals (Sweden)
Gholam Reza Arab
2014-10-01
Full Text Available Recently to eliminate the harmonics and improve the power factor of the power networks, much attention has been attracted to active filters. The advantages of these filters are lower volume and their better compensating characteristics than the passive filters. In conventional sliding mode controllers, the source current waveform is fluctuated in near to zero values. In this paper, using a new sliding technique, lower Total Harmonic Distortion (THD in source current is obtained and the current waveform is improved. As well as, two novel control strategies for two types of active filters, VSI and CSI is proposed and then these two types of filters are compared to reduce THD value of source current.The proposed controlled strategies are simulated by MATLAB/Simulink. The Simulation results confirm that the proposed strategies reduce the THD of source current more than other strategies, and active filter based on CSI has a better performance than active filter based on VSI with a dead time area (for avoiding short circuit of the source in high powers.
Bhanot, Gyan [Princeton, NJ; Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Takken, Todd E [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY
2009-09-08
Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.
A grid-voltage-sensorless resistive active power filter with series LC-filter
DEFF Research Database (Denmark)
Bai, Haofeng; Wang, Xiongfei; Blaabjerg, Frede
2017-01-01
distribution network, for which the voltage sensors are needed in order to obtain the current reference. In this paper a grid-voltage-sensorless control strategy is proposed for the R-APF with series LC-filter. Unlike the traditional resistance emulation method, this proposed control method re...
Algebraic properties of generalized inverses
Cvetković‐Ilić, Dragana S
2017-01-01
This book addresses selected topics in the theory of generalized inverses. Following a discussion of the “reverse order law” problem and certain problems involving completions of operator matrices, it subsequently presents a specific approach to solving the problem of the reverse order law for {1} -generalized inverses. Particular emphasis is placed on the existence of Drazin invertible completions of an upper triangular operator matrix; on the invertibility and different types of generalized invertibility of a linear combination of operators on Hilbert spaces and Banach algebra elements; on the problem of finding representations of the Drazin inverse of a 2x2 block matrix; and on selected additive results and algebraic properties for the Drazin inverse. In addition to the clarity of its content, the book discusses the relevant open problems for each topic discussed. Comments on the latest references on generalized inverses are also included. Accordingly, the book will be useful for graduate students, Ph...
2000-01-01
28. I Kohila keskkoolis kohaspetsiifiline skulptuur ja performance "Filter". Kooli 130. aastapäeva tähistava ettevõtmise eesotsas oli skulptor Paul Rodgers ja kaks viimase klassi noormeest ئ Marko Heinmäe, Hendrik Karm.
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).
DEFF Research Database (Denmark)
D'Agostino, Maria-Antonietta; Boers, Maarten; Kirwan, John
2014-01-01
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Filter provides a framework for the validation of outcome measures for use in rheumatology clinical research. However, imaging and biochemical measures may face additional validation challenges because of their technical nature. The Imagin...
Ke, Yougang; Liu, Zhenxing; Liu, Yachao; Zhou, Junxiao; Shu, Weixing; Luo, Hailu; Wen, Shuangchun
2016-10-01
In this letter, we propose and experimentally demonstrate a compact photonic spin filter formed by integrating a Pancharatnam-Berry phase lens (focal length of ±f ) into a conventional plano-concave lens (focal length of -f). By choosing the input port of the filter, photons with a desired spin state, such as the right-handed component or the left-handed one, propagate alone its original propagation direction, while the unwanted spin component is quickly diverged after passing through the filter. One application of the filter, sorting the spin-dependent components of vector vortex beams on higher-order Poincaré sphere, is also demonstrated. Our scheme provides a simple method to manipulate light, and thereby enables potential applications for photonic devices.
Term frequency inverse document frequency (TF-IDF) technique and ...
African Journals Online (AJOL)
Term frequency inverse document frequency (TF-IDF) technique and artificial neural network in email classification system. ... This has inspired attention for urgent need to manage and maintain e-mail. Email messages are expected to be sent and gathered in a warehouse for recurring use as it ranges from inert institutional ...
Lenczewski, Romuald
2001-01-01
By introducing a color filtration to the multiplicity space, we extend the quantum Ito calculus on multiple symmetric Fock space to the framework of filtered adapted biprocesses. In this new notion of adaptedness,``classical'' time filtration makes the integrands similar to adapted processes, whereas ``quantum'' color filtration produces their deviations from adaptedness. An important feature of this calculus, which we call filtered stochastic calculus, is that it provides an explicit interpo...
Directory of Open Access Journals (Sweden)
Calvez V.
2010-12-01
Full Text Available We consider the radiative transfer equation (RTE with reflection in a three-dimensional domain, infinite in two dimensions, and prove an existence result. Then, we study the inverse problem of retrieving the optical parameters from boundary measurements, with help of existing results by Choulli and Stefanov. This theoretical analysis is the framework of an attempt to model the color of the skin. For this purpose, a code has been developed to solve the RTE and to study the sensitivity of the measurements made by biophysicists with respect to the physiological parameters responsible for the optical properties of this complex, multi-layered material. On étudie l’équation du transfert radiatif (ETR dans un domaine tridimensionnel infini dans deux directions, et on prouve un résultat d’existence. On s’intéresse ensuite à la reconstruction des paramètres optiques à partir de mesures faites au bord, en s’appuyant sur des résultats de Choulli et Stefanov. Cette analyse sert de cadre théorique à un travail de modélisation de la couleur de la peau. Dans cette perspective, un code à été développé pour résoudre l’ETR et étudier la sensibilité des mesures effectuées par les biophysiciens par rapport aux paramètres physiologiques tenus pour responsables des propriétés optiques de ce complexe matériau multicouche.
Filtering for Copyright Enforcement in Europe after the Sabam cases
Kulk, S.; Zuiderveen Borgesius, F.
2012-01-01
Sabam, a Belgian collective rights management organisation, wanted an internet access provider and a social network site to install a filter system to enforce copyrights. In two recent judgments, the Court of Justice of the European Union decided that the social network site and the internet access
Optimized Multichannel Filter Bank with Flat Frequency Response for Texture Segmentation
Directory of Open Access Journals (Sweden)
Kachouie Nezamoddin N
2005-01-01
Full Text Available Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT, and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed.
Optimized Multichannel Filter Bank with Flat Frequency Response for Texture Segmentation
Kachouie, Nezamoddin N.; Alirezaie, Javad
2005-12-01
Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed.
Stelman, David
1989-01-01
A contactor/filter arrangement for removing particulate contaminants from a gaseous stream includes a housing having a substantially vertically oriented granular material retention member with upstream and downstream faces, a substantially vertically oriented microporous gas filter element, wherein the retention member and the filter element are spaced apart to provide a zone for the passage of granular material therethrough. The housing further includes a gas inlet means, a gas outlet means, and means for moving a body of granular material through the zone. A gaseous stream containing particulate contaminants passes through the gas inlet means as well as through the upstream face of the granular material retention member, passing through the retention member, the body of granular material, the microporous gas filter element, exiting out of the gas outlet means. Disposed on the upstream face of the filter element is a cover screen which isolates the filter element from contact with the moving granular bed and collects a portion of the particulates so as to form a dust cake having openings small enough to exclude the granular material, yet large enough to receive the dust particles. In one embodiment, the granular material is comprised of prous alumina impregnated with CuO, with the cover screen cleaned by the action of the moving granular material as well as by backflow pressure pulses.
Directory of Open Access Journals (Sweden)
Eloísa Berbel Manaia
2013-06-01
Full Text Available Nowadays, concern over skin cancer has been growing more and more, especially in tropical countries where the incidence of UVA/B radiation is higher. The correct use of sunscreen is the most efficient way to prevent the development of this disease. The ingredients of sunscreen can be organic and/or inorganic sun filters. Inorganic filters present some advantages over organic filters, such as photostability, non-irritability and broad spectrum protection. Nevertheless, inorganic filters have a whitening effect in sunscreen formulations owing to the high refractive index, decreasing their esthetic appeal. Many techniques have been developed to overcome this problem and among them, the use of nanotechnology stands out. The estimated amount of nanomaterial in use must increase from 2000 tons in 2004 to a projected 58000 tons in 2020. In this context, this article aims to analyze critically both the different features of the production of inorganic filters (synthesis routes proposed in recent years and the permeability, the safety and other characteristics of the new generation of inorganic filters.
Conditioning the full-waveform inversion gradient to welcome anisotropy
Alkhalifah, Tariq Ali
2015-04-23
Multiparameter full-waveform inversion (FWI) suffers from complex nonlinearity in the objective function, compounded by the eventual trade-off between the model parameters. A hierarchical approach based on frequency and arrival time data decimation to maneuver the complex nonlinearity associated with this problem usually falls short in anisotropic media. In place of data decimation, I use a model gradient filter approach to access the parts of the gradient more suitable to combat the potential nonlinearity and parameter trade-off. The filter is based on representing the gradient in the time-lag normalized domain, in which small scattering-angles of the gradient update are initially muted out. The model update hierarchical filtering strategy include applying varying degrees of filtering to the different anisotropic parameter updates, a feature not easily accessible to simple data decimation. Using FWI and reflection-based FWI, when the modeled data are obtained with the single-scattering theory, allows access to additional low model wavenumber components. Combining such access to wavenumbers with scattering-angle filters applied to the individual parameter gradients allows for multiple strategies to avoid complex FWI nonlinearity as well as the parameter trade-off.
Depth Images Filtering In Distributed Streaming
Directory of Open Access Journals (Sweden)
Dziubich Tomasz
2016-04-01
Full Text Available In this paper, we propose a distributed system for point cloud processing and transferring them via computer network regarding to effectiveness-related requirements. We discuss the comparison of point cloud filters focusing on their usage for streaming optimization. For the filtering step of the stream pipeline processing we evaluate four filters: Voxel Grid, Radial Outliner Remover, Statistical Outlier Removal and Pass Through. For each of the filters we perform a series of tests for evaluating the impact on the point cloud size and transmitting frequency (analysed for various fps ratio. We present results of the optimization process used for point cloud consolidation in a distributed environment. We describe the processing of the point clouds before and after the transmission. Pre- and post-processing allow the user to send the cloud via network without any delays. The proposed pre-processing compression of the cloud and the post-processing reconstruction of it are focused on assuring that the end-user application obtains the cloud with a given precision.
Analyzing Meteoroid Flights Using Particle Filters
Sansom, E. K.; Rutten, M. G.; Bland, P. A.
2017-02-01
Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a novel approach to modeling the uncertainty in meteoroid trajectories and incorporates errors in initial parameters, the dynamical model used, and observed position measurements. Unlike other stochastic approaches, a particle filter does not require predefined values for initial conditions or unobservable trajectory parameters. The Bunburra Rockhole fireball, observed by the Australian Desert Fireball Network (DFN) in 2007, is used to determine the effectiveness of a particle filter for use in fireball trajectory modeling. The final mass is determined to be 2.16+/- 1.33 {kg} with a final velocity of 6030+/- 216 {{m}} {{{s}}}-1, similar to previously calculated values. The full automatability of this approach will allow an unbiased evaluation of all events observed by the DFN and lead to a better understanding of the dynamical state and size frequency distribution of asteroid and cometary debris in the inner solar system.
DNN Filter Bank Cepstral Coefficients for Spoofing Detection
DEFF Research Database (Denmark)
Yu, Hong; Tan, Zheng-Hua; Zhang, Yiming
2017-01-01
With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank-based cepstral feature, deep neural network (DNN) filter ban...... 2015 database show that the Gaussian mixture model maximum-likelihood classifier trained by the new feature performs better than the state-of-the-art linear frequency triangle filter bank cepstral coefficients-based classifier, especially on detecting unknown attacks.......With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank-based cepstral feature, deep neural network (DNN) filter bank...... cepstral coefficients, to distinguish between natural and spoofed speech. The DNN filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band limited...
Comparison of Deconvolution Filters for Photoacoustic Tomography.
Directory of Open Access Journals (Sweden)
Dominique Van de Sompel
Full Text Available In this work, we compare the merits of three temporal data deconvolution methods for use in the filtered backprojection algorithm for photoacoustic tomography (PAT. We evaluate the standard Fourier division technique, the Wiener deconvolution filter, and a Tikhonov L-2 norm regularized matrix inversion method. Our experiments were carried out on subjects of various appearances, namely a pencil lead, two man-made phantoms, an in vivo subcutaneous mouse tumor model, and a perfused and excised mouse brain. All subjects were scanned using an imaging system with a rotatable hemispherical bowl, into which 128 ultrasound transducer elements were embedded in a spiral pattern. We characterized the frequency response of each deconvolution method, compared the final image quality achieved by each deconvolution technique, and evaluated each method's robustness to noise. The frequency response was quantified by measuring the accuracy with which each filter recovered the ideal flat frequency spectrum of an experimentally measured impulse response. Image quality under the various scenarios was quantified by computing noise versus resolution curves for a point source phantom, as well as the full width at half maximum (FWHM and contrast-to-noise ratio (CNR of selected image features such as dots and linear structures in additional imaging subjects. It was found that the Tikhonov filter yielded the most accurate balance of lower and higher frequency content (as measured by comparing the spectra of deconvolved impulse response signals to the ideal flat frequency spectrum, achieved a competitive image resolution and contrast-to-noise ratio, and yielded the greatest robustness to noise. While the Wiener filter achieved a similar image resolution, it tended to underrepresent the lower frequency content of the deconvolved signals, and hence of the reconstructed images after backprojection. In addition, its robustness to noise was poorer than that of the Tikhonov
CO2 Network Design for Washington DC/Baltimore
Lopez-Coto, I.; Prasad, K.; Ghosh, S.; Whetstone, J. R.
2015-12-01
The North-East Corridor project aims to use a top-down inversion method to quantify sources of Greenhouse Gas (GHG) emissions in the urban areas of Washington DC and Baltimore at approximately 1km2 resolutions. The aim of this project is to help establish reliable measurement methods for quantifying and validating GHG emissions independently of the inventory methods typically used to guide mitigation efforts. Since inversion methods depend on atmospheric observations of GHG, deploying a suitable network of ground-based measurement stations is a fundamental step in estimating emissions from the perspective of the atmosphere with reasonable levels of uncertainty. The purpose of this work is to design a tower based network of measurement stations that can reduce the uncertainty in emissions by 50% in the central areas of DC and Baltimore. To this end, the Weather Research and Forecasting Model (WRF-ARW) was used along with the Stochastic Time-Inverted Lagrangian Transport model (STILT) to derive the sensitivity of hypothetical observations to surface emissions (footprints) for the months of February and July 2013. An iterative selection algorithm, based on k-means clustering method, was applied in order to minimize the similarities between the temporal response of each site and maximize the urban contribution. Afterwards, a synthetic inversion Kalman Filter was used to evaluate the performances of the observing system based on the merit of the retrieval over time and the amount of a priori uncertainty reduced by the network. We present the performances of various measurement networks that consist of different number of towers and where the location of these towers vary. Results show that too compact networks lose spatial coverage whilst too spread networks lose capabilities of constraining uncertainties in the fluxes. In addition, we explore the possibility of using a very high density network of low-cost, low-accuracy sensors characterized by larger uncertainties and
A Generalization of the Spherical Inversion
Ramírez, José L.; Rubiano, Gustavo N.
2017-01-01
In the present article, we introduce a generalization of the spherical inversion. In particular, we define an inversion with respect to an ellipsoid, and prove several properties of this new transformation. The inversion in an ellipsoid is the generalization of the elliptic inversion to the three-dimensional space. We also study the inverse images…
Inverse Doppler Effects in Flute
Zhao, Xiao P; Liu, Song; Shen, Fang L; Li, Lin L; Luo, Chun R
2015-01-01
Here we report the observation of the inverse Doppler effects in a flute. It is experimentally verified that, when there is a relative movement between the source and the observer, the inverse Doppler effect could be detected for all seven pitches of a musical scale produced by a flute. Higher tone is associated with a greater shift in frequency. The effect of the inverse frequency shift may provide new insights into why the flute, with its euphonious tone, has been popular for thousands of years in Asia and Europe.
Adaptive Filtering Queueing for Improving Fairness
Directory of Open Access Journals (Sweden)
Jui-Pin Yang
2015-06-01
Full Text Available In this paper, we propose a scalable and efficient Active Queue Management (AQM scheme to provide fair bandwidth sharing when traffic is congested dubbed Adaptive Filtering Queueing (AFQ. First, AFQ identifies the filtering level of an arriving packet by comparing it with a flow label selected at random from the first level to an estimated level in the filtering level table. Based on the accepted traffic estimation and the previous fair filtering level, AFQ updates the fair filtering level. Next, AFQ uses a simple packet-dropping algorithm to determine whether arriving packets are accepted or discarded. To enhance AFQ’s feasibility in high-speed networks, we propose a two-layer mapping mechanism to effectively simplify the packet comparison operations. Simulation results demonstrate that AFQ achieves optimal fairness when compared with Rotating Preference Queues (RPQ, Core-Stateless Fair Queueing (CSFQ, CHOose and Keep for responsive flows, CHOose and Kill for unresponsive flows (CHOKe and First-In First-Out (FIFO schemes under a variety of traffic conditions.
Breaking the filter bubble: Democracy and design
Bozdag, E.; van den Hoven, M.J.
2015-01-01
It has been argued that the Internet and social media increase the number of available viewpoints, perspectives, ideas and opinions available, leading to a very diverse pool of information. However, critics have argued that algorithms used by search engines, social networking platforms and other large online intermediaries actually decrease information diversity by forming so-called “filter bubbles”. This may form a serious threat to our democracies. In response to this threat others have dev...
Choosing and using astronomical filters
Griffiths, Martin
2014-01-01
As a casual read through any of the major amateur astronomical magazines will demonstrate, there are filters available for all aspects of optical astronomy. This book provides a ready resource on the use of the following filters, among others, for observational astronomy or for imaging: Light pollution filters Planetary filters Solar filters Neutral density filters for Moon observation Deep-sky filters, for such objects as galaxies, nebulae and more Deep-sky objects can be imaged in much greater detail than was possible many years ago. Amateur astronomers can take
Intelligent investment; Inversion inteligente
Energy Technology Data Exchange (ETDEWEB)
NONE
2007-06-15
In this presentation the company called Energia Renovable De Mexico SA de CV (ERDM), shows not only its obtained objectives but also its wanted objectives. This company is manufacturer and consultant of photovoltaic modules. In the first part, it is given a description of the following issues: the beginnings the company, the implemented marketing strategy, the signed agreement between ERDM and Q-CELLS AG in German, the construction of the San Andres Tuxtla's office as well as the PV module, the reasons why this company is considered a leader not only in Mexico but also in Latin America. Then, It is briefly explained the company's mission, which is mainly focused on the network-connected system that are currently allowed according to the Mexican laws. Besides, there are mentioned the key pieces that have made possible the success of this company. At the same time, there are briefly explained the plans for Mexico, in which there are found the use of both photovoltaic systems and wind turbines in order to feed the electric network. Such plans have as targets to reduce the energy cost in Mexico and to open the profitable market to potential investors. Finally, there are mentioned the future plans that are going to help the company's expansion and to improve some issues related to the energy. [Spanish] En esta presentacion la compania Energia Renovable De Mexico S.A. de C.V. (ERDM), describe tanto los objetivos alcanzados como los que desean alcanzar en el futuro, fungiendo no solo como fabricantes sino tambien como consultores de modulos fotovoltaicos. En la primera parte, se da una descripcion de: los inicios de la compania, las estrategias mercadologicas utilizadas, el acuerdo con Q-CELLS, Alemania; la construccion de la oficina de San Andres Tuxtla y del modulo PV, las causas que la han llevado a ser una empresa lider. Enseguida, se explica escuetamente la mision de la compania; ademas, se mencionan las piezas clave que la han llevado al exito
Filters for cathodic arc plasmas
Anders, Andre; MacGill, Robert A.; Bilek, Marcela M. M.; Brown, Ian G.
2002-01-01
Cathodic arc plasmas are contaminated with macroparticles. A variety of magnetic plasma filters has been used with various success in removing the macroparticles from the plasma. An open-architecture, bent solenoid filter, with additional field coils at the filter entrance and exit, improves macroparticle filtering. In particular, a double-bent filter that is twisted out of plane forms a very compact and efficient filter. The coil turns further have a flat cross-section to promote macroparticle reflection out of the filter volume. An output conditioning system formed of an expander coil, a straightener coil, and a homogenizer, may be used with the magnetic filter for expanding the filtered plasma beam to cover a larger area of the target. A cathodic arc plasma deposition system using this filter can be used for the deposition of ultrathin amorphous hard carbon (a-C) films for the magnetic storage industry.
Sensory pollution from bag filters, carbon filters and combinations.
Bekö, G; Clausen, G; Weschler, C J
2008-02-01
Used ventilation filters are a major source of sensory pollutants in air handling systems. The objective of the present study was to evaluate the net effect that different combinations of filters had on perceived air quality after 5 months of continuous filtration of outdoor suburban air. A panel of 32 subjects assessed different sets of used filters and identical sets consisting of new filters. Additionally, filter weights and pressure drops were measured at the beginning and end of the operation period. The filter sets included single EU5 and EU7 fiberglass filters, an EU7 filter protected by an upstream pre-filter (changed monthly), an EU7 filter protected by an upstream activated carbon (AC) filter, and EU7 filters with an AC filter either downstream or both upstream and downstream. In addition, two types of stand-alone combination filters were evaluated: a bag-type fiberglass filter that contained AC and a synthetic fiber cartridge filter that contained AC. Air that had passed through used filters was most acceptable for those sets in which an AC filter was used downstream of the particle filter. Comparable air quality was achieved with the stand-alone bag filter that contained AC. Furthermore, its pressure drop changed very little during the 5 months of service, and it had the added benefit of removing a large fraction of ozone from the airstream. If similar results are obtained over a wider variety of soiling conditions, such filters may be a viable solution to a long recognized problem. The present study was designed to address the emission of sensory offending pollutants from loaded ventilation filters. The goal was to find a low-polluting solution from commercially available products. The results indicate that the use of activated carbon (AC) filters downstream of fiberglass bag filters can reduce the degradation of air quality that occurs with increasing particle loading. A more practical solution, yet comparably effective, is a stand-alone particle
Multilevel ensemble Kalman filtering
Hoel, Haakon
2016-01-08
The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.
Multilevel Mixture Kalman Filter
Directory of Open Access Journals (Sweden)
Xiaodong Wang
2004-11-01
Full Text Available The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is quite high if the dimension of the indicator sampling space is high. In this paper, we address this difficulty by developing a new Monte Carlo sampling scheme, namely, the multilevel mixture Kalman filter. The basic idea is to make use of the multilevel or hierarchical structure of the space from which the indicator variables take values. That is, we draw samples in a multilevel fashion, beginning with sampling from the highest-level sampling space and then draw samples from the associate subspace of the newly drawn samples in a lower-level sampling space, until reaching the desired sampling space. Such a multilevel sampling scheme can be used in conjunction with the delayed estimation method, such as the delayed-sample method, resulting in delayed multilevel mixture Kalman filter. Examples in wireless communication, specifically the coherent and noncoherent 16-QAM over flat-fading channels, are provided to demonstrate the performance of the proposed multilevel mixture Kalman filter.
Testing earthquake source inversion methodologies
Page, Morgan T.
2011-01-01
Source Inversion Validation Workshop; Palm Springs, California, 11-12 September 2010; Nowadays earthquake source inversions are routinely performed after large earthquakes and represent a key connection between recorded seismic and geodetic data and the complex rupture process at depth. The resulting earthquake source models quantify the spatiotemporal evolution of ruptures. They are also used to provide a rapid assessment of the severity of an earthquake and to estimate losses. However, because of uncertainties in the data, assumed fault geometry and velocity structure, and chosen rupture parameterization, it is not clear which features of these source models are robust. Improved understanding of the uncertainty and reliability of earthquake source inversions will allow the scientific community to use the robust features of kinematic inversions to more thoroughly investigate the complexity of the rupture process and to better constrain other earthquakerelated computations, such as ground motion simulations and static stress change calculations.
Statistical perspectives on inverse problems
DEFF Research Database (Denmark)
Andersen, Kim Emil
of the interior of an object from electrical boundary measurements. One part of this thesis concerns statistical approaches for solving, possibly non-linear, inverse problems. Thus inverse problems are recasted in a form suitable for statistical inference. In particular, a Bayesian approach for regularisation...... is obtained by assuming that the a priori beliefs about the solution before having observed any data can be described by a prior distribution. The solution to the statistical inverse problem is then given by the posterior distribution obtained by Bayes' formula. Hence the solution of an ill-posed inverse...... problem is given in terms of probability distributions. Posterior inference is obtained by Markov chain Monte Carlo methods and new, powerful simulation techniques based on e.g. coupled Markov chains and simulated tempering is developed to improve the computational efficiency of the overall simulation...
Parameter estimation and inverse problems
Aster, Richard C; Thurber, Clifford H
2005-01-01
Parameter Estimation and Inverse Problems primarily serves as a textbook for advanced undergraduate and introductory graduate courses. Class notes have been developed and reside on the World Wide Web for faciliting use and feedback by teaching colleagues. The authors'' treatment promotes an understanding of fundamental and practical issus associated with parameter fitting and inverse problems including basic theory of inverse problems, statistical issues, computational issues, and an understanding of how to analyze the success and limitations of solutions to these probles. The text is also a practical resource for general students and professional researchers, where techniques and concepts can be readily picked up on a chapter-by-chapter basis.Parameter Estimation and Inverse Problems is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who may not have an extensive mathematical background. It is accompanied by a Web site that...
Filters for Submillimeter Electromagnetic Waves
Berdahl, C. M.
1986-01-01
New manufacturing process produces filters strong, yet have small, precise dimensions and smooth surface finish essential for dichroic filtering at submillimeter wavelengths. Many filters, each one essentially wafer containing fine metal grid made at same time. Stacked square wires plated, fused, and etched to form arrays of holes. Grid of nickel and tin held in brass ring. Wall thickness, thickness of filter (hole depth) and lateral hole dimensions all depend upon operating frequency and filter characteristics.
Coin tossing and Laplace inversion
Indian Academy of Sciences (India)
of a probability measure " on Е0Y 1К via the obvious change of variables e└t И xX An inversion formula for " in terms of its moments yields an inversion formula for # in terms of the values of its Laplace transform at n И 0Y 1Y 2Y ... and vice versa. In our discussion we allow " (respectively #) to have positive mass at 0 ...
Thermal measurements and inverse techniques
Orlande, Helcio RB; Maillet, Denis; Cotta, Renato M
2011-01-01
With its uncommon presentation of instructional material regarding mathematical modeling, measurements, and solution of inverse problems, Thermal Measurements and Inverse Techniques is a one-stop reference for those dealing with various aspects of heat transfer. Progress in mathematical modeling of complex industrial and environmental systems has enabled numerical simulations of most physical phenomena. In addition, recent advances in thermal instrumentation and heat transfer modeling have improved experimental procedures and indirect measurements for heat transfer research of both natural phe
Generation of Long Waves using Non-Linear Digital Filters
DEFF Research Database (Denmark)
Høgedal, Michael; Frigaard, Peter; Christensen, Morten
1994-01-01
transform of the 1st order surface elevation and subsequently inverse Fourier transformed. Hence, the methods are unsuitable for real-time applications, for example where white noise are filtered digitally to obtain a wave spectrum with built-in stochastic variabillity. In the present paper an approximative...... method for including the correct 2nd order bound terms in such applications is presented. The technique utilizes non-liner digital filters fitted to the appropriate transfer function is derived only for bounded 2nd order subharmonics, as they laboratory experiments generally are considered the most...
Vibrato in Singing Voice: The Link between Source-Filter and Sinusoidal Models
Directory of Open Access Journals (Sweden)
Arroabarren Ixone
2004-01-01
Full Text Available The application of inverse filtering techniques for high-quality singing voice analysis/synthesis is discussed. In the context of source-filter models, inverse filtering provides a noninvasive method to extract the voice source, and thus to study voice quality. Although this approach is widely used in speech synthesis, this is not the case in singing voice. Several studies have proved that inverse filtering techniques fail in the case of singing voice, the reasons being unclear. In order to shed light on this problem, we will consider here an additional feature of singing voice, not present in speech: the vibrato. Vibrato has been traditionally studied by sinusoidal modeling. As an alternative, we will introduce here a novel noninteractive source filter model that incorporates the mechanisms of vibrato generation. This model will also allow the comparison of the results produced by inverse filtering techniques and by sinusoidal modeling, as they apply to singing voice and not to speech. In this way, the limitations of these conventional techniques, described in previous literature, will be explained. Both synthetic signals and singer recordings are used to validate and compare the techniques presented in the paper.
Vibrato in Singing Voice: The Link between Source-Filter and Sinusoidal Models
Arroabarren, Ixone; Carlosena, Alfonso
2004-12-01
The application of inverse filtering techniques for high-quality singing voice analysis/synthesis is discussed. In the context of source-filter models, inverse filtering provides a noninvasive method to extract the voice source, and thus to study voice quality. Although this approach is widely used in speech synthesis, this is not the case in singing voice. Several studies have proved that inverse filtering techniques fail in the case of singing voice, the reasons being unclear. In order to shed light on this problem, we will consider here an additional feature of singing voice, not present in speech: the vibrato. Vibrato has been traditionally studied by sinusoidal modeling. As an alternative, we will introduce here a novel noninteractive source filter model that incorporates the mechanisms of vibrato generation. This model will also allow the comparison of the results produced by inverse filtering techniques and by sinusoidal modeling, as they apply to singing voice and not to speech. In this way, the limitations of these conventional techniques, described in previous literature, will be explained. Both synthetic signals and singer recordings are used to validate and compare the techniques presented in the paper.
Kovačević, Branko; Milosavljević, Milan
2013-01-01
“Adaptive Digital Filters” presents an important discipline applied to the domain of speech processing. The book first makes the reader acquainted with the basic terms of filtering and adaptive filtering, before introducing the field of advanced modern algorithms, some of which are contributed by the authors themselves. Working in the field of adaptive signal processing requires the use of complex mathematical tools. The book offers a detailed presentation of the mathematical models that is clear and consistent, an approach that allows everyone with a college level of mathematics knowledge to successfully follow the mathematical derivations and descriptions of algorithms. The algorithms are presented in flow charts, which facilitates their practical implementation. The book presents many experimental results and treats the aspects of practical application of adaptive filtering in real systems, making it a valuable resource for both undergraduate and graduate students, and for all others interested in m...
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.
Automated electronic filter design
Banerjee, Amal
2017-01-01
This book describes a novel, efficient and powerful scheme for designing and evaluating the performance characteristics of any electronic filter designed with predefined specifications. The author explains techniques that enable readers to eliminate complicated manual, and thus error-prone and time-consuming, steps of traditional design techniques. The presentation includes demonstration of efficient automation, using an ANSI C language program, which accepts any filter design specification (e.g. Chebyschev low-pass filter, cut-off frequency, pass-band ripple etc.) as input and generates as output a SPICE(Simulation Program with Integrated Circuit Emphasis) format netlist. Readers then can use this netlist to run simulations with any version of the popular SPICE simulator, increasing accuracy of the final results, without violating any of the key principles of the traditional design scheme.
Simple Models of EMI Filters for Low Frequency Range
Directory of Open Access Journals (Sweden)
Z. Raida
2008-09-01
Full Text Available This paper deals with mathematical simulations of EMI filtersÃ¢Â€Â™ performance. These filters are commonly used for the suppressing of electromagnetic interference which penetrates through the power supply networks. The performance of these filters depends on terminating impedances which are plugged to the inputs and outputs clamps of the EMI filters. This paper describes the method by which it is possible to calculate the insertion loss of the filters. The method is based on the modified nodal voltage method. The circuitry of the EMI filters is used for their description. The effect of spurious components is not taken into account. The filter itself is described by set of admittance parameters, which makes the presented method more universal. The calculated results were compared with measured data of several filters for several impedance combinations. Different test setups, like asymmetrical, symmetrical, etc. were taken into account. The simplicity and accuracy of the presented method is discussed in the conclusion. The achieved accuracy is on high level. The described method is universal, but for filters with more than one current compensated inductor, the mentioned method is complicated. The size of the final equation for calculating the insertion loss rapidly increases with the number of current compensated inductors.
Filters in topology optimization
DEFF Research Database (Denmark)
Bourdin, Blaise
1999-01-01
In this article, a modified (``filtered'') version of the minimum compliance topology optimization problem is studied. The direct dependence of the material properties on its pointwise density is replaced by a regularization of the density field using a convolution operator. In this setting...... it is possible to establish the existence of solutions. Moreover, convergence of an approximation by means of finite elements can be obtained. This is illustrated through some numerical experiments. The ``filtering'' technique is also shown to cope with two important numerical problems in topology optimization...
Directory of Open Access Journals (Sweden)
M. Van Looy
1998-12-01
Full Text Available In this paper we present a switched capacitor filter design using the SC22324 1C, which is fitted with an E2PROM. It contains four digitally programmable switched-capacitor filter sections, in order to obtain different responses. The SC22324 also contains the on-chip RAM. We'd like to explain, how could the on-chip RAM controlled via a PC. In this way the chip may be used afterwards with a menu where the user may select the wanted parameters.
DEFF Research Database (Denmark)
Lehn-Schiøler, Tue; Erdogmus, Deniz; Principe, Jose C.
2004-01-01
Using a Parzen density estimator any distribution can be approximated arbitrarily close by a sum of kernels. In particle filtering this fact is utilized to estimate a probability density function with Dirac delta kernels; when the distribution is discretized it becomes possible to solve an otherw......Using a Parzen density estimator any distribution can be approximated arbitrarily close by a sum of kernels. In particle filtering this fact is utilized to estimate a probability density function with Dirac delta kernels; when the distribution is discretized it becomes possible to solve...
Chromatid Painting for Chromosomal Inversion Detection Project
National Aeronautics and Space Administration — We propose a novel approach to the detection of chromosomal inversions. Transmissible chromosome aberrations (translocations and inversions) have profound genetic...
Toward Inverse Control of Physics-Based Sound Synthesis
Pfalz, A.; Berdahl, E.
2017-05-01
Long Short-Term Memory networks (LSTMs) can be trained to realize inverse control of physics-based sound synthesizers. Physics-based sound synthesizers simulate the laws of physics to produce output sound according to input gesture signals. When a user's gestures are measured in real time, she or he can use them to control physics-based sound synthesizers, thereby creating simulated virtual instruments. An intriguing question is how to program a computer to learn to play such physics-based models. This work demonstrates that LSTMs can be trained to accomplish this inverse control task with four physics-based sound synthesizers.
Tissue elasticity measurement method using forward and inversion algorithms
Lee, Jong-Ha; Won, Chang-Hee; Park, Hee-Jun; Ku, Jeonghun; Heo, Yun Seok; Kim, Yoon-Nyun
2013-03-01
Elasticity is an important indicator of tissue health, with increased stiffness pointing to an increased risk of cancer. We investigated a tissue elasticity measurement method using forward and inversion algorithms for the application of early breast tumor identification. An optical based elasticity measurement system is developed to capture images of the embedded lesions using total internal reflection principle. From elasticity images, we developed a novel method to estimate the elasticity of the embedded lesion using 3-D finite-element-model-based forward algorithm, and neural-network-based inversion algorithm. The experimental results showed that the proposed characterization method can be diffierentiate the benign and malignant breast lesions.
A GPU-accelerated extended Kalman filter
Wei, Shih-Chieh; Huang, Bormin
2011-11-01
The extended Kalman filter is one of the most widely used techniques for state estimation of nonlinear systems. In its two steps of forecast and data assimilation, many matrix operations including multiplication and inversion are involved. As recent graphic processor units (GPU) have shown to provide much speedup in matrix operations, we will explore in this work a GPU-based implementation of the extended Kalman filter. The Compute Unified Device Architecture (CUDA) on the Nvidia GeForce GTX 590 GPU hardware will be used for comparison with a single threaded CPU counterpart. Experiments were conducted on typical large-scale over-determined systems with thousands of components in states and measurements. Within the GPU memory limit, a speedup of 1386x is achieved for a system with measurements having 5000 components and states having 3750 components. The speedup profile for various combinations of measurement and state sizes will serve as good reference for future implementation of extended Kalman filter on real large-scale applications.
Inverse carbon dioxide flux estimates for the Netherlands
Energy Technology Data Exchange (ETDEWEB)
Meesters, A.G.C.A.; Tolk, L.F.; Dolman, A.J. [Faculty of Earth and Life Sciences, VU University, Amsterdam (Netherlands); Peters, W.; Hutjes, R.W.A.; Vellinga, O.S.; Elbers, J.A. [Department Meteorology and Air Quality, Wageningen University and Research Centre, Wageningen (Netherlands); Vermeulen, A.T. [Biomass, Coal and Environmental Research, Energy research Center of the Netherlands ECN, Petten (Netherlands); Van der Laan, S.; Neubert, R.E.M.; Meijer, H.A.J. [Centre for Isotope Research, Energy and Sustainability Research Institute Groningen, University of Groningen, Groningen (Netherlands)
2012-10-26
CO2 fluxes for the Netherlands and surroundings are estimated for the year 2008, from concentration measurements at four towers, using an inverse model. The results are compared to direct CO2 flux measurements by aircraft, for 6 flight tracks over the Netherlands, flown multiple times in each season. We applied the Regional Atmospheric Mesoscale Modeling system (RAMS) coupled to a simple carbon flux scheme (including fossil fuel), which was run at 10 km resolution, and inverted with an Ensemble Kalman Filter. The domain had 6 eco-regions, and inversions were performed for the four seasons separately. Inversion methods with pixel-dependent and -independent parameters for each eco-region were compared. The two inversion methods, in general, yield comparable flux averages for each eco-region and season, whereas the difference from the prior flux may be large. Posterior fluxes co-sampled along the aircraft flight tracks are usually much closer to the observations than the priors, with a comparable performance for both inversion methods, and with best performance for summer and autumn. The inversions showed more negative CO2 fluxes than the priors, though the latter are obtained from a biosphere model optimized using the Fluxnet database, containing observations from more than 200 locations worldwide. The two different crop ecotypes showed very different CO2 uptakes, which was unknown from the priors. The annual-average uptake is practically zero for the grassland class and for one of the cropland classes, whereas the other cropland class had a large net uptake, possibly because of the abundance of maize there.
Inverse carbon dioxide flux estimates for the Netherlands
Meesters, A. G. C. A.; Tolk, L. F.; Peters, W.; Hutjes, R. W. A.; Vellinga, O. S.; Elbers, J. A.; Vermeulen, A. T.; van der Laan, S.; Neubert, R. E. M.; Meijer, H. A. J.; Dolman, A. J.
2012-10-01
CO2 fluxes for the Netherlands and surroundings are estimated for the year 2008, from concentration measurements at four towers, using an inverse model. The results are compared to direct CO2flux measurements by aircraft, for 6 flight tracks over the Netherlands, flown multiple times in each season. We applied the Regional Atmospheric Mesoscale Modeling system (RAMS) coupled to a simple carbon flux scheme (including fossil fuel), which was run at 10 km resolution, and inverted with an Ensemble Kalman Filter. The domain had 6 eco-regions, and inversions were performed for the four seasons separately. Inversion methods with pixel-dependent and -independent parameters for each eco-region were compared. The two inversion methods, in general, yield comparable flux averages for each eco-region and season, whereas the difference from the prior flux may be large. Posterior fluxes co-sampled along the aircraft flight tracks are usually much closer to the observations than the priors, with a comparable performance for both inversion methods, and with best performance for summer and autumn. The inversions showed more negative CO2 fluxes than the priors, though the latter are obtained from a biosphere model optimized using the Fluxnet database, containing observations from more than 200 locations worldwide. The two different crop ecotypes showed very different CO2uptakes, which was unknown from the priors. The annual-average uptake is practically zero for the grassland class and for one of the cropland classes, whereas the other cropland class had a large net uptake, possibly because of the abundance of maize there.
Enhanced Optical Filter Design
Cushing, David
2011-01-01
This book serves as a supplement to the classic texts by Angus Macleod and Philip Baumeister, taking an intuitive approach to the enhancement of optical coating (or filter) performance. Drawing from 40 years of experience in thin film design, Cushing introduces the basics of thin films, the commonly used materials and their deposition, the major coatings and their applications, and improvement methods for each.
Beck, H P; Boissat, C; Davis, R; Duval, P Y; Etienne, F; Fede, E; Francis, D; Green, P; Hemmer, F; Jones, R; MacKinnon, J; Mapelli, Livio P; Meessen, C; Mommsen, R K; Mornacchi, Giuseppe; Nacasch, R; Negri, A; Pinfold, James L; Polesello, G; Qian, Z; Rafflin, C; Scannicchio, D A; Stanescu, C; Touchard, F; Vercesi, V
1999-01-01
An overview of the studies for the ATLAS Event Filter is given. The architecture and the high level design of the DAQ-1 prototype is presented. The current status if the prototypes is briefly given. Finally, future plans and milestones are given. (11 refs).
2016-08-01
EXWC) performed the evaluation at the Naval Air Station Lemoore, CA . The two year evaluation period began with one year of sand filter operation...appear dirty? If you answered “ yes ” to the first question and “ yes ” to either of the other questions, investigate this technology for your
Fast Anisotropic Gauss Filters
Geusebroek, J.M.; Smeulders, A.W.M.; van de Weijer, J.
2003-01-01
We derive the decomposition of the anisotropic Gaussian in a one dimensional Gauss filter in the x-direction phi. So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a computing perspective. An implementation scheme for normal covolution and
Energy Technology Data Exchange (ETDEWEB)
Mitchell, M A; Bergman, W; Haslam, J; Brown, E P; Sawyer, S; Beaulieu, R; Althouse, P; Meike, A
2012-04-30
Potential benefits of ceramic filters in nuclear facilities: (1) Short term benefit for DOE, NRC, and industry - (a) CalPoly HTTU provides unique testing capability to answer questions for DOE - High temperature testing of materials, components, filter, (b) Several DNFSB correspondences and presentations by DNFSB members have highlighted the need for HEPA filter R and D - DNFSB Recommendation 2009-2 highlighted a nuclear facility response to an evaluation basis earthquake followed by a fire (aka shake-n-bake) and CalPoly has capability for a shake-n-bake test; (2) Intermediate term benefit for DOE and industry - (a) Filtration for specialty applications, e.g., explosive applications at Nevada, (b) Spin-off technologies applicable to other commercial industries; and (3) Long term benefit for DOE, NRC, and industry - (a) Across industry, strong desire for better performance filter, (b) Engineering solution to safety problem will improve facility safety and decrease dependence on associated support systems, (c) Large potential life-cycle cost savings, and (d) Facilitates development and deployment of LLNL process innovations to allow continuous ventilation system operation during a fire.
Rutger van Aalst; Ines Simic
2015-01-01
This paper describes a possible solution to the underwater sound filtering problem, using Blind Source Separation. The problem regards splitting sound from a boat engine and the water waves to prove the possibility to extract one sound fragment from the other on the open sea. The illustrations shown
Methods and Applications of Inversion
Johnson, Lane
In considering Methods and Applications of Inversions, it is important to realize that the study of inverse problems is not a well-posed endeavor. To begin with, the variety of such problems is extremely broad; any systematic attempt to use observational data to make inferences about a model of the underlying physical processes qualifies as an inverse method. And then, the methods of analysis can branch off in innumerable directions. Many choices must be made in formulating the problem, determining the type and amount of regularization, selecting a solution algorithm, and in representing the results. Finally there is the peculiar process of appraisal, which is often treated as optional, in which one attempts to determine whether a solution was actually obtained and whether it contains any new information. What this means is that when a group gets together to discuss inverse problems, one should not be surprised to encounter a broad variety of problems and approaches. Such is the case with Methods and Applications of Inversions.
Multi-scattering inversion for low model wavenumbers
Alkhalifah, Tariq Ali
2015-08-19
A successful full wavenumber inversion (FWI) implementation updates the low wavenumber model components first for proper wavefield propagation description, and slowly adds the high-wavenumber potentially scattering parts of the model. The low-wavenumber components can be extracted from the transmission parts of the recorded data given by direct arrivals or the transmission parts of the single and double-scattering wave-fields developed from a predicted scatter field. We develop a combined inversion of data modeled from the source and those corresponding to single and double scattering to update both the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data. A scattering angle filter is used to divide the gradient of the combined inversion so initially the high wavenumber (low scattering angle) components of the gradient is directed to the perturbation model and the low wavenumber (high scattering angle) components to the velocity model. As our background velocity matures, the scattering angle divide is slowly lowered to allow for more of the higher wavenumbers to contribute the velocity model.
Practice Utilization of Algorithms for Analog Filter Group Delay Optimization
Directory of Open Access Journals (Sweden)
K. Hajek
2007-04-01
Full Text Available This contribution deals with an application of three different algorithms which optimize a group delay of analog filters. One of them is a part of the professional circuit simulator Micro Cap 7 and the others two original algorithms are developed in the MATLAB environment. An all-pass network is used to optimize the group delay of an arbitrary analog filter. Introduced algorithms look for an optimal order and optimal coefficients of an all-pass network transfer function. Theoretical foundations are introduced and all algorithms are tested using the optimization of testing analog filter. The optimization is always run three times because the second, third and fourth-order all-pass network is used. An equalization of the original group delay is a main objective of these optimizations. All outputs of all algorithms are critically evaluated and also described.
DEMONSTRATION BULLETIN: COLLOID POLISHING FILTER METHOD - FILTER FLOW TECHNOLOGY, INC.
The Filter Flow Technology, Inc. (FFT) Colloid Polishing Filter Method (CPFM) was tested as a transportable, trailer mounted, system that uses sorption and chemical complexing phenomena to remove heavy metals and nontritium radionuclides from water. Contaminated waters can be pro...
Experimental comparison of point-of-use filters for drinking water ultrafiltration.
Totaro, M; Valentini, P; Casini, B; Miccoli, M; Costa, A L; Baggiani, A
2017-06-01
Waterborne pathogens such as Pseudomonas spp. and Legionella spp. may persist in hospital water networks despite chemical disinfection. Point-of-use filtration represents a physical control measure that can be applied in high-risk areas to contain the exposure to such pathogens. New technologies have enabled an extension of filters' lifetimes and have made available faucet hollow-fibre filters for water ultrafiltration. To compare point-of-use filters applied to cold water within their period of validity. Faucet hollow-fibre filters (filter A), shower hollow-fibre filters (filter B) and faucet membrane filters (filter C) were contaminated in two different sets of tests with standard bacterial strains (Pseudomonas aeruginosa DSM 939 and Brevundimonas diminuta ATCC 19146) and installed at points-of-use. Every day, from each faucet, 100 L of water was flushed. Before and after flushing, 250 mL of water was collected and analysed for microbiology. There was a high capacity of microbial retention from filter C; filter B released only low Brevundimonas spp. counts; filter A showed poor retention of both micro-organisms. Hollow-fibre filters did not show good micro-organism retention. All point-of-use filters require an appropriate maintenance of structural parameters to ensure their efficiency. Copyright © 2016 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.
Infrasound data inversion for atmospheric sounding
Lalande, J.-M.; Sèbe, O.; Landès, M.; Blanc-Benon, Ph.; Matoza, R. S.; Le Pichon, A.; Blanc, E.
2012-07-01
The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) continuously records acoustic waves in the 0.01-10 Hz frequency band, known as infrasound. These waves propagate through the layered structure of the atmosphere. Coherent infrasonic waves are produced by a variety of anthropogenic and natural sources and their propagation is controlled by spatiotemporal variations of temperature and wind velocity. Natural stratification of atmospheric properties (e.g. temperature, density and winds) forms waveguides, allowing long-range propagation of infrasound waves. However, atmospheric specifications used in infrasound propagation modelling suffer from lack and sparsity of available data above an altitude of 50 km. As infrasound can propagate in the upper atmosphere up to 120 km, we assume that infrasonic data could be used for sounding the atmosphere, analogous to the use of seismic data to infer solid Earth structure and the use of hydroacoustic data to infer oceanic structure. We therefore develop an inversion scheme for vertical atmospheric wind profiles in the framework of an iterative linear inversion. The forward problem is treated in the high-frequency approximation using a Hamiltonian formulation and complete first-order ray perturbation theory is developed to construct the Fréchet derivatives matrix. We introduce a specific parametrization for the unknown model parameters based on Principal Component Analysis. Finally, our algorithm is tested on synthetic data cases spanning different seasonal periods and network configurations. The results show that our approach is suitable for infrasound atmospheric sounding on a regional scale.
Directory of Open Access Journals (Sweden)
Zhiying Zhu
2017-01-01
Full Text Available Dual-winding bearingless switched reluctance motor (BSRM is a multivariable high-nonlinear system characterized by strong coupling, and it is not completely reversible. In this paper, a new decoupling control strategy based on improved inverse system method is proposed. Robust servo regulator is adopted for the decoupled plants to guarantee control performances and robustness. A phase dynamic compensation filter is also designed to improve system stability at high-speed. In order to explain the advantages of the proposed method, traditional methods are compared. The tracking and decoupling characteristics as well as disturbance rejection and robustness are deeply analyzed. Simulation and experiments results show that the decoupling control of dual-winding BSRM in both reversible and irreversible domains can be successfully resolved with the improved inverse system method. The stability and robustness problems induced by inverse controller can be effectively solved by introducing robust servo regulator and dynamic compensation filter.
Waveform inversion with exponential damping using a deconvolution-based objective function
Choi, Yun Seok
2016-09-06
The lack of low frequency components in seismic data usually leads full waveform inversion into the local minima of its objective function. An exponential damping of the data, on the other hand, generates artificial low frequencies, which can be used to admit long wavelength updates for waveform inversion. Another feature of exponential damping is that the energy of each trace also exponentially decreases with source-receiver offset, where the leastsquare misfit function does not work well. Thus, we propose a deconvolution-based objective function for waveform inversion with an exponential damping. Since the deconvolution filter includes a division process, it can properly address the unbalanced energy levels of the individual traces of the damped wavefield. Numerical examples demonstrate that our proposed FWI based on the deconvolution filter can generate a convergent long wavelength structure from the artificial low frequency components coming from an exponential damping.
Multisource Data Inversion Using Decentralized Fusion
Alzraiee, A. H.; Bau, D. A.
2013-12-01
Field data pertaining hydrological systems typically come from multiple sources and are related to different hydraulic properties. The spatial and temporal coverage of these datasets is also variable. Data fusion techniques allow for the integration of multiple datasets with different quality in order to produce a more informative dataset than any of the original inputs. That is to say, the accuracy and the spatial coverage of the fused data are expected to be superior to any of the original datasets. In this work, we present a 'decentralized' data fusion method stemming from Millman's theory, which has been introduced in the field of signal processing to fuse multiple correlated estimates. Millman's equations are applied to integrate separate estimates of aquifer hydraulic properties, such as the spatial distributions of the hydraulic conductivity K and the specific elastic storage Ss, estimated through the inversion of drawdown data collected over multiple independent pumping tests. For each pumping test, 'local' estimates of K and Ss are obtained by applying an Ensemble Kalman Filter (EnKF) algorithm to assimilate the first and second moments of aquifer drawdown into the response of a corresponding groundwater flow model. Since the application of Millman's theory may be computationally very intensive, we propose a more efficient Millman's fusion algorithm for merging local estimates into a global estimate of the hydraulic properties. Increased computational efficiency is achieved by distributing local estimation processes among multicore computers. Multiple numerical experiments are conducted to investigate the potential of this inversion method. In these experiments, a synthetic aquifer is explored by conducting multiple hypothetical pumping tests at different locations in the aquifer. Finally, the decentralized fusion method is compared to a centralized fusion method where all drawdown data corresponding to multiple pumping tests are fused simultaneously using
Chohan, V C
1975-01-01
The design of filter networks with poles as well as zeros satisfying requirements simultaneously in time and frequency domain has been considered analytically by Schussler for the cases of amplitude step or impulse. This paper suggests a general computer-based procedure for designing band-pass filters optimum to a given time-domain specification in terms of step change in frequency (F.S.K.) together with the insertion loss constraints in the frequency domain. The problem of optimizing the step-frequency response subject to insertion loss constraints is reformulated as that of non-linear minimization using a known penalty function technique which converts the constrained optimization problem into a sequence of unconstrained minimisations. As an example, a two-zero, four-pole filter transfer function is optimized using this procedure and synthesized. (11 refs).
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.
FPGA implementation of filtered image using 2D Gaussian filter
Leila kabbai; Anissa Sghaier; Ali Douik; Mohsen Machhout
2016-01-01
Image filtering is one of the very useful techniques in image processing and computer vision. It is used to eliminate useless details and noise from an image. In this paper, a hardware implementation of image filtered using 2D Gaussian Filter will be present. The Gaussian filter architecture will be described using a different way to implement convolution module. Thus, multiplication is in the heart of convolution module, for this reason, three different ways to implement multiplication opera...
Inverse modeling of carbon monoxide fluxes
Hooghiemstra, Pim; Krol, Maarten
2010-05-01
An inverse modeling framework is used to estimate global emissions of carbon monoxide (CO). In particular, we intend to estimate the magnitude and variability of biomass burning CO emissions because the source strength of these emissions is highly uncertain, and the interannual variability is large. Observations from the National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory (NOAA/CMDL) surface network are assimilated using a four-dimensional variational (4DVAR) data assimilation system with the transport model TM5 and its adjoint for 2 years. The biomass burning emissions in the model are not released in the lowest layer of the model, but a vertical distribution is applied and 40% of the emissions is released above 1000 m. The optimized emissions are validated with a separate set of surface station data and the new version 4 product of the satellite instrument MOPITT. A sensitivity test will be presented in which the biomass burning emissions are released in the surface layer.
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.
Optimization and geophysical inverse problems
Energy Technology Data Exchange (ETDEWEB)
Barhen, J.; Berryman, J.G.; Borcea, L.; Dennis, J.; de Groot-Hedlin, C.; Gilbert, F.; Gill, P.; Heinkenschloss, M.; Johnson, L.; McEvilly, T.; More, J.; Newman, G.; Oldenburg, D.; Parker, P.; Porto, B.; Sen, M.; Torczon, V.; Vasco, D.; Woodward, N.B.
2000-10-01
A fundamental part of geophysics is to make inferences about the interior of the earth on the basis of data collected at or near the surface of the earth. In almost all cases these measured data are only indirectly related to the properties of the earth that are of interest, so an inverse problem must be solved in order to obtain estimates of the physical properties within the earth. In February of 1999 the U.S. Department of Energy sponsored a workshop that was intended to examine the methods currently being used to solve geophysical inverse problems and to consider what new approaches should be explored in the future. The interdisciplinary area between inverse problems in geophysics and optimization methods in mathematics was specifically targeted as one where an interchange of ideas was likely to be fruitful. Thus about half of the participants were actively involved in solving geophysical inverse problems and about half were actively involved in research on general optimization methods. This report presents some of the topics that were explored at the workshop and the conclusions that were reached. In general, the objective of a geophysical inverse problem is to find an earth model, described by a set of physical parameters, that is consistent with the observational data. It is usually assumed that the forward problem, that of calculating simulated data for an earth model, is well enough understood so that reasonably accurate synthetic data can be generated for an arbitrary model. The inverse problem is then posed as an optimization problem, where the function to be optimized is variously called the objective function, misfit function, or fitness function. The objective function is typically some measure of the difference between observational data and synthetic data calculated for a trial model. However, because of incomplete and inaccurate data, the objective function often incorporates some additional form of regularization, such as a measure of smoothness
Computer-Aided Numerical Inversion of Laplace Transform
Directory of Open Access Journals (Sweden)
Umesh Kumar
2000-01-01
Full Text Available This paper explores the technique for the computer aided numerical inversion of Laplace transform. The inversion technique is based on the properties of a family of three parameter exponential probability density functions. The only limitation in the technique is the word length of the computer being used. The Laplace transform has been used extensively in the frequency domain solution of linear, lumped time invariant networks but its application to the time domain has been limited, mainly because of the difficulty in finding the necessary poles and residues. The numerical inversion technique mentioned above does away with the poles and residues but uses precomputed numbers to find the time response. This technique is applicable to the solution of partially differentiable equations and certain classes of linear systems with time varying components.
Subadditive functions and their (pseudo-)inverses
DEFF Research Database (Denmark)
Østerdal, Lars Peter
2006-01-01
The paper considers non-negative increasing functions on intervals with left endpoint closed at zero and investigates the duality between subadditivity and superadditivity via the inverse function and pseudo-inverses......The paper considers non-negative increasing functions on intervals with left endpoint closed at zero and investigates the duality between subadditivity and superadditivity via the inverse function and pseudo-inverses...
Some Phenomena on Negative Inversion Constructions
Sung, Tae-Soo
2013-01-01
We examine the characteristics of NDI (negative degree inversion) and its relation with other inversion phenomena such as SVI (subject-verb inversion) and SAI (subject-auxiliary inversion). The negative element in the NDI construction may be" not," a negative adverbial, or a negative verb. In this respect, NDI has similar licensing…
Size Estimates in Inverse Problems
Di Cristo, Michele
2014-01-06
Detection of inclusions or obstacles inside a body by boundary measurements is an inverse problems very useful in practical applications. When only finite numbers of measurements are available, we try to detect some information on the embedded object such as its size. In this talk we review some recent results on several inverse problems. The idea is to provide constructive upper and lower estimates of the area/volume of the unknown defect in terms of a quantity related to the work that can be expressed with the available boundary data.
-Dimensional Fractional Lagrange's Inversion Theorem
Directory of Open Access Journals (Sweden)
F. A. Abd El-Salam
2013-01-01
Full Text Available Using Riemann-Liouville fractional differential operator, a fractional extension of the Lagrange inversion theorem and related formulas are developed. The required basic definitions, lemmas, and theorems in the fractional calculus are presented. A fractional form of Lagrange's expansion for one implicitly defined independent variable is obtained. Then, a fractional version of Lagrange's expansion in more than one unknown function is generalized. For extending the treatment in higher dimensions, some relevant vectors and tensors definitions and notations are presented. A fractional Taylor expansion of a function of -dimensional polyadics is derived. A fractional -dimensional Lagrange inversion theorem is proved.
Ozonometer M-124 calibration for the Ukrainian network: method and results
Grytsai, A.; Milinevsky, G.; Evtushevsky, O.; Sosonkin, M.; Kravchenko, V.; Danylevsky, V.
2016-12-01
M-124 filter ozonometers are used for total ozone measuring in Ukraine since 1970s. Recently the need to calibrate several M-124 instruments of the Ukrainian filter ozonometer network is raised to continue ozone observations. The calibration became possible owing to the accurate ozone measurements by Dobson spectrophotometer started in 2010 at the Kyiv-Goloseyev WMO station located at the Main Astronomical Observatory of National Academy of Sciences of Ukraine. For calibration purposes the simultaneous M-124 and Dobson Direct Sun measurements were carried out during the 2013-2016 period by researchers from Taras Shevchenko National University of Kyiv and Main Astronomical Observatory. The M-124 instrument has two spectral channels: first is 305 nm and second is 325 nm. Outgoing signal from M-124 is determined by transparency of the terrestrial atmosphere and filter characteristics. Theoretical description of the solar radiation propagation through the atmosphere is determined by the Bouguer-Lambert-Beer law taking into account ozone absorption, Rayleigh and aerosol scattering. Parameters of the aerosol scattering have been determined from observations with the CIMEL sunphotometer of Aerosol Robotic Network which is also located at the Kyiv-Goloseyev station. The ozonometers optical characteristics were studied after M-124 refurbishment and modernization at the Central Geophysical Observatory of Ukraine that includes a significant part of the whole calibration work. Knowing the spectral dependence of each filter is necessary to calculate signal ratios in two channels. This information allowed solving the inverse problem of determining total ozone content in the terrestrial atmosphere. Comparison of these results with Dobson spectrophotometer data shows their good quality even without an additional correction. These results open a possibility to calibrate M-124 filter ozonometers for future ozone measurements at the observation sites of the Ukraine ozonometer network.