Inverse spiking filter based acquisition enhancement in software based global positioning system receiver

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.

Recurrent Neural Network for Computing the Drazin Inverse.

Science.gov (United States)

Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

2015-11-01

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

Quantized, piecewise linear filter network

DEFF Research Database (Denmark)

Sørensen, John Aasted

1993-01-01

A quantization based piecewise linear filter network is defined. A method for the training of this network based on local approximation in the input space is devised. The training is carried out by repeatedly alternating between vector quantization of the training set into quantization classes...... and equalization of the quantization classes linear filter mean square training errors. The equalization of the mean square training errors is carried out by adapting the boundaries between neighbor quantization classes such that the differences in mean square training errors are reduced...

Research on the Random Shock Vibration Test Based on the Filter-X LMS Adaptive Inverse Control Algorithm

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.

Inverse kinematics problem in robotics using neural networks

Science.gov (United States)

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.

Inverse problems in 1D hemodynamics on systemic networks: a sequential approach.

Science.gov (United States)

Lombardi, D

2014-02-01

In this work, a sequential approach based on the unscented Kalman filter is applied to solve inverse problems in 1D hemodynamics, on a systemic network. For instance, the arterial stiffness is estimated by exploiting cross-sectional area and mean speed observations in several locations of the arteries. The results are compared with those ones obtained by estimating the pulse wave velocity and the Moens-Korteweg formula. In the last section, a perspective concerning the identification of the terminal models parameters and peripheral circulation (modeled by a Windkessel circuit) is presented. Copyright © 2013 John Wiley & Sons, Ltd.

Power-Law Radon-Transformed Superimposed Inverse Filter Synthetic Discriminant Correlator for Facial Recognition

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...

Nonlinear adaptive inverse control via the unified model neural network

Science.gov (United States)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Filtering in Hybrid Dynamic Bayesian Networks

Science.gov (United States)

Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin

2000-01-01

We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).

Scattering-angle based filtering of the waveform inversion gradients

KAUST Repository

Alkhalifah, Tariq Ali

2014-01-01

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.

Scattering-angle based filtering of the waveform inversion gradients

KAUST Repository

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.

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

Science.gov (United States)

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. PMID:22163999

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

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.

Recursive inverse kinematics for robot arms via Kalman filtering and Bryson-Frazier smoothing

Science.gov (United States)

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.

Neural network training by Kalman filtering in process system monitoring

International Nuclear Information System (INIS)

Ciftcioglu, Oe.

1996-03-01

Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome.

Science.gov (United States)

Quevedo-Tumailli, Viviana F; Ortega-Tenezaca, Bernabé; González-Díaz, Humbert

2018-03-02

The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of genes are transcribed from bidirectional promoters in humans, and many more are organized into larger clusters. This raises intriguing questions previously asked by different authors. We would like to add a few more questions in this context, related to gene orientation inversions. Does gene orientation (inversion) follow a random pattern? Is it relevant to biological activity somehow? We define a new kind of network coined as the gene orientation inversion network (GOIN). GOIN's complex network encodes short- and long-range patterns of inversion of the orientation of pairs of gene in the chromosome. We selected Plasmodium falciparum as a case of study due to the high relevance of this parasite to public health (causal agent of malaria). We constructed here for the first time all of the GOINs for the genome of this parasite. These networks have an average of 383 nodes (genes in one chromosome) and 1314 links (pairs of gene with inverse orientation). We calculated node centralities and other parameters of these networks. These numerical parameters were used to study different properties of gene inversion patterns, for example, distribution, local communities, similarity to Erdös-Rényi random networks, randomness, and so on. We find clues that seem to indicate that gene orientation inversion does not follow a random pattern. We noted that some gene communities in the GOINs tend to group genes encoding for RIFIN-related proteins in the proteome of the parasite. RIFIN-like proteins are a second family of clonally variant proteins expressed on the surface of red cells infected with Plasmodium falciparum. Consequently, we used these centralities as input of machine learning (ML) models to predict the RIFIN-like activity of 5365 proteins in the proteome of Plasmodium sp. The best linear ML model found discriminates RIFIN-like from other proteins with sensitivity and

Resonant TMR inversion in LiF/EuS based spin-filter tunnel junctions

Directory of Open Access Journals (Sweden)

Fen Liu

2016-08-01

Full Text Available Resonant tunneling can lead to inverse tunnel magnetoresistance when impurity levels rather than direct tunneling dominate the transport process. We fabricated hybrid magnetic tunnel junctions of CoFe/LiF/EuS/Ti, with an epitaxial LiF energy barrier joined with a polycrystalline EuS spin-filter barrier. Due to the water solubility of LiF, the devices were fully packaged in situ. The devices showed sizeable positive TMR up to 16% at low bias voltages but clearly inverted TMR at higher bias voltages. The TMR inversion depends sensitively on the thickness of LiF, and the tendency of inversion disappears when LiF gets thick enough and recovers its intrinsic properties.

Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method

Science.gov (United States)

Chen, Xiaohong; Du, Yukun; Liu, Zhan; Zhao, Wenju; Chen, Xiaocheng

2018-05-01

This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.

Mini-batch optimized full waveform inversion with geological constrained gradient filtering

Science.gov (United States)

Yang, Hui; Jia, Junxiong; Wu, Bangyu; Gao, Jinghuai

2018-05-01

High computation cost and generating solutions without geological sense have hindered the wide application of Full Waveform Inversion (FWI). Source encoding technique is a way to dramatically reduce the cost of FWI but subject to fix-spread acquisition setup requirement and slow convergence for the suppression of cross-talk. Traditionally, gradient regularization or preconditioning is applied to mitigate the ill-posedness. An isotropic smoothing filter applied on gradients generally gives non-geological inversion results, and could also introduce artifacts. In this work, we propose to address both the efficiency and ill-posedness of FWI by a geological constrained mini-batch gradient optimization method. The mini-batch gradient descent optimization is adopted to reduce the computation time by choosing a subset of entire shots for each iteration. By jointly applying the structure-oriented smoothing to the mini-batch gradient, the inversion converges faster and gives results with more geological meaning. Stylized Marmousi model is used to show the performance of the proposed method on realistic synthetic model.

Fault diagnosis system of electromagnetic valve using neural network filter

International Nuclear Information System (INIS)

Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

2008-01-01

This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

Construction and Experimental Implementation of a Model-Based Inverse Filter to Attenuate Hysteresis in Ferroelectric Transducers

National Research Council Canada - National Science Library

Hatch, Andrew G; Smith, Ralph C; De, Tathagata; Salapaka, Murti V

2005-01-01

.... In this paper, we illustrate the construction of inverse filters, based on homogenized energy models, which can be used to approximately linearize the piezoceramic transducer behavior for linear...

Microelectromechanical filter formed from parallel-connected lattice networks of contour-mode resonators

Science.gov (United States)

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.

Reconstructing the Hopfield network as an inverse Ising problem

International Nuclear Information System (INIS)

Huang Haiping

2010-01-01

We test four fast mean-field-type algorithms on Hopfield networks as an inverse Ising problem. The equilibrium behavior of Hopfield networks is simulated through Glauber dynamics. In the low-temperature regime, the simulated annealing technique is adopted. Although performances of these network reconstruction algorithms on the simulated network of spiking neurons are extensively studied recently, the analysis of Hopfield networks is lacking so far. For the Hopfield network, we found that, in the retrieval phase favored when the network wants to memory one of stored patterns, all the reconstruction algorithms fail to extract interactions within a desired accuracy, and the same failure occurs in the spin-glass phase where spurious minima show up, while in the paramagnetic phase, albeit unfavored during the retrieval dynamics, the algorithms work well to reconstruct the network itself. This implies that, as an inverse problem, the paramagnetic phase is conversely useful for reconstructing the network while the retrieval phase loses all the information about interactions in the network except for the case where only one pattern is stored. The performances of algorithms are studied with respect to the system size, memory load, and temperature; sample-to-sample fluctuations are also considered.

Artificial Neural Network Modeling of an Inverse Fluidized Bed ...

African Journals Online (AJOL)

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 decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...

Microwave photonic filters with negative coefficients based on phase inversion in an electro-optic modulator.

Science.gov (United States)

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.

A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator

Directory of Open Access Journals (Sweden)

Panchanand Jha

2014-07-01

Full Text Available Inverse kinematic is one of the most interesting problems of industrial robot. The inverse kinematics problem in robotics is about the determination of joint angles for a desired Cartesian position of the end effector. It comprises of the computation need to find the joint angles for a given Cartesian position and orientation of the end effectors to control a robot arm. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network is one such technique which can be gainfully used to yield the acceptable results. This paper proposes a structured artificial neural network (ANN model to find the inverse kinematics solution of a 4-dof SCARA manipulator. The ANN model used is a multi-layered perceptron neural network (MLPNN, wherein gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that multi-layered perceptron neural network gives minimum mean square error.

Fragmented network subsystem with traffic filtering for microkernel environment

Directory of Open Access Journals (Sweden)

Anna Urievna Budkina

2016-06-01

Full Text Available The TCP/IP stack in a microkernel operating system executed in a user space, which requires the development of a distributed network infrastructure within a single software environment. Its functions are the organization of interaction between the components of the stack with different processes, as well as the organization of filtering mechanisms and routing of internal network traffic. Use of architectural approaches applicable in monolithic-modular systems is impossible, because the network stack is not a shareable component of the system. As a consequence, the microkernel environment requires development of special network subsystem. In this work we provide overview of major conceptions of network architectures in microkernel environments. Also, we provide own architecture which supports filtering of internal network traffic. We evaluate the architecture by development of high-performance "key-value" store.

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.

Science.gov (United States)

Jia, Bin; Wang, Xiaodong

2013-12-17

: The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

Science.gov (United States)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

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.

A New Filter Design Method for Disturbed Multilayer Hopfield Neural Networks

Directory of Open Access Journals (Sweden)

AHN, C. K.

2011-05-01

Full Text Available This paper investigates the passivity based filtering problem for multilayer Hopfield neural networks with external disturbance. A new passivity based filter design method for multilayer Hopfield neural networks is developed to ensure that the filtering error system is exponentially stable and passive from the external disturbance vector to the output error vector. The unknown gain matrix is obtained by solving a linear matrix inequality (LMI, which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed filter.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

Science.gov (United States)

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.

Neutral networks for event filtering at D0

International Nuclear Information System (INIS)

Cutts, D.; Hoftun, J.S.; Sornborger, A.; Johnson, R.C.; Zeller, R.T.

1989-01-01

Neutral networks may provide important tools for pattern recognition in high energy physics. We discuss an initial exploration of these techniques, presenting the result of network simulations of several filter algorithms. The D0 data acquisition system, a MicroVAX farm, will perform critical event selection; we describe a possible implementation of neural network algorithms in this system. (orig.)

TOMOGRAPHY OF PLASMA FLOWS IN THE UPPER SOLAR CONVECTION ZONE USING TIME-DISTANCE INVERSION COMBINING RIDGE AND PHASE-SPEED FILTERING

International Nuclear Information System (INIS)

Švanda, Michal

2013-01-01

The consistency of time-distance inversions for horizontal components of the plasma flow on supergranular scales in the upper solar convection zone is checked by comparing the results derived using two k-ω filtering procedures—ridge filtering and phase-speed filtering—commonly used in time-distance helioseismology. I show that both approaches result in similar flow estimates when finite-frequency sensitivity kernels are used. I further demonstrate that the performance of the inversion improves (in terms of a simultaneously better averaging kernel and a lower noise level) when the two approaches are combined together in one inversion. Using the combined inversion, I invert for horizontal flows in the upper 10 Mm of the solar convection zone. The flows connected with supergranulation seem to be coherent only for the top ∼5 Mm; deeper down there is a hint of change of the convection scales toward structures larger than supergranules

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...

Optical supervised filtering technique based on Hopfield neural network

Science.gov (United States)

Bal, Abdullah

2004-11-01

Hopfield neural network is commonly preferred for optimization problems. In image segmentation, conventional Hopfield neural networks (HNN) are formulated as a cost-function-minimization problem to perform gray level thresholding on the image histogram or the pixels' gray levels arranged in a one-dimensional array [R. Sammouda, N. Niki, H. Nishitani, Pattern Rec. 30 (1997) 921-927; K.S. Cheng, J.S. Lin, C.W. Mao, IEEE Trans. Med. Imag. 15 (1996) 560-567; C. Chang, P. Chung, Image and Vision comp. 19 (2001) 669-678]. In this paper, a new high speed supervised filtering technique is proposed for image feature extraction and enhancement problems by modifying the conventional HNN. The essential improvement in this technique is to use 2D convolution operation instead of weight-matrix multiplication. Thereby, neural network based a new filtering technique has been obtained that is required just 3 × 3 sized filter mask matrix instead of large size weight coefficient matrix. Optical implementation of the proposed filtering technique is executed easily using the joint transform correlator. The requirement of non-negative data for optical implementation is provided by bias technique to convert the bipolar data to non-negative data. Simulation results of the proposed optical supervised filtering technique are reported for various feature extraction problems such as edge detection, corner detection, horizontal and vertical line extraction, and fingerprint enhancement.

Neural networks for event filtering at D/O/

International Nuclear Information System (INIS)

Cutts, D.; Hoftun, J.S.; Sornborger, A.; Johnson, C.R.; Zeller, R.T.

1989-01-01

Neural networks may provide important tools for pattern recognition in high energy physics. We discuss an initial exploration of these techniques, presenting the result of network simulations of several filter algorithms. The D0 data acquisition system, a MicroVAX farm, will perform critical event selection; we describe a possible implementation of neural network algorithms in this system. 7 refs., 4 figs

Inverse Problem of Air Filtration of Nanoparticles: Optimal Quality Factors of Fibrous Filters

Directory of Open Access Journals (Sweden)

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.

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

Science.gov (United States)

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 (R 2 ) 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.

Machine learning of radial basis function neural network based on Kalman filter: Introduction

Directory of Open Access Journals (Sweden)

Vuković Najdan L.

2014-01-01

Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.

Chemical Source Inversion using Assimilated Constituent Observations in an Idealized Two-dimensional System

Science.gov (United States)

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%.

A NEW METHOD OF CHANNEL FRICTION INVERSION BASED ON KALMAN FILTER WITH UNKNOWN PARAMETER VECTOR

Institute of Scientific and Technical Information of China (English)

CHENG Wei-ping; MAO Gen-hai; LIU Guo-hua

2005-01-01

Channel friction is an important parameter in hydraulic analysis.A channel friction parameter inversion method based on Kalman Filter with unknown parameter vector is proposed.Numerical simulations indicate that when the number of monitoring stations exceeds a critical value, the solution is hardly affected.In addition, Kalman Filter with unknown parameter vector is effective only at unsteady state.For the nonlinear equations, computations of sensitivity matrices are time-costly.Two simplified measures can reduce computing time, but not influence the results.One is to reduce sensitivity matrix analysis time, the other is to substitute for sensitivity matrix.

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.

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

Science.gov (United States)

Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

2018-01-01

Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

Information filtering on coupled social networks.

Science.gov (United States)

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.

Adaptive filtering for hidden node detection and tracking in networks.

Science.gov (United States)

Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L

2017-07-01

The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy

Science.gov (United States)

Gabbard, Hunter; Williams, Michael; Hayes, Fergus; Messenger, Chris

2018-04-01

We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy.

Science.gov (United States)

Gabbard, Hunter; Williams, Michael; Hayes, Fergus; Messenger, Chris

2018-04-06

We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.

Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Peng; QI Wen-Juan; DENG Zi-Li

2014-01-01

This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.

A key heterogeneous structure of fractal networks based on inverse renormalization scheme

Science.gov (United States)

Bai, Yanan; Huang, Ning; Sun, Lina

2018-06-01

Self-similarity property of complex networks was found by the application of renormalization group theory. Based on this theory, network topologies can be classified into universality classes in the space of configurations. In return, through inverse renormalization scheme, a given primitive structure can grow into a pure fractal network, then adding different types of shortcuts, it exhibits different characteristics of complex networks. However, the effect of primitive structure on networks structural property has received less attention. In this paper, we introduce a degree variance index to measure the dispersion of nodes degree in the primitive structure, and investigate the effect of the primitive structure on network structural property quantified by network efficiency. Numerical simulations and theoretical analysis show a primitive structure is a key heterogeneous structure of generated networks based on inverse renormalization scheme, whether or not adding shortcuts, and the network efficiency is positively correlated with degree variance of the primitive structure.

Recurrent Neural Network Approach Based on the Integral Representation of the Drazin Inverse.

Science.gov (United States)

Stanimirović, Predrag S; Živković, Ivan S; Wei, Yimin

2015-10-01

In this letter, we present the dynamical equation and corresponding artificial recurrent neural network for computing the Drazin inverse for arbitrary square real matrix, without any restriction on its eigenvalues. Conditions that ensure the stability of the defined recurrent neural network as well as its convergence toward the Drazin inverse are considered. Several illustrative examples present the results of computer simulations.

Filtering and storage working memory networks in younger and older age.

Science.gov (United States)

Vellage, Anne-Katrin; Becke, Andreas; Strumpf, Hendrik; Baier, Bernhard; Schönfeld, Mircea Ariel; Hopf, Jens-Max; Müller, Notger G

2016-11-01

Working memory (WM) is a multi-component model that among others involves the two processes of filtering and storage. The first reflects the necessity to inhibit irrelevant information from entering memory, whereas the latter refers to the active maintenance of object representations in memory. In this study, we aimed at a) redefining the neuronal networks sustaining filtering and storage within visual working memory by avoiding shortcomings of prior studies, and b) assessing age-related changes in these networks. We designed a new paradigm that strictly controlled for perceptual load by presenting the same number of stimuli in each of three conditions. We calculated fMRI contrasts between a baseline condition (low filter and low storage load) and conditions that posed high demands on filtering and storage, respectively, in large samples of younger ( n  = 40) and elder ( n  = 38) participants. Our approach of comparing contrasts between groups revealed more extensive filter and storage WM networks than previous studies. In the younger group, filtering involved the bilateral insulae, the right occipital cortex, the right brainstem, and the right cerebellum. In the elder group, filtering was associated with the bilateral insulae, right precuneus, and bilateral ventromedial prefrontal cortex. An extensive neuronal network was also found during storage of information in the bilateral posterior parietal cortex, the left ventromedial prefrontal cortex, and the right precuneus in the younger participants. In addition to these brain regions, elder participants recruited the bilateral ventral prefrontal cortex, the superior, middle and inferior and temporal cortex, the left cingulum and the bilateral parahippocampal cortex. In general, elder participants recruited more brain regions in comparison to younger participants to reach similar accuracy levels. Furthermore, in elder participants one brain region emerged in both contrasts, namely the left ventromedial prefrontal

Study of 1D complex resistivity inversion using digital linear filter technique; Linear filter ho wo mochiita fukusohi teiko no gyakukaisekiho no kento

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.

MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion

Science.gov (United States)

Zhang, Yunong; Chen, Ke; Ma, Weimu; Li, Xiao-Dong

This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.

Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network

Science.gov (United States)

Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao

2009-10-01

A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.

Adaptive training of feedforward neural networks by Kalman filtering

International Nuclear Information System (INIS)

Ciftcioglu, Oe.

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.)

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...

A new hybrid-FBP inversion algorithm with inverse distance backprojection weight for CT reconstruction

Energy Technology Data Exchange (ETDEWEB)

Narasimhadhan, A.V.; Rajgopal, Kasi

2011-07-01

This paper presents a new hybrid filtered backprojection (FBP) algorithm for fan-beam and cone-beam scan. The hybrid reconstruction kernel is the sum of the ramp and Hilbert filters. We modify the redundancy weighting function to reduce the inverse square distance weighting in the backprojection to inverse distance weight. The modified weight also eliminates the derivative associated with the Hilbert filter kernel. Thus, the proposed reconstruction algorithm has the advantages of the inverse distance weight in the backprojection. We evaluate the performance of the new algorithm in terms of the magnitude level and uniformity in noise for the fan-beam geometry. The computer simulations show that the spatial resolution is nearly identical to the standard fan-beam ramp filtered algorithm while the noise is spatially uniform and the noise variance is reduced. (orig.)

Decoupling control of vehicle chassis system based on neural network inverse system

Science.gov (United States)

Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

2018-06-01

Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

Gravity Effects on Information Filtering and Network Evolving

Science.gov (United States)

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

Improving information filtering via network manipulation

Science.gov (United States)

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.

Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

OpenAIRE

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...

Solving inversion problems with neural networks

Science.gov (United States)

Kamgar-Parsi, Behzad; Gualtieri, J. A.

1990-01-01

A class of inverse problems in remote sensing can be characterized by Q = F(x), where F is a nonlinear and noninvertible (or hard to invert) operator, and the objective is to infer the unknowns, x, from the observed quantities, Q. Since the number of observations is usually greater than the number of unknowns, these problems are formulated as optimization problems, which can be solved by a variety of techniques. The feasibility of neural networks for solving such problems is presently investigated. As an example, the problem of finding the atmospheric ozone profile from measured ultraviolet radiances is studied.

Resolution enhancement of pump-probe microscope with an inverse-annular filter

Science.gov (United States)

Kobayashi, Takayoshi; Kawasumi, Koshi; Miyazaki, Jun; Nakata, Kazuaki

2018-04-01

Optical pump-probe microscopy can provide images by detecting changes in probe light intensity induced by stimulated emission, photoinduced absorbance change, or photothermal-induced refractive index change in either transmission or reflection mode. Photothermal microscopy, which is one type of optical pump-probe microscopy, has intrinsically super resolution capability due to the bilinear dependence of signal intensity of pump and probe. We introduce new techniques for further resolution enhancement and fast imaging in photothermal microscope. First, we introduce a new pupil filter, an inverse-annular pupil filter in a pump-probe photothermal microscope, which provides resolution enhancement in three dimensions. The resolutions are proved to be improved in lateral and axial directions 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. Second, a fast, high-sensitivity photothermal microscope is developed by implementing a spatially segmented balanced detection scheme into a laser scanning microscope using a Galvano mirror. We confirm a 4.9 times improvement in signal-to-noise ratio in the spatially segmented balanced detection compared with that of conventional detection. The system demonstrates simultaneous bi-modal photothermal and confocal fluorescence imaging of transgenic mouse brain tissue with a pixel dwell time of 20 µs. The

Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—applied to GPR crosshole traveltime inversion

Science.gov (United States)

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.

Kalman filter based fault diagnosis of networked control system with white noise

Institute of Scientific and Technical Information of China (English)

Yanwei WANG; Ying ZHENG

2005-01-01

The networked control system NCS is regarded as a sampled control system with output time-variant delay.White noise is considered in the model construction of NCS.By using the Kalman filter theory to compute the filter parameters,a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults.Finally,an example is given to show the feasibility of the approach.

Inverse stochastic resonance in networks of spiking neurons.

Science.gov (United States)

Uzuntarla, Muhammet; Barreto, Ernest; Torres, Joaquin J

2017-07-01

Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

Filtering in hybrid dynamic Bayesian networks

DEFF Research Database (Denmark)

Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin

2004-01-01

for inference. We extend the experiment and perform approximate inference using The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Furthermore, we combine these techniques in a 'non-strict' Rao-Blackwellisation framework and apply it to the watertank system. We show that UKF and UKF in a PF...... framework outperform the generic PF, EKF and EKF in a PF framework with respect to accuracy and robustness in terms of estimation RMSE (root-mean-square error). Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. We also show...... 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...

Data-Filtering System to Avoid Total Data Distortion in IoT Networking

Directory of Open Access Journals (Sweden)

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.

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 ...

An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

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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.

Nonlinear stochastic systems with network-induced phenomena recursive filtering and sliding-mode design

CERN Document Server

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

Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

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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.

Qualitative performance comparison of reactivity estimation between the extended Kalman filter technique and the inverse point kinetic method

International Nuclear Information System (INIS)

Shimazu, Y.; Rooijen, W.F.G. van

2014-01-01

Highlights: • Estimation of the reactivity of nuclear reactor based on neutron flux measurements. • Comparison of the traditional method, and the new approach based on Extended Kalman Filtering (EKF). • Estimation accuracy depends on filter parameters, the selection of which is described in this paper. • The EKF algorithm is preferred if the signal to noise ratio is low (low flux situation). • The accuracy of the EKF depends on the ratio of the filter coefficients. - Abstract: The Extended Kalman Filtering (EKF) technique has been applied for estimation of subcriticality with a good noise filtering and accuracy. The Inverse Point Kinetic (IPK) method has also been widely used for reactivity estimation. The important parameters for the EKF estimation are the process noise covariance, and the measurement noise covariance. However the optimal selection is quite difficult. On the other hand, there is only one parameter in the IPK method, namely the time constant for the first order delay filter. Thus, the selection of this parameter is quite easy. Thus, it is required to give certain idea for the selection of which method should be selected and how to select the required parameters. From this point of view, a qualitative performance comparison is carried out

Applicability of Neural Networks to Etalon Fringe Filtering in Laser Spectrometers

Science.gov (United States)

Nicely, J. M.; Hanisco, T. F.; Riris, H.

2018-01-01

We present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.

Applicability of neural networks to etalon fringe filtering in laser spectrometers

Science.gov (United States)

Nicely, J. M.; Hanisco, T. F.; Riris, H.

2018-05-01

We present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.

High Efficiency, Transparent, Reusable, and Active PM2.5 Filters by Hierarchical Ag Nanowire Percolation Network.

Science.gov (United States)

Jeong, Seongmin; Cho, Hyunmin; Han, Seonggeun; Won, Phillip; Lee, Habeom; Hong, Sukjoon; Yeo, Junyeob; Kwon, Jinhyeong; Ko, Seung Hwan

2017-07-12

Air quality has become a major public health issue in Asia including China, Korea, and India. Particulate matters are the major concern in air quality. We present the first environmental application demonstration of Ag nanowire percolation network for a novel, electrical type transparent, reusable, and active PM2.5 air filter although the Ag nanowire percolation network has been studied as a very promising transparent conductor in optoelectronics. Compared with previous particulate matter air filter study using relatively weaker short-range intermolecular force in polar polymeric nanofiber, Ag nanowire percolation network filters use stronger long-range electrostatic force to capture PM2.5, and they are highly efficient (>99.99%), transparent, working on an active mode, low power consumption, antibacterial, and reusable after simple washing. The proposed new particulate matter filter can be applied for a highly efficient, reusable, active and energy efficient filter for wearable electronics application.

Hybrid inversions of CO2 fluxes at regional scale applied to network design

Science.gov (United States)

Kountouris, Panagiotis; Gerbig, Christoph; -Thomas Koch, Frank

2013-04-01

Long term observations of atmospheric greenhouse gas measuring stations, located at representative regions over the continent, improve our understanding of greenhouse gas sources and sinks. These mixing ratio measurements can be linked to surface fluxes by atmospheric transport inversions. Within the upcoming years new stations are to be deployed, which requires decision making tools with respect to the location and the density of the network. We are developing a method to assess potential greenhouse gas observing networks in terms of their ability to recover specific target quantities. As target quantities we use CO2 fluxes aggregated to specific spatial and temporal scales. We introduce a high resolution inverse modeling framework, which attempts to combine advantages from pixel based inversions with those of a carbon cycle data assimilation system (CCDAS). The hybrid inversion system consists of the Lagrangian transport model STILT, the diagnostic biosphere model VPRM and a Bayesian inversion scheme. We aim to retrieve the spatiotemporal distribution of net ecosystem exchange (NEE) at a high spatial resolution (10 km x 10 km) by inverting for spatially and temporally varying scaling factors for gross ecosystem exchange (GEE) and respiration (R) rather than solving for the fluxes themselves. Thus the state space includes parameters for controlling photosynthesis and respiration, but unlike in a CCDAS it allows for spatial and temporal variations, which can be expressed as NEE(x,y,t) = λG(x,y,t) GEE(x,y,t) + λR(x,y,t) R(x,y,t) . We apply spatially and temporally correlated uncertainties by using error covariance matrices with non-zero off-diagonal elements. Synthetic experiments will test our system and select the optimal a priori error covariance by using different spatial and temporal correlation lengths on the error statistics of the a priori covariance and comparing the optimized fluxes against the 'known truth'. As 'known truth' we use independent fluxes

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.

Enhancing the Statistical Filtering Scheme to Detect False Negative Attacks in Sensor Networks

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Muhammad Akram

2017-06-01

Full Text Available In this paper, we present a technique that detects both false positive and false negative attacks in statistical filtering-based wireless sensor networks. In statistical filtering scheme, legitimate reports are repeatedly verified en route before they reach the base station, which causes heavy energy consumption. While the original statistical filtering scheme detects only false reports, our proposed method promises to detect both attacks.

Whitelists Based Multiple Filtering Techniques in SCADA Sensor Networks

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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.

H∞ Filtering for Networked Markovian Jump Systems with Multiple Stochastic Communication Delays

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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.

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

Science.gov (United States)

Mai, Huanhuan; Song, Gangbing; Liao, Xiaofeng

2013-01-01

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.

Effect of number of of projections on inverse radon transform based image reconstruction by using filtered back-projection for parallel beam transmission tomography

International Nuclear Information System (INIS)

Qureshi, S.A.; Mirza, S.M.; Arif, M.

2007-01-01

This paper present the effect of number of projections on inverse Radon transform (IRT) estimation using filtered back-projection (FBP) technique for parallel beam transmission tomography. The head phantom and the lung phantom have been used in this work. Various filters used in this study include Ram-Lak, Shepp-Logan, Cosin, Hamming and Hanning filters. The slices have been reconstructed by increasing the number of projections through parallel beam transmission tomography keeping the projections uniformly distributed. The Euclidean and Mean Squared errors and peak signal-to-noise ratio (PSNR) have been analyzed for their sensitiveness as functions of number of projections. It has found that image quality improves with the number of projections but at the cost of the computer time. The error has been minimized to get the best approximation of inverse Radon transform (IRT) as the number of projections is enhanced. The value of PSNR has been found to increase from 8.20 to 24.53 dB as the number of projections is raised from 5 to 180 for head phantom. (author)

Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks

International Nuclear Information System (INIS)

El-Kaliouby, Hesham M; Al-Garni, Mansour A

2009-01-01

The modular neural network (MNN) inversion method has been used for inversion of self-potential (SP) data anomalies caused by 2D inclined sheets of infinite horizontal extent. The analysed parameters are the depth (h), the half-width (a), the inclination (α), the zero distance from the origin (x o ) and the polarization amplitude (k). The MNN inversion has been first tested on a synthetic example and then applied to two field examples from the Surda area of Rakha mines, India, and Kalava fault zone, India. The effect of random noise has been studied, and the technique showed satisfactory results. The inversion results show good agreement with the measured field data compared with other inversion techniques in use

Towards Effective Trust-Based Packet Filtering in Collaborative Network Environments

DEFF Research Database (Denmark)

Meng, Weizhi; Li, Wenjuan; Kwok, Lam-For

2017-01-01

compromised by insider attacks. In this paper, we adopt the existing CIDN framework and aim to apply a collaborative trust-based approach to reduce unwanted packets. More specifically, we develop a collaborative trust-based packet filter, which can be deployed in collaborative networks and be robust against...... typical insider attacks (e.g., betrayal attacks). Experimental results in various simulated and practical environments demonstrate that our filter can perform effectively in reducing unwanted traffic and can defend against insider attacks through identifying malicious nodes in a quick manner, as compared...

Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

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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.

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

International Nuclear Information System (INIS)

Mai, Huanhuan; Liao, Xiaofeng; Song, Gangbing

2013-01-01

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller. (paper)

Bessel smoothing filter for spectral-element mesh

Science.gov (United States)

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

Interviewer Effects on a Network-Size Filter Question

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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.

Building a good initial model for full-waveform inversion using frequency shift filter

Science.gov (United States)

Wang, Guanchao; Wang, Shangxu; Yuan, Sanyi; Lian, Shijie

2018-05-01

Accurate initial model or available low-frequency data is an important factor in the success of full waveform inversion (FWI). The low-frequency helps determine the kinematical relevant components, low-wavenumber of the velocity model, which are in turn needed to avoid FWI trap in local minima or cycle-skipping. However, in the field, acquiring data that common point of low- and high-frequency signal, then utilize the high-frequency data to obtain the low-wavenumber velocity model. It is well known that the instantaneous amplitude envelope of a wavelet is invariant under frequency shift. This means that resolution is constant for a given frequency bandwidth, and independent of the actual values of the frequencies. Based on this property, we develop a frequency shift filter (FSF) to build the relationship between low- and high-frequency information with a constant frequency bandwidth. After that, we can use the high-frequency information to get a plausible recovery of the low-wavenumber velocity model. Numerical results using synthetic data from the Marmousi and layer model demonstrate that our proposed envelope misfit function based on the frequency shift filter can build an initial model with more accurate long-wavelength components, when low-frequency signals are absent in recorded data.

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

Science.gov (United States)

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.

Building a Catalog of Time-Dependent Inversions for Cascadia ETS Events

Science.gov (United States)

Bartlow, N. M.; Williams, C. A.; Wallace, L. M.

2017-12-01

Episodic Tremor and Slip (ETS), composed of periodically occurring slow slip events accompanied by tectonic tremor, have been recognized in Cascadia since 1999. While the tremor has been continuously and automatically monitored for a few years (Wech et al., SRL, 2010; pnsn.org/tremor), the geodetically-derived slip has not been systematically monitored in the same way. Instead, numerous time-dependent and static inversions of the geodetic data have been performed for individual ETS events, with many events going unstudied. Careful study of, and monitoring of, ETS is important both to advance the scientific understanding of fault mechanics and to improve earthquake hazard forecasting in Cascadia. Here we present the results of initial efforts to standardize geodetic inversions of slow slip during Cascadia ETS. We use the Network Inversion Filter (NIF, Segall and Matthews,1997; McGuire and Segall, 2003; Miyazaki et al.,2006), applied evenly to an extended time period, to detect and catalog slow slip transients. Bartlow et al., 2014, conducted a similar study for the Hikurangi subduction zone, covering a 2.5 year period. Additionally, we generate Green's functions using the PyLith finite element code (Aagaard et al., 2013) to allow consideration of elastic property variations derived from a Cascadia-wide seismic velocity model (Stephenson, USGS pub., 2007). These Green's functions are then integrated to provide Green's functions compatible with the Network Inversion Filter. The use of heterogeneous elastic Green's functions allows for a more accurate estimation of slip amplitudes, both during individual ETS events and averaged over multiple events. This is useful for constraining the total slip budget in Cascadia, including whether ETS takes up the entire plate motion on the deeper extent of the plate interface where it occurs. The recent study of Williams and Wallace, GRL, 2015 demonstrated that the use heterogeneous elastic Green's Functions in inversions can make a

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.

INVERSE FILTERING TECHNIQUES IN SPEECH ANALYSIS

African Journals Online (AJOL)

Dr Obe

domain or in the frequency domain. However their .... computer to speech analysis led to important elaborations ... tool for the estimation of formant trajectory (10), ... prediction Linear prediction In effect determines the filter .... Radio Res. Lab.

A neural network-based optimal spatial filter design method for motor imagery classification.

Directory of Open Access Journals (Sweden)

Ayhan Yuksel

Full Text Available In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

Science.gov (United States)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

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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.

An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

International Nuclear Information System (INIS)

Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

2015-01-01

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

An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

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.

Filtering Performance Comparison of Kernel and Wavelet Filters for Reactivity Signal Noise

International Nuclear Information System (INIS)

Park, Moon Ghu; Shin, Ho Cheol; Lee, Yong Kwan; You, Skin

2006-01-01

Nuclear reactor power deviation from the critical state is a parameter of specific interest defined by the reactivity measuring neutron population. Reactivity is an extremely important quantity used to define many of the reactor startup physics parameters. The time dependent reactivity is normally determined by solving the using inverse neutron kinetics equation. The reactivity computer is a device to provide an on-line solution of the inverse kinetics equation. The measurement signal of the neutron density is normally noise corrupted and the control rods movement typically gives reactivity variation with edge signals like saw teeth. Those edge regions should be precisely preserved since the measured signal is used to estimate the reactivity wroth which is a crucial parameter to assure the safety of the nuclear reactors. In this paper, three kind of edge preserving noise filters are proposed and their performance is demonstrated using stepwise signals. The tested filters are based on the unilateral, bilateral kernel and wavelet filters which are known to be effective in edge preservation. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters

A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle

Directory of Open Access Journals (Sweden)

Duo Zhang

2014-07-01

Full Text Available Vehicle active safety control is attracting ever increasing attention in the attempt to improve the stability and the maneuverability of electric vehicles. In this paper, a neural network combined inverse (NNCI controller is proposed, incorporating the merits of left-inversion and right-inversion. As the left-inversion soft-sensor can estimate the sideslip angle, while the right-inversion is utilized to decouple control. Then, the proposed NNCI controller not only linearizes and decouples the original nonlinear system, but also directly obtains immeasurable state feedback in constructing the right-inversion. Hence, the proposed controller is very practical in engineering applications. The proposed system is co-simulated based on the vehicle simulation package CarSim in connection with Matlab/Simulink. The results verify the effectiveness of the proposed control strategy.

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

Traditional technologies for solving hand-eye calibration and inverse kinematics are cumbersome and time consuming due to the high nonlinearity in the models. An alternative to the traditional approaches is the articial neural network inspired by the remarkable abilities of the animals in dierent...

Potential of European 14CO2 observation network to estimate the fossil fuel CO2 emissions via atmospheric inversions

Science.gov (United States)

Wang, Yilong; Broquet, Grégoire; Ciais, Philippe; Chevallier, Frédéric; Vogel, Felix; Wu, Lin; Yin, Yi; Wang, Rong; Tao, Shu

2018-03-01

-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and true estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model-data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

Potential of European 14CO2 observation network to estimate the fossil fuel CO2 emissions via atmospheric inversions

Directory of Open Access Journals (Sweden)

Y. Wang

2018-03-01

remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty from the inversion and the posterior estimate of emissions itself, for a given prior and true estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model–data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales, which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

Implementation of non-linear filters for iterative penalized maximum likelihood image reconstruction

International Nuclear Information System (INIS)

Liang, Z.; Gilland, D.; Jaszczak, R.; Coleman, R.

1990-01-01

In this paper, the authors report on the implementation of six edge-preserving, noise-smoothing, non-linear filters applied in image space for iterative penalized maximum-likelihood (ML) SPECT image reconstruction. The non-linear smoothing filters implemented were the median filter, the E 6 filter, the sigma filter, the edge-line filter, the gradient-inverse filter, and the 3-point edge filter with gradient-inverse filter, and the 3-point edge filter with gradient-inverse weight. A 3 x 3 window was used for all these filters. The best image obtained, by viewing the profiles through the image in terms of noise-smoothing, edge-sharpening, and contrast, was the one smoothed with the 3-point edge filter. The computation time for the smoothing was less than 1% of one iteration, and the memory space for the smoothing was negligible. These images were compared with the results obtained using Bayesian analysis

Information Filtering via Clustering Coefficients of User-Object Bipartite Networks

Science.gov (United States)

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.

Image processing with a cellular nonlinear network

International Nuclear Information System (INIS)

Morfu, S.

2005-01-01

A cellular nonlinear network (CNN) based on uncoupled nonlinear oscillators is proposed for image processing purposes. It is shown theoretically and numerically that the contrast of an image loaded at the nodes of the CNN is strongly enhanced, even if this one is initially weak. An image inversion can be also obtained without reconfiguration of the network whereas a gray levels extraction can be performed with an additional threshold filtering. Lastly, an electronic implementation of this CNN is presented

Neural Network-Based Passive Filtering for Delayed Neutral-Type Semi-Markovian Jump Systems.

Science.gov (United States)

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.

Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

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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.

Experimental calibration of forward and inverse neural networks for rotary type magnetorheological damper

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...

BoolFilter: an R package for estimation and identification of partially-observed Boolean dynamical systems.

Science.gov (United States)

Mcclenny, Levi D; Imani, Mahdi; Braga-Neto, Ulisses M

2017-11-25

Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis. BoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package. We introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression

A hopfield-like artificial neural network for solving inverse radiation transport problems

International Nuclear Information System (INIS)

Lee, Sang Hoon

1997-02-01

In this thesis, we solve inverse radiation transport problems by an Artificial Neural Network(ANN) approach. ANNs have many interesting properties such as nonlinear, parallel, and distributed processing. Some of the promising applications of ANNs are optimization, image and signal processing, system control, etc. In some optimization problems, Hopfield Neural Network(HNN) which has one-layered and fully interconnected neurons with feed-back topology showed that it worked well with acceptable fault tolerance and efficiency. The identification of radioactive source in a medium with a limited number of external detectors is treated as an inverse radiation transport problem in this work. This kind of inverse problem is usually ill-posed and severely under-determined; however, its applications are very useful in many fields including medical diagnosis and nondestructive assay of nuclear materials. Therefore, it is desired to develop efficient and robust solution algorithms. Firstly, we study a representative ANN model which has learning ability and fault tolerance, i.e., feed-forward neural network. It has an error backpropagation learning algorithm processed by reducing error in learning patterns that are usually results of test or calculation. Although it has enough fault tolerance and efficiency, a major obstacle is 'curse of dimensionality'--required number of learning patterns and learning time increase exponentially proportional to the problem size. Therefore, in this thesis, this type of ANN is used as benchmarking the reliability of the solution. Secondly, another approach for solving inverse problems, a modified version of HNN is proposed. When diagonal elements of the interconnection matrix are not zero, HNN may become unstable. However, most problems including this identification problem contain non-zero diagonal elements when programmed on neural networks. According to Soulie et al., discrete random iterations could produce the stable minimum state

Radar-to-Radar Interference Suppression for Distributed Radar Sensor Networks

Directory of Open Access Journals (Sweden)

Wen-Qin Wang

2014-01-01

Full Text Available Radar sensor networks, including bi- and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched filtering-based radar-to-radar interference suppression algorithm. This algorithm first uses an iterative filtering algorithm to suppress the radar-to-radar interferences and, then, separately matched filtering for each radar. Besides the detailed algorithm derivation, extensive numerical simulation examples are performed with the down-chirp and up-chirp waveforms, partially overlapped or inverse chirp rate linearly frequency modulation (LFM waveforms and orthogonal frequency division multiplexing (ODFM chirp diverse waveforms. The effectiveness of the algorithm is verified by the simulation results.

Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices

Directory of Open Access Journals (Sweden)

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.

Quantum neural network-based EEG filtering for a brain-computer interface.

Science.gov (United States)

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.

Time reversal mirror and perfect inverse filter in a microscopic model for sound propagation

International Nuclear Information System (INIS)

Calvo, Hernan L.; Danieli, Ernesto P.; Pastawski, Horacio M.

2007-01-01

Time reversal of quantum dynamics can be achieved by a global change of the Hamiltonian sign (a hasty Loschmidt daemon), as in the Loschmidt Echo experiments in NMR, or by a local but persistent procedure (a stubborn daemon) as in the time reversal mirror (TRM) used in ultrasound acoustics. While the first is limited by chaos and disorder, the last procedure seems to benefit from it. As a first step to quantify such stability we develop a procedure, the perfect inverse filter (PIF), that accounts for memory effects, and we apply it to a system of coupled oscillators. In order to ensure a numerical many-body dynamics intrinsically reversible, we develop an algorithm, the pair partitioning, based on the Trotter strategy used for quantum dynamics. We analyze situations where the PIF gives substantial improvements over the TRM

Calculation method of water injection forward modeling and inversion process in oilfield water injection network

Science.gov (United States)

Liu, Long; Liu, Wei

2018-04-01

A forward modeling and inversion algorithm is adopted in order to determine the water injection plan in the oilfield water injection network. The main idea of the algorithm is shown as follows: firstly, the oilfield water injection network is inversely calculated. The pumping station demand flow is calculated. Then, forward modeling calculation is carried out for judging whether all water injection wells meet the requirements of injection allocation or not. If all water injection wells meet the requirements of injection allocation, calculation is stopped, otherwise the demand injection allocation flow rate of certain step size is reduced aiming at water injection wells which do not meet requirements, and next iterative operation is started. It is not necessary to list the algorithm into water injection network system algorithm, which can be realized easily. Iterative method is used, which is suitable for computer programming. Experimental result shows that the algorithm is fast and accurate.

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

Science.gov (United States)

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.

Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances

Institute of Scientific and Technical Information of China (English)

QI Wen-Juan; ZHANG Peng; DENG Zi-Li

2014-01-01

This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.

Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters

Science.gov (United States)

Sadri, Amir Reza; Zekri, Maryam; Sadri, Saeid; Gheissari, Niloofar

2012-01-01

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well. PMID:23493998

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.

Science.gov (United States)

Colclough, Giles L; Woolrich, Mark W; Harrison, Samuel J; Rojas López, Pedro A; Valdes-Sosa, Pedro A; Smith, Stephen M

2018-05-07

A Bayesian model for sparse, hierarchical inverse covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fmri, meg and eeg data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in meg beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity. Copyright © 2018. Published by Elsevier Inc.

High pressure and [Ca2+] produce an inverse modulation of synaptic input strength, network excitability and frequency response in the rat dentate gyrus

Directory of Open Access Journals (Sweden)

Thomas I Talpalar

2016-09-01

Full Text Available Hyperbaric environments induce the high pressure neurological syndrome (HPNS characterized by hyperexcitability of the central nervous system and memory impairment. Human divers and other animals experience the HPNS at pressures beyond 1.1 MPa. High pressure depresses synaptic transmission and alters its dynamics in various animal models. Medial perforant path (MPP synapses connecting the medial entorhinal cortex with the hippocampal formation are suppressed by 50% at 10.1MPa. Reduction of synaptic inputs is paradoxically associated with enhanced ability of dentate gyrus’ granule cells to generate spikes at high pressure. This mechanism allows MPP inputs to elicit standard granule cell outputs at 0.1 -25 Hz frequencies under hyperbaric conditions. An increased postsynaptic gain of MPP inputs probably allows diving animals to perform in hyperbaric environments, but makes them vulnerable to high intensity/frequency stimuli producing hyperexcitability. Increasing extracellular Ca2+ (Ca2+o partially reverted pressure-mediated depression of MPP inputs and increased MPP’s low-pass filter properties. We postulated that raising Ca2+o in addition to increase synaptic inputs may reduce network excitability in the dentate gyrus potentially improving its function and reducing sensitivity to high intensity and pathologic stimuli. For this matter, we activated the MPP with single and 50 Hz frequency stimuli that simulated physiologic and deleterious conditions, while assessing the granule cell’s output under various conditions of pressure and Ca2+o. Our results reveal that pressure and Ca2+o produce an inverse modulation on synaptic input strength and network excitability. These coincident phenomena suggest a potential general mechanism of networks that adjusts gain as an inverse function of synaptic inputs’ strength. Such mechanism may serve for adaptation to variable pressure and other physiological and pathological conditions and may explain the

A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

Science.gov (United States)

Zhao, Haiquan; Zhang, Jiashu

2009-12-01

To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

The discrete Kalman filtering approach for seismic signals deconvolution

International Nuclear Information System (INIS)

Kurniadi, Rizal; Nurhandoko, Bagus Endar B.

2012-01-01

Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.

Synthesis of highly integrated optical network based on microdisk-resonator add-drop filters in silicon-on-insulator technology

Science.gov (United States)

Kaźmierczak, Andrzej; Dortu, Fabian; Giannone, Domenico; Bogaerts, Wim; Drouard, Emmanuel; Rojo-Romeo, Pedro; Gaffiot, Frederic

2009-10-01

We analyze a highly compact optical add-drop filter topology based on a pair of microdisk resonators and a bus waveguide intersection. The filter is further assessed on an integrated optical 4×4 network for optical on-chip communication. The proposed network structure, as compact as 50×50 μm, is fabricated in a CMOS-compatible process on a silicon-on-insulator (SOI) substrate. Finally, the experimental results demonstrate the proper operation of the fabricated devices.

Study on spin filtering and switching action in a double-triangular network chain

Science.gov (United States)

Zhang, Yongmei

2018-04-01

Spin transport properties of a double-triangular quantum network with local magnetic moment on backbones and magnetic flux penetrating the network plane are studied. Numerical simulation results show that such a quantum network will be a good candidate for spin filter and spin switch. Local dispersion and density of states are considered in the framework of tight-binding approximation. Transmission coefficients are calculated by the method of transfer matrix. Spin transmission is regulated by substrate magnetic moment and magnetic flux piercing those triangles. Experimental realization of such theoretical research will be conducive to designing of new spintronic devices.

Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model

International Nuclear Information System (INIS)

Xu Long; Wang Junping; Chen Quanshi

2012-01-01

Highlights: ► A novel extended Kalman Filtering SOC estimation method based on a stochastic fuzzy neural network (SFNN) battery model is proposed. ► The SFNN which has filtering effect on noisy input can model the battery nonlinear dynamic with high accuracy. ► A robust parameter learning algorithm for SFNN is studied so that the parameters can converge to its true value with noisy data. ► The maximum SOC estimation error based on the proposed method is 0.6%. - Abstract: Extended Kalman filtering is an intelligent and optimal means for estimating the state of a dynamic system. In order to use extended Kalman filtering to estimate the state of charge (SOC), we require a mathematical model that can accurately capture the dynamics of battery pack. In this paper, we propose a stochastic fuzzy neural network (SFNN) instead of the traditional neural network that has filtering effect on noisy input to model the battery nonlinear dynamic. Then, the paper studies the extended Kalman filtering SOC estimation method based on a SFNN model. The modeling test is realized on an 80 Ah Ni/MH battery pack and the Federal Urban Driving Schedule (FUDS) cycle is used to verify the SOC estimation method. The maximum SOC estimation error is 0.6% compared with the real SOC obtained from the discharging test.

A Fault Detection Filtering for Networked Control Systems Based on Balanced Reduced-Order

Directory of Open Access Journals (Sweden)

Da-Meng Dai

2015-01-01

Full Text Available Due to the probability of the packet dropout in the networked control systems, a balanced reduced-order fault detection filter is proposed. In this paper, we first analyze the packet dropout effects in the networked control systems. Then, in order to obtain a robust fault detector for the packet dropout, we use the balanced structure to construct a reduced-order model for residual dynamics. Simulation results are provided to testify the proposed method.

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

International Nuclear Information System (INIS)

Huang Jin-Wang; Feng Jiu-Chao

2014-01-01

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF. (general)

Analyses of integrated aircraft cabin contaminant monitoring network based on Kalman consensus filter.

Science.gov (United States)

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.

Some arithmetically symmetrical bandpass filters

Science.gov (United States)

Paranasi, P.; Roy, S. C. D.

1981-01-01

A combination of the conventional and Matthaei lowpass-bandpass transformations is shown to result in some bandpass filters having very good arithmetic symmetry. The technique presented is applicable to the Butterworth and inverse Chebyshev types of magnitude approximations and the Bessel type of delay approximations. It is not valid, however, for the Chebyshev and elliptic varieties of filters.

Focus-based filtering + clustering technique for power-law networks with small world phenomenon

Science.gov (United States)

Boutin, François; Thièvre, Jérôme; Hascoët, Mountaz

2006-01-01

Realistic interaction networks usually present two main properties: a power-law degree distribution and a small world behavior. Few nodes are linked to many nodes and adjacent nodes are likely to share common neighbors. Moreover, graph structure usually presents a dense core that is difficult to explore with classical filtering and clustering techniques. In this paper, we propose a new filtering technique accounting for a user-focus. This technique extracts a tree-like graph with also power-law degree distribution and small world behavior. Resulting structure is easily drawn with classical force-directed drawing algorithms. It is also quickly clustered and displayed into a multi-level silhouette tree (MuSi-Tree) from any user-focus. We built a new graph filtering + clustering + drawing API and report a case study.

A CMOS Gm-C complex filter with on-chip automatic tuning for wireless sensor network application

International Nuclear Information System (INIS)

Wan Chuanchuan; Li Zhiqun; Hou Ningbing

2011-01-01

A G m -C complex filter with on-chip automatic tuning for wireless sensor networks is designed and implemented using 0.18 μm CMOS process. This filter is synthesized from a low-pass 5th-order Chebyshev RLC ladder filter prototype by means of capacitors and fully balanced transconductors. A conventional phase-locked loop is used to realize the on-chip automatic tuning for both center frequency and bandwidth control. The filter is centered at 2 MHz with a bandwidth of 2.4 MHz. The measured results show that the filter provides more than 45 dB image rejection while the ripple in the pass-band is less than 1.2 dB. The complete filter including on-chip tuning circuit consumes 4.9 mA with 1.8 V single supply voltage. (semiconductor integrated circuits)

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.

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Science.gov (United States)

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

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

Science.gov (United States)

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.

Impacts of the filter clogging on the behavior of a ventilation network in the event of fire

International Nuclear Information System (INIS)

Laborde, J.C.; Pourprix, M.; Lopez, M.C.; Savornin, J.

1991-01-01

One of the main roles of ventilation in a nuclear plant is to maintain dynamic containment during normal or accidental operating conditions. Among the incidents likely to affect a nuclear installation, fire is one of those which, coming from the safety standpoint, requires the greatest attention because it is one of the most probable risks. The consequences of a fire have to be analyzed not only in the room where it breaks out, but also for the entire ventilation network. To evaluate these consequences and develop strategies against fire, the Commissariat a l'Energie Atomique uses several test rigs and calculation codes by which the impact of a fire upon the sensitive points of a network can be determined. Research and development studies currently under way give priority to the clogging of High Efficiency Particulate Air filters. Beginning with polymer fires in a 85 m 3 ventilated room, the influence of filter clogging on the characteristic parameters of the associated ventilated network is highlighted. The resultant modeling study following these experiments reveals that coupling of a ventilation code with a fire code cannot be disassociated from the development of a filter clogging model. This paper also gives the first experimental results relative to the determination of the variation, according to time and mass of deposited aerosols, of the air flow resistance of a filter clogged by aerosols derived from combustion of standard polymers used in the nuclear industry (methyl acrylate polymer, polyvinyl chloride). A methodology to extend the results obtained on the clogging test rig to any ventilation network is then described

Robotic fish tracking method based on suboptimal interval Kalman filter

Science.gov (United States)

Tong, Xiaohong; Tang, Chao

2017-11-01

Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

Science.gov (United States)

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.

Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system.

Science.gov (United States)

Meau, Yeong Pong; Ibrahim, Fatimah; Narainasamy, Selvanathan A L; Omar, Razali

2006-05-01

This study presents the development of a hybrid system consisting of an ensemble of Extended Kalman Filter (EKF) based Multi Layer Perceptron Network (MLPN) and a one-pass learning Fuzzy Inference System using Look-up Table Scheme for the recognition of electrocardiogram (ECG) signals. This system can distinguish various types of abnormal ECG signals such as Ventricular Premature Cycle (VPC), T wave inversion (TINV), ST segment depression (STDP), and Supraventricular Tachycardia (SVT) from normal sinus rhythm (NSR) ECG signal.

Use of switched capacitor filters to implement the discrete wavelet transform

Science.gov (United States)

Kaiser, Kraig E.; Peterson, James N.

1993-01-01

This paper analyzes the use of IIR switched capacitor filters to implement the discrete wavelet transform and the inverse transform, using quadrature mirror filters (QMF) which have the necessary symmetry for reconstruction of the data. This is done by examining the sensitivity of the QMF transforms to the manufacturing variance in the desired capacitances. The performance is evaluated at the outputs of the separate filter stages and the error in the reconstruction of the inverse transform is compared with the desired results.

An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

Directory of Open Access Journals (Sweden)

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.

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

Science.gov (United States)

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.

A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

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Carlos Robles Algarín

2018-01-01

Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

Science.gov (United States)

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

Spectral Filtering Criteria for U-Band Test Light for In-Service Line Monitoring in Optical Fiber Networks

Science.gov (United States)

Honda, Nazuki; Izumita, Hisashi; Nakamura, Minoru

2006-06-01

In the fiber-to-the-home era, thousands of optical fibers will have to be accommodated in the central offices of optical access networks. To reduce maintenance costs and improve the service reliability of optical fiber networks, the authors must develop an optical fiber line testing system with a function for in-service line monitoring that uses a test light with a wavelength different from the communication light wavelength. To monitor an in-service line in an optical network, the effective rejection ratio of the test light must be taken into account. This ratio depends on the spectrum of the test light from the optical time-domain reflectometer and the rejection band of the filter in front of the optical network unit. The dependence of the effective rejection ratio as a function of the sideband suppression ratio (SBSR) and of the ratio of the rejection band to the bandwidth of the sideband noise d/D is clarified. When d/D =0.1 and the target effective rejection ratio of the filter is -40 dB, the SBSR and the filter loss of the termination cable must be -70 and -43 dB, respectively, or the SBSR must be -80 dB. When d/D service line monitoring for a 10-Gb/s transmission using a 1650-nm test light with an SBSR of -80 dB is also demonstrated.

Filtering of Discrete-Time Switched Neural Networks Ensuring Exponential Dissipative and $l_{2}$ - $l_{\\infty }$ Performances.

Science.gov (United States)

Choi, Hyun Duck; Ahn, Choon Ki; Karimi, Hamid Reza; Lim, Myo Taeg

2017-10-01

This paper studies delay-dependent exponential dissipative and l 2 - l ∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l 2 - l ∞ senses. The design of the desired exponential dissipative and l 2 - l ∞ filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.

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.

A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

Directory of Open Access Journals (Sweden)

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.

Low-sensitivity active filter realization using a complex all-pass filter

Science.gov (United States)

Regalia, Phillip A.; Mitra, Sanjit K.

1987-04-01

A wide class of continuous-time transfer functions may be implemented as the parallel combination of two all-pass filters, including Butterworth, Chebyshev, and elliptic low-pass approximations of odd order. Here, the realization of even-order low-pass classical approximations is considered, and it is shown that they may be decomposed in terms of complex all-pass functions. A systematic realization approach, based on scattering domain simulation (i.e., wave active filters), allows for a low-sensitivity active filter implementation. Further insight into the low-sensitivity property is gained by connecting the insertion loss of doubly terminated antimetric networks with the imaginary return loss of complex lossless networks.

Varying prior information in Bayesian inversion

International Nuclear Information System (INIS)

Walker, Matthew; Curtis, Andrew

2014-01-01

Bayes' rule is used to combine likelihood and prior probability distributions. The former represents knowledge derived from new data, the latter represents pre-existing knowledge; the Bayesian combination is the so-called posterior distribution, representing the resultant new state of knowledge. While varying the likelihood due to differing data observations is common, there are also situations where the prior distribution must be changed or replaced repeatedly. For example, in mixture density neural network (MDN) inversion, using current methods the neural network employed for inversion needs to be retrained every time prior information changes. We develop a method of prior replacement to vary the prior without re-training the network. Thus the efficiency of MDN inversions can be increased, typically by orders of magnitude when applied to geophysical problems. We demonstrate this for the inversion of seismic attributes in a synthetic subsurface geological reservoir model. We also present results which suggest that prior replacement can be used to control the statistical properties (such as variance) of the final estimate of the posterior in more general (e.g., Monte Carlo based) inverse problem solutions. (paper)

Inversion Estimate of California Methane Emissions Using a Bayesian Inverse Model with Multi-Tower Greenhouse Gas Monitoring Network and Aircraft Measurements

Science.gov (United States)

Cui, Y.; Falk, M.; Chen, Y.; Herner, J.; Croes, B. E.; Vijayan, A.

2017-12-01

Methane (CH4) is an important short-lived climate pollutant (SLCP), and the second most important greenhouse gas (GHG) in California which accounts for 9% of the statewide GHG emissions inventory. Over the years, California has enacted several ambitious climate change mitigation goals, including the California Global Warming Solutions Act of 2006 which requires ARB to reduce statewide GHG emissions to 1990 emission level by 2020, as well as Assembly Bill 1383 which requires implementation of a climate mitigation program to reduce statewide methane emissions by 40% below the 2013 levels. In order to meet these requirements, ARB has proposed a comprehensive SLCP Strategy with goals to reduce oil and gas related emissions and capture methane emissions from dairy operations and organic waste. Achieving these goals will require accurate understanding of the sources of CH4 emissions. Since direct monitoring of CH4 emission sources in large spatial and temporal scales is challenging and resource intensive, we developed a complex inverse technique combined with atmospheric three-dimensional (3D) transport model and atmospheric observations of CH4 concentrations from a regional tower network and aircraft measurements, to gain insights into emission sources in California. In this study, develop a comprehensive inversion estimate using available aircraft measurements from CalNex airborne campaigns (May-June 2010) and three years of hourly continuous measurements from the ARB Statewide GHG Monitoring Network (2014-2016). The inversion analysis is conducted using two independent 3D Lagrangian models (WRF-STILT and WRF-FLEXPART), with a variety of bottom-up prior inputs from national and regional inventories, as well as two different probability density functions (Gaussian and Lognormal). Altogether, our analysis provides a detailed picture of the spatially resolved CH4 emission sources and their temporal variation over a multi-year period.

Optical UWB pulse generator using an N tap microwave photonic filter and phase inversion adaptable to different pulse modulation formats.

Science.gov (United States)

Bolea, Mario; Mora, José; Ortega, Beatriz; Capmany, José

2009-03-30

We propose theoretically and demonstrate experimentally an optical architecture for flexible Ultra-Wideband pulse generation. It is based on an N-tap reconfigurable microwave photonic filter fed by a laser array by using phase inversion in a Mach-Zehnder modulator. Since a large number of positive and negative coefficients can be easily implemented, UWB pulses fitted to the FCC mask requirements can be generated. As an example, a four tap pulse generator is experimentally demonstrated which complies with the FCC regulation. The proposed pulse generator allows different pulse modulation formats since the amplitude, polarity and time delay of generated pulse is controlled.

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)

Hardware design of the median filter based on window structure and batcher′s oddeven sort network

Directory of Open Access Journals (Sweden)

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 realtime 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.

Truncation of power law behavior in 'scale-free' network models due to information filtering

International Nuclear Information System (INIS)

Mossa, Stefano; Barthelemy, Marc; Eugene Stanley, H.; Nunes Amaral, Luis A.

2002-01-01

We formulate a general model for the growth of scale-free networks under filtering information conditions--that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network 'accessible' to the node. We test our model with empirical data for the World Wide Web and find agreement

Multicasting based optical inverse multiplexing in elastic optical network.

Science.gov (United States)

Guo, Bingli; Xu, Yingying; Zhu, Paikun; Zhong, Yucheng; Chen, Yuanxiang; Li, Juhao; Chen, Zhangyuan; He, Yongqi

2014-06-16

Optical multicasting based inverse multiplexing (IM) is introduced in spectrum allocation of elastic optical network to resolve the spectrum fragmentation problem, where superchannels could be split and fit into several discrete spectrum blocks in the intermediate node. We experimentally demonstrate it with a 1-to-7 optical superchannel multicasting module and selecting/coupling components. Also, simulation results show that, comparing with several emerging spectrum defragmentation solutions (e.g., spectrum conversion, split spectrum), IM could reduce blocking performance significantly but without adding too much system complexity as split spectrum. On the other hand, service fairness for traffic with different granularity of these schemes is investigated for the first time and it shows that IM performs better than spectrum conversion and almost as well as split spectrum, especially for smaller size traffic under light traffic intensity.

A CMOS G{sub m}-C complex filter with on-chip automatic tuning for wireless sensor network application

Energy Technology Data Exchange (ETDEWEB)

Wan Chuanchuan; Li Zhiqun; Hou Ningbing, E-mail: zhiqunli@seu.edu.cn [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China)

2011-05-15

A G{sub m}-C complex filter with on-chip automatic tuning for wireless sensor networks is designed and implemented using 0.18 {mu}m CMOS process. This filter is synthesized from a low-pass 5th-order Chebyshev RLC ladder filter prototype by means of capacitors and fully balanced transconductors. A conventional phase-locked loop is used to realize the on-chip automatic tuning for both center frequency and bandwidth control. The filter is centered at 2 MHz with a bandwidth of 2.4 MHz. The measured results show that the filter provides more than 45 dB image rejection while the ripple in the pass-band is less than 1.2 dB. The complete filter including on-chip tuning circuit consumes 4.9 mA with 1.8 V single supply voltage. (semiconductor integrated circuits)

Neural Network Molecule: a Solution of the Inverse Biometry Problem through Software Support of Quantum Superposition on Outputs of the Network of Artificial Neurons

Directory of Open Access Journals (Sweden)

Vladimir I. Volchikhin

2017-12-01

Full Text Available Introduction: The aim of the study is to accelerate the solution of neural network biometrics inverse problem on an ordinary desktop computer. Materials and Methods: To speed up the calculations, the artificial neural network is introduced into the dynamic mode of “jittering” of the states of all 256 output bits. At the same time, too many output states of the neural network are logarithmically folded by transitioning to the Hamming distance space between the code of the image “Own” and the codes of the images “Alien”. From the database of images of “Alien” 2.5 % of the most similar images are selected. In the next generation, 97.5 % of the discarded images are restored with GOST R 52633.2-2010 procedures by crossing parent images and obtaining descendant images from them. Results: Over a period of about 10 minutes, 60 generations of directed search for the solution of the inverse problem can be realized that allows inversing matrices of neural network functionals of dimension 416 inputs to 256 outputs with restoration of up to 97 % information on unknown biometric parameters of the image “Own”. Discussion and Conclusions: Supporting for 10 minutes of computer time the 256 qubit quantum superposition allows on a conventional computer to bypass the actual infinity of analyzed states in 5050 (50 to 50 times more than the same computer could process realizing the usual calculations. The increase in the length of the supported quantum superposition by 40 qubits is equivalent to increasing the processor clock speed by about a billion times. It is for this reason that it is more profitable to increase the number of quantum superpositions supported by the software emulator in comparison with the creation of a more powerful processor.

Hydraulic tomography of discrete networks of conduits and fractures in a karstic aquifer by using a deterministic inversion algorithm

Science.gov (United States)

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.

Bayesian ISOLA: new tool for automated centroid moment tensor inversion

Science.gov (United States)

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

Germinal Center Optimization Applied to Neural Inverse Optimal Control for an All-Terrain Tracked Robot

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.

A simple structure wavelet transform circuit employing function link neural networks and SI filters

Science.gov (United States)

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.

A convolutional neural network to filter artifacts in spectroscopic MRI.

Science.gov (United States)

Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D

2018-03-09

Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.

Modeling reveals bistability and low-pass filtering in the network module determining blood stem cell fate.

Directory of Open Access Journals (Sweden)

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.

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration.

Science.gov (United States)

Carrière, Olivier; Hermand, Jean-Pierre

2012-04-01

Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.

Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images

Directory of Open Access Journals (Sweden)

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.

Energy-Efficient Distributed Filtering in Sensor Networks: A Unified Switched System Approach.

Science.gov (United States)

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.

Aerosol Robotic Network (AERONET) Version 3 Aerosol Optical Depth and Inversion Products

Science.gov (United States)

Giles, D. M.; Holben, B. N.; Eck, T. F.; Smirnov, A.; Sinyuk, A.; Schafer, J.; Sorokin, M. G.; Slutsker, I.

2017-12-01

The Aerosol Robotic Network (AERONET) surface-based aerosol optical depth (AOD) database has been a principal component of many Earth science remote sensing applications and modelling for more than two decades. During this time, the AERONET AOD database had utilized a semiautomatic quality assurance approach (Smirnov et al., 2000). Data quality automation developed for AERONET Version 3 (V3) was achieved by augmenting and improving upon the combination of Version 2 (V2) automatic and manual procedures to provide a more refined near real time (NRT) and historical worldwide database of AOD. The combined effect of these new changes provides a historical V3 AOD Level 2.0 data set comparable to V2 Level 2.0 AOD. The recently released V3 Level 2.0 AOD product uses Level 1.5 data with automated cloud screening and quality controls and applies pre-field and post-field calibrations and wavelength-dependent temperature characterizations. For V3, the AERONET aerosol retrieval code inverts AOD and almucantar sky radiances using a full vector radiative transfer called Successive ORDers of scattering (SORD; Korkin et al., 2017). The full vector code allows for potentially improving the real part of the complex index of refraction and the sphericity parameter and computing the radiation field in the UV (e.g., 380nm) and degree of linear depolarization. Effective lidar ratio and depolarization ratio products are also available with the V3 inversion release. Inputs to the inversion code were updated to the accommodate H2O, O3 and NO2 absorption to be consistent with the computation of V3 AOD. All of the inversion products are associated with estimated uncertainties that include the random error plus biases due to the uncertainty in measured AOD, absolute sky radiance calibration, and retrieved MODIS BRDF for snow-free and snow covered surfaces. The V3 inversion products use the same data quality assurance criteria as V2 inversions (Holben et al. 2006). The entire AERONET V3

Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

International Nuclear Information System (INIS)

Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan

2011-01-01

On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)

Multiple estimation channel decoupling and optimization method based on inverse system

Science.gov (United States)

Wu, Peng; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

This paper addressed the intelligent autonomous navigation request of intelligent deformation missile, based on the intelligent deformation missile dynamics and kinematics modeling, navigation subsystem solution method and error modeling, and then focuses on the corresponding data fusion and decision fusion technology, decouples the sensitive channel of the filter input through the inverse system of design dynamics to reduce the influence of sudden change of the measurement information on the filter input. Then carrying out a series of simulation experiments, which verified the feasibility of the inverse system decoupling algorithm effectiveness.

Alternate MIMO AF relaying networks with interference alignment: Spectral efficient protocol and linear filter design

KAUST Repository

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.

LCL filter design for three-phase two-level power factor correction using line impedance stabilization network

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...... for filter. By introducing of fast power switches for PFC applications such as silicon-carbide, major current harmonics around the switching frequency drops in the region that LISN can actively provide well-defined impedance for measuring the harmonics (i.e. 9 kHz- 30MHz). Therefore, LISN can be replaced...... 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...

Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

Directory of Open Access Journals (Sweden)

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.

Balanced microwave filters

CERN Document Server

Hong, Jiasheng; Medina, Francisco; Martiacuten, Ferran

2018-01-01

This book presents and discusses strategies for the design and implementation of common-mode suppressed balanced microwave filters, including, narrowband, wideband, and ultra-wideband filters This book examines differential-mode, or balanced, microwave filters by discussing several implementations of practical realizations of these passive components. Topics covered include selective mode suppression, designs based on distributed and semi-lumped approaches, multilayer technologies, defect ground structures, coupled resonators, metamaterials, interference techniques, and substrate integrated waveguides, among others. Divided into five parts, Balanced Microwave Filters begins with an introduction that presents the fundamentals of balanced lines, circuits, and networks. Part 2 covers balanced transmission lines with common-mode noise suppression, including several types of common-mode filters and the application of such filters to enhance common-mode suppression in balanced bandpass filters. Next, Part 3 exa...

A Performance Comparison Between Extended Kalman Filter and Unscented Kalman Filter in Power System Dynamic State Estimation

DEFF Research Database (Denmark)

Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth

2016-01-01

Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve......-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks.This article will focus on comparing...

Moment tensor inversions using strong motion waveforms of Taiwan TSMIP data, 1993–2009

Science.gov (United States)

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

Bayesian inversion of surface wave data for discontinuities and velocity structure in the upper mantle using Neural Networks. Geologica Ultraiectina (287)

NARCIS (Netherlands)

Meier, U.

2008-01-01

We present a neural network approach to invert surface wave data for discontinuities and velocity structure in the upper mantle. We show how such a neural network can be trained on a set of random samples to give a continuous approximation to the inverse relation in a compact and computationally

Information filtering via biased random walk on coupled social network.

Science.gov (United States)

Nie, Da-Cheng; Zhang, Zi-Ke; Dong, Qiang; Sun, Chongjing; Fu, Yan

2014-01-01

The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods.

Method and system for training dynamic nonlinear adaptive filters which have embedded memory

Science.gov (United States)

Rabinowitz, Matthew (Inventor)

2002-01-01

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

Robust Event-Triggered Energy-to-Peak Filtering for Polytopic Uncertain Systems over Lossy Network with Quantized Measurements

Directory of Open Access Journals (Sweden)

Jidong Wang

2016-01-01

Full Text Available The event-triggered energy-to-peak filtering for polytopic discrete-time linear systems is studied with the consideration of lossy network and quantization error. Because of the communication imperfections from the packet dropout of lossy link, the event-triggered condition used to determine the data release instant at the event generator (EG can not be directly applied to update the filter input at the zero order holder (ZOH when performing filter performance analysis and synthesis. In order to balance such nonuniform time series between the triggered instant of EG and the updated instant of ZOH, two event-triggered conditions are defined, respectively, whereafter a worst-case bound on the number of consecutive packet losses of the transmitted data from EG is given, which marginally guarantees the effectiveness of the filter that will be designed based on the event-triggered updating condition of ZOH. Then, the filter performance analysis conditions are obtained under the assumption that the maximum number of packet losses is allowable for the worst-case bound. In what follows, a two-stage LMI-based alternative optimization approach is proposed to separately design the filter, which reduces the conservatism of the traditional linearization method of filter analysis conditions. Subsequently a codesign algorithm is developed to determine the communication and filter parameters simultaneously. Finally, an illustrative example is provided to verify the validity of the obtained results.

Cleaning metal filters by pulse-jet

International Nuclear Information System (INIS)

Pickard, P.; Perry, R.A.

1986-01-01

Cleanable metal filters have an established use in the Nuclear Industry. The filters that have been installed in the past have not proved to be sufficiently cleanable. A series of tests were undertaken to study the application of pulse-jet cleaning to metal fibre filter elements. The efficiency of dust removal was examined under various operating conditions. A very high degree of particulate removal was achieved, with a return to almost clean pressure drop. The effectiveness of cleaning was found to vary inversely with blowback pressure. The position of the blowback nozzle with respect to the filter element throat was also found to be important to cleaning efficiency. Under the test conditions the effect of re-entrainment when cleaning on line was found to be minimal. (author)

Resonator memories and optical novelty filters

Science.gov (United States)

Anderson, Dana Z.; Erle, Marie C.

Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive materials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydreaming" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.

EDITORIAL: Inverse Problems in Engineering

Science.gov (United States)

West, Robert M.; Lesnic, Daniel

2007-01-01

Presented here are 11 noteworthy papers selected from the Fifth International Conference on Inverse Problems in Engineering: Theory and Practice held in Cambridge, UK during 11-15 July 2005. The papers have been peer-reviewed to the usual high standards of this journal and the contributions of reviewers are much appreciated. The conference featured a good balance of the fundamental mathematical concepts of inverse problems with a diverse range of important and interesting applications, which are represented here by the selected papers. Aspects of finite-element modelling and the performance of inverse algorithms are investigated by Autrique et al and Leduc et al. Statistical aspects are considered by Emery et al and Watzenig et al with regard to Bayesian parameter estimation and inversion using particle filters. Electrostatic applications are demonstrated by van Berkel and Lionheart and also Nakatani et al. Contributions to the applications of electrical techniques and specifically electrical tomographies are provided by Wakatsuki and Kagawa, Kim et al and Kortschak et al. Aspects of inversion in optical tomography are investigated by Wright et al and Douiri et al. The authors are representative of the worldwide interest in inverse problems relating to engineering applications and their efforts in producing these excellent papers will be appreciated by many readers of this journal.

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 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...... and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof...

Optimal experiment design in a filtering context with application to sampled network data

OpenAIRE

Singhal, Harsh; Michailidis, George

2010-01-01

We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, w...

Decomposition of ECG by linear filtering.

Science.gov (United States)

Murthy, I S; Niranjan, U C

1992-01-01

A simple method is developed for the delineation of a given electrocardiogram (ECG) signal into its component waves. The properties of discrete cosine transform (DCT) are exploited for the purpose. The transformed signal is convolved with appropriate filters and the component waves are obtained by computing the inverse transform (IDCT) of the filtered signals. The filters are derived from the time signal itself. Analysis of continuous strips of ECG signals with various arrhythmias showed that the performance of the method is satisfactory both qualitatively and quantitatively. The small amplitude P wave usually had a high percentage rms difference (PRD) compared to the other large component waves.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

Science.gov (United States)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI 1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Handling of impact forces in inverse dynamics

NARCIS (Netherlands)

Bisseling, Rob W.; Hof, At L.

2006-01-01

In the standard inverse dynamic method, joint moments are assessed from ground reaction force data and position data, where segmental accelerations are calculated by numerical differentiation of position data after low-pass filtering. This method falls short in analyzing the impact phase, e.g.

Multi-Antenna Data Collector for Smart Metering Networks with Integrated Source Separation by Spatial Filtering

Science.gov (United States)

Quednau, Philipp; Trommer, Ralph; Schmidt, Lorenz-Peter

2016-03-01

Wireless transmission systems in smart metering networks share the advantage of lower installation costs due to the expandability of separate infrastructure but suffer from transmission problems. In this paper the issue of interference of wireless transmitted smart meter data with third party systems and data from other meters is investigated and an approach for solving the problem is presented. A multi-channel wireless m-bus receiver was developed to separate the desired data from unwanted interferers by spatial filtering. The according algorithms are presented and the influence of different antenna types on the spatial filtering is investigated. The performance of the spatial filtering is evaluated by extensive measurements in a realistic surrounding with several hundreds of active wireless m-bus transponders. These measurements correspond to the future environment for data-collectors as they took place in rural and urban areas with smart gas meters equipped with wireless m-bus transponders installed in almost all surrounding buildings.

Bayesian Parameter Estimation via Filtering and Functional Approximations

KAUST Repository

Matthies, Hermann G.

2016-11-25

The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.

Bayesian Parameter Estimation via Filtering and Functional Approximations

KAUST Repository

Matthies, Hermann G.; Litvinenko, Alexander; Rosic, Bojana V.; Zander, Elmar

2016-01-01

The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.

Network performance measurements as part of feasibility studies on moving an ATLAS event filter to off-site institutes

CERN Document Server

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...

Source-independent time-domain waveform inversion using convolved wavefields: Application to the encoded multisource waveform inversion

KAUST Repository

Choi, Yun Seok

2011-09-01

Full waveform inversion requires a good estimation of the source wavelet to improve our chances of a successful inversion. This is especially true for an encoded multisource time-domain implementation, which, conventionally, requires separate-source modeling, as well as the Fourier transform of wavefields. As an alternative, we have developed the source-independent time-domain waveform inversion using convolved wavefields. Specifically, the misfit function consists of the convolution of the observed wavefields with a reference trace from the modeled wavefield, plus the convolution of the modeled wavefields with a reference trace from the observed wavefield. In this case, the source wavelet of the observed and the modeled wavefields are equally convolved with both terms in the misfit function, and thus, the effects of the source wavelets are eliminated. Furthermore, because the modeled wavefields play a role of low-pass filtering, the observed wavefields in the misfit function, the frequency-selection strategy from low to high can be easily adopted just by setting the maximum frequency of the source wavelet of the modeled wavefields; and thus, no filtering is required. The gradient of the misfit function is computed by back-propagating the new residual seismograms and applying the imaging condition, similar to reverse-time migration. In the synthetic data evaluations, our waveform inversion yields inverted models that are close to the true model, but demonstrates, as predicted, some limitations when random noise is added to the synthetic data. We also realized that an average of traces is a better choice for the reference trace than using a single trace. © 2011 Society of Exploration Geophysicists.

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

Science.gov (United States)

Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

2014-03-01

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

Interactive inverse kinematics for human motion estimation

DEFF Research Database (Denmark)

Engell-Nørregård, Morten Pol; Hauberg, Søren; Lapuyade, Jerome

2009-01-01

We present an application of a fast interactive inverse kinematics method as a dimensionality reduction for monocular human motion estimation. The inverse kinematics solver deals efficiently and robustly with box constraints and does not suffer from shaking artifacts. The presented motion...... to significantly speed up the particle filtering. It should be stressed that the observation part of the system has not been our focus, and as such is described only from a sense of completeness. With our approach it is possible to construct a robust and computationally efficient system for human motion estimation....

Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network

Directory of Open Access Journals (Sweden)

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.

Systematic Design of the Lead-Lag Network Method for Active Damping in LCL-Filter Based Three Phase Converters

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...

Waveform inversion with exponential damping using a deconvolution-based objective function

KAUST Repository

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.

[Investigation of fast filter of ECG signals with lifting wavelet and smooth filter].

Science.gov (United States)

Li, Xuefei; Mao, Yuxing; He, Wei; Yang, Fan; Zhou, Liang

2008-02-01

The lifting wavelet is used to decompose the original ECG signals and separate them into the approach signals with low frequency and the detail signals with high frequency, based on frequency characteristic. Parts of the detail signals are ignored according to the frequency characteristic. To avoid the distortion of QRS Complexes, the approach signals are filtered by an adaptive smooth filter with a proper threshold value. Through the inverse transform of the lifting wavelet, the reserved approach signals are reconstructed, and the three primary kinds of noise are limited effectively. In addition, the method is fast and there is no time delay between input and output.

One-dimensional nonlinear inverse heat conduction technique

International Nuclear Information System (INIS)

Hills, R.G.; Hensel, E.C. Jr.

1986-01-01

The one-dimensional nonlinear problem of heat conduction is considered. A noniterative space-marching finite-difference algorithm is developed to estimate the surface temperature and heat flux from temperature measurements at subsurface locations. The trade-off between resolution and variance of the estimates of the surface conditions is discussed quantitatively. The inverse algorithm is stabilized through the use of digital filters applied recursively. The effect of the filters on the resolution and variance of the surface estimates is quantified. Results are presented which indicate that the technique is capable of handling noisy measurement data

A Multipath Routing Protocol Based on Bloom Filter for Multihop Wireless Networks

Directory of Open Access Journals (Sweden)

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.

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

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

Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks.

Science.gov (United States)

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-11-11

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer-Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

Science.gov (United States)

Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

Science.gov (United States)

Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

2017-11-01

The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

OpenAIRE

Naikwad, S. N.; Dudul, S. V.

2009-01-01

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 tempora...

Tower-Based Greenhouse Gas Measurement Network Design---The National Institute of Standards and Technology North East Corridor Testbed.

Science.gov (United States)

Lopez-Coto, Israel; Ghosh, Subhomoy; Prasad, Kuldeep; Whetstone, James

2017-09-01

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k -means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Information filtering in evolving online networks

Science.gov (United States)

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.

Towards effective and robust list-based packet filter for signature-based network intrusion detection: an engineering approach

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...... this problem, packet filtration is a promising solution to reduce unwanted traffic. Motivated by this, in this work, a list-based packet filter was designed and an engineering method of combining both blacklist and whitelist techniques was introduced. To further secure such filters against IP spoofing attacks...... in traffic filtration as well as workload reduction, and is robust against IP spoofing attacks....

Filtering Undesirable Flows in Networks

NARCIS (Netherlands)

Polevoy, G.; Trajanovski, S.; Grosso, P.; de Laat, C.; Gao, X.; Du, H.; Han, M.

2017-01-01

We study the problem of fully mitigating the effects of denial of service by filtering the minimum necessary set of the undesirable flows. First, we model this problem and then we concentrate on a subproblem where every good flow has a bottleneck. We prove that unless P=NP, this subproblem is

"Blocking" and "Filtering": a Commentary on Mobile Technology, Racism, and the Sexual Networks of Young Black MSM (YBMSM).

Science.gov (United States)

Winder, Terrell J A; Lea, Charles H

2018-04-30

While research investigates the role and influence of geo-social networking (GSN) applications on HIV, less is known about the impact of GSN functions on disease transmission. In our formative research on young Black men who have sex with men's (YBMSM) technology use patterns and preferences for a smartphone-based HIV prevention intervention, we found that study participants used GSN "block" and "filter" functions as protective mechanisms against racism and racial sexual discrimination. Yet, we suggest that these functions may unintentionally create restrictive sexual networks that likely increase their risk for disease transmission. As such, we contend that attention to the unintended effects of these protective mechanisms against racism on GSN applications is fundamentally a public health issue that requires more research and explicit intervention. Ultimately, we use this work to hypothesize the role of blocking and filtering as a strategy to avoid racism on GSN applications that may partly explain HIV disparities among YBMSM.

Investigating the Effects of the 0.05 Hz First-order High-pass Filter on the Electrocardiogram

DEFF Research Database (Denmark)

Isaksen, Jonas; Leber, Remo; Schmid, Ramun

2016-01-01

Background: A thorough review is needed for the first-order 0.05 Hz high-pass filter, which was introduced almost fifty years ago before modern techniques were available. We quantify the effectiveness of inverse filtering and assess the changes that the filter imposes on the electrocardiogram (ECG...

Logical spin-filtering in a triangular network of quantum nanorings with a Rashba spin-orbit interaction

Science.gov (United States)

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.

AN APPLE GRADING SYSTEM ACCORDING TO EUROPEAN FRUIT QUALITY STANDARDS USING GABOR FILTER AND ARTIFICIAL NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

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.

Advanced Multivariate Inversion Techniques for High Resolution 3D Geophysical Modeling

Science.gov (United States)

2011-09-01

2005). We implemented a method to increase the usefulness of gravity data by filtering the Bouguer anomaly map. Though commonly applied 40 km 30 35...remove the long-wavelength components from the Bouguer gravity map we follow Tessema and Antoine (2004), who use an upward continuation method and...inversion of group velocities and gravity. (a) Top: Group velocities from a representative cell in the model. Bottom: Filtered Bouguer anomalies. (b

Distributed Event-Based Set-Membership Filtering for a Class of Nonlinear Systems With Sensor Saturations Over Sensor Networks.

Science.gov (United States)

Ma, Lifeng; Wang, Zidong; Lam, Hak-Keung; Kyriakoulis, Nikos

2017-11-01

In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

Electrodeposited Structurally Stable V2O5 Inverse Opal Networks as High Performance Thin Film Lithium Batteries.

Science.gov (United States)

Armstrong, Eileen; McNulty, David; Geaney, Hugh; O'Dwyer, Colm

2015-12-09

High performance thin film lithium batteries using structurally stable electrodeposited V2O5 inverse opal (IO) networks as cathodes provide high capacity and outstanding cycling capability and also were demonstrated on transparent conducting oxide current collectors. The superior electrochemical performance of the inverse opal structures was evaluated through galvanostatic and potentiodynamic cycling, and the IO thin film battery offers increased capacity retention compared to micron-scale bulk particles from improved mechanical stability and electrical contact to stainless steel or transparent conducting current collectors from bottom-up electrodeposition growth. Li(+) is inserted into planar and IO structures at different potentials, and correlated to a preferential exposure of insertion sites of the IO network to the electrolyte. Additionally, potentiodynamic testing quantified the portion of the capacity stored as surface bound capacitive charge. Raman scattering and XRD characterization showed how the IO allows swelling into the pore volume rather than away from the current collector. V2O5 IO coin cells offer high initial capacities, but capacity fading can occur with limited electrolyte. Finally, we demonstrate that a V2O5 IO thin film battery prepared on a transparent conducting current collector with excess electrolyte exhibits high capacities (∼200 mAh g(-1)) and outstanding capacity retention and rate capability.

Removing tidal-period variations from time-series data using low-pass digital filters

Science.gov (United States)

Walters, Roy A.; Heston, Cynthia

1982-01-01

Several low-pass, digital filters are examined for their ability to remove tidal Period Variations from a time-series of water surface elevation for San Francisco Bay. The most efficient filter is the one which is applied to the Fourier coefficients of the transformed data, and the filtered data recovered through an inverse transform. The ability of the filters to remove the tidal components increased in the following order: 1) cosine-Lanczos filter, 2) cosine-Lanczos squared filter; 3) Godin filter; and 4) a transform fitter. The Godin fitter is not sufficiently sharp to prevent severe attenuation of 2–3 day variations in surface elevation resulting from weather events.

Using active power filter to compensate the current component of asymmetrical non-linear load in the four wire network

Directory of Open Access Journals (Sweden)

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

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.

A family of inversion formulas in thermoacoustic tomography

KAUST Repository

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.

Inverse Problems in a Bayesian Setting

KAUST Repository

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.

Inverse Problems in a Bayesian Setting

KAUST Repository

Matthies, Hermann G.; Zander, Elmar; Rosić, Bojana V.; Litvinenko, Alexander; Pajonk, Oliver

2016-01-01

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.

The role of the umbrella inversion mode in proton diffusion

Science.gov (United States)

Hassanali, Ali A.; Giberti, Federico; Sosso, Gabriele C.; Parrinello, Michele

2014-04-01

Here, using ab initio molecular dynamics (AIMD) simulations, we elucidate the role of the umbrella inversion mode of the hydronium in proton transfer (PT) in liquid water. The hydrophobic face of the hydronium oxygen experiences asymmetries in the solvent potential along the inversion coordinate and this has a rather drastic effect on the barrier for proton transfer. This behavior is coupled to the fluctuations of voids or cavities in the vicinity of the hydronium in the water network. The peculiar inversion mode can either trap or release the proton from different parts of the water network.

Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection

Science.gov (United States)

Janssens, Eline; De Beenhouwer, Jan; Van Dael, Mattias; De Schryver, Thomas; Van Hoorebeke, Luc; Verboven, Pieter; Nicolai, Bart; Sijbers, Jan

2018-03-01

X-ray imaging is an important tool for quality control since it allows to inspect the interior of products in a non-destructive way. Conventional x-ray imaging, however, is slow and expensive. Inline x-ray inspection, on the other hand, can pave the way towards fast and individual quality control, provided that a sufficiently high throughput can be achieved at a minimal cost. To meet these criteria, an inline inspection acquisition geometry is proposed where the object moves and rotates on a conveyor belt while it passes a fixed source and detector. Moreover, for this acquisition geometry, a new neural-network-based reconstruction algorithm is introduced: the neural network Hilbert transform based filtered backprojection. The proposed algorithm is evaluated both on simulated and real inline x-ray data and has shown to generate high quality reconstructions of 400  ×  400 reconstruction pixels within 200 ms, thereby meeting the high throughput criteria.

Cerebellum-inspired neural network solution of the inverse kinematics problem.

Science.gov (United States)

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.

Classification of movement intention by spatially filtered electromagnetic inverse solutions

International Nuclear Information System (INIS)

Congedo, M; Lotte, F; Lecuyer, A

2006-01-01

We couple standardized low-resolution electromagnetic tomography, an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (brain-computer interface) competition 2003, data set IV. The data set is from an experiment where a subject performed a self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represent uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and our extraction of only one attribute per class, our method yields accuracy similar to the winners of the competition for this data set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers

The application of neural network techniques to magnetic and optical inverse problems

International Nuclear Information System (INIS)

Jones, H.V.

2000-12-01

The processing power of the computer has increased at unimaginable rates over the last few decades. However, even today's fastest computer can take several hours to find solutions to some mathematical problems; and there are instances where a high powered supercomputer may be impractical, with the need for near instant solutions just as important (such as in an on-line testing system). This led us to believe that such complex problems could be solved using a novel approach, whereby the system would have prior knowledge about the expected solutions through a process of learning. One method of approaching this kind of problem is through the use of machine learning. Just as a human can be trained and is able to learn from past experiences, a machine is can do just the same. This is the concept of neural networks. The research which was conducted involves the investigation of various neural network techniques, and their applicability to solve some known complex inverse problems in the field of magnetic and optical recording. In some cases a comparison is also made to more conventional methods of solving the problems, from which it was possible to outline some key advantages of using a neural network approach. We initially investigated the application of neural networks to transverse susceptibility data in order to determine anisotropy distributions. This area of research is proving to be very important, as it gives us information about the switching field distribution, which then determines the minimum transition width achievable in a medium, and affects the overwrite characteristics of the media. Secondly, we investigated a similar situation, but applied to an optical problem. This involved the determination of important compact disc parameters from the diffraction pattern of a laser from a disc. This technique was then intended for use in an on-line testing system. Finally we investigated another area of neural networks with the analysis of magnetisation maps and

Multiscale Phase Inversion of Seismic Data

KAUST Repository

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.

Group inverses of M-matrices and their applications

CERN Document Server

Kirkland, Stephen J

2013-01-01

Group inverses for singular M-matrices are useful tools not only in matrix analysis, but also in the analysis of stochastic processes, graph theory, electrical networks, and demographic models. Group Inverses of M-Matrices and Their Applications highlights the importance and utility of the group inverses of M-matrices in several application areas. After introducing sample problems associated with Leslie matrices and stochastic matrices, the authors develop the basic algebraic and spectral properties of the group inverse of a general matrix. They then derive formulas for derivatives of matrix f

Theory and design of microwave filters

CERN Document Server

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.

Temperature profile retrievals with extended Kalman-Bucy filters

Science.gov (United States)

Ledsham, W. H.; Staelin, D. H.

1979-01-01

The Extended Kalman-Bucy Filter is a powerful technique for estimating non-stationary random parameters in situations where the received signal is a noisy non-linear function of those parameters. A practical causal filter for retrieving atmospheric temperature profiles from radiances observed at a single scan angle by the Scanning Microwave Spectrometer (SCAMS) carried on the Nimbus 6 satellite typically shows approximately a 10-30% reduction in rms error about the mean at almost all levels below 70 mb when compared with a regression inversion.

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

Neural network based multiscale image restoration approach

Science.gov (United States)

de Castro, Ana Paula A.; da Silva, José D. S.

2007-02-01

This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

Application of Extreme Learning Machines to inverse neutron kinetics

International Nuclear Information System (INIS)

Picca, Paolo; Furfaro, Roberto

2017-01-01

Highlights: • The paper applies the Extreme Learning Machines (ELMs) to inverse reactor problems. • Multi-group transport model is used for the inversion as opposed to point kinetics. • ELMs are compared against Artificial Neural Networks (ANNs). • Various options are tested to improve the reliability of the estimation. • Results highlight the potential of the ELM approach. - Abstract: The paper presents the application of Extreme Leaning Machines (ELMs) for inverse reactor kinetic applications. ELMs were proposed by Huang and co-workers (2004, 2006a,b, 2015), which showed their enhances capabilities in terms of training speed and generalization with respect to classical Artificial Neural Networks (ANNs). ELMs are here implemented for reactivity determination as an alternative to ANNs (e.g. Picca et al. (2008)) and Gaussian Processes (Picca and Furfaro, 2012). After a review of the main features of ELMs, their application to inverse kinetic problems is proposed. The ELMs performance is tested on a typical accelerator drive system configuration (Yalina reactor) and the inversion is carried out on an accurate kinetic model (multi-group transport).

Multiscattering inversion for low-model wavenumbers

KAUST Repository

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.

Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability

Science.gov (United States)

Kar, Soummya; Moura, José M. F.

2011-04-01

The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where inter-sensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1. the GIKF error process remains stochastically bounded, irrespective of the instability properties of the random process dynamics; and 2. the network achieves \\emph{weak consensus}, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semi-definite matrices (independent of the initial state.) To prove these results, we interpret the filtered states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a non-stationary Markov chain on the network graph.

Volterra Filtering for ADC Error Correction

Directory of Open Access Journals (Sweden)

J. Saliga

2001-09-01

Full Text Available Dynamic non-linearity of analog-to-digital converters (ADCcontributes significantly to the distortion of digitized signals. Thispaper introduces a new effective method for compensation such adistortion based on application of Volterra filtering. Considering ana-priori error model of ADC allows finding an efficient inverseVolterra model for error correction. Efficiency of proposed method isdemonstrated on experimental results.

Air radioactivity: to assess risks. Tools answer citizen questions. The Opera-Air network: the journey of a filter

International Nuclear Information System (INIS)

Didier, Damien; Gariel, Jean-Christophe; Bruno, Valerie; Debayle, Christophe

2017-01-01

Very highly efficient filters containing a porous glass fibre fabric are used in industrial installations to trap radioactive or toxic particles in order to limit their release, notably in accidental situations. Thus this set of articles discusses various issues related to the use of such filters. A first one describes how air radioactivity is continuously monitored by two coexisting networks: Opera-Air and Teleray. It indicates where air radioactivity comes from, and how the origin of a release can be determined, and outlines the importance of modelling tools. Air monitoring about the Gravelines nuclear power plant is briefly presented with a drawing. A second article comments the existence of numerous tools which are used as information channels about the monitoring of air radioactivity: web sites, mobile application, and so on. The last article briefly describes the journey of a filter from its removal on a Monday to a complete and validated analysis which lasts between two and four weeks

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.

Evaluation of cancer detection efficiency by means of hybrid and inverse filter in chest radiography

International Nuclear Information System (INIS)

Kim, Youn Young; Kim, Tae Young; Kim, Hyun Ji; Kim, Jung Min; Park, Min Seock

2013-01-01

The purpose of this study is to evaluate usefulness of Hybrid image and Inverse image about detection of tumor shadow in chest radiography using ROC analysis. Original images of 60 cases are selected from Standards digital image date base issued by the Japanese Society of Radiological Technology. Through computer language of C, Inverse images of 60 cases and Hybrid image of 30 cases are made. The continues reading experiment was conducted. In the case of inverse image were observed by 5 radiographer and 2 radiologist. In the case of In case of Hybrid image were observed by 3 student radiographer and 2 experienced radiographer. ROC curve are constructed using ROCKIT Program made by Metz. In Inverse image, a Az of average ROC curve was increases from 0.742 of original image to 0.775 of inverse image. In normal cases, the effect of the detrimental is same to that of the beneficial, however In abnormal cases, the beneficial effect is greater than detrimental effect. However in Hybrid image, a Az of average ROC curve was decreases from 0.5253 of original image to 0.4868 of Hybrid image. In Normal cases, the effect of the detrimental is greater than that of the Beneficial, however In abnormal cases, the Beneficial effect is greater than detrimental effect. The inverse image can be more positively considered for the detecting of tumor than the hybrid image

A new strategy for weak events in sparse networks: the first-motion polarity solutions constrained by single-station waveform inversion

Czech Academy of Sciences Publication Activity Database

Fojtíková, Lucia; Zahradník, J.

2014-01-01

Roč. 85, č. 6 (2014), s. 1265-1274 ISSN 0895-0695 R&D Projects: GA ČR GAP210/12/2336 Institutional support: RVO:67985891 Keywords : weak events * sparse networks * focal mechanism * waveform inversion Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 2.156, year: 2014 http://srl.geoscienceworld.org/content/85/6/1265.full

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.

Plasmonic band-stop filter with asymmetric rectangular ring for WDM networks

International Nuclear Information System (INIS)

Nezhad, Vahid Foroughi; Abrishamian, Mohammad Sadegh; Abaslou, Siamak

2013-01-01

We proposed a simple asymmetric rectangular band-stop filter based on metal–insulator–metal plasmonic waveguides. It is shown that the performance of the structure as a filter strongly depends on the asymmetry of the rectangular structure. An analytical model based on the analogy between MDM waveguides and the microwave transmission line is used to calculate the resonance wavelengths and explain the behavior of the filter. The bandwidth of spectra can be easily manipulated by adjusting the topological parameters of the filter. It is also demonstrated that by adjusting the bandwidth, the filter can be used for CWDM standard channels. The filter behavior is verified using the numerical finite difference time domain (FDTD) method. The filter is compact and has a footprint of 1 μm × 0.5 μm, which is suitable for integrated optical circuits. (paper)

The Ellipticity Filter-A Proposed Solution to the Mixed Event Problem in Nuclear Seismic Discrimination

Science.gov (United States)

1974-09-07

ellipticity filter. The source waveforms are recreated by an inverse transform of those complex ampli- tudes associated with the same azimuth...terms of the three complex data points and the ellipticity. Having solved the equations for all frequency bins, the inverse transform of...Transform of those complex amplitudes associated with Source 1, yielding the signal a (t). Similarly, take the inverse Transform of all

Improved pulsed photoacoustic detection by means of an adapted filter

Science.gov (United States)

González, M.; Santiago, G.; Peuriot, A.; Slezak, V.; Mosquera, C.

2005-06-01

We present a numerical and experimental study of two adapted filters devised to the quantitative analysis of weak photoacoustic signals. The first one is a simple convolution-type one and the other is based on neural networks of the multilayer perceptron type. The theoretical signal used as one of the inputs in both filters is derived from the solution of the transient response of the acoustic cell modeled with a simple transmission-line analogue. The filters were tested numerically by using the theoretical signal corrupted with white noise. After 500 iterations it was possible to define an average error for the returned value of each filter. Since the neural network outperformed the convolution-type, we assessed its performance by measuring SF6 traces diluted in N2 and excited by tuned TEA CO2 laser. The results show the use of the neural network filter allows recovering a signal with poor signal-to-noise ratio without resorting to extensive averaging, thus reducing the acquisition time while improving the precision of the measurement.

A simple procedure to estimate reactivity with good noise filtering characteristics

International Nuclear Information System (INIS)

Shimazu, Yoichiro

2014-01-01

Highlights: • A new and simple on-line reactivity estimation method is proposed. • The estimator has robust noise filtering characteristics. • The noise filtering is equivalent to those of conventional reactivity meters. • The new estimator eliminates the burden of selecting optimum filter constants. • The new estimation performance is assessed without and with measurement noise. - Abstract: A new and simple on-line reactivity estimation method is proposed. The estimator has robust noise filtering characteristics without the use of complex filters. The noise filtering capability is equivalent to or better than that of a conventional estimator based on Inverse Point Kinetics (IPK). The new estimator can also eliminate the burden of selecting optimum filter time constants, such as would be required for the IPK-based estimator, or noise covariance matrices, which are needed if the extended Kalman filter (EKF) technique is used. In this paper, the new estimation method is introduced and its performance assessed without and with measurement noise

Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays.

Science.gov (United States)

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.

Imaging disturbance zones ahead of a tunnel by elastic full-waveform inversion: Adjoint gradient based inversion vs. parameter space reduction using a level-set method

Directory of Open Access Journals (Sweden)

Andre Lamert

2018-03-01

Full Text Available We present and compare two flexible and effective methodologies to predict disturbance zones ahead of underground tunnels by using elastic full-waveform inversion. One methodology uses a linearized, iterative approach based on misfit gradients computed with the adjoint method while the other uses iterative, gradient-free unscented Kalman filtering in conjunction with a level-set representation. Whereas the former does not involve a priori assumptions on the distribution of elastic properties ahead of the tunnel, the latter introduces a massive reduction in the number of explicit model parameters to be inverted for by focusing on the geometric form of potential disturbances and their average elastic properties. Both imaging methodologies are validated through successful reconstructions of simple disturbances. As an application, we consider an elastic multiple disturbance scenario. By using identical synthetic time-domain seismograms as test data, we obtain satisfactory, albeit different, reconstruction results from the two inversion methodologies. The computational costs of both approaches are of the same order of magnitude, with the gradient-based approach showing a slight advantage. The model parameter space reduction approach compensates for this by additionally providing a posteriori estimates of model parameter uncertainty. Keywords: Tunnel seismics, Full waveform inversion, Seismic waves, Level-set method, Adjoint method, Kalman filter

Multi-scattering inversion for low model wavenumbers

KAUST Repository

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.

An Ensemble of Neural Networks for Online Electron Filtering at the ATLAS Experiment.

CERN Document Server

Da Fonseca Pinto, Joao Victor; The ATLAS collaboration

2018-01-01

In 2017 the ATLAS experiment implemented an ensemble of neural networks (NeuralRinger algorithm) dedicated to improving the performance of filtering events containing electrons in the high-input rate online environment of the Large Hadron Collider at CERN, Geneva. The ensemble employs a concept of calorimetry rings. The training procedure and final structure of the ensemble are used to minimize fluctuations from detector response, according to the particle energy and position of incidence. A detailed study was carried out to assess profile distortions in crucial offline quantities through the usage of statistical tests and residual analysis. These details and the online performance of this algorithm during the 2017 data-taking will be presented.

Conditioning the full waveform inversion gradient to welcome anisotropy

KAUST Repository

Alkhalifah, Tariq Ali

2014-01-01

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.

Conditioning the full waveform inversion gradient to welcome anisotropy

KAUST Repository

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.

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

Voltage-sensorless control has been investigated for Voltage Source Inverters (VSIs) for many years due to the reduced system cost and potentially improved system reliability. The VSI based Resistive Active Power Filters (R-APFs) are now widely used to prevent the harmonic resonance in power...... 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...

A Grid-Voltage-Sensorless Resistive Active Power Filter with Series LC-Filter

DEFF Research Database (Denmark)

Bai, Haofeng; Wang, Xiongfei; Blaabjerg, Frede

2018-01-01

Voltage-sensorless control has been investigated for Voltage Source Inverters (VSIs) for many years due to the reduced system cost and potentially improved system reliability. The VSI based Resistive Active Power Filters (R-APFs) are now widely used to prevent the harmonic resonance in power...... 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...

Molecular surface mesh generation by filtering electron density map.

Science.gov (United States)

Giard, Joachim; Macq, Benoît

2010-01-01

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Molecular Surface Mesh Generation by Filtering Electron Density Map

Directory of Open Access Journals (Sweden)

Joachim Giard

2010-01-01

Full Text Available Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Signal filtering algorithm for depth-selective diffuse optical topography

International Nuclear Information System (INIS)

Fujii, M; Nakayama, K

2009-01-01

A compact filtered backprojection algorithm that suppresses the undesirable effects of skin circulation for near-infrared diffuse optical topography is proposed. Our approach centers around a depth-selective filtering algorithm that uses an inverse problem technique and extracts target signals from observation data contaminated by noise from a shallow region. The filtering algorithm is reduced to a compact matrix and is therefore easily incorporated into a real-time system. To demonstrate the validity of this method, we developed a demonstration prototype for depth-selective diffuse optical topography and performed both computer simulations and phantom experiments. The results show that the proposed method significantly suppresses the noise from the shallow region with a minimal degradation of the target signal.

Inverse Problem and Variation Method to Optimize Cascade Heat Exchange Network in Central Heating System

Institute of Scientific and Technical Information of China (English)

ZHANG Yin; WEI Zhiyuan; ZHANG Yinping; WANG Xin

2017-01-01

Urban heating in northern China accounts for 40％ of total building energy usage.In central heating systems,heat is often transfened from heat source to users by the heat network where several heat exchangers arc installed at heat source,substations and terminals respectively.For given overall heating capacity and heat source temperarure,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 thernmal 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.

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...

Correlation filtering in financial time series (Invited Paper)

Science.gov (United States)

Aste, T.; Di Matteo, Tiziana; Tumminello, M.; Mantegna, R. N.

2005-05-01

We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al.,1 we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).

Preconditioner-free Wiener filtering with a dense noise matrix

Science.gov (United States)

Huffenberger, Kevin M.

2018-05-01

This work extends the Elsner & Wandelt (2013) iterative method for efficient, preconditioner-free Wiener filtering to cases in which the noise covariance matrix is dense, but can be decomposed into a sum whose parts are sparse in convenient bases. The new method, which uses multiple messenger fields, reproduces Wiener-filter solutions for test problems, and we apply it to a case beyond the reach of the Elsner & Wandelt (2013) method. We compute the Wiener-filter solution for a simulated Cosmic Microwave Background (CMB) map that contains spatially varying, uncorrelated noise, isotropic 1/f noise, and large-scale horizontal stripes (like those caused by atmospheric noise). We discuss simple extensions that can filter contaminated modes or inverse-noise-filter the data. These techniques help to address complications in the noise properties of maps from current and future generations of ground-based Microwave Background experiments, like Advanced ACTPol, Simons Observatory, and CMB-S4.

MR fingerprinting reconstruction with Kalman filter.

Science.gov (United States)

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.

Performance Analysis of ZigBee Wireless Networks for AAL through Hybrid Ray Launching and Collaborative Filtering

Directory of Open Access Journals (Sweden)

Peio Lopez-Iturri

2016-01-01

Full Text Available This paper presents a novel hybrid simulation method based on the combination of an in-house developed 3D ray launching algorithm and a collaborative filtering (CF technique, which will be used to analyze the performance of ZigBee-based wireless sensor networks (WSNs to enable ambient assisted living (AAL. The combination of Low Definition results obtained by means of a deterministic ray launching method and the application of a CF technique leads to a drastic reduction of the time and computational cost required to obtain accurate simulation results. The paper also reports that this kind of AAL indoor complex scenario with multiple wireless devices needs a thorough and personalized radioplanning analysis as radiopropagation has a strong dependence on the network topology and the specific morphology of the scenario. The wireless channel analysis performed by our hybrid method provides valuable insight into network design phases of complex wireless systems, typical in AAL-oriented environments. Thus, it results in optimizing network deployment, reducing overall interference levels, and increasing the overall system performance in terms of cost reduction, transmission rates, and energy efficiency.

Inverse modelling of fluvial sediment connectivity identifies characteristics and spatial distribution of sediment sources in a large river network.

Science.gov (United States)

Schmitt, R. J. P.; Bizzi, S.; Kondolf, G. M.; Rubin, Z.; Castelletti, A.

2016-12-01

Field and laboratory evidence indicates that the spatial distribution of transport in both alluvial and bedrock rivers is an adaptation to sediment supply. Sediment supply, in turn, depends on spatial distribution and properties (e.g., grain sizes and supply rates) of individual sediment sources. Analyzing the distribution of transport capacity in a river network could hence clarify the spatial distribution and properties of sediment sources. Yet, challenges include a) identifying magnitude and spatial distribution of transport capacity for each of multiple grain sizes being simultaneously transported, and b) estimating source grain sizes and supply rates, both at network scales. Herein, we approach the problem of identifying the spatial distribution of sediment sources and the resulting network sediment fluxes in a major, poorly monitored tributary (80,000 km2) of the Mekong. Therefore, we apply the CASCADE modeling framework (Schmitt et al. (2016)). CASCADE calculates transport capacities and sediment fluxes for multiple grainsizes on the network scale based on remotely-sensed morphology and modelled hydrology. CASCADE is run in an inverse Monte Carlo approach for 7500 random initializations of source grain sizes. In all runs, supply of each source is inferred from the minimum downstream transport capacity for the source grain size. Results for each realization are compared to sparse available sedimentary records. Only 1 % of initializations reproduced the sedimentary record. Results for these realizations revealed a spatial pattern in source supply rates, grain sizes, and network sediment fluxes that correlated well with map-derived patterns in lithology and river-morphology. Hence, we propose that observable river hydro-morphology contains information on upstream source properties that can be back-calculated using an inverse modeling approach. Such an approach could be coupled to more detailed models of hillslope processes in future to derive integrated models

Calculation of reactivity using a finite impulse response filter

Energy Technology Data Exchange (ETDEWEB)

Suescun Diaz, Daniel [COPPE/UFRJ, Programa de Engenharia Nuclear, Caixa Postal 68509, CEP 21941-914, RJ (Brazil); Senra Martinez, Aquilino [COPPE/UFRJ, Programa de Engenharia Nuclear, Caixa Postal 68509, CEP 21941-914, RJ (Brazil)], E-mail: aquilino@lmp.ufrj.br; Carvalho Da Silva, Fernando [COPPE/UFRJ, Programa de Engenharia Nuclear, Caixa Postal 68509, CEP 21941-914, RJ (Brazil)

2008-03-15

A new formulation is presented in this paper to solve the inverse kinetics equation. This method is based on the Laplace transform of the point kinetics equations, resulting in an expression equivalent to the inverse kinetics equation as a function of the power history. Reactivity can be written in terms of the summation of convolution with response to impulse, characteristic of a linear system. For its digital form the Z-transform is used, which is the discrete version of the Laplace transform. This new method of reactivity calculation has very special features, amongst which it can be pointed out that the linear part is characterized by a filter named finite impulse response (FIR). The FIR filter will always be, stable and non-varying in time, and, apart from this, it can be implemented in the non-recursive form. This type of implementation does not require feedback, allowing the calculation of reactivity in a continuous way.

Consistence of Network Filtering Rules

Institute of Scientific and Technical Information of China (English)

SHE Kun; WU Yuancheng; HUANG Juncai; ZHOU Mingtian

2004-01-01

The inconsistence of firewall/VPN(Virtual Private Network) rule makes a huge maintainable cost.With development of Multinational Company,SOHO office,E-government the number of firewalls/VPN will increase rapidly.Rule table in stand-alone or network will be increased in geometric series accordingly.Checking the consistence of rule table manually is inadequate.A formal approach can define semantic consistence,make a theoretic foundation of intelligent management about rule tables.In this paper,a kind of formalization of host rules and network ones for auto rule-validation based on SET theory were proporsed and a rule validation scheme was defined.The analysis results show the superior performance of the methods and demonstrate its potential for the intelligent management based on rule tables.

A Kalman-filter based approach to identification of time-varying gene regulatory networks.

Directory of Open Access Journals (Sweden)

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.

Generation of Long Waves using Non-Linear Digital Filters

DEFF Research Database (Denmark)

Høgedal, Michael; Frigaard, Peter

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...

A regional high-resolution carbon flux inversion of North America for 2004

Science.gov (United States)

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

Inverse calculation of strain profiles from ETDR measurements using artificial neural networks

Directory of Open Access Journals (Sweden)

R. Höhne

2017-12-01

Full Text Available A novel carbon fibre sensor is developed for the spatially resolved strain measurement. A unique feature of the sensor is the fibre-break resistive measurement principle and the two-core transmission line design. The electrical time domain reflectometry (ETDR is used in order to realize a spatially resolved measurement of the electrical parameters of the sensor. In this contribution, the process of mapping between the ETDR signals to the existing strain profile is described. Artificial neural networks (ANNs are used to solve the inverse electromagnetic problem. The investigations were carried out with a sensor patch in a cantilever arm configuration. Overall, 136 experiments with varying strain distribution over the sensor length were performed to generate the necessary training data to learn the ANN model. The validation of the ANN highlights the feasibility as well as the current limits concerning the quantitative accuracy of mapping ETDR signals to strain profiles.

Inverse problem of radiofrequency sounding of ionosphere

Science.gov (United States)

Velichko, E. N.; Yu. Grishentsev, A.; Korobeynikov, A. G.

2016-01-01

An algorithm for the solution of the inverse problem of vertical ionosphere sounding and a mathematical model of noise filtering are presented. An automated system for processing and analysis of spectrograms of vertical ionosphere sounding based on our algorithm is described. It is shown that the algorithm we suggest has a rather high efficiency. This is supported by the data obtained at the ionospheric stations of the so-called “AIS-M” type.

Method and means for filtering polychlorinated biphenyls from a gas stream

International Nuclear Information System (INIS)

Sowinski, R.F.

1992-01-01

This patent describes a method of filtering, adjacent to an end user-customer's residence or business in which at least a single gas appliance is located, a natural gas stream in which polychlorinated biphenyls (PCB's) and degraded PCB products have been concentrated at sufficient levels to be a health threat in a natural gas gathering and distributing network. It comprises: introducing the natural gas stream to a filter selected from a group that includes impingement, absorbing and adsorbing media whereby PCB's and degraded PCB products concentrated in the gas stream at sufficient levels to be a health threat by a periodic loading of the natural gas within the gathering and distributing network, are filtered from the gas stream and captured irrespective of mode of transport, passing the filtered natural gas stream to the customer's gas appliance wherein safe use of the energy associated with the stream occurs; periodically and safely removing the filter, inserting a new filter in place of the removed filter

Ensemble Kalman methods for inverse problems

International Nuclear Information System (INIS)

Iglesias, Marco A; Law, Kody J H; Stuart, Andrew M

2013-01-01

The ensemble Kalman filter (EnKF) was introduced by Evensen in 1994 (Evensen 1994 J. Geophys. Res. 99 10143–62) as a novel method for data assimilation: state estimation for noisily observed time-dependent problems. Since that time it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. In this paper we propose the application of an iterative ensemble Kalman method for the solution of a wide class of inverse problems. In this context we show that the estimate of the unknown function that we obtain with the ensemble Kalman method lies in a subspace A spanned by the initial ensemble. Hence the resulting error may be bounded above by the error found from the best approximation in this subspace. We provide numerical experiments which compare the error incurred by the ensemble Kalman method for inverse problems with the error of the best approximation in A, and with variants on traditional least-squares approaches, restricted to the subspace A. In so doing we demonstrate that the ensemble Kalman method for inverse problems provides a derivative-free optimization method with comparable accuracy to that achieved by traditional least-squares approaches. Furthermore, we also demonstrate that the accuracy is of the same order of magnitude as that achieved by the best approximation. Three examples are used to demonstrate these assertions: inversion of a compact linear operator; inversion of piezometric head to determine hydraulic conductivity in a Darcy model of groundwater flow; and inversion of Eulerian velocity measurements at positive times to determine the initial condition in an incompressible fluid. (paper)

New Smith Internal Model Control of Two-Motor Drive System Based on Neural Network Generalized Inverse

Directory of Open Access Journals (Sweden)

Guohai Liu

2016-01-01

Full Text Available Multimotor drive system is widely applied in industrial control system. Considering the characteristics of multi-input multioutput, nonlinear, strong-coupling, and time-varying delay in two-motor drive systems, this paper proposes a new Smith internal model (SIM control method, which is based on neural network generalized inverse (NNGI. This control strategy adopts the NNGI system to settle the decoupling issue and utilizes the SIM control structure to solve the delay problem. The NNGI method can decouple the original system into several composite pseudolinear subsystems and also complete the pole-zero allocation of subsystems. Furthermore, based on the precise model of pseudolinear system, the proposed SIM control structure is used to compensate the network delay and enhance the interference resisting the ability of the whole system. Both simulation and experimental results are given, verifying that the proposed control strategy can effectively solve the decoupling problem and exhibits the strong robustness to load impact disturbance at various operations.

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.

Method for filtering radon from a gas system

International Nuclear Information System (INIS)

Sowinski, R.F.

1992-01-01

This patent describes a method of filtering, adjacent to an end user-customer's residence, or business in which at least a single gas appliance is located, a natural gas stream in which benz-a-anthracene has been concentrated at sufficient levels to be a health threat in a natural gas gathering and distributing network. It comprises introducing the natural gas stream to a filter selected from a group that includes impingement, passing the filtered natural gas stream to the customer's gas appliance wherein safe use of the energy associated with the stream occurs, periodically and safely removing the filter for disposing of captured benz-a-anthracene, inserting a new filter in place of the removed filter of step

Full waveform inversion based on scattering angle enrichment with application to real dataset

KAUST Repository

Wu, Zedong

2015-08-19

Reflected waveform inversion (RWI) provides a method to reduce the nonlinearity of the standard full waveform inversion (FWI). However, the drawback of the existing RWI methods is inability to utilize diving waves and the extra sensitivity to the migrated image. We propose a combined FWI and RWI optimization problem through dividing the velocity into the background and perturbed components. We optimize both the background and perturbed components, as independent parameters. The new objective function is quadratic with respect to the perturbed component, which will reduce the nonlinearity of the optimization problem. Solving this optimization provides a true amplitude image and utilizes the diving waves to update the velocity of the shallow parts. To insure a proper wavenumber continuation, we use an efficient scattering angle filter to direct the inversion at the early stages to direct energy corresponding to large (smooth velocity) scattering angles to the background velocity update and the small (high wavenumber) scattering angles to the perturbed velocity update. This efficient implementation of the filter is fast and requires less memory than the conventional approach based on extended images. Thus, the new FWI procedure updates the background velocity mainly along the wavepath for both diving and reflected waves in the initial stages. At the same time, it updates the perturbation with mainly reflections (filtering out the diving waves). To demonstrate the capability of this method, we apply it to a real 2D marine dataset.

Analog fault diagnosis by inverse problem technique

KAUST Repository

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.

Conditioning the full-waveform inversion gradient to welcome anisotropy

KAUST Repository

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.

Visualizing deep neural network by alternately image blurring and deblurring.

Science.gov (United States)

Wang, Feng; Liu, Haijun; Cheng, Jian

2018-01-01

Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created. Our algorithm is good at extracting the details in the images, which are usually filtered by previous methods in the visualizations. Extensive experiments on AlexNet, VGGNet and GoogLeNet illustrate that we can better understand the neural networks utilizing the knowledge obtained by the visualization. Copyright © 2017 Elsevier Ltd. All rights reserved.

An exact algorithm for optimal MAE stack filter design.

Science.gov (United States)

Dellamonica, Domingos; Silva, Paulo J S; Humes, Carlos; Hirata, Nina S T; Barrera, Junior

2007-02-01

We propose a new algorithm for optimal MAE stack filter design. It is based on three main ingredients. First, we show that the dual of the integer programming formulation of the filter design problem is a minimum cost network flow problem. Next, we present a decomposition principle that can be used to break this dual problem into smaller subproblems. Finally, we propose a specialization of the network Simplex algorithm based on column generation to solve these smaller subproblems. Using our method, we were able to efficiently solve instances of the filter problem with window size up to 25 pixels. To the best of our knowledge, this is the largest dimension for which this problem was ever solved exactly.

An inversion formula for the exponential Radon transform in spatial domain with variable focal-length fan-beam collimation geometry

International Nuclear Information System (INIS)

Wen Junhai; Liang Zhengrong

2006-01-01

Inverting the exponential Radon transform has a potential use for SPECT (single photon emission computed tomography) imaging in cases where a uniform attenuation can be approximated, such as in brain and abdominal imaging. Tretiak and Metz derived in the frequency domain an explicit inversion formula for the exponential Radon transform in two dimensions for parallel-beam collimator geometry. Progress has been made to extend the inversion formula for fan-beam and varying focal-length fan-beam (VFF) collimator geometries. These previous fan-beam and VFF inversion formulas require a spatially variant filtering operation, which complicates the implementation and imposes a heavy computing burden. In this paper, we present an explicit inversion formula, in which a spatially invariant filter is involved. The formula is derived and implemented in the spatial domain for VFF geometry (where parallel-beam and fan-beam geometries are two special cases). Phantom simulations mimicking SPECT studies demonstrate its accuracy in reconstructing the phantom images and efficiency in computation for the considered collimator geometries

Rotation speed measurement for turbine governor: torsion filtering by using Kalman filter

International Nuclear Information System (INIS)

Houry, M.P.; Bourles, H.

1996-01-01

The rotation speed of a turbogenerator is disturbed by its shaft torsion. Obtaining a filtered measure of this speed is a problem of a great practical importance for turbine governor. A good filtering of this speed must meet two requirements: it must cut frequencies of the shaft torsion oscillation and it must not reduce or delay the signal in the pass-band, i.e. at lower frequencies. At Electricite de France, the speed measure is used to set in motion the fast valving system as quickly as possible, after a short circuit close to the unit or rather an islanding. It is difficult to satisfy these two requirements by using conventional filtering methods. The standard solution consists in a first order filter: at Electricite de France, its time constant is equal to 80 ms. We have decided to improve this filtering by designing a new filter which cuts the frequencies of the shaft torsion oscillation without reducing the bandwidth to the speed measure. If one uses conventional methods to obtain a band stop filter, it is easy to obtain the desired magnitude but not a phase near zero in the whole pass-band. Therefore, we have chosen to design the filter by using Kalman'a theory. The measurement noise is modeled as a colored one, generated by a very lightly damped system driven by a while noise. The resulting Kalman filter is an effective band stop filter, whose phase nicely remains near zero in the whole pass-band. The digital simulations we made and the tests we carried out with the Electricite de France Micro Network laboratory show the advantages of the rotation speed filter we designed using Kalman's theory. With the proposed filter, the speed measure filtering is better in terms of reduction and phase shift. the result is that there are less untimely solicitations of the fast valving system. Consequently, this device improves the power systems stability by minimizing the risks of deep perturbations due to a temporary lack of generation and the risks of under-speed loss

Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks

Science.gov (United States)

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

Verification of the helioseismology travel-time measurement technique and the inversion procedure for sound speed using artificial data

Energy Technology Data Exchange (ETDEWEB)

Parchevsky, K. V.; Zhao, J.; Hartlep, T.; Kosovichev, A. G., E-mail: akosovichev@solar.stanford.edu [Stanford University, HEPL, Stanford, CA 94305 (United States)

2014-04-10

We performed three-dimensional numerical simulations of the solar surface acoustic wave field for the quiet Sun and for three models with different localized sound-speed perturbations in the interior with deep, shallow, and two-layer structures. We used the simulated data generated by two solar acoustics codes that employ the same standard solar model as a background model, but utilize different integration techniques and different models of stochastic wave excitation. Acoustic travel times were measured using a time-distance helioseismology technique, and compared with predictions from ray theory frequently used for helioseismic travel-time inversions. It is found that the measured travel-time shifts agree well with the helioseismic theory for sound-speed perturbations, and for the measurement procedure with and without phase-speed filtering of the oscillation signals. This testing verifies the whole measuring-filtering-inversion procedure for static sound-speed anomalies with small amplitude inside the Sun outside regions of strong magnetic field. It is shown that the phase-speed filtering, frequently used to extract specific wave packets and improve the signal-to-noise ratio, does not introduce significant systematic errors. Results of the sound-speed inversion procedure show good agreement with the perturbation models in all cases. Due to its smoothing nature, the inversion procedure may overestimate sound-speed variations in regions with sharp gradients of the sound-speed profile.

Verification of the helioseismology travel-time measurement technique and the inversion procedure for sound speed using artificial data

International Nuclear Information System (INIS)

Parchevsky, K. V.; Zhao, J.; Hartlep, T.; Kosovichev, A. G.

2014-01-01

We performed three-dimensional numerical simulations of the solar surface acoustic wave field for the quiet Sun and for three models with different localized sound-speed perturbations in the interior with deep, shallow, and two-layer structures. We used the simulated data generated by two solar acoustics codes that employ the same standard solar model as a background model, but utilize different integration techniques and different models of stochastic wave excitation. Acoustic travel times were measured using a time-distance helioseismology technique, and compared with predictions from ray theory frequently used for helioseismic travel-time inversions. It is found that the measured travel-time shifts agree well with the helioseismic theory for sound-speed perturbations, and for the measurement procedure with and without phase-speed filtering of the oscillation signals. This testing verifies the whole measuring-filtering-inversion procedure for static sound-speed anomalies with small amplitude inside the Sun outside regions of strong magnetic field. It is shown that the phase-speed filtering, frequently used to extract specific wave packets and improve the signal-to-noise ratio, does not introduce significant systematic errors. Results of the sound-speed inversion procedure show good agreement with the perturbation models in all cases. Due to its smoothing nature, the inversion procedure may overestimate sound-speed variations in regions with sharp gradients of the sound-speed profile.

The Inverse Contagion Problem (ICP) vs.. Predicting site contagion in real time, when network links are not observable

Science.gov (United States)

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

Cascaded Subpatch Networks for Effective CNNs.

Science.gov (United States)

Jiang, Xiaoheng; Pang, Yanwei; Sun, Manli; Li, Xuelong

2017-05-12

Conventional convolutional neural networks use either a linear or a nonlinear filter to extract features from an image patch (region) of spatial size Hx W (typically, H is small and is equal to W, e.g., H is 5 or 7 ). Generally, the size of the filter is equal to the size Hx W of the input patch. We argue that the representational ability of equal-size strategy is not strong enough. To overcome the drawback, we propose to use subpatch filter whose spatial size hx w is smaller than Hx W . The proposed subpatch filter consists of two subsequent filters. The first one is a linear filter of spatial size hx w and is aimed at extracting features from spatial domain. The second one is of spatial size 1x 1 and is used for strengthening the connection between different input feature channels and for reducing the number of parameters. The subpatch filter convolves with the input patch and the resulting network is called a subpatch network. Taking the output of one subpatch network as input, we further repeat constructing subpatch networks until the output contains only one neuron in spatial domain. These subpatch networks form a new network called the cascaded subpatch network (CSNet). The feature layer generated by CSNet is called the csconv layer. For the whole input image, we construct a deep neural network by stacking a sequence of csconv layers. Experimental results on five benchmark data sets demonstrate the effectiveness and compactness of the proposed CSNet. For example, our CSNet reaches a test error of 5.68% on the CIFAR10 data set without model averaging. To the best of our knowledge, this is the best result ever obtained on the CIFAR10 data set.

Tomographic pseudo-inversion of resistivity profiles

Directory of Open Access Journals (Sweden)

D. Luzio

1997-06-01

Full Text Available A new approach to construct vertical and/or horizontal pseudosections starting from sets of resistivity (and/or IP data is presented. In principle it consists in the division of the subsoil into a number of pixels (discretization, arranged in a 3D halfspace. The resistivity of each pixel is then obtained by a back-projection of the set of acquired experimental data, that is by arranging a set of convolutions using 3D filters. The coefficients of the filters are calculated, depending on the geometry of the electrode array used, on the basis of a mask reproducing the «influence coefficients» of all the pixels. The aim of these representations is to match the shape of the investigated structures as close as possible, so that, even if it can be considered a fast arrangement of the experimental data rather than a real inversion, it can be a useful tool in interpretation, at least as a simple preliminary sketch. This method is discussed, focussing on some critical choices regarding the construction of the filters and the use of smoothing factors; some applications on synthetic data calculated on simple models of buried resistive spheres are also presented.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

Science.gov (United States)

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.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

Directory of Open Access Journals (Sweden)

Ke Li

2016-01-01

Full Text Available 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.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

Science.gov (United States)

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-01

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. PMID:26761006

A low-complexity interacting multiple model filter for maneuvering target tracking

KAUST Repository

Khalid, Syed Safwan; Abrar, Shafayat

2017-01-01

In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.

A low-complexity interacting multiple model filter for maneuvering target tracking

KAUST Repository

Khalid, Syed Safwan

2017-01-22

In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.

Event-Based Variance-Constrained ${\\mathcal {H}}_{\\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements.

Science.gov (United States)

Wang, Licheng; Wang, Zidong; Han, Qing-Long; Wei, Guoliang

2018-03-01

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

Extracting Low-Frequency Information from Time Attenuation in Elastic Waveform Inversion

Science.gov (United States)

Guo, Xuebao; Liu, Hong; Shi, Ying; Wang, Weihong

2017-03-01

Low-frequency information is crucial for recovering background velocity, but the lack of low-frequency information in field data makes inversion impractical without accurate initial models. Laplace-Fourier domain waveform inversion can recover a smooth model from real data without low-frequency information, which can be used for subsequent inversion as an ideal starting model. In general, it also starts with low frequencies and includes higher frequencies at later inversion stages, while the difference is that its ultralow frequency information comes from the Laplace-Fourier domain. Meanwhile, a direct implementation of the Laplace-transformed wavefield using frequency domain inversion is also very convenient. However, because broad frequency bands are often used in the pure time domain waveform inversion, it is difficult to extract the wavefields dominated by low frequencies in this case. In this paper, low-frequency components are constructed by introducing time attenuation into the recorded residuals, and the rest of the method is identical to the traditional time domain inversion. Time windowing and frequency filtering are also applied to mitigate the ambiguity of the inverse problem. Therefore, we can start at low frequencies and to move to higher frequencies. The experiment shows that the proposed method can achieve a good inversion result in the presence of a linear initial model and records without low-frequency information.

Derivation of Oscillators from Biquadratic Band Pass Filters Using Circuit Transformations

Directory of Open Access Journals (Sweden)

Hung-Yu Wang

2014-09-01

Full Text Available Network transformations are the techniques to obtain new functional schemes from available circuits. They are systematic methodologies, since each transformation technique can be applied to many circuits to obtain the desired functions or characteristics. A convenient network transformation method, exploiting different circuit transformations, for deriving linear sinusoidal oscillators from biquadratic band pass filters is proposed. This method with generality can be applied to any band pass filter. The oscillation frequency of the new obtained oscillator is identical to the center frequency of the original band pass filter, and the useful properties of the selected band pass filter can be retained. Two examples are illustrated to confirm the feasibility of the proposed approach. The workability of the obtained oscillators is verified with PSPICE simulations.

Individua l tree identification in airborne LASER data BY inverse SEARCH window

Directory of Open Access Journals (Sweden)

Eric Bastos Gorgens

2015-03-01

Full Text Available The local maximum filtering performance is highly dependent of the window size definition. This paper proposes that the window size should be determined by an inverse relationship to the canopy height model, and test the hypothesis that a windowsize inversely proportional will have better performance than the window proportional to the canopy height model. The study area is located in the southeastern region of the State of British Columbia, Canada. The natural vegetation is the boreal type and is characterized by the dominance of two species Picea engelmannii Parry ex. Engelmann (Engelmann spruce and Abies lasiocarpa (Hook. Nutt. (sub-alpine fir. The relief is mountainous with altitudes ranging from 650-2400 meters. 62 plots with 256 square meters were measured in the field. The airborne LiDAR had discrete returns, 2 points per square meter density and small-footprint. The performance of the search windows was evaluated based on success percentage, absolute average error and also compared to the observed values of the field plots. The local maximum filter underestimated the number of trees per hectare for both window sizing methods. The use of the inverse proportional window size has resulted in superior results, particularly for regions with highest density of trees.

Low-pass parabolic FFT filter for airborne and satellite lidar signal processing.

Science.gov (United States)

Jiao, Zhongke; Liu, Bo; Liu, Enhai; Yue, Yongjian

2015-10-14

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 lidar signal processing has been verified. The method has been tested on real lidar signal from Wyoming Cloud Lidar (WCL). Results show that PFFTF has advantages over the other methods. It keeps the high frequency components well and reduces much of the random noise simultaneously for lidar signal processing.

Neutron flux filtration using Kalman filter

International Nuclear Information System (INIS)

Urcikan, Marian

2005-01-01

In the course of the WWER-440 start-up procedure the time dependent reactivity is determined from the measured ionization chamber signal by inverse kinetic method. Due to the random nature of the fission process and random nature the detection process the measured ionization chamber signal contains certain noise content. To minimize the unwonted noise on measured reactivity one of the possibility method is utilization Kalman filter, based on a stochastic model of reactor system (Author)

Improving Artificial eural etwork Forecasts with Kalman Filtering

African Journals Online (AJOL)

Nafiisah

technique in financial time series and the application of a Kalman filter ... networks (ANN) model using a Kalman filter leads to significant improvements in .... 3-rd order polynomial (Galanis et al. (2006)): 1 t p. 2 t p. 3 t p. 4 t p. 1 t h. 2 t h tr t r ...

Nonlinear stochastic systems with incomplete information filtering and control

CERN Document Server

Shen, Bo; Shu, Huisheng

2013-01-01

Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: ·         a unified framework for handling filtering and control problems in complex communication networks with limited bandwidth; ·         new concepts such as random sensor and signal saturations for more realistic modeling; and ·         demonstration of the use of techniques such...

Virtual Seismic Observation (VSO) with Sparsity-Promotion Inversion

Science.gov (United States)

Tiezhao, B.; Ning, J.; Jianwei, M.

2017-12-01

Large station interval leads to low resolution images, sometimes prevents people from obtaining images in concerned regions. Sparsity-promotion inversion, a useful method to recover missing data in industrial field acquisition, can be lent to interpolate seismic data on none-sampled sites, forming Virtual Seismic Observation (VSO). Traditional sparsity-promotion inversion suffers when coming up with large time difference in adjacent sites, which we concern most and use shift method to improve it. The procedure of the interpolation is that we first employ low-pass filter to get long wavelength waveform data and shift the waveforms of the same wave in different seismograms to nearly same arrival time. Then we use wavelet-transform-based sparsity-promotion inversion to interpolate waveform data on none-sampled sites and filling a phase in each missing trace. Finally, we shift back the waveforms to their original arrival times. We call our method FSIS (Filtering, Shift, Interpolation, Shift) interpolation. By this way, we can insert different virtually observed seismic phases into none-sampled sites and get dense seismic observation data. For testing our method, we randomly hide the real data in a site and use the rest to interpolate the observation on that site, using direct interpolation or FSIS method. Compared with directly interpolated data, interpolated data with FSIS can keep amplitude better. Results also show that the arrival times and waveforms of those VSOs well express the real data, which convince us that our method to form VSOs are applicable. In this way, we can provide needed data for some advanced seismic technique like RTM to illuminate shallow structures.

I6-FPS: Automating the ICMPv6 Filtering Rules

Directory of Open Access Journals (Sweden)

Wan Ali Wan Nor Ashiqin

2018-01-01

Full Text Available Enterprises are required to utilize Internet Control Message Protocol version 6 (ICMPv6 when IPv6 is deployed. In IPv4, Internet Control Message Protocol (ICMP is aggressively filtered by a network administrator while in IPv6, ICMPv6 messages cannot be aggressively filtered due to the function of ICMPv6 message. ICMPv6 security risks increase when ICMPv6 threats and vulnerabilities are exploited. Thus, it is very crucial for enterprises to address the issues. In practice, network researchers must review several resources to identify ICMPv6 related attacks occurring due to the exploitation of ICMPv6 vulnerabilities. Overlooking any of these issues will jeopardize the security of ICMPv6. While conducting the attack scenarios testing, IPv6-Filtering Prototype System (I6-FPS was developed to overcome the deficiency and limited filtering tools that supported IPv6 filtering rules (ip6table. I6-FPS is used to automate and simplify the writing of ip6table and it was developed using PHP5 and Shell script languages. This research revealed that I6-FPS is significant in the initial phase of securing IPv6 deployment as well as focusing on the ICMPv6 filtering rules. The I6-FPS has the potential to be enhanced and developed over time by including more functions to that system in generating specific filtering ip6table rules.

A cooperative positioning with Kalman filters and handover mechanism for indoor microcellular visible light communication network

Science.gov (United States)

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.

Towards self-organizing Kalman filters

NARCIS (Netherlands)

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,

Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

Science.gov (United States)

Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal

2017-08-18

The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.

Bank of Weight Filters for Deep CNNs

Science.gov (United States)

2016-11-22

very large even on the best available hardware . In some studies in transfer learning it has been observed that the network learnt on one task can be...CNNs. Keywords: CNN, deep learning , neural networks, transfer learning , bank of weigh filters, BWF 1. Introduction Object recognition is an important...of CNNs (or, in general, of deep neural networks) is that feature generation part is fused with the classifier part and both parts are learned together

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.

High-performance implementation of Chebyshev filter diagonalization for interior eigenvalue computations

Energy Technology Data Exchange (ETDEWEB)

Pieper, Andreas [Ernst-Moritz-Arndt-Universität Greifswald (Germany); Kreutzer, Moritz [Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany); Alvermann, Andreas, E-mail: alvermann@physik.uni-greifswald.de [Ernst-Moritz-Arndt-Universität Greifswald (Germany); Galgon, Martin [Bergische Universität Wuppertal (Germany); Fehske, Holger [Ernst-Moritz-Arndt-Universität Greifswald (Germany); Hager, Georg [Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany); Lang, Bruno [Bergische Universität Wuppertal (Germany); Wellein, Gerhard [Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)

2016-11-15

We study Chebyshev filter diagonalization as a tool for the computation of many interior eigenvalues of very large sparse symmetric matrices. In this technique the subspace projection onto the target space of wanted eigenvectors is approximated with filter polynomials obtained from Chebyshev expansions of window functions. After the discussion of the conceptual foundations of Chebyshev filter diagonalization we analyze the impact of the choice of the damping kernel, search space size, and filter polynomial degree on the computational accuracy and effort, before we describe the necessary steps towards a parallel high-performance implementation. Because Chebyshev filter diagonalization avoids the need for matrix inversion it can deal with matrices and problem sizes that are presently not accessible with rational function methods based on direct or iterative linear solvers. To demonstrate the potential of Chebyshev filter diagonalization for large-scale problems of this kind we include as an example the computation of the 10{sup 2} innermost eigenpairs of a topological insulator matrix with dimension 10{sup 9} derived from quantum physics applications.

Software tool for resolution of inverse problems using artificial intelligence techniques: an application in neutron spectrometry

International Nuclear Information System (INIS)

Castaneda M, V. H.; Martinez B, M. R.; Solis S, L. O.; Castaneda M, R.; Leon P, A. A.; Hernandez P, C. F.; Espinoza G, J. G.; Ortiz R, J. M.; Vega C, H. R.; Mendez, R.; Gallego, E.; Sousa L, M. A.

2016-10-01

The Taguchi methodology has proved to be highly efficient to solve inverse problems, in which the values of some parameters of the model must be obtained from the observed data. There are intrinsic mathematical characteristics that make a problem known as inverse. Inverse problems appear in many branches of science, engineering and mathematics. To solve this type of problem, researches have used different techniques. Recently, the use of techniques based on Artificial Intelligence technology is being explored by researches. This paper presents the use of a software tool based on artificial neural networks of generalized regression in the solution of inverse problems with application in high energy physics, specifically in the solution of the problem of neutron spectrometry. To solve this problem we use a software tool developed in the Mat Lab programming environment, which employs a friendly user interface, intuitive and easy to use for the user. This computational tool solves the inverse problem involved in the reconstruction of the neutron spectrum based on measurements made with a Bonner spheres spectrometric system. Introducing this information, the neural network is able to reconstruct the neutron spectrum with high performance and generalization capability. The tool allows that the end user does not require great training or technical knowledge in development and/or use of software, so it facilitates the use of the program for the resolution of inverse problems that are in several areas of knowledge. The techniques of Artificial Intelligence present singular veracity to solve inverse problems, given the characteristics of artificial neural networks and their network topology, therefore, the tool developed has been very useful, since the results generated by the Artificial Neural Network require few time in comparison to other techniques and are correct results comparing them with the actual data of the experiment. (Author)

A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada

Science.gov (United States)

Coogan, A.; Avanzi, F.; Akella, R.; Conklin, M. H.; Bales, R. C.; Glaser, S. D.

2017-12-01

Automatic meteorological and snow stations provide large amounts of information at dense temporal resolution, but data quality is often compromised by noise and missing values. We present a new gap-filling and cleaning procedure for networks of these stations based on Kalman filtering and expectation maximization. Our method utilizes a multi-sensor, regime-switching Kalman filter to learn a latent process that captures dependencies between nearby stations and handles sharp changes in snowfall rate. Since the latent process is inferred using observations across working stations in the network, it can be used to fill in large data gaps for a malfunctioning station. The procedure was tested on meteorological and snow data from Wireless Sensor Networks (WSN) in the American River basin of the Sierra Nevada. Data include air temperature, relative humidity, and snow depth from dense networks of 10 to 12 stations within 1 km2 swaths. Both wet and dry water years have similar data issues. Data with artificially created gaps was used to quantify the method's performance. Our multi-sensor approach performs better than a single-sensor one, especially with large data gaps, as it learns and exploits the dominant underlying processes in snowpack at each site.

Multi-layered dielectric cladding plasmonic microdisk resonator filter and coupler

International Nuclear Information System (INIS)

Han Cheng, Bo; Lan, Yung-Chiang

2013-01-01

This work develops the plasmonic microdisk filter/coupler, whose effectiveness is evaluated by finite-difference time-domain simulation and theoretical analyses. Multi-layer dielectric cladding is used to prevent the scattering of surface plasmons (SPs) from a silver microdisk. This method allows devices that efficiently perform filter/coupler functions to be developed. The resonant conditions and the effective refractive index of bounded SP modes on the microdisk are determined herein. The waveguide-to-microdisk distance barely influences the resonant wavelength but it is inversely related to the bandwidth. These findings are consistent with predictions made using the typical ring resonator model.

An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.

Science.gov (United States)

Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica

2009-01-01

In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.

Class network routing

Science.gov (United States)

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.

Analysis of design parameters for crosstalk cancellation filters applied to different loudspeaker configurations

DEFF Research Database (Denmark)

Parodi, Yesenia Lacouture

2008-01-01

Several approaches to render binaural signals through loudspeakers have been proposed previously. Some studies had focused on the optimum loudspeaker arrangement while others had proposed efficient filters. However, to our knowledge, the identification of optimal parameters for inverse methods ap...... loudspeaker arrangements. Least square approximations in frequency and time domain are evaluated along with a crosstalk canceler based on minimum-phase approximation. Filter parameters, such as length and regularization, are varied and simulated for different span and elevation angles....

Passive Noise Filtering by Cellular Compartmentalization.

Science.gov (United States)

Stoeger, Thomas; Battich, Nico; Pelkmans, Lucas

2016-03-10

Chemical reactions contain an inherent element of randomness, which presents itself as noise that interferes with cellular processes and communication. Here we discuss the ability of the spatial partitioning of molecular systems to filter and, thus, remove noise, while preserving regulated and predictable differences between single living cells. In contrast to active noise filtering by network motifs, cellular compartmentalization is highly effective and easily scales to numerous systems without requiring a substantial usage of cellular energy. We will use passive noise filtering by the eukaryotic cell nucleus as an example of how this increases predictability of transcriptional output, with possible implications for the evolution of complex multicellularity. Copyright © 2016 Elsevier Inc. All rights reserved.

Service discovery using Bloom filters

NARCIS (Netherlands)

Goering, P.T.H.; Heijenk, Geert; Lelieveldt, B.P.F.; Haverkort, Boudewijn R.H.M.; de Laat, C.T.A.M.; Heijnsdijk, J.W.J.

A protocol to perform service discovery in adhoc networks is introduced in this paper. Attenuated Bloom filters are used to distribute services to nodes in the neighborhood and thus enable local service discovery. The protocol has been implemented in a discrete event simulator to investigate the

Time stamp generation with inverse FIR filters for Positron Emission Tomography

International Nuclear Information System (INIS)

Namias, Mauro

2009-01-01

Photon coincidence detection is the process by which Positron Emission Tomography (PET) works. This requires the determination of the time of impact of each coincident photon at the detector system, also known as time stamp. In this work, the timestamp was generated by means of digital time-domain deconvolution with FIR filters for a INa(Tl) based system. The detector deadtime was reduced from 350 ns to 175 ns while preserving the system's energy resolution and a direct relation between the amount of light collected and the temporal resolution was found.(author)

Observation and inverse problems in coupled cell networks

International Nuclear Information System (INIS)

Joly, Romain

2012-01-01

A coupled cell network is a model for many situations such as food webs in ecosystems, cellular metabolism and economic networks. It consists in a directed graph G, each node (or cell) representing an agent of the network and each directed arrow representing which agent acts on which. It yields a system of differential equations .x(t)=f(x(t)), where the component i of f depends only on the cells x j (t) for which the arrow j → i exists in G. In this paper, we investigate the observation problems in coupled cell networks: can one deduce the behaviour of the whole network (oscillations, stabilization, etc) by observing only one of the cells? We show that the natural observation properties hold for almost all the interactions f

A novel cooperative localization algorithm using enhanced particle filter technique in maritime search and rescue wireless sensor network.

Science.gov (United States)

Wu, Huafeng; Mei, Xiaojun; Chen, Xinqiang; Li, Junjun; Wang, Jun; Mohapatra, Prasant

2018-07-01

Maritime search and rescue (MSR) play a significant role in Safety of Life at Sea (SOLAS). However, it suffers from scenarios that the measurement information is inaccurate due to wave shadow effect when utilizing wireless Sensor Network (WSN) technology in MSR. In this paper, we develop a Novel Cooperative Localization Algorithm (NCLA) in MSR by using an enhanced particle filter method to reduce measurement errors on observation model caused by wave shadow effect. First, we take into account the mobility of nodes at sea to develop a motion model-Lagrangian model. Furthermore, we introduce both state model and observation model to constitute a system model for particle filter (PF). To address the impact of the wave shadow effect on the observation model, we develop an optimal parameter derived by Kullback-Leibler divergence (KLD) to mitigate the error. After the optimal parameter is acquired, an improved likelihood function is presented. Finally, the estimated position is acquired. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Cake filtration modeling: Analytical cake filtration model and filter medium characterization

Energy Technology Data Exchange (ETDEWEB)

Koch, Michael

2008-05-15

Cake filtration is a unit operation to separate solids from fluids in industrial processes. The build up of a filter cake is usually accompanied with a decrease in overall permeability over the filter leading to an increased pressure drop over the filter. For an incompressible filter cake that builds up on a homogeneous filter cloth, a linear pressure drop profile over time is expected for a constant fluid volume flow. However, experiments show curved pressure drop profiles, which are also attributed to inhomogeneities of the filter (filter medium and/or residual filter cake). In this work, a mathematical filter model is developed to describe the relationship between time and overall permeability. The model considers a filter with an inhomogeneous permeability and accounts for fluid mechanics by a one-dimensional formulation of Darcy's law and for the cake build up by solid continuity. The model can be solved analytically in the time domain. The analytic solution allows for the unambiguous inversion of the model to determine the inhomogeneous permeability from the time resolved overall permeability, e.g. pressure drop measurements. An error estimation of the method is provided by rewriting the model as convolution transformation. This method is applied to simulated and experimental pressure drop data of gas filters with textile filter cloths and various situations with non-uniform flow situations in practical problems are explored. A routine is developed to generate characteristic filter cycles from semi-continuous filter plant operation. The model is modified to investigate the impact of non-uniform dust concentrations. (author). 34 refs., 40 figs., 1 tab

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

and derives fundamental concepts about the control of the resulting hybrid power filter. From this analysis, a specifc implementation of a three-phase four-wire hybrid power filter is presented as an illustrative application of the filtering topology. An extensive evaluation using simulation and experimental......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...

Collaborative filtering on a family of biological targets.

Science.gov (United States)

Erhan, Dumitru; L'heureux, Pierre-Jean; Yue, Shi Yi; Bengio, Yoshua

2006-01-01

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

SampleCNN: End-to-End Deep Convolutional Neural Networks Using Very Small Filters for Music Classification

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.

Optimisation of digital noise filtering in the deconvolution of ultrafast kinetic data

International Nuclear Information System (INIS)

Banyasz, Akos; Dancs, Gabor; Keszei, Erno

2005-01-01

Ultrafast kinetic measurements in the sub-picosecond time range are always distorted by a convolution with the instrumental response function. To restore the undistorted signal, deconvolution of the measured data is needed, which can be done via inverse filtering, using Fourier transforms, if experimental noise can be successfully filtered. However, in the case of experimental data when no underlying physical model is available, no quantitative criteria are known to find an optimal noise filter which would remove excessive noise without distorting the signal itself. In this paper, we analyse the Fourier transforms used during deconvolution and describe a graphical method to find such optimal noise filters. Comparison of graphically found optima to those found by quantitative criteria in the case of known synthetic kinetic signals shows the reliability of the proposed method to get fairly good deconvolved kinetic curves. A few examples of deconvolution of real-life experimental curves with the graphical noise filter optimisation are also shown

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.

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.

Real-time digital filtering, event triggering, and tomographic reconstruction of JET soft x-ray data (abstract)

Science.gov (United States)

Edwards, A. W.; Blackler, K.; Gill, R. D.; van der Goot, E.; Holm, J.

1990-10-01

Based upon the experience gained with the present soft x-ray data acquisition system, new techniques are being developed which make extensive use of digital signal processors (DSPs). Digital filters make 13 further frequencies available in real time from the input sampling frequency of 200 kHz. In parallel, various algorithms running on further DSPs generate triggers in response to a range of events in the plasma. The sawtooth crash can be detected, for example, with a delay of only 50 μs from the onset of the collapse. The trigger processor interacts with the digital filter boards to ensure data of the appropriate frequency is recorded throughout a plasma discharge. An independent link is used to pass 780 and 24 Hz filtered data to a network of transputers. A full tomographic inversion and display of the 24 Hz data is carried out in real time using this 15 transputer array. The 780 Hz data are stored for immediate detailed playback following the pulse. Such a system could considerably improve the quality of present plasma diagnostic data which is, in general, sampled at one fixed frequency throughout a discharge. Further, it should provide valuable information towards designing diagnostic data acquisition systems for future long pulse operation machines when a high degree of real-time processing will be required, while retaining the ability to detect, record, and analyze events of interest within such long plasma discharges.

Sparse adaptive filters for echo cancellation

CERN Document Server

Paleologu, Constantin

2011-01-01

Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling

Science.gov (United States)

Li, Weixuan; Lin, Guang; Zhang, Dongxiao

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect-except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functions is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and demonstrated

Communication Policies in Knowledge Networks

Science.gov (United States)

Ioannidis, Evangelos; Varsakelis, Nikos; Antoniou, Ioannis

2018-02-01

Faster knowledge attainment within organizations leads to improved innovation, and therefore competitive advantage. Interventions on the organizational network may be risky or costly or time-demanding. We investigate several communication policies in knowledge networks, which reduce the knowledge attainment time without interventions. We examine the resulting knowledge dynamics for real organizational networks, as well as for artificial networks. More specifically, we investigate the dependence of knowledge dynamics on: (1) the Selection Rule of agents for knowledge acquisition, and (2) the Order of implementation of "Selection" and "Filtering". Significant decrease of the knowledge attainment time (up to -74%) can be achieved by: (1) selecting agents of both high knowledge level and high knowledge transfer efficiency, and (2) implementing "Selection" after "Filtering" in contrast to the converse, implicitly assumed, conventional prioritization. The Non-Commutativity of "Selection" and "Filtering", reveals a Non-Boolean Logic of the Network Operations. The results demonstrate that significant improvement of knowledge dynamics can be achieved by implementing "fruitful" communication policies, by raising the awareness of agents, without any intervention on the network structure.

Modeling, simulation, and design of SAW grating filters

Science.gov (United States)

Schwelb, Otto; Adler, E. L.; Slaboszewicz, J. K.

1990-05-01

A systematic procedure for modeling, simulating, and designing SAW (surface acoustic wave) grating filters, taking losses into account, is described. Grating structures and IDTs (interdigital transducers) coupling to SAWs are defined by cascadable transmission-matrix building blocks. Driving point and transfer characteristics (immittances) of complex architectures consisting of gratings, transducers, and coupling networks are obtained by chain-multiplying building-block matrices. This modular approach to resonator filter analysis and design combines the elements of lossy filter synthesis with the transmission-matrix description of SAW components. A multipole filter design procedure based on a lumped-element-model approximation of one-pole two-port resonator building blocks is given and the range of validity of this model examined. The software for simulating the performance of SAW grating devices based on this matrix approach is described, and its performance, when linked to the design procedure to form a CAD/CAA (computer-aided design and analysis) multiple-filter design package, is illustrated with a resonator filter design example.

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

Science.gov (United States)

Raza, Khalid; Alam, Mansaf

2016-10-01

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Imaging the Flow Networks from a Harmonic Pumping in a Karstic Field with an Inversion Algorithm

Science.gov (United States)

Fischer, P.; Lecoq, N.; Jardani, A.; Jourde, H.; Wang, X.; Chedeville, S.; Cardiff, M. A.

2017-12-01

Identifying flow paths within karstic fields remains a complex task because of the high dependency of the hydraulic responses to the relative locations between the observation boreholes and the karstic conduits and interconnected fractures that control the main flows of the hydrosystem. In this context, harmonic pumping is a new investigation tool that permits to inform on the flow paths connectivity between the boreholes. We have shown that the amplitude and phase offset values in the periodic responses of a hydrosystem to a harmonic pumping test characterize three different type of flow behavior between the measurement boreholes and the pumping borehole: a direct connectivity response (conduit flow), an indirect connectivity (conduit and short matrix flows), and an absence of connectivity (matrix). When the hydraulic responses to study are numerous and complex, the interpretation of the flow paths requires an inverse modeling. Therefore, we have recently developed a Cellular Automata-based Deterministic Inversion (CADI) approach that permits to infer the spatial distribution of field hydraulic conductivities in a structurally constrained model. This method distributes hydraulic conductivities along linear structures (i.e. karst conduits) and iteratively modifies the structural geometry of this conduits network to progressively match the observed responses to the modeled ones. As a result, this method produces a conductivity model that is composed of a discrete conduit network embedded in the background matrix, capable of producing the same flow behavior as the investigated hydrologic system. We applied the CADI approach in order to reproduce, in a model, the amplitude and phase offset values of a set of periodic responses generated from harmonic pumping tests conducted in different boreholes at the Terrieu karstic field site (Southern France). This association of oscillatory responses with the CADI method provides an interpretation of the flow paths within the

Data and modelling requirements for CO2 inversions using high-frequency data

International Nuclear Information System (INIS)

Law, R.M.; Rayner, P.J.; Steele, L.P.; Enting, I.G.

2003-01-01

We explore the future possibilities for CO 2 source estimation from atmospheric concentration data by performing synthetic data experiments. Synthetic data are used to test seasonal CO 2 inversions using high-frequency data. Monthly CO 2 sources over the Australian region are calculated for inversions with data at 4-hourly frequency and averaged over 1 d, 2.5 d, 5 d, 12.17 d and 1 month. The inversion quality, as determined by bias and uncertainty, is degraded when averaging over longer periods. This shows the value of the strong but relatively short-lived signals present in high-frequency records that are removed in averaged and particularly filtered records. Sensitivity tests are performed in which the synthetic data are 'corrupted' to simulate systematic measurement errors such as intercalibration differences or to simulate transport modelling errors. The inversion is also used to estimate the effect of calibration offsets between sites. We find that at short data-averaging periods the inversion is reasonably robust to measurement-type errors. For transport-type errors, the best results are achieved for synoptic (2-5 d) timescales. Overall the tests indicate that improved source estimates should be possible by incorporating continuous measurements into CO 2 inversions

A filtering approach to edge preserving MAP estimation of images.

Science.gov (United States)

Humphrey, David; Taubman, David

2011-05-01

The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both blur and noise. The image is divided into statistically independent regions. Each region is modelled with a WSS Gaussian prior. Classical Wiener filter theory is used to generate a set of convex sets in the solution space, with the solution to the MAP estimation problem lying at the intersection of these sets. The proposed algorithm uses an underlying segmentation of the image, and a means of determining the segmentation and refining it are described. The algorithm is suitable for a range of image restoration problems, as it provides a computationally efficient means to deal with the shortcomings of Wiener filtering without sacrificing the computational simplicity of the filtering approach. The algorithm is also of interest from a theoretical viewpoint as it provides a continuum of solutions between Wiener filtering and Inverse filtering depending upon the segmentation used. We do not attempt to show here that the proposed method is the best general approach to the image reconstruction problem. However, related work referenced herein shows excellent performance in the specific problem of demosaicing.

Feasibility analysis of using inverse modeling for estimating natural groundwater recharge from a large-scale soil moisture monitoring network

Science.gov (United States)

Wang, Tiejun; Franz, Trenton E.; Yue, Weifeng; Szilagyi, Jozsef; Zlotnik, Vitaly A.; You, Jinsheng; Chen, Xunhong; Shulski, Martha D.; Young, Aaron

2016-02-01

Despite the importance of groundwater recharge (GR), its accurate estimation still remains one of the most challenging tasks in the field of hydrology. In this study, with the help of inverse modeling, long-term (6 years) soil moisture data at 34 sites from the Automated Weather Data Network (AWDN) were used to estimate the spatial distribution of GR across Nebraska, USA, where significant spatial variability exists in soil properties and precipitation (P). To ensure the generality of this study and its potential broad applications, data from public domains and literature were used to parameterize the standard Hydrus-1D model. Although observed soil moisture differed significantly across the AWDN sites mainly due to the variations in P and soil properties, the simulations were able to capture the dynamics of observed soil moisture under different climatic and soil conditions. The inferred mean annual GR from the calibrated models varied over three orders of magnitude across the study area. To assess the uncertainties of the approach, estimates of GR and actual evapotranspiration (ETa) from the calibrated models were compared to the GR and ETa obtained from other techniques in the study area (e.g., remote sensing, tracers, and regional water balance). Comparison clearly demonstrated the feasibility of inverse modeling and large-scale (>104 km2) soil moisture monitoring networks for estimating GR. In addition, the model results were used to further examine the impacts of climate and soil on GR. The data showed that both P and soil properties had significant impacts on GR in the study area with coarser soils generating higher GR; however, different relationships between GR and P emerged at the AWDN sites, defined by local climatic and soil conditions. In general, positive correlations existed between annual GR and P for the sites with coarser-textured soils or under wetter climatic conditions. With the rapidly expanding soil moisture monitoring networks around the

Study on Rayleigh Wave Inversion for Estimating Shear-wave Velocity Profile

Directory of Open Access Journals (Sweden)

T.A. Sanny

2003-05-01

Full Text Available Rayleigh wave or ground roll is a noise in seismic body waves. However, how to use this noise for soil characterization is very interesting since Rayleigh wave phase velocity is a function of compression-wave velocity, shear-wave velocity, density and layer thickness. In layered-medium Rayleigh wave velocity also depends on wavelength or frequency, and this phenomenon is called dispersion. Inversion procedure to get shear-wave velocity profile needs a priori information about the solution of the problem to limit the unknown parameters. The Lagrange multiplier method was used to solve the constrained optimization problems or well known as a smoothing parameter in inversion problems. The advantage of our inversion procedure is that it can guarantee the convergence of solution even though the field data is incomplete, insufficient, and inconsistent. The addition of smoothing parameter can reduce the time to converge. Beside numerical stability, the statistical stability is also involved in inversion procedure. In field experiment we extracted ground roll data from seismic refraction record. The dispersion curves had been constructed by applying f-k analysis and f-k dip filtering. The dispersion curves show the dependence of Rayleigh wave phase velocities in layered media to frequency. The synthetic models also demonstrate the stability and the speed of inversion procedure.

Airborne Network Optimization with Dynamic Network Update

Science.gov (United States)

2015-03-26

source si and a target ti . For each commodity (si, ki) the commodity specifies a non- negative demand di [5]. The objective of the multi-commodity...queue predictions, and network con- gestion [15]. The implementation of the DRQC uses the Kalman filter to predict the state of the network and optimize

CONSISTENT USE OF THE KALMAN FILTER IN CHEMICAL TRANSPORT MODELS (CTMS) FOR DEDUCING EMISSIONS

Science.gov (United States)

Past research has shown that emissions can be deduced using observed concentrations of a chemical, a Chemical Transport Model (CTM), and the Kalman filter in an inverse modeling application. An expression was derived for the relationship between the "observable" (i.e., the con...

Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

Energy Technology Data Exchange (ETDEWEB)

Lobato, Justo; Canizares, Pablo; Rodrigo, Manuel A.; Linares, Jose J. [Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004 Ciudad Real (Spain); Piuleac, Ciprian-George; Curteanu, Silvia [Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, ' ' Gh. Asachi' ' Technical University Iasi Bd. D. Mangeron, No. 71A, 700050 IASI (Romania)

2010-08-15

This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties. (author)

Assimilating irregularly spaced sparsely observed turbulent signals with hierarchical Bayesian reduced stochastic filters

International Nuclear Information System (INIS)

Brown, Kristen A.; Harlim, John

2013-01-01

In this paper, we consider a practical filtering approach for assimilating irregularly spaced, sparsely observed turbulent signals through a hierarchical Bayesian reduced stochastic filtering framework. The proposed hierarchical Bayesian approach consists of two steps, blending a data-driven interpolation scheme and the Mean Stochastic Model (MSM) filter. We examine the potential of using the deterministic piecewise linear interpolation scheme and the ordinary kriging scheme in interpolating irregularly spaced raw data to regularly spaced processed data and the importance of dynamical constraint (through MSM) in filtering the processed data on a numerically stiff state estimation problem. In particular, we test this approach on a two-layer quasi-geostrophic model in a two-dimensional domain with a small radius of deformation to mimic ocean turbulence. Our numerical results suggest that the dynamical constraint becomes important when the observation noise variance is large. Second, we find that the filtered estimates with ordinary kriging are superior to those with linear interpolation when observation networks are not too sparse; such robust results are found from numerical simulations with many randomly simulated irregularly spaced observation networks, various observation time intervals, and observation error variances. Third, when the observation network is very sparse, we find that both the kriging and linear interpolations are comparable

Filter's importance in nuclear cardiology imaging

International Nuclear Information System (INIS)

Jesus, Maria C. de; Lima, Ana L.S.; Santos, Joyra A. dos; Megueriam, Berdj A.

2008-01-01

Full text: Nuclear Medicine is a medical speciality which employs tomography procedures for the diagnosis, treatment and prevention of diseases. One of the most commonly used apparatus is the Single Photon Emission Computed Tomography (SPECT). To perform exams, a very small amount of a radiopharmaceutical must be given to the patient. Then, a gamma camera is placed in convenient positions to perform the photon counting, which is used to reconstruct a full 3 dimensional distribution of the radionuclide inside the body or organ. This reconstruction provides a 3-dimensional image in spatial coordinates, of the body or organ under study, allowing the physician to give the diagnostic. Image reconstruction is usually worked in the frequency domain, due to a great simplification introduced by the Fourier decomposition of image spectra. After the reconstruction, an inverse Fourier transform must be applied to trace back the image into spatial coordinates. To optimize this reconstruction procedure, digital filters are used to remove undesirable components of frequency, which can 'shadow' relevant physical signatures of diseases. Unfortunately, the efficiency of the applied filter is strongly dependent on its own mathematical parameters. In this work we demonstrate how filters interfere on image quality in cardiology examinations with SPECT, concerning perfusion and myocardial viability and the importance of the medical physicist in the choice of the right filters avoiding some serious problems that could occur in the inadequate processing of an image damaging the medical diagnosis. (author)

Solving of L0 norm constrained EEG inverse problem.

Science.gov (United States)

Xu, Peng; Lei, Xu; Hu, Xiao; Yao, Dezhong

2009-01-01

l(0) norm is an effective constraint used to solve EEG inverse problem for a sparse solution. However, due to the discontinuous and un-differentiable properties, it is an open issue to solve the l(0) norm constrained problem, which is usually instead solved by using some alternative functions like l(1) norm to approximate l(0) norm. In this paper, a continuous and differentiable function having the same form as the transfer function of Butterworth low-pass filter is introduced to approximate l(0) norm constraint involved in EEG inverse problem. The new approximation based approach was compared with l(1) norm and LORETA solutions on a realistic head model using simulated sources. The preliminary results show that this alternative approximation to l(0) norm is promising for the estimation of EEG sources with sparse distribution.

Inverse targeting —An effective immunization strategy

Science.gov (United States)

Schneider, C. M.; Mihaljev, T.; Herrmann, H. J.

2012-05-01

We propose a new method to immunize populations or computer networks against epidemics which is more efficient than any continuous immunization method considered before. The novelty of our method resides in the way of determining the immunization targets. First we identify those individuals or computers that contribute the least to the disease spreading measured through their contribution to the size of the largest connected cluster in the social or a computer network. The immunization process follows the list of identified individuals or computers in inverse order, immunizing first those which are most relevant for the epidemic spreading. We have applied our immunization strategy to several model networks and two real networks, the Internet and the collaboration network of high-energy physicists. We find that our new immunization strategy is in the case of model networks up to 14%, and for real networks up to 33% more efficient than immunizing dynamically the most connected nodes in a network. Our strategy is also numerically efficient and can therefore be applied to large systems.

Active filtering applied to radiographic images unfolded by the Richardson-Lucy algorithm

International Nuclear Information System (INIS)

Almeida, Gevaldo L. de; Silvani, Maria Ines; Lopes, Ricardo T.

2011-01-01

Degradation of images caused by systematic uncertainties can be reduced when one knows the features of the spoiling agent. Typical uncertainties of this kind arise in radiographic images due to the non - zero resolution of the detector used to acquire them, and from the non-punctual character of the source employed in the acquisition, or from the beam divergence when extended sources are used. Both features blur the image, which, instead of a single point exhibits a spot with a vanishing edge, reproducing hence the point spread function - PSF of the system. Once this spoiling function is known, an inverse problem approach, involving inversion of matrices, can then be used to retrieve the original image. As these matrices are generally ill-conditioned, due to statistical fluctuation and truncation errors, iterative procedures should be applied, such as the Richardson-Lucy algorithm. This algorithm has been applied in this work to unfold radiographic images acquired by transmission of thermal neutrons and gamma-rays. After this procedure, the resulting images undergo an active filtering which fairly improves their final quality at a negligible cost in terms of processing time. The filter ruling the process is based on the matrix of the correction factors for the last iteration of the deconvolution procedure. Synthetic images degraded with a known PSF, and undergone to the same treatment, have been used as benchmark to evaluate the soundness of the developed active filtering procedure. The deconvolution and filtering algorithms have been incorporated to a Fortran program, written to deal with real images, generate the synthetic ones and display both. (author)

Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation

KAUST Repository

Alkhalifah, Tariq Ali

2016-04-06

A model of the earth can be described using a Fourier basis represented by its wavenumber content. In full-waveform inversion (FWI), the wavenumber description of the model is natural because our Born-approximation-based velocity updates are made up of wavefields. Our objective in FWI is to access all the model wavenumbers available in our limited aperture and bandwidth recorded data that are not yet accurately present in the initial velocity model. To invert for those model wavenumbers, we need to locate their imprint in the data. Thus, I review the relation between the model wavenumber buildup and the inversion process. Specifically, I emphasize a focus on the model wavenumber components and identified their individual influence on the data. Missing the energy for a single vertical low-model wavenumber from the residual between the true Marmousi model and some initial linearly increasing velocity model produced a worse least-squares fit to the data than the initial model itself, in which all the residual model wavenumbers were missing. This stern realization validated the importance of wavenumber continuation, specifically starting from the low-model wavenumbers, to higher (resolution) wavenumbers, especially those attained in an order dictated by the scattering angle filter. A numerical Marmousi example determined the important role that the scattering angle filter played in managing the wavenumber continuation from low to high. An application on the SEG2014 blind test data set with frequencies lower than 7 Hz muted out further validated the versatility of the scattering angle filtering.

Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation

KAUST Repository

Alkhalifah, Tariq Ali

2016-01-01

A model of the earth can be described using a Fourier basis represented by its wavenumber content. In full-waveform inversion (FWI), the wavenumber description of the model is natural because our Born-approximation-based velocity updates are made up of wavefields. Our objective in FWI is to access all the model wavenumbers available in our limited aperture and bandwidth recorded data that are not yet accurately present in the initial velocity model. To invert for those model wavenumbers, we need to locate their imprint in the data. Thus, I review the relation between the model wavenumber buildup and the inversion process. Specifically, I emphasize a focus on the model wavenumber components and identified their individual influence on the data. Missing the energy for a single vertical low-model wavenumber from the residual between the true Marmousi model and some initial linearly increasing velocity model produced a worse least-squares fit to the data than the initial model itself, in which all the residual model wavenumbers were missing. This stern realization validated the importance of wavenumber continuation, specifically starting from the low-model wavenumbers, to higher (resolution) wavenumbers, especially those attained in an order dictated by the scattering angle filter. A numerical Marmousi example determined the important role that the scattering angle filter played in managing the wavenumber continuation from low to high. An application on the SEG2014 blind test data set with frequencies lower than 7 Hz muted out further validated the versatility of the scattering angle filtering.

Linear network theory

CERN Document Server

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

Inverse approach for determination of the coils location during magnetic stimulation

International Nuclear Information System (INIS)

Marinova, Iliana; Kovachev, Ludmil

2002-01-01

An inverse approach using neural networks is extended and applied for determination of coils location during magnetic stimulation. The major constructions of magnetic stimulation coils have been investigated. The electric and magnetic fields are modelled using finite element method and integral equation method. The effects of changing the construction of coils and the frequency to the effect of magnetic stimulation are analysed. The results show that the coils for magnetic stimulation characterize with different focality and magnetic field concentration. The proposed inverse approach using neural networks is very useful for determination the spatial position of the stimulation coils especially when the location of the coil system is required to be changed dynamically. (Author)

A novel and generalized approach in the inversion of geoelectrical ...

Indian Academy of Sciences (India)

can automate this process and substantially reduce development .... In this network, the information moves in only ..... inversion scheme for deep resistivity sounding data using artificial ... Tiwari 2013 Delineation of shallow resistivity structure.

A generalized model via random walks for information filtering

Science.gov (United States)

Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng

2016-08-01

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation.

Toward Inverse Control of Physics-Based Sound Synthesis

Science.gov (United States)

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.

Digital high-pass filter deconvolution by means of an infinite impulse response filter

Energy Technology Data Exchange (ETDEWEB)

Födisch, P., E-mail: p.foedisch@hzdr.de [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Wohsmann, J. [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Dresden University of Applied Sciences, Faculty of Electrical Engineering, Friedrich-List-Platz 1, 01069 Dresden (Germany); Lange, B. [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Schönherr, J. [Dresden University of Applied Sciences, Faculty of Electrical Engineering, Friedrich-List-Platz 1, 01069 Dresden (Germany); Enghardt, W. [OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, PF 41, 01307 Dresden (Germany); Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology, Bautzner Landstr. 400, 01328 Dresden (Germany); German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg (Germany); Kaever, P. [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Dresden University of Applied Sciences, Faculty of Electrical Engineering, Friedrich-List-Platz 1, 01069 Dresden (Germany)

2016-09-11

In the application of semiconductor detectors, the charge-sensitive amplifier is widely used in front-end electronics. The output signal is shaped by a typical exponential decay. Depending on the feedback network, this type of front-end electronics suffers from the ballistic deficit problem, or an increased rate of pulse pile-ups. Moreover, spectroscopy applications require a correction of the pulse-height, while a shortened pulse-width is desirable for high-throughput applications. For both objectives, digital deconvolution of the exponential decay is convenient. With a general method and the signals of our custom charge-sensitive amplifier for cadmium zinc telluride detectors, we show how the transfer function of an amplifier is adapted to an infinite impulse response (IIR) filter. This paper investigates different design methods for an IIR filter in the discrete-time domain and verifies the obtained filter coefficients with respect to the equivalent continuous-time frequency response. Finally, the exponential decay is shaped to a step-like output signal that is exploited by a forward-looking pulse processing.

A highly linear baseband Gm—C filter for WLAN application

Science.gov (United States)

Lijun, Yang; Zheng, Gong; Yin, Shi; Zhiming, Chen

2011-09-01

A low voltage, highly linear transconductan—C (Gm—C) low-pass filter for wireless local area network (WLAN) transceiver application is proposed. This transmitter (Tx) filter adopts a 9.8 MHz 3rd-order Chebyshev low pass prototype and achieves 35 dB stop-band attenuation at 30 MHz frequency. By utilizing pseudo-differential linear-region MOS transconductors, the filter IIP3 is measured to be as high as 9.5 dBm. Fabricated in a 0.35 μm standard CMOS technology, the proposed filter chip occupies a 0.41 × 0.17 mm2 die area and consumes 3.36 mA from a 3.3-V power supply.

Social Collaborative Filtering by Trust.

Science.gov (United States)

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.

Stochastic forward and inverse groundwater flow and solute transport modeling

NARCIS (Netherlands)

Janssen, G.M.C.M.

2008-01-01

Keywords: calibration, inverse modeling, stochastic modeling, nonlinear biodegradation, stochastic-convective, advective-dispersive, travel time, network design, non-Gaussian distribution, multimodal distribution, representers

This thesis offers three new approaches that contribute

Inverse problems using ANN in long range atmospheric dispersion with signature analysis picked scattered numerical sensors from CFD

International Nuclear Information System (INIS)

Sharma, Pavan K.; Gera, B.; Ghosh, A.K.; Kushwaha, H.S.

2010-01-01

Scalar dispersion in the atmosphere is an important area wherein different approaches are followed in development of good analytical model. The analyses based on Computational Fluid Dynamics (CFD) codes offer an opportunity of model development based on first principles of physics and hence such models have an edge over the existing models. Both forward and backward calculation methods are being developed for atmospheric dispersion around NPPs at BARC Forward modeling methods, which describe the atmospheric transport from sources to receptors, use forward-running transport and dispersion models or computational fluid dynamics models which are run many times, and the resulting dispersion field is compared to observations from multiple sensors. Backward or inverse modeling methods use only one model run in the reverse direction from the receptors to estimate the upwind sources. Inverse modeling methods include adjoint and tangent linear models, Kalman filters, and variational data assimilation, and neural network. The present paper is aimed at developing a new approach where the identified specific signatures at receptor points form the basis for source estimation or inversions. This approach is expected to reduce the large transient data sets to reduced and meaningful data sets. In fact this reduces the inherently transient data set into a time independent mean data set. Forward computation were carried out with CFD code for various case to generate a large set of data to train the ANN. Specific signature analysis was carried out to find the parameters of interest for ANN training like peak concentration, time to reach peak concentration and time to fall, the ANN was trained with data and source strength and location were predicted from ANN. Inverse problem was performed using ANN approach in long range atmospheric dispersion. An illustration of application of CFD code for atmospheric dispersion studies for a hypothetical case is also included in the paper. (author)

A cost-effective structure of a centralized-light-source WDM-PON utilizing inverse-duobinary-RZ downstream and DPSK upstream

International Nuclear Information System (INIS)

Chen Long-Quan; Qiao Yao-Jun; Ji Yue-Feng

2013-01-01

In this paper, we propose a new structure of a centralized-light-source wavelength division multiplexed passive optical network (WDM-PON) utilizing inverse-duobinary-return-to-zero (inverse-duobinary-RZ) downstream and DPSK upstream. It reuses downstream light for the upstream modulation, which retrenches lasers assembled at each optical network unit (ONU), and ultimately cuts down the cost of ONUs a great deal. Meanwhile, a 50-km-reach WDM-PON experiment with 10-Gb/s inverse-duobinary-RZ downstream and 6-Gb/s DPSK upstream is demonstrated here. It is revealed to be a novel cost-effective alternative for the next generation access network. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise.

Science.gov (United States)

Vuković, Najdan; Miljković, Zoran

2015-03-01

Feedforward neural networks (FFNN) are among the most used neural networks for modeling of various nonlinear problems in engineering. In sequential and especially real time processing all neural networks models fail when faced with outliers. Outliers are found across a wide range of engineering problems. Recent research results in the field have shown that to avoid overfitting or divergence of the model, new approach is needed especially if FFNN is to run sequentially or in real time. To accommodate limitations of FFNN when training data contains a certain number of outliers, this paper presents new learning algorithm based on improvement of conventional extended Kalman filter (EKF). Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is not constant; the sequence of noise measurement covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is modeled as inverse Wishart distribution. In each iteration EKF-OR simultaneously estimates noise estimates and current best estimate of FFNN parameters. Bayesian framework enables one to mathematically derive expressions, while analytical intractability of the Bayes' update step is solved by using structured variational approximation. All mathematical expressions in the paper are derived using the first principles. Extensive experimental study shows that FFNN trained with developed learning algorithm, achieves low prediction error and good generalization quality regardless of outliers' presence in training data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Cone-beam local reconstruction based on a Radon inversion transformation

International Nuclear Information System (INIS)

Wang Xian-Chao; Yan Bin; Li Lei; Hu Guo-En

2012-01-01

The local reconstruction from truncated projection data is one area of interest in image reconstruction for computed tomography (CT), which creates the possibility for dose reduction. In this paper, a filtered-backprojection (FBP) algorithm based on the Radon inversion transform is presented to deal with the three-dimensional (3D) local reconstruction in the circular geometry. The algorithm achieves the data filtering in two steps. The first step is the derivative of projections, which acts locally on the data and can thus be carried out accurately even in the presence of data truncation. The second step is the nonlocal Hilbert filtering. The numerical simulations and the real data reconstructions have been conducted to validate the new reconstruction algorithm. Compared with the approximate truncation resistant algorithm for computed tomography (ATRACT), not only it has a comparable ability to restrain truncation artifacts, but also its reconstruction efficiency is improved. It is about twice as fast as that of the ATRACT. Therefore, this work provides a simple and efficient approach for the approximate reconstruction from truncated projections in the circular cone-beam CT

Word2Vec inversion and traditional text classifiers for phenotyping lupus.

Science.gov (United States)

Turner, Clayton A; Jacobs, Alexander D; Marques, Cassios K; Oates, James C; Kamen, Diane L; Anderson, Paul E; Obeid, Jihad S

2017-08-22

Identifying patients with certain clinical criteria based on manual chart review of doctors' notes is a daunting task given the massive amounts of text notes in the electronic health records (EHR). This task can be automated using text classifiers based on Natural Language Processing (NLP) techniques along with pattern recognition machine learning (ML) algorithms. The aim of this research is to evaluate the performance of traditional classifiers for identifying patients with Systemic Lupus Erythematosus (SLE) in comparison with a newer Bayesian word vector method. We obtained clinical notes for patients with SLE diagnosis along with controls from the Rheumatology Clinic (662 total patients). Sparse bag-of-words (BOWs) and Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) matrices were produced using NLP pipelines. These matrices were subjected to several different NLP classifiers: neural networks, random forests, naïve Bayes, support vector machines, and Word2Vec inversion, a Bayesian inversion method. Performance was measured by calculating accuracy and area under the Receiver Operating Characteristic (ROC) curve (AUC) of a cross-validated (CV) set and a separate testing set. We calculated the accuracy of the ICD-9 billing codes as a baseline to be 90.00% with an AUC of 0.900, the shallow neural network with CUIs to be 92.10% with an AUC of 0.970, the random forest with BOWs to be 95.25% with an AUC of 0.994, the random forest with CUIs to be 95.00% with an AUC of 0.979, and the Word2Vec inversion to be 90.03% with an AUC of 0.905. Our results suggest that a shallow neural network with CUIs and random forests with both CUIs and BOWs are the best classifiers for this lupus phenotyping task. The Word2Vec inversion method failed to significantly beat the ICD-9 code classification, but yielded promising results. This method does not require explicit features and is more adaptable to non-binary classification tasks. The Word2Vec inversion is

Efficient Underwater RSS Value to Distance Inversion Using the Lambert Function

Directory of Open Access Journals (Sweden)

Majid Hosseini

2014-01-01

Full Text Available There are many applications for using wireless sensor networks (WSN in ocean science; however, identifying the exact location of a sensor by itself (localization is still a challenging problem, where global positioning system (GPS devices are not applicable underwater. Precise distance measurement between two sensors is a tool of localization and received signal strength (RSS, reflecting transmission loss (TL phenomena, is widely used in terrestrial WSNs for that matter. Underwater acoustic sensor networks have not been used (UASN, due to the complexity of the TL function. In this paper, we addressed these problems by expressing underwater TL via the Lambert W function, for accurate distance inversion by the Halley method, and compared this to Newton-Raphson inversion. Mathematical proof, MATLAB simulation, and real device implementation demonstrate the accuracy and efficiency of the proposed equation in distance calculation, with fewer iterations, computation stability for short and long distances, and remarkably short processing time. Then, the sensitivities of Lambert W function and Newton-Raphson inversion to alteration in TL were examined. The simulation results showed that Lambert W function is more stable to errors than Newton-Raphson inversion. Finally, with a likelihood method, it was shown that RSS is a practical tool for distance measurement in UASN.

Edge and line enhancement by adaptive lattice filtering

International Nuclear Information System (INIS)

Brolley, J.E.

1979-01-01

Digitized images have been two-dimensionally transformed to the Haar sequency domain. High-sequency boosting was performed and the inverse Haar two-dimensional transform applied. The resulting image was then raster-scanned with a continuously adaptive lattice filter. This procedure was applied to a simple image of a photographic step tablet and a complex scene. All of the lines of the step tablet were well defined over the whole dynamic range. Useful definition of lines in the complex scene was obtained

Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

Directory of Open Access Journals (Sweden)

Hossein Foroozand

2018-03-01

Full Text Available Recently, the Entropy Ensemble Filter (EEF method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (NN modeling of El Nino-southern oscillation. Specifically, we forecast the first five principal components (PCs of sea surface temperature monthly anomaly fields over tropical Pacific, at different lead times (from 3 to 15 months, with a three-month increment for the period 1979–2017. We apply the EEF method in a multiple-linear regression (MLR model and two NN models, one using Bayesian regularization and one Levenberg-Marquardt algorithm for training, and evaluate their performance and computational efficiency relative to the same models with conventional bagging. All models perform equally well at the lead time of 3 and 6 months, while at higher lead times, the MLR model’s skill deteriorates faster than the nonlinear models. The neural network models with both bagging methods produce equally successful forecasts with the same computational efficiency. It remains to be shown whether this finding is sensitive to the dataset size.

Kalman and particle filtering methods for full vehicle and tyre identification

Science.gov (United States)

Bogdanski, Karol; Best, Matthew C.

2018-05-01

This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.

Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

Science.gov (United States)

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

Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

Directory of Open Access Journals (Sweden)

Stavros I. Dimitriadis

2017-12-01

Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node

Analysis of positron lifetime spectra using quantified maximum entropy and a general linear filter

International Nuclear Information System (INIS)

Shukla, A.; Peter, M.; Hoffmann, L.

1993-01-01

Two new approaches are used to analyze positron annihilation lifetime spectra. A general linear filter is designed to filter the noise from lifetime data. The quantified maximum entropy method is used to solve the inverse problem of finding the lifetimes and intensities present in data. We determine optimal values of parameters needed for fitting using Bayesian methods. Estimates of errors are provided. We present results on simulated and experimental data with extensive tests to show the utility of this method and compare it with other existing methods. (orig.)

Analytical inversion formula for uniformly attenuated fan-beam projections

International Nuclear Information System (INIS)

Weng, Y.; Zeng, G.L.; Gullberg, G.T.

1997-01-01

In deriving algorithms to reconstruct single photon emission computed tomography (SPECT) projection data, it is important that the algorithm compensates for photon attenuation in order to obtain quantitative reconstruction results. A convolution backprojection algorithm was derived by Tretiak and Metz to reconstruct two-dimensional (2-D) transaxial slices from uniformly attenuated parallel-beam projections. Using transformation of coordinates, this algorithm can be modified to obtain a formulation useful to reconstruct uniformly attenuated fan-beam projections. Unlike that for parallel-beam projections, this formulation does not produce a filtered backprojection reconstruction algorithm but instead has a formulation that is an inverse integral operator with a spatially varying kernel. This algorithm thus requires more computation time than does the filtered backprojection reconstruction algorithm for the uniformly attenuated parallel-beam case. However, the fan-beam reconstructions demonstrate the same image quality as that of parallel-beam reconstructions

A direct inversion scheme for deep resistivity sounding data using ...

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor ... ing the network to acquire important information on the problem being ... the processing element (PE), called neuron, which.

Kalman filter for statistical monitoring of forest cover across sub-continental regions [Symposium

Science.gov (United States)

Raymond L. Czaplewski

1991-01-01

The Kalman filter is a generalization of the composite estimator. The univariate composite estimate combines 2 prior estimates of population parameter with a weighted average where the scalar weight is inversely proportional to the variances. The composite estimator is a minimum variance estimator that requires no distributional assumptions other than estimates of the...

A highly linear baseband Gm-C filter for WLAN application

International Nuclear Information System (INIS)

Yang Lijun; Chen Zhiming; Gong Zheng; Shi Yin

2011-01-01

A low voltage, highly linear transconductan-C (G m -C) low-pass filter for wireless local area network (WLAN) transceiver application is proposed. This transmitter (Tx) filter adopts a 9.8 MHz 3rd-order Chebyshev low pass prototype and achieves 35 dB stop-band attenuation at 30 MHz frequency. By utilizing pseudo-differential linear-region MOS transconductors, the filter IIP 3 is measured to be as high as 9.5 dBm. Fabricated in a 0.35 μm standard CMOS technology, the proposed filter chip occupies a 0.41 x 0.17 mm 2 die area and consumes 3.36 mA from a 3.3-V power supply. (semiconductor integrated circuits)

Enhancing time resolution by stabilized inverse filter and Q estimated on instantaneous spectra

OpenAIRE

Corrales, Álvaro; Cabrera, Francisco; Montes, Luis

2014-01-01

Physical phenomena, such as attenuation of high frequency components and velocity dispersion, deteriorate seismic images. To enhance seismic resolution, Q filtering is usually applied, where the accurate estimation of Q is the core of this approach. The Matching Pursuit (MP) approach is an instantaneous spectral analysis method that overcomes windowing problems caused by decomposing a seismic trace, providing a frequency spectrum for each time sample of the trace. By changing variables, the s...

Compact Unequal Power Divider with Filtering Response

Directory of Open Access Journals (Sweden)

Wei-Qiang Pan

2015-01-01

Full Text Available We present a novel unequal power divider with bandpass responses. The proposed power divider consists of five resonators and a resistor. The power division ratio is controlled by altering the coupling strength among the resonators. The output ports have the characteristic impedance of 50 Ω and impedance transformers in classical Wilkinson power dividers are not required in this design. Use of resonators enables the filtering function of the power divider. Two transmission zeros are generated near the passband edges, resulting in quasielliptic bandpass responses. For validation, a 2 : 1 filtering power divider is implemented. The fabricated circuit size is 0.22 λg × 0.08 λg, featuring compact size for unequal filtering power dividers, which is suitable for the feeding networks of antenna arrays.

Basic methodology of tomographic imaging by filtered inverse projection at a turbo-pump. Project report; Methodische Grundlagen fuer die Tomographie durch gefilterte Rueckprojektion an einer Axialpumpe. Projektbericht

Energy Technology Data Exchange (ETDEWEB)

Hoppe, D.

2000-11-01

A two-phase medium consisting of a fluid containing gas is transported in a turbo-pump via an impeller. The interaction between the gaseous phase and the impeller is to be examined by tomography with gamma rays. Reconstruction of the image of the object is to be made by way of filtered inverse projection. The methodology of using this principle in the given system (geometry and measuring conditions) is explained. (orig./CB) [German] Ein zweiphasiges, aus einer gashaltigen Fluessigkeit bestehendes Medium wird in einer Axialpumpe von einem propellerartigen Laufrad senkrecht zur Drehachse dieses Laufrades transportiert. Die Wechselwirkung zwischen der Gasphase und dem Laufrad soll unter Verwendung von Gamma-Strahlung mittels Tomographie untersucht werden. Dabei ist fuer die Rekonstruktion des Objektbildes das Prinzip der sogenannten gefilterten Rueckprojektion vorgesehen. Die methodischen Grundlagen fuer die Nutzung dieses Prinzips unter von vorgesehenen geometrischen und messtechnischen Bedingungen sind Gegenstand dieser Arbeit. (orig.)

Polarization-Insensitive Tunable Optical Filters based on Liquid Crystal Polarization Gratings

Science.gov (United States)

Nicolescu, Elena

Tunable optical filters are widely used for a variety of applications including spectroscopy, optical communication networks, remote sensing, and biomedical imaging and diagnostics. All of these application areas can greatly benefit from improvements in the key characteristics of the tunable optical filters embedded in them. Some of these key parameters include peak transmittance, bandwidth, tuning range, and transition width. In recent years research efforts have also focused on miniaturizing tunable optical filters into physically small packages for compact portable spectroscopy and hyperspectral imaging applications such as real-time medical diagnostics and defense applications. However, it is important that miniaturization not have a detrimental effect on filter performance. The overarching theme of this dissertation is to explore novel configurations of Polarization Gratings (PGs) as simple, low-cost, polarization-insensitive alternatives to conventional optical filtering technologies for applications including hyperspectral imaging and telecommunications. We approach this goal from several directions with a combination of theory and experimental demonstration leading to, in our opinion, a significant contribution to the field. We present three classes of tunable optical filters, the first of which is an angle-filtering scheme where the stop-band wavelengths are redirected off axis and the passband is transmitted on-axis. This is achieved using a stacked configuration of polarization gratings of various thicknesses. To improve this class of filter, we also introduce a novel optical element, the Bilayer Polarization Grating, exhibiting unique optical properties and demonstrating complex anchoring conditions with high quality. The second class of optical filter is analogous to a Lyot filter, utilizing stacks of static or tunable waveplates sandwiched with polarizing elements. However, we introduce a new configuration using PGs and static waveplates to replace

Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data

International Nuclear Information System (INIS)

Zhuang Tingliang; Leng Shuai; Nett, Brian E; Chen Guanghong

2004-01-01

In this paper, a new image reconstruction scheme is presented based on Tuy's cone-beam inversion scheme and its fan-beam counterpart. It is demonstrated that Tuy's inversion scheme may be used to derive a new framework for fan-beam and cone-beam image reconstruction. In this new framework, images are reconstructed via filtering the backprojection image of differentiated projection data. The new framework is mathematically exact and is applicable to a general source trajectory provided the Tuy data sufficiency condition is satisfied. By choosing a piece-wise constant function for one of the components in the factorized weighting function, the filtering kernel is one dimensional, viz. the filtering process is along a straight line. Thus, the derived image reconstruction algorithm is mathematically exact and efficient. In the cone-beam case, the derived reconstruction algorithm is applicable to a large class of source trajectories where the pi-lines or the generalized pi-lines exist. In addition, the new reconstruction scheme survives the super-short scan mode in both the fan-beam and cone-beam cases provided the data are not transversely truncated. Numerical simulations were conducted to validate the new reconstruction scheme for the fan-beam case

Electron volt spectroscopy on a pulsed neutron source using resonance absorption filters

International Nuclear Information System (INIS)

Newport, R.J.; Williams, W.G.

1983-05-01

The design aspects of an inelastic neutron spectrometer based on energy selection by the resonance absorption filter difference method are discussed. Detailed calculations of the accessible dynamical range (Q, ω), energy and momentum transfer resolutions and representative count rates are presented for Sm and Ta resonance filters in an inverse geometry spectrometer on a high intensity pulsed source such as the RAL Spallation Neutron Source (SNS). A discussion is given of the double-difference method, which provides a means of improving the resonance attenuation peak shape. As a result of this study, as well as preliminary experimental results, recommendations are made for the future development of the technique. (author)

A filtering approach to image reconstruction in 3D SPECT

International Nuclear Information System (INIS)

Bronnikov, Andrei V.

2000-01-01

We present a new approach to three-dimensional (3D) image reconstruction using analytical inversion of the exponential divergent beam transform, which can serve as a mathematical model for cone-beam 3D SPECT imaging. We apply a circular cone-beam scan and assume constant attenuation inside a convex area with a known boundary, which is satisfactory in brain imaging. The reconstruction problem is reduced to an image restoration problem characterized by a shift-variant point spread function which is given analytically. The method requires two computation steps: backprojection and filtering. The modulation transfer function (MTF) of the filter is derived by means of an original methodology using the 2D Laplace transform. The filter is implemented in the frequency domain and requires 2D Fourier transform of transverse slices. In order to obtain a shift-invariant cone-beam projection-backprojection operator we resort to an approximation, assuming that the collimator has a relatively large focal length. Nevertheless, numerical experiments demonstrate surprisingly good results for detectors with relatively short focal lengths. The use of a wavelet-based filtering algorithm greatly improves the stability to Poisson noise. (author)

Ultranarrow-bandwidth filter based on a thermal EIT medium.

Science.gov (United States)

Wang, Gang; Wang, Yu-Sheng; Huang, Emily Kay; Hung, Weilun; Chao, Kai-Lin; Wu, Ping-Yeh; Chen, Yi-Hsin; Yu, Ite A

2018-05-21

We present high-contrast electromagnetically-induced-transparency (EIT) spectra in a heated vapor cell of single isotope 87 Rb atoms. The EIT spectrum has both high resonant transmission up to 67% and narrow linewidth of 1.1 MHz. We get rid of the possible amplification resulted from the effects of amplification without population inversion and four-wave mixing. Therefore, this high transmitted light is not artificial. The theoretical prediction of the probe transmission agrees well with the data and the experimental parameters can be derived reasonably from the model. Such narrow and high-contrast spectral profile can be employed as a high precision bandpass filter, which provides a significant advantage in terms of stability and tunability. The central frequency tuning range of the filter is larger than 100 MHz with out-of-band blocking ≥15 dB. This bandpass filter can effectively produce light fields with subnatural linewidth. Nonlinearity associating with the narrow-linewidth and high-contrast EIT profile can be very useful in the applications utilizing the EIT effect.

Neural Model for Left-Handed CPW Bandpass Filter Loaded Split Ring Resonator

Science.gov (United States)

Liu, Haiwen; Wang, Shuxin; Tan, Mingtao; Zhang, Qijun

2010-02-01

Compact left-handed coplanar waveguide (CPW) bandpass filter loaded split ring resonator (SRR) is presented in this paper. The proposed filter exhibits a quasi-elliptic function response and its circuit size occupies only 12 × 11.8 mm2 (≈0.21 λg × 0.20 λg). Also, a simple circuit model is given and the parametric study of this filter is discussed. Then, with the aid of NeuroModeler software, a five-layer feed-forward perceptron neural networks model is built up to optimize the proposed filter design fast and accurately. Finally, this newly left-handed CPW bandpass filter was fabricated and measured. A good agreement between simulations and measurement verifies the proposed left-handed filter and the validity of design methodology.

Direct and accelerated parameter mapping using the unscented Kalman filter.

Science.gov (United States)

Zhao, Li; Feng, Xue; Meyer, Craig H

2016-05-01

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Directory of Open Access Journals (Sweden)

Yongyang Xu

2018-01-01

Full Text Available Very high resolution (VHR remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.

Sensitivity study on hydraulic well testing inversion using simulated annealing

International Nuclear Information System (INIS)

Nakao, Shinsuke; Najita, J.; Karasaki, Kenzi

1997-11-01

For environmental remediation, management of nuclear waste disposal, or geothermal reservoir engineering, it is very important to evaluate the permeabilities, spacing, and sizes of the subsurface fractures which control ground water flow. Cluster variable aperture (CVA) simulated annealing has been used as an inversion technique to construct fluid flow models of fractured formations based on transient pressure data from hydraulic tests. A two-dimensional fracture network system is represented as a filled regular lattice of fracture elements. The algorithm iteratively changes an aperture of cluster of fracture elements, which are chosen randomly from a list of discrete apertures, to improve the match to observed pressure transients. The size of the clusters is held constant throughout the iterations. Sensitivity studies using simple fracture models with eight wells show that, in general, it is necessary to conduct interference tests using at least three different wells as pumping well in order to reconstruct the fracture network with a transmissivity contrast of one order of magnitude, particularly when the cluster size is not known a priori. Because hydraulic inversion is inherently non-unique, it is important to utilize additional information. The authors investigated the relationship between the scale of heterogeneity and the optimum cluster size (and its shape) to enhance the reliability and convergence of the inversion. It appears that the cluster size corresponding to about 20--40 % of the practical range of the spatial correlation is optimal. Inversion results of the Raymond test site data are also presented and the practical range of spatial correlation is evaluated to be about 5--10 m from the optimal cluster size in the inversion

Sensitivity study on hydraulic well testing inversion using simulated annealing

Energy Technology Data Exchange (ETDEWEB)

Nakao, Shinsuke; Najita, J.; Karasaki, Kenzi

1997-11-01

For environmental remediation, management of nuclear waste disposal, or geothermal reservoir engineering, it is very important to evaluate the permeabilities, spacing, and sizes of the subsurface fractures which control ground water flow. Cluster variable aperture (CVA) simulated annealing has been used as an inversion technique to construct fluid flow models of fractured formations based on transient pressure data from hydraulic tests. A two-dimensional fracture network system is represented as a filled regular lattice of fracture elements. The algorithm iteratively changes an aperture of cluster of fracture elements, which are chosen randomly from a list of discrete apertures, to improve the match to observed pressure transients. The size of the clusters is held constant throughout the iterations. Sensitivity studies using simple fracture models with eight wells show that, in general, it is necessary to conduct interference tests using at least three different wells as pumping well in order to reconstruct the fracture network with a transmissivity contrast of one order of magnitude, particularly when the cluster size is not known a priori. Because hydraulic inversion is inherently non-unique, it is important to utilize additional information. The authors investigated the relationship between the scale of heterogeneity and the optimum cluster size (and its shape) to enhance the reliability and convergence of the inversion. It appears that the cluster size corresponding to about 20--40 % of the practical range of the spatial correlation is optimal. Inversion results of the Raymond test site data are also presented and the practical range of spatial correlation is evaluated to be about 5--10 m from the optimal cluster size in the inversion.

Transfer functions of double- and multiple-cavity Fabry-Perot filters driven by Lorentzian sources.

Science.gov (United States)

Marti, J; Capmany, J

1996-12-20

We derive expressions for the transfer functions of double- and multiple-cavity Fabry-Perot filters driven by laser sources with Lorentzian spectrum. These are of interest because of their applications in sensing and channel filtering in optical frequency-division multiplexing networks.

Use of Savitzky-Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors.

Science.gov (United States)

de Oliveira, Mario A; Araujo, Nelcileno V S; da Silva, Rodolfo N; da Silva, Tony I; Epaarachchi, Jayantha

2018-01-08

A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky-Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Science.gov (United States)

Araujo, Nelcileno V. S.; da Silva, Rodolfo N.; da Silva, Tony I.; Epaarachchi, Jayantha

2018-01-01

A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario. PMID:29316693

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Directory of Open Access Journals (Sweden)

Mario A. de Oliveira

2018-01-01

Full Text Available A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1 Savitzky–Golay (SG filter, using both first and second derivatives; (2 Probabilistic Neural Network (PNN; and, (3 Simplified Fuzzy ARTMAP Network (SFAN. Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

Neural network approach to radiologic lesion detection

International Nuclear Information System (INIS)

Newman, F.D.; Raff, U.; Stroud, D.

1989-01-01

An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition. The authors explore the use of neural networks in radiologic lesion detection with what is known in the literature as the novelty filter. This filter uses a linear model; images of normal patterns become training vectors and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or novelty is extracted. A VAX 750 was used to encode the novelty filter, and two experiments have been examined

Unscented Kalman filter assimilation of time-lapse self-potential data for monitoring solute transport

Science.gov (United States)

Cui, Yi-an; Liu, Lanbo; Zhu, Xiaoxiong

2017-08-01

Monitoring the extent and evolution of contaminant plumes in local and regional groundwater systems from existing landfills is critical in contamination control and remediation. The self-potential survey is an efficient and economical nondestructive geophysical technique that can be used to investigate underground contaminant plumes. Based on the unscented transform, we have built a Kalman filtering cycle to conduct time-lapse data assimilation for monitoring the transport of solute based on the solute transport experiment using a bench-scale physical model. The data assimilation was formed by modeling the evolution based on the random walk model and observation correcting based on the self-potential forward. Thus, monitoring self-potential data can be inverted by the data assimilation technique. As a result, we can reconstruct the dynamic process of the contaminant plume instead of using traditional frame-to-frame static inversion, which may cause inversion artifacts. The data assimilation inversion algorithm was evaluated through noise-added synthetic time-lapse self-potential data. The result of the numerical experiment shows validity, accuracy and tolerance to the noise of the dynamic inversion. To validate the proposed algorithm, we conducted a scaled-down sandbox self-potential observation experiment to generate time-lapse data that closely mimics the real-world contaminant monitoring setup. The results of physical experiments support the idea that the data assimilation method is a potentially useful approach for characterizing the transport of contamination plumes using the unscented Kalman filter (UKF) data assimilation technique applied to field time-lapse self-potential data.

An improved exact inversion formula for solenoidal fields in cone beam vector tomography

Science.gov (United States)

Katsevich, Alexander; Rothermel, Dimitri; Schuster, Thomas

2017-06-01

In this paper we present an improved inversion formula for the 3D cone beam transform of vector fields supported in the unit ball which is exact for solenoidal fields. It is well known that only the solenoidal part of a vector field can be determined from the longitudinal ray transform of a vector field in cone beam geometry. The inversion formula, as it was developed in Katsevich and Schuster (2013 An exact inversion formula for cone beam vector tomography Inverse Problems 29 065013), consists of two parts. The first part is of the filtered backprojection type, whereas the second part is a costly 4D integration and very inefficient. In this article we tackle this second term and obtain an improved formula, which is easy to implement and saves one order of integration. We also show that the first part contains all information about the curl of the field, whereas the second part has information about the boundary values. More precisely, the second part vanishes if the solenoidal part of the original field is tangential at the boundary. A number of numerical tests presented in the paper confirm the theoretical results and the exactness of the formula. Also, we obtain an inversion algorithm that works for general convex domains.

A Network of Kalman Filters for MAI and ISI Compensation in a Non-Gaussian Environment

Directory of Open Access Journals (Sweden)

Sayadi Bessem

2005-01-01

Full Text Available This paper develops a new multiuser detector based on a network of kalman filters (NKF dealing with multiple access-interference (MAI, intersymbol Interference (ISI, and an impulsive observation noise. The two proposed schemes are based on the modeling of the DS-CDMA system by a discrete-time linear system that has non-Gaussian state and measurement noises. By approximating the non-Gaussian densities of the noises by a weighted sum of Gaussian terms and under the common MMSE estimation criterion, we first derive an NKF detector. This version is further optimized by introducing a feedback exploiting the ISI interference structure. The resulting scheme is an NKF detector based on a likelihood ratio test (LRT. Monte-Carlo simulations have shown that the NKF and the NKF based on LRT detectors significantly improve the efficiency and the performance of the classical Kalman algorithm.

PENGEMBANGAN SISTEM PENGENDALIAN TRAFFIC DAN WEB FILTERING PADA JARINGAN INTERNET BERBASIS HOTSPOT

Directory of Open Access Journals (Sweden)

Riska Robianto

2016-12-01

Full Text Available Utilization of Internet services to the hotspot network raises particular problems in traffic control and web filtering. Linux as an operating system that is open (open source, offers a variety of internet firewall systems to assist in the management of traffic, bandwidth, web filtering and one using Smoothwall Express to Linux which can convert a personal computer (PC to the Internet firewall reliable and stable. Smoothwall allows to integrated with the add-ons extends like AdvProxy and URL Filter to improve the performance of Smoohtwall and simplify configuration of the traffic control and web filtering. The research was conducted by collecting data from various sources related, then experiment with implementing Smoohtwall by adding add-ons extends to help administrators manage and regulate the bandwidth allocation of each client, block websites (URLs that are not needed. Based on these results bandwidth management can be divided equally and can block websites (URLs that are not needed like porn sites, warez, bad word, and also the quality of the network connection updatesites more stable.

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

Pyramidal resistor networks for electrical impedance tomography with partial boundary measurements

International Nuclear Information System (INIS)

Borcea, L; Mamonov, A V; Druskin, V; Vasquez, F Guevara

2010-01-01

We introduce an inversion algorithm for electrical impedance tomography (EIT) with partial boundary measurements in two dimensions. It gives stable and fast reconstructions using sparse parameterizations of the unknown conductivity on optimal grids that are computed as part of the inversion. We follow the approach in Borcea et al (2008 Inverse Problems 24 035013) and Vasquez (2006 PhD thesis Rice University, Houston, TX, USA) that connects inverse discrete problems for resistor networks to continuum EIT problems, using optimal grids. The algorithm in Borcea et al (2008 Inverse Problems 24 035013) and Vasquez (2006 PhD Thesis Rice University, Houston, TX, USA) is based on circular resistor networks, and solves the EIT problem with full boundary measurements. It is extended in Borcea et al (2010 Inverse Problems 26 045010) to EIT with partial boundary measurements, using extremal quasi-conformal mappings that transform the problem to one with full boundary measurements. Here we introduce a different class of optimal grids, based on resistor networks with pyramidal topology, that is better suited for the partial measurements setup. We prove the unique solvability of the discrete inverse problem for these networks and develop an algorithm for finding them from the measurements of the Dirichlet to Neumann map. Then, we show how to use the networks to define the optimal grids and to approximate the unknown conductivity. We assess the performance of our approach with numerical simulations and compare the results with those in Borcea et al (2010)

TransCom 3 CO2 inversion intercomparison: 2. Sensitivity of annual mean results to data choices

International Nuclear Information System (INIS)

Law, Rachel M.; Yu-Han Chen

2003-01-01

TransCom 3 is an intercomparison project for CO 2 source inversions. Annual mean CO 2 concentration data are used to estimate CO 2 sources using 16 different atmospheric transport models. Here we test the sensitivity of the inversion to the concentration data. We examine data network choice, time period of data, baseline data selection and the choice of data uncertainty used. We find that in most cases regional source estimates lie within the source uncertainty range of the control inversion. This indicates that the estimated sources are relatively insensitive to the changes in data that were tested. In the data network tests, only the Australian region source estimates varied over a much larger range than that given by the control case uncertainty estimate. For the other regions, the sensitivity to data network was within or close to the uncertainty range. Most of the sensitivity was found to be associated with a small number of sites (e.g. Darwin, Easter Island). These sites are often identified by the inability of the inversion to fit the data at these locations. The model-mean inversion values are mostly insensitive to the time period of data used, with the exception of temperate North America and the tropical Indian ocean. Data selection has a small impact on source estimates for the mean across models, but individual model sensitivity can be large. The magnitude of data uncertainties controls the relative magnitude of the estimated source uncertainty and the spread in model source estimates. Smaller data uncertainties lead to larger differences in source estimates between models. Overall, the data sensitivity tests performed here support the robustness of the control inversion source estimates presented in Gurney et al. (2003. Tellus 55B, this issue). The test results also provide guidance in setting up and interpreting other inversions

Inverse photoemission

International Nuclear Information System (INIS)

Namatame, Hirofumi; Taniguchi, Masaki

1994-01-01

Photoelectron spectroscopy is regarded as the most powerful means since it can measure almost perfectly the occupied electron state. On the other hand, inverse photoelectron spectroscopy is the technique for measuring unoccupied electron state by using the inverse process of photoelectron spectroscopy, and in principle, the similar experiment to photoelectron spectroscopy becomes feasible. The development of the experimental technology for inverse photoelectron spectroscopy has been carried out energetically by many research groups so far. At present, the heightening of resolution of inverse photoelectron spectroscopy, the development of inverse photoelectron spectroscope in which light energy is variable and so on are carried out. But the inverse photoelectron spectroscope for vacuum ultraviolet region is not on the market. In this report, the principle of inverse photoelectron spectroscopy and the present state of the spectroscope are described, and the direction of the development hereafter is groped. As the experimental equipment, electron guns, light detectors and so on are explained. As the examples of the experiment, the inverse photoelectron spectroscopy of semimagnetic semiconductors and resonance inverse photoelectron spectroscopy are reported. (K.I.)

Multiple attenuation to reflection seismic data using Radon filter and Wave Equation Multiple Rejection (WEMR) method

Energy Technology Data Exchange (ETDEWEB)

Erlangga, Mokhammad Puput [Geophysical Engineering, Institut Teknologi Bandung, Ganesha Street no.10 Basic Science B Buliding fl.2-3 Bandung, 40132, West Java Indonesia puput.erlangga@gmail.com (Indonesia)

2015-04-16

Separation between signal and noise, incoherent or coherent, is important in seismic data processing. Although we have processed the seismic data, the coherent noise is still mixing with the primary signal. Multiple reflections are a kind of coherent noise. In this research, we processed seismic data to attenuate multiple reflections in the both synthetic and real seismic data of Mentawai. There are several methods to attenuate multiple reflection, one of them is Radon filter method that discriminates between primary reflection and multiple reflection in the τ-p domain based on move out difference between primary reflection and multiple reflection. However, in case where the move out difference is too small, the Radon filter method is not enough to attenuate the multiple reflections. The Radon filter also produces the artifacts on the gathers data. Except the Radon filter method, we also use the Wave Equation Multiple Elimination (WEMR) method to attenuate the long period multiple reflection. The WEMR method can attenuate the long period multiple reflection based on wave equation inversion. Refer to the inversion of wave equation and the magnitude of the seismic wave amplitude that observed on the free surface, we get the water bottom reflectivity which is used to eliminate the multiple reflections. The WEMR method does not depend on the move out difference to attenuate the long period multiple reflection. Therefore, the WEMR method can be applied to the seismic data which has small move out difference as the Mentawai seismic data. The small move out difference on the Mentawai seismic data is caused by the restrictiveness of far offset, which is only 705 meter. We compared the real free multiple stacking data after processing with Radon filter and WEMR process. The conclusion is the WEMR method can more attenuate the long period multiple reflection than the Radon filter method on the real (Mentawai) seismic data.

Ultra-short FBG based distributed sensing using shifted optical Gaussian filters and microwave-network analysis.

Science.gov (United States)

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.

Adaptive filters and internal models: multilevel description of cerebellar function.

Science.gov (United States)

Porrill, John; Dean, Paul; Anderson, Sean R

2013-11-01

Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles. Copyright © 2012 Elsevier Ltd. All rights reserved.

All-optical VPN utilizing DSP-based digital orthogonal filters access for PONs

Science.gov (United States)

Zhang, Xiaoling; Zhang, Chongfu; Chen, Chen; Jin, Wei; Qiu, Kun

2018-04-01

Utilizing digital filtering-enabled signal multiplexing and de-multiplexing, a cost-effective all-optical virtual private network (VPN) system is proposed, for the first time to our best knowledge, in digital filter multiple access passive optical networks (DFMA-PONs). Based on the DFMA technology, the proposed system can be easily designed to meet the requirements of next generation network's flexibility, elasticity, adaptability and compatibility. Through dynamic digital filter allocation and recycling, the proposed all-optical VPN system can provide dynamic establishments and cancellations of multiple VPN communications with arbitrary traffic volumes. More importantly, due to the employment of DFMA technology, the system is not limited to a fixed signal format and different signal formats such as pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM) and orthogonal frequency division multiplexing (OFDM) can be used. Moreover, one transceiver is sufficient to simultaneously transmit upstream (US)/VPN data to optical line terminal (OLT) or other VPN optical network units (ONUs), thus leading to great reduction in network constructions and operation expenditures. The proposed all-optical VPN system is demonstrated with the transceiver incorporating the formats of QAM and OFDM, which can be made transparent to downstream (DS), US and VPN communications. The bit error rates (BERs) of DS, US and VPN for OFDM signals are below the forward-error-correction (FEC) limit of 3 . 8 × 10-3 when the received optical powers are about -16.8 dBm, -14.5 dBm and -15.7 dBm, respectively.

Inverse Stochastic Resonance in Cerebellar Purkinje Cells.

Directory of Open Access Journals (Sweden)

Anatoly Buchin

2016-08-01

Full Text Available Purkinje neurons play an important role in cerebellar computation since their axons are the only projection from the cerebellar cortex to deeper cerebellar structures. They have complex internal dynamics, which allow them to fire spontaneously, display bistability, and also to be involved in network phenomena such as high frequency oscillations and travelling waves. Purkinje cells exhibit type II excitability, which can be revealed by a discontinuity in their f-I curves. We show that this excitability mechanism allows Purkinje cells to be efficiently inhibited by noise of a particular variance, a phenomenon known as inverse stochastic resonance (ISR. While ISR has been described in theoretical models of single neurons, here we provide the first experimental evidence for this effect. We find that an adaptive exponential integrate-and-fire model fitted to the basic Purkinje cell characteristics using a modified dynamic IV method displays ISR and bistability between the resting state and a repetitive activity limit cycle. ISR allows the Purkinje cell to operate in different functional regimes: the all-or-none toggle or the linear filter mode, depending on the variance of the synaptic input. We propose that synaptic noise allows Purkinje cells to quickly switch between these functional regimes. Using mutual information analysis, we demonstrate that ISR can lead to a locally optimal information transfer between the input and output spike train of the Purkinje cell. These results provide the first experimental evidence for ISR and suggest a functional role for ISR in cerebellar information processing.

Application Filters for TCP/IP Industrial Automation Protocols

Science.gov (United States)

Batista, Aguinaldo B.; Kobayashi, Tiago H.; Medeiros, João Paulo S.; Brito, Agostinho M.; Motta Pires, Paulo S.

The use of firewalls is a common approach usually meant to secure Automation Technology (AT) from Information Technology (TI) networks. This work proposes a filtering system for TCP/IP-based automation networks in which only certain kind of industrial traffic is permitted. All network traffic which does not conform with a proper industrial protocol pattern or with specific rules for its actions is supposed to be abnormal and must be blocked. As a case study, we developed a seventh layer firewall application with the ability of blocking spurious traffic, using an IP packet queueing engine and a regular expression library.

A New Class of Pulse Compression Codes and Techniques.

Science.gov (United States)

1980-03-26

04 11 01 12 02 13 03 14 OA DIALFL I NOTE’ BO𔃾T TRANSFORM AND DIGITAL FILTER NETWORK INVERSE TRANSFORM DRIVE FRANK CODE SAME DIGITAL FILTER ; ! ! I I...function from circuit of Fig. I with N =9 TRANSFORM INVERSE TRANSFORM SINGLE _WORD S1A ~b,.ISR -.- ISR I- SR I--~ SR SIC-- I1GENERATOR 0 fJFJ $ J$ .. J...FOR I 1 1 13 11 12 13 FROM RECEIVER TRANSMIT Q- j ~ ~ 01 02 03 0, 02 03 11 01 12 02 13 03 4 1 1 ~ 4 NOTrE: BOTH TRANSFORM ANDI I I I INVERSE TRANSFORM DRIVE

Decoupling control of a five-phase fault-tolerant permanent magnet motor by radial basis function neural network inverse

Science.gov (United States)

Chen, Qian; Liu, Guohai; Xu, Dezhi; Xu, Liang; Xu, Gaohong; Aamir, Nazir

2018-05-01

This paper proposes a new decoupled control for a five-phase in-wheel fault-tolerant permanent magnet (IW-FTPM) motor drive, in which radial basis function neural network inverse (RBF-NNI) and internal model control (IMC) are combined. The RBF-NNI system is introduced into original system to construct a pseudo-linear system, and IMC is used as a robust controller. Hence, the newly proposed control system incorporates the merits of the IMC and RBF-NNI methods. In order to verify the proposed strategy, an IW-FTPM motor drive is designed based on dSPACE real-time control platform. Then, the experimental results are offered to verify that the d-axis current and the rotor speed are successfully decoupled. Besides, the proposed motor drive exhibits strong robustness even under load torque disturbance.

Particulate removal processes and hydraulics of porous gravel media filters

Science.gov (United States)

Minto, J. M.; Phoenix, V. R.; Dorea, C. C.; Haynes, H.; Sloan, W. T.

2013-12-01

Sustainable urban Drainage Systems (SuDS) are rapidly gaining acceptance as a low-cost tool for treating urban runoff pollutants close to source. Road runoff water in particular requires treatment due to the presence of high levels of suspended particles and heavy metals adsorbed to these particles. The aim of this research is to elucidate the particle removal processes that occur within gravel filters that have so far been considered as 'black-box' systems. Based on these findings, a better understanding will be attained on what influences gravel filter removal efficiency and how this changes throughout their design life; leading to a more rational design of this useful technology. This has been achieved by tying together three disparate research elements: tracer residence time distribution curves of filters during clogging; 3D magnetic resonance imaging (MRI) of clogging filters and computational fluid dynamics (CFD) modelling of complex filter pore networks. This research relates column average changes in particle removal efficiency and tracer residence time distributions (RTDs) due to clogging with non-invasive measurement of the spatial variability in particle deposition. The CFD modelling provides a link between observed deposition patterns, flow velocities and wall shear stresses as well as the explanations for the change in RTD with clogging and the effect on particle transport. Results show that, as a filter clogs, particles take a longer, more tortuous path through the filter. This is offset by a reduction in filter volume resulting in higher flow velocities and more rapid particle transport. Higher velocities result in higher shear stresses and the development of preferential pathways in which the velocity exceeds the deposition threshold and the overall efficiency of the filter decreases. Initial pore geometry is linked to the pattern of deposition and subsequent formation of preferential pathways. These results shed light on the 'black-box' internal

Exact Inverse Matrices of Fermat and Mersenne Circulant Matrix

Directory of Open Access Journals (Sweden)

Yanpeng Zheng

2015-01-01

Full Text Available The well known circulant matrices are applied to solve networked systems. In this paper, circulant and left circulant matrices with the Fermat and Mersenne numbers are considered. The nonsingularity of these special matrices is discussed. Meanwhile, the exact determinants and inverse matrices of these special matrices are presented.

Analysis of a spectrum of a positron annihilation half life through inverse problem studies

International Nuclear Information System (INIS)

Monteiro, Roberto Pellacani G.; Viterbo, Vanessa C.; Braga, Joao Pedro; Magalhaes, Wellington F. de; Braga, A.P.

2002-01-01

Inversion of positron annihilation lifetime spectroscopy, based on a neural network Hopfield model and singular value decomposition (SVD) associated to Tikhonov regularization is presented in this work. From a previous reported density function for lysozyme in water a simulated spectrum, without spectrometer resolution effects, was generated. The precision of the inverted density function was analyzed taking into account the number of neurons and the learning time of the Hopfield network and the maximum position and areas for the spectral peaks in the SVD method considering noise and noiseless data. A fair agreement was obtained when comparing the inversion results with direct exact results. (author)

Windows 2012 Server network security securing your Windows network systems and infrastructure

CERN Document Server

Rountree, Derrick

2013-01-01

Windows 2012 Server Network Security provides the most in-depth guide to deploying and maintaining a secure Windows network. The book drills down into all the new features of Windows 2012 and provides practical, hands-on methods for securing your Windows systems networks, including: Secure remote access Network vulnerabilities and mitigations DHCP installations configuration MAC filtering DNS server security WINS installation configuration Securing wired and wireless connections Windows personal firewall

Optimal Linear Filters. 2. Pulse Time Measurements in the Presence of Noise