Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occur...
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Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding condition. In this paper, a novel frequency calculation algorithm based on extended Kalman filter was proposed to track dynamic frequency of the microgrid. Taylor series expansion was introduced to solve nonlinear state equations. In addition, a typical microgrid model was built using MATLAB/SIMULINK. Simulation results demonstrated that the proposed algorithm achieved great stability and strong robustness in of tracking dynamic frequency.
The goal of this paper is to gain insight into the equations arising in nonlinear filtering, as well as into the feedback particle filter introduced in recent research. The analysis is inspired by the optimal transpor...
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ISBN:
(纸本)9781467360890
The goal of this paper is to gain insight into the equations arising in nonlinear filtering, as well as into the feedback particle filter introduced in recent research. The analysis is inspired by the optimal transportation literature and by prior work on variational formulation of nonlinear filtering. The construction involves a discrete-time recursion based on the successive solution of minimization problems involving the so-called forward variational representation of the elementary Bayes' formula. The construction shows that the dynamics of the nonlinear filter may be regarded as a gradient flow, or a steepest descent, for a certain energy functional with respect to the Kullback-Leibler divergence pseudo-metric. The feedback particle filter algorithm is obtained using similar analysis. This filter is a controlled system, where the control is obtained via consideration of the first order optimality conditions for the variational problem. The filter is shown to be exact, i.e., the posterior distribution of the particle matches exactly the true posterior, provided the filter is initialized with the true prior.
In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati...
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ISBN:
(纸本)9781479932757
In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.
The particle filter algorithm has been applied to mobile robot localization problem for more than a decade. In this paper, we present two extensions of the basic particle filter algorithm for humanoid robot vision-bas...
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ISBN:
(纸本)9781479910731
The particle filter algorithm has been applied to mobile robot localization problem for more than a decade. In this paper, we present two extensions of the basic particle filter algorithm for humanoid robot vision-based localization in a soccer game. The individual particle reset module allows localization even in kidnapped robot problem. The "switching algorithm" is developed to save computing time through adapting the population size of particles. The accuracy and efficiency of the proposed approaches have been verified in a simulated humanoid soccer environment.
This paper presents a new interactive way to solve the problem of human-computer interaction, with a pen as the media to achieve some operations. Firstly, this paper proposes a particle filter algorithm based on immun...
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ISBN:
(纸本)9781479925483
This paper presents a new interactive way to solve the problem of human-computer interaction, with a pen as the media to achieve some operations. Firstly, this paper proposes a particle filter algorithm based on immune mechanism, and extracts the user pen operations by camera video. Then extract the line of pen's motor trend pen movement trend with the least square method and judge the legality of the pen operation and calculate the operation direction of the pen. Finally, we will gain mapping relation in the change in of machine interface.
Electromagnetic source localization is a technique that enables the study of neural dynamical activities on a millisecond timescale using Magnetoencephalography (MEG) or Electroencephalography (EEG) data. It aims to r...
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ISBN:
(纸本)9781479903573
Electromagnetic source localization is a technique that enables the study of neural dynamical activities on a millisecond timescale using Magnetoencephalography (MEG) or Electroencephalography (EEG) data. It aims to reveal neural activities in the brain cortical region which cannot be seen with imaging methods that operate on a slower timescale such as fMRI. In this paper, we model the problem under a Bayesian multi-target tracking framework. A multi-target detection and particle filtering algorithm is developed to estimate the dipolar source dynamics, and a minimum norm (MN) based estimation method is incorporated to construct the birth-death move for the dynamical number of dipolar sources. The algorithm is tested using both simulated and experimental data. The results demonstrate that the proposed algorithm performs better than that in previous works in terms of both localization accuracy and computational cost.
This paper presents a method to evaluate the attitude of a rigid body under condition of high non-gravitational acceleration. Most of the attitude estimation algorithms based on data from low cost Inertial Measurement...
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ISBN:
(纸本)9781479927456
This paper presents a method to evaluate the attitude of a rigid body under condition of high non-gravitational acceleration. Most of the attitude estimation algorithms based on data from low cost Inertial Measurement Units (IMU), assume that the total acceleration perceived by the accelerometer be gravity or at most small variations of it. When the actual conditions are far away from such assumption, the attitude estimation results in wrong evaluations. We propose a method that uses an external RGB-D camera to measure The noninertial linear acceleration. Such acceleration is subtracted to the total acceleration reading of the accelerometer in order to obtain a truthful gravity direction that will be fed into the fusion algorithm. Performance of our attitude estimation has been evaluated empirically under non-gravitational acceleration. We compare our results against the output of a commercially avalaible IMU sensor based on a Kalman Filter algorithm as well as the estimation of a recently developed fusion algorithm based on a gradient descent algorithm, showing significant improvement.
To achieve accurate visual object tracking and overcome the difficulties brought by the object deformation, occlusion, and illumination variations, a particle filter for object tracking algorithm based on color local ...
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To achieve accurate visual object tracking and overcome the difficulties brought by the object deformation, occlusion, and illumination variations, a particle filter for object tracking algorithm based on color local entropy (CLE) is proposed. First we improved the traditional histogram weighted function by using a scale factor. Then, for the shortcoming that the color feature is sensitive to illumination and environmental interference, a color local entropy object observation model is constructed by mapping the object from color feature space to local entropy space. In addition, an adaptive updating strategy of the object template is designed and the number of particle is adjusted dynamically according to the tracking performance. The experimental results show that compared with several existing algorithms, the proposed algorithm is more effective and robust for the real-time object tracking under the situation of illumination variation, object occlusion, and nonlinear motion.
MapReduce is a domain-independent programming model for processing data in a highly parallel fashion. With MapReduce, parallel computing can be automatically carried out in large-scale commodity machines. This paper p...
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ISBN:
(纸本)9781627480338
MapReduce is a domain-independent programming model for processing data in a highly parallel fashion. With MapReduce, parallel computing can be automatically carried out in large-scale commodity machines. This paper presents a method that utilizes the parallel and distributed processing capability of Hadoop MapReduce for particle filter-based data assimilation in wildfire spread simulation. We parallelize the sampling and weight computation steps of the particle filtering algorithm based on the MapReduce programming model. Experiment results show that our approach significantly increases the performance of particle filter-based data assimilation.
We develop a particle filter algorithm to simultaneously estimate and track the instantaneous peak frequency, amplitude, and bandwidth of multiple concurrent non-stationary components of an EEG signal in the time-freq...
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ISBN:
(纸本)9781457702150
We develop a particle filter algorithm to simultaneously estimate and track the instantaneous peak frequency, amplitude, and bandwidth of multiple concurrent non-stationary components of an EEG signal in the time-frequency domain. We use this method to characterize human EEG activity during anesthesia-induced unconsciousness.
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