An innovatory on–line method of power system frequency estimation and voltage/current signals resampling is presented. The method is designed to increase the accuracy of power system equivalent harmonic impedance estimation using simple DAQ systems. Estimation of instantaneous frequency uses 5–th degree polynomial interpolation of filtered signal zero crossings time positions. Final resampling ...

0950-7051/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.knosys.2011.06.013 ⇑ Corresponding author. E-mail addresses: [email protected] (V. García), s [email protected] (R.A. Mollineda). The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluat...

In Simulation-based Evolutionary Multi-objective Optimization (EMO) the available time for optimization usually is limited. Since many real-world optimization problems are stochastic models, the optimization algorithm has to employ a noise compensation technique for the objective values. This article analyzes Dynamic Resampling algorithms for handling the objective noise. Dynamic Resampling imp...

The present paper establishes conditional and unconditional central limit theorems for various resampling procedures for the t-statistic. The results work under fairly general conditions and the underlying random variables need not to be independent. Specific examples are then the 're(n) (double) bootstrap out of k(n) observations, the Bayesian bootstrap and two-sample t-type permutation statis...

Searched Space DEFINITION 1 State space: The total state space an evolutionary algorithm is able to search in is defined as . We assume all are equal, thus we know that and . DEFINITION 2 Searched space of an algorithm: The searched space is the set of points taken from the state space that is visited by a particular evolutionary algorithm during a run. DEFINITION 3 Resampling ratio: First we d...

We describe several new sampling strategies for Rao-Blackwellized particle filtering SLAM. Two of the strategies, called fixed-lag roughening and the block proposal distribution, attempt to exploit “future” information, when it becomes available, to improve the filter’s estimation for previous time steps. Fixed-lag roughening perturbs trajectory samples over a fixed lag time according to a Mark...

Particle filtering methods are powerful tools for online estimation and tracking in nonlinear and non-Gaussian dynamical systems. They commonly consist of three steps: (1) drawing samples in the state-space of the system, (2) computing proper importance weights of each sample and (3) resampling. Steps 1 and 2 can be carried out concurrently for each sample, but standard resampling techniques re...

Image resampling is frequently employed in geometric correction as a part of preprocessing of a remotely sensed image. The resampled images will have a noticeable change in image quality, which may have an adverse impact on the accuracy of subsequent image analysis for information extraction. Information of edges is very important, especially in high spatial resolution imagery. The present stud...

We propose an on-line algorithm for exact filtering of multiple changepoint problems. This algorithm enables simulation from the true joint posterior distribution of the number and position of the changepoints for a class of changepoint models. The computational cost of this exact algorithm is quadratic in the number of observations. We further show how resampling ideas from particle filters ca...

This paper derives lower bounds on the L2-norms of digital resampling filters with zero-valued input samples. This emanates from uniform-grid sampling but where some of the samples are missing. One application is found in timeinterleaved analog-to-digital converters with missing samples due to calibration at certain time instances. The square of the L2-norms correspond to scaling of the round-o...