In this paper a novel approach for lexicon reduction of Farsi words is proposed. For this purpose we extract upper and lower profiles, vertical projection profile and black/white transition from word images. Using DTW similarity between words in the database is measured. The Isoclus algorithm is used to cluster handwritten word images of training dataset. The initial center of clusters is determined from agglomerative hierarchical clustering algorithm.Experimental results on IRANSHAHR dataset show a promising result. It yields a lexicon reduction of 77% with accuracy of 94%. We also evaluate the proposed system when combination of statistical features and different type of distance measures are used.
Nowadays electric power quality problems such as flicker (voltage fluctuation) are major concern of electric companies and industrial consumers. Identification of flicker sources is an important stage in cus tomers convincing and in flicker reduction process. For equal fluctuation frequency of flicker sources, detec tion of coupling points of these sources is not an easy task. In this paper, voltage envelope is extracted by Enhanced Phase Locked Loop (EPLL), which acts as a nonlinear adaptive filter, and then the amplitude of flicker tone is used as a flicker index. The power system with arbitrary flicker sources is assumed and simulated to generate a set of training data. Then two methods based on K means clustering algorithm and correlation coefficient are introduced to identify the location of flicker sources in a non radial power system. Using pro posed methods location of flicker sources with similar fluctuation frequency in non radial power system is determined. In a typical test system, several simulations have been conducted. The simulation results show the performance of the proposed method is very promising and flicker sources can be detected tolerably.
Attenuation of random noise is a major concern in seismic data processing. This kind of noise is usually characterized by random oscillation in seismic data over the entire time and frequency. We introduced and evaluated a low-rank and sparse decomposition-based method for seismic random noise attenuation. The proposed method, which is a trace by trace algorithm, starts by transforming the seismic signal into a new sparse subspace using the synchrosqueezing transform. Then, the sparse time-frequency representation (TFR) matrix is decomposed into two parts: (a) a low-rank component and (b) a sparse component using bilateral random projection. Although seismic data are not exactly low-rank in the sparse TFR domain, they can be assumed as being of semi-low-rank or approximately low-rank type. Hence, we can recover the denoised seismic signal by minimizing the mixed [Formula: see text] norms’ objective function by considering the intrinsically semilow-rank property of the seismic data and sparsity feature of random noise in the sparse TFR domain. The proposed method was tested on synthetic and real data. In the synthetic case, the data were contaminated by random noise. Denoising was carried out by means of the [Formula: see text] classical singular spectrum analysis (SSA) and [Formula: see text] deconvolution method for comparison. The [Formula: see text] deconvolution and the classical [Formula: see text] SSA method failed to properly reduce the noise and to recover the desired signal. We have also tested the proposed method on a prestack real data set from an oil field in the southwest of Iran. Through synthetic and real tests, the proposed method is determined to be an effective, amplitude preserving, and robust tool that gives superior results over classical [Formula: see text] SSA as conventional algorithm for denoising seismic data.
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