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The objective of this work is to detect delay times between two ultrasonic signals, one of them considered as a reference and the other signal as the signal to be evaluated, using the wavelet transform, to indirectly estimate temperature changes. In this work the evaluation of 3 types of wavelets (Morlet, Mexican Hat and Daubechies 5) was made to detect the delay time in echo ultrasonic signals which...
Precursor pattern identification addresses the problem of detecting warning signals in data that herald an impending event of extraordinary interest. In the context of electrical power systems, identifying precursors to fluctuations in power generation in advance would enable engineers to put in place measures that mitigate against the effects of such fluctuations. In this research we use the Morlet...
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling:...
The paper presents a mathematical model of processing and forecasting time series data. The mathematical model is based on the methods of artificial neural networks and preliminary data processing using wavelet transform. Various classes of algorithms for predicting changes in the parameters of continuous functions and time series that occur in the interval called the prediction horizon are considered...
Recently, Deep belief networks(DBNs) have been applied in classification and regression, proved to be superior to general algorithms. But its powerful deep feature extraction ability has not yet been fully played so that a novel algorithm, multi-scale DBNs fusing wavelet transform(WT), is proposed in this paper. Based on the advantages of predicting high frequency components from WT by DBN confirmed,...
In side channel attacks (SCA), noise has been a hot topic for affecting the quality of obtained observations. In this paper, we propose a kind of improved wavelet transform denoising method based on singular spectral analysis (SSA) and detrended fluctuation analysis (DFA). Principal signal component in SSA can be selected by DFA adaptively, and residual part can be denoised by wavelet transform to...
In an electric power system with high penetration of wind power, the sudden increase or rapid decrease of power output in a short time, known as wind power ramp event, poses a serious threat to the power system. The wind power ramp events forecasting can help the grid operators to minimize this impact in advance by electric grid scheduling. In this paper, a forecasting model, WT-ARMA (Wavelet Transform-Auto...
The aim of this study is to detect variability at low frequencies and trend of time series connected with climate using two different processing techniques. In previous work the wavelet transform and models of pure oscillations with statistical parameter setting were applied to the series of surface temperatures of the Orcadas Antarctic Station (Argentina) over 110 years. Periods of about 20 and 50...
Given the importance of an accurate wind speed forecasting for efficient utilization of wind farms, and the volatile nature of wind speed data including its non-linear and uncertain nature, the wind speed forecasting has remained an active field of research. In this study, the non-linearity of wind speed is tackled using artificial neural network and its uncertainty by wavelet transform. To avoid...
This paper aims to improve our knowledge of the complex vegetation-climate relationship in subtropical humid region in the context of global warming, by taking into considerations of spatio-temporal variation of both vegetation and climate change. A multi-resolution analysis (MRA) based on the wavelet transform (WT) is applied to examine the vegetation growth and its relationship with climate factors...
A new hybrid model that combines wavelet transform(WT) and least squares support vector machines(LSSVM) called the wavelet least squares support vector machines(WT-LSSVM) model is proposed and applied for runoff time series prediction. Time series of monthly runoff of Tangnaihai Station located in Yellow River upper stream were analyzed by the WT-LSSVM model. The observed time series are decomposed...
The current gold market show a high degree of nonlinearity and uncertainty, in order to predicted the gold price, Empirical Mode Decomposition (EMD) is introduced, the use the EMD orthogonal decompose the special functions into a finite number of independent intrinsic mode functions (IMFs), then Grouping the IMFs according different frequencies, using support vector regression (SVR) to predict each...
In this paper, we propose an approach for predicting time series. This approach is based on the Stationary Wavelet Transform (SWT) and two types of forecasting models, such as based on Auto-Regressive Integrated Moving Average (ARIMA) and based on Artificial Neural Networks (ANNs). The forecasting performance of these models was evaluated using three well-known evaluation criteria: Mean Absolute Error...
Internal information systems play an important role in keeping the enterprises running well. To detect system anomalies, previous research achieved good results with system symptoms; however, the presented results are primarily performed on a relatively small scale and within a short time period. To understand the system's long-term profiles, we collected four common symptom data including CPU usage,...
The vibration of internal structure and car body of rolling stock influences the safety of train operation directly. Due to non-stationary feature of vibration signal, traditional spectrum analysis and PSD methods are limited. In this paper, mechanical vibration transmission relationship is researched based on wavelet denoising technique and higher order spectrum analysis by the use of the character...
In order to coordinate the heat supply of heat source and users' heat demand and also maintain the whole system work in a high efficiency, the wavelet analysis was utilized to make short-term prediction on heating load. The heating load of heat supply system was chosen as the output variation. After applying wavelet transform to the heat load sequence, the low-frequency signals and high-frequency...
Wind power is widely used to replace conventional power plant and reduce carbon emission. However, the variability and intermittency of wind makes the wind power output uncertain, which will bring great challenges to the electricity dispatch and the system reliability. So it is very important to predict the wind power generation. Two different signal decomposition methods are introduced into the prediction...
We first pursue the study of how hierarchy provides a well-adapted tool for the analysis of change. Then, using time sequence-constrained hierarchical clustering, we develop the practical aspects of a new approach to wavelet regression. This provides a new way to link hierarchical relationships in a multivariate time-series data set with external signals. Violence data from the Colombian conflict...
Time series classification based on wavelet transforms and Fourier transform is discussed in this paper. Wavelet transforms have the time-variant characteristic, and are relatively sensitive to the time series with some mutations. Fourier transform is able to reflect various periodic variation of time series clearly. The test proves that the hierarchical clustering based on wavelet transforms can...
The wave spectral density function, more commonly known as the wave spectrum, has seen widespread use as a tool to measure the wave energy resource at possible sites for the deployment of Wave Energy Converters (WECs). The wave spectrum provides a useful means to calculate summary statistics such as significant wave height and average period from measured time series of surface elevation, which allows...
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