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A new method of magnitude square coherence (MSC) estimation by an auto-regressive moving average (ARMA) model based on analytic discrete cosine transform (ADCT) and group delay (GD) property is proposed. The estimation is achieved by modeling the Welch-MSC derived from ADCT and the ARMA model realized by GD. The ADCT provides twice frequency resolution and reduced variance compared to those of conventional...
In this work, experimental data, carried out on a twin-cylinder turbocharged engine at full load operations and referred to a spark advance of borderline knock, are used to characterize the effects of cyclic dispersion on knock phenomena. 200 consecutive in-cylinder pressure signals are processed through a refined Auto-Regressive Moving Average (ARMA) mathematical technique, adopted to define the...
Wind can be considered as an interesting alternative to fossil fuels, but as a power source, is both intermittent and diffuse. It is necessary to calculate wind equivalent capacities in order to introduce a coherent evaluation of wind production in the management of the centralized production park. Base on the sequential Monte Carlo simulation technique for wind speeds, an auto-regressive and moving...
A common technique to deploy linear prediction to non-stationary signals is time segmentation and local analysis. Variations of a process within such a segment cause inaccuracies. In this paper, we model the temporal changes of linear prediction coefficients (LPCs) as a Fourier series. We obtain a compact description of the vocal tract model limited by the predictor order and the maximum Doppler frequency...
Uncertainty and variability in the wind resource create obstacles for the participation of wind power in forward markets, such as regional day ahead electricity markets. Studies performed in various states have developed methods to improve wind forecasting and so reduce the inherent uncertainty in a day ahead schedule for wind power generation. This paper addresses the issue of the variability in...
The analysis of historical time series data that reflects equipment failures is becoming increasingly important in maintenance policies in manufacturing plant. This paper presents a novel methodology to use auto-regressive moving average (ARMA) model for device down time forecasting based on transformed historical data. The 8 orders moving average method was adopted to obtain mean stationary time...
The analysis of historical time series data that reflects equipment failures is becoming increasingly important in maintenance policies in manufacturing plant. In this paper, we propose a two-level hierarchical modeling framework whose higher level is a model for trend prediction, while whose lower level is a model for residual prediction. Solving the lower level problem is the main focus of this...
This paper presents a methodology of robot arm teleoperation, using electromyographic (EMG) signals and a bio-inspired motion law. The methodology is implemented in planar catching movements, in situations that the user reaches and grasps objects lying on a table in front of him. EMG signals from the flexor and extensor muscles of both the elbow and the wrist joint are used to predict the elbow and...
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