An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition

The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.

Transient behavior of drift and ionization in atmospheric pressure nitrogen discharge

The fluid models are frequently used to describe a non-thermal plasma such as a streamer discharge. The required electron transport data and rate coefficients for the fluid model are parametrized using the local field approximation (LFA) in first order models and the local-mean-energy approximation (LMEA) in second order models. We performed Monte Carlo simulations in Nitrogen gas with step changes in the E/N (reduced electric field) to study the behavior of the transport properties in the transient phase. During the transient phase of the simulation, we extract the instantaneous electron mean energy, which is different from the steady state mean electron energy, and the corresponding transport parameters and rate coefficients. Our results indicate that the mean electron energy is not a suitable parameter for mobility/drift of electrons due to big difference in momentum relaxation and energy relaxation. However, the high energy threshold rates such as ionization show a strong correlation to mean electron energy. In second order models where the energy-balance equation is solved, we suggest that it would rather be appropriate to use the local electric field to find electron drift velocity in gases such as Nitrogen and the local mean electron energy to determine the ionization and excitation rates.

On the uncertainty associated with detecting global and local mean sea level drifts on Sentinel-3A and Sentinel-3B altimetry missions

An instrumental drift in the Point Target Response (PTR) parameters has been detected on the Copernicus Sentinel-3A (S3A) altimetry mission. It could have an impact on sea level rise of a few tenths of mm yr−1. In order to accurately evaluate this drift, a method for detecting global and local mean sea level relative drifts between two altimetry missions is implemented. Associated uncertainties are also accurately calculated thanks to a detailed error budget analysis. A drift on both S3A and S3B GMSL is detected with values significantly higher than expected. For S3A, the relative GMSL drift detected is 1.0 mm yr−1 with Jason-3 and 1.3 mm yr−1 with SARAL/AltiKa. For S3B, the relative GMSL drift detected is −2.2 mm yr−1 with SARAL/AltiKa and −3.4 mm yr−1 with Jason-3. The drift detected at global level does not show detectable regional variations above the uncertainty level of the proposed method. The investigations led by the altimeter experts can now explain the origin of this drift for S3A, while it is still under investigation for S3B. The ability of the implemented method to detect a sea level drift with respect to the length of the common period is also analysed. We find that the maximum detectable sea-level drift over a 5 years period is 0.3 mm yr−1 at the global scale, and 1.5 mm yr−1 at local scales (2400 km). However, these levels of uncertainty do not meet the sea-level stability requirements for climate change studies.