52,193 research outputs found
Fault Detection, Isolation and Restoration Test Platform Based on Smart Grid Architecture Model Using Internet-of-Things Approaches
To systematically shift existing distribution outage management paradigms to smart and more efficient schemes, we need to have an architectural overview of Smart Grids to reuse the assets as much as possible. Smart Grid Architecture Model offers a support to design such emerging use cases by representing interoperability aspects among component, function, communication, information, and business layers. To allow this kind of interoperability analysis for design and implementation of Fault Detection, Isolation and Restoration function in outage management systems, we develop an Internet-of-Things-based platform to perform real time co-simulations. Physical components of the grid are modeled in Opal-RT real time simulator, an automated Fault Detection, Isolation and Restoration algorithm is developed in MATLAB and an MQTT communication has been adopted. A 2-feeder MV network with a normally open switch for reconfiguration is modeled to realize the performance of the developed co-simulation platform
Image processing as state reconstruction in optics
The image reconstruction of partially coherent light is interpreted as the
quantum state reconstruction. The efficient method based on maximum-likelihood
estimation is proposed to acquire information from registered intensity
measurements affected by noise. The connection with totally incoherent image
restoration is pointed out. The feasibility of the method is demonstrated
numerically. Spatial and correlation details significantly smaller than the
diffraction limit are revealed in the reconstructed pattern.Comment: 10 pages, 5 figure
Deep Mean-Shift Priors for Image Restoration
In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our prior corresponds to the mean-shift vector on the natural image
distribution. In addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes risk
minimization. We demonstrate competitive results for noise-blind deblurring,
super-resolution, and demosaicing.Comment: NIPS 201
Impulsive noise removal from color images with morphological filtering
This paper deals with impulse noise removal from color images. The proposed
noise removal algorithm employs a novel approach with morphological filtering
for color image denoising; that is, detection of corrupted pixels and removal
of the detected noise by means of morphological filtering. With the help of
computer simulation we show that the proposed algorithm can effectively remove
impulse noise. The performance of the proposed algorithm is compared in terms
of image restoration metrics and processing speed with that of common
successful algorithms.Comment: The 6th international conference on analysis of images, social
networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi
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