Characterization of Wavelet-Based Image Coding Systems for Algorithmic Fault Detection

2005 ◽  
Author(s):  
L. Costas ◽  
J.J. Rodriguez-Andina
Keyword(s):  
Fault Detection ◽  
Image Coding ◽  
Coding Systems

Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection

2020 ◽  
Author(s):  
Selorme Agbleze ◽  
Fernando V. Lima ◽  
Natarianto Indrawan ◽  
Rupendranath Panday ◽  
Paolo Pezzini ◽  
...  
Keyword(s):  
Power Plant ◽  
Fault Detection ◽  
Power Plants ◽  
Energy Content ◽  
Leak Detection ◽  
Primary Component ◽  
Plant Performance ◽  
Saturated Steam ◽  
Power Sources

Abstract Due to the increased penetration of renewable power sources into the electric grid, the current number of existing coal-fired power plants shifting from baseload to load-following operations has also increased. This shift creates challenges especially for the power industry as coal-fired power plants were not designed for ramping situations, leading to added stress on major components of these plants. This stress causes the system to degrade over time and eventually develop faults. As boilers are still the primary component that fails and causes forced outages, accurate characterization of faults and fractures of boilers is now becoming increasingly critical to reduce plant downtime and extend the plant life during cycling operations. This work focuses on modeling sections of a subcritical coal-fired power plant and proposes algorithms for fault detection in MATLAB/Simulink. The developed model simulates the process dynamics including steam and feedwater flow regulating valves, drum-boiler, and heat rate on the regulation of pressure, drum level and production of saturated steam. The model also simulates the dynamics of superheaters for increasing the energy content of steam, and a spray section for regulating the temperature of steam upstream of the high-pressure turbine to allow for power output adjustment within a given valve operating range. Furthermore, an extension to a leak detection framework proposed by co-authors in previous work is explored. The new framework includes a modification to the threshold analysis portion of the previous work. The extended framework is then applied to a subcritical coal-fired power plant model for leak detection. In particular, this framework analyzes mismatches or deviations in expected plant dynamics with an identified transfer function model. The mismatch is flagged after it exceeds a threshold. The developed algorithm thus aids in rapid detection of faults to reduce impeded plant performance. The results of this work will support real plant operations by providing an accurate characterization of faults in the operation of coal-fired power plants.

scholarly journals SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1939 ◽  
Author(s):  
Seok Bong Yoo ◽  
Mikyong Han
Keyword(s):  
Noise Reduction ◽  
Image Coding ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Poisson Noise ◽  
Art Restoration ◽  
Compressed Images ◽  
Coding Systems ◽  
Satisfactory Performance ◽  
Block Based

In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have recently been proposed for compressed images, they do not provide satisfactory performance on the challenging compressed Poisson noises. This is mainly due to (i) inaccurate modeling regarding the image degradation, (ii) the signal-dependent noise property, and (iii) the lack of analysis on intercorrelation distortion. In this paper, we focused on the challenging issues in practical image coding systems and propose a compressed Poisson noise reduction scheme based on a secondary domain intercorrelation enhanced network. Specifically, we introduced a compressed Poisson noise corruption model and combined the secondary domain intercorrelation prior with a deep neural network especially designed for signal-dependent compression noise reduction. Experimental results showed that the proposed network is superior to the existing state-of-the-art restoration alternatives on classical images, the LIVE1 dataset, and the SIDD dataset.

REAL TIME FRACTAL VIDEO FOR PERSONAL COMMUNICATIONS

Fractals ◽  
1994 ◽  
Vol 02 (03) ◽  
pp. 391-394 ◽  
Author(s):  
DONALD M MONRO ◽  
JEREMY A NICHOLLS
Keyword(s):  
Image Coding ◽  
Video Sequence ◽  
Consumer Electronics ◽  
Video Sequences ◽  
Personal Communications ◽  
First Order ◽  
Coding Scheme ◽  
Fractal Image ◽  
Coding Systems ◽  
Talking Heads

Using a first order (bilinear) Bath Fractal Transform (BFT), we have obtained useful video sequences of talking heads with transmission rates as low as 40 KBits/sec. Previously, fractal image coding had been computationally asymmetric. In our work, 8 by 8 pixel blocks are coded in 250 µs and decoded in 200 µs on a 33 MHz i-486 based PC. This is of significance in consumer electronics, such as personal communications, where inexpensive coding systems will have an advantage over more expensive methods using DSP or custom chips to achieve the necessary speed. With a simple quantization and entropy coding scheme applied to standard QCIF frames, at 40 KBits/sec we achieve coding of 40% of each frame in a 25 Hz video sequence, equivalent to 100% at 10 Hz.

On a new characterization of finite jump discontinuities and its application to vertical fault detection

2008 ◽  
Vol 77 (2-3) ◽  
pp. 247-256 ◽  
Author(s):  
María Cruz López de Silanes ◽  
María Cruz Parra ◽  
Juan José Torrens
Keyword(s):  
Fault Detection ◽  
Jump Discontinuities ◽  
Vertical Fault
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