Robust Detection Algorithms for Time Reversal Matched Field Processing

2006 ◽  
Author(s):  
Amir Asif ◽  
Qian Bai
Keyword(s):  
Time Reversal ◽  
Matched Field Processing ◽  
Robust Detection ◽  
Detection Algorithms

A new database for Intense Mediterranean Cyclones 

2021 ◽  
Author(s):  
M. Carmen Alvarez-Castro ◽  
David Gallego ◽  
Pedro Ribera ◽  
Cristina Peña-Ortiz ◽  
Davide Faranda
Keyword(s):  
Relative Vorticity ◽  
Sea Level Pressure ◽  
Robust Detection ◽  
Detection Algorithms ◽  
Mediterranean Cyclones ◽  
Detection And Tracking ◽  
Detection Approach ◽  
Local Extrema ◽  
Atmospheric Mass ◽  
Selection Of

<p>To better assess the future risks associated with Intense Mediterranean Cyclones (IMC) a better understanding of their features, variability, frequency and intensity is required, including a robust detection method. The application of different detection algorithms provides results that are remarkably similar in some aspects but may be very different in others even using the same data. Thus, the selection of a particular method can significantly affect the results. For these reasons it is necessary to use different approaches and datasets to study the sensitivity and robustness of the detection approach. Those approaches often use minima in sea-level pressure (SLP) or extrema in relative vorticity or both to first identify the eye of the cyclone. SLP reflects the atmospheric mass distribution, and is representative of synoptic-scale atmospheric processes. On the other hand, the relative vorticity displays higher variability and is representative of the atmospheric circulation, being able to detect several local extrema (more than one centre), it can reduce uncertainties in the cyclone detection and tracking.</p><p>Therefore, within the framework of the EFIMERA project and to detect and track IMC we use a combination of different methods based on previous studies found in the literature. This new list of detected IMC events, together with the observed and well documented ones, are used here to create a new IMC database to be used for the study of their impacts and risk associated.</p>

scholarly journals Robust Detection of Minute Faults in Uncertain Systems Using Energy Activity

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1801
Author(s):  
Manarshhjot Singh ◽  
Anne-Lise Gehin ◽  
Belkacem Ould-Boaumama
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Integral Form ◽  
Fault Detection And Isolation ◽  
Detection Methods ◽  
Robust Detection ◽  
Detection Algorithms ◽  
Model Based ◽  
Tank System ◽  
Key Steps

Fault detection is one of the key steps in Fault Detection and Isolation (FDI) and, therefore, critical for subsequent prognosis or implementation of Fault Tolerant Control (FTC). It is, therefore, advisable to utilize detection algorithms which are quick and can detect the smallest faults. Model-based detection methods satisfy both these criteria and should be preferred. However, a big limitation for model-based methods is that they require the accurate value of the component parameters, which is difficult to obtain in real situations. This limits the accuracy of model-based methods. This paper proposes a new method for fault detection using Energy Activity (EA) which can detect minute levels of fault in systems with high component uncertainty. Different forms of EA are developed for use as an FDI metric. The proposed forms are simulated using a two-tank system under various types of faults. The results are compared with each other and with the traditional model-based FDI method using Analytical Redundancy Relations (ARRs). The simulations are performed considering model uncertainties to check the inherent performance of the methods. From initial simulations, it is established that the integral form of EA is most suited for fault detection. The integral for if EA is then tested using a real two-tank system considering both the model and measurement uncertainties.

scholarly journals Market Manipulation: An Adversarial Learning Framework for Detection and Evasion

2020 ◽  
Author(s):  
Xintong Wang ◽  
Michael P. Wellman
Keyword(s):  
Market Manipulation ◽  
Adversarial Learning ◽  
Market Making ◽  
Agent Based ◽  
Robust Detection ◽  
Detection Algorithms ◽  
Learning Framework ◽  
Trading Patterns ◽  
Trading Activities ◽  
Manipulation Strategy

We propose an adversarial learning framework to capture the evolving game between a regulator who develops tools to detect market manipulation and a manipulator who obfuscates actions to evade detection. The model includes three main parts: (1) a generator that learns to adapt original manipulation order streams to resemble trading patterns of a normal trader while preserving the manipulation intent; (2) a discriminator that differentiates the adversarially adapted manipulation order streams from normal trading activities; and (3) an agent-based simulator that evaluates the manipulation effect of adapted outputs. We conduct experiments on simulated order streams associated with a manipulator and a market-making agent respectively. We show examples of adapted manipulation order streams that mimic a specified market maker's quoting patterns and appear qualitatively different from the original manipulation strategy we implemented in the simulator. These results demonstrate the possibility of automatically generating a diverse set of (unseen) manipulation strategies that can facilitate the training of more robust detection algorithms.

Matched-field processing using time-reversal concept in a range-dependent environment

2012 ◽  
Vol 131 (4) ◽  
pp. 3239-3239 ◽  
Author(s):  
Kunde Yang ◽  
Tongwei Zhang ◽  
Yuanliang Ma
Keyword(s):  
Time Reversal ◽  
Matched Field Processing

scholarly journals A Robust Detection Algorithm for Infrared Maritime Small and Dim Targets

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1237 ◽  
Author(s):  
Yuwei Lu ◽  
Lili Dong ◽  
Tong Zhang ◽  
Wenhai Xu
Keyword(s):  
Target Detection ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Texture Features ◽  
Detection Algorithm ◽  
Robust Detection ◽  
Detection Algorithms ◽  
Region Selection ◽  
The Difference ◽  
Small Targets

Infrared maritime target detection is the key technology of maritime target search systems. However, infrared images generally have the defects of low signal-to-noise ratio and low resolution. At the same time, the maritime environment is complicated and changeable. Under the interference of islands, waves and other disturbances, the brightness of small dim targets is easily obscured, which makes them difficult to distinguish. This is difficult for traditional target detection algorithms to deal with. In order to solve these problems, through the analysis of infrared maritime images under a variety of sea conditions including small dim targets, this paper concludes that in infrared maritime images, small targets occupy very few pixels, often do not have any edge contour information, and the gray value and contrast values are very low. The background such as island and strong sea wave occupies a large number of pixels, with obvious texture features, and often has a high gray value. By deeply analyzing the difference between the target and the background, this paper proposes a detection algorithm (SRGM) for infrared small dim targets under different maritime background. Firstly, this algorithm proposes an efficient maritime background filter for the common background in the infrared maritime image. Firstly, the median filter based on the sensitive region selection is used to extract the image background accurately, and then the background is eliminated by image difference with the original image. In addition, this article analyzes the differences in gradient features between strong interference caused by the background and targets, proposes a small dim target extraction operator with two analysis factors that fit the target features perfectly and combines the adaptive threshold segmentation to realize the accurate extraction of the small dim target. The experimental results show that compared with the current popular small dim target detection algorithms, this paper has better performance for target detection in various maritime environments.

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