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 Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Data characterization for automatic selection of valve stiction detection algorithms

2013 ◽  
Author(s):  
Alexey Zakharov ◽  
Elena Zattoni ◽  
Lei Xie ◽  
Octavio Pozo ◽  
Sirkka-Liisa Jamsa-Jounela
Keyword(s):  
Detection Algorithms ◽  
Automatic Selection ◽  
Valve Stiction ◽  
Selection Of

scholarly journals Research on Robustness of Related Images for Power Equipment Inspection

2021 ◽  
Vol 2113 (1) ◽  
pp. 012045
Author(s):  
Chunlei Zhou ◽  
Xiangzhou Chen ◽  
Wenli Liu ◽  
Tianyu Dong ◽  
Huang Yun
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Power Equipment ◽  
Actual Measurement ◽  
Practical Applications ◽  
Detection Algorithms ◽  
Traction Substations ◽  
Deep Learning Model ◽  
Selection Of

Abstract With the increase in the number of traction substations year by year, manual inspections are gradually being replaced by unattended inspections. Target detection algorithms based on deep learning are more widely used in intelligent inspections of power equipment. However, in practical applications, it is found that due to the small target to be detected, the accuracy of the deep learning model will decrease when the shooting angle is inclined and the light conditions are poor. This is because the algorithm’s robustness is low, and the detection ability of the model will be seriously affected when the angle or illumination difference with the sample is large. Based on this, the feature fusion part of the YOLOv3 algorithm and the selection of the loss function and the size of the anchor frame are improved, and the improved ASFF fusion method is used to classify various images in the power equipment. Actual measurement and repeated experiments show that the proposed method can be effectively applied to image recognition of various power equipment, optimize robustness, and greatly improve the image recognition efficiency of power equipment.

scholarly journals A New Image Registration Algorithm Based on Evidential Reasoning

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1091 ◽  
Author(s):  
Zhe Zhang ◽  
Deqiang Han ◽  
Jean Dezert ◽  
Yi Yang
Keyword(s):  
Image Registration ◽  
Fundamental Problem ◽  
Similarity Measures ◽  
Detection Algorithms ◽  
Image Registration Algorithm ◽  
Theory Of Belief Functions ◽  
Pros And Cons ◽  
Registration Result ◽  
Different Levels ◽  
Selection Of

Image registration is a crucial and fundamental problem in image processing and computer vision, which aims to align two or more images of the same scene acquired from different views or at different times. In image registration, since different keypoints (e.g., corners) or similarity measures might lead to different registration results, the selection of keypoint detection algorithms or similarity measures would bring uncertainty. These different keypoint detectors or similarity measures have their own pros and cons and can be jointly used to expect a better registration result. In this paper, the uncertainty caused by the selection of keypoint detector or similarity measure is addressed using the theory of belief functions, and image information at different levels are jointly used to achieve a more accurate image registration. Experimental results and related analyses show that our proposed algorithm can achieve more precise image registration results compared to several prevailing algorithms.

scholarly journals Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1017 ◽  
Author(s):  
Abdulmohsen Almalawi ◽  
Adil Fahad ◽  
Zahir Tari ◽  
Asif Irshad Khan ◽  
Nouf Alzahrani ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Supervisory Control ◽  
State Of The Art ◽  
Current Approach ◽  
Industrial Processes ◽  
Parameter Choice ◽  
Detection Algorithms ◽  
Detection Approach ◽  
Unsupervised Anomaly Detection ◽  
Threshold Technique

Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms.

Glacial-interglacial mean sea level pressure change due to sea level, ice sheet and atmospheric mass changes

1991 ◽  
Vol 3 (4) ◽  
pp. 333-340 ◽  
Author(s):  
Marie-Antoinette Mélières ◽  
Patricia Martinerie ◽  
Dominique Raynaud ◽  
Louis Lliboutry
Keyword(s):  
Sea Level ◽  
Ice Sheet ◽  
Pressure Change ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Level Ice ◽  
Atmospheric Mass
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