A Fast SNR-based Vibration Events Detection Algorithm for AETA Geo-acoustic Data

2020 ◽  
Author(s):  
Binyan Ma ◽  
Shanshan Yong ◽  
Xin'an Wang
Keyword(s):  
Detection Algorithm ◽  
Acoustic Data ◽  
Events Detection

scholarly journals Opportunistically recorded acoustic data support Northeast Atlantic mackerel expansion theory

2015 ◽  
Vol 73 (4) ◽  
pp. 1115-1126 ◽  
Author(s):  
Jeroen van der Kooij ◽  
Sascha M.M. Fässler ◽  
David Stephens ◽  
Lisa Readdy ◽  
Beth E. Scott ◽  
...  
Keyword(s):  
North Sea ◽  
Stock Assessment ◽  
Detection Algorithm ◽  
Trawl Survey ◽  
Northeast Atlantic ◽  
Acoustic Data ◽  
The North ◽  
The North Sea ◽  
Northern North Sea ◽  
Feeding Grounds

Abstract Fisheries independent monitoring of widely distributed pelagic fish species which conduct large seasonal migrations is logistically complex and expensive. One of the commercially most important examples of such a species in the Northeast Atlantic Ocean is mackerel for which up to recently only an international triennial egg survey contributed to the stock assessment. In this study, we explore whether fisheries acoustic data, recorded opportunistically during the English component of the North Sea International Bottom Trawl Survey, can contribute to an improved understanding of mackerel distribution and provide supplementary data to existing dedicated monitoring surveys. Using a previously published multifrequency acoustic mackerel detection algorithm, we extracted the distribution and abundance of schooling mackerel for the whole of the North Sea during August and September between 2007 and 2013. The spatio-temporal coverage of this unique dataset is of particular interest because it includes part of the unsurveyed summer mackerel feeding grounds in the northern North Sea. Recent increases in landings in Icelandic waters during this season suggested that changes have occurred in the mackerel feeding distribution. Thus far it is poorly understood whether these changes are due to a shift, i.e. mackerel moving away from their traditional feeding grounds in the northern North Sea and southern Norwegian Sea, or whether the species' distribution has expanded. We therefore explored whether acoustically derived biomass of schooling mackerel declined in the northern North Sea during the study period, which would suggest a shift in mackerel distribution rather than an expansion. The results of this study show that in the North Sea, schooling mackerel abundance has increased and that its distribution in this area has not changed over this period. Both of these findings provide, to our knowledge, the first evidence in support of the hypothesis that mackerel have expanded their distribution rather than moved away.

scholarly journals Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-16
Author(s):  
Haoran Niu ◽  
Olufemi A. Omitaomu ◽  
Qing C. Cao
Keyword(s):  
Event Detection ◽  
Change Point ◽  
Power Grid ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Disturbance Analysis ◽  
Events Detection ◽  
Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

Rare events detection algorithm: Application to sonar images

2014 ◽  
Author(s):  
Imen Mandhouj ◽  
Frederic Maussang ◽  
Basel Solaiman ◽  
Hamid Amiri
Keyword(s):  
Rare Events ◽  
Detection Algorithm ◽  
Sonar Images ◽  
Events Detection

scholarly journals MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6081
Author(s):  
Sébastien Bonnieux ◽  
Dorian Cazau ◽  
Sébastien Mosser ◽  
Mireille Blay-Fornarino ◽  
Yann Hello ◽  
...  
Keyword(s):  
Programming Language ◽  
Embedded Software ◽  
Detection Algorithm ◽  
Global Scale ◽  
P Wave ◽  
Anthropogenic Activity ◽  
Acoustic Data ◽  
Signal Processing Algorithms ◽  
Blue Whales ◽  
Argo Project

At 2000 m depth in the oceans, one can hear biological, seismological, meteorological, and anthropogenic activity. Acoustic monitoring of the oceans at a global scale and over long periods of time could bring important information for various sciences. The Argo project monitors the physical properties of the oceans with autonomous floats, some of which are also equipped with a hydrophone. These have a limited transmission bandwidth requiring acoustic data to be processed on board. However, developing signal processing algorithms for these instruments requires one to be an expert in embedded software. To reduce the need of such expertise, we have developed a programming language, called MeLa. The language hides several aspects of embedded software with specialized programming concepts. It uses models to compute energy consumption, processor usage, and data transmission costs early during the development of applications; this helps to choose a strategy of data processing that has a minimum impact on performances. Simulations on a computer allow for verifying the performance of the algorithms before their deployment on the instrument. We have implemented a seismic P wave detection and a blue whales D call detection algorithm with the MeLa language to show its capabilities. These are the first efforts toward multidisciplinary monitoring of the oceans, which can extend beyond acoustic applications.

scholarly journals A Real-Time Early Warning Seismic Event Detection Algorithm Using Smart Geo-Spatial Bi-Axial Inclinometer Nodes for Industry 4.0 Applications

2019 ◽  
Vol 9 (18) ◽  
pp. 3650 ◽  
Author(s):  
Hasan Tariq ◽  
Farid Touati ◽  
Mohammed Abdulla E. Al-Hitmi ◽  
Damiano Crescini ◽  
Adel Ben Mnaouer
Keyword(s):  
Real Time ◽  
Seismic Wave ◽  
Early Warning ◽  
Event Detection ◽  
Seismic Waves ◽  
Detection Algorithm ◽  
Time Data ◽  
Time Scaling ◽  
Time Average ◽  
Events Detection

Earthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky–Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved.

scholarly journals Spatial–Temporal Distribution of the Euphausiid Euphausia pacifica and Fish Schools in the Coastal Southwestern East Sea

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 203
Author(s):  
Hyungbeen Lee ◽  
Junghwa Choi ◽  
Yangjae Im ◽  
Wooseok Oh ◽  
Kangseok Hwang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Temporal Distribution ◽  
Detection Algorithm ◽  
East Sea ◽  
Acoustic Backscatter ◽  
Euphausia Pacifica ◽  
Temporal And Spatial Distribution ◽  
Fish Schools ◽  
Acoustic Data ◽  
Temporal And Spatial

The spatial and temporal distribution of euphausiid Euphausia pacifica and fish schools were observed along acoustic transects in the coastal southwestern East Sea. Two-frequency (38- and 120-kHz) acoustic backscatter data were examined from April to July 2010. A dB identification window (SV120–38) and school detection algorithm identified E. pacifica and fish schools in the acoustic backscatter, respectively. The E. pacifica was regularly observed in middle of southern waters, where phytoplankton was abundant during spring, and irregularly during summer, when phytoplankton was homogeneously distributed. Using the distorted-wave Born approximation model, the acoustic density of E. pacifica calculated was higher in spring (April: 75.9 mg m−2, May: 85.3 mg m−2) than in summer (June: 71.4 mg m−2, July: 54.1 mg m−2). The fish schools in the acoustic data tended to significantly increase from spring to summer. Although major fish species, such as anchovies and herring, fed on copepods and euphausiids in the survey area, the temporal and spatial distribution of E. pacifica was weakly correlated with the distribution of the fish schools. These findings aid in our understanding of the temporal and spatial distribution dynamics of euphausiids and fish schools in the food web of the coastal southwestern East Sea.

scholarly journals Sensitivity analysis and parameter selection for detecting aggregations in acoustic data

2006 ◽  
Vol 64 (1) ◽  
pp. 160-168 ◽  
Author(s):  
Julian M. Burgos ◽  
John K. Horne
Keyword(s):  
Sensitivity Analysis ◽  
Latin Hypercube Sampling ◽  
Detection Algorithm ◽  
Design Sensitivity ◽  
Walleye Pollock ◽  
Parameter Selection ◽  
Minimum Size ◽  
Acoustic Data ◽  
Input Parameters ◽  
Selection For

Abstract Burgos, J. M., and Horne, J. K. 2007. Sensitivity analysis and parameter selection for detecting aggregations in acoustic data. ICES Journal of Marine Science, 64: 160–168. A global sensitivity analysis was conducted on the algorithm implemented in the Echoview ® software to detect and describe aggregations in acoustic backscatter. Multiple aggregation detections were performed using walleye pollock (Theragra chalcogramma) data from the eastern Bering Sea. Walleye pollock form distinct aggregations and dense and diffuse layers. In each aggregation detection, input parameters defining minimum size, density, and distance to other aggregations were selected at random using a Latin hypercube sampling design. Sensitivity was quantified by testing for correlation among input parameters and a series of aggregation descriptors. In all, 336 correlation tests were performed, corresponding to a combination of seven detection input parameters, eight aggregation descriptors, and six transects. Among these, 181 tests were significant, indicating sensitivity between input parameters and aggregation descriptors. The aggregation-detection algorithm is sensitive to changes in threshold and minimum size, but less sensitive to changes in the connectivity criterion among aggregations.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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