scholarly journals Calibration of a real-time tsunami detection algorithm for sites with no instrumental tsunami records: application to stations in Eastern Sicily, Italy

2013 ◽  
Vol 1 (3) ◽  
pp. 2455-2493 ◽  
Author(s):  
L. Bressan ◽  
F. Zaniboni ◽  
S. Tinti
Keyword(s):  
Real Time ◽  
Tide Gauge ◽  
Warning System ◽  
Detection Algorithm ◽  
Tsunami Warning System ◽  
Discrimination Power ◽  
Detection Algorithms ◽  
Tsunami Detection ◽  
Efficient Detection ◽  
Level Data

Abstract. Coastal tide-gauges play a very important role in a Tsunami Warning System, since sea-level data are needed for a correct evaluation of the tsunami threat and the tsunami arrival has to be recognised as early as possible. Real-time tsunami detection algorithms serve this purpose. For an efficient detection they have to be calibrated and adapted to the specific local characteristics of the site where they are installed, which is easily done when the station has recorded a sufficiently large number of tsunamis. In this case the recorded database can be used to select the best set of parameters enhancing the discrimination power of the algorithm and minimizing the detection time. This chance is however rare, since most of the coastal tide-gauge stations, either historical or of new installation, have recorded only a few tsunamis in their lifetime, if not any. In this case calibration must be carried out by using synthetic tsunami signals, which poses the problem of how to generate them and how to use them. This paper investigates this issue and proposes a calibration approach by using as an example a specific case, that is the calibration of a real-time detection algorithm called TEDA for two stations, namely Tremestieri and Catania, in eastern Sicily, Italy, that have been recently installed in the frame of the Italian project TSUNET, aiming at improving the tsunami monitoring capacity in a region that is one of the most hazardous tsunami areas of Italy and of the Mediterranean.

scholarly journals Calibration of a real-time tsunami detection algorithm for sites with no instrumental tsunami records: application to coastal tide-gauge stations in eastern Sicily, Italy

2013 ◽  
Vol 13 (12) ◽  
pp. 3129-3144 ◽  
Author(s):  
L. Bressan ◽  
F. Zaniboni ◽  
S. Tinti
Keyword(s):  
Real Time ◽  
Tide Gauge ◽  
Warning System ◽  
Detection Algorithm ◽  
Tsunami Warning System ◽  
Discrimination Power ◽  
Detection Algorithms ◽  
Tsunami Detection ◽  
Efficient Detection ◽  
Level Data

Abstract. Coastal tide gauges play a very important role in a tsunami warning system, since sea-level data are needed for a correct evaluation of the tsunami threat, and the tsunami arrival has to be recognized as early as possible. Real-time tsunami detection algorithms serve this purpose. For an efficient detection, they have to be calibrated and adapted to the specific local characteristics of the site where they are installed, which is easily done when the station has recorded a sufficiently large number of tsunamis. In this case the recorded database can be used to select the best set of parameters enhancing the discrimination power of the algorithm and minimizing the detection time. This chance is however rare, since most of the coastal tide-gauge stations, either historical or of new installation, have recorded only a few tsunamis in their lifetimes, if any. In this case calibration must be carried out by using synthetic tsunami signals, which poses the problem of how to generate them and how to use them. This paper investigates this issue and proposes a calibration approach by using as an example a specific case, which is the calibration of a real-time detection algorithm called TEDA (Tsunami Early Detection Algorithm) for two stations (namely Tremestieri and Catania) in eastern Sicily, Italy, which were recently installed in the frame of the Italian project TSUNET, aiming at improving the tsunami monitoring capacity in a region that is one of the most hazardous tsunami areas of Italy and of the Mediterranean.

scholarly journals Source modeling and inversion with near real-time GPS: a GITEWS perspective for Indonesia

2010 ◽  
Vol 10 (7) ◽  
pp. 1617-1627 ◽  
Author(s):  
A. Y. Babeyko ◽  
A. Hoechner ◽  
S. V. Sobolev
Keyword(s):  
Real Time ◽  
Scaling Laws ◽  
Near Field ◽  
Warning System ◽  
Source Characterization ◽  
Source Modeling ◽  
Plate Interface ◽  
Tsunami Warning System ◽  
Tsunami Early Warning ◽  
Source Models

Abstract. We present the GITEWS approach to source modeling for the tsunami early warning in Indonesia. Near-field tsunami implies special requirements to both warning time and details of source characterization. To meet these requirements, we employ geophysical and geological information to predefine a maximum number of rupture parameters. We discretize the tsunamigenic Sunda plate interface into an ordered grid of patches (150×25) and employ the concept of Green's functions for forward and inverse rupture modeling. Rupture Generator, a forward modeling tool, additionally employs different scaling laws and slip shape functions to construct physically reasonable source models using basic seismic information only (magnitude and epicenter location). GITEWS runs a library of semi- and fully-synthetic scenarios to be extensively employed by system testing as well as by warning center personnel teaching and training. Near real-time GPS observations are a very valuable complement to the local tsunami warning system. Their inversion provides quick (within a few minutes on an event) estimation of the earthquake magnitude, rupture position and, in case of sufficient station coverage, details of slip distribution.

scholarly journals STABILIZATION OF SENTINEL-1 SAR TIME-SERIES USING CLIMATE AND FOREST STRUCTURE DATA FOR EARLY TROPICAL DEFORESTATION DETECTION

2020 ◽  
Vol V-3-2020 ◽  
pp. 89-96
Author(s):  
J. Doblas ◽  
A. Carneiro ◽  
Y. Shimabukuro ◽  
S. Sant’Anna ◽  
L. Aragão ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Structure ◽  
Detection Algorithm ◽  
Structure Information ◽  
Detection Algorithms ◽  
Alternative State ◽  
Level Data ◽  
Relevant Variables ◽  
Long Time ◽  
Omission Errors

Abstract. In this study we analyse the factors of variability of Sentinel-1 C-band radar backscattering over tropical rainforests, and propose a method to reduce the effects of this variability on deforestation detection algorithms. To do so, we developed a random forest regression model that relates Sentinel-1 gamma nought values with local climatological data and forest structure information. The model was trained using long time-series of 26 relevant variables, sampled over 6 undisturbed tropical forests areas. The resulting model explained 71.64% and 73.28% of the SAR signal variability for VV and VH polarizations, respectively. Once the best model for every polarization was selected, it was used to stabilize extracted pixel-level data of forested and non-deforested areas, which resulted on a 10 to 14% reduction of time-series variability, in terms of standard deviation. Then a statistically robust deforestation detection algorithm was applied to the stabilized time-series. The results show that the proposed method reduced the rate of false positives on both polarizations, especially on VV (from 21% to 2%, α=0.01). Meanwhile, the omission errors increased on both polarizations (from 27% to 37% in VV and from 27% to 33% on VV, α=0.01). The proposed method yielded slightly better results when compared with an alternative state-of-the-art approach (spatial normalization).

scholarly journals Near real-time GPS applications for tsunami early warning systems

2010 ◽  
Vol 10 (2) ◽  
pp. 181-189 ◽  
Author(s):  
C. Falck ◽  
M. Ramatschi ◽  
C. Subarya ◽  
M. Bartsch ◽  
A. Merx ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Tide Gauge ◽  
Warning System ◽  
Early Warning Systems ◽  
Time Data ◽  
Essential Information ◽  
Tsunami Early Warning ◽  
Land Mass

Abstract. GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components.

scholarly journals Accurate and Lightweight RailNet for Real-Time Rail Line Detection

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2038
Author(s):  
Zhen Tao ◽  
Shiwei Ren ◽  
Yueting Shi ◽  
Xiaohua Wang ◽  
Weijiang Wang
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Social Progress ◽  
Line Detection ◽  
Detection Accuracy ◽  
Railway Transportation ◽  
Deep Convolutional Neural Networks ◽  
Detection Algorithms ◽  
Rail Line ◽  
Binary Segmentation

Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.

scholarly journals Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas

2018 ◽  
Vol 12 (3) ◽  
pp. 599-607 ◽  
Author(s):  
Daniel P. Howsmon ◽  
Nihat Baysal ◽  
Bruce A. Buckingham ◽  
Gregory P. Forlenza ◽  
Trang T. Ly ◽  
...  
Keyword(s):  
Real Time ◽  
Artificial Pancreas ◽  
Intervention Strategy ◽  
Failure Detection ◽  
Detection Algorithm ◽  
Outpatient Setting ◽  
False Positives ◽  
Zone Model ◽  
Infusion Site ◽  
Detection Algorithms

Background: As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient’s glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures. Methods: An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed. Results: In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58). Conclusions: As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875

Real-Time Tsunami Data Assimilation of S-Net Pressure Gauge Records during the 2016 Fukushima Earthquake

2021 ◽  
Author(s):  
Yuchen Wang ◽  
Kenji Satake
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Pressure Gauge ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Tsunami Detection ◽  
Seafloor Observation ◽  
Net Pressure

Abstract The 2016 Fukushima earthquake (M 7.4) generated a moderate tsunami, which was recorded by the offshore pressure gauges of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net). We used 28 S-net pressure gauge records for tsunami data assimilation and forecasted the tsunami waveforms at four tide gauges on the Sanriku coast. The S-net raw records were processed using two different methods. In the first method, we removed the tidal components by polynomial fitting and applied a low-pass filter. In the second method, we used a real-time tsunami detection algorithm based on ensemble empirical mode decomposition to extract the tsunami signals, imitating real-time operations for tsunami early warning. The forecast accuracy scores of the two detection methods are 60% and 74%, respectively, for a time window of 35 min, but they improve to 89% and 94% if we neglect the stations with imperfect modeling or insufficient offshore observations. Hence, the tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.

scholarly journals A Joint Learning Approach to Face Detection in Wavelet Compressed Domain

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Szu-Hao Huang ◽  
Shang-Hong Lai
Keyword(s):  
Face Detection ◽  
Learning Algorithm ◽  
Feature Space ◽  
Detection Algorithm ◽  
Feature Representation ◽  
Compressed Domain ◽  
Discrimination Power ◽  
Detection Algorithms ◽  
The Face ◽  
Active Research

Face detection has been an important and active research topic in computer vision and image processing. In recent years, learning-based face detection algorithms have prevailed with successful applications. In this paper, we propose a new face detection algorithm that works directly in wavelet compressed domain. In order to simplify the processes of image decompression and feature extraction, we modify the AdaBoost learning algorithm to select a set of complimentary joint-coefficient classifiers and integrate them to achieve optimal face detection. Since the face detection on the wavelet compression domain is restricted by the limited discrimination power of the designated feature space, the proposed learning mechanism is developed to achieve the best discrimination from the restricted feature space. The major contributions in the proposed AdaBoost face detection learning algorithm contain the feature space warping, joint feature representation, ID3-like plane quantization, and weak probabilistic classifier, which dramatically increase the discrimination power of the face classifier. Experimental results on the CBCL benchmark and the MIT + CMU real image dataset show that the proposed algorithm can detect faces in the wavelet compressed domain accurately and efficiently.

Hierarchy Topic Detection and Hot Topic Identification

2014 ◽  
Vol 701-702 ◽  
pp. 180-186
Author(s):  
Xue Mei Zhou ◽  
Shan Ying Cheng
Keyword(s):  
Public Opinion ◽  
Real Time ◽  
Hierarchical Structure ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Topic Detection ◽  
Topic Identification ◽  
Evaluation Indexes ◽  
Detection Algorithms ◽  
The Hierarchical Structure

Due to the problem that the existing topic detection algorithms can not satisfy accuracy,real time and topic hierarchical clustering at the same time, this article builds a hierarchy topic detection algorithm based on improved single pass clustering algorithm. In addition, using public opinion evaluation indexes to analyze topic temperature,the method proposed in this paper can detect hot topics accurately and timely while showing the hierarchical structure of the topic .

scholarly journals THE HELLENIC NATIONAL TSUNAMI WARNING CENTER: RESEARCH, OPERATIONAL AND TRAINING ACTIVITIES

2017 ◽  
Vol 50 (2) ◽  
pp. 1100 ◽  
Author(s):  
G.A. Papadopoulos ◽  
G.A. Tselentis ◽  
M. Charalampakis ◽  
All the scientific staff of the Institut All the scientific staff of the Institute of Geodynamics1
Keyword(s):  
Tide Gauge ◽  
Eastern Mediterranean ◽  
Warning System ◽  
Tsunami Warning ◽  
Tsunami Warning System ◽  
Warning Messages ◽  
The North ◽  
Operational Life ◽  
High Level ◽  
And Training

The Hellenic National Tsunami Warning Center (HL-NTWC), which is a unit of the Institute of Geodynamics of the National Observatory of Athens (NOA-IG), was officially established in Greece by law in September 2010. It operates a 24/7 tsunami monitoring service for Greece and the eastern Mediterranean Sea, providing warning messages to the General Secretariat for Civil Protection in Greece. Since August 2012, HL-NTWC acts as Candidate Tsunami Service Provider (CTSP) in the framework of the North-Eastern Atlantic, the Mediterranean and connected seas Tsunami Warning System (NEAMTWS) of the IOC/UNESCO providing tsunami messages to a large number of subscribers. The HL-NTWC function is based on the national seismograph and tide gauge networks of NOA-IG and incorporates several data bases, algorithms and computational tools. Collaboration with top class research institutions in the framework of important EC funded tsunami research projects strengthens the scientific background of the center. Tests, exercises and training of the duty officers involved in the 24/7 operation of the HL-NTWC are carried on constantly, in order to maintain a high level of readiness and response in case of emergency. In its operational life since August 2012 the HL-NTWC has timely issued tsunami warning messages for 14 potentially tsunamigenic earthquake events.

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