A Source Identification Algorithm for INTEGRAL

2008 ◽  
Author(s):  
Simone Scaringi ◽  
Antony J. Bird ◽  
David J. Clark ◽  
Anthony J. Dean ◽  
Adam B. Hill ◽  
...  
Keyword(s):  
Source Identification ◽  
Identification Algorithm

Time-Series Segmentation Using Predictive Modular Neural Networks

1997 ◽  
Vol 9 (8) ◽  
pp. 1691-1709 ◽  
Author(s):  
Athanasios Kehagias ◽  
Vassilios Petridis
Keyword(s):  
Time Series ◽  
Source Identification ◽  
Predictive Accuracy ◽  
Identification Algorithm ◽  
Time Series Classification ◽  
Time Step ◽  
Modular Neural Networks ◽  
Time Series Segmentation ◽  
Network Method

A predictive modular neural network method is applied to the problem of unsupervised time-series segmentation. The method consists of the concurrent application of two algorithms: one for source identification, the other for time-series classification. The source identification algorithm discovers the sources generating the time series, assigns data to each source, and trains one predictor for each source. The classification algorithm recursively computes a credit function for each source, based on the competition of the respective predictors, according to their predictive accuracy; the credit function is used for classification of the time-series observation at each time step. The method is tested by numerical experiments.

scholarly journals Efficient Online Source Identification Algorithm for Integration within Contamination Event Management System

2017 ◽  
Author(s):  
Jochen Deuerlein ◽  
Lea Meyer-Harries ◽  
Nicolai Guth
Keyword(s):  
Real Time ◽  
Source Identification ◽  
Management System ◽  
Automatic Identification ◽  
Identification Algorithm ◽  
Event Management ◽  
Supply Networks ◽  
Web Based ◽  
Security Enhancement ◽  
Online Source

<p><strong>Abstract.</strong> The automatic identification of the source of a contamination is an important component of an early warning and event management system for security enhancement of water supply networks. Whilst a number of algorithms have been published on the algorithmic development, only few information exists about the integration within a comprehensive real-time Event Detection and Management System. In the following the analytical solution and the software implementation of a real-time source identification module and its integration within a web-based Event Management System is described. The development was part of the project SAFEWATER, which was funded under FP 7 of the European Commission.</p>

scholarly journals Technical note: Efficient online source identification algorithm for integration within a contamination event management system

2017 ◽  
Vol 10 (2) ◽  
pp. 53-59
Author(s):  
Jochen Deuerlein ◽  
Lea Meyer-Harries ◽  
Nicolai Guth
Keyword(s):  
Drinking Water ◽  
Real Time ◽  
Early Warning ◽  
Source Identification ◽  
Management System ◽  
Distribution Networks ◽  
Early Warning Systems ◽  
Optimal Placement ◽  
Identification Algorithm ◽  
Event Management

Abstract. Drinking water distribution networks are part of critical infrastructures and are exposed to a number of different risks. One of them is the risk of unintended or deliberate contamination of the drinking water within the pipe network. Over the past decade research has focused on the development of new sensors that are able to detect malicious substances in the network and early warning systems for contamination. In addition to the optimal placement of sensors, the automatic identification of the source of a contamination is an important component of an early warning and event management system for security enhancement of water supply networks. Many publications deal with the algorithmic development; however, only little information exists about the integration within a comprehensive real-time event detection and management system. In the following the analytical solution and the software implementation of a real-time source identification module and its integration within a web-based event management system are described. The development was part of the SAFEWATER project, which was funded under FP 7 of the European Commission.

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