Validation of an Odor Source Identification Algorithm via an Underwater Vehicle

2012 ◽  
Author(s):  
Xiaodong Kang ◽  
Wei Li ◽  
Hongli Xu ◽  
Xisheng Feng ◽  
Yiping Li
Keyword(s):  
Source Identification ◽  
Underwater Vehicle ◽  
Identification Algorithm ◽  
Odor Source

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

Developing data fusion and recursive estimation methods for online identification of dive plane dynamics of an autonomous underwater vehicle

2019 ◽  
Vol 234 (2) ◽  
pp. 520-533
Author(s):  
Seid Farhad Abtahi ◽  
Mohammad Mehdi Alishahi ◽  
Ehsan Azadi Yazdi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Unscented Kalman Filter ◽  
Underwater Vehicle ◽  
Recursive Estimation ◽  
High Rate ◽  
Pole Placement ◽  
Estimation Methods ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients

The purpose of this article is to develop an online method to identify the hydrodynamic coefficients of pitch plane of an autonomous underwater vehicle. To obtain necessary data for the identification, the dive plane dynamics should be excited through diving maneuvers. Hence, a controller is needed whose performance and stability are appropriate. To design such a controller, first, hydrodynamic coefficients are approximated using semi-empirical methods. Based on these approximated coefficients, a classic controller is designed at the next step. Since the estimation of these coefficients is uncertain, µ-analysis is employed to verify the robustness of stability and performance of the controller. Using the verified robust controller, some oscillating maneuvers are carried out that excite the dive plane dynamics. Using sensor fusion and unscented Kalman filter, smooth and high-rate data of depth is provided for the depth controller. A recursive identification algorithm is developed to identify the hydrodynamic coefficients of heave and pitch motions. It turns out that some inputs required by the identification are not measured directly by the sensors. But the devised fusion algorithm is able to provide the necessary data for identification. Finally, using the identified coefficients and employing pole placement method, a new controller with better performance is synthesized online. To evaluate the performance of the identification and fusion algorithms, a 6-degree-of-freedom simulation of an autonomous underwater vehicle is carried out.

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.

Odor source identification by grounding linguistic descriptions in an artificial nose

2001 ◽  
Author(s):  
Amy Loutfi ◽  
Silvia Coradeschi ◽  
Tom Duckett ◽  
Peter Wide
Keyword(s):  
Source Identification ◽  
Odor Source ◽  
Artificial Nose

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>

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