Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis
Sonar signals recognition is an important task in detecting the presence of some significant objects under the sea. In military, sonar signals are used in lieu of visuals to navigate underwater and/or locate enemy submarines in proximity. In particular, classification algorithm in data mining has be...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-05-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/635834 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832545216673873920 |
|---|---|
| author | Simon Fong Suash Deb Raymond Wong Guangmin Sun |
| author_facet | Simon Fong Suash Deb Raymond Wong Guangmin Sun |
| author_sort | Simon Fong |
| collection | DOAJ |
| description | Sonar signals recognition is an important task in detecting the presence of some significant objects under the sea. In military, sonar signals are used in lieu of visuals to navigate underwater and/or locate enemy submarines in proximity. In particular, classification algorithm in data mining has been applied in sonar signal recognition for recognizing the type of surfaces from which the sonar waves are bounced. Classification algorithms in traditional data mining approach offer fair accuracy by training a classification model with the full dataset, in batches. It is well known that sonar signals are continuous and they are collected as data streams. Although the earlier classification algorithms are effective in traditional batch training, it may not be practical for incremental classifier learning. Since sonar signal data streams can amount to infinity, the data preprocessing time must be kept to a minimum to fulfill the need for high speed. This paper presents an alternative data mining strategy suitable for the progressive purging of noisy data via fast conflict analysis from the data stream without the need to learn from the whole dataset at one time. Simulation experiments are conducted and superior results are observed in supporting the efficacy of the methodology. |
| format | Article |
| id | doaj-art-1a4c18b5549f4f3888cf47042238c8d6 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-1a4c18b5549f4f3888cf47042238c8d62025-02-03T07:26:21ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-05-011010.1155/2014/635834635834Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict AnalysisSimon Fong0Suash Deb1Raymond Wong2Guangmin Sun3 Department of Computer and Information Science, University of Macau, Taipa, Macau Department of Computer Science and Engineering, Cambridge Institute of Technology, Ranchi 835103, India School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia Department of Electronic Engineering, Beijing University of Technology, Beijing 100022, ChinaSonar signals recognition is an important task in detecting the presence of some significant objects under the sea. In military, sonar signals are used in lieu of visuals to navigate underwater and/or locate enemy submarines in proximity. In particular, classification algorithm in data mining has been applied in sonar signal recognition for recognizing the type of surfaces from which the sonar waves are bounced. Classification algorithms in traditional data mining approach offer fair accuracy by training a classification model with the full dataset, in batches. It is well known that sonar signals are continuous and they are collected as data streams. Although the earlier classification algorithms are effective in traditional batch training, it may not be practical for incremental classifier learning. Since sonar signal data streams can amount to infinity, the data preprocessing time must be kept to a minimum to fulfill the need for high speed. This paper presents an alternative data mining strategy suitable for the progressive purging of noisy data via fast conflict analysis from the data stream without the need to learn from the whole dataset at one time. Simulation experiments are conducted and superior results are observed in supporting the efficacy of the methodology.https://doi.org/10.1155/2014/635834 |
| spellingShingle | Simon Fong Suash Deb Raymond Wong Guangmin Sun Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis International Journal of Distributed Sensor Networks |
| title | Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis |
| title_full | Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis |
| title_fullStr | Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis |
| title_full_unstemmed | Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis |
| title_short | Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis |
| title_sort | underwater sonar signals recognition by incremental data stream mining with conflict analysis |
| url | https://doi.org/10.1155/2014/635834 |
| work_keys_str_mv | AT simonfong underwatersonarsignalsrecognitionbyincrementaldatastreamminingwithconflictanalysis AT suashdeb underwatersonarsignalsrecognitionbyincrementaldatastreamminingwithconflictanalysis AT raymondwong underwatersonarsignalsrecognitionbyincrementaldatastreamminingwithconflictanalysis AT guangminsun underwatersonarsignalsrecognitionbyincrementaldatastreamminingwithconflictanalysis |