All Issue

2023 Vol.11, Issue 3 Preview Page

Research Article

Automatic Frequency Line Detection Method for DEMON and LOFAR Gram using Transfer Learning

전이 학습을 이용한 DEMON 및 LOFAR 그램의 자동 주파수 선 검출 방법

Do Kyung Shin1, Young Dae Kim2

신도경1, 김영대2

1,2Chief Research Engineer, Maritime Surface Sonar System LIG Nex1
1,2엘아이지 넥스원 해양연구소 소나체계개발단 수석연구원
30 September 2023. pp. 83-99
PDF Citation
Abstract
해상경계감시는 국가 안보에 매우 중요한 관심사로, 감시 및 정찰을 위한 다양한 방법의 배치를 필요로 한다. 연안 감시 작전에서 잠수함은 은밀한 기동을 수행하고 수중 센서를 통해 해상 상황에 대한 정보를 수집하고 분석하는 데 중요한 역할을 하는 중추적인 역할을 한다. 본 논문에서는 잠수함에 탑재된 수동 소나를 통해 획득한 음향 신호의 주요 특성 검출 정확도를 높이기 위해 딥러닝 기법을 적용하여 엔진 및 프로펠러 소음과 같은 주요 음원 탐지 정확도를 높이는 것을 목표로 한다. 음향 신호는 일반적으로 엔진 및 프로펠러와 같은 기계적 소음으로 구성되며 일반적으로 DEMON 및 LOFAR 그램을 분석하여 소음원에 대한 주요 특성을 추정한다. 그러나 딥러닝 기술을 적용하기 위해서는 대용량 데이터셋이 필요하고, 한반도 주변 해역에서 실제 데이터를 수집하는 데는 높은 비용과 국가 안보 문제로 인해 상당한 어려움이 따른다. 따라서 이 논문에서는 DEMON 및 LOFAR 신호와 유사한 심전도(ECG) 신호 분할 모델에서 사전에 학습된 모델을 활용하여 대용량 데이터셋 구축의 문제를 해결하기 위한 전이 학습을 통해 음향 신호의 주요 특성을 검출하는 방법을 제안한다.
Maritime border surveillance is a very important concern for national security, requiring the deployment of various methods for surveillance and reconnaissance. In coastal surveillance operations, submarines play a pivotal role in performing covert maneuvers and playing an important role in collecting and analyzing information about the sea situation through underwater sensors. In this paper, we aim to improve the detection accuracy of major sound sources such as engine and propeller noise by applying a deep learning technique to improve the detection accuracy of the main characteristics of the acoustic signal acquired through the passive sonar mounted on the submarine. Ship noise usually consists of mechanical noises such as engines and propellers, and key characteristics are usually estimated for noise sources by analyzing DEMON and LOFAR grams. However, in order to apply deep learning technology, a considerable amount of data sets are required, and there are considerable difficulties due to the high cost and national security issues of collecting actual data in the waters around the Korean Peninsula. Therefore, in this paper, we propose an approach to solve the problem of constructing a large data set through transfer learning using a pre-trained model of an electrocardiogram (ECG) signal segmentation model similar to DEMON and LOFAR signals.
Keywords
DEMON
LOFAR
Acoustic signal
Frequency line detection
Transfer learning
Ship noise
LSTM
ECG
References
  1. P. Stinco, A. Tesei, R. Dreo, and M. Micheli, "Detection of Envelope Modulation and Direction of Arrival Estimation of Multiple Noise Sources with an Acoustic Vector Sensor", Journal of the Acoustical Society of America, Vol. 149, No. 3, pp. 1596-1608, 2021. 10.1121/10.0003628 33765795
  2. A. Pollara, A. Sutin, and H. Salloum, "Improvement of the Detection of Envelope Modulation on Noise (DEMON) and its application to small boats", OCEANS 2016 MTS/IEEE Monterey, Monterey, pp. 1-10, 2016. 10.1109/OCEANS.2016.7761197
  3. J. Li, L. Jiang, H. Chen, Y. Zhang, and Y. Xie, "Propeller Feature Extraction of UUVs Study based on CEEMD Combined with Symmetric Correlation", MATEC Web of Conferences, Vol. 336, pp. 1-9, 2021. 10.1051/matecconf/202133602006
  4. G. J. Lowes, J. Neaham, R. Burett, B. Sherlock, and C. Tsimenidis, "Passive Acoustic Detection of Vessel Activity by Low-Energy Wireless Sensors", Journal of Marine Science and Engineering, Vol. 10, No. 2, pp. 1-35, 2022. 10.3390/jmse10020248
  5. J. Fernandes, N. Junior, and J. Seixas, "Deep Learning Models for Passive Sonar Signal Classification of Military Data", Remote Sensing, 2648, Vol. 14, No. 11, 2022. 10.3390/rs14112648
  6. J. Chen, B. Han, X. Ma, and J. Zhang, "Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach", Future Internet, Vol. 13, No. 10, pp. 1-21, 2021. 10.3390/fi13100265
  7. X. Luo, M. Zhang, T. Liu, M. Huang, and X. Xu, "An Underwater Acoustic Target Recognition Method Based on Spectrograms with Different Resolutions", Journal of Marine Science and Engineering, Vol. 9, No. 11, pp. 1-20, 2021. 10.3390/jmse9111246
  8. D. Ju, C. Chi, Z. Li, Y. Li, C. Zhang, and H. Huang, "Deep-Learning-based Line Enhancer for Passive Sonar Systems", IET Radar, Sonar & Navigation, Vol. 16, No. 3, pp. 589-601, 2021. 10.1049/rsn2.12205
  9. S. Kamal, S. C. Chandran, and M. H. Supriya, "Passive Sonar Automated Target Classifier for Shallow Waters using End-to-End Learnable Deep Convolutional LSTMs", Engineering Science and Technology, van International Journal, Vol. 24, No. 4, pp. 860-871, 2021. 10.1016/j.jestch.2021.01.014
  10. L. Domingos, P. Santos, P. Skelton, R, Brinkworth, and K. Sammut, "A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance", Sensors, Vol. 22, No. 6, pp. 1-30, 2022. 10.3390/s22062181 35336352 PMC8954367
  11. J. Fischer, M. Orescanin, P. Leary, and K. B. Smith, "Active Bayesian Deep Learning With Vector Sensor for Passive Sonar Sensing of the Ocean", in IEEE Journal of Oceanic Engineering, Vol. 48, No. 3, pp. 837-852, 2023. 10.1109/JOE.2023.3252624
  12. A. Hosna, E. Merry, J. Gyalmo, Z. Alom, Z. Aung, and M. A. Azim, "Transfer Learning: A Friendly Introduction", Journal of Big Data, Vol. 9, No. 102, pp. 1-19, 2022. 10.1186/s40537-022-00652-w 36313477 PMC9589764
  13. Z. Zhu, K. Lin, A.K. Jain, and J. Zhou, "Transfer Learning in Deep Reinforcement Learning: A Survey", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-20, 2023. 10.1109/TPAMI.2023.3292075 37402188
  14. S. Saadatnejad, M. Oveisi, and M. Haxhemi, "LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices", IEEE Journal of Biomedical and Health Informatics, Vol. 24, No. 2, pp. 1-9, 2020. 10.1109/JBHI.2019.2911367 30990452
  15. B. H. Kim, and J. Y. Pyun, "ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks", Sensors, Vol. 20, No. 11, pp. 1-17, 2020. 10.3390/s20113069 32485827 PMC7309053
  16. S. Dalal, and V. P. Vishwakarma, "Classification of ECG Signals using Multi-Cumulants based Evolutionary Hybrid Classifier", Scientific Reports, Vol. 11, pp. 1-25, 2021. 10.1038/s41598-021-94363-6 34301998 PMC8302656
  17. S. Sowmya, and D. Jose, "Contemplate on ECG Signals and Classification of Arrhythmia Signals using CNN-LSTM Deep Learning Model", Sensors, Vol. 24, pp. 100558, 2022. 10.1016/j.measen.2022.100558
  18. S. Hochreiter, J. Schmidhuber, "Long Short-Term Memory", Neural Computation, Vol. 9 No. 8, pp. 1735-1780, 1997. 10.1162/neco.1997.9.8.1735 9377276
  19. H. P. Martinez, Y. Bengio and G. N. Yannakakis, "Learning deep physiological models of affect," in IEEE Computational Intelligence Magazine, Vol. 8, No. 2, pp. 20-33, 2013. 10.1109/MCI.2013.2247823
  20. P. Xiong, H. Wang, M. Liu, S. Zhou, Z. Hou, and X. Liu, "ECG signal enhancement based on improved denoising auto-encoder", Engineering Applications of Artificial Intelligence, Vol. 52, pp. 194-202, 2016. 10.1016/j.engappai.2016.02.015
  21. S. D. Goodfellow, A. Goodwin, R. Greer, P.C. Laussen, M. Mazwi, and D. Eytan, "Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings", Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR, Vol. 85, pp. 83-101, 2018.
  22. Z. Xiong, Z. M. P. Nash, E. Cheng, V. V. Fedorov, M. K. Stiles, and J. Zhao, "ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network", Journal of Physiol Meas, Vol. 39, No. 9, pp. 1-14, 2018. 10.1088/1361-6579/aad9ed 30102248 PMC6377428
  23. S. Saadatnejad, M. Oveisi, and M. Hashemi, "LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices", IEEE Journal of Biomediacl and Health Informatics(JBHI), Vol. 24, No. 2, pp. 515-523, 2020. 10.1109/JBHI.2019.2911367 30990452
  24. R. Smisek, J. Hejc, M. Ronzhina, A. Nemcova, L. Marsanova, J. Kolarova, L. Smital, and M. Vitek, "Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device", Journal of Physiol Meas, Vol. 39, No. 9, pp. 1-12, 2021. 10.1088/1361-6579/aad9e7 30102239
  25. M. Pieter, B. Ruud, and B. Wim, "Machine Vision for a Selective Broccoli Harvesting Robot", International Federation of Automatic Control(IFAC), pp. 1-7, 2017.
  26. K. S. Knumar, K. Venkatalakshmi, and K. Karthikeyan, "Lung Cancer Detection Using Image Segmentation by means ofVarious Evolutionary Algorithms", Computational and Mathematical Methods in Medicine, Vol. 2019, pp. 1-16, 2019. 10.1155/2019/4909846 30728852 PMC6341460
Information
  • Publisher :The Society of Convergence Knowledge
  • Publisher(Ko) :융복합지식학회
  • Journal Title :The Society of Convergence Knowledge Transactions
  • Journal Title(Ko) :융복합지식학회논문지
  • Volume : 11
  • No :3
  • Pages :83-99
  • DOI :https://doi.org/10.22716/sckt.2023.11.3.028
Journal Informaiton The Society of Convergence Knowledge Transactions The Society of Convergence Knowledge Transactions
Online Submission
  • NRF
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close