Nghiên cứu giải pháp nâng cao chất lượng phân loại tín hiệu thủy âm tại vùng biển nước nông ứng dụng trí tuệ nhân tạo

MINISTRY OF EDUCATION AND TRAINING MINISTRY OF DEFENSE ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY BACH NHAT HOANG RESEARCHING ON SOLUTION OF IMPROVING THE QUALITY OF UNDERWATER SIGNAL CLASSIFICATION IN SHALLOW WATERS APPLYING ARITIFICIAL INTELLIGENCE SUMMARY OF PhD THESIS IN ENGINEERING HANOI – 2023 MINISTRY OF EDUCATION AND TRAINING MINISTRY OF DEFENSE ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY BACH NHAT HOANG RESEARCHING ON SOLUTION OF IMPROVING THE QUALITY OF UNDERWATER SIGNAL CLASSIFICATION IN SHALLOW WATERS APPLYING ARITIFICIAL INTELLIGENCE Specialization: Electronic Engineering Code: 52 02 03 SUMMARY OF PhD THESIS IN ENGINEERING SCIENTIFIC SUPERVISORS: Assoc, Prof Dr Nguyen Van Duc Dr Vu Le Ha HANOI – 2023 i DECLARATION I hereby declare that this is my own research work under the guidance of my supervisors The statistics and results presented in this thesis are completely honest and have not been published in any other works References are fully cited Hanoi, April 12nd, 2023 Author Bach Nhat Hoang ii ACKNOWLEDGEMENT During the process of conducting research and completing this thesis, the PhD student has received valuable guidance, supports and comments, as well as sincere encouragements from scientists, teachers, colleagues and family members First of all, the PhD student would like to express his gratitude to Assoc Prof Dr Nguyen Van Duc and Dr Vu Le Ha for their enthusiastic guidance and support in the entire process of conducting research and completing the thesis Also, the PhD student would like to show his sincere thanks to the Board of Management of the Academy Military of Science and Technology, the Training Department and the Institute of Electronics for giving the PhD student all the favorable conditions to study, research and complete the thesis Additionally, the PhD student sincerely thank the teachers, scientists, and colleagues at the Academy Military of Science and Technology, the Institute of Electronics, and Hanoi University of Science and Technology, etc for instructing and providing valuable suggestions to the PhD student throughout the process of conducting this thesis Last but not least, the PhD student is thankful for the encouragement, support, sharing and sacrifice of his family that help the PhD student to overcome difficulties and achieve the research results in this thesis Author Bach Nhat Hoang iii TABLE OF CONTENTS LIST OF ABBREVIATIONS vii LIST OF TABLES xi LIST OF FIGURES xi INTRODUCTION CHAPTER 1.OVERVIEW ON THE CLASSIFICATION OF BIOTIC AND ABIOTIC UNDERWATER SIGNALS IN SHALLOW WATERS 1.1 Biotic-Abiotic underwater signals and uderwater signal classification system in shallow waters 1.1.1 1.2 1.3 1.4 7 The ocean and acoustic propagation characteristics in shallow waters 1.1.2 Biotic and Abiotic sound sources 11 1.1.3 Underwater signal detection system based on sonar principle 13 Classical approaches in underwater signal classification 19 1.2.1 Time-frequency domain transformation 19 1.2.2 LOFAR algorithm 22 1.2.3 CMS algorithm 23 1.2.4 DEMON algorithm 24 Modern approaches in underwater signal classification 26 1.3.1 Restricted Boltzmann Machine 27 1.3.2 Auto-Encoder 28 1.3.3 Convolution Neural Network 29 The current state of research in underwater signal classification and limitation 30 1.4.1 The sole use of artificial intelligence 32 1.4.2 The use of pre-processing combined with artificial intelligence 35 1.4.3 The use of transfer learning 38 1.4.4 Limitations 39 iv 1.5 1.6 Research directions of the thesis and actual underwater datasets 40 1.5.1 Research directions of the thesis 41 1.5.2 Actual underwater datasets used in the thesis 43 Chapter conclusion 48 CHAPTER 2.CLASSIFICATION OF PROPELLER SHIP SIGNALS USING THE SOLUTION OF PROPOSED SPECTRAL AMPLITUDE VARIATION COMBINED WITH A CUSTOMIZED CONVOLUTION NEURAL NETWORK 2.1 2.2 2.3 The formation process of the propeller ship signals during movement 49 2.1.1 Signals generated from moving propeller ships 49 2.1.2 Cavitation phenomenon 51 Proposal of spectral amplitude variation pre-processing 52 2.2.1 Drawbacks of the DEMON algorithm 52 2.2.2 Mathematical analysis of the proposed algorithm 54 2.2.3 Structure of the proposed algorithm 57 2.2.4 Evaluation of the proposed algorithm on actual ship data 60 2.2.5 Evaluation of the proposed algorithm on actual diver breath data 66 Proposal of a customized convolution neural network 69 2.3.1 Reasons for choosing convolution neural network 69 2.3.2 Proposed network configuration 70 2.3.3 Evaluation of the proposed convolution neural network complexity 2.4 49 74 Classification results using the combination of two proposed solutions on propeller signals 2.4.1 Classification results of DEMON-Hilbert combined with LeNet and VGG19 as control results 2.4.2 76 77 Classification results of the proposed spectral amplitude variation algorithm with LeNet and VGG19 78 v 2.4.3 Classification results of DEMON-Hilbert and the proposed spectral amplitude variation algorithm combined with the proposed CNN 80 Evaluation of the proposed network 83 Chapter conclusion 85 2.4.4 2.5 CHAPTER 3.CLASSIFICATION OF MARINE MAMMAL AND PROPELLER SHIP SIGNALS USING THE PROPOSED SOLUTION OF CUBIC SPLINES INTERPOLATION COMBINED WITH PROBABILITY DISTRIBUTION IN THE HIDDEN SPACE DOMAIN 88 3.1 Marine mammals communication signal structure 88 3.2 Proposal of the cubic-splines interpolation pre-processing 89 3.2.1 Theoretical basis for using cubic-splines interpolation 90 3.2.2 Interpolations on the frequency domain and proposed solutions 91 3.2.3 Structure of the proposed algorithm 3.2.4 Evaluation of the proposed algorithm on actual marine mammal data 3.3 95 97 Proposal of probability distribution in the hidden space for Siamese triple loss network 101 3.3.1 Structure of Siamese triple loss network 101 3.3.2 Structure of Rep-VGG model 102 3.3.3 Proposed solution using probability distribution in the hidden space 103 3.3.4 3.4 Proposed solution using SNN-VAE model 105 Classification results using the combination of two proposed solutions on marine mammal and propeller signals 108 3.4.1 Classification results of the proposed SNN-VAE on propeller signals 108 3.4.2 Classification results of the proposed cubic-splines interpolation and SNN-VAE on marine mammal signals 113 3.4.3 Classification results of the proposed cubic-splines interpolation and SNN-VAE on marine mammal and propeller signals 119 vi 3.5 Chapter conclusion 125 CONCLUSION 127 LIST OF SCIENTIFIC PUBLICATION 130 REFERENCES 132 vii LIST OF ABBREVIATIONS Ak The last convolution layer feature map c Speed of sound underwater (m/s) C Number of input channels E Orthogonal function E(z) Vector mean of random variable z f Signal frequency (Hz) F Window size of convolution network F (x) Spatial Signal sequence I Input spectrogram size L Total global average weight L Loss function of Siamese network nk (t) Cavitation noise N0 /2 Power spectral density of white noise b O The size of the network P Padding R Set of real numbers s(t) Noise generated from propeller blades S Stride Sa Salinity (per thousand) t Time of signal (second) Tk Propeller signal period Tp Temperature of enviroment (Celcius) V(z) Vector variance of random variable z x(t) Signal at the receiver zh The depth of the water (meter) αkc Weight of filter k when classifying class c γ Scale parameter on the time axis υ Position parameter on the time axis C.vT.Bg.Jy.Lj.Tai lieu Luan vT.Bg.Jy.Lj van Luan an.vT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.Lj Do an.Tai lieu Luan van Luan an Do an.Tai lieu Luan van Luan an Do an viii Ψλ,τ Wavelets families function [.]∗ Complex conjugation := ⟨⟩ Equals function in the frequency domain ≈ Approximately σk2 Power spectral density λ Signal variance [∗] Convolution multiplication τ Signal delay ≜ Equals function by definition mathematic µB The mean of a subset ϵ Random bias of convolutional networks ACT Anisotropic chirplet transforms ADCNN Auditory perception inspired deep convolutional neural network AE Auto-encoder AG Array gain AV Amplitude variation BN Batch Norm BOC-NOAA Best of CUT-National Oceanic and Atmospheric Administration CMS Cyclic modulation Spectrum CNN Convolution neural network CSDM Cross-spectral density matrices CSI Cubic-splines interpolation CWT Continuos wavelet transform DL Deep learning DEMON Demodulation of envelope modulation on noise DT Detection threshold DWT Discrete wavelet transform ELM Extreme learning machine FC Fully connected FCNN Fully connected neural network FT Fourier Transform FFT Fast fourier transform Stt.010.Mssv.BKD002ac.email.ninhd.vT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.Lj.dtt@edu.gmail.com.vn.bkc19134.hmu.edu.vn.Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn.bkc19134.hmu.edu.vn C.vT.Bg.Jy.Lj.Tai lieu Luan vT.Bg.Jy.Lj van Luan an.vT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.LjvT.Bg.Jy.Lj Do an.Tai lieu Luan van Luan an Do 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