압축센싱 기반 접근법을 이용한 수중음향 소음원의 위치 추정 기법 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 조선해양공학과, 2021. 2. 성우제.삼차원 음향 소음원의 위치추정은 잠수체, 산란체, 캐비테이션 소음원의 분석을 위해 필수적인 과정이다. 전통적인 빔형성 기법은 강인한 위치 추정 결과를 제공하나, 하나의 소음원의 위치만을 구분할 수 있는 저해상도의 결과를 보인다. 고해상도의 위치 추정 결과를 얻기 위해 최근 압축센싱 기반의 위치 추정 기법들이 사용되어 지고 있다. 압축센싱 기법은 희소성을 가진 신호의 획득,처리,복원에 효과적인 방법이며 영상처리, 수중음향, 최적화 문제 등에서 널리 활용되어지고 있다. 수중 소음원의 위치 추정을 위하여 압축센싱 기밥의 기법들이 적용되어 왔으며 전통적인 빔형성 기법에 비하여 해상도 측면에서 더 나은 성능을 보여주었다. 하지만 이러한 해상도 측면의 성능 향상에도 불구하고 압축센싱 기반의 방법은 여전히 문제점들을 가지고 있다. 첫번째, 압축센싱 기법은 전통적인 빔형성 기법에 비해 수치 연산 과정이 불안전성을 가진다. 비록 고해상도의 성능을 보여주나 압축센싱 기법은 수치해석 과정에서 불안정한 모습을 보여주며, 안정적인 복원을 저해한다. 두번째, 기저불일치로 인한 오차가 정확한 소음원의 위치 추정을 저해한다. 게다가 3차원 소음원의 위치 추정 문제는 이러한 기저 불일치를 해결할 수 있는 기법이 아직까지 개발되지 못하였다. 본 논문은 기존의 압축센싱 기반의 위치 추정 기법이 가지는 문제점을 파악하고 3차원 위치 추정 문제를 다룰 수 있는 향상된 압축센싱 기법을 소개한다. 탐색 공간 사이의 높은 상관관계로 인하여 발생하는 해의 불안정성을 해결아기 위하여 ``다중주파수 상관 처리기법"을 소개하고, 3차원 위치 추정문제에서 기저불일치 문제를 해결할 수 있는 ``유동 탐색 격자 기법"을 소개한다. 제안된 기법은 전통적인 빔형성 기법에 비하여 정확한 위치 추정 결과를 제공하며 실험 데이터를 통한 위치 추정결과를 이용하여 이러한 주장을 뒷받침하였다. 본 연구에서는 수중음향 소음원의 3차원 위치 추정 문제를 중점적으로 다루었으나, 제안된 기법은 소나 및 레이더, 음향 소음원 위치 추정 문제에도 효과적으로 적용될 수 있을 것으로 기대된다.Three-dimensional acoustic localization is an essential process to analyze the underwater sound sources such as submarine, scatterer, marine cavitation. Traditional beamforming processors provide robust localization results, however, the results show a low-resolution result which only reveals one dominant source location. In order to obtain the high resolution localization results, compressive sensing(CS) based approaches have been used recently. CS technique is an effective way for acquiring, processing, reconstructing the sparse signal and has wide applicability to many research fields such as image processing, underwater acoustics and optimization problems. For localizing the underwater acoustic sources, CS-based approaches have been adopted in many research fields and have shown better localization performance compared to the traditional beamforming processors in terms of resolution. Despite the performance improvement in resolution, there are still problems that need to be resolved when using the CS-based method. First, the CS-based method does not appear to be robust compared with the traditional beamforming processors. CS-based method provides high-resolution results, however, it suffers from computational instability which hinders the stable reconstruction. Second, basis mismatch error hindrances estimating the exact source locations. Moreover, there is no basis mismatch estimation technique applicable to 3D source localization problem. This dissertation points out the limitation of conventional CS-based localization method and introduces the advanced CS-based localization method which deals with 3D source localization problem. The ``coherent multiple-frequency processing" is introduced to overcome the instability of solution induced by high correlation of spatial grids and ``flexible searching-grid technique" is introduced to solve the basis mismatch problem which is developed for 3D source localization problem. The suggested techniques provide more accurate localization results compared to traditional beamforming processors or conventional CS-based beamforming processors and the arguments are backed with actual experimental data which was conducted in a cavitation tunnel. Though underwater acoustic source localization problems are presented in this dissertation, the proposed technique can be extended to many research fields, such as radar detection, sonar detection, ultrasound imaging.1 Introduction 2 1.1 Issue 1 : Computational Stability 4 1.2 Issue 2 : Basis Mismatch 5 1.3 Organization of the Dissertation 5 2 CS techniques for three-dimensional source localization 9 2.1 Compressive Sensing (CS) 9 2.2 Block-Sparse Compressive Sensing (BSCS) 11 2.3 Sparse Bayesian learning (SBL) 12 2.4 Off-Grid Sparse Bayesian Inference (OGSBI) 14 3 3D CS-based source localization method using multiple-frequency components 18 3.1 Introduction 18 3.2 Block-sparse Compressive Sensing for Incipient Tip Vortex Cavitation Localization 24 3.2.1 System framework for incipient tip vortex cavitation localization 24 3.2.2 Incoherent multiple-frequency localization with compressive sensing 26 3.2.3 Coherent multiple-frequency localization with block-sparse compressive sensing 28 3.3 Localization Results for Incipient TVC 32 3.3.1 Transducer source experiment 33 3.3.2 Incipient TVC Noise Source Experiment 36 3.4 Conclusion 41 3.5 Acknowledgments 43 4 3D CS-based source localization method by reducing the basis mismatch error 48 4.1 Introduction 48 4.2 Off grid system framework for 3D source localization 50 4.2.1 System framework for 3-dimensional off gird source localization 50 4.2.2 Coherent multiple-frequency localization with block-sparse Bayesian learning technique 53 4.2.3 3-dimensional off grid source localization method 55 4.3 Simulation and Experiment Results 62 4.4 Conclusion 65 5 Summary 70 Abstract (In Korean) 73Docto

Efficient direction of arrival estimation based on sparse covariance fitting criterion with modeling mismatch

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My thesis presents several novel methods to facilitate solving large-scale inverse problems by utilizing recent advances in machine learning, and particularly deep generative modeling. Inverse problems involve reliably estimating unknown parameters of a physical model from indirect observed data that are noisy. Solving inverse problems presents primarily two challenges. The first challenge is to capture and incorporate prior knowledge into ill-posed inverse problems whose solutions cannot be uniquely identified. The second challenge is the computational complexity of solving inverse problems, particularly the cost of quantifying uncertainty. The main goal of this thesis is to address these issues by developing practical data-driven methods that are scalable to geophysical applications in which access to high-quality training data is often limited. There are six papers included in this thesis. A majority of these papers focus on addressing computational challenges associated with Bayesian inference and uncertainty quantification, while others focus on developing regularization techniques to improve inverse problem solution quality and accelerate the solution process. These papers demonstrate the applicability of the proposed methods to seismic imaging, a large-scale geophysical inverse problem with a computationally expensive forward operator for which sufficiently capturing the variability in the Earth's heterogeneous subsurface through a training dataset is challenging. The first two papers present computationally feasible methods of applying a class of methods commonly referred to as deep priors to seismic imaging and uncertainty quantification. I also present a systematic Bayesian approach to translate uncertainty in seismic imaging to uncertainty in downstream tasks performed on the image. The next two papers aim to address the reliability concerns surrounding data-driven methods for solving Bayesian inverse problems by leveraging variational inference formulations that offer the benefits of fully-learned posteriors while being directly informed by physics and data. The last two papers are concerned with correcting forward modeling errors where the first proposes an adversarially learned postprocessing step to attenuate numerical dispersion artifacts in wave-equation simulations due to coarse finite-difference discretizations, while the second trains a Fourier neural operator surrogate forward model in order to accelerate the qualification of uncertainty due to errors in the forward model parameterization.Ph.D

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The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

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The importance of seismic wave research lies not only in our ability to understand and predict earthquakes and tsunamis, it also reveals information on the Earth's composition and features in much the same way as it led to the discovery of Mohorovicic's discontinuity. As our theoretical understanding of the physics behind seismic waves has grown, physical and numerical modeling have greatly advanced and now augment applied seismology for better prediction and engineering practices. This has led to some novel applications such as using artificially-induced shocks for exploration of the Earth's subsurface and seismic stimulation for increasing the productivity of oil wells. This book demonstrates the latest techniques and advances in seismic wave analysis from theoretical approach, data acquisition and interpretation, to analyses and numerical simulations, as well as research applications. A review process was conducted in cooperation with sincere support by Drs. Hiroshi Takenaka, Yoshio Murai, Jun Matsushima, and Genti Toyokuni

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Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2017", die vom 21. bis 25.08.2017, in Lausanne stattfand

Microscopy Conference 2017 (MC 2017) - Proceedings

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2017", die vom 21. bis 25.08.2017, in Lausanne stattfand

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Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp