파이프 시스템 내 소음원 위치 추정에 관한 딥러닝 접근법

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2023. 2. 성우제.조선산업에서 선박과 잠수함의 소음원을 규명은 중요한 문제로 대두되고 있다. 주 장비와 같은 다양한 소음원으로부터 발생하는 소음은 선체 외부, 작업장, 그리고 거주구역을 비롯한 선체 대부분에 전파될 수 있다. 게다가, 이러한 노이즈는 작업자들과 거주자들, 그리고 수중 방사 소음에도 여향을 미친다. 특히 잠수함의 경우, 수중 방사 소음은 생존성과 직접적으로 관련되며 소나의 탐지 성능을 저해시키는 원인이다. 도착시간 차이에 의한 기존 소음원 위치 추정방법은 노이즈에 너무 민감하기 때문에 복잡한 시스템에 적용하기는 어렵다. 게다가 이러한 방법들은 두 개 이상의 센서를 사용해서, 파이프 결함이나 누수에 의한 일시적인 소음원이거나 한 가지 파이프 종류에 대해서 한정된 방법이다. 최근 소음원 위치추정에 대한 연구들은 딥러닝 적용이 이러한 문제들을 해결 가능하다는 것을 보여주고있다. 따라서, 우리는 한 개의 센서를 이용해 파이프 시스템에서 소음원의 위치를 추정하는 방법을 제시했다. 전이학습과 미세조정이 제안된 파이프 소음원 위치 추정 방법에 적용되었다. 사전 학습된 컨볼루셔널 신경망으로서, 분류를 위한 VGG 16과 회귀를 위한 ResNet 50이 사용되었다. 우리는 2차원 파이프 위치 추정을 위해 분류를 이용한 방법과 분류 모델이 결합된 회귀를 위한 방법을 제시하였고, 이것은 3차원 파이프에서도 적용이 가능하다. 이것은 또한 복잡하고 좁은 파이프 시스템으로 인해 볼 수 없는 데이터에 대한 해결책도 될것이다. 모델들을 학습시키기 위해, 구조 전달 소음 데이터셋이 4가지 전형적인 조건들에 따라 실험을 통해 얻어졌고, 그 4가지 조건은 다양한 소음원 유형, 파이프 내에 물이 존재하는지 여부, 경계조건, 그리고 파이프의 형상에 대해 다뤘다. 이렇게 모아진 신호들은 딥러닝 모델을 학습시키기 위해 원신호에서 로그-멜 스펙트로그램 이미지로 전처리 되었다. 모델의 성능은 5-겹 교차검증으로 평가되었다. 비록 이 논문은 파이프 소음원 위치 추정에 초점을 맞춰져있지만, 이 제안된 방법들은 다른 산업에서의 파이프 시스템뿐만 아니라 다른 시스템에서의 소음원 위치 추정에도 이용될 수 있다.The identification of noise on a piping systems become a serious issue ships and submarines in the industries of shipbuilding. This noise originating from various sources such as main machinery can be propagated to many parts of a ship including outer hull, workshops, and accomodation areas. In addition, this noise exerts a negative effect on workers, residents and also underwater radiated noise (URN). Especially in submarines, URN is directly related to suvivability and degrades detection performance of sonar (sound navigation and ranging). Conventional source localization methods with time difference of arrival are not applicable in complex systems because it is too noise sensitive. Moreover, these methods are limited to transient noise sources from defects and leak and a single type of pipe by using more than two sensors. Recent studies on source localization have shown that deep learning application have the potential to solve these problems. Therefore, We propose the deep learning approach to noise source localization on a pipe system with a single accelerometer. Transfer learning and fine tuning are applied for the suggested method for noise source localization on pipes. As pre-trained convolutional neural network (CNN) models, VGG 16 for classification and ResNet 50 for regression are used. We suggest both the classification method and the regression method combined with classification model for 2D pipe source localization, which is applicable in 3D pipes. This can also be possible solution for unseen data due to complex and narrow piping systems. For training the models, the structure-borne noise dataset is acquired from the experiments according to four typical conditions which are a variety of source types, with and without the presence of water in the pipe, boundary conditions, and configuration of pipes. The collected signals are pre-processed from raw signals to Log-Mel spectrogram images so as to train the deep learning model. The performance of the model are evaluated by 5-folds cross validation. Though this thesis is focused on the pipe noise source localization, the proposed methods can be employed for source localization methods in other systems as well as in the pipe systems of other industries.Chapter 1. Introduction 1 1.1 Backgrounds 1 1.2 Purpose of research 2 1.3 Related works 4 1.3.1 Source localization methods on a piping system 4 1.3.2 Source localization methods using deep learning 6 1.4 Approach 7 1.5 Organization of the thesis 9 Chapter 2. Experiemnt and data processing 10 2.1 Experiments 10 2.1.1 Features of noise on a piping system 10 2.1.2. Purpose of experiment 11 2.1.3 Configuration of experiment 12 2.1.4 Data acquisition 14 2.2 Data processing 16 Chapter 3. Source localization on a piping system 20 3.1 Convolutional neural network (CNN) 20 3.2 CNN classification and regression for source localization on a piping system 24 3.3 CNN model training 26 3.4 Source localization results 29 Chapter 4. Conclusions 34 4.1 Summary 34 4.2 Applications 35 4.3 Future study 36 Appendix 38 A. TDOA based source localization method 38 Reference 40 Abstract in Korean 44석

A Study on Flame Propagation Through a Mixture of H<sub>2</sub>/Air and Inert Particles with Radiation Effect

The characteristics of flame propagation in inert particle-laden H2/Air premixed gas are numerically investigated on this study. The 2nd order TVD scheme is applied to numerical analysis of governing equations and multi-step chemical reaction model and detailed transport properties are sued to solve chemical reaction terms. Radiation heat transfer is computed by applying the finite volume method to a radiative transfer equation. The burning velocities against the mole fractions of hydrogen agree well with results performed by different workers. The inert particles play significant roles in the flame propagation on account of momentum and heat transfer between gas and particles. Gas temperature, pressure and flame propagation speed are decreased as the loading ratio of particle is increased. Also the products behind flame zone contain lots of water vapor whose absorption coefficient is much larger than that of unburned gas. Thus, the radiation effect of gas and particles must be considered simultaneously for the flame propagation in a mixture of H2/Air and inert particles. As a result, it is founded that because the water vapor emits much radiation and this emitted radiation is released at boundaries as radiant heat loss as well as reabsorbed by gas and particles, flame propagation speed and flame structure are altered with radiation effect

A study on flame propagation through a mixture of Hydrogen/air and inert particles with radiation effect

학위논문(석사) - 한국과학기술원 : 항공우주공학과, 1999.2, [ ix, 66 p. ]한국과학기술원 : 항공우주공학과

비디오 스트리밍에서 동적인 적응변환을 위한 실시간 스케일러블 비디오 코딩 비트스트림 추출

학위논문(석사) - 한국정보통신대학교 : 공학부, 2006, [ viii, 70 p. ]The video streaming service is widely used thanks to the development of network environment. The scalable video coding is the coding scheme that supports the spatial, temporal, and quality scalability under being standardization by JVT. As using SVC bitstream, it is possible to serve dynamically adapted video streaming by extracting SVC bitstream with changed scalability according to current network condition. It is very important to find how the extractor extracts the necessary data from the SVC bitstream extracts in real-time. In this paper, we propose the real-time SVC bitstream extractor for dynamic adaptation in video streaming under dynamically changing network environment. First, we check the constraints for proposing extractor. And then, we propose the structure of real-time SVC bitstream extractor which consists of two parts: bitstream analyzer and bitstream extractor. And we propose the algorithm for extraction process of proposed extractor. Lastly, we propose the extraction methods of the bitstream with varied scalability for spatial, temporal, and quality scalability. We implemented the proposed extractor by using JSVM 2.0. To check validity of the proposed extraction method and extractor, we test the performance of proposed extractor by comparing the required bitrate with bitrate of extracted bitstream. From the experiments, we could verify that the proposed real-time SVC bitstream extractor extracts the bitstream with changed scalability according to varying the input parameters of the proposed extractor한국정보통신대학교 : 공학부