그래프 신경망의 스펙트럴 해석과 그 응용

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 수리과학부, 2023. 8. 강명주.In this dissertation, we present a theoretical analysis of spectral-based graph neural networks and their practical performance. We analyze how the spectra of a graph Laplacian relates to the convolution operation of a graph neural network, and we discuss how expressive a graph convolutional model can be and how competent expressiveness can be achieved by implementing various convolutions on a graph based on this spectra. The results show that spectral-based graph neural networks can perform well on graph-based tasks, and we discuss what improvements can be made in the future to improve their performance in practice. As an extension, we apply it to traditional computer vision tasks in addition to graph-based tasks and show that it is comparably expressive. In addition, we present several results of its applications utilizing graphs. Specifically, we conducted experiments on the task of salient object detection using directed acyclic graphs. We also show experimental results of applying the simple model based on the theory of Fourier analysis to practical applications such as the rain removal task. These experiments empirically demonstrate that incorporating the knowledge of graph theory and Fourier analysis into the model helps improve performance.본 눈문에서는 스펙트럼 기반 그래프 인공신경망의 이론적 분석과 그 실용적 성능에 대해 다룬다. 그래프 신경망에서의 컨볼루션 연산과 라플라시안 그래프의 스펙트럼 간의 관계를 조사하고, 어떤 가정들 하에서 그래프 컨볼루션이 정의될 수 있으며, 그러한 가정 아래에서 어떤 방식으로 그래프 컨볼루션을 정확히 표현할 수 있는 지에 대해 분석하였다. 이러한 분석을 기반으로 다양한 그래프 컨볼루션을 실험하여 모델의 표현력과 성능을 논의하였다. 결과적으로, 스펙트럼 기반 그래프 신경망이 그래프 기반 작업에서 우수한 성능을 보여줌을 확인하며, 실제 성능을 향상시키기 위한 개선 가능한 부분에 대해 논의한다. 더불어, 이론과 적용 영역을 확장하여 그래프 기반 작업뿐만 아니라 전통적인 컴퓨터 비전 작업 등에도 적용할 수 있음을 보여주어 이러한 방식의 확장성을 보여주었다. 마지막으로 이 논문에서는 그래프를 활용한 몇 가지 응용 사례와 결과를 제시하였다. 구체적인 실험으로 방향성 비순환 그래프(DAG)를 이용한 두드러진 물체 검출 작업에 대한 실험을 수행하였다. 이외에도 푸리에 변환을 활용한 모듈을 활용하여 비를 제거하는 태스크 같은 실용적 분야에 적용한 모델과 실험 결과들을 살펴본다. 이러한 실험들을 통해, 그래프 이론과 푸리에 분석 지식과 같은 수학적 지식을 모델에 통합하고 이를 분석하는 것이 성능 향상에 유용함을 실증적으로 보여주었다.Abstract 1 Introduction 1 2 Preliminaries 4 2.1 Graph Neural Networks 4 2.1.1 Mathematical Terminologies 4 2.1.2 Graph Message Passing 5 2.1.3 Spatial-based Graph Neural Networks 6 2.1.4 Spectral-based Graph Neural Networks 8 2.2 Collaborative Filtering 8 2.3 Directed Acyclic Graphs Learning 10 3 Related Works 12 3.1 Spectral-based Graph Neural Networks 12 3.1.1 Spectral Network 12 3.1.2 ChebNet 12 3.1.3 Graph Convolutional Networks 13 3.2 Collaborative Filtering 13 3.3 Salient Object Detection 15 3.4 Rain Removal Tasks 17 4 Spectral Analysis of Graph Neural Networks 20 4.1 Schwartz space S (Rd) and Ring graph Rn 20 4.2 Convolution on General Graphs 25 5 Proposed Method 30 5.1 Proposal Background 30 5.2 Spectral GNNs to Computational Fluid Dynamics 31 5.3 Collaborative Filtering 33 5.4 Salient Object Detection 34 5.5 Rain Removal Task 36 6 Experiments 39 6.1 Spectral GNNs to Computational Fluid Dynamics 39 6.1.1 Datasets 39 6.1.2 Experimental Results 40 6.2 Collaborative Filtering 45 6.2.1 Datasets 45 6.2.2 Evaluation Metric 46 6.2.3 Bayesian Personalized Ranking 47 6.2.4 Experimental Results 49 6.3 Salient Object Detection 50 6.3.1 Datasets 50 6.3.2 Evaluation metrics 51 6.3.3 Experimental Results 52 6.4 Rain Removal Task 57 6.4.1 Datasets 57 6.4.2 Experimental Results 57 7 Conclusion 63 References 65 Abstract (in Korean) 73박

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called Pixel- Based Nowcasting (PBN) tracks severe storms with a hierarchical mesh-tracking algorithm to capture storm advection in space and time at high resolution from radar imagers. The extracted advection field is then extended to nowcast the rainfall field in the next 3. hr based on a pixel-based Lagrangian dynamic model. The proposed algorithm is compared with two other nowcasting algorithms (WCN: Watershed-Clustering Nowcasting and PER: PERsistency) for ten thunderstorm events over the conterminous United States. Object-based verification metric and traditional statistics have been used to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm is superior over comparison algorithms and is effective in tracking and predicting severe storm events for the next few hours. © 2012 Elsevier B.V

Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation

Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment, has been a subject of growing research in the past decades. Here, we introduce a unified framework that ties together the problems of downscaling, data fusion and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing proper regularization, which are constraints consistent with the degree of smoothness and probabilistic structure of the underlying state. We review relevant regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on hydrologic and atmospheric applications. Informed by the statistical characteristics of the state variable of interest, the central results of the paper suggest that proper regularization can lead to a more accurate and stable recovery of the true state and hence more skillful forecasts. In particular, using the Tikhonov and Huber regularization in the derivative space, the promise of the proposed framework is demonstrated in static downscaling and fusion of synthetic multi-sensor precipitation data, while a data assimilation numerical experiment is presented using the heat equation in a variational setting

Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Coherent, super resolved radar beamforming using self-supervised learning

High resolution automotive radar sensors are required in order to meet the high bar of autonomous vehicles needs and regulations. However, current radar systems are limited in their angular resolution causing a technological gap. An industry and academic trend to improve angular resolution by increasing the number of physical channels, also increases system complexity, requires sensitive calibration processes, lowers robustness to hardware malfunctions and drives higher costs. We offer an alternative approach, named Radar signal Reconstruction using Self Supervision (R2-S2), which significantly improves the angular resolution of a given radar array without increasing the number of physical channels. R2-S2 is a family of algorithms which use a Deep Neural Network (DNN) with complex range-Doppler radar data as input and trained in a self-supervised method using a loss function which operates in multiple data representation spaces. Improvement of 4x in angular resolution was demonstrated using a real-world dataset collected in urban and highway environments during clear and rainy weather conditions.Comment: 28 pages 10 figure

Sea surface wind and wave parameter estimation from X-band marine radar images with rain detection and mitigation

In this research, the application of X-band marine radar backscatter images for sea surface wind and wave parameter estimation with rain detection and mitigation is investigated. In the presence of rain, the rain echoes in the radar image blur the wave signatures and negatively affect estimation accuracy. Hence, in order to improve estimation accuracy, it is meaningful to detect the presence of those rain echoes and mitigate their influence on estimation results. Since rain alters radar backscatter intensity distribution, features are extracted from the normalized histogram of each radar image. Then, a support vector machine (SVM)-based rain detection model is proposed to classify radar images obtained between rainless and rainy conditions. The classification accuracy shows significant improvement compared to the existing threshold-based method. By further observing images obtained under rainy conditions, it is found that many of them are only partially contaminated by rain echoes. Therefore, in order to segment between rain-contaminated regions and those that are less or unaffected by rain, two types of methods are developed based on unsupervised learning techniques and convolutional neural network (CNN), respectively. Specifically, for the unsupervised learning-based method, texture features are first extracted from each pixel and then trained using a self organizing map (SOM)-based clustering model, which is able to conduct pixel-based identification of rain-contaminated regions. As for the CNN-based method, a SegNet-based semantic segmentation CNN is �rst designed and then trained using images with manually annotated labels. Both shipborne and shore-based marine radar data are used to train and validate the proposed methods and high classification accuracies of around 90% are obtained. Due to the similarities between how haze affects terrestrial images and how rain affects marine radar images, a type of CNN for image dehazing purposes, i.e., DehazeNet, is applied to rain-contaminated regions in radar images for correcting the in uence of rain, which reduces the estimation error of wind direction significantly. Besides, after extracting histogram and texture features from rain-corrected radar images, a support vector regression (SVR)-based model, which achieves high estimation accuracy, is trained for wind speed estimation. Finally, a convolutional gated recurrent unit (CGRU) network is designed and trained for significant wave height (SWH) estimation. As an end-to-end system, the proposed network is able to generate estimation results directly from radar image sequences by extracting multi-scale spatial and temporal features in radar image sequences automatically. Compared to the classic signal-to-noise (SNR)-based method, the CGRU-based model shows significant improvement in both estimation accuracy (under both rainless and rainy conditions) and computational efficiency

Investigation of Enhanced Performance in Flexible Solar Cells Using Passive Cooling Technique

The lack of flexibility and enormous weight in conventional photovoltaic (PV) modules limits their applications. The advantages of flexibility and lightweight have made flexible solar cells popular in various applications. However, flexible PVs have an efficiency degradation due to an increase in module temperature through incoming solar infrared radiation. Both the power output and the electrical efficiency of the PV module depend linearly on the operating temperature. For every degree increase in the PV temperature, the efficiency decreases by 0.45-0.65%. Here, the novel concept of applying a nanomaterial-based heat-resistant coating for the passive cooling of flexible solar cells was experimentally investigated. A heat-resistant coating generally keeps buildings cooled by filtering UV and infrared rays and transmitting visible rays. This approach works by controlling the incoming solar radiation, thereby decreasing the overall temperature of flexible solar cells passively without adding much weight. Here, a transparent flexible polyacrylic sheet 0.25 mm in thickness was used, and two coats of silver nanomaterial-based coating were applied. The sheet was placed over a flexible solar photovoltaic module with a power rating of 6 watts. The temperature of the flexible solar photovoltaic module was recorded at different time intervals for August, September, and October using temperature sensors, taking note of factors such as wind speed and solar irradiation. These readings were compared with those taken from the solar panel without any coating. A temperature reduction of 6-7°C and an improved solar power efficiency of 2.5-4 % were observed for cooled flexible solar panels