Image-based Relighting Using Implicit Neural Representation

Rendering a scene under novel lighting has been a problem in all fields that require computer graphics knowledge, and Image-based relighting is one of the best ways to reconstruct the scene correctly. Current research on Image-based relighting uses discrete convolutional neural networks, which tend to be less fit-able to different spatial resolutions and take up massive memory spaces. However, the implicit neural representation solves the problem by mapping the coordinates of the image directly to the value of the coordinate with a continuous function modeled through the neural network. In this way, despite the changing of the image resolution, the parameters taken in by the neural network stay the same, so the complexity stays the same. Also, the rectified linear activation unit (ReLU) based network used in current research lacks the representation of information of second and higher derivatives. On the other hand, the sinusoidal representation networks (SIREN) provide a new way to solve this problem by using periodic activation functions like the sin curve. Hence, my research intends to leverage implicit neural representation with periodic activation functions in image-based relighting. To tackle the research question, we proposed to base our image-relighting network on the SIREN network in the research by Sitzmann. Our method is to modify the SIREN network so that it takes in not only coordinates but also light positions. Then we train it with a set of input images depicting the same set of sparse objects in different lighting conditions and their corresponding light positions, as in previous image-based relighting research. We test our network by giving the network new lighting positions, and the result we aim for is to acquire a good representation of optimal sparse samples under novel lighting with high-frequency details. Eventually, we run the training and test with several different input sets and acquire their results. We also compare and evaluate the results, in order to find the advantage or limitation of the method

3D LiDAR 포인트 클라우드에서 데이터 변환을 통한 정확하고 빠른 사람 감지

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. U Kang.Given an object and its 3D LiDAR point cloud, how can we detect human accurately? Human detection from 3D LiDAR points is an important task in autonomous systems. The shape of a point cloud of an object varies at different distances due to the issues of the vertical and horizontal resolutions of 3D LiDAR sensors. The sparse density of 3D points far from 3D LiDAR sensors directly results in inferior quality of features, and affects the detection performance. In this thesis, we propose ObjectZoom, an accurate and fast human detection method for a 3D LiDAR point cloud. ObjectZoom improves the accuracy of the detection task by carefully transforming the given point cloud considering the characteristics of the 3D LiDAR sensor for a better feature extraction of point clouds. Our extensive experiments on four real world datasets show that ObjectZoom provides the state-of-the-art accuracy and a fast running time in the human detection task.물체와 그의 3D 라이다 포인트 클라우드가 주어지면 어떻게 사람을 정확하게 감 지할 수 있을까? 3D 라이다 포인트들에서 사람을 감지하는 것은 자율 시스템에서 중요한 작업이다. 3D 라이다 센서의 수직 및 수평 해상도 문제로 인해 물체의 모양이 거리에 따라 다른다. 3D 라이다 센서에서 멀리 떨어진 3D 포인트의 희박한 밀도는 직접적으로 피쳐들의 품질을 저하시키고 감지 성능에 영향을 미친다.을 이 논문에서는 3D LiDAR 포인트 클라우드를 위한 정확하고 빠른 사람 감지 방 법 인 ObjectZoom 을 제안한다. ObjectZoom 은 더 나은 피쳐 추출을 위해 3D LiDAR 센서의 특성을 고려하여 주어진 포인트 클라우드를 신중하게 변환하여 작업의 정확 성을 향상시킨다. 4 가지 실제 데이터 세트에 대한 광범위한 실험은 ObjectZoom 이 사람 감지 작업에서 최첨단의 정확도와 빠른 실행 시간을 제공한다는 것을 보여준 다.I. Introduction 1 II. Background and Related Works 4 III. Proposed Method 8 3.1 Overview 8 3.2 Grouping 3D LiDAR Points by Channels 9 3.3 Determination of Standard Distance 10 3.4 Mapping Heights to Standard Distance 11 3.5 Augmentation 12 3.5.1 Vertical Augmentation 13 3.5.2 Horizontal Augmentation 15 3.6 ObjectZoom-HV 16 3.7 Complexity of ObjectZoom 18 IV. Experiments 19 4.1 Settings 19 4.1.1 Dataset 19 4.1.2 Competitors 20 4.1.3 Features 20 4.1.4 Training 21 4.1.5 Evaluation 22 4.2 Accuracy 22 4.3 Running Time 25 V. Conclusion 26 References 27 Abstract in Korean 29Maste

Longwall mining-induced fracture characterisation based on seismic monitoring

Despite several technological advancements, mining-induced fractures are still critical for the safety of underground coal mines. Rocking fracturing as a natural response to mining activities can pose a potential hazard to mine operators, equipment, and infrastructures. The fractures occur not only around the working face that can be visually measured but also above and in front of the working face and where geological structures are affected by mining activities. Therefore, it is of importance to detect and investigate the properties of mining-induced fractures. Mining-induced seismicity has been generated due to rock fracturing during progressive mining activities and can provide critical fracture information. Currently, the application of using seismic monitoring to characterise fractures has remained relatively challenged in mining because mining-induced fractures are initiated by stress change and strata movement after mineral extraction. Compared to seismic monitoring in the oil and gas industry, the fractures and seismic responses may show different characteristics. Therefore, seismic monitoring in mines lacks a comprehensive investigation of received seismic signals to the properties of induced fractures and the effect on mine workings by these fractures. Additionally, constraints such as the quality of seismic signals and the deficiency of correlation analysis of seismic events in underground mining pose great challenges in using seismic data for hazard prediction. This thesis aims to address these challenges in using seismic monitoring to understand and characterise mining-induced fractures by (1) calculating fracture properties related to seismic source location, magnitude and mechanism based on uniaxial seismic data, (2) spatial and temporal correlation analysis of seismic events, and (3) inspecting fracture distributions and simulation of the fractured zone in longwall coal mines. Firstly, since cheap and easily removable uniaxial geophones close to production areas are preferable in coal mines, a novel method to use uniaxial signal and moment tensor inversion to generate synthetic triaxial waves is designed for a comprehensive description of the fracture properties, including location, radius, aperture and orientation. Secondly, to apply seismic data for advanced analysis, such as rockburst prediction and caving assessment, the correlation of seismic events is proved to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process. The spatial and temporal correlation of seismic event energy is quantitatively analysed using various statistical methods, including autocorrelation function (ACF), semivariogram and Moran's I analysis. In addition, based on the integrated spatial-temporal (ST) correlation assessment, seismic events are further classified into seven clusters to assess the correlations within individual clusters. Finally, several source parameters such as seismic moment (M0), seismic source radius (R), fracture aperture (τ), failure type and fracture orientation were used to characterise fractures induced by longwall mining. This thesis also presents the fracture patterns induced caused progressive longwall mining for the first time. Besides, a discrete element method (DEM) model with seismic-derived fractures is generated and proves the impact of mining-induced fractures on altering stress conditions during mineral extraction. In addition, with the analysis of the seismic source mechanism and a synthetic triaxial method, a discrete fracture network (DFN) is generated from monitored seismic events to restore complete induced fractures. Overall, the outcomes of this study lead to a comprehensive assessment of mining-induced fracture properties based on real-time seismic monitoring, demonstrating its significant potential for hazard prediction and improving the safety of resource recovery

Solitons in One-Dimensional Bose Einstein Condensate with Higher-Order Interactions

National Natural Science Foundation of China [11791240178, 11674338, 11547024]We model a one-dimensional Bose-Einstein condensate with the one-dimensional Gross-Pitaevskii equation (1D GPE) incorporating higher-order interaction effects. Based on the F-expansion method, we analytically solve the 1D GPE, identifying the typical soliton solution under certain experimental settings within the general wave-like solution set, and demonstrating the applicability of the theoretical treatment that is employed

Current advances in capillarity: Theories and applications

As common physical phenomena in porous media, capillarity behaviors exist in many engineering applications and natural science fields. The experimental, theoretical and numerical research on capillarity in porous media has lasted for more than a century, and the research results have been widely used in various fields, such as the development of conventional and unconventional resources. However, although the research has made great progress, the complex imbibition mechanism poses new challenges to us. The 1st National Conference on Imbibition Theory and Application in Porous Media was held in Beijing from April 22 to 24, 2023, to gather  researchers who are interested in imbibition research, exchange the latest progress and achievements in the field of imbibition in porous media, and discuss research hotspots and difficulties.Cited as: Cai, J., Sun, S., Wang, H. Current advances in capillarity: Theories and applications. Capillarity, 2023, 7(2): 25-31. https://doi.org/10.46690/capi.2023.05.0

Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing

Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance