Capillary Effects on Fluid Transport in Granular Media

Fluid transport phenomena in granular media are of great importance due to various natural and industrial applications, including CO2 sequestration, enhanced oil recovery, remediation of contamination, and water infiltration into soil. Although numerous studies exist in the literature with aims to understand how fluid properties and flow conditions impact the transport process, some key mechanisms at microscale are often not considered due to simplifications of physical phenomenon and geometry, limited computational resources, or limited temporal/spatial resolution of existing imaging techniques. In this Thesis, we investigate fluid transport phenomena in granular media with a focus on the capillary effects. We move from relatively simple scenario on patterned surfaces to more complex granular media, tackling a variety of liquid-transport related problems that all have extensive industrial applications. The bulk of this Thesis is composed of six published papers. Each chapter is prefaced by an introductory section presenting the motivation for the corresponding paper and its context within the greater body of work. This Thesis reveals the impact of some previously neglected physical phenomena at microscale on the fluid transport in granular materials, providing new insights and methodology for describing and modelling fluid transport process in porous media

Theory and Practice Integrated Research on Engineering Project Management Course Based on Multi-Role-Playing Method

The goal of cultivating applied talents in colleges and universities is that students not only need to have sufficient theoretical knowledge and professional qualities, but also be able to comprehensively use professional skills to solve practical problems and create economic and social benefits for society. Therefore, in view of the current disconnect between theory and practice in construction engineering education. We should promote innovation in teaching systems and reform of internal operating mechanisms of courses. In the engineering project management course, we designed a real engineering project background, where students play the roles of seven types of participating parties and manage them independently to complete the main engineering activities from the project decision-making stage to the implementation stage. The result is that we have fundamentally subverted the previous pure case-based teaching with unclear goals

CASA: Category-agnostic Skeletal Animal Reconstruction

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge. Prevailing animal reconstruction methods often adopt a control-point driven animation model and optimize bone transforms individually without considering skeletal topology, yielding unsatisfactory shape and articulation. In contrast, humans can easily infer the articulation structure of an unknown animal by associating it with a seen articulated character in their memory. Inspired by this fact, we present CASA, a novel Category-Agnostic Skeletal Animal reconstruction method consisting of two major components: a video-to-shape retrieval process and a neural inverse graphics framework. During inference, CASA first retrieves an articulated shape from a 3D character assets bank so that the input video scores highly with the rendered image, according to a pretrained language-vision model. CASA then integrates the retrieved character into an inverse graphics framework and jointly infers the shape deformation, skeleton structure, and skinning weights through optimization. Experiments validate the efficacy of CASA regarding shape reconstruction and articulation. We further demonstrate that the resulting skeletal-animated characters can be used for re-animation.Comment: Accepted to NeurIPS 202

Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.Comment: Paper appears in Bioengineering 2023, 10(7), 86

A survey of speech enhancement algorithms

speech is easy to be interfered by the external environment in real applications, resulting in the reduction of speech intelligibility and signal-to-noise ratio. In the past few decades, due to the wide application of speech based solutions in practical applications, speech enhancement of noisy speech signals has aroused considerable research interest. This paper classifi es and introduces several main speech enhancement methods, summarizes the advantages and disadvantages of several main methods, and fi nally puts forward the next research direction of speech enhancement methods

Monitoring and Research on urban impervious surface rainfall runoff pollution

Urban impervious surface rainfall runoff pollution is an important part of non-point source pollution, and the pollutants accumulated on urban impervious surface in non rainy days are the main source of pollutants in rainfall runoff. Taking the first 10 rainfall events in 2015 as an example, the impervious surfaces such as the roof of teaching buildings, campus roads and adjacent main traffic roads within the university campus in southeast Beijing were selected as the research objects to conduct field sampling and Analysis on the natural rainfall and the rainfall runoff pollution. The results show that the first rainfall runoff pollution after winter is serious, and the water quality is inferior to class v. After that, the rainfall runoff pollution is reduced; the severity of water pollution is different at different sampling points; the closer to the building toilet exhaust outlet, the higher the ammonia nitrogen pollution concentration; the existence of pervious surface facilities can reduce the degree of runoff pollution. According to the analysis and research results, some suggestions for controlling and harnessing urban rainfall runoff pollution are put forward

Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

Surrogate-assisted evolutionary algorithms have been widely developed to solve complex and computationally expensive multi-objective optimization problems in recent years. However, when dealing with high-dimensional optimization problems, the performance of these surrogate-assisted multi-objective evolutionary algorithms deteriorate drastically. In this work, a novel Classifier-assisted rank-based learning and Local Model based multi-objective Evolutionary Algorithm (CLMEA) is proposed for high-dimensional expensive multi-objective optimization problems. The proposed algorithm consists of three parts: classifier-assisted rank-based learning, hypervolume-based non-dominated search, and local search in the relatively sparse objective space. Specifically, a probabilistic neural network is built as classifier to divide the offspring into a number of ranks. The offspring in different ranks uses rank-based learning strategy to generate more promising and informative candidates for real function evaluations. Then, radial basis function networks are built as surrogates to approximate the objective functions. After searching non-dominated solutions assisted by the surrogate model, the candidates with higher hypervolume improvement are selected for real evaluations. Subsequently, in order to maintain the diversity of solutions, the most uncertain sample point from the non-dominated solutions measured by the crowding distance is selected as the guided parent to further infill in the uncertain region of the front. The experimental results of benchmark problems and a real-world application on geothermal reservoir heat extraction optimization demonstrate that the proposed algorithm shows superior performance compared with the state-of-the-art surrogate-assisted multi-objective evolutionary algorithms. The source code for this work is available at https://github.com/JellyChen7/CLMEA