Novel system of pavement cracking detection algorithms using 1mm 3D surface data

Pavement cracking is one of the major concerns for pavement design and management. There have been rapid developments of automated pavement cracking detection in recent years. However, none of them has been widely accepted so far due to lack of capability of maintaining consistently high detection accuracy for various pavement surfaces. Using 1mm 3D data collected by WayLink Digital Highway Data Vehicle (DHDV), an entire system of algorithms, which consists of Fully Automated Cracking Detection Subsystem, Interactive Cracking Detection Subsystem and Noisy Pattern Detection Subsystem, is proposed in this study for improvements in adaptability, reliability and interactivity of pavement cracking detection.The Fully Automated Cracking Detection Subsystem utilizes 3D Shadow Simulation to find lower areas in local neighborhood, and then eliminates noises by subsequent noise suppressing procedures. The assumption behind 3D Shadow Simulation is that local lower areas will be shadowed under light with a certain projection angle. According to the Precision-Recall Analysis on two real pavement segments, the fully automated subsystem can achieve a high level of Precision and Recall on both pavement segments.The Interactive Cracking Detection Subsystem implements an interactive algorithm proposed in this study, which is capable of improving its detection accuracy by adjustments based on the operator's feedback, to provide a slower but more flexible as well as confident approach to pavement cracking detection. It is demonstrated in the case study that the interactive subsystem can retrieve almost 100 percent of cracks with nearly no noises.The Noisy Pattern Detection Subsystem is proposed to exclude pavement joints and grooves from cracking detection so that false-positive errors on rigid pavements can be reduced significantly. This subsystem applies Support Vector Machines (SVM) to train the classifiers for the recognition of transverse groove, transverse joint, longitudinal groove and longitudinal joint respectively. Based on the trained classifiers, pattern extraction procedures are developed to find the exact locations of pavement joints and grooves.Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is one of multi objective genetic algorithms, is employed in this study to optimize parameters of the fully automated subsystem for the pursuing of high Precision and high Recall simultaneously. In addition to NSGA-II, an Auxiliary Prediction Model (APM) is proposed in this study to assist NSGA-II for faster convergence and better diversity.Finally, CPU-based and GPU-based Parallel Computing Techniques, including MultiGPU, GPU streaming, Multi-Core and Multi-Threading are combined in this study to increase the processing speed for all computational tasks that can be synchronous

Composing Deep Learning and Bayesian Nonparametric Methods

Recent progress in Bayesian methods largely focus on non-conjugate models featured with extensive use of black-box functions: continuous functions implemented with neural networks. Using deep neural networks, Bayesian models can reasonably fit big data while at the same time capturing model uncertainty. This thesis targets at a more challenging problem: how do we model general random objects, including discrete ones, using random functions? Our conclusion is: many (discrete) random objects are in nature a composition of Poisson processes and random functions}. Thus, all discreteness is handled through the Poisson process while random functions captures the rest complexities of the object. Thus the title: composing deep learning and Bayesian nonparametric methods. This conclusion is not a conjecture. In spacial cases such as latent feature models , we can prove this claim by working on infinite dimensional spaces, and that is how Bayesian nonparametric kicks in. Moreover, we will assume some regularity assumptions on random objects such as exchangeability. Then the representations will show up magically using representation theorems. We will see this two times throughout this thesis. One may ask: when a random object is too simple, such as a non-negative random vector in the case of latent feature models, how can we exploit exchangeability? The answer is to aggregate infinite random objects and map them altogether onto an infinite dimensional space. And then assume exchangeability on the infinite dimensional space. We demonstrate two examples of latent feature models by (1) concatenating them as an infinite sequence (Section 2,3) and (2) stacking them as a 2d array (Section 4). Besides, we will see that Bayesian nonparametric methods are useful to model discrete patterns in time series data. We will showcase two examples: (1) using variance Gamma processes to model change points (Section 5), and (2) using Chinese restaurant processes to model speech with switching speakers (Section 6). We also aware that the inference problem can be non-trivial in popular Bayesian nonparametric models. In Section 7, we find a novel solution of online inference for the popular HDP-HMM model

Experimental test of contextuality in quantum and classical systems

Contextuality is considered as an intrinsic signature of non-classicality, and a crucial resource for achieving unique advantages of quantum information processing. However, recently there have been debates on whether classical fields may also demonstrate contextuality. Here we experimentally configure a contextuality test for optical fields, adopting various definitions of measurement events, and analyse how the definitions affect the emergence of non-classical correlations. The heralded single photon state, a typical non-classical light field, manifests contextuality in our setup, while contextuality for classical coherent fields strongly depends on the specific definition of measurement events which is equivalent to filtering the non-classical component of the input state. Our results highlight the importance of definition of measurement events to demonstrate contextuality, and link the contextual correlations to non-classicality defined by quasi-probabilities in phase space.Comment: 17 pages, 7 figure

NVDiff: Graph Generation through the Diffusion of Node Vectors

Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures. Recently, several works have proposed graph generative models based on normalizing flows or score-based diffusion models. However, these models need to generate nodes and edges in parallel from the same process, whose dimensionality is unnecessarily high. We propose NVDiff, which takes the VGAE structure and uses a score-based generative model (SGM) as a flexible prior to sample node vectors. By modeling only node vectors in the latent space, NVDiff significantly reduces the dimension of the diffusion process and thus improves sampling speed. Built on the NVDiff framework, we introduce an attention-based score network capable of capturing both local and global contexts of graphs. Experiments indicate that NVDiff significantly reduces computations and can model much larger graphs than competing methods. At the same time, it achieves superior or competitive performances over various datasets compared to previous methods

Effects of 4A Zeolite Additions on the Structure and Performance of LDPE Blend Microfiltration Membrane through Thermally Induced Phase Separation Method

Microfiltration membranes, 4A zeolite/LDPE, were prepared by blending low density polyethylene (LDPE) and4A zeolite through thermally induced phase separation (TIPS) process with diphenyl ether (DPE) as diluent. The effects of 4A zeolite loading on the pore structure and water permeation performance of the 4A zeolite/LDPE blend membranes were investigated. The incorporation of 4A zeolite particles greatly enhanced the connectivity of membrane pores, the pore size, and thus the water flux of 4A zeolite/LDPE blend membranes due to the gradually stronger DPE-zeolite affinity with the increase of the 4A zeolite loading. The water flux increased from 0 of LDPE control membrane to 87 L/m2h of 4A zeolite/LDPE blend membrane with 4A zeolite loading of 10 wt%. In addition, increasing the DPE content and cooling bath temperature is in favor of the water flux of 4A zeolite/LDPE blend membranes