Doctor of Philosophy

dissertationIn this dissertation research, Molecular Dynamics Simulation (MDS), Sum Frequency Vibrational Spectroscopy (SFVS), and contact angle measurement have been used to investigate the wettability and interfacial water structure at selected mineral surfaces. The primary objective is to provide fundamental understanding of the hydrophobic surface state, a state of special interest in particle separations by froth flotation. First, MDS interfacial water features, including water number density profile, water residence time, water dipole orientation, and hydrogen bonding analysis, at selected hydrophobic mineral surfaces (graphite (001) surface and octadecyltrichlorosilane (OTS) monolayer on quartz) and at selected hydrophilic mineral surfaces (quartz (001), sapphire (001), and gibssite (001) surfaces) have been evaluated and compared to the corresponding SFVS experimental results. A "water exclusion zone" of 3 A accounts for the "free OH" vibration (from both MDS water dipole orientation analysis and SFVS spectrum) at hydrophobic surfaces. In addition, a water residence time of less than 10 ps and about 2 hydrogen bonds have been found for surface water molecules at the selected hydrophobic mineral surfaces. Sessile drop wetting characteristics of the hydrophobic molybdenite (001) surface and the hydrophilic quartz (001) surface have been examined by MDS and by contact angle experiments to determine the effect of drop size, advancing/receding contact angles, and spreading time on wettability. In addition, film stability and bubble attachment at the hydrophobic molybdenite (001) surface and the hydrophilic quartz (001) surface have been studied by MDS for the first time and the results compared with corresponding experimental captive bubble contact angles. At the hydrophobic molybdenite (001) surface, the water film is unstable and ruptures, while the water film at the hydrophilic quartz (001) surface does not. Finally, the wettability and interfacial water features of sulfide/telluride mineral surfaces have been described with MDS for the first time. The interfacial water features of selected sulfide/telluride mineral surfaces under anaerobic conditions have been 2 + examined, as well as Cu activated sphalerite (110) and oxidized pyrite (100) surfaces, to determine which interfacial water features best identify the wetting characteristics of the selected mineral surfaces. In summary, it has been found that "water exclusion zone" and "free OH" vibration present for hydrophobic mineral surfaces, whereas, for hydrophilic mineral surfaces, the interfacial water is characterized by hydrogen bonding with the surface and relatively long water residence time. The interfacial water analysis of the selected mineral surfaces increases our fundamental understanding of the flotation chemistry associated with the mineral systems and is expected to provide a foundation for improved flotation technology in the future

Effect of surface oxidation on interfacial water structure at the pyrite (100) surface as studied by MDS

journal articleFlotation provides a number of alternatives for the processing of gold ores containing sulfide minerals. e.g. flotation of free gold and gold-bearing sulfides to produce a gold-rich concentrate for regrinding, oxidative pretreatment and cyanidation

HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks

Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to transform recorded speech to sound as though it had been recorded in a studio. We use an end-to-end feed-forward WaveNet architecture, trained with multi-scale adversarial discriminators in both the time domain and the time-frequency domain. It relies on the deep feature matching losses of the discriminators to improve the perceptual quality of enhanced speech. The proposed model generalizes well to new speakers, new speech content, and new environments. It significantly outperforms state-of-the-art baseline methods in both objective and subjective experiments.Comment: Accepted by INTERSPEECH 202

"Taking down the monitoring": Privacy protection for social media user in the era of big data

In the era of big data, the development of technology has promoted social progress and given rise to various types of social media. The popularity of social media has made it an essential platform for information sharing and communication. However, the monitoring, collecting, and using of personal information on social media platforms have exposed users' privacy to risks. While technology has greatly facilitated people's lives, it has also brought about many ethical challenges to privacy. A balance is needed between the development of information technology and the protection of personal privacy. Through in-depth interviews with nine social media users, this paper discusses three situations where user privacy is compromised: user profiling, precision marketing and fraud. It also gives some strategies for protecting privacy based on the need for three different subjects to work together: individuals, platforms and governments

Domain Adaptation based Enhanced Detection for Autonomous Driving in Foggy and Rainy Weather

Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, however such assumption may fail in different weather conditions. Due to the domain gap, a detection model trained under clear weather may not perform well in foggy and rainy conditions. Overcoming detection bottlenecks in foggy and rainy weather is a real challenge for autonomous vehicles deployed in the wild. To bridge the domain gap and improve the performance of object detectionin foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection. The adaptations at both the image-level and object-level are intended to minimize the differences in image style and object appearance between domains. Furthermore, in order to improve the model's performance on challenging examples, we introduce a novel adversarial gradient reversal layer that conducts adversarial mining on difficult instances in addition to domain adaptation. Additionally, we suggest generating an auxiliary domain through data augmentation to enforce a new domain-level metric regularization. Experimental findings on public V2V benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios.Comment: only change the title of this pape

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

Most object detection methods for autonomous driving usually assume a consistent feature distribution between training and testing data, which is not always the case when weathers differ significantly. The object detection model trained under clear weather might not be effective enough in foggy weather because of the domain gap. This paper proposes a novel domain adaptive object detection framework for autonomous driving under foggy weather. Our method leverages both image-level and object-level adaptation to diminish the domain discrepancy in image style and object appearance. To further enhance the model's capabilities under challenging samples, we also come up with a new adversarial gradient reversal layer to perform adversarial mining for the hard examples together with domain adaptation. Moreover, we propose to generate an auxiliary domain by data augmentation to enforce a new domain-level metric regularization. Experimental results on public benchmarks show the effectiveness and accuracy of the proposed method. The code is available at https://github.com/jinlong17/DA-Detect.Comment: Accepted by WACV2023. Code is available at https://github.com/jinlong17/DA-Detec

Deep Industrial Image Anomaly Detection: A Survey

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection