UV Curing and Micromolding of Polymer Coatings

University of Minnesota Ph.D. dissertation.August 2018. Major: Material Science and Engineering. Advisors: Lorraine Francis, Alon McCormick. 1 computer file (PDF); xii, 143 pages.UV curable films and coatings have a wide range of applications in everyday life and various industrial sectors. With surface microstructures, patterned coatings can be used as a general way to provide surface textures or serve critical design purposes, such as providing engineered optical performances and altering the surface hydrophobicity. This thesis addresses key challenges in the UV curing process and their patterning applications, aiming to make UV curing faster, better, and cheaper. Firstly, the UV curing speed is the bottleneck of the process throughput for many applications. Traditionally, high-intensity light sources are used to achieve fast cure but bring with potential problems, e.g. significant heat accumulation. In this thesis, the intense pulsed light was investigated as an alternative curing method, where the UV energy is delivered in discrete pulses with a dark period between pulses. A systematic study was performed on a model acrylate system to understand the curing conversion as a function of various processing parameter, including illumination conditions, the photoinitiator concentration, and the curing temperature. It was revealed that sufficient curing of acrylates was achieved within seconds without significant heat built-up in the substrate. Second, this thesis investigates the fabrication of surface microstructures with UV curable materials. The UV micromolding process is used for pattern replication, where a liquid coating is brought into contact with a patterned mold and then UV cured to obtain surface microstructures. However, the wide application of this process is limited the stringent material requirements of the process: low viscosities, fast cure, low surface energy, and tunable mechanical properties. This thesis describes the design of thiol-ene based coating formulations for the UV micromolding process. The coating system allows for the preparation of microstructured coatings within seconds and significantly expands the achievable mechanical and surface properties of cured materials. Finally, continuous fabrication of microstructured coatings was explored to move the process a step further towards mass production. The roll-to-roll imprinting process with thiol-ene based formulations was discussed. In the process, a roller mold was used for pattern replication on large-area substrates. The coating formulations were optimized for a fast cure and high curing extents. In addition, the influences of processing variables on the curing extents were studied systematically

Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction

Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. In neuroimaging, DL methods can reconstruct high-quality images from undersampled data. However, it is essential to consider fairness in DL algorithms, particularly in terms of demographic characteristics. This study presents the first fairness analysis in a DL-based brain MRI reconstruction model. The model utilises the U-Net architecture for image reconstruction and explores the presence and sources of unfairness by implementing baseline Empirical Risk Minimisation (ERM) and rebalancing strategies. Model performance is evaluated using image reconstruction metrics. Our findings reveal statistically significant performance biases between the gender and age subgroups. Surprisingly, data imbalance and training discrimination are not the main sources of bias. This analysis provides insights of fairness in DL-based image reconstruction and aims to improve equity in medical AI applications.Comment: Accepted for publication at FAIMI 2023 (Fairness of AI in Medical Imaging) at MICCA

AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density

DBSCAN has been widely used in density-based clustering algorithms. However, with the increasing demand for Multi-density clustering, previous traditional DSBCAN can not have good clustering results on Multi-density datasets. In order to address this problem, an adaptive Multi-density DBSCAN algorithm (AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and MinPts), which are the key parameters to determine the clustering results and performance, therefore allowing the model to be applied to Multi-density datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid the complicated repetitive initialization operations. Furthermore, the variance of the number of neighbors (VNN) is proposed to measure the difference in density between each cluster. The experimental results show that our AMD-DBSCAN reduces execution time by an average of 75% due to lower algorithm complexity compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN improves accuracy by 24.7% on average over the state-of-the-art design on Multi-density datasets of extremely variable density, while having no performance loss in Single-density scenarios. Our code and datasets are available at https://github.com/AlexandreWANG915/AMD-DBSCAN.Comment: Accepted at DSAA202

Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement

Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the $k$-space is employed to reduce the scan duration, thus increasing patient comfort and reducing the risk of motion artefacts, at the cost of reduced image quality. In this challenge paper, we investigate the use of a convolutional recurrent neural network (CRNN) architecture to exploit temporal correlations in supervised cine cardiac MRI reconstruction. This is combined with a single-image super-resolution refinement module to improve single coil reconstruction by 4.4\% in structural similarity and 3.9\% in normalised mean square error compared to a plain CRNN implementation. We deploy a high-pass filter to our $\ell_1$ loss to allow greater emphasis on high-frequency details which are missing in the original data. The proposed model demonstrates considerable enhancements compared to the baseline case and holds promising potential for further improving cardiac MRI reconstruction.Comment: MICCAI STACOM workshop 202

Coordinated economic dispatch of the primary and secondary heating systems considering the boiler’s supplemental heating

District heating systems have been widely used in large and medium-sized cities. Typical district heating systems consist of the primary heating system (PHS) and the secondary heating system (SHS) operating in isolation. However, the isolated dispatch of the PHS and the SHS has poor adjustability and large losses, resulting in unnecessary operation costs. To address these issues, a coordinated economic dispatching model (CEDM) for the primary and secondary heating systems considering the boiler’s supplemental heating is proposed in this study, which characterized the physical properties of the PHS and the SHS in detail. Considering that the PHS and the SHS are controlled separately without central operators in practice, it is difficult to dispatch them in a centralized method. Thus, the master-slave splitting algorithm is innovatively introduced to solve the CEDM in a decentralized way. Finally, a P6S12 system is utilized to analyze and verify the effectiveness and optimality of the proposed algorithm