Iterative Bayesian Learning for Crowdsourced Regression

Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fully harness the wisdom of the crowd, one needs to learn the heterogeneous quality of each worker. We resolve this fundamental challenge in crowdsourced regression tasks, i.e., the answer takes continuous labels, where identifying good or bad workers becomes much more non-trivial compared to a classification setting of discrete labels. In particular, we introduce a Bayesian iterative scheme and show that it provably achieves the optimal mean squared error. Our evaluations on synthetic and real-world datasets support our theoretical results and show the superiority of the proposed scheme

Development of Data Analytics and Modeling Tools for Civil Infrastructure Condition Monitoring Applications

This dissertation focuses on the development of data analytics approaches to two distinct important condition monitoring applications in civil infrastructure: structural health monitoring and road surface monitoring. In the first part, measured vibration responses of a major long-span bridge are used to identify its modal properties. Variations in natural frequencies over a daily cycle have been observed with measured data, which are probably due to environmental effects such as temperature and traffic. With a focus on understanding the relationships between natural frequencies and temperatures, a controlled simulation-based study is conducted with the use of a full-scale finite element (FE) model and four regression models. In addition to the temperature effect study, the identified modal properties and the FE model are used to explore both deterministic and probabilistic model updating approaches. In the deterministic approach (sensitivity-based model updating), the regularization technique is applied to deal with a trade-off between natural frequency and mode shape agreements. Specific nonlinear constraints on mode shape agreements are suggested here. Their capabilities to adjust mode shape agreements are validated with the FE model. To the best of the author's knowledge, the sensitivity-based clustering technique, which enables one to determine efficient updating parameters based on a sensitivity analysis, has not previously been applied to any civil structure. Therefore, this technique is adapted and applied to a full-scale bridge model for the first time to highlight its capability and robustness to select physically meaningful updating parameters based on the sensitivity of natural frequencies with respect to both mass and stiffness-related physical parameters. Efficient and physically meaningful updating parameters are determined by the sensitivity-based clustering technique, resulting in an updated model that has a better agreement with measured data sets. When it comes to the probabilistic approach, the application of Bayesian model updating to large-scale civil structures based on real data is very rare and challenging due to the high level of uncertainties associated with the complexity of a large-scale model and variations in natural frequencies and mode shapes identified from real measured data. In this dissertation, the full-scale FE model is updated via the Bayesian model updating framework in an effort to explore the applicability of Bayesian model updating to a more complex and realistic problem. Uncertainties of updating parameters, uncertainty reductions due to information provided by data sets, and uncertainty propagations to modal properties of the FE model are estimated based on generated posterior samples. In the second part of this dissertation, a new innovative framework is developed to collect pavement distress data via multiple vehicles. Vehicle vibration responses are used to detect isolated pavement distress and rough road surfaces. GPS positioning data are used to localize identified road conditions. A real-time local data logging algorithm is developed to increase the efficiency of data logging in each vehicle client. Supervised machine learning algorithms are implemented to classify measured dynamic responses into three categories. Since data are collected from multiple vehicles, the trajectory clustering algorithm is introduced to integrate various trajectories to provide a compact format of information about road surface conditions. The suggested framework is tested and evaluated in real road networks

Finite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization

Finite element model updating seeks to modify a structural model to reduce discrepancies between predicted and measured data, often from vibration studies. An updated model provides more accurate prediction of structural behavior in future analyses. Sensitivity-based parameter clustering and regularization are two techniques used to improve model updating solutions, particularly for high-dimensional parameter spaces and ill-posed updating problems. In this paper, a novel parameter clustering scheme is proposed which considers the structure of the objective function to facilitate simultaneous updating of disparate data, such as natural frequencies and mode shapes. In a small-scale updating example with simulated data, the proposed clustering scheme is shown to provide moderate to excellent improvement over existing parameter clustering methods, depending on the accuracy of initial model. A full-scale updating example on a large suspension bridge shows similar improvement using the proposed parametrization scheme. Levenberg-Marquardt minimization with Bayesian regularization is also implemented, providing an optimal regularized solution and insight into parametrization efficiency

Symmetry properties of natural frequency and mode shape sensitivities in symmetric structures

When updating a finite element (FE) model to match the measured properties of its corresponding structure, the sensitivities of FE model outputs to parameter changes are of significant interest. These sensitivities form the core of sensitivity-based model updating algorithms, but they are also used for developing reduced parametrizations, such as in subset selection and clustering. In this work, the sensitivities of natural frequencies and mode shapes are studied for structures having at least one plane of reflectional symmetry. It is first shown that the mode shapes of these structures are either symmetric and anti-symmetric, which is used to prove that natural frequency sensitivities are equal for symmetric parameters. Conversely, mode shape sensitivities are shown to be unequal for symmetric parameters, as measured by cosine distance. These topics are explored with a small numerical example, where it is noted that mode shape sensitivities for symmetric parameters exhibit similar properties to asymmetric parameters

Sensitivity‐based singular value decomposition parametrization and optimal regularization in finite element model updating

Model updating is used to reduce error between measured structural responses and corresponding finite element (FE) model outputs, which allows accurate prediction of structural behavior in future analyses. In this work, reduced‐order parametrizations of an underlying FE model are developed from singular value decomposition (SVD) of the sensitivity matrix, thereby improving efficiency and posedness in model updating. A deterministic error minimization scheme is combined with asymptotic Bayesian inference to provide optimal regularization with estimates for model evidence and parameter efficiency. Natural frequencies and mode shapes are targeted for updating in a small‐scale example with simulated data and a full‐scale example with real data. In both cases, SVD‐based parametrization is shown to have good or better results than subset selection with very strong results on the full‐scale model, as assessed by Bayes factor

Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder

Despite its practical importance across a wide range of modalities, recent advances in self-supervised learning (SSL) have been primarily focused on a few well-curated domains, e.g., vision and language, often relying on their domain-specific knowledge. For example, Masked Auto-Encoder (MAE) has become one of the popular architectures in these domains, but less has explored its potential in other modalities. In this paper, we develop MAE as a unified, modality-agnostic SSL framework. In turn, we argue meta-learning as a key to interpreting MAE as a modality-agnostic learner, and propose enhancements to MAE from the motivation to jointly improve its SSL across diverse modalities, coined MetaMAE as a result. Our key idea is to view the mask reconstruction of MAE as a meta-learning task: masked tokens are predicted by adapting the Transformer meta-learner through the amortization of unmasked tokens. Based on this novel interpretation, we propose to integrate two advanced meta-learning techniques. First, we adapt the amortized latent of the Transformer encoder using gradient-based meta-learning to enhance the reconstruction. Then, we maximize the alignment between amortized and adapted latents through task contrastive learning which guides the Transformer encoder to better encode the task-specific knowledge. Our experiment demonstrates the superiority of MetaMAE in the modality-agnostic SSL benchmark (called DABS), significantly outperforming prior baselines. Code is available at https://github.com/alinlab/MetaMAE.Comment: Accepted to NeurIPS 2023. The first two authors contributed equall

Methods and Tools for Monitoring Driver's Behavior

In-vehicle sensing technology has gained tremendous attention due to its ability to support major technological developments, such as connected vehicles and self-driving cars. In-vehicle sensing data are invaluable and important data sources for traffic management systems. In this paper we propose an innovative architecture of unobtrusive in-vehicle sensors and present methods and tools that are used to measure the behavior of drivers. The proposed architecture including methods and tools are used in our NIH project to monitor and identify older drivers with early dementi

Inkjet-Printed Silver Gate Electrode and Organic Dielectric Materials for Bottom-Gate Pentacene Thin-Film Transistors

An inkjet-printed silver electrode and a spin-coated cross-linked poly(4-vinylphenol)(PVP) dielectric layer were used as a gate electrode and a gate insulator for a bottom-gate pentacene thin-film transistor (TFT), respectively. The printing and the curing conditions of the printed silver electrode were optimized and tested on various substrates, such as glass, silicon, silicon dioxide, polyethersulfone, polyethyleneterephthalate, polyimide and polyarylate, to produce a good sheet resistance of 0.2 $\sim$ 0.4 $\Omega$/$\square$ and a good surface roughness of 2.38 nm in RMS value and 20.14 nm in peak-to-valley (P2V) value, which are very similar to those of conventionally-sputtered indium-tin-oxide (ITO) or thermally-evaporated silver electrodes. The coated PVP layer of metal/PVP/metal devices showed a good insulation property of 10.4 nA/$\rm cm^{2}$ at 0.5 MV/cm. The PVP layer further reduced the surface roughness of the gate electrode to provide a good interface to the pentance layer. The pentacene TFT with a structure of glass/printed silver/PVP/pentacene/Au showed a good saturation region mobility of 0.13 $\rm cm^{2}$/Vs and a good on/off ratio of larger than 10$^{5}$, which are similar to the performance of a pentacene TFT with a conventional ITO gate electrode.This work was supported by \SystemIC2010" project of Korea Ministry of Knowledge Economy and by the Seoul R&BD Program (CRO70048)

Superaerophobic hydrogels for enhanced electrochemical and photoelectrochemical hydrogen production

The efficient removal of gas bubbles in (photo)electrochemical gas evolution reactions is an important but underexplored issue. Conventionally, researchers have attempted to impart bubble-repellent properties (so-called superaerophobicity) to electrodes by controlling their microstructures. However, conventional approaches have limitations, as they are material specific, difficult to scale up, possibly detrimental to the electrodes' catalytic activity and stability, and incompatible with photoelectrochemical applications. To address these issues, we report a simple strategy for the realization of superaerophobic (photo)electrodes via the deposition of hydrogels on a desired electrode surface. For a proof-of-concept demonstration, we deposited a transparent hydrogel assembled from M13 virus onto (photo)electrodes for a hydrogen evolution reaction. The hydrogel overlayer facilitated the elimination of hydrogen bubbles and substantially improved the (photo)electrodes' performances by maintaining high catalytic activity and minimizing the concentration overpotential. This study can contribute to the practical application of various types of (photo)electrochemical gas evolution reactions