74 research outputs found
Magnetic sensing supported by machine learning
No full textThis dissertation explores how magnetic sensing can be advanced by integrating machine learning (ML) with magnetically responsive materials. Grounded in the concept of bioinspired adaptation, the core approach leverages soft ferromagnetic assemblies, magnetoelectric interfaces, and mouldable magnetic soft composites as reconfigurable platforms that readily adjust sensing sensitivity and functionality under varying stimuli. Coupled with ML methods—from basic classification to optimization strategies—these magnetically driven sensors transcend conventional static designs, enabling tasks such as shape detection, adaptive mechanosensing, secure information encoding, and magnetic field measurement.
The research progresses from trainable, bioinspired sensor concepts to increasingly integrated systems that demonstrate broader functionality and higher autonomy along this spectrum of applications. Underpinning the whole work is the concept of magnetic fields serving not only as a stimulus but also as a tunable “control knob” to reconfigure material properties. Machine learning methods then classify complex patterns, interpret sensor data, and enhance sensing resolution by addressing performance trade-offs. Although the main emphasis is on magnetics, a final demonstration of pressure-based handwriting recognition illustrates the broader applicability of integrating advanced material engineering with ML. The result is a cohesive framework where ML augments magnetic sensing systems toward enhanced adaptability, robustness, and intelligence.
By seamlessly uniting magnetic field manipulation with data-driven algorithms, this dissertation proposes a framework for developing advanced sensing devices in applications ranging from soft robotics and biomedical diagnostics to secure communication and wearable electronics. Beyond individual device advancements, the work underscores the broader potential of cross-disciplinary research—where merging materials science, magnetics, and ML can catalyse transformative innovations in sensor design and functionality
Fast algorithms for Bayesian variable selection
No full textVariable selection of regression and classification models is an important but challenging problem. There are generally two approaches, one based on penalized likelihood, and the other based on Bayesian framework. We focus on the Bayesian framework in which a hierarchical prior is imposed on all unknown parameters including the unknown variable set. The Bayesian approach has many advantages, for example, we can access unknown obtain the posterior distribution of the sub-models. And more accurate prediction may be obtained by model averaging.
However, as the posterior distribution of the model parameters is usually not in closed form, posterior inference that relies on Markov Chain Monte Carlo (MCMC) has high computational cost especially in high-dimensional settings, which makes Bayesian approaches undesirable. In order to deal with datasets with large number of features, we aim to develop fast algorithms for Bayesian variable selection, which approximate the true posterior distribution, but yet still return the right inference (at least asymptotically).
In this thesis, we start with a variational algorithm for linear regression. Our algorithm is based on the work by Carbonetto and Stephens (2012), and with essential modifications including updating scheme and truncation of posterior inclusion probabilities. We have shown that our algorithm achieves both frequentist and Bayesian variable selection consistency.
Then we extend our variational algorithm to logistic regression by incorporating the Polya-Gamma data-augmentation trick (Polson et al., 2013), which links our algorithm for linear regression with logistic regression. However, as the variational algorithm needs to update the variational distribution of all the latent Polya-Gamma random variables of the same size of the observations at every iteration, this algorithm is slow when there are huge amount of observations, or even be infeasible when the data is too large to be loaded into computer memory. We propose an online algorithm for the logistic regression, under the framework of online convex optimization. Our algorithm is fast, and achieves similar accuracy (log-loss) as the state-of-art algorithm (Follow-the-Regularized-Proximal algorithm).Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01The student, Xichen Huang, accepted the attached license on 2017-07-07 at 15:07.The student, Xichen Huang, submitted this Dissertation for approval on 2017-07-07 at 15:23.This Dissertation was approved for publication on 2017-07-10 at 12:40.DSpace SAF Submission Ingestion Package generated from Vireo submission #11339 on 2017-09-29 at 10:46:46Made available in DSpace on 2017-09-29T17:45:38Z (GMT). No. of bitstreams: 2
HUANG-DISSERTATION-2017.pdf: 610142 bytes, checksum: 132966a902cf66c70d837edb312274ec (MD5)
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Previous issue date: 2017-07-10Embargo set by: Colleen Fallaw for item 103486
Lift date: 2019-09-29T17:48:06Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 103486
Lift date: 2020-03-02T19:56:41Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 103486
Lift date: 2020-03-02T19:59:52Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 103486
Lift date: 2020-03-02T20:02:46Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 103486 on 2020-03-03T10:15:29Z
Exploration of the Motion Graphics Educational tools based on the animations ‘Weddings’
No full textWith the development of science and technology, computer networks and smart devices are widely used in our life and strongly driving social growth, including the field of education. Cities are becoming bigger and bigger. People are getting busier in modern times. People have lots of free but short time slots every day, but attending traditional classes is limited by the factors of geographical location, distance, time, etc. Thus the demand for on-line meetings and remote learning is sharply increasing. People are seeking a convenient and effective method to gain knowledge or information with their fragment time. This thesis is under such a background and focuses on the exploration of the Motion Graphics Educational tools based on the animations ‘Weddings’.
This thesis project presents an applicable design solution to facilitate extensive online educational classes. The author designed a series of animations of weddings in two specific times as an Exploratorium attempt via motion graphics from four aspects, audio, time, graphics, and motion effects. This thesis describes the process discussion on the topics of design and the benefits based on these four factors. For further accessible cultural communication, the author set up a website of weddings to work as a public channel to display the animations to the audience. On purpose for better understand the benefits of motion graphic educational tools better and functional realization effectiveness of this project, the author conducted two surveys and analyzed the responses and confirmed the educational tools do work to convey the expected information to the audience.
This thesis project demonstrates how motion graphics are implicated as a useful educational tool based on the animations ‘Weddings.’ It creates a more learner-friendly and accessible manner for the audience to gain knowledge or information conveniently and efficiently
Colloidal Magnetoelectric Shape Recognition Based on Machine Learning
No full textPublisher Copyright: © 2025 The Author(s). Small Structures published by Wiley-VCH GmbH.Functionalized particles ranging from nanoscale to microscale and their assemblies have facilitated a wide variety of sensing concepts, from molecular-scale chemical and biological detection to large-scale engineering defect testing. Related to macroscopic object shape sensing, visual recognition is generally the most versatile approach whenever possible. However, under certain conditions where visual perception is hindered, for example, dark space or underwater, electrosensing can serve as an alternative sensation manner. Inspired by this concept, the sensing of rudimentary object shapes using electrically conductive, soft ferromagnetic Ni particles is demonstrated, herein denoted as colloidal magnetoelectric shape recognition. By confining the target and sensory particles between two planar electrodes and using a magnetic field to drive the particles toward object edges, changes in electrical conductivity are monitored. Machine learning is then used to resolve the exact object shapes with high fidelity. This study introduces a colloidal magnetoelectric shape recognition strategy for short-range shape sensing, with potential applications suggested for the fields such as soft robotics, drug delivery, and biomedical diagnostics.Peer reviewe
Visual analysis of research hotspots and trends of external therapies in traditional Chinese medicine for depression
No full textObjective: Based on the visualization and analysis of the CiteSpace software, we aimed to explore the current research status and development trend of depression caused by external therapies in traditional Chinese medicine (TCM) and to provide a reference for further research in this field. Methods: In the China National Knowledge Infrastructure, Wanfang, Web of Science, and PubMed databases, relevant articles on external therapies in TCM for depression were selected as the research objects, and CiteSpace performed the bibliometric analysis. Results: In total, 1672 Chinese and 441 English articles were included after CiteSpace was used to remove duplicate articles and perform manual screening. The Chinese articles were analyzed, and the overall issuance showed an upward trend; the core author was Tu Ya, and the institution with the highest article production was Heilongjiang University of Traditional Chinese Medicine. The English articles were analyzed, and the overall issuance showed an upward trend; the core author was Macpherson, Hugh, and the institution with the highest article production was Guangzhou University of Traditional Chinese Medicine. China ranked first in terms of number and centrality of publications, followed by the United States. The keywords of Chinese and English articles were analyzed to conclude that the research trends in this field were an exploration of therapeutic mechanisms, acupoint application therapy, and assessment of sleep quality, and the research hotspots were the clinical application of external therapies in TCM and the types of underlying diseases. Conclusion: This study comprehensively and objectively summarized the relevant literature on external therapies in TCM for depression. It highlights the direction for further exploration by revealing and analyzing the research hotspots and trends in this field
The Coupling of Strain and Lithium Diffusion: A Theoretical Model Based on First-Principles Calculations
No full textMost electrodes undergo volume changes in lithium-ion batteries, and it turns out that the volume changes also significantly affect lithium diffusion kinetics based on first-principles calculations. To study the mechano-electro-chemical coupling, a theoretical model of spherical electrode has been developed and the effect of strain on diffusion coefficient is explicitly considered. The results show that strain-enhanced diffusion greatly alleviates diffusion-induced stresses, and the lithiation and delithiation are extremely asymmetric in lithium concentration distribution and stresses due to asymmetrical changes of diffusion coefficient with time and positions along the radial direction. (C) 2015 The Electrochemical Society.National Natural Science Foundation of China [11172231, 11372241]; ARPA-E [DE-AR0000396]; AFOSR [FA9550-12-1-0159]SCI(E)[email protected]; [email protected]
An Optimization Model Applied to Active Solar Energy System for Buildings in Cold Plateau Area
No full textAbstractThe large-scale utilization of solar energy in buildings is one of the most promising technologies to solve the global energy shortage problem and reduce the carbon dioxide emissions. The present paper has proposed an optimization model coupled with solar thermal and photovoltaic systems. Optimization results of active solar energy system from the energy saving view and economical view have been obtained for typical hotel and office buildings in cold plateau area, respectively
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