287 research outputs found
Advanced Materials and Technologies in Nanogenerators
This reprint discusses the various applications, new materials, and evolution in the field of nanogenerators. This lays the foundation for the popularization of their broad applications in energy science, environmental protection, wearable electronics, self-powered sensors, medical science, robotics, and artificial intelligence
Blind Hyperspectral Unmixing Using Autoencoders
The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds.Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing)
með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning).
Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar
leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur
aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning)
og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional
neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar.
Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem
hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og
útfærslur á þeim.
í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara
spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með
sjálfkóðurum.
Megin framlag ritgerðarinnar er eftirfarandi:
- Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í
fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm
til að aðgreina grennd af rófum í einu.
- Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara
og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches)
og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina.
- Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru
kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu
sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum.The Icelandic Research Fund under Grants 174075-05 and 207233-05
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
Three recent breakthroughs due to AI in arts and science serve as motivation:
An award winning digital image, protein folding, fast matrix multiplication.
Many recent developments in artificial neural networks, particularly deep
learning (DL), applied and relevant to computational mechanics (solid, fluids,
finite-element technology) are reviewed in detail. Both hybrid and pure machine
learning (ML) methods are discussed. Hybrid methods combine traditional PDE
discretizations with ML methods either (1) to help model complex nonlinear
constitutive relations, (2) to nonlinearly reduce the model order for efficient
simulation (turbulence), or (3) to accelerate the simulation by predicting
certain components in the traditional integration methods. Here, methods (1)
and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3)
relying on convolutional neural networks. Pure ML methods to solve (nonlinear)
PDEs are represented by Physics-Informed Neural network (PINN) methods, which
could be combined with attention mechanism to address discontinuous solutions.
Both LSTM and attention architectures, together with modern and generalized
classic optimizers to include stochasticity for DL networks, are extensively
reviewed. Kernel machines, including Gaussian processes, are provided to
sufficient depth for more advanced works such as shallow networks with infinite
width. Not only addressing experts, readers are assumed familiar with
computational mechanics, but not with DL, whose concepts and applications are
built up from the basics, aiming at bringing first-time learners quickly to the
forefront of research. History and limitations of AI are recounted and
discussed, with particular attention at pointing out misstatements or
misconceptions of the classics, even in well-known references. Positioning and
pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at
CMES-Computer Modeling in Engineering & Science
Structured Mixture Models
Finite mixture models are a staple of model-based clustering approaches for distinguishing subgroups. A common mixture model is the finite Gaussian mixture model, whose degrees of freedom scales quadratically with increasing data dimension. Methods in the literature often tackle the degrees of freedom of the Gaussian mixture model by sharing parameters between the eigendecomposition of covariance matrices across all mixture components. We posit finite Gaussian mixture models with alternate forms of parameter sharing by imposing additional structure on the parameters, such as sharing parameters with other components as a convex combination of the corresponding parent components or by imposing a sequence of hierarchical clustering structure in orthogonal subspaces with common parameters across levels. Estimation procedures using the Expectation-Maximization (EM) algorithm are derived throughout, with application to simulated and real-world datasets. As well, the proposed model structures have an interpretable meaning that can shed light on clustering analyses performed by practitioners in the context of their data.
The EM algorithm is a popular estimation method for tackling issues of latent data, such as in finite mixture models where component memberships are often latent. One aspect of the EM algorithm that hampers estimation is a slow rate of convergence, which affects the estimation of finite Gaussian mixture models. To explore avenues of improvement, we explore the extrapolation of the sequence of conditional expectations admitting general EM procedures, with minimal modifications for many common models. With the same mindset of accelerating iterative algorithms, we also examine the use of approximate sketching methods in estimating generalized linear models via iteratively re-weighted least squares, with emphasis on practical data infrastructure constraints. We propose a sketching method that controls for both data transfer and computation costs, the former of which is often overlooked in asymptotic complexity analyses, and are able to achieve an approximate result in much faster wall-clock time compared to the exact solution on real-world hardware, and can estimate standard errors in addition to point estimates
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático.
de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
Advances of Machine Learning in Materials Science: Ideas and Techniques
In this big data era, the use of large dataset in conjunction with machine
learning (ML) has been increasingly popular in both industry and academia. In
recent times, the field of materials science is also undergoing a big data
revolution, with large database and repositories appearing everywhere.
Traditionally, materials science is a trial-and-error field, in both the
computational and experimental departments. With the advent of machine
learning-based techniques, there has been a paradigm shift: materials can now
be screened quickly using ML models and even generated based on materials with
similar properties; ML has also quietly infiltrated many sub-disciplinary under
materials science. However, ML remains relatively new to the field and is
expanding its wing quickly. There are a plethora of readily-available big data
architectures and abundance of ML models and software; The call to integrate
all these elements in a comprehensive research procedure is becoming an
important direction of material science research. In this review, we attempt to
provide an introduction and reference of ML to materials scientists, covering
as much as possible the commonly used methods and applications, and discussing
the future possibilities.Comment: 80 pages; 22 figures. To be published in Frontiers of Physics, 18,
xxxxx, (2023
- …