10,631 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures
In this work we present a non-parametric online market regime detection
method for multidimensional data structures using a path-wise two-sample test
derived from a maximum mean discrepancy-based similarity metric on path space
that uses rough path signatures as a feature map. The latter similarity metric
has been developed and applied as a discriminator in recent generative models
for small data environments, and has been optimised here to the setting where
the size of new incoming data is particularly small, for faster reactivity.
On the same principles, we also present a path-wise method for regime
clustering which extends our previous work. The presented regime clustering
techniques were designed as ex-ante market analysis tools that can identify
periods of approximatively similar market activity, but the new results also
apply to path-wise, high dimensional-, and to non-Markovian settings as well as
to data structures that exhibit autocorrelation.
We demonstrate our clustering tools on easily verifiable synthetic datasets
of increasing complexity, and also show how the outlined regime detection
techniques can be used as fast on-line automatic regime change detectors or as
outlier detection tools, including a fully automated pipeline. Finally, we
apply the fine-tuned algorithms to real-world historical data including
high-dimensional baskets of equities and the recent price evolution of crypto
assets, and we show that our methodology swiftly and accurately indicated
historical periods of market turmoil.Comment: 65 pages, 52 figure
Investigation of Deep Learning-Based Filtered Density Function for Large Eddy Simulation of Turbulent Scalar Mixing
The present investigation focuses on the application of deep neural network
(DNN) models to predict the filtered density function (FDF) of mixture fraction
in large eddy simulation (LES) of variable density mixing layers with conserved
scalar mixing. A systematic training method is proposed to select the DNN-FDF
model training sample size and architecture via learning curves, thereby
reducing bias and variance. Two DNN-FDF models are developed: one trained on
the FDFs generated from direct numerical simulation (DNS), and another trained
with low-fidelity simulations in a zero-dimensional pairwise mixing stirred
reactor (PMSR). The accuracy and consistency of both DNN-FDF models are
established by comparing their predicted scalar filtered moments with those of
conventional LES, in which the transport equations corresponding to these
moments are directly solved. Further, DNN-FDF approach is shown to perform
better than the widely used -FDF method, particularly for multi-modal
FDF shapes and higher variances. Additionally, DNN-FDF results are also
assessed via comparison with data obtained by DNS and the transported FDF
method. The latter involves LES simulations coupled with the Monte Carlo (MC)
methods which directly account for the mixture fraction FDF. The DNN-FDF
results compare favorably with those of DNS and transported FDF method.
Furthermore, DNN-FDF models exhibit good predictive capabilities compared to
filtered DNS for filtering of highly non-linear functions, highlighting their
potential for applications in turbulent reacting flow simulations. Overall, the
DNN-FDF approach offers a more accurate alternative to the conventional
presumed FDF method for describing turbulent scalar transport in a
cost-effective manner
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques
The rapid growth of demanding applications in domains applying multimedia
processing and machine learning has marked a new era for edge and cloud
computing. These applications involve massive data and compute-intensive tasks,
and thus, typical computing paradigms in embedded systems and data centers are
stressed to meet the worldwide demand for high performance. Concurrently, the
landscape of the semiconductor field in the last 15 years has constituted power
as a first-class design concern. As a result, the community of computing
systems is forced to find alternative design approaches to facilitate
high-performance and/or power-efficient computing. Among the examined
solutions, Approximate Computing has attracted an ever-increasing interest,
with research works applying approximations across the entire traditional
computing stack, i.e., at software, hardware, and architectural levels. Over
the last decade, there is a plethora of approximation techniques in software
(programs, frameworks, compilers, runtimes, languages), hardware (circuits,
accelerators), and architectures (processors, memories). The current article is
Part I of our comprehensive survey on Approximate Computing, and it reviews its
motivation, terminology and principles, as well it classifies and presents the
technical details of the state-of-the-art software and hardware approximation
techniques.Comment: Under Review at ACM Computing Survey
Technology for Low Resolution Space Based RSO Detection and Characterisation
Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment
Fault diagnosis in aircraft fuel system components with machine learning algorithms
There is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced
businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario.
The CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models
to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components.
The aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that
impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that
monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure,
the fuel in the aircraft will become unusable/unavailable to reach the destination.
It is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner.
This thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is
similar to Boeing-777. The data is collected is processed through Machine learning Techniques and the results are comparedPhD in Manufacturin
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Homeostasis in Immunity-Related Pupal Tissues of the Malaria Mosquito Anopheles gambiae and its regulation by the NF-kappaB-like Factor Rel2
Die Haut ist eine oft übersehene Komponente des angeborenen Immunsystems der Mücken. Die Haut der Mücke bildet eine physische Barriere, die die mikrobielle Homöostase aufrechterhält, das Eindringen von Toxinen wie Insektiziden verhindert und das Austrocknen verhindert. Die am meisten untersuchten Akteure des Immunsystems von Stechmücken sind das Fettgewebe und die Blutzellen, aber die Hauttalg-Fabriken, die Oenozyten, werden in Studien nur selten berücksichtigt.
Mückenpuppen haben aktiv funktionierende immunitätsbezogene Organe, einschließlich derjenigen, die Hautbarrieren produzieren. Ihre biologische Rolle in diesem Entwicklungsstadium ist kaum bekannt, aber der Übergang von der Puppen- zur Erwachsenenhaut und die Auffälligkeit der talgproduzierenden Zellen machen dieses Stadium zu einem vielversprechenden Entwicklungsstadium für die Untersuchung der Hautbildung.
Mit Hilfe der Transkriptomanalyse beschreiben wir die Rolle der Blutzellen bei der Entwicklung des chitinösen Teils der Insektenhaut, die Beteiligung des Fettkörpers an der Immunität und bestätigen die Rolle der talgproduzierenden Zellen im Lipidstoffwechsel. Darüber hinaus beschreiben wir talgsezernierende Zellen als einen bedeutenden Wirkungsort des NF-kappaB-ähnlichen IMD-Rel2-Pathway, in dem der Transkriptionsfaktor Rel2 die Retinoid-Homöostase reguliert. Schließlich bestätigen wir eine 100 Jahre alte Beobachtung, wonach sebumsezernierende Zellen der Stechmücke ihren Zellinhalt in einem Netzwerk von Vesikeln absondern. Wir beschreiben extrazelluläres Chromatin als Fracht in diesem Vesikelnetzwerk und sein antimikrobielles Potenzial.The skin is an often overlooked component of the mosquito's innate immune system. The mosquito skin provides a physical barrier that maintains microbial homeostasis, prevents the entry of toxins like insecticides, and avoids desiccation. The most studied players in the immune system of mosquitoes are the adipose tissue and blood cells, but studies rarely consider the skin sebum factories, oenocytes.
Mosquito pupae have actively functional immunity-related organs, including those producing skin barriers. Their biological roles at this developmental stage are poorly understood, but the pupae-to-adult metamorphic skin transition and the conspicuity of sebum-secreting cells make it a promising developmental stage to study skin formation.
We use transcriptomics to describe the role of blood cells in the development of the chitinous section of the insect skin, the involvement of the fat body in immunity, and confirm the lipid metabolism role of sebum-secreting cells. Furthermore, we describe sebum-secreting cells as a significant action site of the NF-kappaB-like IMD-Rel2 pathway where the transcription factor Rel2 regulates retinoid homeostasis. Finally, we confirm a 100-year-old observation of how mosquito sebum-secreting cells secrete their cellular contents in a network of vesicles. We describe extracellular chromatin as cargo inside this vesicle network and its antimicrobial potential
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
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