2,509 research outputs found
Machine learning applications in search algorithms for gravitational waves from compact binary mergers
Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe.
However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing.
In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software.
Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
The dynamics and control of large space structures with distributed actuation
Future large space structures are likely to be constructed at much greater length-scales, and lower areal mass densities than has been achieved to-date. This could be enabled by ongoing developments in on-orbit manufacturing, whereby large structures are 3D-printed in space from raw feedstock materials. This thesis proposes and analyses a number of attitude control strategies which could be adopted for this next generation of ultra-lightweight, large space structures. Each of the strategies proposed makes use of distributed actuation, which is demonstrated early in the thesis to reduce structural deformations during attitude manoeuvres. All of the proposed strategies are considered to be particularly suitable for structures which are 3d-printed on-orbit, due to the relative simplicity of the actuators and ease with which the actuator placement or construction could be integrated with the on-orbit fabrication of the structure itself.
The first strategy proposed is the use of distributed arrays of magnetorquer rods. First, distributed torques are shown to effectively rotate highly flexible structures. This is compared with torques applied to the centre-of-mass of the structure, which cause large surface deformations and can fail to enact a rotation. This is demonstrated using a spring-mass model of a planar structure with embedded actuators. A torque distribution algorithm is then developed to control an individually addressable array of actuators. Attitude control simulations are performed, using the array to control a large space structure, again modelled as a spring-mass system. The attitude control system is demonstrated to effectively detumble a representative 75×75m flexible structure, and perform slew manoeuvres, in the presence of both gravity-gradient torques and a realistic magnetic field model.
The development of a Distributed Magnetorquer Demonstration Platform is then presented, a laboratory-scale implementation of the distributed magnetorquer array concept. The platform consists of 48 addressable magnetorquers, arranged with two perpendicular torquers at the nodes of a 5×5 grid. The control algorithms proposed previously in the thesis are implemented and tested on this hardware, demonstrating the practical feasibility of the concept. Results of experiments using a spherical air bearing and Helmholtz cage are presented, demonstrating rest-to-rest slew manoeuvres and detumbling around a single axis using the developed algorithms.
The next attitude control strategy presented is the use of embedded current loops, conductive pathways which can be integrated with a spacecraft support structure and used to generate control torques through interaction with the Earth’s magnetic field. Length-scaling laws are derived by determining what fraction of a planar spacecraft’s mass would need to be allocated to the conductive current loops in order to produce a torque at least as large as the gravity gradient torque. Simulations are then performed of a flexible truss structure, modelled as a spring-mass system, for a range of structural flexibilities and a variety of current loop geometries. Simulations demonstrate rotation of the structure via the electromagnetic force on the current carrying elements, and are also used to characterise the structural deformations caused by the various current loop geometries. An attitude control simulation is then performed, demonstrating a 90◦ slew manoeuvre of a 250×250 m flexible structure through the use of three orthogonal sets of current loops embedded within the spacecraft.
The final concept investigated in this thesis is a self-reconfiguring OrigamiSat, where reconfiguration of the proposed OrigamiSat is triggered by changes in the local surface optical properties of an origami structure to harness the solar radiation pressure induced acceleration. OrigamiSats are origami spacecraft with reflective panels which, when flat, operate as a conventional solar sail. Shape reconfiguration, i.e. “folding” of the origami design, allows the OrigamiSat to change operational modes, performing different functions as per mission requirements. For example, a flat OrigamiSat could be reconfigured into the shape of a parabolic reflector, before returning to the flat configuration when required to again operate as a solar sail, providing propellant-free propulsion. Shape reconfiguration or folding of OrigamiSats through the use of surface reflectivity modulation is investigated in this thesis. First, a simplified, folding facet model is used to perform a length-scaling analysis, and then a 2d multibody dynamics simulation is used to demonstrate the principle of solar radiation presure induced folding. A 3d multibody dynamics simulation is then developed and used to demonstrate shape reconfiguration for different origami folding patterns. Here, the attitude dynamics and shape reconfiguration of OrigamiSats are found to be highly coupled, and thus present a challenge from a control perspective. The problem of integrating attitude and shape control of a Miura-fold pattern OrigamiSat through the use of variable reflectivity is then investigated, and a control algorithm developed which uses surface reflectivity modulation of the OrigamiSat facets to enact shape reconfiguration and attitude manoeuvres simultaneously
Selected Analytical Techniques of Solid State, Structure Identification, and Dissolution Testing in Drug Life Cycle
The textbook provides an overview of the main techniques applied in pharmaceutical industry, with the focus on solid-state analysis. It discusses spectral methods, thermal analysis, and dissolution testing, explains the theoretical background for each method and shows practical examples from a real-life drug-design and quality control applications. The textbook is thus intended for both pharmacy students and early career professionals
2023-2024 academic bulletin & course catalog
University of South Carolina Aiken publishes a catalog with information about the university, student life, undergraduate and graduate academic programs, and faculty and staff listings
Authentic alignment : toward an Interpretative Phenomenological Analysis (IPA) informed model of the learning environment in health professions education
It is well established that the goals of education can only be achieved through the constructive alignment of instruction, learning and assessment. There is a gap in research interpreting the lived experiences of stakeholders within the UK learning environment toward understanding the real impact – authenticity – of curricular alignment. This investigation uses a critical realist framework to explore the emergent quality of authenticity as a function of alignment.This project deals broadly with alignment of anatomy pedagogy within UK undergraduate medical education. The thread of alignment is woven through four aims: 1) to understand the alignment of anatomy within the medical curriculum via the relationships of its stakeholders; 2) to explore the apparent complexity of the learning environment (LE); 3) to generate a critical evaluation of the methodology, Interpretative Phenomenological Analysis as an approach appropriate for realist research in the complex fields of medical and health professions education; 4) to propose a functional, authentic model of the learning environment.Findings indicate that the complexity and uncertainty inherent in the LE can be reflected in spatiotemporal models. Findings meet the thesis aims, suggesting: 1) the alignment of anatomy within the medical curriculum is complex and forms a multiplicity of perspectives; 2) this complexity is ripe for phenomenological exploration; 3) IPA is particularly suitable for realist research exploring complexity in HPE; 4) Authentic Alignment theory offers a spatiotemporal model of the complex HPE learning environment:the T-icosa
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Novel deep learning architectures for marine and aquaculture applications
Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices
Data journeys in the sciences
This is the final version. Available from Springer via the DOI in this record. This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.European CommissionAustralian Research CouncilAlan Turing Institut
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