Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 18 of 18 for “"Learning and Control"”.
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Learning and Control of Network Phenomena
The intersection of dynamical systems and networks are used to model a huge variety of phenomena. From social networks, to traffic routes and self-driving cars, to swarms of robots and multiagent systems, to individuals moving about in a geographical area, networks can represent an enormous variety …
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Optimization, Learning, and Control for Energy Networks
… role in everyday human lives. Development and operation of these networks is extremely capital-intensive. Moreover, security and reliability of these networks is critical. This work identifies and addresses a diverse class of computationally challenging and time-critical problems pertaining …
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Stochastic Optimization For Multi-Agent Statistical Learning And Control
… a mathematical framework for optimal, accurate, and affordable complexity statistical learning among networks of autonomous agents. We begin by noting the connection between statistical inference and stochastic programming, and consider extensions of this setup to settings in which a network of …
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Game-Theoretic Learning and Control for Resilience of Complex Adaptive Systems
… That is, they are systems of high complexity and heterogeneity, consisting of various digital and analog components that communicate with one another through multiple communication channels. For example, something as small as our phone is a CPS, but also something as large as a power grid. …
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Augmenting physics simulators with neural networks for model learning and control
… role in robot state estimation, planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Therefore, most physics simulators employ approximations that lead to a loss in precision. We propose a hybrid …
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MODEL-BASED LEARNING AND CONTROL OF ADVECTION-DIFFUSION TRANSPORT USING MOBILE ROBOTS
… models that describe different processes and phenomena are of paramount importance in many robotics applications. Nevertheless, utilization of high-fidelity models, particularly Partial Differential Equations (PDEs), has been hindered for many years due to the lack of adequate …
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Sensorimotor learning and control in autism spectrum disorders: The role of sensorimotor integration.
… of sensorimotor integration during sensorimotor learning and control processes in autism spectrum disorders. Autistic participants were matched (IQ, age, gender) with control participants across three experimental chapters (chapters three-five) within the contexts of motor learning, imitation and …
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Neural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems
… nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network …
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Predictive Time-Variant Photovoltaic Electrodialysis Reversal: A Novel Design Optimization using Predictive Machine Learning and Control Theory
This paper introduces a novel control theory and system design optimization to reduce the Levelized Cost of Water (LCOW) and maximize the reliability of Photovoltaic Electrodialysis Reversal (PV-EDR) groundwater desalination systems. This work aims to exploit the relationship between water …
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Data-driven robust solution schemes for sequential decision making
This dissertation develops robust and data-efficient methodologies for sequential decision making under uncertainty, motivated by challenges arising in operations research, control, and machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk …
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Development of Automated Gait Assessment and Intelligent Virtual Reality Based Gait Training Systems for Post-Stroke Rehabilitation
… that severely compromise mobility, independence, and quality of life. This thesis aims to integrate advanced technologies, including artificial intelligence (AI) and virtual reality (VR) into post-stroke gait assessment and training, thereby developing solutions that are technically advanced, …
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Geometric Properties of Learned Representations
In machine learning, reprensentation learning refers to optimizing a mapping from data to some representation space (usually generic vectors in Rᵈ for some pre-determined 𝑑 much lower than data dimensions). While such training often uses no supervised labels, the learned representations have proved …
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Differentiable Multiscale Molecular Simulations
… simulation is a critical tool to understand matter. The multiscale picture of simulations views observables at different granularities, providing explanation and prediction of phenomena across a wide range of spatial and temporal scales. Recently, data-driven modeling has shown great …
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Adaptive auditory-motor control of the time-varying formant trajectories in vowels and its patterns of generalization
… elucidating the role of auditory feedback in the learning and planning of complex articulatory gestures in time-varying phonemes. To this end, we studied native Mandarin speakers' responses to perturbations of their auditory feedback of the first and second formant trajectories during the …
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Teachers' Perceptions of an Online Social Network as an Instructional Platform: The Impact of an Edmodo-Based Professional Development Workshop
Today's students are learning and communicating in increasingly digital ways, which is challenging instructors to rethink their practice in order to meet their students' needs. These needs include instant access to information, student-centered learning, and control over their own learning. With …
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Reinforcement Learning for Cybersecurity Risk Assessment of Advanced Air Mobility Systems
… systems relies on highly granular, reliable, and trustworthy sensor data. This thesis is motivated by the need to assess safety risks due to cyber vulnerabilities in the surveillance components of AAM systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) and the Airborne Collision …
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Learning-Based Methods for Spacecraft Dynamics Modeling, Filtering, and Predictive Control
… arises in many on-orbit assembly, servicing, and assistance scenarios, including tasks requiring manipulation of unknown grappled objects. Traditionally, adaptive model-based control approaches have relied on an analytical dynamics model with a set of parameters that are estimated from …
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Variability of Practice and Strength Training Periodization: When Theories Collide
… strength programs. Forty one subjects (23 women and 18 men) were assigned to either the control group or one of two treatment groups by a blocked-random method. Subject's one repetition maximum (1RM) for the kettlebell press and leg press were measured at baseline, after 4 weeks of training, and …