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 20 of 30 for “"observation model"”.
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DEVELOPMENT OF SPATIOTEMPORAL CONGESTION PATTERN OBSERVATION MODEL USING HISTORICAL AND NEAR REAL TIME DATA
<p>Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at …
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Structure incorporation of model uncertainty for Bayesian adaptive tracking and its application to maritime surveillance
… phrasing the entire task, including the adaptive observation model, within the Bayesian inference. In this thesis we develop a framework for simultaneous modelling and estimation (SMAE), in which the common Bayesian recursive estimator (BRE) is extended to include estimation of the underlying …
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Dynamic Bayesian networks for the classification of spinning discs
… dynamics describing rotation with a nonlinear observation model determined by the disc pattern, which is parameterized by angle. A consequence of the nonlinear observation model is that the posterior state distribution of angle and spin-rate is multi-modal. This detail motivates the use of …
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Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter
… which has linear signal dynamics and nonlinear observation dynamics. Different parameters of the signal model and observation model will be varied and performance of the two filtering techniques FPF, EKF will be compared.
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Computationally Efficient Reinforcement Learning under Partial Observability
… of Markov decision processes (MDPs) that model this challenge. Unfortunately, planning and learning near-optimal policies in POMDPs is computationally intractable. Most existing algorithms either lack provable guarantees, require exponential time, or only apply under stringent assumptions …
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Statistical Inference and Learning for Stochastic and Partial Differential Equations
Learning differential equation models from data is of significant interest to the scientific and engineering communities. Fundamental to many areas of science, differential equations are the mathematical description of change, derived from physical laws and modelling assumptions. Practically, …
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23 Illinois Administrative Code 50: Redefining the Formal Observation in Teacher Evaluation; A Policy Advocacy Document
… how a change in the definition of the formal observation may improve the teacher evaluation system in the state of Illinois. Currently, the formal observation must be conducted in person as defined by administrative code. In an effort to increase the value and impact of the formal observation …
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Particle Filtering for Continuous Time Problems
… of a stochastic system from noisy and incomplete observations, where both the state and observations evolve continuously—a problem known as ``continuous time filtering.’’ This problem presents difficulties such as irregular observation times and the intractability of transition densities, …
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Knowledge and Ignorance in Reinforcement Learning
… I distinguish three: the state-space/transition model, the reward function, and the observation model. In this thesis, I present a framework for studying how the state of knowledge or uncertainty of each component affects the Reinforcement Learning process. I focus on the reward function and the …
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Exploring the Relationship between Imitation and Social Communication in Infants
… & Prizant, 2002). This study used a naturalistic observation model so the one-hour play sessions took place in the infants' homes. Sessions were digitally recorded for later scoring and analysis. This study demonstrated a concurrent and predictive relationship between imitation and language …
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Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments
… find this requires the robot to have a spatial observation model that can predict where to find various phenomena, a reward model which can measure how relevant these phenomena are to the scientific mission objectives, and an adaptive path planner which can use this information to plan high …
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A population dynamics model for analysing the effect of rainfall seasonality on vegetation in the Karoo
… biomes in the Cape Floristic Region is used to model the growth of the two growth forms post- fire. Rainfall in this experiment is artificially manipulated on replicated plots at the two experiment sites. The population growth is modelled using state-space models, incorporating both an …
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Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments
… find this requires the robot to have a spatial observation model that can predict where to find various phenomena, a reward model which can measure how relevant these phenomena are to the scientific mission objectives, and an adaptive path planner which can use this information to plan high …
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Remote Operator Blended Intelligence System for Environmental Navigation and Discernment (RobiSEND)
… and human, for system integration purposes. Observation models relevant to both human and robotic collaborators are tracked through a boundary based approach deemed AIM-SHIFT. A system is developed to classify the semantic and functional relevance of an observation model to local search …
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Teacher Evaluation: The Change We Wish to See; Improving the Formal Observation Process to Improve Student Learning
… instruction and student learning. Formal observations are a central component of that process. This change model explores the option of a videotaped observation model as an alternative to the current Illinois state-mandated in-person formal observation to increase teacher ownership, …
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Mitigating timing noise in ADCs through digital post-processing
… noise. Two approaches are considered: classical, observation model-driven estimation, and Bayesian estimation that incorporates a prior model of the signal parameters. For both approaches, algorithms are derived that achieve lower mean-squared-error (MSE) by taking the non-linear effect of the …
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Decentralized control of multi-robot systems using partially observable Markov Decision Processes and belief space macro-actions
… each robot should take at a given time. The observation model, a function of the robots' sensors, may be noisy or partial, meaning that deterministic knowledge of the team's state is often impossible to attain. Robots designed for real-world applications require careful consideration of such …
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Snow Hydrology and Streamflow Response to Snowmelt: A Case Study on SWE Dataset Comparisons, Regional Water Balance, and Snowmelt-Induced Runoff within the Smith River Watershed, Montana
… WUS-SR, SNODAS, and Noah-MP via WLDAS) and SnowModel were validated against observations from five Snow Telemetry (SNOTEL) stations using correlation (r), mean absolute error (MAE), and root mean square error (RMSE). Models assimilating SNOTEL data (SNODAS, UA 800m, and UA 4km) performed best. …
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Development of dynamic recursive models for freeway travel time predictions
… the development of a sound prediction model for travel times is desirable. A comprehensive literature review about existing prediction models was conducted by investigating the advantages, disadvantages, and limitations of each model. Based on the features and properties of previous …
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Bug vision : experiments in low resolution vision
… are well understood, because the generative models need describe the dynamics of simple point objects. In addition, the radar tracking problem assumes that measurements are noise corrupted positions, which makes it easy to cast the tracking problem in a Bayesian framework. Unlike radar, …
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