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.
Results
Showing 1 to 20 of 42 for “"Neural Model"”.
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Neural Model-Based Advanced Control of Chylla-Haase Reactor
… start with the development of mathematical model of the process. The sub-models for monomer concentration, polymerization rate, reactor temperature and jacket outlet/inlet temperature are developed and implemented in Matlab/Simulink. Four conventional control methods were applied to the …
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The Development and Validation of a Neural Model of Affective States
… and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the …
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Named entity recognition for Icelandic: comparing and combining different machine learning methods
… NER is a subtask of Information Extraction. A neural model for NER has already been implemented for Icelandic (NeuroNER), but this is as far as we know, the only previous Machine Learning (ML) model for the task in the Icelandic language. The goal of this project was to develop other ML methods …
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Neural models of modulation frequency analysis in the auditory system
… auditory system. A biologically motivated neural model of AM processing has been developed. The first main component of the model allows for the simulation of the response properties of cochlear nucleus ideal onset units, a neuron type that is known to encode the modulation frequency of AM …
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Imaging neural correlates of syntactic complexity in a naturalistic context
… within which it is embedded, is to delineate a neural model of grammatical competence. For this purpose, we develop here a novel integrated, multi-disciplinary experimental paradigm that endorses the fundamental premise of generative grammar, that the study of language is in essence, the study …
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The Role of Precedent in Computational Models of Law
… new decision increases rapidly over time. Robust models of precedential reasoning could help in mitigating this problem. Therefore, the goal of this thesis is to model precedent, understand how it works and how to use it to explain system predictions. Towards this, I will use modern techniques …
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The Functional Role for Chromatin Loops in Gene Expression Control During Human Neuron Maturation
… and chromatin in neuroscience, examine neural model systems, and build an auxin-inducible degron to deplete the architectural protein CTCF and disrupt loops genome-wide on short time scales during human induced pluripotent stem cell (iPSC)-derived post-mitotic neuronal maturation. We …
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Data-Efficient Machine Learning for Computational Imaging
… by incorporating prior knowledge from physical models into machine learning algorithms. Our approach optimizes image reconstruction from sparse and noisy datasets by utilizing physical constraints to guide deep learning models. This integration accelerates the imaging workflow, minimizes the …
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Hierarchical neural control of human postural balance and bipedal walking in sagittal plane
… This thesis proposes an integrated hierarchical neural model of sagittal planar human postural balance and biped walking to 1) investigate an explicit mechanism of the cerebrocerebellar and other related neural systems, 2) explain the principles of human postural balancing and biped walking …
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Non invasive brain stimulation : modeling and experimental analysis of transcranial magnetic stimulations and transcranial DC stimulation as a modality for neuropathology treatment
… treatment by means of both experimental and modeling paradigms. The first and primary modality that will be analyzed is Transcranial Magnetic Stimulation (TMS). TMS is a technique that uses the principle of electromagnetic induction to focus induced currents in the brain and modulate cortical …
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SparkSim: A Counterfactual Approach for Spark Cluster Scheduling
… policy, our method consists of training a neural model to learn about unseen and unbiased computation elements of the cluster, extracting them, and using them as latents in predicting the duration of a workload from an existing trace. We implement this using a counterfactual approach, which …
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A study of synchronization of nonlinear oscillators: Application to epileptic seizures
… a network of a Hodgkin-Huxley type neocortical neural model is constructed. Phase reduction, which is a dimension reduction technique for a stable limit cycle, is applied to the system. The results propose a possible mechanism for the initiation of the drug-induced seizure as a result of a …
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Biophysical model of core & matrix thalamocortical circuitry in rodents & primates
… we created both a rodent and primate biophysical neural model of the core and matrix thalamocortical circuit. We found that the primate model was able to synchronize the network activity faster than the rodent model; primate TC relay neurons were less likely to sustain their activity, and primate …
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Weakly supervised aspect extraction for domain-specific texts
… per each aspect. Specifically, our proposed neural model is equipped with multi-head attention and self-training. The multi-head attention is learned from the seed words to ensure that the aspect-related words in text segments are weighted higher than those unrelated ones. The self-training …
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Towards a Neural Measure of Value and the Modelling of Choice in Strategic Games
Neuroeconomic models take economic theory literally, interpreting hypothesized quantities as observables in the brain in order to provide insight into choice behaviour. This thesis develops a model of the neural decision process in strategic games with a unique mixed strategy equilibrium. In such …
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Mechanisms of Sensory Adaptation in the Primate Visual System
… the signal is weak. We proposed a new dynamic neural network that can account for adaptive properties of motion integration and segregation under various luminance and contrast conditions. Finally, in order to clarify the implications of attentional mechanisms deployed to select inputs …
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Trustworthy Reinforcement Learning under Constraints and Perturbations
… allocation across a sequence, and a disjunctive model to represent situational constraints. (3) To enable multi-agent coordination under situational constraints, we design the Situational-Constrained DBCE (SC-DBCE) solution concept and the Situational-Constrained Correlated Policy Iteration …
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Semantic chunking
… on sentence strings. We present three chunking models: a) rule-based proof-of-concept DMRS chunking system; b) a semi-supervised sequence labelling neural model for surface semantic chunking; c) a system capable of finding semantic chunk boundaries based on the inherent structure of DMRS graphs, …
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The functionality of spatial and time domain artificial neural models.
… Artificial Intelligence systems. Artificial Neural Networks form the foundation of the research and their units, Artificial Neurons, are first compared with alternative models. This initial work is mainly in the spatial-domain and introduces a new neural model, termed a Taylor Series neuron. …
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Monitoring and control for NGL recovery plant
… under typical disturbances. Feedforward neural networks (FFNs) were used for the development of soft sensors used in data-driven control schemes. Given the multitude of data made available by the process simulator, this work aims to develop a demethanizer digital twin that can approximate …
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