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 57 for “"GRU"”.
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Implantación de un ERP en Muebles GRU
Este Trabajo Fin de Grado estudia cómo en Muebles GRU, una pyme de venta de muebles de Cambrils formada cinco empleados, ha implantado un sistema ERP para mejorar la gestión de sus ventas, el control de inventario, la contabilidad y su actividad de comercio electrónico. Se analizaron distintas …
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TranDynaMo: A comparative study of transformer and GRU performance in modeling dynamical systems
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
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GEOSPATIAL MODELLING OF PRAIRIE RIVERS: LINKING PHYSICAL INDICATORS OF FISH HABITAT TO LARGE SCALE GEOMORPHIC PATTERNS IN RIVER SYSTEMS USING GEOMORPHIC RESPONSE UNITS (GRU)
… has been lacking. The Geomorphic Response Unit (GRU) method provides a novel approach to identifying large scale patterns in geomorphic character that provide a link between the hydrological regime and different habitat types to which species respond. Specifically, I investigated whether …
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Data-Driven Decoding of Quantum Surface Codes using Recurrent Neural Networks
… architectures, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), are investigated as their are able to capture temporal dependencies and model sequential data. GRU and LSTM models are trained using the data generated with the help of the Stim simulator for quantum circuits …
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Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries
… method that integrates a gated recurrent unit (GRU) network with physics-informed neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical physical parameters, …
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Engineering Design Automation via Imitation Learning and Reinforcement Learning
… employing recurrent neural networks such as GRU, LSTM, and simple-RNN. We define a metric Q-score, which quantifies design quality on a scale between 0 and 1, with higher values indicating better design quality. Our findings demonstrate that the GRU architecture outperforms both LSTM and …
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A New Machine Learning Algorithm for Detection of Stray Clays
… data, which is based on gated recurrent units (GRU) in neural networks. The combination of DOG and GRU methods assists to discover the accurate position of stray clays with time-series analysis. We optimize the strength of stray clay from clay seam number 414 by suppressing the stray clay signal …
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Efficient methods for mapping neural machine translator on FPGAs
… such as bidirectional gated recurrent units (GRU), attention mechanisms, and beam-search algorithms, for improved translation quality. However, with the increasing problem size, the real-life NMT models have become much more complicated and difficult to implement on hardware for acceleration …
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Short-term traffic forecasting for a smart satellite communications system
… (LSTM) or Gated Recurrent Unit neural networks (GRU) to forecast terminal traffic. Each algorithm was tuned using a two-stage design of experiments process consisting of a fractional screening design to identify impactful hyper-parameters and a central composite design to find optimal model …
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Development of risk-based groundwater operating rules: a case study of Siloam Village, South Africa
… variability. A groundwater resource unit (GRU) was delineated and its hydrogeological conceptual model developed. Automatic curve matching was used to identify appropriate aquifer models and test solutions for estimating hydraulic characteristics (storativity, transmissivity and hydraulic …
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Path Following using Frequency Modulated Continuous Wave millimetre-wave Automotive Radar
… result is an efficient, Gated Recurrent Unit (GRU) deep neural network that has been trained on custom synthetically trained data of a vehicle following a path of retro-reflectors. The GRU network was then experimentally validated using a 60 kg skid-steer robot showing promising path planning …
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Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security.
… a combination of a gated recurrent unit (GRU)-based recurrent neural network (RNN) model and a time-based model. CAN-CID is designed to detect injection and masquerade attacks on the CAN bus. It achieved an F1 score of over 99% on three publicly available CAN attack datasets for 10 …
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Application of data-driven neural networks to bio-inspired lattice design and prediction of multiphysics solution fields
… transverse compression. A gated recurrent unit (GRU) predicts stress-strain curves and energy absorption from geometric inputs. Systematic variations of design parameters, such as tubule shapes and orientations, enable the creation of a dataset comprising 128,000 designs. This approach reveals …
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Machine learning approaches for malware classification based on hybrid artefacts
… concatenating the Gated Recurrent Unit (GRU) and Transformers model can yield the highest accuracy at 97% for Noriben operations, while GRU can achieve the maximum accuracy for Opcode sequences at 89%.
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Využití strojového učení pro predikci poruchových stavů tepelného čerpadla
… se pro tyto účely ukázala rekurentní síť GRU, která vykazovala vysokou přesnost a robustnost vůči nastavení hyperparametrů. Práce zároveň identifikovala limity přenositelnosti modelů mezi různými instalacemi a zdůraznila význam měření průtoků pro zvýšení obecnosti predikcí.
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Using deep learning to classify community network traffic
… Neural Networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU to classify encrypted internet traffic collected from a community network. In this study, we performed a comparative analysis by adopting an ML model (Support vector machine). Machine against DL models (Convolutional …
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On the computational power of RNNs
… neural network (RNN) and Gated Recurrent Unit (GRU) have gained prominence as end-to-end learning architectures for natural language processing tasks. But what is the computational power of such systems? We prove that finite precision RNNs with one hidden layer and ReLU activation and finite …
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Time series forecasting with recurrent neural networks
… series. RNN models such as LSTM networks and GRU networks are widely used in literature. Besides, different feature engineering methods such as CEEMDAN are also tools employed in the literature to improve prediction accuracy. In this paper, we will introduce different models and methods of …
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