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 123 for “"RNN"”.
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RNN-Based Generation of Polyphonic Music and Jazz Improvisation
… recurrent neural network architecture <em>char-rnn</em>. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.</p>
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Application of RMT-RNN improved decomposition onto defected system
… Matrix Theory coupled with Neural Networks (RMT-RNN) to large static systems with relatively large disorder in mesoscopic systems. It is a new algorithm that can quickly decompose random matrices with real eigenvalues for further study of physical properties, such as transmission probability, …
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Investigating the Use of Inductive Transfer Learning and RNN to Quantify Extreme Event Statistics of Ship Motions
Ship motion software has been a critical tool for designers to study the extreme responses of ships in irregular waves. These studies and simulations often take thousands of hours to predict and analyze the ship’s motion. Simulation results are often imperative to ensure the development of accurate …
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Modeling of electrical circuit with recurrent neural networks
… methodology using recurrent neural networks (RNNs) is developed. The methodology covers model structure selection, data generation, training, and model implementation for circuit simulation. Several different RNN structures are investigated and their capabilities in circuit modeling are …
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Forecasting Energy Consumption using Sequence to Sequence Attention models
… (S2S) Recurrent Neural Networks (RNNs) with attention for electrical load forecasting. The S2S and S2S attention architectures commonly used for neural machine translation are adapted for energy forecasting. An RNN enables capturing time dependencies present in the load data, while …
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Development of an Emotion Recognition Classifier from Body Language Using Deep Learning for the Children with Autism to Help Identifying Human Emotions
… of CNN and Recurrent Neural Network (CNN+RNN), and the combination of CNN and Long Short-Term Memory (CNN+LSTM). For this research, though the CNN model provides better accuracy than CNN+RNN and CNN+LSTM, CNN model ignores the temporal information from the sequenced video data. The CNN+RNN …
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Multi-objective evolutionary neural architecture search for recurrent neural networks
… for NAS to evolve recurrent neural network (RNN) architectures. This allows for the consideration of the underlying computational resource requirements of the RNN models while maintaining an acceptable model performance-related objective. Additionally, methods such as weight inheritance, …
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Time series forecasting with recurrent neural networks
… series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM networks and GRU networks are …
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A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches
… algorithm and Recurrent Neural Network (RNN) for use on videos of real collegiate pitchers, we aim to distinguish physiological differences between these types of pitches and use our RNN to automatically detect illegal pitches. Our pose estimation results demonstrate the algorithm's …
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An improved algorithm for iris classification by using support vector machine and binary random machine learning
… Neighbors(k-NN) and Random Nearest Neighbors (RNN) as a method. The first objective of this study is to improve a new algorithm technique for classification. The new algorithm come from a combination of an ideas of k-NN algorithm and ensemble concept. The second objective is to conduct a …
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Long short-term memory recurrent neural networks for classification of acute hypotensive episodes
… short-term memory recurrent neural network (LSTM RNN) models which predict whether a patient will experience an AHE or not based on a time series of mean arterial blood pressure (ABP). A 2-layer, 128-hidden unit LSTM RNN trained with rmsprop and dropout regularization achieves sensitivity of 78% …
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Gesture Recognition in Tennis Biomechanics
… sequence classifiers, recurrent neural networks (RNN), to predict tennis ball trajectory. In attempt to learn temporal dependencies within a tennis swing, we implemented gate-augmented RNNs. This study compared the RNN to two gated models; gated recurrent units (GRU), and long short-term memory …
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Modeling repairable system failure data using NHPP reliability growth mode.
… Time to Failure recurrent neural network (WTTE-RNN) framework, a probabilistic deep learning model for failure data, is also explored. However, we find that the WTTE-RNN framework is only appropriate failure data with independent and identically distributed interarrival times of successive …
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Barometer-Based Tactile Sensing: Characterization, Processing, and Applications for Dynamic Manipulation
… thesis introduces a recurrent neural network (RNN) architecture that captures viscoelastic transients in the sensor response. The proposed methods are evaluated on two sensor geometries: a spherical sensor and a slimmer ellipsoid variant. An automated data collection pipeline is developed to …
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Scalable and Reconfigurable True-Time Delay Line for Integrated Radio-Frequency Recurrent Neural Processors
… for analog recurrent neural networks (RNNs) operating directly in the RF-domain. Previous research has shown the RF-domain RNNs are capable of performing real-time anomaly detection in wireless systems while reducing the inference latency of the wireless system to be one RF clock cycle. …
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Predicting price volatility crytocurrency ethereum
… absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309.
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ADVANCED MACHINE LEARNING MODELS IN PREDICTION OF MEDICAL CONDITIONS
… Memory (LSTM) Recurrent Neural Network (RNN) deep learning model to predict will the patient develop AD. The LSTM RNN method performed significantly better when learning from the SCRP dataset than when datasets were selected naïvely. Accurate prediction of AD is significant in the …
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MACHINE LEARNING FOR CONSTITUTIVE MODELLING
… materials, the recurrent neural network (RNN) is adopted for the training of path dependent mechanical responses, due to its advanced learning ability for history dependent behavior. However, the huge data generation time of RNN surrogate model limits the off-line learning efficiency. This …
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Optimización de la configuración mecánica y de los efectos del entrenamiento en parques biosaludables sobre la condición física y la salud.
… y en el nuevo prototipo con un agarre neutro (RNN), en pronación (RNP), en supinación (RNS), pronación cerrado (RNPC), y también dichos agarres de la nueva maquinaria con una sobrecarga (+S) en la musculatura (tríceps braquial, porción superior y medial del trapecio, bíceps braquial, dorsal …
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