Global ETD Search
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Showing 1 to 8 of 8 for “"ConvLSTM"”.
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Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model
… and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and Sentinel Hub burned scars satellite data to train a logistic method for …
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Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks
… and hybrid models such as CNN- LSTM and ConvLSTM. Computer simulation results show that all artificial neural networks are able to provide satisfactory prediction. Among them, the multi-layer feedforward neural networks require less training time and often give reasonable results; while …
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Modeling Hourly Storm Surges using Deep Learning Techniques
… Long Short-Term Memory (LSTM), and a hybrid ConvLSTM are employed to model surges using atmospheric variables (e.g., sea level pressure and winds) as predictors. ConvLSTM outperforms the others in predicting the overall variability of surges, extreme surge events, and statistical attributes …
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Machine Learning Application in Energy Storage System’s State Estimation: State of Health (SOH)
… short-term memory (LSTM), convolutional LSTM (ConvLSTM), and deep convolutional neural network (DCNN) architecture are acceptable by industry standards, with mean absolute percentage error (MAPE) less than 3%. The promising results obtained in this study indicate that the presented DL models in …
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DEVELOPMENT OF SPATIOTEMPORAL CONGESTION PATTERN OBSERVATION MODEL USING HISTORICAL AND NEAR REAL TIME DATA
… using Convolutional Long Short Term Memory (ConvLSTM) networks.</p>
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The role of representations in human activity recognition
… deep learning representation based on the DeepConvLSTM architecture. This is motivated by the promises deep learning methods offer – they learn end-to-end, eliminate the necessity for hand crafting features and generalize well across tasks and datasets. The choice of studying unsupervised …
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A machine learning study of wind-driven runback/flow-off multiphase flows pertinent to aircraft icing phenomena
… In the present study, a deep-learning framework ConvLSTM-AE is developed to forecast the intricate spatial-temporal progression of an experimental multiphase WDRWF flow on a flat plate, considering different water flow rates and wind speeds. To predict the WDRWF flow with a long-time evolution …
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Hluboké učení pro analýzu 3D geometrie v medicíně
Architektúry hlbokých neurónových sietí navrhnuté pre tradičné signály, ako sú pravidelne vzorkované obrázky a mriežky, sa nedajú priamo previesť na geometrické reprezentácie s nepravidelným charakterom, ako napríklad triangulované povrchy či mračná bodov. Keďže nástroje, ktoré produkujú tieto 3D …