Sökning: "Convolutional LSTM"

Visar resultat 1 - 5 av 12 avhandlingar innehållade orden Convolutional LSTM.

1. 1. Supervised and Unsupervised Deep Learning Models for Flood Detection

   Författare :Ritu Yadav; Yifang Ban; Andrea Nascetti; Nicolas Audebert; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Floods; Remote Sensing; Sentinel-1 SAR; Segmentation; Change Detection; DEM; Data Fusion; Time Series; Deep Learning; Unsupervised Learning; Contrastive Learning; Self-Attention; Convolutional LSTM; Variational AutoEncoder VAE ; Geoinformatik; Geoinformatics;

   Sammanfattning : Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. LÄS MER

3. 2. Multi-LSTM Acceleration and CNN Fault Tolerance

   Författare :Stefano Ribes; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Compression; SVD; LSTMs; CNNs; Fault Tolerance; Machine Learning; FPGA; Roofline Model; HLS; Caffe;

   Sammanfattning : This thesis addresses the following two problems related to the field of Machine Learning: the acceleration of multiple Long Short Term Memory (LSTM) models on FPGAs and the fault tolerance of compressed Convolutional Neural Networks (CNN). LSTMs represent an effective solution to capture long-term dependencies in sequential data, like sentences in Natural Language Processing applications, video frames in Scene Labeling tasks or temporal series in Time Series Forecasting. LÄS MER

4. 3. Drill Failure Detection based on Sound using Artificial Intelligence

   Författare :Thanh Tran; Jan Thim; Sebastian Bader; Kalle Åström; Mittuniversitetet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Convolutional neural network; machine failure detection; Mel-spectrogram; long short-term memory; sound signal processing;

   Sammanfattning : In industry, it is crucial to be able to detect damage or abnormal behavior in machines. A machine's downtime can be minimized by detecting and repairing faulty components of the machine as early as possible. It is, however, economically inefficient and labor-intensive to detect machine fault sounds manual. LÄS MER

5. 4. Time, space and control: deep-learning applications to turbulent flows

   Författare :Luca Guastoni; Ricardo Vinuesa; Hossein Azizpour; Philipp Schlatter; Andrea Beck; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; turbulence; deep learning; deep reinforcement learning; flow control; turbulens; djupinlärning; djupförstärkningsinlärning; flödeskontroll; Teknisk mekanik; Engineering Mechanics;

   Sammanfattning : In the present thesis, the application of deep learning and deep reinforcement learning to turbulent-flow simulations is investigated. Deep-learning models are trained to perform temporal and spatial predictions, while deep reinforcement learning is applied to a flow-control problem, namely the reduction of drag in an open channel flow. LÄS MER

7. 5. On Deep Machine Learning Based Techniques for Electric Power Systems

   Författare :Ebrahim Balouji; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep Learning; Cable faults; phase locked loop; Flicker; Harmonics and Interharmonics; Reinforcement learning; Voltage Dip; Active Power filter; Machine Learning; Voltage fluctuation; Partial Discharges;

   Sammanfattning : This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. LÄS MER