Essays about: "ConvLSTM"
Showing result 1 - 5 of 7 essays containing the word ConvLSTM.
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1. Demand Forecasting of Outbound Logistics Using Neural Networks
University essay from Malmö universitet/Fakulteten för teknik och samhälle (TS)Abstract : Long short-term volume forecasting is essential for companies regarding their logistics service operations. It is crucial for logistic companies to predict the volumes of goods that will be delivered to various centers at any given day, as this will assist in managing the efficiency of their business operations. READ MORE
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2. Data-driven cyberattack detection for microgrids
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Microgrids are undergoing higher penetrations of renewables and associated power electronics, along with precise and sophisticated control and communication networks. However, such cyber-physical systems might suffer from potential cybersecurity threats and inherent low inertia. READ MORE
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3. Replicating noise in video : a comparison between physics-based and deep learning models for simulating noise
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Algorithms that track objects in video following Newtonian physics can often be affected by noise in the data. Some types of noise might be hard or expensive to capture, so to be able to augment or generate a new data set from models replicating a certain type of noise can be useful. READ MORE
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4. Video Saliency Detection using Deep Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : A deep learning model for video saliency detection is proposed and trained. The neural network architecture combines recent innovations in the field: A twostream approach merges two separate input streams for appearance and motion aspects of saliency. Pre-trained convolutional features detect objectness. READ MORE
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5. Short-Term Forecasting of Taxi Demand using a two Channelled Convolutional LSTM network
University essay from Linköpings universitet/Artificiell intelligens och integrerade datorsystemAbstract : In this thesis a model capable of predicting taxidemand with high accuracy across five different real world single company datasets is presented. The model uses historical drop off and arrival information to make accurate shortterm predictions about future taxi demand. The model is compared to and outperforms both LSTM and statistical baselines. READ MORE