Essays about: "neural network power system"

Showing result 1 - 5 of 86 essays containing the words neural network power system.

  1. 1. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods

    University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

    Author : Nikolaos Staikos; [2024]
    Keywords : Physical Geography; Ecosystem Analysis; Bike-sharing demand; Machine learning; Deep learning; Spatial regression; Graph Convolutional Neural Network; Multiple Linear Regression; Multilayer Perceptron Regressor; Support Vector Machine; Random Forest Regressor; Urban environment; Micro-mobility; Station planning; Geomatics; Earth and Environmental Sciences;

    Abstract : Public Bike-Sharing systems have been employed in many cities around the globe. Shared bikes are an efficient and convenient means of transportation in advanced societies. Nonetheless, station planning and local bike-sharing network effectiveness can be challenging. READ MORE

  2. 2. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jiayi Feng; [2023]
    Keywords : DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

    Abstract : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. READ MORE

  3. 3. Anomaly Detection in the EtherCAT Network of a Power Station : Improving a Graph Convolutional Neural Network Framework

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Niklas Barth; [2023]
    Keywords : Unsupervised Learning; Multivariate Time Series; Graph Convolutional Neural Networks; Anomaly Detection; Industrial Control System; EtherCAT; Power Station; Electricity Grid;

    Abstract : In this thesis, an anomaly detection framework is assessed and fine-tuned to detect and explain anomalies in a power station, where EtherCAT, an Industrial Control System, is employed for monitoring. The chosen framework is based on a previously published Graph Neural Network (GNN) model, utilizing attention mechanisms to capture complex relationships between diverse measurements within the EtherCAT system. READ MORE

  4. 4. Deep Neural Networks as SurrogateModels for Fuel Performance Codes

    University essay from Uppsala universitet/Tillämpad kärnfysik

    Author : Wenhan Zhou; [2023]
    Keywords : Transuranus; AI; Nuclear Fuel Rods;

    Abstract : The core component of a nuclear power plant is the reactor and the fuel rods that supply it with fission fuel. Efficient and safe energy extraction is thus highly dependent on the reactor design and the conditions of the fuel rods. To anticipate high-quality operation and potential risks in advance, one must perform simulations on the fuel rods. READ MORE

  5. 5. Rotor temperature estimation in Induction Motors with Supervised Machine Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Christopher Gauffin; [2023]
    Keywords : Induction motors; Supervised Machine learning; Power converters; Parameter estimation; Embedded systems; Induktionsmotorer; Övervakad maskininlärning; Strömkonverterare; Parameterestimering; Inbyggda system;

    Abstract : The electrification of the automotive industry and artificial intelligence are both growing rapidly and can be greatly beneficial for a more sustainable future when combined. Induction machines exhibit many complex relationships between physical and electromagnetic properties that must be calculated in order to produce the correct quantities of torque and speed commanded by the driver. READ MORE