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Showing result 1 - 5 of 110 essays matching the above criteria.

1. 1. Long-term Forecasting Heat Use in Sweden's Residential Sector using Genetic Algorithms and Neural Network

   University essay from Högskolan i Halmstad/Akademin för företagande, innovation och hållbarhet

   Author : Alireza Momtaz; Mohammad Befkin; [2024]
   Keywords : Genetic Algorithm; Neural Network; Forecasting; Heat Use;

   Abstract : In this study, the parameters of population, gross domestic product (GDP), heat price, U-value, and temperature have been used to predict heat consumption for Sweden till 2050. It should be noted that the heat consumption has been considered for multi-family houses. Most multi-family houses (MFH) get their primary heat from district heating (DH). READ MORE

3. 2. Resource Usage Prediction for Parameter Sweeps with Biochemical System Simulations

   University essay from Uppsala universitet/Tillämpad beräkningsvetenskap

   Author : Minjia Zhou; [2024]
   Keywords : ;

   Abstract : Exploring the behavior of biochemical systems when subjected to certain internal and external changes is fascinating, and these variations can be investigated through computational simulations. However, the computational cost of simulations is often quite high, necessitating an understanding of the computational requirements and resource utilization of these simulations. READ MORE

4. 3. Implementations and evaluation of machine learning algorithms on a microcontroller unit for myoelectric prosthesis control

   University essay from Lunds universitet/Avdelningen för Biomedicinsk teknik

   Author : Jonathan Benitez; [2023]
   Keywords : microcontroller; deep learning; artificial neural network; electromyography; myoelectric prosthesis; Technology and Engineering;

   Abstract : Using a microcontroller unit to implement different machine learning algorithms for myoelectric prosthesis control is currently feasible. Still there are hardware and timing constraints that need to be accounted for. READ MORE

5. 4. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   University essay from KTH/Mekatronik och inbyggda styrsystem

   Author : Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Keywords : Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Abstract : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. READ MORE

6. 5. Physiology-Guided Machine Learning for Improved Reliability of Non-Invasive Assessment of Pulmonary Hypertension

   University essay from Linköpings universitet/Avdelningen för medicinsk teknik

   Author : Frida Hermansson; [2023]
   Keywords : Pulmonary Hypertension; pulmonary hypertension; improving; physiological-guided; machine learning; neural networks; NN; artificial neural networks; non-invasive; PH; tricuspid regurgitation; peak tricuspid regurgitation velocity; tricuspid regurgitation velocity; right ventricular systolic pressure; VGG16; Unet; TR-CNN; CNN; pulmonell hypertension; förbättra; fysiologisk-guidning; neurala nätverk; trikuspidal regurgitation; maximal trikuspidal regurgitation; icke-invasivt;

   Abstract : Diagnosing pulmonary hypertension (PH) with right heart catheterization (RHC) is associated with a risk for complications and high expenses, leading to late diagnoses [1]. Transthoracic echocardiography can be used to assess non-invasive indicators for PH such as right ventricular systolic pressure (RVSP), which can be estimated by combining the peak tricuspid regurgitation velocity (TRV) with the estimated right arterial pressure (RAP). READ MORE