Sökning: "Initialization method"

Visar resultat 1 - 5 av 38 uppsatser innehållade orden Initialization method.

1. 1. Heart rate estimation from wrist-PPG signals in activity by deep learning methods

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Marie-Ange Stefanos; [2023]
   Nyckelord :Deep Learning; Medical Data; Signal Processing; Heart Rate Estimation; Wrist Photoplethysmography; Djup lärning; Medicinska Data; Signalbehandling; Pulsuppskattning; Handledsfotopletysmograf;

   Sammanfattning : In the context of health improving, the measurement of vital parameters such as heart rate (HR) can provide solutions for health monitoring, prevention and screening for certain chronic diseases. Among the different technologies for HR measuring, photoplethysmography (PPG) technique embedded in smart watches is the most commonly used in the field of consumer electronics since it is comfortable and does not require any user intervention. LÄS MER

3. 2. Collaborative Mapping with Drone Swarms Utilizing Relative Distance Measurements

   Master-uppsats, Linköpings universitet/Reglerteknik

   Författare :Johan Forsman; Carl Tidén; [2023]
   Nyckelord :;

   Sammanfattning : The field of use for unmanned aerial vehicles, UAVs, has completely exploded in the last decade. Today they are used for surveillance missions and inspecting places that are difficult for people to access. To increase the efficiency and robustness in the execution of these types of missions, swarms of cooperating drones can be used. LÄS MER

4. 3. Initialization of the k-means algorithm : A comparison of three methods

   Kandidat-uppsats, Stockholms universitet/Matematiska institutionen

   Författare :Simon Jorstedt; [2023]
   Nyckelord :k-means algorithm; clustering algorithm; Unsupervised Machine Learning;

   Sammanfattning : k-means is a simple and flexible clustering algorithm that has remained in common use for 50+ years. In this thesis, we discuss the algorithm in general, its advantages, weaknesses and how its ability to locate clusters can be enhanced with a suitable initialization method. LÄS MER

5. 4. Investigating Relations between Regularization and Weight Initialization in Artificial Neural Networks

   Kandidat-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisation

   Författare :Rasmus Sjöö; [2022]
   Nyckelord :Artificial Neural Networks; L1 Regularization; L2 Regularization; Loss Function; Maximum Likelihood; Regularization Strength Synthetic Data Generation; Weight Initialization; Physics and Astronomy;

   Sammanfattning : L2 regularization is a common method used to prevent overtraining in artificial neural networks. However, an issue with this method is that the regularization strength has to be properly adjusted for it to work as intended. This value is usually found by trial and error which can take some time, especially for larger networks. LÄS MER

7. 5. Comparison of initialization methods of K-means clustering for small data

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Liam Tabibzadeh; [2022]
   Nyckelord :Statistics; Clustering; K-means; Hierarchical; Initialization method; Simulation; Experiment; Factorial Design; Monte Carlo Simulation; Monte Carlo; Data; Classification; Cluster Analysis; R;

   Sammanfattning : Clustering of observations into groups arises as a fundamental challenge both in academia and industry. Many clustering algorithms exist, and the most widely used clustering algorithm, the K-means, notably suffers from sensitivity to initial allocation of cluster centers. LÄS MER