Sökning: "Particle Swarm optimering"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden Particle Swarm optimering.

1. 1. Ray-Tracing Modeling of Grating Lobe Level Reduction by Using a Dielectric Dome Antenna

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

   Författare :Lukas Jonasson; [2023]
   Nyckelord :Array antenna; Dielectric dome; Ray-Tracing; Grating lobe; Particle Swarm optimization; Gruppantenn; Dielektrisk kupol; Strålspårning; Gallerlob; Particle Swarm optimering;

   Sammanfattning : With the newly deployed fifth-generation telecommunications system and upcoming sixth-generation, high-gain antennas with hemispherical scanning capabilities are of high interest. Phased array antennas allow for fast scanning capabilities with electronic beam-steering. LÄS MER

3. 2. Investigating the Use of Digital Twins to Optimize Waste Collection Routes : A holistic approach towards unlocking the potential of IoT and AI in waste management

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

   Författare :Aarati Medehal; [2023]
   Nyckelord :Internet of Things; Industry 4.0; Smart Cities; Artificial Intelligence; Travelling Salesman problem; Vehicle Routing Problems; Digital Twins; Waste collection; Optimization; Metaheuristics algorithms; Ant Colony Optimization; Simulated Annealing; Particle Swarm; Tabu Search; Genetic Algorithm; Ontology; Digital Twin Definition Language; Internet of Things; Industri 4.0; Smarta städer; Artificiell intelligens; Travelling Salesman problem; Vehicle Routing Problems; Digitala tvillingar; Sophämtning; Optimering; Metaheuristika algoritmer; Ant Colony optimering; Simulerad glödgning; Partikelsvärm; Tabu-sökning; Genetisk algoritm; Ontologi; Digitala tvillingar defintitionsspråk;

   Sammanfattning : Solid waste management is a global issue that affects everyone. The management of waste collection routes is a critical challenge in urban environments, primarily due to inefficient routing. This thesis investigates the use of real-time virtual replicas, namely Digital Twins to optimize waste collection routes. LÄS MER

4. 3. Auto-Tuning Apache Spark Parameters for Processing Large Datasets

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

   Författare :Shidi Zhou; [2023]
   Nyckelord :Apache Spark; Cloud Environment; Spark Configuration Parameter; Resource Utilization; Ridge Regression; Elastic Net; Random Forest; Deep Neural Network; Bayesian Optimization; Particle Swarm Optimization.; Apache Spark; Molnmiljö; Apache Spark konfigurationsparameter; Resursutnyttjande; Ridge-regression; Elastisk nät; Slumpskog; Djupt neuralt nätverk; Bayesiansk optimering; Partikelsvärmsoptimering.;

   Sammanfattning : Apache Spark is a popular open-source distributed processing framework that enables efficient processing of large amounts of data. Apache Spark has a large number of configuration parameters that are strongly related to performance. Selecting an optimal configuration for Apache Spark application deployed in a cloud environment is a complex task. LÄS MER

5. 4. Computing Equivalent hydropower models in Sweden using inflow clustering

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

   Författare :Daniel Lilja; [2023]
   Nyckelord :Hydropower; Equivalent; Bilevel; Optimization; Clustering; Inflow; Sweden; Vattenkraft; Ekvivalent; Bilevel; Optimering; Klusteranalys; Inflöde; Sverige;

   Sammanfattning : To simulate a hydropower system, one can use what is known as a Detailed model. However, due to the complexity of river systems, this is often a computationally heavy task. Equivalent models, which aim to reproduce the result of a Detailed model, are used to significantly reduce the computation time for these simulations. LÄS MER

7. 5. Evaluation of hyperparameter optimization methods for Random Forest classifiers

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

   Författare :Rasmus Nygren; Aleks Petkov; [2021]
   Nyckelord :;

   Sammanfattning : In order to create a machine learning model, one is often tasked with selecting certain hyperparameters which configure the behavior of the model. The performance of the model can vary greatly depending on how these hyperparameters are selected, thus making it relevant to investigate the effects of hyperparameter optimization on the classification accuracy of a machine learning model. LÄS MER