Sökning: "KDE"

Visar resultat 1 - 5 av 13 uppsatser innehållade ordet KDE.

1. 1. Clustering on groups for human tracking with 3D LiDAR

   Master-uppsats, Umeå universitet/Institutionen för datavetenskap

   Författare :Simon Utterström; [2023]
   Nyckelord :Computer Vision; Computer Science; AI; Machine Learning; clustering; Kernel Density Clustering; tracking; LiDAR; 3D LiDAR; tracking; human; pedestrian; real time; Datavetenskap; Dataseende; clustering; SLR; CVC; KDEG; KDE; Kernel Density Clustering; HDBSCAN; DBSCAN; LiDAR; point cloud; tracking; human; pedestrian;

   Sammanfattning : 3D LiDAR people detection and tracking applications rely on extracting individual people from the point cloud for reliable tracking. A recurring problem for these applications is under-segmentation caused by people standing close or interacting with each other, which in turn causes the system to lose tracking. LÄS MER

3. 2. Sentiment Analysis and Time-series Analysis for the COVID-19 vaccine Tweets

   Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Gowtham Kumar Sandaka; Bala Namratha Gaekwade; [2021]
   Nyckelord :COVID-19 vaccine; Sentiment analysis; Time-based analysis; Twitter data; VADER.;

   Sammanfattning : Background: The implicit nature of social media information brings many advantages to realistic sentiment analysis applications. Sentiment Analysis is the process of extracting opinions and emotions from data. As a research topic, sentiment analysis of Twitter data has received much attention in recent years. LÄS MER

4. 3. Kernel density estimators as a tool for atmospheric dispersion models

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysik

   Författare :Daniel Egelrud; [2021]
   Nyckelord :Particle model; LPELLO; kernel density estimation; field of concentration; post-processing;

   Sammanfattning : Lagrangian particle models are useful for modelling pollutants in the atmosphere. They simulate the spread of pollutants by modelling trajectories of individual particles. However, to be useful, these models require a density estimate. The standard method to use has been boxcounting, but kernel density estimator (KDE) is an alternative. LÄS MER

5. 4. Probabilistic Regression using Conditional Generative Adversarial Networks

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Joel Oskarsson; [2020]
   Nyckelord :machine learning; ml; regression; probabilistic; distribution; cgan; gan; conditional gan; adversarial networks; neural network; deep learning; f-gan; f-cgan; f-divergence; adversarial training; bimodal; heteroskedastic; mmd; maximum mean discrepancy; gmmn; generative moment matching network; conditional gmmn; ipm; kde; cgan evaluation; cgan regression; gan regression; cgan-regression; regression using gan; deep; nn; implicit; generative; conditional; model; complex noise; aleatoric; uncertainty; dctd; mdn; heteroskedastic regression; gp;

   Sammanfattning : Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. LÄS MER

7. 5. The Missing People of Malthi : A kernel density analysis based on Middle Helladic Ceramics

   Master-uppsats, Uppsala universitet/Institutionen för arkeologi och antik historia

   Författare :Anna Sunneborn Gudnadottir; [2019]
   Nyckelord :GIS; KDE; Bronze Age Greece; Middle Helladic; Malthi; household archaeology; Formation processes; Messenia;

   Sammanfattning : This study aims to identify human interference and tendencies in the Bronze Age settlement of Malthi, Greece. It has employed a spatial analysis, a Kernel Density Estimate, to locate areas of anthropic interference and evaluate if the initial excavation report, despite its flaws, can be used in newer research. LÄS MER