Sökning: "novelty detection"

Visar resultat 1 - 5 av 19 uppsatser innehållade orden novelty detection.

1. 1. Neural Networks for Predictive Maintenance on Highly Imbalanced Industrial Data

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Oscar Montilla Tabares; [2023]
   Nyckelord :Class Imbalance; Cost Sensitivity; Cost-Sensitive Learning; Focal Loss; Binary Classification; Machine Learning; Deep Learning;

   Sammanfattning : Preventive maintenance plays a vital role in optimizing industrial operations. However, detecting equipment needing such maintenance using available data can be particularly challenging due to the class imbalance prevalent in real-world applications. LÄS MER

3. 2. Phosphorous Precipitation in Source Separated Greywater for Direct Environmental Release.

   Master-uppsats, Lunds universitet/Kemiteknik (CI)

   Författare :Ashley Hall; [2022]
   Nyckelord :water; wastewater; source separation; greywater; phosphorous; precipitation; utfällning; Chemical Engineering; Technology and Engineering;

   Sammanfattning : As our understanding of the impact humans have on the environment changes, so too do the mitigation strategies we employ to prevent it. One major source of anthropogenic pollution is wastewater effluent. LÄS MER

4. 3. Anomaly or not Anomaly, that is the Question of Uncertainty : Investigating the relation between model uncertainty and anomalies using a recurrent autoencoder approach to market time series

   Master-uppsats, Umeå universitet/Institutionen för datavetenskap

   Författare :Anton Vidmark; [2022]
   Nyckelord :Uncertainty in deep learning; Bayesian; anomaly detection; novelty detection; stock market; time series;

   Sammanfattning : Knowing when one does not know is crucial in decision making. By estimating uncertainties humans can recognize novelty both by intuition and reason, but most AI systems lack this self-reflective ability. In anomaly detection, a common approach is to train a model to learn the distinction between some notion of normal and some notion of anomalies. LÄS MER

5. 4. Enhancing Simulated Sonar Images With CycleGAN for Deep Learning in Autonomous Underwater Vehicles

   Master-uppsats, KTH/Matematisk statistik

   Författare :Aron Norén; [2021]
   Nyckelord :Deep Learning; Machine Learning; Sonar; Simulation; GAN; cycleGAN; YOLO-v4; Data Sparsity; Uncertainty Estimations; Djupinlärning; maskininlärning; sonar; simulering; GAN; cycleGAN; YOLO-v4; gles data; osäkerhetsanalys;

   Sammanfattning : This thesis addresses the issues of data sparsity in the sonar domain. A data pipeline is set up to generate and enhance sonar data. The possibilities and limitations of using cycleGAN as a tool to enhance simulated sonar images for the purpose of training neural networks for detection and classification is studied. LÄS MER

7. 5. A Real- time Log Correlation System for Security Information and Event Management

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

   Författare :Clémence Dubuc; [2021]
   Nyckelord :Correlation; SIEM; Security Logs; Apache Spark; Elastic Search; Korrelation; SIEM; Säkerhetsloggar; Apache Spark; Elastic Search;

   Sammanfattning : The correlation of several events in a period of time is a necessity for a threat detection platform. In the case of multistep attacks (attacks characterized by a sequence of executed commands), it allows detecting the different steps one by one and correlating them to raise an alert. LÄS MER