Sökning: "Automated data labeling"

Visar resultat 1 - 5 av 14 uppsatser innehållade orden Automated data labeling.

1. 1. Image Quality Assessment Pipeline and Semi-Automated Annotation method for Synthetic Data

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Liam Le Tran; Edina Dedovic; [2023-10-24]
   Nyckelord :Facial Expression Recognition; FACS; Action Units; styleGAN2-ada; synthetic data; Image Quality Assessment; Multi-stage Pre-training; Pipeline Processing; Semi-automated Human Annotation;

   Sammanfattning : Predicting human emotions through facial expression, particularly in relation to medication field such as clinical trial settings, has garnered scientific interest in recent years due to significant understanding of the impact of treatment on emotions and social functioning. This thesis aims to improve performance of a FER model using large scale of synthetic data. LÄS MER

3. 2. Duplicate detection of multimodal and domain-specific trouble reports when having few samples : An evaluation of models using natural language processing, machine learning, and Siamese networks pre-trained on automatically labeled data

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

   Författare :Viktor Karlstrand; [2022]
   Nyckelord :Duplicate detection; Bug reports; Trouble reports; Natural language processing; Information retrieval; Machine learning; Siamese neural network; Transformers; Automated data labeling; Shapley values; Dubblettdetektering; Felrapporter; Buggrapporter; Naturlig språkbehandling; Informationssökning; Maskininlärning; Siamesiska neurala nätverk; Transformatorer; Automatiserad datamärkning; Shapley-värden;

   Sammanfattning : Trouble and bug reports are essential in software maintenance and for identifying faults—a challenging and time-consuming task. In cases when the fault and reports are similar or identical to previous and already resolved ones, the effort can be reduced significantly making the prospect of automatically detecting duplicates very compelling. LÄS MER

4. 3. Defect classification in LPBF images using semi-supervised learning

   Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013); Karlstads universitet/Avdelningen för datavetenskap

   Författare :Anton Göransson; [2022]
   Nyckelord :Additive manufacturing; Laser powder bed fusion; Machine learning; Siamese neural networks; Deep learning; Defect classification;

   Sammanfattning : Laser powder bed fusion is an additive manufacturing technique that is capable of building metallic parts by spreading many layers of metal powder over a build surface and using a laser to melt specific sections of the surface. The part is built by melting consecutive layers on top of each other until the design is completed. LÄS MER

5. 4. Predictive maintenance using NLP and clustering support messages

   Magister-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Författare :Ugur Yilmaz; [2022]
   Nyckelord :Predictive maintenance; support messages; NLP; unsupervised clustering; intent recognition; LDA; UMAP; HDBSCAN; BERT; Swedish BERT KB-BERT ; Billogram;

   Sammanfattning : Communication with customers is a major part of customer experience as well as a great source of data mining. More businesses are engaging with consumers via text messages. Before 2020, 39% of businesses already use some form of text messaging to communicate with their consumers. Many more were expected to adopt the technology after 2020[1]. LÄS MER

7. 5. Collision Avoidance for Complex and Dynamic Obstacles : A study for warehouse safety

   Master-uppsats, Linköpings universitet/Reglerteknik

   Författare :Sandra Ljungberg; Ester Brandås; [2022]
   Nyckelord :Object Detection; Object Tracking; Obstacle Avoidance; Collision Avoidance; Automated Guided Vehicle;

   Sammanfattning : Today a group of automated guided vehicles at Toyota Material Handling Manufacturing Sweden detect and avoid objects primarily by using 2D-LiDAR, with shortcomings being the limitation of only scanning the area in a 2D plane and missing objects close to the ground. Several dynamic obstacles exist in the environment of the vehicles. LÄS MER