Search for dissertations about: "Damage detection"

Showing result 11 - 15 of 169 swedish dissertations containing the words Damage detection.

2. 11. Infrared Laser Stimulation of Cerebral Cortex Cells - Aspects of Heating and Cellular Responses

   Author : Rickard Liljemalm; Tobias Nyberg; Hans von Holst; Claus-Peter Richter; KTH; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; laser; modeling; heating; infrared neural stimulation; astrocytes; neurons; damage;

   Abstract : The research of functional stimulation of neural tissue is of great interest within the field of clinical neuroscience to further develop new neural prosthetics. A technique which has gained increased interest during the last couple of years is the stimulation of nervous tissue using infrared laser light. READ MORE

4. 12. Methods for Processing and Analysis of Biomedical TEM Images

   Author : Amit Suveer; Ida-Maria Sintorn; Carolina Wählby; Natasa Sladoje; Jari Hyttinen; Uppsala universitet; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; image analysis; image processing; deep learning; transmission electron microscopy; denoising; super-resolution reconstruction; registration; detection; Computerized Image Processing; Datoriserad bildbehandling;

   Abstract : Transmission Electron Microscopy (TEM) has the high resolving capability and high clinical significance; however, the current manual diagnostic procedure using TEM is complicated and time-consuming, requiring rarely available expertise for analyzing TEM images of the biological specimen. This thesis addresses the bottlenecks of TEM-based analysis by proposing image analysis methods to automate and improve critical time-consuming steps of currently manual diagnostic procedures. READ MORE

5. 13. Multispectral Remote Sensing and Deep Learning for Wildfire Detection

   Author : Xikun Hu; Yifang Ban; Ioannis Gitas; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; active fire detection; biome; multi-criteria; Sentinel-2; Landsat-8; burned area mapping; deep learning; semantic segmentation; machine learning.; aktiv branddetektering; biom; multikriterietillvägagångssätt; Sentinel-2; Landsat-8; kartläggning av bränt område; djupinlärning; semantisk segmentering; maskininlärningsmetoderna; Geoinformatik; Geoinformatics;

   Abstract : Remote sensing data has great potential for wildfire detection and monitoring with enhanced spatial resolution and temporal coverage. Earth Observation satellites have been employed to systematically monitor fire activity over large regions in two ways: (i) to detect the location of actively burning spots (during the fire event), and (ii) to map the spatial extent of the burned scars (during or after the event). READ MORE

6. 14. Drill Failure Detection based on Sound using Artificial Intelligence

   Author : Thanh Tran; Jan Thim; Sebastian Bader; Kalle Åström; Mittuniversitetet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Convolutional neural network; machine failure detection; Mel-spectrogram; long short-term memory; sound signal processing;

   Abstract : In industry, it is crucial to be able to detect damage or abnormal behavior in machines. A machine's downtime can be minimized by detecting and repairing faulty components of the machine as early as possible. It is, however, economically inefficient and labor-intensive to detect machine fault sounds manual. READ MORE

7. 15. Enhancing Machine Failure Detection with Artificial Intelligence and sound Analysis

   Author : Thanh Tran; Jan Lundgren; Sebastian Bader; Domenico Capriglione; Mittuniversitetet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine Failure Detection; Machine Learning; Deep Learning; Sound Signal Processing; Audio Augmentation;

   Abstract : The detection of damage or abnormal behavior in machines is critical in industry, as it allows faulty components to be detected and repaired as early as possible, reducing downtime and minimizing operating and personnel costs. However, manual detection of machine fault sounds is economically inefficient and labor-intensive. READ MORE