Browsing Publikasjoner fra CRIStin by Subject "Machine learning"
Now showing items 1-20 of 27
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Anomaly Detection with Unknown Anomalies: Application to Maritime Machinery
(Peer reviewed; Journal article, 2021)We present a framework for deriving anomaly detection algorithms on timeseries data when the time and expression of anomalous behaviour is unknown. The framework is suited for problems in which individual machine learning ... -
Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests
(Peer reviewed; Journal article, 2020)Geotechnical classification is vital for site characterization and geotechnical design. Field tests such as the cone penetration test with pore water pressure measurement (CPTu) are widespread because they represent a ... -
Applying Object Detection to Marine Data and Exploring Explainability of a Fully Convolutional Neural Network Using Principal Component Analysis
(Peer reviewed; Journal article, 2021)With the rise of focus on man made changes to our planet and wildlife therein, more and more emphasis is put on sustainable and responsible gathering of resources. In an effort to preserve maritime wildlife the Norwegian ... -
Automated morphometrics on microscopy images of Atlantic cod larvae using Mask R-CNN and classical machine vision techniques
(Peer reviewed; Journal article, 2022)Measurements of morphometrical parameters on i.e., fish larvae are useful for assessing the quality and condition of the specimen in environmental research or optimal growth in the cultivation industry. Manually acquiring ... -
Classification of Individual Finger Movements from Right Hand Using fNIRS Signal
(Peer reviewed; Journal article, 2021)Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using ... -
COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics
(Peer reviewed; Journal article, 2022)Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting ... -
Deep learning to predict power output from respiratory inductive plethysmography data
(Peer reviewed; Journal article, 2022)Power output is one of the most accurate methods for measuring exercise intensity during outdoor endurance sports, since it records the actual effect of the work performed by the muscles over time. However, power meters ... -
A flow-through imaging system for automated measurement of ichthyoplankton
(Peer reviewed; Journal article, 2022)Microscopic imaging and morphometric measurement of fish embryos and larvae is essential in environmental monitoring of fish populations and to evaluate larvae development in aquaculture. Traditional microscopy methods ... -
Improving Computer Vision-Based Perception for Collaborative Indoor Navigation
(Peer reviewed; Journal article, 2021)Collaborative navigation is the most promising technique for infrastructure-free indoor navigation for a group of pedestrians, such as rescue personnel. Infrastructure-free navigation means using a system that is able to ... -
An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic
(Peer reviewed; Journal article, 2022)This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing ... -
Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis
(Peer reviewed; Journal article, 2021)Low lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed using density functional theory (DFT), typically at a high computational cost. ... -
Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
(Peer reviewed; Journal article, 2022)With the advancing integration of fluctuating renewables, a more dynamic demand-side, and a grid running closer to its operational limits, future power system operators require new tools to anticipate unwanted events. ... -
Machine Learning for Identifying Group Trajectory Outliers
(Peer reviewed; Journal article, 2021)Prior works on the trajectory outlier detection problem solely consider individual outliers. However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the ... -
Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses
(Peer reviewed; Journal article, 2023)This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling ... -
Magnesiothermic Reduction of Silica: A Machine Learning Study
(Journal article; Peer reviewed, 2023)undamental studies have been carried out experimentally and theoretically on the magnesiothermic reduction of silica with different Mg/SiO2 molar ratios (1–4) in the temperature range of 1073 to 1373 K with different ... -
NEMO: Internet of Things based Real-time Noise and Emissions MOnitoring System for Smart Cities
(Peer reviewed; Journal article, 2022)With the advent of ubiquitous sensors and Internet of Things (IoT) applications, research and development initiatives on smart cities are ramping up worldwide. It enables remote monitoring, management, and control of devices ... -
On the value of popular crystallographic databases for machine learning prediction of space groups
(Peer reviewed; Journal article, 2022)Predicting crystal structure information is a challenging problem in materials science that clearly benefits from artificial intelligence approaches. The leading strategies in machine learning are notoriously data-hungry ... -
Prediction of Strain in Embedded Rebars for RC Member, Application of Hybrid Learning Approach
(Peer reviewed; Journal article, 2023)The aim of this study was to find strains in embedded reinforcement by monitoring surface deformations. Compared with analytical methods, application of the machine learning regression technique imparts a noteworthy reduction ... -
Reduced well path parameterization for optimization problems through machine learning
(Peer reviewed; Journal article, 2021)In this work we apply a recently developed machine learning routine for automatic well planning to simplify well parameterization in reservoir simulation models. This reduced-order parameterization is shown to be beneficial ... -
A Robust Method for Automatic Flight Detection
(SINTEF Rapport;10, Research report, 2021)Here, we present a robust method for detecting flight automatically for use in digital luggage tags. The method is based on simple statistical aggregates using air pressure and 3D accelerometer measurements and complies ...