Blar i Publikasjoner fra CRIStin på emneord "maskinlæring"
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AI and Machine Learning in Industrial Asset Management: Insights from CIAM Meetings
(Journal article, 2023)This paper investigates the influence of Artificial Intelligence (AI) and Machine Learning (ML) in Industrial Asset Management as reflected in the discussions from various Cluster for Industrial Asset Management (CIAM) ... -
Application of machine learning to assess the value of information in polymer flooding
(Peer reviewed; Journal article, 2021-12)In this work, we provide a more consistent alternative for performing value of information (VOI) analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir ... -
Applying a machine learning method for cumulative fatigue damage estimation of the IEA 15MW wind turbine with monopile support structures
(Peer reviewed; Journal article, 2023)Offshore support structures are critical for offshore bottom-fixed wind turbines, as they bear nearly all the mass and loading of wind turbine systems. In addition, the support structures are generally subjected to a harsh ... -
Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support
(Peer reviewed; Journal article, 2023)Prostate cancer (PCa) is a significant health concern for men worldwide, where early detection and effective diagnosis can be crucial for successful treatment. Multiparametric magnetic resonance imaging (mpMRI) has evolved ... -
Deployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids
(Peer reviewed; Journal article, 2023)Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart ... -
Exploring of the incompatibility of marine residual fuel: A case study using machine learning methods
(Peer reviewed; Journal article, 2021-12)Providing quality fuel to ships with reduced SOx content is a priority task. Marine residual fuels are one of the main sources of atmospheric pollution during the operation of ships and sea tankers. Hence, the International ... -
Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures
(Peer reviewed; Journal article, 2019-02)The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local ... -
Impact of data pre-processing techniques on recurrent neural network performance in context of real-time drilling logs in an automated prediction framework
(Peer reviewed; Journal article, 2021-11)Recurrent neural networks (RNN), which are able to capture temporal natures of a signal, are becoming more common in machine learning applied to petroleum engineering, particularly drilling. With this technology come ... -
Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis
(Peer reviewed; Journal article, 2022-03)Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for ... -
Learning the nonlinear flux function of a hidden scalar conservation law from data
(Peer reviewed; Journal article, 2023-10)Nonlinear conservation laws are widely used in fluid mechanics, biology, physics, and chemical engineering. However, deriving such nonlinear conservation laws is a significant and challenging problem. A possible attractive ... -
Machine Learning Approach for Risk-Based Inspection Screening Assessment
(Peer reviewed; Journal article, 2019-05)Risk-based inspection (RBI) screening assessment is used to identify equipment that makes a significant contribution to the system's total risk of failure (RoF), so that the RBI detailed assessment can focus on analyzing ... -
Machine learning based decline curve analysis for short-term oil production forecast
(Peer reviewed; Journal article, 2021-05)Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional ... -
Machine learning in reservoir permeability prediction and modelling of fluid flow in porous media
(Peer reviewed; Journal article, 2019)Reliable data on the properties of the porous medium are necessary for the correct description of the process of displacing hydrocarbons from the reservoirs and forecasting reservoir performance. The true permeability of ... -
Physics-Based Swab and Surge Simulations and the Machine Learning Modeling of Field Telemetry Swab Datasets
(Peer reviewed; Journal article, 2023)Drilling operations are the major cost factor for the oil industry. Appropriately designed operations are essential for successful drilling. Optimized drilling operations also enhance drilling performance and reduce drilling ... -
Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating-Gas Injection based on PSO-, GSA-, GWO-, and GA-LSSVM
(Peer reviewed; Journal article, 2022-01)In this study, we solve the challenge of predicting oil recovery factor (RF) in layered heterogeneous reservoirs after 1.5 pore volumes of water-, gas- or water-alternating-gas (WAG) injection. A dataset of ~2500 reservoir ... -
Reference Dataset for Rate of Penetration Benchmarking
(Peer reviewed; Journal article, 2020-10)In recent years, there were multiple papers published related to rate of penetration prediction using machine learning vastly outperforming analytical methods. There are models proposed reportedly achieving R2 values as ... -
Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis
(Peer reviewed; Journal article, 2020-04)Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings ... -
Texture-based probability mapping for automatic scar assessment in late gadolinium-enhanced cardiovascular magnetic resonance images
(Peer reviewed; Journal article, 2021-12)Purpose To evaluate a novel texture-based probability mapping (TPM) method for scar size estimation in LGE-CMRI. Methods This retrospective proof-of-concept study included chronic myocardial scars from 52 patients. ...