Browsing Publikasjoner fra CRIStin - NTNU by Title
Now showing items 21097-21116 of 38678
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A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living
(Journal article; Peer reviewed, 2021)Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity ... -
Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water
(Journal article; Peer reviewed, 2024)Various synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) ... -
A machine learning enabled ultra-fine grain design strategy of Mg–Mn-based alloys
(Peer reviewed; Journal article, 2023)Grain size is the critical characteristic of ultra-fine grain Magnesium (Mg), which is a concrete representation of the whole heat deformation procedure. In this paper, a design strategy was proposed to quantitatively ... -
Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions
(Peer reviewed; Journal article, 2020)This paper investigates and discusses the current and future role of machine learning (ML) within the hydropower sector. An overview of the main applications of ML in the field of hydropower operations is presented to show ... -
Machine learning for predicting dimensions of extrusion blow molded parts: A comparison of three algorithms
(Journal article; Peer reviewed, 2023)In the perspective of feed-forward control through a manufacturing process chain, production data can be used downstream to correct subsequent processes and improve product quality. In an extrusion blow-moulding (EBM) use ... -
Machine Learning for PV System Operational Fault Analysis: Literature Review
(Chapter, 2022)This review paper aims to discover the research gap and assess the feasibility of a holistic approach for photovoltaic (PV) system operational fault analysis using machine learning (ML) methods. The analysis includes the ... -
Machine Learning for Smart Building Applications: Review and Taxonomy
(Journal article; Peer reviewed, 2019)The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions ... -
Machine Learning in Financial Market Surveillance: A Survey
(Peer reviewed; Journal article, 2021)The use of machine learning for anomaly detection is a well-studied topic within various application domains. However, the detection problem for market surveillance remains challenging due to the lack of labeled data and ... -
Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives
(Journal article; Peer reviewed, 2018)Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance ... -
Machine learning methods for prediction of hot water demands in integrated R744 system for hotels
(Chapter, 2020)Load forecasting can help modern energy systems achieve more efficient operation by means of more accurate peak power shaving and more reliable control. This paper proposes a framework based on machine learning algorithms ... -
A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information
(Journal article; Peer reviewed, 2024)Purpose: Body-worn accelerometers are commonly used to estimate sleep duration in population-based studies. However, since accelerometry-based sleep/wake-scoring relies on detecting body movements, the prediction of sleep ... -
Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
(Journal article; Peer reviewed, 2019)Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the ... -
Machine learning techniques for modeling chemical absorption in CO2 capture process
(Chapter, 2022)Post-combustion carbon capture (PCC) technologies play an important role in the reduction of CO2 emissions to address climate challenges. This process is usually simulated in process simulation software based on first-principle ... -
Machine learning-based estimation of buildings' characteristics employing electrical and chilled water consumption data: Pipeline optimization
(Peer reviewed; Journal article, 2023)Smart meter-driven remote auditing of buildings, as an alternative to the labor-intensive on-site visits, permits large-scale and rapid identification of buildings with low energy performance. The existing literature has ... -
Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial
(Peer reviewed; Journal article, 2023)Background We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478 ). Molecular and genomic features associated with immune ... -
Machine Learning-Based Methods for Code Smell Detection: A Survey
(Journal article; Peer reviewed, 2024)Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern ... -
A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources
(Peer reviewed; Journal article, 2022)A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the ... -
Machine Learning-based Occupancy Estimation Using Multivariate Sensor Nodes
(Chapter, 2019)In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems. One way to optimize their usage is to make them demand-driven depending on human occupancy. This paper focuses on accurately ... -
Machine learning-based predictive maintenance: A cost-oriented model for implementation
(Journal article; Peer reviewed, 2021)Predictive Maintenance (PdM) is a condition-based maintenance strategy (CBM) that carries out maintenance action when needed, avoiding unnecessary preventive actions or failures. Machine learning (ML), in the form of ... -
Machine Learning-based Uptime-Prediction for Battery-friendly Passenger Information Displays
(Chapter, 2020)Personal Information Displays (PID) at bus stops help making the usage of public transport more attractive. If no electric grid is nearby, however, the installation of PIDs is very expensive due to the high wiring costs. ...