Browsing Publikasjoner fra CRIStin - NTNU by Title
Now showing items 7666-7685 of 37384
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Data-driven Machine Learning Model in District Heating System for Heat Load Prediction: A Comparison Study
(Journal article; Peer reviewed, 2016)We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, ... -
Data-Driven Maritime Processes Management Using Executable Models
(Journal article, 2018)In this paper we describe a decision support system for maritime traffic and operations, based on formal models and driven by data from the environment. To handle the complexity of system description, we work with a ... -
Data-Driven Methodology for the Analysis of Operational Profile and the Quantification of Electrical Power Variability on Marine Vessels
(Journal article; Peer reviewed, 2018)Measurements from the on-board systems of marine vessels are increasingly available for data analysis and are growing in importance as the ship industry enters a phase of digitalization. The purposes of the data analysis ... -
A Data-Driven Model for Ice-Breaking Resistance of Structure Based on Non-Smooth Discrete Element Method and Artificial Neural Network Method
(Peer reviewed; Journal article, 2023)In this paper, a data-driven model based on the Non-smooth Discrete Element Method (NDEM) and Artificial Neural Network Method (ANN) is proposed for the computation of the ice-breaking resistance of the structure. The idea ... -
Data-driven modeling of a CO2 refrigeration system
(Peer reviewed; Journal article, 2019)This paper describes a data-driven method for system identification of a CO 2 refrigeration system. Traditionally, the interaction between the measured variables is not utilized as they are highly dependent on the refrigeration ... -
Data-driven modeling of fatigue effects following repeated muscular contractions
(Chapter, 2022)We propose a novel dynamical model for describing muscular fatigue and cramping following repeated muscular contractions in the case of female Kegel exercising. The proposed compartmental model adds opportune parameters ... -
Data-driven modeling of fatigue effects following repeated muscular contractions
(Chapter, 2022)We propose a novel dynamical model for describing muscular fatigue and cramping following repeated muscular contractions in the case of female Kegel exercising. The proposed compartmental model adds opportune parameters ... -
Data-driven modeling of hydrodynamic loading using NARX and harmonic probing
(Peer reviewed; Journal article, 2023)Optimizing floating wind turbines and their mooring systems requires validated computational models that predict wave-frequency and low-frequency hydrodynamic loads. Low-frequency loads are crucial for determining extreme ... -
Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression
(Journal article; Peer reviewed, 2017)This paper presents a flexible system structure to analyze and model for the potential use of huge ship sensor data to generate efficient ship motion prediction model. The noisy raw data is cleaned using noise reduction, ... -
Data-driven modelling of a R744 refrigeration system with parallel compression configuration
(Chapter, 2019)The type and characteristics of the employed components, subsystems and refrigerant in a commercial refrigeration system varies significantly depending on required functionalities (e.g. MT (medium temperature), LT (low ... -
Data-driven models of pelvic floor muscles dynamics subject to psychological and physiological stimuli
(Journal article; Peer reviewed, 2019)This paper proposes individualized, dynamical and data-driven models to describe pelvic floor muscle responses in women undergoing vaginal dilation. Specifically, the models describe how the aggregated pressure exerted by ... -
Data-Driven Personalized Cervical Cancer Risk Prediction: A Graph-Perspective
(Chapter, 2021)Routine cervical cancer screening at regular periodic intervals leads to either over-screening or too infrequent screening of patients. For this purpose, personalized screening intervals are desirable that account for ... -
Data-driven prediction of mean wind turbulence from topographic data
(Peer reviewed; Journal article, 2021)This study presents a data-driven model to predict mean turbulence intensities at desired generic locations, for all wind directions. The model, a multilayer perceptron, requires only information about the local topography ... -
Data-Driven Prediction of Ship Propulsion Power Using Spark Parallel Random Forest on Comprehensive Ship Operation Data
(Chapter, 2022)This paper aims to propose an efficient machine learning framework for maritime big data and use it to train a random forest model to estimate ships’ propulsion power based on ship operation data. The comprehensive data ... -
Data-Driven Prediction of Vortex-Induced Vibration Response of Marine Risers Subjected to Three-Dimensional Current
(Chapter, 2019)Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage. The ocean current is ... -
Data-Driven Real-Time Price-Based Demand Response for Industrial Facilities Energy Management
(Journal article; Peer reviewed, 2021)Recent advances in smart grid technologies have highlighted demand response (DR) as an important tool to alleviate electricity demand–supply mismatches. In this paper, a real-time price (RTP)-based DR algorithm is proposed ... -
Data-driven recovery of hidden hysics in reduced order modeling of fluid flows
(Peer reviewed; Journal article, 2020)In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework ... -
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
(Peer reviewed; Journal article, 2020)In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework ... -
Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters
(Peer reviewed; Journal article, 2020)Industrial waste heat recovery is an attractive option having the simultaneous benefits of reducing energy costs as well as carbon emissions. In this context, thermal energy storage can be used along with an optimal operation ... -
Data-driven Scenario Selection for Multistage Robust Model Predictive Control
(Journal article; Peer reviewed, 2018)A main assumption in many works considering multistage model predictive control (MPC) is that discrete realizations of the uncertainty are chosen a-priori and that the scenario tree is given. In this work, we focus on ...