Now showing items 2869-2888 of 3669

    • Regulering og optimalisering av Shell Eco-maraton kjøretøy 

      Øverby, Jardar Sølna (Master thesis, 2011)
      Å bygge en bil som kan kjøre tusen kilometer på energien i en eneste liter bensin; er ingeniørkunst. Når tusenvis av Europas ypperste studenter innen teknologi og ingeniørvitenskap hvert år møtes i konkurransen Shell ...
    • Reinforcement Learning and Predictive Safety Filtering for Floating Offshore Wind Turbine Control 

      Malmin, Vebjørn; Ødegård Teigen, Halvor (Master thesis, 2021)
      Kunstig intelligens blir sett på som et av de mest betydningsfulle sprangene innen teknologi de siste årene. Underkategorien forsterkende læring har vist eksepsjonelle resultater for problemer som tidligere var antatt ...
    • Reinforcement Learning and Predictive Safety Filtering for Floating Offshore Wind Turbine Control 

      Malmin, Vebjørn; Ødegård Teigen, Halvor (Master thesis, 2021)
      Kunstig intelligens blir sett på som et av de mest betydningsfulle sprangene innen teknologi de siste årene. Underkategorien forsterkende læring har vist eksepsjonelle resultater for problemer som tidligere var antatt ...
    • Reinforcement Learning for Batch Bioprocess Optimization 

      Petsagkourakis, Panagiotis; Sandoval, Ilya Orson; Bradford, Eric; Zhang, Dongda; del Rio-Chanona, Ehecatl Antonio (Peer reviewed; Journal article, 2020)
      Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and ...
    • Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation 

      Petsagkourakis, Panagiotis; Sandoval, Ilya Orson; Bradford, Eric; Zhang, Dongda; del Rio-Chanona, Ehecatl Antonio (Chapter, 2019)
      Bioprocesses have received great attention from the scientific community as an alternative to fossil-based products by microorganisms-synthesised counterparts. However, bioprocesses are generally operated at unsteady-state ...
    • Reinforcement Learning for Fast, Map-Free Navigation in Cluttered Environments Using Aerial Robots 

      Nitschke, Patrick (Master thesis, 2022)
      Autonom navigering i stadig mer komplekse domener byr på nye utfordringer og stiller spørsmål ved effektiviteten og kapasiteten til tradisjonelle modellbaserte metoder. Selv om tradisjonelle metoder har vært vellykkede for ...
    • Reinforcement Learning for Optimization of Nonlinear and Predictive Control 

      Bøhn, Eivind (Doctoral theses at NTNU;2022:45, Doctoral thesis, 2022)
      Autonomous systems extend upon human capabilities and can be equipped with superhuman attributes in terms of durability, strength, and perception to name a few, and can provide numerous benefits such as superior efficiency, ...
    • Reinforcement Learning for Robotic Ultrasound 

      Ackre, Susanne Dorethea (Master thesis, 2022)
      Denne masteroppgaven er et studie av “deep reinforcement learning” for robotisert ultralyd. Å bruke autonome roboter i helsesektoren er et komplekst bruksområde med mange faktorer å ta hensyn til. For eksempel, utfordingen ...
    • Reinforcement Learning for Robotic Manipulation 

      Vagle, Anders Haver (Master thesis, 2019)
      Denne oppgaven tar for seg detaljer rundt implementasjon av PPO-algoritme for trening på egendefinerte miljøer designet for robotikk-basert manipulasjon. Resultatene er lovende for de forenklede miljøene i simulering, men ...
    • Reinforcement learning for robotic soft-body interaction 

      Jakobsen, Herman Kolstad (Master thesis, 2021)
      Innen klinisk medisin ansees det som utfordrende å benytte autonome robotsystemer. Håndtering av bevegelige objekter og myke materialer, samt variasjon mellom pasienter, har vist seg å være en hindring for robotassisterte ...
    • Reinforcement learning-based NMPC for tracking control of ASVs: Theory and experiments 

      Martinsen, Andreas Bell; Lekkas, Anastasios; Gros, Sebastien (Peer reviewed; Journal article, 2022)
      We present a reinforcement learning-based (RL) model predictive control (MPC) method for trajectory tracking of surface vessels. The proposed method uses an MPC controller in order to perform both trajectory tracking and ...
    • Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions 

      Martinsen, Andreas Bell; Lekkas, Anastasios M.; Gros, Sebastien (Peer reviewed; Journal article, 2020)
      We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing ...
    • Rejecting heave-induced pressure oscillations in a semilinear hyperbolic well model 

      Strecker, Timm; Aamo, Ole Morten (Journal article; Peer reviewed, 2017)
      In this paper, we apply recent results on the output feedback control of 2×2 semilinear systems to the problem of rejecting heave-induced pressure oscillations in offshore drilling. The well is modeled as a transmission ...
    • Rejecting pressure fluctuations induced by string movement in drilling 

      Strecker, Timm; Aamo, Ole Morten (Journal article; Peer reviewed, 2016)
      Keeping the pressure within predefined bounds is essential for the safety of managed pressure drilling operations, but drill string movements can induce pressure fluctuations that violate these margins. We extend previous ...
    • Rejection of Sinusoidal Disturbance Approach Based on High-Gain Principle 

      Pyrkin, Anton; Bobtsov, Alexey; Kolyubin, Sergey (Journal article; Peer reviewed, 2012)
      This paper deals with the output stabilization of linear systems with unknown parameters and sinusoidal disturbance. The approach is based on a hybrid algorithm of frequency estimation, that is used for compensation of the ...
    • Rejection of Sinusoidal Disturbance Approach Based on High-Gain Principle 

      Pyrkin, Anton; Bobtsov, Alexey; Kolyubin, Sergey (Journal article; Peer reviewed, 2012)
      This paper deals with the output stabilization of linear systems with unknown parameters and sinusoidal disturbance. The approach is based on a hybrid algorithm of frequency estimation, that is used for compensation of the ...
    • Relationship between Finite Set Statistics and the Multiple Hypothesis Tracker 

      Brekke, Edmund Førland; Chitre, Mandar (Journal article; Peer reviewed, 2018)
      The multiple hypothesis tracker (MHT) and finite set statistics (FISST) are two approaches to multitarget tracking, which both have been heralded as optimal. In this paper, we show that the multitarget Bayes filter with ...
    • Relevance-based Channel Selection for EEG Source Reconstruction: An Approach to Identify Low-density Channel Subsets 

      Soler, Andres; Giraldo, Eduardo; Lundheim, Lars Magne; Molinas Cabrera, Maria Marta (Chapter, 2022)
      Abstract: Electroencephalography (EEG) Source Reconstruction is the estimation of the underlying neural activity at cortical areas. Currently, the most accurate estimations are done by combining the information registered ...
    • Reliability Analysis of Shore-to-Ship Fast Charging Systems 

      Karimi, Siamak; Zadeh, Mehdi; Suul, Jon Are Wold; Thieme, Christoph Alexander (Chapter, 2021)
      This paper presents a reliability assessment of Shore-to-Ship Sharging (S2SC) systems with focus on the two most common topologies of ac and dc charging. In the proposed reliability model, the Markov chain and reliability ...
    • Reliability and barrier assessment of series–parallel systems subject to cascading failures 

      Xie, Lin; Lundteigen, Mary Ann; Liu, Yiliu (Peer reviewed; Journal article, 2020)
      Cascading failures can occur in many technical systems where the components are organized as in series–parallel structures. The failures in these systems may propagate from one component to the other, not only within the ...