Blar i Institutt for datateknologi, elektroteknologi og realfag på dokumenttype "Chapter"

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    • AUV Pipeline Following using Reinforcement Learning 

      Fjerdingen, Sigurd Aksnes; Kyrkjebø, Erik; Transeth, Aksel Andreas (Chapter, 2010)
      This paper analyzes the application of several reinforcement learning techniques for continuous state and action spaces to pipeline following for an autonomous underwater vehicle (AUV). Continuous space SARSA is compared ...

    • A Data-Driven Security Game to Facilitate Information Security Education 

      Løvgren, Dag Erik Homdrum; Li, Jingyue; Oyetoyan, Tosin Daniel (Chapter, 2019)
      Many universities have started to educate students on how to develop secure software and systems. One challenge of teaching information security is that the curriculum can easily be outdated, because new attacks and ...

    • Different E-learning Paradigms - a Survey 

      Kristensen, Terje; Lamo, Yngve; Hole, Grete Oline; Hinna, Kristin (Chapter, 2007-10-28)
      At the beginning of this decade a new Learning Management System(LMS)was developed at Bergen University College as a continuation of its for-runners. This system has been a great commercial succcessw with the foundation ...

    • Education model for future 

      Fojcik, Marcin Andrzej; Fojcik, Martyna Katarzyna (Chapter, 2023)
      The modern work environment requires many new abilities. Hence, there are often called 21st-century skills. It can be caused to the increasingly common use of digital tools or by more and more individualization (fitting) ...

    • Field trials of two 802.11 residual bandwidth estimation methods 

      Nielsen, Martin N.; Øvsthus, Knut; Landmark, lars (Chapter, 2006)
      Ad hoc networks have attracted much attention due to their decentralized architecture and potential mobility. The latter promise has sparked research aimed towards routing and quality of service (QoS) admission schemes. ...

    • Fingerprint identification – a support vector machine approach 

      Kristensen, Terje (Chapter, 2010)
      In this work a hybrid technique for classification of fingerprint identification has been developed to decrease the matching time. For classification a Support Vector Machine is described and used. Automatic Fingerprint ...

    • Hardware-Assisted Online Data Race Detection 

      Ahishakiye, Faustin; Requeno Jarabo, Jose Ignacio; Pun, Ka I; Stolz, Volker (Chapter; Peer reviewed, 2021)
      Dynamic data race detection techniques usually involve invasive instrumentation that makes it impossible to deploy an executable with such checking in the field, hence making errors difficult to debug and reproduce. This ...

    • Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection 

      Lee, Ming-Chang; Lin, Jia-Chun (Chapter; Peer reviewed, 2023)
      Providing online adaptive lightweight time series anomaly detection without human intervention and domain knowledge is highly valuable. Several such anomaly detection approaches have been introduced in the past years, but ...

    • Inertial Human Motion Estimation for Physical Human-Robot Interaction Using an Interaction Velocity Update to Reduce Drift 

      Kyrkjebø, Erik (Chapter, 2018)
      Robots used for physical human-robot interaction (pHRI) are currently advancing from being simple stand-alone manipulators passing tools or parts to human collaborators to becoming autonomous co-workers that continuously ...

    • MC/DC Test Cases Generation Based on BDDs 

      Ahishakiye, Faustin; Requeno Jarabo, Jose Ignacio; Kristensen, Lars Michael; Stolz, Volker (Chapter; Peer reviewed, 2021)
      We present a greedy approach to test-cases selection for single decisions to achieve MC/DC-coverage of their Boolean conditions. Our heuristics take into account “don’t care” inputs through three-valued truth values that ...

    • A ML-Based Stock Trading Model for Profit Predication 

      Wu, Jimmy Ming-Tai; Sun, Lingyun; Srivastava, Gautam; Lin, Jerry Chun-Wei (Chapter, 2021)
      This paper uses a new convolutional neural network framework to collect data on leading indicators including historical prices and their futures and options, and use arrays as the input map of the CNN framework for stock ...

    • A Model Based Slicing Technique for Process Mining Healthcare Information 

      Rabbi, Fazle; Lamo, Yngve; MacCaull, Wendy (Chapter, 2020)
      Process mining is a powerful technique which uses an organization’s event data to extract and analyse process flow information and develop useful process models. However, it is difficult to apply process mining techniques ...

    • Multi-objective Search for Model-based Testing 

      Wang, Rui; Artho, Cyrille; Kristensen, Lars Michael; Stolz, Volker (Chapter; Peer reviewed, 2020)
      This paper presents a search-based approach relying on multi-objective reinforcement learning and optimization for test case generation in model-based software testing. Our approach considers test case generation as an ...

    • Preserved Structure Constants for Red Refinements of Product Elements 

      Korotov, Sergey; Vatne, Jon Eivind (Chapter; Peer reviewed, 2021)
      In this paper we discuss some strategy for red refinements of product elements and show that there are certain structure characteristics (d-sines of angles formed by certain edges in the initial partition) which remain ...

    • Priors Inspired by Speed-Accuracy Trade-Offs for Incremental Learning of Probabilistic Movement Primitives 

      Schäle, Daniel; Stølen, Martin Fodstad; Kyrkjebø, Erik (Chapter; Peer reviewed, 2021)
      Probabilistic Movement Primitives (ProMPs) model robot motor skills by capturing the mean and variance of a set of demonstrations provided by a human teacher. Such a probabilistic representation of motor skills is beneficial ...

    • Reusable data visualization patterns for clinical practice 

      Rabbi, Fazle; Wake, Jo Dugstad; Nordgreen, Tine (Chapter, 2020)
      Among clinical psychologists involved in guided internet-facilitated interventions, there is an overarching need to understand patients symptom development and learn about patients need for treatment support. Data ...

    • RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series 

      Lee, Ming-Chang; Lin, Jia-Chun (Chapter; Peer reviewed, 2023)
      A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivariate time series and detect anomalies in ...

    • A smart ocean observation system for reliable real-time measurements 

      Sætre, Camilla; Skålvik, Astrid Marie; Frøysa, Kjell Eivind; Holstad, Marie Bueie (Chapter; Peer reviewed, 2023)
      This paper presents a research and innovation centre for a smart ocean observation system. The main goal of the centre, SFI Smart Ocean, is to enable sustainable ocean management through real-time measurements from autonomous ...

    • Sparse-Data Based 3D Surface Reconstruction for Cartoon and Map 

      Wu, Bin; Rahman, Talal; Tai, Xue-Cheng (Chapter; Peer reviewed, 2018)
      A model combining the first-order and the second-order variational regularizations for the purpose of 3D surface reconstruction based on 2D sparse data is proposed. The model includes a hybrid fidelity constraint which ...

    • Student Perspectives on Online Hybrid Learning in an Undergraduate Robotics Course 

      Kyrkjebø, Erik; Stølen, Martin F (Chapter; Peer reviewed, 2023)
      Many institutions in higher education worldwide are transforming classes into online courses, or into hybrid courses with students participating both physically in the classroom and digitally through video conferencing ...