Blar i SINTEF Open på forfatter "Sen, Sagar"

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    • AutoConf: Automated Configuration of Unsupervised Learning Systems using Metamorphic Testing and Bayesian Optimization 

      Shar, Lwin Khin; Goknil, Arda; Husom, Erik Johannes; Sen, Sagar; Tun, Yan Naing; Kim, Kisub (Chapter, 2023)
      Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their ...

    • A blockchain-based framework for trusted quality data sharing towards zero-defect manufacturing 

      Isaja, Mauro; Nguyen, Phu Hong; Goknil, Arda; Sen, Sagar; Husom, Erik Johannes; Tverdal, Simeon; Anand, Abhilash Ramanathapuram; Jiang, Yunman; Pedersen, Karl John; Myrseth, Per; Stang, Jørgen; Niavis, Harris; Pfeifhofer, Simon; Lamplmair, Patrick (Peer reviewed; Journal article, 2023)
      There is a current wave of a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure quality in smart factories. Such digital solutions heavily ...

    • Deep learning to predict power output from respiratory inductive plethysmography data 

      Husom, Erik Johannes; Bernabé, Pierre; Sen, Sagar (Peer reviewed; Journal article, 2022)
      Power output is one of the most accurate methods for measuring exercise intensity during outdoor endurance sports, since it records the actual effect of the work performed by the muscles over time. However, power meters ...

    • Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review 

      Cassoli, Beatriz; Jourdan, Nicolas; Nguyen, Phu Hong; Sen, Sagar; Garcia-Ceja, Enrique; Metternich, Joachim (Peer reviewed; Journal article, 2022)
      Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the ...

    • Impact of the Mobile Game FightHPV on Cervical Cancer Screening Attendance: Retrospective Cohort Study 

      Orumaa, Madleen; Campbell, Suzanne; Støer, Nathalie Charlotte; Castle, Philip E.; Sen, Sagar; Trope, Ameli; Adedimeji, Adebola; Nygård, Mari (Peer reviewed; Journal article, 2022)
      Background: The wide availability of mobile phones has made it easy to disseminate health-related information and make it accessible. With gamification, mobile apps can nudge people to make informed health choices, including ...

    • Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns 

      Marijan, Dusica; Sen, Sagar (Peer reviewed; Journal article, 2022)
      Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community. The problem of two cultures, research ...

    • On The Reliability Of Machine Learning Applications In Manufacturing Environments 

      Jourdan, Nicolas; Sen, Sagar; Husom, Erik Johannes; Garcia-Ceja, Enrique; Biegel, Tobias; Metternich, Joachim (Chapter, 2021)
      The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the productive use of machine learning (ML) ...

    • Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI 

      Ahuja, Mohit Kumar; Mohamed-Bachir, Belaid; Pierre, Bernabet; Mathieu, Collet; Arnaud, Gotlieb; Chhagan, Lal; Dusica, Marijan; Sen, Sagar; Aizaz, Sharif; Helge, Spieker (Peer reviewed; Journal article, 2020)
      Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against ...

    • A Systematic Mapping Study on Approaches for AI-Supported Security Risk Assessment 

      Erdogan, Gencer; Garcia-Ceja, Enrique; Hugo, Åsmund Pedersen; Nguyen, Phu Hong; Sen, Sagar (Chapter; Peer reviewed, 2021)
      Effective assessment of cyber risks in the increasingly dynamic threat landscape must be supported by artificial intelligence techniques due to their ability to dynamically scale and adapt. This article provides the state ...

    • A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 

      Goknil, Arda; Nguyen, Phu Hong; Sen, Sagar; Politaki, Dimitra; Niavis, Harris; Pedersen, Karl John; Suyuthi, Abdillah; Anand, Abhilash Ramanathapuram; Ziegenbein, Amina (Peer reviewed; Journal article, 2023)
      The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or ...

    • Taming Data Quality in AI-Enabled Industrial Internet of Things 

      Sen, Sagar; Husom, Erik Johannes; Goknil, Arda; Tverdal, Simeon; Nguyen, Phu Hong; Mancisidor, Iker (Peer reviewed; Journal article, 2022)
      We address the problem of taming data quality in artificial intelligence (AI)-enabled Industrial Internet of Things systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. We ...

    • UDAVA: an unsupervised learning pipeline for sensor data validation in manufacturing 

      Husom, Erik Johannes; Tverdal, Simeon; Goknil, Arda; Sen, Sagar (Chapter, 2022)
      Manufacturing has enabled the mechanized mass production of the same (or similar) products by replacing craftsmen with assembly lines of machines. The quality of each product in an assembly line greatly hinges on continual ...

    • Uncertainty-aware Virtual Sensors for Cyber-Physical Systems 

      Sen, Sagar; Husom, Erik Johannes; Goknil, Arda; Tverdal, Simeon; Nguyen, Phu Hong (Peer reviewed; Journal article, 2023)
      We present a data pipeline to train and deploy uncertainty-aware virtual sensors in cyber-physical systems. Our virtual sensor predicts the expected values of a physical sensor and a standard deviation indicating the degree ...

    • Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline 

      Sen, Sagar; Husom, Erik Johannes; Goknil, Arda; Politaki, Dimitra; Tverdal, Simeon; Nguyen, Phu Hong; Jourdan, Nicolas (Peer reviewed; Journal article, 2023)
      Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure ...