Lehrstuhl SRS - Team

Dr.-Ing. Daniel Adofo Ameyaw

Daniel Adofo Ameyaw

Kurzvita / CV

2020 Dr.-Ing. Mechanical Engineering,
Faculty of Engineering,
University of Duisburg-Essen, Germany
2014M.Sc. in Mechanical Engineering (Applied Mechanics),
Department of Mechanical Engineering,
Kwame Nkrumah University of Science and Technology, Ghana
2013Research Assistant,
Department of Mechanical Engineering,
Kwame Nkrumah University of Science and Technology, Ghana
2011Teaching Assistant,
Department of Mechanical Engineering,
Kwame Nkrumah University of Science and Technology, Ghana
2011B.Sc. in Mechanical Engineering,
Department of Mechanical Engineering,
Kwame Nkrumah University of Science and Technology, Ghana

Veröffentlichungen / Publications

  • Bakhshande, F.; Ameyaw, D. A.; Madan, N.; Söffker, D.: New Metric for Evaluation of Deep Neural Network Applied in Vision-Based Systems. Applied Science, Vol. 12, No. 7, 2022, pp. 3251.
    • , [PDF], [Link]
  • Ameyaw, D. A.; Deng, Q.; Söffker, D.: How to evaluate classifier performance in the presence of additional effects: A new POD-based approach allowing certification of machine learning approaches. Machine Learning with Applications (Elsevier), Volume 7, 15 March, 2022.
    • , [PDF], [Link]
  • Thind, N. S.; Ameyaw, D. A.; Söffker, D.: Adaptive situated and reliable prediction of object trajectories. European Conference of Safety and Reliability (ESREL), Dublin, 2022.
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  • Ameyaw, D. A.; Deng, Q.; Söffker, D.: Evaluating Machine Learning-Based Classification Approaches: A New Method for Comparing Classifiers Applied to Human Driver Prediction Intentions. IEEE Access, Vol. 10, 2022, pp. 62429-62439.
    • , [PDF], [Link]
  • Ameyaw, D. A.; Rothe, S.; Söffker, D.: A novel feature-based Probability of Detection (POD) assessment and fusion approach for reliability evaluation of vibration-based diagnosis systems. SHM Journal, Vol. 19, No. 3, 2020, pp. 649-660.
    • , [Link]
  • Ameyaw, D. A.; Söffker, D.: False alarm improved detection capabilities of multi-sensor-based monitoring of vibrating systems. 10th European Workshop on Structural Health Monitoring (EWSHM 2020) - Special Collection, 2020, pp. 467-480.
    • , [Link]
  • Ameyaw, D. A.; Deng, Q.; Söffker, D.: Probability of Detection (POD)-based metric for evaluation of Classifiers used in Driving Behavior Prediction. Proceedings of the Annual Conference of the PHM Society, 11(1) , Scottsdale, Arizona, USA, 2019.
    • , [Link]
  • Ameyaw, D. A.; Rothe, S.; Söffker, D.: Fault diagnosis using Probability of Detection (POD)-based sensor/information fusion for vibration-based analysis of elastic structures. PAMM-Wiley Online, 2018.
    • , [Link]
  • Ameyaw, D. A.; Rothe, S.; Söffker, D.: Adaptation and Implementation of Probability of Detection (POD)-based Fault diagnosis in elastic structures through vibration-based SHM approach. The 9th European Workshop on Structural Health Monitoring (EWSHM), Manchester, 2018.
    • , [PDF]
  • Ameyaw, D. A.; Rothe, S.; Söffker, D.: Probability of Detection (POD)-oriented view to Fault Diagnosis for Reliability assessment of FDI approaches. ASME 2018 International Design Engineering Technical Conferences & Computers(IDETC/CIE). 30th Conference on Mechanical Vibration and Noise, Quebec, Canada, 2018, DETC2018-85554, pp. V008T10A041.