1. 2018
  2. Neocleous, A. C., Syngelaki, A., Nicolaides, K. H., & Schizas, C. N. (2018). Two-stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems. In 29th World Congress on ultrasound in obstetrics and gynecology (4 ed., pp. 503-508). (Ultrasound in Obstetrics & Gynecology; Vol. 51). Wiley. https://doi.org/10.1002/uog.17558
  3. Dijkhuis, T., Blok, J., & Velthuijsen, H. (2018). Virtual Coach: Predict Physical Activity Using a Machine Learning Approach. Paper presented at eTELEMED 2018, Rome, Italy.
  4. van der Meulen, P., Wolf, B., Pirih, P., & van Netten, S. (2018). Performance of Neural Networks in Source Localization using Artificial Lateral Line Sensor Configurations. Poster session presented at ICT OPEN 2018: The Interface for Dutch ICT-Research, Amersfoort, Netherlands.
  5. Arvanitou, E. M., Ampatzoglou, A., Tzouvalidis, K., Chatzigeorgiou, A., Avgeriou, P., & Deligiannis, I. (2018). Assessing change proneness at the architecture level: An empirical validation. In 2017 24th Asia-Pacific Software Engineering Conference Workshops, APSECW 2017: Proceedings (pp. 98-105). (Proceedings - 2017 24th Asia-Pacific Software Engineering Conference Workshops, APSECW 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSECW.2017.21
  6. Dymitruk, M., Markovich, R., Liepiņa, R., El Ghosh, M., van Doesburg, R., Governatori, G., & Verheij, B. (2018). Research in progress: Report on the ICAIL 2017 doctoral . Artificial Intelligence and Law, 26(1), 49-97. https://doi.org/10.1007/s10506-018-9220-6
  7. De Persis, C., Cucuzzella, M., Trip, S., Cheng, X., Ferrara, A., & van der Schaft, A. (2018). A robust consensus algorithm for DC microgrids. 40-40. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  8. De Persis, C., Stegink, T. W., Cherukuri, A., van der Schaft, A., & Cortés, J. (2018). Frequency-driven market mechanisms for optimal power dispatch. 52-52. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  9. Wu, H. C., & van der Schaft, A. (2018). Nonlinear trajectory tracking via incremental passivity. 102-102. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  10. Eising, J., & Camlibel, M. (2018). Optimal control for a class of differential inclusions. 78-78. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  11. Borja Rosales, L. P., Scherpen, J. M. A., & van der Schaft, A. (2018). Passivity-based control of gradient systems. 99-99. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  12. Monshizadeh Naini, P., Machado, J., Ortega, R., & van der Schaft, A. (2018). Power-controlled Hamiltonian systems. 50-50. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  13. Jia, J., Trentelman, H. L., Camlibel, M., & Baar, W. (2018). Strong structural controllability of systems on colored graphs. 63-63. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  14. van Waarde, H., Tesi, P., & Camlibel, M. (2018). Topology reconstruction of dynamical networks via constrained Lyapunov equations. 115-115. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  15. Reyes Báez, R., van der Schaft, A., & Jayawardhana, B. (2018). Virtual differential passivity based control of mechanical systems in the port-Hamiltonian framework. 100-100. Abstract from 37th Benelux meeting on Systems and Control, Soesterberg, Netherlands.
  16. Francalanza, A., Pérez, J. A., & Sánchez, C. (2018). Runtime Verification for Decentralised and Distributed Systems. In E. Bartocci, & Y. Falcone (Eds.), Lectures on Runtime Verification (pp. 176-210). Springer. https://doi.org/10.1007/978-3-319-75632-5_6
  17. Szymanik, J., & Verbrugge, R. (2018). Tractability and the computational mind. In M. Sprevak, & M. Colombo (Eds.), The Routledge Handbook of the Computational Mind (1st Editon ed., pp. 339-353). Oxford: Routledge.
  18. Sabatelli, M., Bidoia, F., Codreanu, V., & Wiering, M. (2018). Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead. Paper presented at 7th International Conference on Pattern Recognition Applications and Methods, Madeira, Portugal.
  19. Mencer, O., Boucher, B., Robinson, G., Gregory, J., & Gaydadjiev, G. (2018). Multiscale dataflow computing in finance. In High-Performance Computing in Finance: Problems, Methods, and Solutions (pp. 441-470). Taylor and Francis Ltd. https://doi.org/10.1201/9781315372006
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