Aarhus Universitets segl

Publikationer fra instituttet

Under publikationsliste finder du en samlet liste over de publikationer, som er lavet af medarbejdere ved Institut for Elektro- og Computerteknologi.

Publikationsliste

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Nielsen, A. H. (2022). Data-Driven Weather Forecasting using Deep Learning. [Ph.d.-afhandling, Aarhus Universitet]. Aarhus Universitet.
Gheisarnejad, M., Fathollahi, A., Sharifzadeh, M., Laurendeau, E. & Al-Haddad, K. (2025). Data-Driven Switching Control Technique Based on Deep Reinforcement Learning for Packed E-Cell as Smart EV Charger. IEEE Transactions on Transportation Electrification, 11(1), 3194-3203. https://doi.org/10.1109/TTE.2024.3435763
Shabani, M. (2023). Data-driven Inter-Stock Predictive Analytics. [Ph.d.-afhandling, Aarhus Universitet]. Aarhus Universitet.
Böttjer, T., Ørnskov Rønsch, G., Gonçalves Gomes, C. Â., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A.-L. Andersen, R. Andersen, D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconfigurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer. https://www.springerprofessional.de/en/data-driven-identification-of-remaining-useful-life-for-plastic-/19816878
Böttjer, T., Ørnskov Rønsch, G., Gomes, C., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2022). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (s. 431-439). Springer. https://doi.org/10.1007/978-3-030-90700-6_49
Fathollahi, A., Sharifzadeh, M., Oshnoei, S., Gheisarnejad, M., Laurendeau, E. & Al-Haddad, K. (2026). Data-Driven Fault-Tolerant Control of Grid-Tied Multilevel PEC Inverter Using Soft Reinforcement Learning under Switching-Level Transition. IEEE Transactions on Industry Applications. Advance online publication. https://doi.org/10.1109/TIA.2026.3714398
Fernández, F. J., Alfonso-González, L., Praga, M., Mikkelsen, K. & Vega, M. C. (2026). Data-augmented machine learning refines the effective-concentration estimate for eculizumab in complement-mediated diseases. Frontiers in Immunology, 17, Artikel 1800803. https://doi.org/10.3389/fimmu.2026.1800803
Hallerstede, S., Robby & Hatcliff, J. (2025). Danvy’s Mystery Functions in Slang. I F. Henglein, J. Lawall, J. Palsberg & S. Ilya (red.), OLIVIERFEST 2025 - Proceedings of the Workshop Dedicated to Olivier Danvy on the Occasion of His 64th Birthday (s. 14-25). Association for Computing Machinery. https://doi.org/10.1145/3759427.3760363
Fitzgerald, J., Gamble, C., Larsen, P. G., Pierce, K. & Woodcock, J. (2015). Cyber-Physical Systems Design: Formal Foundations, Methods and Integrated Tool Chains. I Proceedings of the 2015 IEEE/ACM 3rd FME Workshop on Formal Methods in Software Engineering (FormaliSE) (s. 40-46). IEEE. https://doi.org/10.1109/FormaliSE.2015.14
Minh Nguyen, Q., Tran, D. T., Kanniainen, J., Iosifidis, A. & Gabbouj, M. (2024). Cryptocurrency Portfolio Optimization by Neural Networks. I 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (s. 25-32). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371855
Nhut, T. D., Bonen, S., Cooke, G., Jager, T., Spasaro, M., Sufra, D., Voinigescu, S. P. & Zito, D. (2022). Cryogenic Compact mm-Wave Broadband SPST Switch in 22nm FDSOI CMOS for Monolithic Quantum Processors. I 2022 IEEE/MTT-S International Microwave Symposium, IMS 2022 (s. 168-171). IEEE. https://doi.org/10.1109/IMS37962.2022.9865577
Spasaro, M., Bonen, S., Cooke, G., Jager, T., Nhut, T. D., Sufra, D., Voinigescu, S. P. & Zito, D. (2022). Cryogenic Compact Low-Power 60GHz Amplifier for Spin Qubit Control in Monolithic Silicon Quantum Processors. I 2022 IEEE/MTT-S International Microwave Symposium, IMS 2022 (s. 164-167). IEEE. https://doi.org/10.1109/IMS37962.2022.9865561
Falay, B., Wilfling, S., Alfalouji, Q., Exenberger, J., Schranz, T., Legaard, C. M., Leusbrock, I. & Schweiger, G. (2021). Coupling physical and machine learning models: case study of a single-family house. I Proceedings of 14th Modelica Conference 2021 (s. 335-341) https://doi.org/10.3384/ecp21181335
Banaei, M., Boudjadar, J., Dragicevic, T. & Khooban, M. H. (2020). Cost Effective Operation of a Hybrid Zero-Emission Ferry Ship. I 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2020 (s. 23-28). Artikel 9244456 IEEE. https://doi.org/10.1109/PEDG48541.2020.9244456
Banaei, M., Ghanami, F., Khooban, M. H. & Boudjadar, J. (2021). Cost-effective control of Roll-on/Roll-off Emission-Free Ships. I 2021 25th International Conference on Methods and Models in Automation and Robotics, MMAR 2021 (s. 315-320). IEEE. https://doi.org/10.1109/MMAR49549.2021.9528473
Alfalouji, Q., Schranz, T., Falay, B., Wilfling, S., Exenberger, J., Mattausch, T., Gomes, C. & Schweiger, G. (2023). Co-simulation for buildings and smart energy systems — A taxonomic review. Simulation Modelling Practice and Theory, 126, Artikel 102770. https://doi.org/10.1016/j.simpat.2023.102770
Ulsig, E. Z., Stanton, E. J., Reddy, D. V., Shalm, K., Volet, N. & Mirin, R. (2023). Correlated Photon Pairs Obtained by SPDC in AlGaAs-OI Waveguides. I Quantum 2.0: Proceedings Optica Quantum 2.0 Conference and Exhibition (s. QTh4C-2) https://doi.org/10.1364/QUANTUM.2023.QTh4C.2
Frasheri, M., Esterle, L. & Papadopoulos, A. V. (2021). Cooperative Multi-agent Systems for the Multi-target κ -Coverage Problem. I A. P. Rocha, L. Steels & J. van den Herik (red.), Agents and Artificial Intelligence: ICAART 2020 (s. 106-131). Springer. https://doi.org/10.1007/978-3-030-71158-0_5
Laakom, F., Sohrab, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2025). Convolutional autoencoder-based multimodal one-class classification. I 2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media Companion, CISM Companion 2025 IEEE. https://doi.org/10.1109/CISMCompanion65074.2025.11032368
Gil Arboleda, S., Zapata-Madrigal, G. D., García-Sierra, R. & Cruz Salazar, L. A. (2022). Converging IoT protocols for the data integration of automation systems in the electrical industry. Journal of Electrical Systems and Information Technology, 9(1), 1-21. Artikel 1. https://doi.org/10.1186/s43067-022-00043-4
Tamirat, T. W., Pedersen, S. M., Farquharson, R. J., de Bruin, S., Forristal, P. D., Sørensen, C. G., Nuyttens, D., Pedersen, H. H. & Thomsen, M. N. (2022). Controlled traffic farming and field traffic management: Perceptions of farmers groups from Northern and Western European countries. Soil and Tillage Research, 217, Artikel 105288. https://doi.org/10.1016/j.still.2021.105288
Bentsen, P., Nybo, J., Ring, C. & Wagner, S. (2010). Continuous healthcare: Removing the inconvenience of recharging. I 2010 4th International Conference on Pervasive Computing Technologies for Healthcare, Pervasive Health 2010 Artikel 5482263 https://doi.org/10.4108/ICST.PERVASIVEHEALTH2010.8900
Qiao, Z., Pham, X. H., Ramasamy, S., Jiang, X., Kayacan, E. & Sarabakha, A. (2024). Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing. I 2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings IEEE. https://doi.org/10.1109/ijcnn60899.2024.10649903
Hedegaard, L. & Iosifidis, A. (2023). Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch. I L. Karlinsky, T. Michaeli & K. Nishino (red.), Computer Vision – ECCV 2022 Workshops, Proceedings (s. 21-34). Springer. https://doi.org/10.1007/978-3-031-25082-8_2
Hedegaard Morsing, L. & Iosifidis, A. (2022). Continual 3D Convolutional Neural Networks for Real-time Processing of Videos. I S. Avidan, G. Brostow, M. Cissé, G. M. Farinella & T. Hassner (red.), Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings (s. 369-385). Springer. https://doi.org/10.1007/978-3-031-19772-7_22
Swain, K. C., Zaman, Q. U., Schumann, A. W., Percival, D. C. & Bochtis, D. (2010). Computer vision system for wild blueberry fruit yield mapping. Biosystems Engineering, 106(4), 389-394. https://doi.org/10.1016/j.biosystemseng.2010.05.001