@inproceedings{61492,
  abstract     = {{This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios.
Various machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved.
For the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.}},
  author       = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}},
  booktitle    = {{2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}},
  keywords     = {{ML, Prediction, Tree Ensembles, GLOSA}},
  location     = {{Gold Coast (Australia)}},
  publisher    = {{IEEE}},
  title        = {{{ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}}},
  volume       = {{28}},
  year         = {{2026}},
}

@article{63498,
  author       = {{Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}},
  journal      = {{IEEE Transactions on Power Electronics}},
  keywords     = {{Mathematical models, Estimation, Data models, Convolutional neural networks, Accuracy, Magnetic hysteresis, Magnetic cores, Temperature measurement, Magnetic domains, Temperature distribution, Convolutional neural network (CNN), machine learning (ML), magnetics}},
  number       = {{2}},
  pages        = {{3326--3335}},
  title        = {{{HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}}},
  doi          = {{10.1109/TPEL.2024.3488174}},
  volume       = {{40}},
  year         = {{2025}},
}

@article{3510,
  abstract     = {{Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.}},
  author       = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  issn         = {{1573-0565}},
  journal      = {{Machine Learning}},
  keywords     = {{AutoML, Hierarchical Planning, HTN planning, ML-Plan}},
  location     = {{Dublin, Ireland}},
  pages        = {{1495--1515}},
  publisher    = {{Springer}},
  title        = {{{ML-Plan: Automated Machine Learning via Hierarchical Planning}}},
  doi          = {{10.1007/s10994-018-5735-z}},
  year         = {{2018}},
}

@inproceedings{11816,
  abstract     = {{In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.}},
  author       = {{Hoang, Manh Kha and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian processes, Global Positioning System, convergence, expectation-maximisation algorithm, fingerprint identification, indoor radio, signal classification, wireless LAN, EM algorithm, ML estimation, WiFi indoor positioning, censored Gaussian data classification, clipped data, convergence properties, expectation maximization algorithm, fingerprinting method, maximum likelihood estimation, optimal classification, parameters estimation, portable devices sensitivity, signal strength measurements, wireless LAN positioning systems, Convergence, IEEE 802.11 Standards, Maximum likelihood estimation, Parameter estimation, Position measurement, Training, Indoor positioning, censored data, expectation maximization, signal strength, wireless LAN}},
  pages        = {{3721--3725}},
  title        = {{{Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}}},
  doi          = {{10.1109/ICASSP.2013.6638353}},
  year         = {{2013}},
}