---
_id: '61492'
abstract:
- lang: eng
  text: "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.\r\nVarious 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.\r\nFor 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:
- first_name: Kevin
  full_name: Malena, Kevin
  id: '36303'
  last_name: Malena
  orcid: 0000-0003-1183-4679
- first_name: Christopher
  full_name: Link, Christopher
  id: '38249'
  last_name: Link
- first_name: Sandra
  full_name: Gausemeier, Sandra
  id: '17793'
  last_name: Gausemeier
- first_name: Ansgar
  full_name: Trächtler, Ansgar
  id: '552'
  last_name: Trächtler
citation:
  ama: 'Malena K, Link C, Gausemeier S, Trächtler A. ML-based Prediction Framework
    for varying Traffic Signal Control Strategies and its GLOSA-application. In: <i>2025
    IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>.
    Vol 28. IEEE.'
  apa: Malena, K., Link, C., Gausemeier, S., &#38; Trächtler, A. (n.d.). ML-based
    Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application.
    <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems
    (ITSC)</i>, <i>28</i>.
  bibtex: '@inproceedings{Malena_Link_Gausemeier_Trächtler, title={ML-based Prediction
    Framework for varying Traffic Signal Control Strategies and its GLOSA-application},
    volume={28}, booktitle={2025 IEEE 28th International Conference on Intelligent
    Transportation Systems (ITSC)}, publisher={IEEE}, author={Malena, Kevin and Link,
    Christopher and Gausemeier, Sandra and Trächtler, Ansgar} }'
  chicago: Malena, Kevin, Christopher Link, Sandra Gausemeier, and Ansgar Trächtler.
    “ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and
    Its GLOSA-Application.” In <i>2025 IEEE 28th International Conference on Intelligent
    Transportation Systems (ITSC)</i>, Vol. 28. IEEE, n.d.
  ieee: K. Malena, C. Link, S. Gausemeier, and A. Trächtler, “ML-based Prediction
    Framework for varying Traffic Signal Control Strategies and its GLOSA-application,”
    in <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems
    (ITSC)</i>, Gold Coast (Australia), vol. 28.
  mla: Malena, Kevin, et al. “ML-Based Prediction Framework for Varying Traffic Signal
    Control Strategies and Its GLOSA-Application.” <i>2025 IEEE 28th International
    Conference on Intelligent Transportation Systems (ITSC)</i>, vol. 28, IEEE.
  short: 'K. Malena, C. Link, S. Gausemeier, A. Trächtler, in: 2025 IEEE 28th International
    Conference on Intelligent Transportation Systems (ITSC), IEEE, n.d.'
conference:
  end_date: 2025-11-21
  location: Gold Coast (Australia)
  name: 28th International Conference on Intelligent Transportation Systems (ITSC)
  start_date: 2025-11-18
date_created: 2025-10-01T11:20:34Z
date_updated: 2026-01-26T08:50:37Z
department:
- _id: '153'
intvolume: '        28'
keyword:
- ML
- Prediction
- Tree Ensembles
- GLOSA
language:
- iso: eng
publication: 2025 IEEE 28th International Conference on Intelligent Transportation
  Systems (ITSC)
publication_status: accepted
publisher: IEEE
quality_controlled: '1'
status: public
title: ML-based Prediction Framework for varying Traffic Signal Control Strategies
  and its GLOSA-application
type: conference
user_id: '36303'
volume: 28
year: '2026'
...
---
_id: '63498'
author:
- first_name: Wilhelm
  full_name: Kirchgässner, Wilhelm
  last_name: Kirchgässner
- first_name: Nikolas
  full_name: Förster, Nikolas
  last_name: Förster
- first_name: Till
  full_name: Piepenbrock, Till
  last_name: Piepenbrock
- first_name: Oliver
  full_name: Schweins, Oliver
  last_name: Schweins
- first_name: Oliver
  full_name: Wallscheid, Oliver
  last_name: Wallscheid
citation:
  ama: 'Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE:
    H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
    Convolutional Neural Networks in Ferrite Cores. <i>IEEE Transactions on Power
    Electronics</i>. 2025;40(2):3326-3335. doi:<a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>'
  apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., &#38; Wallscheid,
    O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms
    With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. <i>IEEE
    Transactions on Power Electronics</i>, <i>40</i>(2), 3326–3335. <a href="https://doi.org/10.1109/TPEL.2024.3488174">https://doi.org/10.1109/TPEL.2024.3488174</a>'
  bibtex: '@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE:
    H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
    Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={<a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>},
    number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner,
    Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid,
    Oliver}, year={2025}, pages={3326–3335} }'
  chicago: 'Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins,
    and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary
    Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.”
    <i>IEEE Transactions on Power Electronics</i> 40, no. 2 (2025): 3326–35. <a href="https://doi.org/10.1109/TPEL.2024.3488174">https://doi.org/10.1109/TPEL.2024.3488174</a>.'
  ieee: 'W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid,
    “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
    Dilated Convolutional Neural Networks in Ferrite Cores,” <i>IEEE Transactions
    on Power Electronics</i>, vol. 40, no. 2, pp. 3326–3335, 2025, doi: <a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>.'
  mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation
    for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in
    Ferrite Cores.” <i>IEEE Transactions on Power Electronics</i>, vol. 40, no. 2,
    2025, pp. 3326–35, doi:<a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>.'
  short: W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid,
    IEEE Transactions on Power Electronics 40 (2025) 3326–3335.
date_created: 2026-01-06T08:07:13Z
date_updated: 2026-01-06T08:08:01Z
department:
- _id: '52'
doi: 10.1109/TPEL.2024.3488174
intvolume: '        40'
issue: '2'
keyword:
- 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
page: 3326-3335
publication: IEEE Transactions on Power Electronics
status: public
title: 'HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
  Dilated Convolutional Neural Networks in Ferrite Cores'
type: journal_article
user_id: '83383'
volume: 40
year: '2025'
...
---
_id: '3510'
abstract:
- lang: eng
  text: 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.
article_type: original
author:
- first_name: Felix
  full_name: Mohr, Felix
  last_name: Mohr
- first_name: Marcel Dominik
  full_name: Wever, Marcel Dominik
  id: '33176'
  last_name: Wever
  orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical
    Planning. <i>Machine Learning</i>. Published online 2018:1495-1515. doi:<a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>'
  apa: 'Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). ML-Plan: Automated Machine
    Learning via Hierarchical Planning. <i>Machine Learning</i>, 1495–1515. <a href="https://doi.org/10.1007/s10994-018-5735-z">https://doi.org/10.1007/s10994-018-5735-z</a>'
  bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine
    Learning via Hierarchical Planning}, DOI={<a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>},
    journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever,
    Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }'
  chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated
    Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, 2018, 1495–1515.
    <a href="https://doi.org/10.1007/s10994-018-5735-z">https://doi.org/10.1007/s10994-018-5735-z</a>.'
  ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning
    via Hierarchical Planning,” <i>Machine Learning</i>, pp. 1495–1515, 2018, doi:
    <a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>.'
  mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical
    Planning.” <i>Machine Learning</i>, Springer, 2018, pp. 1495–515, doi:<a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>.'
  short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
conference:
  end_date: 2018-09-14
  location: Dublin, Ireland
  name: European Conference on Machine Learning and Principles and Practice of Knowledge
    Discovery in Databases
  start_date: 2018-09-10
date_created: 2018-07-08T14:06:14Z
date_updated: 2022-01-06T06:59:21Z
ddc:
- '000'
department:
- _id: '355'
- _id: '34'
- _id: '7'
- _id: '26'
doi: 10.1007/s10994-018-5735-z
file:
- access_level: closed
  content_type: application/pdf
  creator: ups
  date_created: 2018-11-02T15:32:16Z
  date_updated: 2018-11-02T15:32:16Z
  file_id: '5306'
  file_name: ML-PlanAutomatedMachineLearnin.pdf
  file_size: 1070937
  relation: main_file
  success: 1
file_date_updated: 2018-11-02T15:32:16Z
has_accepted_license: '1'
keyword:
- AutoML
- Hierarchical Planning
- HTN planning
- ML-Plan
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://rdcu.be/3Nc2
oa: '1'
page: 1495-1515
project:
- _id: '1'
  name: SFB 901
- _id: '3'
  name: SFB 901 - Project Area B
- _id: '10'
  name: SFB 901 - Subproject B2
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Machine Learning
publication_identifier:
  eissn:
  - 1573-0565
  issn:
  - 0885-6125
publication_status: epub_ahead
publisher: Springer
status: public
title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning'
type: journal_article
user_id: '5786'
year: '2018'
...
---
_id: '11816'
abstract:
- lang: eng
  text: 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:
- first_name: Manh Kha
  full_name: Hoang, Manh Kha
  last_name: Hoang
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored
    Gaussian data with application to WiFi indoor positioning. In: <i>38th International
    Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>'
  apa: Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification
    of censored Gaussian data with application to WiFi indoor positioning. In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>
    (pp. 3721–3725). <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>
  bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and
    classification of censored Gaussian data with application to WiFi indoor positioning},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>},
    booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
    (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013},
    pages={3721–3725} }'
  chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    3721–25, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>.
  ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of
    censored Gaussian data with application to WiFi indoor positioning,” in <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–3725.
  mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–25, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>.
  short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.'
date_created: 2019-07-12T05:28:48Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638353
keyword:
- 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
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf
oa: '1'
page: 3721-3725
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf
status: public
title: Parameter estimation and classification of censored Gaussian data with application
  to WiFi indoor positioning
type: conference
user_id: '44006'
year: '2013'
...