---
_id: '63157'
abstract:
- lang: eng
text: Three-phase cascaded H-bridge converters (CHBs) in star configuration require
reliable current controllers to evenly charge the module DC-link capacitors. Conventionally,
a current control in dq-coordinates is utilized. At steady state, the resulting
calculated reference arm voltages are sinusoidal, have identical amplitudes and
show a phase shift of 120 degree to each other. For balanced grid inductors, the
resulting grid currents also have the same amplitude. However, own simulations
show that unbalanced grid inductors always lead to different grid current amplitudes
(4% difference in this case). As a result, the averaged charging module powers
differ and the peak DC-link capacitor voltage rises as well. In the first step,
an adaptation of an existing zero-sequence voltage injection is proposed. For
balanced grid inductors, it converges to the 3rd harmonic voltage injection which
can reduce the peak-to-peak DC-link voltage ripple up by to 50% and balances the
power between the phases. However, unbalanced grid inductors still lead to the
same unbalanced grid currents of 4%. Therefore, a new method with 4 integrators
based on linear regression is proposed to achieve sinusoidal grid currents for
unbalanced inductors. The proposed method has a similar transient dynamic as the
conventional dq control, but balances the grid currents nearly ideally. Simulation
results of a 1MW cascaded H bridge and a scaled-down prototype verify the proposed
method.
author:
- first_name: Roland
full_name: Unruh, Roland
id: '34289'
last_name: Unruh
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
- first_name: Frank
full_name: Schafmeister, Frank
id: '71291'
last_name: Schafmeister
citation:
ama: 'Unruh R, Böcker J, Schafmeister F. Three-Phase Instantaneous Current Controller
for Unbalanced Grid Inductors Without DQ Transform for Cascaded H-Bridge Converters.
In: 2025 Energy Conversion Congress &Amp; Expo Europe (ECCE Europe).
IEEE; 2025. doi:10.1109/ecce-europe62795.2025.11238538'
apa: Unruh, R., Böcker, J., & Schafmeister, F. (2025). Three-Phase Instantaneous
Current Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded
H-Bridge Converters. 2025 Energy Conversion Congress &Amp; Expo Europe
(ECCE Europe). 2025 Energy Conversion Congress & Expo Europe (ECCE Europe),
Birmingham, United Kingdom. https://doi.org/10.1109/ecce-europe62795.2025.11238538
bibtex: '@inproceedings{Unruh_Böcker_Schafmeister_2025, title={Three-Phase Instantaneous
Current Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded
H-Bridge Converters}, DOI={10.1109/ecce-europe62795.2025.11238538},
booktitle={2025 Energy Conversion Congress & Expo Europe (ECCE Europe)},
publisher={IEEE}, author={Unruh, Roland and Böcker, Joachim and Schafmeister,
Frank}, year={2025} }'
chicago: Unruh, Roland, Joachim Böcker, and Frank Schafmeister. “Three-Phase Instantaneous
Current Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded
H-Bridge Converters.” In 2025 Energy Conversion Congress &Amp; Expo
Europe (ECCE Europe). IEEE, 2025. https://doi.org/10.1109/ecce-europe62795.2025.11238538.
ieee: 'R. Unruh, J. Böcker, and F. Schafmeister, “Three-Phase Instantaneous Current
Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded H-Bridge
Converters,” presented at the 2025 Energy Conversion Congress & Expo Europe
(ECCE Europe), Birmingham, United Kingdom, 2025, doi: 10.1109/ecce-europe62795.2025.11238538.'
mla: Unruh, Roland, et al. “Three-Phase Instantaneous Current Controller for Unbalanced
Grid Inductors Without DQ Transform for Cascaded H-Bridge Converters.” 2025
Energy Conversion Congress &Amp; Expo Europe (ECCE Europe), IEEE,
2025, doi:10.1109/ecce-europe62795.2025.11238538.
short: 'R. Unruh, J. Böcker, F. Schafmeister, in: 2025 Energy Conversion Congress
&Amp; Expo Europe (ECCE Europe), IEEE, 2025.'
conference:
end_date: 2025-09-04
location: Birmingham, United Kingdom
name: 2025 Energy Conversion Congress & Expo Europe (ECCE Europe)
start_date: 2025-09-01
date_created: 2025-12-16T15:20:55Z
date_updated: 2025-12-16T15:25:38Z
department:
- _id: '52'
doi: 10.1109/ecce-europe62795.2025.11238538
keyword:
- Cascaded H-Bridge
- Current Control
- dq Transformation
- Linear Regression
- Unbalanced Inductors
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/11238538
publication: 2025 Energy Conversion Congress & Expo Europe (ECCE Europe)
publication_status: published
publisher: IEEE
status: public
title: Three-Phase Instantaneous Current Controller for Unbalanced Grid Inductors
Without DQ Transform for Cascaded H-Bridge Converters
type: conference
user_id: '34289'
year: '2025'
...
---
_id: '27506'
abstract:
- lang: eng
text: Explainability for machine learning gets more and more important in high-stakes
decisions like real estate appraisal. While traditional hedonic house pricing
models are fed with hard information based on housing attributes, recently also
soft information has been incorporated to increase the predictive performance.
This soft information can be extracted from image data by complex models like
Convolutional Neural Networks (CNNs). However, these are intransparent which excludes
their use for high-stakes financial decisions. To overcome this limitation, we
examine if a two-stage modeling approach can provide explainability. We combine
visual interpretability by Regression Activation Maps (RAM) for the CNN and a
linear regression for the overall prediction. Our experiments are based on 62.000
family homes in Philadelphia and the results indicate that the CNN learns aspects
related to vegetation and quality aspects of the house from exterior images, improving
the predictive accuracy of real estate appraisal by up to 5.4%.
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
citation:
ama: 'Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal.
In: 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
; 2022.'
apa: Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate
Appraisal. 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
Hawaii International Conference on System Science (HICSS), Virtual.
bibtex: '@inproceedings{Kucklick_2022, title={Visual Interpretability of Image-based
Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference
on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }'
chicago: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate
Appraisal.” In 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 2022.
ieee: J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,”
presented at the Hawaii International Conference on System Science (HICSS), Virtual,
2022.
mla: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.”
55th Annual Hawaii International Conference on System Sciences (HICSS-55),
2022.
short: 'J.-P. Kucklick, in: 55th Annual Hawaii International Conference on System
Sciences (HICSS-55), 2022.'
conference:
end_date: 2022-01-07
location: Virtual
name: Hawaii International Conference on System Science (HICSS)
start_date: 2022-01-03
date_created: 2021-11-17T07:08:15Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence (XAI)
- Regression Activation Maps
- Real Estate Appraisal
- Convolutional Block Attention Module
- Computer Vision
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Visual Interpretability of Image-based Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '27507'
abstract:
- lang: eng
text: Accurate real estate appraisal is essential in decision making processes of
financial institutions, governments, and trending real estate platforms like Zillow.
One of the most important factors of a property’s value is its location. However,
creating accurate quantifications of location remains a challenge. While traditional
approaches rely on Geographical Information Systems (GIS), recently unstructured
data in form of images was incorporated in the appraisal process, but text data
remains an untapped reservoir. Our study shows that using text data in form of
geolocated Wikipedia articles can increase predictive performance over traditional
GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to
automatically extract geographically weighted vector representations for text
is established and used alongside traditional structural housing features to make
predictions and to uncover local patterns on sale price for real estate transactions
between 2015 and 2020 in Allegheny County, Pennsylvania.
author:
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location
in Real Estate Appraisal. In: 55th Annual Hawaii International Conference on
System Sciences (HICSS-55). ; 2022.'
apa: Heuwinkel, T., Kucklick, J.-P., & Müller, O. (2022). Using Geolocated Text
to Quantify Location in Real Estate Appraisal. 55th Annual Hawaii International
Conference on System Sciences (HICSS-55). Hawaii International Conference
on System Science (HICSS), Virtual.
bibtex: '@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated
Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii
International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim
and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }'
chicago: Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated
Text to Quantify Location in Real Estate Appraisal.” In 55th Annual Hawaii
International Conference on System Sciences (HICSS-55), 2022.
ieee: T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify
Location in Real Estate Appraisal,” presented at the Hawaii International Conference
on System Science (HICSS), Virtual, 2022.
mla: Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real
Estate Appraisal.” 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 2022.
short: 'T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International
Conference on System Sciences (HICSS-55), 2022.'
conference:
end_date: 2022-01-07
location: Virtual
name: Hawaii International Conference on System Science (HICSS)
start_date: 2022-01-03
date_created: 2021-11-17T07:12:03Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
keyword:
- Real Estate Appraisal
- Text Regression
- Natural Language Processing (NLP)
- Location Intelligence
- Wikipedia
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Using Geolocated Text to Quantify Location in Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '48849'
abstract:
- lang: eng
text: One-shot optimization tasks require to determine the set of solution candidates
prior to their evaluation, i.e., without possibility for adaptive sampling. We
consider two variants, classic one-shot optimization (where our aim is to find
at least one solution of high quality) and one-shot regression (where the goal
is to fit a model that resembles the true problem as well as possible). For both
tasks it seems intuitive that well-distributed samples should perform better than
uniform or grid-based samples, since they show a better coverage of the decision
space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy
point sets are indeed very commonly used designs for one-shot optimization tasks.
We study in this work how well low star discrepancy correlates with performance
in one-shot optimization. Our results confirm an advantage of low-discrepancy
designs, but also indicate the correlation between discrepancy values and overall
performance is rather weak. We then demonstrate that commonly used designs may
be far from optimal. More precisely, we evolve 24 very specific designs that each
achieve good performance on one of our benchmark problems. Interestingly, we find
that these specifically designed samples yield surprisingly good performance across
the whole benchmark set. Our results therefore give strong indication that significant
performance gains over state-of-the-art one-shot sampling techniques are possible,
and that evolutionary algorithms can be an efficient means to evolve these.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies
for One-Shot Optimization Tasks. In: Parallel Problem Solving from Nature (PPSN
XVI). Springer-Verlag; 2020:111–124. doi:10.1007/978-3-030-58112-1_8'
apa: Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020).
Evolving Sampling Strategies for One-Shot Optimization Tasks. Parallel Problem
Solving from Nature (PPSN XVI), 111–124. https://doi.org/10.1007/978-3-030-58112-1_8
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin,
Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks},
DOI={10.1007/978-3-030-58112-1_8},
booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag},
author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta
and Neumann, Frank}, year={2020}, pages={111–124} }'
chicago: 'Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank
Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In Parallel
Problem Solving from Nature (PPSN XVI), 111–124. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_8.'
ieee: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling
Strategies for One-Shot Optimization Tasks,” in Parallel Problem Solving from
Nature (PPSN XVI), 2020, pp. 111–124, doi: 10.1007/978-3-030-58112-1_8.'
mla: Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization
Tasks.” Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag,
2020, pp. 111–124, doi:10.1007/978-3-030-58112-1_8.
short: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem
Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
111–124.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:53Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_8
extern: '1'
keyword:
- Continuous optimization
- Fully parallel search
- One-shot optimization
- Regression
- Surrogate-assisted optimization
language:
- iso: eng
page: 111–124
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publication_status: published
publisher: Springer-Verlag
status: public
title: Evolving Sampling Strategies for One-Shot Optimization Tasks
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '11846'
abstract:
- lang: eng
text: In this paper, we present a new technique for automatic speech recognition
(ASR) in reverberant environments. Our approach is aimed at the enhancement of
the logarithmic Mel power spectrum, which is computed at an intermediate stage
to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the
reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean
square error estimate of the clean LMPSCs is computed by carrying out Bayesian
inference. We employ switching linear dynamical models as an a priori model for
the dynamics of the clean LMPSCs. Further, we derive a stochastic observation
model which relates the clean to the reverberant LMPSCs through a simplified model
of the room impulse response (RIR). This model requires only two parameters, namely
RIR energy and reverberation time, which can be estimated from the captured microphone
signal. The performance of the proposed enhancement technique is studied on the
AURORA5 database and compared to that of constrained maximum-likelihood linear
regression (CMLLR). It is shown by experimental results that our approach significantly
outperforms CMLLR and that up to 80\% of the errors caused by the reverberation
are recovered. In addition to the fact that the approach is compatible with the
standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of
moderate computational complexity and suitable for real time applications.
author:
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Krueger A, Haeb-Umbach R. Model-Based Feature Enhancement for Reverberant Speech
Recognition. IEEE Transactions on Audio, Speech, and Language Processing.
2010;18(7):1692-1707. doi:10.1109/TASL.2010.2049684
apa: Krueger, A., & Haeb-Umbach, R. (2010). Model-Based Feature Enhancement
for Reverberant Speech Recognition. IEEE Transactions on Audio, Speech, and
Language Processing, 18(7), 1692–1707. https://doi.org/10.1109/TASL.2010.2049684
bibtex: '@article{Krueger_Haeb-Umbach_2010, title={Model-Based Feature Enhancement
for Reverberant Speech Recognition}, volume={18}, DOI={10.1109/TASL.2010.2049684},
number={7}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}, pages={1692–1707}
}'
chicago: 'Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
for Reverberant Speech Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing 18, no. 7 (2010): 1692–1707. https://doi.org/10.1109/TASL.2010.2049684.'
ieee: A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant
Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing,
vol. 18, no. 7, pp. 1692–1707, 2010.
mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
for Reverberant Speech Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing, vol. 18, no. 7, 2010, pp. 1692–707, doi:10.1109/TASL.2010.2049684.
short: A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 18 (2010) 1692–1707.
date_created: 2019-07-12T05:29:23Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2049684
intvolume: ' 18'
issue: '7'
keyword:
- ASR
- AURORA5 database
- automatic speech recognition
- Bayesian inference
- belief networks
- CMLLR
- computational complexity
- constrained maximum likelihood linear regression
- least mean squares methods
- LMPSC computation
- logarithmic Mel power spectrum
- maximum likelihood estimation
- Mel frequency cepstral coefficients
- MFCC feature vectors
- microphone signal
- minimum mean square error estimation
- model-based feature enhancement
- regression analysis
- reverberant speech recognition
- reverberation
- RIR energy
- room impulse response
- speech recognition
- stochastic observation model
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf
oa: '1'
page: 1692-1707
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Model-Based Feature Enhancement for Reverberant Speech Recognition
type: journal_article
user_id: '44006'
volume: 18
year: '2010'
...
---
_id: '11778'
abstract:
- lang: eng
text: In this paper, it is shown that a correlation criterion is the appropriate
criterion for bottom-up clustering to obtain broad phonetic class regression trees
for maximum likelihood linear regression (MLLR)-based speaker adaptation. The
correlation structure among speech units is estimated on the speaker-independent
training data. In adaptation experiments the tree outperformed a regression tree
obtained from clustering according to closeness in acoustic space and achieved
results comparable with those of a manually designed broad phonetic class tree
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Haeb-Umbach R. Automatic generation of phonetic regression class trees for
MLLR adaptation. IEEE Transactions on Speech and Audio Processing. 2001;9(3):299-302.
doi:10.1109/89.906003
apa: Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees
for MLLR adaptation. IEEE Transactions on Speech and Audio Processing,
9(3), 299–302. https://doi.org/10.1109/89.906003
bibtex: '@article{Haeb-Umbach_2001, title={Automatic generation of phonetic regression
class trees for MLLR adaptation}, volume={9}, DOI={10.1109/89.906003},
number={3}, journal={IEEE Transactions on Speech and Audio Processing}, author={Haeb-Umbach,
Reinhold}, year={2001}, pages={299–302} }'
chicago: 'Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class
Trees for MLLR Adaptation.” IEEE Transactions on Speech and Audio Processing
9, no. 3 (2001): 299–302. https://doi.org/10.1109/89.906003.'
ieee: R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for
MLLR adaptation,” IEEE Transactions on Speech and Audio Processing, vol.
9, no. 3, pp. 299–302, 2001.
mla: Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees
for MLLR Adaptation.” IEEE Transactions on Speech and Audio Processing,
vol. 9, no. 3, 2001, pp. 299–302, doi:10.1109/89.906003.
short: R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001)
299–302.
date_created: 2019-07-12T05:28:04Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/89.906003
intvolume: ' 9'
issue: '3'
keyword:
- acoustic space
- adaptation experiments
- automatic generation
- bottom-up clustering
- broad phonetic class regression trees
- correlation criterion
- correlation methods
- maximum likelihood estimation
- maximum likelihood linear regression based speaker adaptation
- MLLR adaptation
- pattern clustering
- phonetic regression class trees
- speaker-independent training data
- speech recognition
- speech units
- statistical analysis
- trees (mathematics)
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2001/Ha01.pdf
oa: '1'
page: 299-302
publication: IEEE Transactions on Speech and Audio Processing
status: public
title: Automatic generation of phonetic regression class trees for MLLR adaptation
type: journal_article
user_id: '44006'
volume: 9
year: '2001'
...