TY - CONF
AU - Mattei, Annalisa
AU - Eremin, Oxana
ID - 45326
T2 - Bericht zur Jahrestagung der Sektion Frauen- und Geschlechterforschung in der Erziehungswissenschaft in der Deutschen Gesellschaft für Erziehungswissenschaft
TI - Corona und Krise. Perspektiven erziehungswissenschaftlicher Frauen- und Geschlechterforschung
ER -
TY - CHAP
AU - Schemmer, Susanne Jutta
AU - Heisler, Dietmar
AU - Rink, Julia
ED - Weyland, Ulrike
ED - Ziegler, Birgit
ED - Driesel-Lange, Katja
ED - Kruse, Annika
ID - 45319
T2 - Entwicklungen und Perspektiven in der Berufsorientierung. Stand und Herausforderungen. Online: (06.12.2021)
TI - Entwicklungschance oder Warteschleife? Berufsorientierung und Berufswahl in der Berufsfachschule
ER -
TY - CONF
AU - Kenter, Tobias
AU - Shambhu, Adesh
AU - Faghih-Naini, Sara
AU - Aizinger, Vadym
ID - 46194
T2 - Proceedings of the Platform for Advanced Scientific Computing Conference
TI - Algorithm-hardware co-design of a discontinuous Galerkin shallow-water model for a dataflow architecture on FPGA
ER -
TY - BOOK
AB - Trotz massiver Förderungen für die Digitalisierung ist die Präsenzlehre noch immer der Standard an deutschen Hochschulen. Aufgrund des Physical Distancing im Zuge der Corona-Pandemie musste sie jedoch kurzfristig fast vollständig digitalisiert werden. Die Beiträge des Bandes bieten einen multiperspektivischen Zugang zu den damit verbundenen Herausforderungen und beleuchten, wie die verschiedenen Akteur*innen die Umstellung auf digitale Lehr- und Lernformate umgesetzt und erlebt haben. Durch die Zusammenführung der verschiedenen Sichtweisen können die Bedarfe und Wünsche der einzelnen Akteursgruppen zusammengebracht und bei der nachhaltigen Weiterentwicklung der Hochschullehre besser berücksichtigt werden.
ED - Neiske, Iris
ED - Osthushenrich, Judith
ED - Schaper, Niclas
ED - Trier, Ulrike
ED - Vöing, Nerea
ID - 46613
SN - 2749-7623
TI - Hochschule auf Abstand
ER -
TY - CONF
AB -
Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach.
In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation.
The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties.
Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties.
Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results.
AU - Bender, Amelie
AU - Sextro, Walter
ED - Do, Phuc
ED - King, Steve
ED - Fink, Olga
ID - 22724
IS - 1
KW - Hybrid prediction method
KW - Multi-model particle filtering
KW - Uncertainty quantification
KW - RUL estimation
T2 - Proceedings of the European Conference of the PHM Society 2021
TI - Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties
VL - 6
ER -
TY - CONF
AB - Several methods, including order analysis, wavelet analysis and empirical mode decomposition have been proposed and successfully employed for the health state estimation of technical systems operating under varying conditions. However, where information such as the speed of rotating machinery, component specifications or other domain-specific information is unavailable, such methods are often infeasible. Thus, this paper investigates the application of classical time-domain features, features from the medical field and novel features from the highly comparative time-series analysis (HCTSA) package, for the health state estimation of rotating machinery operating under varying conditions. Furthermore, several feature selection methods are investigated to identify features as viable health indicators for the diagnostics and prognostics of technical systems. As a case study, the presented methods are evaluated on real-world and experimentally acquired vibration data of bearings operating under varying speed. The results show that the selected features can successfully be employed as health indicators for technical systems operating under varying conditions.
AU - Aimiyekagbon, Osarenren Kennedy
AU - Bender, Amelie
AU - Sextro, Walter
ID - 22507
KW - Wind turbine diagnostics
KW - bearing diagnostics
KW - non-stationary operating conditions
KW - varying operating conditions
KW - feature extraction
KW - feature selection
KW - fault detection
KW - failure detection
T2 - Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)
TI - On the applicability of time series features as health indicators for technical systems operating under varying conditions
ER -
TY - CONF
AB - In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.
AU - Aimiyekagbon, Osarenren Kennedy
AU - Muth, Lars
AU - Wohlleben, Meike Claudia
AU - Bender, Amelie
AU - Sextro, Walter
ED - Do, Phuc
ED - King, Steve
ED - Fink, Olga
ID - 27111
IS - 1
KW - PHME 2021
KW - Feature Selection Classification
KW - Feature Selection Clustering
KW - Interpretable Model
KW - Transparent Model
KW - Industry 4.0
KW - Real-World Diagnostics
KW - Quality Control
KW - Predictive Maintenance
T2 - Proceedings of the European Conference of the PHM Society 2021
TI - Rule-based Diagnostics of a Production Line
VL - 6
ER -
TY - JOUR
AU - Alhaddad, Samer
AU - Förstner, Jens
AU - Groth, Stefan
AU - Grünewald, Daniel
AU - Grynko, Yevgen
AU - Hannig, Frank
AU - Kenter, Tobias
AU - Pfreundt, Franz‐Josef
AU - Plessl, Christian
AU - Schotte, Merlind
AU - Steinke, Thomas
AU - Teich, Jürgen
AU - Weiser, Martin
AU - Wende, Florian
ID - 24788
JF - Concurrency and Computation: Practice and Experience
KW - tet_topic_hpc
SN - 1532-0626
TI - The HighPerMeshes framework for numerical algorithms on unstructured grids
ER -
TY - GEN
AU - Lammer, Christina
ID - 30961
TI - Andrea Karimé: Sterne im Kopf und ein unglaublicher Plan. Köln: Peter Hammer Verlag 2021
ER -
TY - JOUR
AB - AbstractQuantum well (QW) heterostructures have been extensively used for the realization of a wide range of optical and electronic devices. Exploiting their potential for further improvement and development requires a fundamental understanding of their electronic structure. So far, the most commonly used experimental techniques for this purpose have been all-optical spectroscopy methods that, however, are generally averaging in momentum space. Additional information can be gained by angle-resolved photoelectron spectroscopy (ARPES), which measures the electronic structure with momentum resolution. Here we report on the use of extremely low-energy ARPES (photon energy ~ 7 eV) to increase depth sensitivity and access buried QW states, located at 3 nm and 6 nm below the surface of cubic-GaN/AlN and GaAs/AlGaAs heterostructures, respectively. We find that the QW states in cubic-GaN/AlN can indeed be observed, but not their energy dispersion, because of the high surface roughness. The GaAs/AlGaAs QW states, on the other hand, are buried too deep to be detected by extremely low-energy ARPES. Since the sample surface is much flatter, the ARPES spectra of the GaAs/AlGaAs show distinct features in momentum space, which can be reconducted to the band structure of the topmost surface layer of the QW structure. Our results provide important information about the samples’ properties required to perform extremely low-energy ARPES experiments on electronic states buried in semiconductor heterostructures.
AU - Hajlaoui, Mahdi
AU - Ponzoni, Stefano
AU - Deppe, Michael
AU - Henksmeier, Tobias
AU - As, Donat Josef
AU - Reuter, Dirk
AU - Zentgraf, Thomas
AU - Springholz, Gunther
AU - Schneider, Claus Michael
AU - Cramm, Stefan
AU - Cinchetti, Mirko
ID - 25227
JF - Scientific Reports
SN - 2045-2322
TI - Extremely low-energy ARPES of quantum well states in cubic-GaN/AlN and GaAs/AlGaAs heterostructures
VL - 11
ER -
TY - CONF
AB - Unsupervised blind source separation methods do not require a training phase
and thus cannot suffer from a train-test mismatch, which is a common concern in
neural network based source separation. The unsupervised techniques can be
categorized in two classes, those building upon the sparsity of speech in the
Short-Time Fourier transform domain and those exploiting non-Gaussianity or
non-stationarity of the source signals. In this contribution, spatial mixture
models which fall in the first category and independent vector analysis (IVA)
as a representative of the second category are compared w.r.t. their separation
performance and the performance of a downstream speech recognizer on a
reverberant dataset of reasonable size. Furthermore, we introduce a serial
concatenation of the two, where the result of the mixture model serves as
initialization of IVA, which achieves significantly better WER performance than
each algorithm individually and even approaches the performance of a much more
complex neural network based technique.
AU - Boeddeker, Christoph
AU - Rautenberg, Frederik
AU - Haeb-Umbach, Reinhold
ID - 44843
T2 - ITG Conference on Speech Communication
TI - A Comparison and Combination of Unsupervised Blind Source Separation Techniques
ER -
TY - CONF
AU - Boeddeker, Christoph
AU - Zhang, Wangyou
AU - Nakatani, Tomohiro
AU - Kinoshita, Keisuke
AU - Ochiai, Tsubasa
AU - Delcroix, Marc
AU - Kamo, Naoyuki
AU - Qian, Yanmin
AU - Haeb-Umbach, Reinhold
ID - 28259
T2 - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
TI - Convolutive Transfer Function Invariant SDR Training Criteria for Multi-Channel Reverberant Speech Separation
ER -
TY - CONF
AU - Schmalenstroeer, Joerg
AU - Heitkaemper, Jens
AU - Ullmann, Joerg
AU - Haeb-Umbach, Reinhold
ID - 23998
T2 - 29th European Signal Processing Conference (EUSIPCO)
TI - Open Range Pitch Tracking for Carrier Frequency Difference Estimation from HF Transmitted Speech
ER -
TY - GEN
AU - Hartung, Olaf
ID - 48787
T2 - H-Soz-Kult
TI - Rezension von: Jörg van Norden, Thomas Must, Lars Deile, Peter Riedel, Susan Krause und Wanda Schürenberg (Hgg.): Geschichtsdidaktische Grundbegriffe. Ein Bilderbuch für Studium, Lehre und Beruf. Hannover 2020
ER -
TY - JOUR
AB - Due to the ad hoc nature of wireless acoustic sensor networks, the position of the sensor nodes is typically unknown. This contribution proposes a technique to estimate the position and orientation of the sensor nodes from the recorded speech signals. The method assumes that a node comprises a microphone array with synchronously sampled microphones rather than a single microphone, but does not require the sampling clocks of the nodes to be synchronized. From the observed audio signals, the distances between the acoustic sources and arrays, as well as the directions of arrival, are estimated. They serve as input to a non-linear least squares problem, from which both the sensor nodes’ positions and orientations, as well as the source positions, are alternatingly estimated in an iterative process. Given one set of unknowns, i.e., either the source positions or the sensor nodes’ geometry, the other set of unknowns can be computed in closed-form. The proposed approach is computationally efficient and the first one, which employs both distance and directional information for geometry calibration in a common cost function. Since both distance and direction of arrival measurements suffer from outliers, e.g., caused by strong reflections of the sound waves on the surfaces of the room, we introduce measures to deemphasize or remove unreliable measurements. Additionally, we discuss modifications of our previously proposed deep neural network-based acoustic distance estimator, to account not only for omnidirectional sources but also for directional sources. Simulation results show good positioning accuracy and compare very favorably with alternative approaches from the literature.
AU - Gburrek, Tobias
AU - Schmalenstroeer, Joerg
AU - Haeb-Umbach, Reinhold
ID - 22528
JF - EURASIP Journal on Audio, Speech, and Music Processing
SN - 1687-4722
TI - Geometry calibration in wireless acoustic sensor networks utilizing DoA and distance information
ER -
TY - CONF
AU - Gburrek, Tobias
AU - Schmalenstroeer, Joerg
AU - Haeb-Umbach, Reinhold
ID - 23994
T2 - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
TI - Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks
ER -
TY - CONF
AU - Gburrek, Tobias
AU - Schmalenstroeer, Joerg
AU - Haeb-Umbach, Reinhold
ID - 23999
T2 - Speech Communication; 14th ITG-Symposium
TI - On Source-Microphone Distance Estimation Using Convolutional Recurrent Neural Networks
ER -
TY - CONF
AU - Chinaev, Aleksej
AU - Enzner, Gerald
AU - Gburrek, Tobias
AU - Schmalenstroeer, Joerg
ID - 23997
T2 - 29th European Signal Processing Conference (EUSIPCO)
TI - Online Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks with Packet Loss
ER -
TY - GEN
AU - Moritz, Tilman
ID - 49017
IS - 10
T2 - sehepunkte
TI - A. Seidler, I. Monok (Hgg.): Reformation und Bücher. Zentren der Ideen – Zentren der Buchproduktion. Wiesbaden 2020
VL - 21
ER -
TY - CONF
AB - In this work we address disentanglement of style and content in speech signals. We propose a fully convolutional variational autoencoder employing two encoders: a content encoder and a style encoder. To foster disentanglement, we propose adversarial contrastive predictive coding. This new disentanglement method does neither need parallel data nor any supervision. We show that the proposed technique is capable of separating speaker and content traits into the two different representations and show competitive speaker-content disentanglement performance compared to other unsupervised approaches. We further demonstrate an increased robustness of the content representation against a train-test mismatch compared to spectral features, when used for phone recognition.
AU - Ebbers, Janek
AU - Kuhlmann, Michael
AU - Cord-Landwehr, Tobias
AU - Haeb-Umbach, Reinhold
ID - 29304
T2 - Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
TI - Contrastive Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised Learning of Disentangled Speech Representations
ER -