--- _id: '34171' abstract: - lang: eng text: State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models. author: - first_name: Ricarda-Samantha full_name: Götte, Ricarda-Samantha id: '43992' last_name: Götte - first_name: Julia full_name: Timmermann, Julia id: '15402' last_name: Timmermann citation: ama: 'Götte R-S, Timmermann J. Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. In: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022). Vol 56. ; 2023:85-90. doi:https://doi.org/10.1016/j.ifacol.2023.02.015' apa: Götte, R.-S., & Timmermann, J. (2023). Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 56(1), 85–90. https://doi.org/10.1016/j.ifacol.2023.02.015 bibtex: '@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={https://doi.org/10.1016/j.ifacol.2023.02.015}, number={1}, booktitle={12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={85–90} }' chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” In 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 56:85–90, 2023. https://doi.org/10.1016/j.ifacol.2023.02.015. ieee: 'R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF,” in 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90, doi: https://doi.org/10.1016/j.ifacol.2023.02.015.' mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), vol. 56, no. 1, 2023, pp. 85–90, doi:https://doi.org/10.1016/j.ifacol.2023.02.015. short: 'R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 2023, pp. 85–90.' conference: end_date: 2023-01-06 location: Canberra, Australien name: 12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022 start_date: 2023-01-04 date_created: 2022-12-01T07:17:00Z date_updated: 2023-05-02T15:17:47Z department: - _id: '153' doi: https://doi.org/10.1016/j.ifacol.2023.02.015 intvolume: ' 56' issue: '1' keyword: - joint estimation - unscented transform - Kalman filter - sparsity - data-driven - compressed sensing language: - iso: eng page: 85-90 publication: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022) quality_controlled: '1' status: public title: Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF type: conference user_id: '43992' volume: 56 year: '2023' ... --- _id: '44326' abstract: - lang: eng text: "Low-quality models that miss relevant dynamics lead to major challenges in modelbased\r\nstate estimation. We address this issue by simultaneously estimating the system’s states\r\nand its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely,\r\nwe augment the state with the parameter vector of a linear combination containing suitable\r\nfunctions that approximate the lacking dynamics. Presuming that only a few dynamical terms\r\nare relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like\r\nsparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace\r\ndistribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF,\r\nthe regularized horseshoe distribution, a Gaussian that approximately features sparsity, is\r\napplied instead. Results exhibit small estimation errors with model improvements detected by\r\nan automated model reduction technique." author: - first_name: Ricarda-Samantha full_name: Götte, Ricarda-Samantha id: '43992' last_name: Götte - first_name: Julia full_name: Timmermann, Julia id: '15402' last_name: Timmermann citation: ama: 'Götte R-S, Timmermann J. Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. In: IFAC-PapersOnLine. Vol 56. ; 2023:869-874.' apa: Götte, R.-S., & Timmermann, J. (2023). Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. IFAC-PapersOnLine, 56(2), 869–874. bibtex: '@inproceedings{Götte_Timmermann_2023, title={Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF}, volume={56}, number={2}, booktitle={IFAC-PapersOnLine}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={869–874} }' chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” In IFAC-PapersOnLine, 56:869–74, 2023. ieee: R.-S. Götte and J. Timmermann, “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF,” in IFAC-PapersOnLine, Yokohama, Japan, 2023, vol. 56, no. 2, pp. 869–874. mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” IFAC-PapersOnLine, vol. 56, no. 2, 2023, pp. 869–74. short: 'R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.' conference: end_date: 2023-07-14 location: Yokohama, Japan name: 22nd IFAC World Congress start_date: 2023-07-09 date_created: 2023-05-02T15:16:43Z date_updated: 2023-11-27T07:42:51Z department: - _id: '153' intvolume: ' 56' issue: '2' keyword: - joint estimation - unscented Kalman filter - sparsity - Laplacian prior - regularized horseshoe - principal component analysis language: - iso: eng page: 869-874 publication: IFAC-PapersOnLine quality_controlled: '1' status: public title: Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF type: conference user_id: '43992' volume: 56 year: '2023' ... --- _id: '33849' abstract: - lang: eng text: Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed. 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: Leon full_name: Bußemas, Leon id: '51118' last_name: Bußemas - 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, Bußemas L, Gausemeier S, Trächtler A. Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments. In: Klein C, Jarke M, Helfert M, Berns K, Gusikhin O, eds. Communications in Computer and Information Science. Vol 1612. Communications in Computer and Information Science. Springer International Publishing; 2022:232–254. doi:10.1007/978-3-031-17098-0_12' apa: Malena, K., Link, C., Bußemas, L., Gausemeier, S., & Trächtler, A. (2022). Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments. In C. Klein, M. Jarke, M. Helfert, K. Berns, & O. Gusikhin (Eds.), Communications in Computer and Information Science (Vol. 1612, pp. 232–254). Springer International Publishing. https://doi.org/10.1007/978-3-031-17098-0_12 bibtex: '@inbook{Malena_Link_Bußemas_Gausemeier_Trächtler_2022, place={Cham}, series={Communications in Computer and Information Science}, title={Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}, volume={1612}, DOI={10.1007/978-3-031-17098-0_12}, booktitle={Communications in Computer and Information Science}, publisher={Springer International Publishing}, author={Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}, editor={Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}, year={2022}, pages={232–254}, collection={Communications in Computer and Information Science} }' chicago: 'Malena, Kevin, Christopher Link, Leon Bußemas, Sandra Gausemeier, and Ansgar Trächtler. “Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments.” In Communications in Computer and Information Science, edited by Cornel Klein, Mathias Jarke, Markus Helfert, Karsten Berns, and Oleg Gusikhin, 1612:232–254. Communications in Computer and Information Science. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-17098-0_12.' ieee: 'K. Malena, C. Link, L. Bußemas, S. Gausemeier, and A. Trächtler, “Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments,” in Communications in Computer and Information Science, vol. 1612, C. Klein, M. Jarke, M. Helfert, K. Berns, and O. Gusikhin, Eds. Cham: Springer International Publishing, 2022, pp. 232–254.' mla: Malena, Kevin, et al. “Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments.” Communications in Computer and Information Science, edited by Cornel Klein et al., vol. 1612, Springer International Publishing, 2022, pp. 232–254, doi:10.1007/978-3-031-17098-0_12. short: 'K. Malena, C. Link, L. Bußemas, S. Gausemeier, A. Trächtler, in: C. Klein, M. Jarke, M. Helfert, K. Berns, O. Gusikhin (Eds.), Communications in Computer and Information Science, Springer International Publishing, Cham, 2022, pp. 232–254.' date_created: 2022-10-20T15:06:39Z date_updated: 2023-04-27T12:08:25Z department: - _id: '153' doi: 10.1007/978-3-031-17098-0_12 editor: - first_name: Cornel full_name: Klein, Cornel last_name: Klein - first_name: Mathias full_name: Jarke, Mathias last_name: Jarke - first_name: Markus full_name: Helfert, Markus last_name: Helfert - first_name: Karsten full_name: Berns, Karsten last_name: Berns - first_name: Oleg full_name: Gusikhin, Oleg last_name: Gusikhin intvolume: ' 1612' keyword: - Traffic control - Traffic estimation - Real-time - MPC - Fuzzy - Isolated intersection - Networked intersection - Sensor fusion language: - iso: eng page: 232–254 place: Cham publication: Communications in Computer and Information Science publication_identifier: isbn: - '9783031170973' - '9783031170980' issn: - 1865-0929 - 1865-0937 publication_status: published publisher: Springer International Publishing quality_controlled: '1' related_material: record: - id: '24159' relation: continues status: public series_title: Communications in Computer and Information Science status: public title: Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments type: book_chapter user_id: '552' volume: 1612 year: '2022' ... --- _id: '24551' abstract: - lang: eng text: "Access to precise meteorological data is crucial to be able to plan and install renewable energy systems \r\nsuch as solar power plants and wind farms. In case of solar energy, knowledge of local irradiance and air temperature \r\nvalues is very important. For this, various methods can be used such as installing local weather stations or using \r\nmeteorological data from different organizations such as Meteonorm or official Deutscher Wetterdienst (DWD). An \r\nalternative is to use satellite reanalysis datasets provided by organizations like the National Aeronautics and Space \r\nAdministration (NASA) and European Centre for Medium-Range Weather Forecasts (ECMWF). In this paper the \r\n“Modern-Era Retrospective analysis for Research and Applications” dataset version 2 (MERRA-2) will be presented, \r\nand its performance will be evaluated by comparing it to locally measured datasets provided by Meteonorm and DWD. \r\nThe analysis shows very high correlation between MERRA-2 and local measurements (correlation coefficients of 0.99) \r\nfor monthly global irradiance and air temperature values. The results prove the suitability of MERRA-2 data for \r\napplications requiring long historical data. Moreover, availability of MERRA-2 for the whole world with an acceptable \r\nresolution makes it a very valuable dataset." author: - first_name: Arash full_name: Khatibi, Arash id: '43538' last_name: Khatibi - first_name: Stefan full_name: Krauter, Stefan id: '28836' last_name: Krauter orcid: 0000-0002-3594-260X citation: ama: 'Khatibi A, Krauter S. Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD). In: Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021). ; 2021:1141-1147. doi:10.4229/EUPVSEC20212021-5BV.4.11' apa: 'Khatibi, A., & Krauter, S. (2021). Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD). Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021), 1141–1147. https://doi.org/10.4229/EUPVSEC20212021-5BV.4.11' bibtex: '@inproceedings{Khatibi_Krauter_2021, title={Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD)}, DOI={10.4229/EUPVSEC20212021-5BV.4.11}, booktitle={Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021)}, author={Khatibi, Arash and Krauter, Stefan}, year={2021}, pages={1141–1147} }' chicago: 'Khatibi, Arash, and Stefan Krauter. “Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD).” In Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021), 1141–47, 2021. https://doi.org/10.4229/EUPVSEC20212021-5BV.4.11.' ieee: 'A. Khatibi and S. Krauter, “Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD),” in Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021), 2021, pp. 1141–1147, doi: 10.4229/EUPVSEC20212021-5BV.4.11.' mla: 'Khatibi, Arash, and Stefan Krauter. “Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD).” Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021), 2021, pp. 1141–47, doi:10.4229/EUPVSEC20212021-5BV.4.11.' short: 'A. Khatibi, S. Krauter, in: Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021), 2021, pp. 1141–1147.' conference: end_date: 2021-09-10 name: 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021) start_date: 2021-09-06 date_created: 2021-09-16T10:20:41Z date_updated: 2022-01-06T13:29:51Z ddc: - '550' department: - _id: '53' doi: 10.4229/EUPVSEC20212021-5BV.4.11 file: - access_level: closed content_type: application/pdf creator: krauter date_created: 2022-01-06T13:26:47Z date_updated: 2022-01-06T13:26:47Z file_id: '29176' file_name: Khatibi Krauter - MERRA 2 vs Meteonorm - EUPVSEC 2021.pdf file_size: 2475972 relation: main_file success: 1 file_date_updated: 2022-01-06T13:26:47Z has_accepted_license: '1' keyword: - Energy potential estimation - Photovoltaic - Solar radiation - Temperature measurement - Satellite data - Meteonorm - MERRA-2 - DWD language: - iso: eng page: 1141 - 1147 publication: Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021) publication_identifier: isbn: - 3-936338-78-7 publication_status: published quality_controlled: '1' status: public title: 'Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD)' type: conference user_id: '28836' year: '2021' ... --- _id: '24159' abstract: - lang: eng text: "The online fitting of a microscopic traffic simulation model to reconstruct the current state of a real traffic\r\narea can be challenging depending on the provided data. This paper presents a novel method based on limited\r\ndata from sensors positioned at specific locations and guarantees a general accordance of reality and\r\nsimulation in terms of multimodal road traffic counts and vehicle speeds. In these considerations, the actual\r\npurpose of research is of particular importance. Here, the research aims at improving the traffic flow by\r\ncontrolling the Traffic Light Systems (TLS) of the examined area which is why the current traffic state and\r\nthe route choices of individual road users are the matter of interest. An integer optimization problem is derived\r\nto fit the current simulation to the latest field measurements. The concept can be transferred to any road traffic\r\nnetwork and results in an observation of the current multimodal traffic state matching at the given sensor\r\nposition. First case studies show promosing results in terms of deviations between reality and simulation." 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: Sven full_name: Mertin, Sven id: '13195' last_name: Mertin - 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, Mertin S, Gausemeier S, Trächtler A. Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources*. In: VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems. Vol 7. VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS; 2021:386-395.' apa: Malena, K., Link, C., Mertin, S., Gausemeier, S., & Trächtler, A. (2021). Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources*. VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, 7, 386–395. bibtex: '@inproceedings{Malena_Link_Mertin_Gausemeier_Trächtler_2021, place={Portugal}, series={VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems}, title={Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources*}, volume={7}, booktitle={VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems}, publisher={SCITEPRESS}, author={Malena, Kevin and Link, Christopher and Mertin, Sven and Gausemeier, Sandra and Trächtler, Ansgar}, year={2021}, pages={386–395}, collection={VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems} }' chicago: 'Malena, Kevin, Christopher Link, Sven Mertin, Sandra Gausemeier, and Ansgar Trächtler. “Online State Estimation for Microscopic Traffic Simulations Using Multiple Data Sources*.” In VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, 7:386–95. VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems. Portugal: SCITEPRESS, 2021.' ieee: K. Malena, C. Link, S. Mertin, S. Gausemeier, and A. Trächtler, “Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources*,” in VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, Online Streaming, 2021, vol. 7, pp. 386–395. mla: Malena, Kevin, et al. “Online State Estimation for Microscopic Traffic Simulations Using Multiple Data Sources*.” VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, vol. 7, SCITEPRESS, 2021, pp. 386–95. short: 'K. Malena, C. Link, S. Mertin, S. Gausemeier, A. Trächtler, in: VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, SCITEPRESS, Portugal, 2021, pp. 386–395.' conference: end_date: 2021-04-30 location: Online Streaming name: 7th International Conference on Vehicle Technology and Intelligent Transport Systems start_date: 2021-04-28 date_created: 2021-09-10T12:19:14Z date_updated: 2023-04-27T12:08:24Z department: - _id: '153' intvolume: ' 7' keyword: - Microscopic Traffic Simulation - Online State Estimation - Mixed Road Users - Sensor Fusion - Integer Programming - Route Choice - Vehicle2Infrastructure language: - iso: eng main_file_link: - url: https://www.scitepress.org/PublicationsDetail.aspx?ID=3xZWfOSENWk=&t=1 page: 386-395 place: Portugal publication: VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems publication_identifier: isbn: - 978-989-758-513-5 publication_status: published publisher: SCITEPRESS quality_controlled: '1' related_material: link: - relation: confirmation url: https://www.scitepress.org/PublicationsDetail.aspx?ID=3xZWfOSENWk=&t=1 record: - id: '33849' relation: is_continued_by status: public series_title: VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems status: public title: Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources* type: conference user_id: '36303' volume: 7 year: '2021' ... --- _id: '22724' abstract: - lang: eng text: "\r\nPredictive 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.\r\n\r\n \r\n\r\nIn 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.\r\n\r\nThe 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.\r\n\r\n \r\n\r\nRubber-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.\r\n\r\nFinally, 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." author: - first_name: Amelie full_name: Bender, Amelie id: '54290' last_name: Bender - first_name: Walter full_name: Sextro, Walter id: '21220' last_name: Sextro citation: ama: 'Bender A, Sextro W. Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties. In: Do P, King S, Fink Olga, eds. Proceedings of the European Conference of the PHM Society 2021. Vol 6. ; 2021. doi:https://doi.org/10.36001/phme.2021.v6i1.2843 ' apa: Bender, A., & Sextro, W. (2021). Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties. In P. Do, S. King, & Olga Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021 (Vol. 6, Issue 1). https://doi.org/10.36001/phme.2021.v6i1.2843 bibtex: '@inproceedings{Bender_Sextro_2021, title={Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties}, volume={6}, DOI={https://doi.org/10.36001/phme.2021.v6i1.2843 }, number={1}, booktitle={Proceedings of the European Conference of the PHM Society 2021}, author={Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve and Fink, Olga}, year={2021} }' chicago: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties.” In Proceedings of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve King, and Olga Fink, Vol. 6, 2021. https://doi.org/10.36001/phme.2021.v6i1.2843 . ieee: 'A. Bender and W. Sextro, “Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties,” in Proceedings of the European Conference of the PHM Society 2021, 2021, vol. 6, no. 1, doi: https://doi.org/10.36001/phme.2021.v6i1.2843 .' mla: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties.” Proceedings of the European Conference of the PHM Society 2021, edited by Phuc Do et al., vol. 6, no. 1, 2021, doi:https://doi.org/10.36001/phme.2021.v6i1.2843 . short: 'A. Bender, W. Sextro, in: P. Do, S. King, Olga Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021, 2021.' conference: end_date: 2021-07-02 name: 6th European Conference of Prognostics and Health Management start_date: 2021-06-28 date_created: 2021-07-14T06:29:08Z date_updated: 2023-09-22T07:19:48Z department: - _id: '151' doi: 'https://doi.org/10.36001/phme.2021.v6i1.2843 ' editor: - first_name: 'Phuc ' full_name: 'Do, Phuc ' last_name: Do - first_name: Steve full_name: King, Steve last_name: King - first_name: ' Olga' full_name: Fink, Olga last_name: Fink intvolume: ' 6' issue: '1' keyword: - Hybrid prediction method - Multi-model particle filtering - Uncertainty quantification - RUL estimation language: - iso: eng main_file_link: - open_access: '1' url: https://papers.phmsociety.org/index.php/phme/article/view/2843 oa: '1' publication: Proceedings of the European Conference of the PHM Society 2021 publication_identifier: unknown: - 978-1-936263-34-9 publication_status: published quality_controlled: '1' status: public title: Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties type: conference user_id: '54290' volume: 6 year: '2021' ... --- _id: '9978' abstract: - lang: eng text: Piezoelectric transducers are used in a wide range of applications. Reliability of these transducers is an important aspect in their application. Prognostics, which involve continuous monitoring of the health of technical systems and using this information to estimate the current health state and consequently predict the remaining useful lifetime (RUL), can be used to increase the reliability, safety, and availability of the transducers. This is achieved by utilizing the health state and RUL predictions to adaptively control the usage of the components or to schedule appropriate maintenance without interrupting operation. In this work, a prognostic approach utilizing self-sensing, where electric signals of a piezoelectric transducer are used as the condition monitoring data, is proposed. The approach involves training machine learning algorithms to model the degradation of the transducers through a health index and the use of the learned model to estimate the health index of similar transducers. The current health index is then used to estimate RUL of test components. The feasibility of the approach is demonstrated using piezoelectric bimorphs and the results show that the method is accurate in predicting the health index and RUL. author: - first_name: James Kuria full_name: Kimotho, James Kuria last_name: Kimotho - first_name: Walter full_name: Sextro, Walter id: '21220' last_name: Sextro - first_name: Tobias full_name: Hemsel, Tobias id: '210' last_name: Hemsel citation: ama: 'Kimotho JK, Sextro W, Hemsel T. Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing. In: IEEE Transactions on Reliability. ; 2017:1-10. doi:10.1109/TR.2017.2710260' apa: Kimotho, J. K., Sextro, W., & Hemsel, T. (2017). Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing. In IEEE Transactions on Reliability (pp. 1–10). https://doi.org/10.1109/TR.2017.2710260 bibtex: '@inproceedings{Kimotho_Sextro_Hemsel_2017, title={Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing}, DOI={10.1109/TR.2017.2710260}, booktitle={IEEE Transactions on Reliability}, author={Kimotho, James Kuria and Sextro, Walter and Hemsel, Tobias}, year={2017}, pages={1–10} }' chicago: Kimotho, James Kuria, Walter Sextro, and Tobias Hemsel. “Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing.” In IEEE Transactions on Reliability, 1–10, 2017. https://doi.org/10.1109/TR.2017.2710260. ieee: J. K. Kimotho, W. Sextro, and T. Hemsel, “Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing,” in IEEE Transactions on Reliability, 2017, pp. 1–10. mla: Kimotho, James Kuria, et al. “Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing.” IEEE Transactions on Reliability, 2017, pp. 1–10, doi:10.1109/TR.2017.2710260. short: 'J.K. Kimotho, W. Sextro, T. Hemsel, in: IEEE Transactions on Reliability, 2017, pp. 1–10.' date_created: 2019-05-27T09:41:06Z date_updated: 2019-09-16T10:32:05Z department: - _id: '151' doi: 10.1109/TR.2017.2710260 keyword: - Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing language: - iso: eng page: 1 - 10 publication: IEEE Transactions on Reliability quality_controlled: '1' status: public title: Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing type: conference user_id: '55222' year: '2017' ... --- _id: '9879' abstract: - lang: eng text: Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge. author: - first_name: 'James Kuria ' full_name: 'Kimotho, James Kuria ' last_name: Kimotho - first_name: Tobias full_name: Meyer, Tobias last_name: Meyer - first_name: Walter full_name: Sextro, Walter id: '21220' last_name: Sextro citation: ama: 'Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter with model parameter adaptation. In: Prognostics and Health Management (PHM), 2014 IEEE Conference On. ; 2014:1-6. doi:10.1109/ICPHM.2014.7036406' apa: Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In Prognostics and Health Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406 bibtex: '@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics using particle filter with model parameter adaptation}, DOI={10.1109/ICPHM.2014.7036406}, booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on}, author={Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}, year={2014}, pages={1–6} }' chicago: Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” In Prognostics and Health Management (PHM), 2014 IEEE Conference On, 1–6, 2014. https://doi.org/10.1109/ICPHM.2014.7036406. ieee: J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle filter with model parameter adaptation,” in Prognostics and Health Management (PHM), 2014 IEEE Conference on, 2014, pp. 1–6. mla: Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6, doi:10.1109/ICPHM.2014.7036406. short: 'J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6.' date_created: 2019-05-20T13:11:02Z date_updated: 2019-05-20T13:12:27Z department: - _id: '151' doi: 10.1109/ICPHM.2014.7036406 keyword: - ageing - particle filtering (numerical methods) - proton exchange membrane fuel cells - remaining life assessment - PEM fuel cell prognostics - PHM - RUL predictions - accelerated degradation - adaptive particle filter algorithm - dynamic loading - model parameter adaptation - prognostics and health management - proton exchange membrane fuel cells - remaining useful life estimation - self-healing effect - Adaptation models - Data models - Degradation - Estimation - Fuel cells - Mathematical model - Prognostics and health management language: - iso: eng page: 1-6 publication: Prognostics and Health Management (PHM), 2014 IEEE Conference on status: public title: PEM fuel cell prognostics using particle filter with model parameter adaptation type: conference user_id: '55222' year: '2014' ... --- _id: '11753' abstract: - lang: eng text: This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Dang Hai full_name: Tran Vu, Dang Hai last_name: Tran Vu - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014). ; 2014:213-217.' apa: Drude, L., Chinaev, A., Tran Vu, D. H., & Haeb-Umbach, R. (2014). Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014) (pp. 213–217). bibtex: '@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models}, booktitle={14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2014}, pages={213–217} }' chicago: Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” In 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 213–17, 2014. ieee: L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models,” in 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217. mla: Drude, Lukas, et al. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–17. short: 'L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.' date_created: 2019-07-12T05:27:35Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' keyword: - Accuracy - Acoustics - Estimation - Mathematical model - Soruce separation - Speech - Vectors - Bayes methods - Blind source separation - Directional statistics - Number of speakers - Speaker diarization language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14.pdf oa: '1' page: 213-217 publication: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014) related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14_Poster.pdf status: public title: Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models type: conference user_id: '44006' year: '2014' ... --- _id: '11716' abstract: - lang: eng text: The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios. author: - first_name: Ahmed H. full_name: Abdelaziz, Ahmed H. last_name: Abdelaziz - first_name: Steffen full_name: Zeiler, Steffen last_name: Zeiler - first_name: Dorothea full_name: Kolossa, Dorothea last_name: Kolossa - first_name: Volker full_name: Leutnant, Volker last_name: Leutnant - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance decoding. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On. ; 2013:6827-6831. doi:10.1109/ICASSP.2013.6638984' apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., & Haeb-Umbach, R. (2013). GMM-based significance decoding. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 6827–6831). https://doi.org/10.1109/ICASSP.2013.6638984 bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based significance decoding}, DOI={10.1109/ICASSP.2013.6638984}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831} }' chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant, and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On, 6827–31, 2013. https://doi.org/10.1109/ICASSP.2013.6638984. ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based significance decoding,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013, pp. 6827–6831. mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On, 2013, pp. 6827–31, doi:10.1109/ICASSP.2013.6638984. short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On, 2013, pp. 6827–6831.' date_created: 2019-07-12T05:26:53Z date_updated: 2022-01-06T06:51:07Z department: - _id: '54' doi: 10.1109/ICASSP.2013.6638984 keyword: - Bayes methods - Gaussian processes - convolution - decision theory - decoding - noise - reverberation - speech coding - speech recognition - Bayesian decision rule - GMM - Gaussian mixture models - additive noise scenarios - automatic speech recognition systems - convolutive noise scenarios - decoding approach - mathematical framework - reverberant environments - significance decoding - speech feature estimation - uncertainty-of-observation techniques - Hidden Markov models - Maximum likelihood decoding - Noise - Speech - Speech recognition - Uncertainty - Uncertainty-of-observation - modified imputation - noise robust speech recognition - significance decoding - uncertainty decoding language: - iso: eng page: 6827-6831 publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on publication_identifier: issn: - 1520-6149 status: public title: GMM-based significance decoding type: conference user_id: '44006' year: '2013' ... --- _id: '11740' abstract: - lang: eng text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner. author: - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:3352-3356. doi:10.1109/ICASSP.2013.6638279' apa: Chinaev, A., & Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) (pp. 3352–3356). https://doi.org/10.1109/ICASSP.2013.6638279 bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations}, DOI={10.1109/ICASSP.2013.6638279}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013}, pages={3352–3356} }' chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.” In 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 3352–56, 2013. https://doi.org/10.1109/ICASSP.2013.6638279. ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations,” in 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356. mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.” 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–56, doi:10.1109/ICASSP.2013.6638279. short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.' date_created: 2019-07-12T05:27:20Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' doi: 10.1109/ICASSP.2013.6638279 keyword: - Gaussian noise - maximum likelihood estimation - parameter estimation - GMM parameter - Gaussian mixture model - MAP estimation - Map-based estimation - maximum a-posteriori estimation - maximum likelihood technique - noisy observation - sequential estimation framework - white Gaussian noise - Additive noise - Gaussian mixture model - Maximum likelihood estimation - Noise measurement - Gaussian mixture model - Maximum a posteriori estimation - Maximum likelihood estimation language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf oa: '1' page: 3352-3356 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/ChHa13_Poster.pdf status: public title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations type: conference user_id: '44006' year: '2013' ... --- _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: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013). ; 2013:3721-3725. doi:10.1109/ICASSP.2013.6638353' apa: Hoang, M. K., & Haeb-Umbach, R. (2013). Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) (pp. 3721–3725). https://doi.org/10.1109/ICASSP.2013.6638353 bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}, DOI={10.1109/ICASSP.2013.6638353}, 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 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 3721–25, 2013. https://doi.org/10.1109/ICASSP.2013.6638353. ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning,” in 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 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.” 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–25, doi:10.1109/ICASSP.2013.6638353. 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' ... --- _id: '11917' abstract: - lang: eng text: In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm. author: - first_name: Dang Hai Tran full_name: Vu, Dang Hai Tran last_name: Vu - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:863-867. doi:10.1109/ICASSP.2013.6637771' apa: Vu, D. H. T., & Haeb-Umbach, R. (2013). Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) (pp. 863–867). https://doi.org/10.1109/ICASSP.2013.6637771 bibtex: '@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation}, DOI={10.1109/ICASSP.2013.6637771}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013}, pages={863–867} }' chicago: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” In 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 863–67, 2013. https://doi.org/10.1109/ICASSP.2013.6637771. ieee: D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation,” in 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867. mla: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–67, doi:10.1109/ICASSP.2013.6637771. short: 'D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.' date_created: 2019-07-12T05:30:45Z date_updated: 2022-01-06T06:51:12Z department: - _id: '54' doi: 10.1109/ICASSP.2013.6637771 keyword: - correlation methods - estimation theory - hidden Markov models - iterative methods - probability - spectral analysis - speech processing - 2D HMM - SPP estimates - iterative algorithm - posterior probability estimation - spectral correlation - speech presence probability estimation - state-of-the-art SPP estimation algorithm - temporal correlation - turbo principle - two-dimensional hidden Markov model - Correlation - Decoding - Estimation - Iterative decoding - Noise - Speech - Vectors language: - iso: eng page: 863-867 publication: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) publication_identifier: issn: - 1520-6149 status: public title: Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation type: conference user_id: '44006' year: '2013' ... --- _id: '11745' abstract: - lang: eng text: In this paper we present a novel noise power spectral density tracking algorithm and its use in single-channel speech enhancement. It has the unique feature that it is able to track the noise statistics even if speech is dominant in a given time-frequency bin. As a consequence it can follow non-stationary noise superposed by speech, even in the critical case of rising noise power. The algorithm requires an initial estimate of the power spectrum of speech and is thus meant to be used as a postprocessor to a first speech enhancement stage. An experimental comparison with a state-of-the-art noise tracking algorithm demonstrates lower estimation errors under low SNR conditions and smaller fluctuations of the estimated values, resulting in improved speech quality as measured by PESQ scores. author: - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Alexander full_name: Krueger, Alexander last_name: Krueger - first_name: Dang Hai full_name: Tran Vu, Dang Hai last_name: Tran Vu - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Chinaev A, Krueger A, Tran Vu DH, Haeb-Umbach R. Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In: 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012). ; 2012.' apa: Chinaev, A., Krueger, A., Tran Vu, D. H., & Haeb-Umbach, R. (2012). Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012). bibtex: '@inproceedings{Chinaev_Krueger_Tran Vu_Haeb-Umbach_2012, title={Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor}, booktitle={37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)}, author={Chinaev, Aleksej and Krueger, Alexander and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2012} }' chicago: Chinaev, Aleksej, Alexander Krueger, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.” In 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012. ieee: A. Chinaev, A. Krueger, D. H. Tran Vu, and R. Haeb-Umbach, “Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor,” in 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012. mla: Chinaev, Aleksej, et al. “Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.” 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012. short: 'A. Chinaev, A. Krueger, D.H. Tran Vu, R. Haeb-Umbach, in: 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.' date_created: 2019-07-12T05:27:26Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' keyword: - MAP parameter estimation - noise power estimation - speech enhancement language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12.pdf oa: '1' publication: 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012) related_material: link: - description: Presentation relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12_Talk.pdf status: public title: Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor type: conference user_id: '44006' year: '2012' ... --- _id: '11845' abstract: - lang: eng text: The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is superposed by additive Gaussian observation errors of known variance. Simulations on artificially generated data demonstrate the superiority of the proposed method. While reducing to the ordinary MAP approach in the absence of observation noise, the improvement becomes the more pronounced the larger the variance of the observation noise. The method is further extended to track the parameters in case of non-stationary Gaussian processes. 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. MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011). ; 2011:3596-3599. doi:10.1109/ICASSP.2011.5946256' apa: Krueger, A., & Haeb-Umbach, R. (2011). MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) (pp. 3596–3599). https://doi.org/10.1109/ICASSP.2011.5946256 bibtex: '@inproceedings{Krueger_Haeb-Umbach_2011, title={MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations}, DOI={10.1109/ICASSP.2011.5946256}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2011}, pages={3596–3599} }' chicago: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of Non-Stationary Gaussian Processes from Noisy Observations.” In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 3596–99, 2011. https://doi.org/10.1109/ICASSP.2011.5946256. ieee: A. Krueger and R. Haeb-Umbach, “MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 2011, pp. 3596–3599. mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of Non-Stationary Gaussian Processes from Noisy Observations.” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 2011, pp. 3596–99, doi:10.1109/ICASSP.2011.5946256. short: 'A. Krueger, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 2011, pp. 3596–3599.' date_created: 2019-07-12T05:29:22Z date_updated: 2022-01-06T06:51:11Z department: - _id: '54' doi: 10.1109/ICASSP.2011.5946256 keyword: - Gaussian processes - MAP-based estimation - maximum a posteriori method - maximum likelihood estimation - nonstationary Gaussian processes language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2011/KrHa11.pdf oa: '1' page: 3596-3599 publication: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) status: public title: MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations type: conference user_id: '44006' year: '2011' ... --- _id: '11850' abstract: - lang: eng text: In this paper, we present a novel blocking matrix and fixed beamformer design for a generalized sidelobe canceler for speech enhancement in a reverberant enclosure. They are based on a new method for estimating the acoustical transfer function ratios in the presence of stationary noise. The estimation method relies on solving a generalized eigenvalue problem in each frequency bin. An adaptive eigenvector tracking utilizing the power iteration method is employed and shown to achieve a high convergence speed. Simulation results demonstrate that the proposed beamformer leads to better noise and interference reduction and reduced speech distortions compared to other blocking matrix designs from the literature. author: - first_name: Alexander full_name: Krueger, Alexander last_name: Krueger - first_name: Ernst full_name: Warsitz, Ernst last_name: Warsitz - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: Krueger A, Warsitz E, Haeb-Umbach R. Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation. IEEE Transactions on Audio, Speech, and Language Processing. 2011;19(1):206-219. doi:10.1109/TASL.2010.2047324 apa: Krueger, A., Warsitz, E., & Haeb-Umbach, R. (2011). Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation. IEEE Transactions on Audio, Speech, and Language Processing, 19(1), 206–219. https://doi.org/10.1109/TASL.2010.2047324 bibtex: '@article{Krueger_Warsitz_Haeb-Umbach_2011, title={Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation}, volume={19}, DOI={10.1109/TASL.2010.2047324}, number={1}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Krueger, Alexander and Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2011}, pages={206–219} }' chicago: 'Krueger, Alexander, Ernst Warsitz, and Reinhold Haeb-Umbach. “Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation.” IEEE Transactions on Audio, Speech, and Language Processing 19, no. 1 (2011): 206–19. https://doi.org/10.1109/TASL.2010.2047324.' ieee: A. Krueger, E. Warsitz, and R. Haeb-Umbach, “Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 1, pp. 206–219, 2011. mla: Krueger, Alexander, et al. “Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 1, 2011, pp. 206–19, doi:10.1109/TASL.2010.2047324. short: A. Krueger, E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 19 (2011) 206–219. date_created: 2019-07-12T05:29:28Z date_updated: 2022-01-06T06:51:11Z department: - _id: '54' doi: 10.1109/TASL.2010.2047324 intvolume: ' 19' issue: '1' keyword: - acoustical transfer function ratio - adaptive eigenvector tracking - array signal processing - beamformer design - blocking matrix - eigenvalues and eigenfunctions - eigenvector-based transfer function ratios estimation - generalized sidelobe canceler - interference reduction - iterative methods - power iteration method - reduced speech distortions - reverberant enclosure - reverberation - speech enhancement - stationary noise language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2011/KrWaHa11.pdf oa: '1' page: 206-219 publication: IEEE Transactions on Audio, Speech, and Language Processing status: public title: Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation type: journal_article user_id: '44006' volume: 19 year: '2011' ... --- _id: '2200' author: - first_name: Tobias full_name: Kenter, Tobias id: '3145' last_name: Kenter - first_name: Marco full_name: Platzner, Marco id: '398' last_name: Platzner - first_name: Christian full_name: Plessl, Christian id: '16153' last_name: Plessl orcid: 0000-0001-5728-9982 - first_name: Michael full_name: Kauschke, Michael last_name: Kauschke citation: ama: 'Kenter T, Platzner M, Plessl C, Kauschke M. Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures. In: Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA). ACM; 2011:177-180. doi:10.1145/1950413.1950448' apa: Kenter, T., Platzner, M., Plessl, C., & Kauschke, M. (2011). Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures. Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA), 177–180. https://doi.org/10.1145/1950413.1950448 bibtex: '@inproceedings{Kenter_Platzner_Plessl_Kauschke_2011, place={New York, NY, USA}, title={Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures}, DOI={10.1145/1950413.1950448}, booktitle={Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA)}, publisher={ACM}, author={Kenter, Tobias and Platzner, Marco and Plessl, Christian and Kauschke, Michael}, year={2011}, pages={177–180} }' chicago: 'Kenter, Tobias, Marco Platzner, Christian Plessl, and Michael Kauschke. “Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures.” In Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA), 177–80. New York, NY, USA: ACM, 2011. https://doi.org/10.1145/1950413.1950448.' ieee: 'T. Kenter, M. Platzner, C. Plessl, and M. Kauschke, “Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures,” in Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA), 2011, pp. 177–180, doi: 10.1145/1950413.1950448.' mla: Kenter, Tobias, et al. “Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures.” Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA), ACM, 2011, pp. 177–80, doi:10.1145/1950413.1950448. short: 'T. Kenter, M. Platzner, C. Plessl, M. Kauschke, in: Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA), ACM, New York, NY, USA, 2011, pp. 177–180.' date_created: 2018-04-03T15:08:13Z date_updated: 2023-09-26T13:45:04Z department: - _id: '27' - _id: '518' - _id: '78' doi: 10.1145/1950413.1950448 keyword: - design space exploration - LLVM - partitioning - performance - estimation - funding-intel language: - iso: eng page: 177-180 place: New York, NY, USA publication: Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA) publication_identifier: isbn: - 978-1-4503-0554-9 publisher: ACM quality_controlled: '1' status: public title: Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures type: conference user_id: '15278' year: '2011' ... --- _id: '11726' abstract: - lang: eng text: In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car. author: - first_name: Maik full_name: Bevermeier, Maik last_name: Bevermeier - first_name: Oliver full_name: Walter, Oliver last_name: Walter - first_name: Sven full_name: Peschke, Sven last_name: Peschke - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Bevermeier M, Walter O, Peschke S, Haeb-Umbach R. Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter. In: 7th Workshop on Positioning Navigation and Communication (WPNC 2010). ; 2010:128-134. doi:10.1109/WPNC.2010.5650745' apa: Bevermeier, M., Walter, O., Peschke, S., & Haeb-Umbach, R. (2010). Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter. In 7th Workshop on Positioning Navigation and Communication (WPNC 2010) (pp. 128–134). https://doi.org/10.1109/WPNC.2010.5650745 bibtex: '@inproceedings{Bevermeier_Walter_Peschke_Haeb-Umbach_2010, title={Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter}, DOI={10.1109/WPNC.2010.5650745}, booktitle={7th Workshop on Positioning Navigation and Communication (WPNC 2010)}, author={Bevermeier, Maik and Walter, Oliver and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2010}, pages={128–134} }' chicago: Bevermeier, Maik, Oliver Walter, Sven Peschke, and Reinhold Haeb-Umbach. “Barometric Height Estimation Combined with Map-Matching in a Loosely-Coupled Kalman-Filter.” In 7th Workshop on Positioning Navigation and Communication (WPNC 2010), 128–34, 2010. https://doi.org/10.1109/WPNC.2010.5650745. ieee: M. Bevermeier, O. Walter, S. Peschke, and R. Haeb-Umbach, “Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter,” in 7th Workshop on Positioning Navigation and Communication (WPNC 2010), 2010, pp. 128–134. mla: Bevermeier, Maik, et al. “Barometric Height Estimation Combined with Map-Matching in a Loosely-Coupled Kalman-Filter.” 7th Workshop on Positioning Navigation and Communication (WPNC 2010), 2010, pp. 128–34, doi:10.1109/WPNC.2010.5650745. short: 'M. Bevermeier, O. Walter, S. Peschke, R. Haeb-Umbach, in: 7th Workshop on Positioning Navigation and Communication (WPNC 2010), 2010, pp. 128–134.' date_created: 2019-07-12T05:27:04Z date_updated: 2022-01-06T06:51:07Z department: - _id: '54' doi: 10.1109/WPNC.2010.5650745 keyword: - altitude measurement unit - barometers - barometric altimeter - barometric error Kalman filter - barometric height estimation - direct cosine matrix - global positioning system - Global Positioning System - GPS device - height information - height measurement - inertial measurement unit - Kalman filters - loosely-coupled error state space Kalman filter - loosely-coupled Kalman-filter - map matching - robust information - robust location estimation - sensor fusion - topographic map information - vertical user position language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2010/BeWaPeHa10.pdf oa: '1' page: 128-134 publication: 7th Workshop on Positioning Navigation and Communication (WPNC 2010) status: public title: Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter type: conference user_id: '44006' year: '2010' ... --- _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: '11723' abstract: - lang: eng text: In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding. author: - first_name: Maik full_name: Bevermeier, Maik last_name: Bevermeier - first_name: Sven full_name: Peschke, Sven last_name: Peschke - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Bevermeier M, Peschke S, Haeb-Umbach R. Robust vehicle localization based on multi-level sensor fusion and online parameter estimation. In: 6th Workshop on Positioning Navigation and Communication (WPNC 2009). ; 2009:235-242. doi:10.1109/WPNC.2009.4907833' apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Robust vehicle localization based on multi-level sensor fusion and online parameter estimation. In 6th Workshop on Positioning Navigation and Communication (WPNC 2009) (pp. 235–242). https://doi.org/10.1109/WPNC.2009.4907833 bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}, DOI={10.1109/WPNC.2009.4907833}, booktitle={6th Workshop on Positioning Navigation and Communication (WPNC 2009)}, author={Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009}, pages={235–242} }' chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Robust Vehicle Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.” In 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 235–42, 2009. https://doi.org/10.1109/WPNC.2009.4907833. ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Robust vehicle localization based on multi-level sensor fusion and online parameter estimation,” in 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–242. mla: Bevermeier, Maik, et al. “Robust Vehicle Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.” 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–42, doi:10.1109/WPNC.2009.4907833. short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–242.' date_created: 2019-07-12T05:27:01Z date_updated: 2022-01-06T06:51:07Z department: - _id: '54' doi: 10.1109/WPNC.2009.4907833 keyword: - covariance matrices - expectation-maximisation algorithm - expectation-maximization algorithm - global positioning system - Global Positioning System - GPS - inertial measurement unit - interacting multiple model approach - Kalman filters - multilevel sensor fusion - narrow street canyons - narrow tunnels - online parameter estimation - parameter estimation - road vehicles - robust vehicle localization - sensor fusion - state noise covariances - time-variant multilevel Kalman filter - vehicle tracking algorithm language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf oa: '1' page: 235-242 publication: 6th Workshop on Positioning Navigation and Communication (WPNC 2009) status: public title: Robust vehicle localization based on multi-level sensor fusion and online parameter estimation type: conference user_id: '44006' year: '2009' ...