@article{BiselliVanderPolDeGooijeretal.1996,
  author    = {Biselli, Manfred and Van der Pol, Jens J. and De Gooijer, Cornelis D. and Wandrey, Christian},
  title     = {Automation of selective assays for on-line bioprocess monitoring by flow-injection analysis / van der Pol, Jens J. ; de Gooijer, Cornelis D. ; Biselli, Manfred ; Wandrey, Christian ; Tramper, Johannes},
  series = {Trends in Biotechnology. 14 (1996), H. 12},
  journal   = {Trends in Biotechnology. 14 (1996), H. 12},
  isbn      = {0167-7799},
  pages     = {471 -- 477},
  year      = {1996},
  language  = {en}
}
@article{EnningBernhardRake1994,
  author    = {Enning, Manfred and Bernhard, S. and Rake, H.},
  title     = {Automation of a laboratory plant for direct casting of thin steel strips / Bernhard, S. ; Enning, M. ; Rake, H.},
  series = {Control Engineering Practice. 2 (1994), H. 6},
  journal   = {Control Engineering Practice. 2 (1994), H. 6},
  isbn      = {0967-0661},
  pages     = {961 -- 967},
  year      = {1994},
  language  = {en}
}
@article{MarxSchenkBehrensetal.2013,
  author    = {Marx, Ulrich and Schenk, Friedrich and Behrens, Jan and Meyr, Ulrike and Wanek, Paul and Zang, Werner and Schmitt, Robert and Br{\"u}stle, Oliver and Zenke, Martin and Klocke, Fritz},
  title     = {Automatic production of induced pluripotent stem cells},
  series = {Procedia CIRP : First CIRP Conference on BioManufacturing},
  volume    = {Vol. 5},
  journal   = {Procedia CIRP : First CIRP Conference on BioManufacturing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  pages     = {2 -- 6},
  year      = {2013},
  language  = {en}
}
@article{SchmitzOligschlaegerEifleretal.1994,
  author    = {Schmitz, G{\"u}nter and Oligschl{\"a}ger, U. and Eifler, G. and Lechner, H.},
  title     = {Automated System for Optimized Calibration of Engine Management Systems},
  year      = {1994},
  language  = {en}
}
@article{NeuJanserKhatibietal.2016,
  author    = {Neu, Eugen and Janser, Frank and Khatibi, Akbar A. and Orifici, Adrian C.},
  title     = {Automated modal parameter-based anomaly detection under varying wind excitation},
  series = {Structural Health Monitoring},
  volume    = {15},
  journal   = {Structural Health Monitoring},
  number    = {6},
  publisher = {Sage},
  address   = {London},
  issn      = {1475-9217},
  doi       = {10.1177/1475921716665803},
  pages     = {1 -- 20},
  year      = {2016},
  abstract  = {Wind-induced operational variability is one of the major challenges for structural health monitoring of slender engineering structures like aircraft wings or wind turbine blades. Damage sensitive features often show an even bigger sensitivity to operational variability. In this study a composite cantilever was subjected to multiple mass configurations, velocities and angles of attack in a controlled wind tunnel environment. A small-scale impact damage was introduced to the specimen and the structural response measurements were repeated. The proposed damage detection methodology is based on automated operational modal analysis. A novel baseline preparation procedure is described that reduces the amount of user interaction to the provision of a single consistency threshold. The procedure starts with an indeterminate number of operational modal analysis identifications from a large number of datasets and returns a complete baseline matrix of natural frequencies and damping ratios that is suitable for subsequent anomaly detection. Mahalanobis distance-based anomaly detection is then applied to successfully detect the damage under varying severities of operational variability and with various degrees of knowledge about the present operational conditions. The damage detection capabilities of the proposed methodology were found to be excellent under varying velocities and angles of attack. Damage detection was less successful under joint mass and wind variability but could be significantly improved through the provision of the currently encountered operational conditions.},
  language  = {en}
}
@article{GorzalkaSchmiedtSchorn2021,
  author    = {Gorzalka, Philip and Schmiedt, Jacob Estevam and Schorn, Christian},
  title     = {Automated Generation of an Energy Simulation Model for an Existing Building from UAV Imagery},
  series = {Buildings},
  volume    = {11},
  journal   = {Buildings},
  number    = {9},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2075-5309},
  doi       = {10.3390/buildings11090380},
  pages     = {15 Seiten},
  year      = {2021},
  abstract  = {An approach to automatically generate a dynamic energy simulation model in Modelica for a single existing building is presented. It aims at collecting data about the status quo in the preparation of energy retrofits with low effort and costs. The proposed method starts from a polygon model of the outer building envelope obtained from photogrammetrically generated point clouds. The open-source tools TEASER and AixLib are used for data enrichment and model generation. A case study was conducted on a single-family house. The resulting model can accurately reproduce the internal air temperatures during synthetical heating up and cooling down. Modelled and measured whole building heat transfer coefficients (HTC) agree within a 12\% range. A sensitivity analysis emphasises the importance of accurate window characterisations and justifies the use of a very simplified interior geometry. Uncertainties arising from the use of archetype U-values are estimated by comparing different typologies, with best- and worst-case estimates showing differences in pre-retrofit heat demand of about ±20\% to the average; however, as the assumptions made are permitted by some national standards, the method is already close to practical applicability and opens up a path to quickly estimate possible financial and energy savings after refurbishment.},
  language  = {en}
}
@article{SchwabedalSippelBrandtetal.2018,
  author    = {Schwabedal, Justus T. C. and Sippel, Daniel and Brandt, Moritz D. and Bialonski, Stephan},
  title     = {Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning},
  doi       = {10.48550/arXiv.1809.08443},
  year      = {2018},
  abstract  = {Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.},
  language  = {en}
}
@article{Pietsch2015,
  author    = {Pietsch, Wolfram},
  title     = {Augmenting voice of the customer analysis by analysis of belief},
  series = {QFD-Forum},
  journal   = {QFD-Forum},
  number    = {30},
  issn      = {1431-6951},
  pages     = {1 -- 5},
  year      = {2015},
  language  = {en}
}
@article{BeverungenEggertVoigtetal.2013,
  author    = {Beverungen, Daniel and Eggert, Mathias and Voigt, Matthias and Rosemann, Michael},
  title     = {Augmenting Analytical CRM Strategies with Social BI},
  series = {International Journal of Business Intelligence Research (IJBIR)},
  volume    = {4},
  journal   = {International Journal of Business Intelligence Research (IJBIR)},
  number    = {3},
  publisher = {IGI Global},
  address   = {Hershey},
  issn      = {1947-3591},
  doi       = {10.4018/ijbir.2013070103},
  pages     = {32 -- 49},
  year      = {2013},
  language  = {en}
}
@article{FoersterRosenauerRemmele1997,
  author    = {F{\"o}rster, Arnold and Rosenauer, A. and Remmele, T.},
  title     = {Atomic scale strain measurements by the digital analysis of transmission electron microscopic lattice images / A. Rosenauer ; T. Remmele ; D. Gerthsen ... A. F{\"o}rster},
  series = {Optik : international journal for light and electron optics. 105 (1997), H. 3},
  journal   = {Optik : international journal for light and electron optics. 105 (1997), H. 3},
  isbn      = {0030-4026},
  pages     = {99 -- 107},
  year      = {1997},
  language  = {en}
}