TY  - JOUR
A1  - Biselli, Manfred
A1  - Van der Pol, Jens J.
A1  - De Gooijer, Cornelis D.
A1  - Wandrey, Christian
T1  - 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
JF  - Trends in Biotechnology. 14 (1996), H. 12
Y1  - 1996
SN  - 0167-7799
SP  - 471
EP  - 477
ER  - 
TY  - JOUR
A1  - Enning, Manfred
A1  - Bernhard, S.
A1  - Rake, H.
T1  - Automation of a laboratory plant for direct casting of thin steel strips / Bernhard, S. ; Enning, M. ; Rake, H.
JF  - Control Engineering Practice. 2 (1994), H. 6
Y1  - 1994
SN  - 0967-0661
SP  - 961
EP  - 967
ER  - 
TY  - JOUR
A1  - Marx, Ulrich
A1  - Schenk, Friedrich
A1  - Behrens, Jan
A1  - Meyr, Ulrike
A1  - Wanek, Paul
A1  - Zang, Werner
A1  - Schmitt, Robert
A1  - Brüstle, Oliver
A1  - Zenke, Martin
A1  - Klocke, Fritz
T1  - Automatic production of induced pluripotent stem cells
JF  - Procedia CIRP : First CIRP Conference on BioManufacturing
Y1  - 2013
SN  - 2212-8271
VL  - Vol. 5
SP  - 2
EP  - 6
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Schmitz, Günter
A1  - Oligschläger, U.
A1  - Eifler, G.
A1  - Lechner, H.
T1  - Automated System for Optimized Calibration of Engine Management Systems
Y1  - 1994
N1  - SAE International Congress and Exposition, Detroit, Feb. 28 - March 3 ; SAE- Paper-No.: 940151 ; <a href=
ER  - 
TY  - JOUR
A1  - Neu, Eugen
A1  - Janser, Frank
A1  - Khatibi, Akbar A.
A1  - Orifici, Adrian C.
T1  - Automated modal parameter-based anomaly detection under varying wind excitation
JF  - Structural Health Monitoring
N2  - 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.
Y1  - 2016
U6  - https://doi.org/10.1177/1475921716665803
SN  - 1475-9217
VL  - 15
IS  - 6
SP  - 1
EP  - 20
PB  - Sage
CY  - London
ER  - 
TY  - JOUR
A1  - Gorzalka, Philip
A1  - Schmiedt, Jacob Estevam
A1  - Schorn, Christian
T1  - Automated Generation of an Energy Simulation Model for an Existing Building from UAV Imagery
JF  - Buildings
N2  - 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.
KW  - Modelica
KW  - heat transfer coefficient
KW  - heat demand
KW  - building energy modelling
KW  - building energy simulation
Y1  - 2021
U6  - https://doi.org/10.3390/buildings11090380
SN  - 2075-5309
N1  - This article belongs to the Special Issue "Application of Computer Technology in Buildings"
VL  - 11
IS  - 9
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Schwabedal, Justus T. C.
A1  - Sippel, Daniel
A1  - Brandt, Moritz D.
A1  - Bialonski, Stephan
T1  - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
N2  - 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.
Y1  - 2018
U6  - https://doi.org/10.48550/arXiv.1809.08443
ER  - 
TY  - JOUR
A1  - Pietsch, Wolfram
T1  - Augmenting voice of the customer analysis by analysis of belief
JF  - QFD-Forum
Y1  - 2015
SN  - 1431-6951
IS  - 30
SP  - 1
EP  - 5
ER  - 
TY  - JOUR
A1  - Beverungen, Daniel
A1  - Eggert, Mathias
A1  - Voigt, Matthias
A1  - Rosemann, Michael
T1  - Augmenting Analytical CRM Strategies with Social BI
JF  - International Journal of Business Intelligence Research (IJBIR)
Y1  - 2013
U6  - https://doi.org/10.4018/ijbir.2013070103
SN  - 1947-3591
VL  - 4
IS  - 3
SP  - 32
EP  - 49
PB  - IGI Global
CY  - Hershey
ER  - 
TY  - JOUR
A1  - Förster, Arnold
A1  - Rosenauer, A.
A1  - Remmele, T.
T1  - Atomic scale strain measurements by the digital analysis of transmission electron microscopic lattice images / A. Rosenauer ; T. Remmele ; D. Gerthsen ... A. Förster
JF  - Optik : international journal for light and electron optics. 105 (1997), H. 3
Y1  - 1997
SN  - 0030-4026
SP  - 99
EP  - 107
ER  -