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 - 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 - 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 - 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 - 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 - 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 - 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 ;