Hochschule Bonn-Rhein-Sieg
Publikationsserver der Hochschule Bonn-Rhein-Sieg - pub H-BRSNot a member yet
7939 research outputs found
Sort by
epicsave - Enhanced ParamedIC vocational training with Serious games And Virtual Environments - Optimierung der Berufsausbildung von Notfallsanitätern durch Training mittels Serious Games und Virtuellen Umgebungen: Schlussbericht
No full text
The clinical spectrum and molecular heterogeneity of Succinyl- CoA:3-oxoacid coenzyme A transferase (SCOT) deficiency
No full textEvaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition
No full textIt has been well proved that deep networks are efficient at extracting features from a given (source) labeled dataset. However, it is not always the case that they can generalize well to other (target) datasets which very often have a different underlying distribution. In this report, we evaluate four different domain adaptation techniques for image classification tasks: DeepCORAL, DeepDomainConfusion, CDAN and CDAN+E. These techniques are unsupervised given that the target dataset dopes not carry any labels during training phase. We evaluate model performance on the office-31 dataset. A link to the github repository of this report can be found here: https://github.com/agrija9/Deep-Unsupervised-Domain-Adaptation
Entwicklung innovativer satellitengestützter Methoden zur verbesserten PV-Ertragsvorhersage auf verschiedenen Zeitskalen für Anwendungen auf Verteilnetzebene: Schlussbericht
No full textAnhand detaillierter Netzanalysen für ein reales Mittelspannungsnetzgebiet konnte gezeigt werden, dass sowohl die Einbindung von Prognosedaten auf Basis von Satelliten und Wetterdaten, als auch die Verbesserung von Folgetagsprognosen auf der Basis numerischer Wettermodelle einen deutlichen Mehrwert für ein prognosebasiertes Engpassmanagement bzw. Redispatch und Blindleistungsmanagement im Verteilnetz aufweisen. Auch Kurzfristprognosen auf der Basis von Satellitendaten haben einen positiven Effekt. Ein weiterer wichtiger Mehrwert des Projektes ist auch die Rückmeldung der kritischen Prognosesituationen aus Sicht der Anwendungsfälle, so dass wie bereits im Projekt gezeigt und darüber hinaus, Prognosen zielgerichteter auf die Anwendung im Verteilnetzbetrieb ausgelegt und optimiert werden können.
Weiterhin konnten Prognoseverbesserungen für das Vorhersagemodell des Deutschen Wetterdienstes durch die Assimilation von sichtbaren Satellitenbildern erreicht werden. Darüber hinaus wurden Wolken- und Strahlungsprodukte aus Satelliten verbessert und somit die Datenbasis für die Kurzfristprognose als auch für die Assimilation.
Darüber hinaus wurden verschiedene Methoden entwickelt, die zukünftig zu einer weiteren Prognoseverbesserung, insbesondere für Wettersituationen mit hohen Prognosefehlern, führen könnten. Solche Situationen wurden aus Sicht des Netzbetriebs und mithilfe von satellitenbasierten Analysen der Gesamtwetterlage für die Perioden der MetPVNet Messkampagnen identifiziert. Hierbei handelte es sich insbesondere um Situationen mit starker oder stark wechselhafter Bewölkung.
Für die MetPVNet Messkampagnen wurde auf der Basis eines Trainingsdatensatzes und in Abhängigkeit der Variabilitätsklasse die Abweichung der bodennahen Einstrahlung von Satellitendaten oder von Strahlungsprognosen quantifiziert. Diese Art der Informationen bietet zukünftig die Möglichkeit zur Bewertung der Prognosegüte.Detailed network analyses for a real medium-voltage grid area showed that both the integration of forecast data based on satellites and weather data and the improvement of next-day forecasts based on numerical weather models have clear added value for forecast-based congestion management or redispatch and reactive power management in the distribution grid. Short-term forecasts based on satellite data also have a positive effect. Another important added value of the project is the feedback of critical forecast situations from the point of view of the use cases, so that, as already shown in the project and beyond, forecasts can be designed and optimised more specifically for the specific application in distribution grid operation.
Furthermore, improvements to the forecast model of the German Weather Service were achieved through the assimilation of visible satellite images. Furthermore, cloud and radiation products from satellites were improved and through this also the data basis for both short-term forecasts as well as data assimilation.
In addition, various methods were developed that could lead to further forecast improvements in the future, especially for weather situations with high forecast errors. Such situations were identified from the perspective of grid operation and with the help of satellite-based analyses of the overall weather situation for the periods of the MetPVNet measurement campaigns. In particular, these were situations with heavy or very changeable cloud cover.
For the MetPVNet measurement campaigns, the deviation of the near-surface irradiance from satellite data or radiation forecasts was quantified on the basis of a training data set and depending on the variability class. In the future, this type of information will offer the possibility of evaluating the forecast quality
Clinical and molecular characterization of the R751L-CFTR mutation
No full textCysticfibrosis (CF) arises from mutations in the CF transmembrane conductance regulator (CFTR) gene, resulting in progressiveand life-limiting respiratory disease. R751L is a rare CFTR mutation that is poorly characterized. Our aims were to describe theclinical and molecular phenotypes associated with R751L. Relevant clinical data were collected from three heterozygote individu-als harboring R751L (2 patients with G551D/R751L and 1 with F508del/R751L). Assessment of R751L-CFTR function was made inprimary human bronchial epithelial cultures (HBEs) andXenopusoocytes. Molecular properties of R751L-CFTR were investigatedin the presence of known CFTR modulators. Although sweat chloride was elevated in all three patients, the clinical phenotypeassociated with R751L was mild. Chloride secretion in F508del/R751L HBEs was reduced compared with non-CF HBEs and asso-ciated with a reduction in sodium absorption by the epithelial sodium channel (ENaC). However, R751L-CFTR function inXenopusoocytes, together with folding and cell surface transport of R751L-CFTR, was not different from wild-type CFTR. Overall,R751L-CFTR was associated with reduced sodium chloride absorption but had functional properties similar to wild-type CFTR.This is thefirst report of R751L-CFTR that combines clinical phenotype with characterization of functional and biological proper-ties of the mutant channel. Our work will build upon existing knowledge of mutations within this region of CFTR and, importantly,inform approaches for clinical management. Elevated sweat chloride and reduced chloride secretion in HBEs may be due to al-ternative non-CFTR factors, which require further investigation
Current practices, challenges, and design implications for collaborative AR/VR application development
No full textAugmented/Virtual Reality (AR/VR) is still a fragmented space to design for due to the rapidly evolving hardware, the interdisciplinarity of teams, and a lack of standards and best practices. We interviewed 26 professional AR/VR designers and developers to shed light on their tasks, approaches, tools, and challenges. Based on their work and the artifacts they generated, we found that AR/VR application creators fulfill four roles: concept developers, interaction designers, content authors, and technical developers. One person often incorporates multiple roles and faces a variety of challenges during the design process from the initial contextual analysis to the deployment. From analysis of their tool sets, methods, and artifacts, we describe critical key challenges. Finally, we discuss the importance of prototyping for the communication in AR/VR development teams and highlight design implications for future tools to create a more usable AR/VR tool chain
Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms
No full textGhana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) 1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector
