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The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach.
Members of the species Bacillus pumilus get more and more in focus of the biotechnological industry as potential new production strains. Based on exoproteome analysis, B. pumilus strain Jo2, possessing a high secretion capability, was chosen for an omics-based investigation. The proteome and metabolome of B. pumilus cells growing either in minimal or complex medium was analyzed. In total, 1542 proteins were identified in growing B. pumilus cells, among them 1182 cytosolic proteins, 297 membrane and lipoproteins and 63 secreted proteins. This accounts for about 43% of the 3616 proteins encoded in the B. pumilus Jo2 genome sequence. By using GC–MS, IP-LC/MS and H NMR methods numerous metabolites were analyzed and assigned to reconstructed metabolic pathways. In the genome sequence a functional secretion system including the components of the Sec- and Tat-secretion machinery was found. Analysis of the exoproteome revealed secretion of about 70 proteins with predicted secretion signals. In addition, selected production-relevant genome features such as restriction modification systems and NRPS clusters of B. pumilus Jo2 are discussed.
The invention pertains to a CellDrum electrode arrangement for measuring mechanical stress, comprising a mechanical holder (1 ) and a non-conductive membrane (4), whereby the membrane (4) is at least partially fixed at its circumference to the mechanical holder (1), keeping it in place when the membrane (4) may bend due to forces acting on the membrane (4), the mechanical holder (1) and the membrane (4) forming a container, whereby the membrane (1) within the container comprises an cell- membrane compound layer or biological material (3) adhered to the deformable membrane 4 which in response to stimulation by an agent may exert mechanical stress to the membrane (4) such that the membrane bending stage changes whereby the container may be filled with an electrolyte, whereby an electric contact (2) is arranged allowing to contact said electrolyte when filled into to the container, whereby within a predefined geometry to the fixing of the membrane (4) an electrode (7) is arranged, whereby the electrode (7) is electrically insulated with respect to the electric contact (2) as well as said electrolyte, whereby mechanical stress due to an agent may be measured as a change in capacitance.
Since certification of Concorde new certification standards were introduced including many new regulations to improve flight safety. Most of these standards are to prevent severe accidents in the future which happened in the past (here: after Concorde’s certification). A new SCT has to fulfill these standards, although Concorde had none of these accidents. But accidents - although they sometimes occurred only for a specific aircraft type - have to be avoided for any (new) aircraft. Because of existing aircraft without typical accident types having demonstrated their reliability, they are allowed to go on based on their old certification; although sometimes new rules prevent accident types which are not connected to specific aircraft types - like e.g. evacuation rules. Anyway, Concorde is allowed to fly based on its old certification, and hopefully in the future will fly as safely as in the past. But a new SCT has to fulfill updated rules like any other aircraft, and it has to be “just another aircraft” [75].
Combined with the use of renewable energy sources for its production, Hydrogen represents a possible alternative gas turbine fuel within future low emission power generation. Due to the large difference in the physical properties of Hydrogen compared to other fuels such as natural gas, well established gas turbine combustion systems cannot be directly applied for Dry Low NOx (DLN) Hydrogen combustion. Thus, the development of DLN combustion technologies is an essential and challenging task for the future of Hydrogen fuelled gas turbines. The DLN Micromix combustion principle for hydrogen fuel has been developed to significantly reduce NOx-emissions. This combustion principle is based on cross-flow mixing of air and gaseous hydrogen which reacts in multiple miniaturized diffusion-type flames. The major advantages of this combustion principle are the inherent safety against flash-back and the low NOx-emissions due to a very short residence time of reactants in the flame region of the micro-flames. The Micromix Combustion technology has been already proven experimentally and numerically for pure Hydrogen fuel operation at different energy density levels. The aim of the present study is to analyze the influence of different geometry parameter variations on the flame structure and the NOx emission and to identify the most relevant design parameters, aiming to provide a physical understanding of the Micromix flame sensitivity to the burner design and identify further optimization potential of this innovative combustion technology while increasing its energy density and making it mature enough for real gas turbine application. The study reveals great optimization potential of the Micromix Combustion technology with respect to the DLN characteristics and gives insight into the impact of geometry modifications on flame structure and NOx emission. This allows to further increase the energy density of the Micromix burners and to integrate this technology in industrial gas turbines.
Today, the assembly of laser systems requires a large share of manual operations due to its complexity regarding the optimal alignment of optics. Although the feasibility of automated alignment of laser optics has been shown in research labs, the development effort for the automation of assembly does not meet economic requirements – especially for low-volume laser production. This paper presents a model-based and sensor-integrated assembly execution approach for flexible assembly cells consisting of a macro-positioner covering a large workspace and a compact micromanipulator with camera attached to the positioner. In order to make full use of available models from computer-aided design (CAD) and optical simulation, sensor systems at different levels of accuracy are used for matching perceived information with model data. This approach is named "chain of refined perception", and it allows for automated planning of complex assembly tasks along all major phases of assembly such as collision-free path planning, part feeding, and active and passive alignment. The focus of the paper is put on the in-process image-based metrology and information extraction used for identifying and calibrating local coordinate systems as well as the exploitation of that information for a part feeding process for micro-optics. Results will be presented regarding the processes of automated calibration of the robot camera as well as the local coordinate systems of part feeding area and robot base.