Deep learning based liquid level extraction from video observations of gas-liquid flows

The slug flow pattern is one of the most common gas–liquid flow patterns in multiphase transportation pipelines, particularly in the oil and gas industry. This flow pattern can cause severe problems for industrial processes. Hence, a detailed description of the spatial distribution of the different phases in the pipe is needed for automated process control and calibration of predictive models. In this paper, a deep-learning based image processing technique is presented that extracts the gas–liquid interface from video observations of multiphase flows in horizontal pipes. The supervised deep learning model consists of a convolutional neural network, which was trained and tested with video data from slug flow experiments. The consistency of the hand-labelled data and the predictions of the trained model have been evaluated in an inter-observer reliability test. The model was further tested with other data sets, which also included recordings of a different flow pattern. It is shown that the presented method provides accurate and reliable predictions of the gas–liquid interface for slug flow as well as for other separate flow patterns. Moreover, it is demonstrated how flow characteristics can be obtained from the results of the deep-learning based image processing technique

Untersuchungen zur Inhibition der Sialyl- und Galactosyltransferasen mit chemischen und enzymatischen Methoden; Synthese von Inhibitoren

SIGLEAvailable from TIB Hannover: DW 8231 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Formal, model- and scenario-based requirement patterns

Distributed, software-intensive systems such as automotive electronic control units have to handle various situations employing message-based coordination. The growing complexity of such systems results in an increasing difficulty to achieve a high quality of the systemsâ requirements specifications. Scenario-based requirements engineering addresses the message-based coordination of such systems and enables, if underpinned with formal modeling languages, automatic analyses for ensuring the quality of requirements specifications. However, formal requirements modeling languages require high expertise of the requirements engineers and many manual iterations until specifications reach high quality. Patterns provide a constructive means for assembling high-quality solutions by applying reusable and established building blocks. Thus, they also gained momentum in requirements documentation. In order to support the requirements engineers in the systematic conception of formal , scenario-based requirements specification models, we hence introduce in this paper a requirement pattern catalog for a requirements modeling language. We illustrate and discuss the application of the requirement patterns with an example of requirements for an automotive electronic control unit

Hybrid framework of management systems supporting cluster-oriented smart grid operations

The concept of smart grids has been widely introduced to cope with energy transition towards sustainable energy system, which results in decentralization of the energy supply system. Among various solutions to accomplish the smart grids, clustering power systems approach (CPSA) has been proposed. Based on this approach, active interconnected cluster areas, operating similar to interconnected grids in transmission systems, are created in bottom-up direction from distribution level upwards to the upstream systems. In order to manage emerging cluster areas and concurrently facilitate their cooperation, this paper proposes hybrid framework of management systems. That is, both centralized and decentralized management systems are employed for operations under the CPSA. Besides, to add a level of cyber security, this paper also presents the integration of the adapted access control mechanism based on XACML data-flow model to the management systems. Lastly, the applications of the proposed hybrid framework are illustrated and discussed in use cases

Data management and visualization for cluster-based grid operations

The increase of electricity demand has raised requirements of more reliable and efficient grid operations as well as higher security of supply. Meanwhile, the transition towards clean and sustainable energy supply systems in the present power systems is under the spotlight [1]–[3]. High penetration of renewable energy sources (RESs), which are usually in the form of distributed generation (DG), can be expected. The RESs-based DG units can reside in distribution level, whose original purpose is to distribute power from electricity utilities to end users. Presently, to cope with the power penetration in distribution level, conventional power grids are being evolved into the smarter ones, known as smart grids. A smart grid is proposed to overcome the arising environmental and technical challenges [4], [5]. To smarten the grid, information and communication technologies are incorporated into the conventional power grids. They allow the cooperation of heterogeneous grid components, e.g. control centers and DG units, or users, e.g. operators and customers. Decentralization of grid control architecture is possible [6], and many actors can actively participate in the operation of the grid

Multilevel and 4-leg topology for smart grid inverter

As the amount of decentralized generation is continuously increasing the control and maintaining of the grid stability becomes more complex. This is caused by the change of production capacities from the high voltage area into the distribution network. In order to ensure the stability of future smart grids a new 4-leg 4-wire inverter as a part of a power electronic regulator is introduced. The features of the 4-leg 4-wire topology in comparison to the 3-leg 4-wire topology and advantages of a multilevel inverter for harmonic compensation are shown. In combination with a powerful FPGA based controller complex algorithms for Space Vector Modulation and for control can be implemented. Finally the actual status of the hardware development including the technical data of the inverter is shown