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This paper introduces the concept of Universal Memory Automata (UMA) and automated compilation of Verilog Hardware Description Language (HDL) code at Register Transfer Level (RTL) from UMA graphs for digital designs. The idea is based on the observation that Push Down Automata (PDA) are able to process the Dyk-Language - commonly known as the balanced bracket problem - with a finite set of states while Finite State Machines (FSM) require an infinite set of states. Since infinite sets of states are not applicable to real designs, PDAs appear promising for types of problems similar to the Dyk-Language. PDAs suffer from the problem that complex memory operations need to be emulated by a specific stack management. The presented UMA therefore extends the PDA by other types of memory, e.g. Queue, RAM or CAM. Memories that are eligible for UMAs are supposed to have at least one read and one write port and a one-cycle read/write latency. With their modified state-transfer- and output-function, UMAs are able to operate user-defined numbers, configurations and types of memories. Proof of concept is given by an implementation of a cache coherency protocol, i.e. a practical problem in microprocessor design.
SyNumSeS is a Python package for numerical simulation of semiconductor devices. It uses the Scharfetter-Gummel discretization for solving the one dimensional Van Roosbroeck system which describes the free electron and hole transport by the drift-diffusion model. As boundary conditions voltages can be applied to Ohmic contacts. It is suited for the simulation of pn-diodes, MOS-diodes, LEDs (hetero junction), solar cells, and (hetero) bipolar transistors.
Summary of the 9th workshop on metallization and interconnection for crystalline silicon solar cells
(2021)
The 9th edition of the Workshop on Metallization and Interconnection for Crystalline Silicon Solar Cells was held as an online event but nevertheless reached the workshop goals of knowledge sharing and networking. The technology of screen-printed contacts of high temperature pastes continues its fast progress enabled by better understanding of the phenomena taking place during printing and firing, and progress in materials. Great improvements were also achieved in low temperature paste printing and plated metallization. In the field of interconnection, progress was reported on multiwire approaches, electrically conductive adhesives and on foil-based approaches. Common to many contributions at the workshop was the use of advanced laser processes to improve performance or throughput.
Since its first edition in 2008, the Workshop on Metallization and Interconnection for Crystalline Silicon SolarCells has been a key event where knowledge in the critical fields of crystalline silicon solar cell metallization andinterconnection is shared between experts from academia and industry. It has become a highly recognized event forthe quality of the contributions, the lively Q&A sessions, and the exceptional networking opportunity.The situation with the Covid-19 pandemic made organizing the 9th edition as an in-person event impossible andforced us to reconsider the event format. The event took place virtually on October 5th and 6th 2020. We used aninnovative online platform that enabled not only presentations followed by Q&A but also more informal interactions,where participants could see and talk directly to other participants. 120 experts from 22 countries took part andattended 21 contributions presented live. In spite of a few technical glitches, the workshop was successful and thegoals of exchanging on the state-of-the-art in research/industry and connecting experts in the field were achieved.All presentations are available on www.miworkshop.info as .pdf documents. These proceedings contain asummary of the 9th edition (MIW2020) and peer-reviewed papers based on the workshop contributions. The organizerswish to thank the members of the Scientific Committee for the time spent reviewing the MIW2020 abstracts andproceedings. The organizers also wish to thank again the sponsors and supporters for their financial contributionswhich made the 9th Workshop on Metallization and Interconnection for Crystalline Silicon Solar Cells possible.
In order to elaborate inflation and deflation tendencies due to the COVID-19 pandemic and how they are tried to be actively influenced, this paper compares news regarding the measurements of central banks in Europe, USA and Japan. Factors affecting inflation are defined in conjunction with the typical measurements of central banks and conclusions are drawn in respect to differences of the most recent correcting behavior. The paper is concluded by discussing how price levels might develop during and after the crisis.
Das Potential der Offshore-Windenergie, welches hauptsächlich auf hohe mittlere Windgeschwindigkeiten zurückzuführen ist, kann nicht ignoriert werden. Trotzdem zeigt die Betrachtung der aktuell installierten Leistung und der Stromgestehungskosten, dass zusätzliche Risiko- und Kostenfaktoren existieren. Diese sind vor allem auf die Installation, die Energiewandlersysteme und die Netzanbindung zurückzuführen. Getriebeschäden sind einer dieser großen Kostenfaktoren. Aus diesem Grund gewinnen getriebelosen Windkraftanlagen mit permanentmagneterregten Synchrongeneratoren immer mehr an Relevanz. In der Netzanbindung von ganzen Offshore-Windparks überwiegt die Hochspannungs-Gleichstrom-Übertragung (HGÜ) ab einer Übertragungsdistanz von 80 km. Diese Tendenz ist sinkend. Steigende windparkinterne Spannungen auf 66 kV fördern zusätzlich den Verzicht auf Umspannplattformen, welche für die HGÜ-Technik aktuell sinnvoll sind. Diese und weitere bereits in Aussicht stehenden Entwicklungen führen zu einer Einschränkung der Risiko- und Kostenfaktoren. Es wird demnach davon ausgegangen, dass die Offshore-Windenergie, als Ergänzung zur Onshore-Windenergie, eine wichtige Rolle im Rahmen der Energiewende einnimmt.
Das hier vorgestellte Netzoptimierungstool kann dem Verteilnetzbetreiber bei einem Störfall im Netz in Echtzeit eine Lösung zur Steuerung seiner Betriebsmittel vorschlagen. Dadurch kann das bestehende Netz optimal genutzt werden und ein kostenintensiver Netzausbau im Mittel- und Niederspannungsnetz verringert oder sogar verhindert werden. Als Grundlage für den Netzoptimierer dient ein künstliches neuronales Netz (KNN). Zum Training des KNN wurden Störfälle generiert, die auf reellen Erzeugungs- und Lastprofilen aus dem CoSSMic-Projekt basieren [1]. Für jeden Störfall wurde aus allen möglichen und sinnvollen Netzkonfigurationen eine optimierte Netztopologie anhand von Lastflussberechnungen ermittelt. Durch die Variation der Stufenschalter der Transformatoren und der Stellungen aller installierten Schalter im Netz wurde berechnet, wie der Stromfluss gelenkt werden muss, damit keines der Betriebsmittel die zulässigen Belastungsgrenzen mehr überschreitet. Für ein virtuelles Testnetz konnte mit einem trainierten KNN zu 90 Prozent die optimale Lösung des jeweiligen Störfalls erkannt werden. Durch die Anwendung der N-Best Methode konnte die Vorhersagewahrscheinlichkeit auf annähernd 99 Prozent erhöht werden.
We present an alternative approach to grid management in low voltage grids by the use of artificial intelligence. The developed decision support system is based on an artificial neural network (ANN). Due to the fast reaction time of our system, real time grid management will be possible. Remote controllable switches and tap changers in transformer stations are used to actively manage the grid infrastructure. The algorithm can support the distribution system operators to keep the grid in a safe state at any time. Its functionality is demonstrated by a case study using a virtual test grid. The ANN achieves a prediction rate of around 90% for the different grid management strategies. By considering the four most likely solutions proposed by the ANN, the prediction rate increases to 98.8%, with a 0.1 second increase in the running time of the model.
We present an innovative decision support system (DSS) for distribution system operators (DSO) based on an artificial neural network (ANN). A trained ANN has the ability to recognize problem patterns and to propose solutions that can be implemented directly in real time grid management. The principle functionality of this ANN based optimizer has been demonstrated by means of a simple virtual electrical grid. For this grid, the trained ANN predicted the solution minimizing the total line power dissipation in 98 percent of the cases considered. In 99 percent of the cases, a valid solution in compliance with the specified operating conditions was found. First ANN tests on a more realistic grid, calibrated with household load measurements, revealed a prediction rate between 88 and 90 percent depending on the optimization criteria. This approach promises a faster, more cost-efficient and potentially secure method to support distribution system operators in grid management.