Enhanced contactless vital sign estimation from real-time multimodal 3D image data. - In: Journal of imaging, ISSN 2313-433X, Bd. 6 (2020), 11, 123, S. 1-15
https://doi.org/10.3390/jimaging6110123
Designing an event store for a modern three-layer storage hierarchy. - In: Datenbank-Spektrum, ISSN 1610-1995, Bd. 20 (2020), 3, S. 211-222
Event stores face the difficult challenge of continuously ingesting massive temporal data streams while satisfying demanding query and recovery requirements. Many of today’s systems deal with multiple hardware-based trade-offs. For instance, long-term storage solutions balance keeping data in cheap secondary media (SSDs, HDDs) and performance-oriented main-memory caches. As an alternative, in-memory systems focus on performance, while sacrificing monetary costs, and, to some degree, recovery guarantees. The advent of persistent memory (PMem) led to a multitude of novel research proposals aiming to alleviate those trade-offs in various fields. So far, however, there is no proposal for a PMem-powered specialized event store.
https://doi.org/10.1007/s13222-020-00356-6
Programmable multiple plasmonic resonances of nanoparticle superlattice for enhancing photoelectrochemical activity. - In: Advanced functional materials, ISSN 1616-3028, Bd. 30 (2020), 48, 2005170, insges. 10 S.
https://doi.org/10.1002/adfm.202005170
Thermal efficiency in laser-assisted joining of polymer-metal composites. - In: Materials, ISSN 1996-1944, Bd. 13 (2020), 21, 4875, insges. 16 S.
https://doi.org/10.3390/ma13214875
Multi-model standard for bitstream-, pixel-based and hybrid video quality assessment of UHD/4K: ITU-T P.1204. - In: IEEE access, ISSN 2169-3536, Bd. 8 (2020), S. 193020-193049
https://doi.org/10.1109/ACCESS.2020.3032080
Liquid-based battery temperature control of electric buses. - In: Energies, ISSN 1996-1073, Bd. 13 (2020), 19, 4990, S. 1-20
Previous research identified that battery temperature control is critical to the safety, lifetime, and performance of electric vehicles. In this paper, the liquid-based battery temperature control of electric buses is investigated subject to heat transfer behavior and control strategy. Therefore, a new transient calculation method is proposed to simulate the thermal behavior of a coolant-cooled battery system. The method is based on the system identification technique and combines the advantage of low computational effort and high accuracy. In detail, four transfer functions are extracted by a thermo-hydraulic 3D simulation model comprising 12 prismatic lithium nickel manganese cobalt oxide (NMC) cells, housing, arrestors, and a cooling plate. The transfer functions describe the relationship between heat generation, cell temperature, and coolant temperature. A vehicle model calculates the power consumption of an electric bus and thus provides the input for the transient calculation. Furthermore, a cell temperature control strategy is developed with respect to the constraints of a refrigerant-based battery cooling unit. The data obtained from the simulation demonstrate the high thermal inertia of the system and suggest sufficient control of the battery temperature using a quasi-stationary cooling strategy. Thereby, the study reveals a crucial design input for battery cooling systems in terms of heat transfer behavior and control strategy.
https://doi.org/10.3390/en13194990
The influence of the stimulus design on the harmonic components of the steady-state visual evoked potential. - In: Frontiers in human neuroscience, ISSN 1662-5161, Bd. 14 (2020), 343, insges. 11 S.
https://doi.org/10.3389/fnhum.2020.00343
Faster & strong: string dictionary compression using sampling and fast vectorized decompression. - In: The VLDB journal, ISSN 0949-877X, Bd. 29 (2020), 6, S. 1263-1285
String dictionaries constitute a large portion of the memory footprint of database applications. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. Therefore, lightweight algorithms such as front coding (PFC) are favored in practice. This paper endeavors to make strong string dictionary compression practical. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. To accelerate compression times, we propose block-based RPFC (BRPFC) which consists in independently compressing small blocks of the dictionary. For further accelerated compression times especially on large string dictionaries, we also propose an alternative version of BRPFC that uses sampling to speed up compression. Moreover, to accelerate access times, we devise a vectorized access method, using Intel® Advanced Vector Extensions 512 (Intel® AVX-512). Our experimental evaluation shows that sampled BRPFC offers compression times up to 190 × faster than RPFC, and random string lookups 2.3 × faster than RPFC on average. These results move our modified RPFC into a practical range for use in database systems because the overhead of Re-Pair-based compression for access times can be reduced by 2 ×.
https://doi.org/10.1007/s00778-020-00620-x
A gray-box modeling methodology for runtime prediction of Apache Spark jobs. - In: Distributed and parallel databases, ISSN 1573-7578, Bd. 38 (2020), 4, S. 819-839
Apache Spark jobs are often characterized by processing huge data sets and, therefore, require runtimes in the range of minutes to hours. Thus, being able to predict the runtime of such jobs would be useful not only to know when the job will finish, but also for scheduling purposes, to estimate monetary costs for cloud deployment, or to determine an appropriate cluster configuration, such as the number of nodes. However, predicting Spark job runtimes is much more challenging than for standard database queries: cluster configuration and parameters have a significant performance impact and jobs usually contain a lot of user-defined code making it difficult to estimate cardinalities and execution costs. In this paper, we present a gray-box modeling methodology for runtime prediction of Apache Spark jobs. Our approach comprises two steps: first, a white-box model for predicting the cardinalities of the input RDDs of each operator is built based on prior knowledge about the behavior and application parameters such as applied filters data, number of iterations, etc. In the second step, a black-box model for each task constructed by monitoring runtime metrics while varying allocated resources and input RDD cardinalities is used. We further show how to use this gray-box approach not only for predicting the runtime of a given job, but also as part of a decision model for reusing intermediate cached results of Spark jobs. Our methodology is validated with experimental evaluation showing a highly accurate prediction of the actual job runtime and a performance improvement if intermediate results can be reused.
https://doi.org/10.1007/s10619-020-07286-y
The new E.ON on the German electricity market - competitive impact of the innogy acquisition :
Die neue E.ON auf dem deutschen Strommarkt - wettbewerbliche Auswirkungen der innogy-Übernahme. - In: List Forum für Wirtschafts- und Finanzpolitik, ISSN 2364-3943, Bd. 45 (2020), 3, S. 295-317
Der Deal der beiden größten deutschen Energielieferanten RWE und E.ON zum Tausch verschiedener Geschäftseinheiten, welcher Mitte September 2019 genehmigt wurde, wird den deutschen Energiemarkt wesentlich umstrukturieren und sowohl im Bereich Erzeugung als auch im Vertrieb zu jeweils einem dominanten Wettbewerber führen. E.ON wird dabei durch die Übernahme der innogy Geschäfte im Bereich des klassischen Energievertriebs und der Ladeinfrastruktur für Elektrofahrzeuge wesentliche Wettbewerbsvorteile erhalten. Dazu zählt unter anderem der Zugang zu einer Vielzahl an Messstellen und damit Datensätzen im Bereich des Haushalts- und Geschäftskundenvertriebs. Die Auswertung und Nutzung dieser Datensätze eröffnet dem zusammengeschlossenen Unternehmen neue Geschäftsfelder, aber auch Möglichkeiten die dominante Stellung auf dem Markt zu missbrauchen. Dieser Beitrag widmet sich den potenziellen Auswirkungen der innogy-Übernahme durch E.ON in den Bereichen klassischer Vertrieb und E‑Mobilität, in welchen die angesprochenen Aspekte der Datenökonomik eine wesentliche Rolle spielen. Des Weiteren werden die Auswirkungen der Marktumstrukturierung auf den Konzessionsmarkt betrachtet und die politökonomische Dimension des Zusammenschlusses erläutert. Wir schließen mit einer Kurzanalyse der Erlaubnisentscheidung und der damit verbundenen Auflagen und kommen zu dem Schluss, dass diese nicht geeignet sind, die erheblichen anti-kompetitiven Auswirkungen des Zusammenschlusses einzudämmen oder zu verhindern.
https://doi.org/10.1007/s41025-020-00185-1