Mood of Music is among the most relevant and commercially promising, yet challenging attributes for retrieval in large music collections. In this respect this article first provides a short overview on methods and performances in the field. While most past research so far dealt with low-level audio descriptors to this aim, this article reports on results exploiting information on middlelevel as the rhythmic and chordal structure or lyrics of a musical piece. Special attention is given to realism and nonprototypicality of the selected songs in the database: all feature information is obtained by fully automatic preclassification apart from the lyrics which are automatically retrieved from on-line sources. Further more, instead of exclusively picking songs with agreement of several annotators upon perceived mood, a full collection of 69 double CDs, or 2 648 titles, respectively, is processed. Due to the severity of this task; different modelling forms in the arousal and valence space are investigated, and relevance per feature group is reported.
Unlocking and managing flexibility is an important contribution to the integration of renewable energy and an efficient and resilient operation of the power system. In this paper, we discuss how the potential of a fleet of battery-electric transportation vehicles can be used to provide frequency containment reserve. To this end, we first examine the use case in detail and then present the system designed to meet this challenge. We give an overview of the tasks and individual sub-components, consisting of (a) an artificial neural network to predict the availability of Automated Guided Vehicles (AGVs) day-ahead, (b) a heuristic approach to compute marketable flexibility, (c) a simulation to check the plausibility of flexibility schedules, (d) a multi-agent system to continuously monitor and control the AGVs and (e) the integration of fleet flexibility into a virtual power plant. We also present our approach to the economic analysis of this provision of a system-critical service in a logistical context characterised by high uncertainty and variability.
Mood of Music is among the most relevant and commercially promising, yet challenging attributes for retrieval in large music collections. In this respect this article first provides a short overview on methods and performances in the field. While most past research so far dealt with low-level audio descriptors to this aim, this article reports on results exploiting information on middlelevel as the rhythmic and chordal structure or lyrics of a musical piece. Special attention is given to realism and nonprototypicality of the selected songs in the database: all feature information is obtained by fully automatic preclassification apart from the lyrics which are automatically retrieved from on-line sources. Further more, instead of exclusively picking songs with agreement of several annotators upon perceived mood, a full collection of 69 double CDs, or 2 648 titles, respectively, is processed. Due to the severity of this task; different modelling forms in the arousal and valence space are investigated, and relevance per feature group is reported.
Nuclear fusion can be considered as a base-load power plant technology: High investment costs and limited operational flexibility require continuous operation. Wind and solar, on the other hand, as the putative main pillars of a future renewable energy system, are intermittent power sources. The resulting variations that occur on many different time scales require at first sight a rather flexible back-up system to balance this stochastic behavior. Fusion would appear not to be well suited for this task. The situation changes, however, if a large-scale renewable energy system is envisaged based on a transnational, or even transcontinental power grid. The present paper discusses a possible European power system in the year 2050 and beyond. A high percentage share of renewable energies and a strong power grid spanning the whole of Europe and involving neighboring countries, in particular those in North Africa, are assumed. The linear programming model URBS is used to describe the power system. The model optimizes the overall system costs and simulates power plant operation with an hourly resolution for one whole year. The geographical resolution is at least at the country level. The renewable technologies are modeled first on a more local level and then summed together at the country or regional level. The results indicate that the smoothing effects of the large-scale power grid transform the intermittent renewable supply, which is then more compatible with base-load power plants such as fusion reactors.
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