Abstract
While in the past the installation of distributed generation systems was oftentimes motivated by attractive feed-in tariffs, many consumers now install such systems to increase their self-sufficiency in order to avoid rising electricity cost. However, due to the intermittency of photovoltaic systems and the dependency of cogeneration units on the thermal demand, there remains a significant mismatch between local generation and demand. Electrical storage devices can align generation and demand, minimizing the mentioned gap and hence the amount of energy that needs to be taken from the grid. This paper describes a model to determine the optimal dispatch of such a system, taking the thermal system into account in order to account for the dependency of cogeneration units. A mixed integer program is presented, which identifies the cost minimizing operation schedule. This model is tested using a case study considering realistic values taken from the German case.

Abstract
Presently, many industries are facing strong challenges related to the demand of customized and high-quality products. These pressures lead to internal company's conflicts where current production systems have a rigid structure, forcing the company into a organization stall when a fast product change is required. Therefore, the need to smoothly migrate traditional systems into more feature-rich and cost-effective systems, namely Cyber-Physical Production Systems (CPPS), became a highly discussed topic. PERFoRM project focuses the conceptual transformation of existing production systems towards plug&produce ones to achieve flexible and reconfigurable manufacturing environments. In particular, the smooth migration process is considered crucial to effectively transpose existing production systems into truly CPPS. This paper describes the use of Petri nets to design the migration process under the PERFoRM perspective, taking advantage of its inherent capabilities to design, analyze, simulate and validate such complex processes.

Abstract

Abstract
Context-aware pervasive applications can improve user experiences by tracking people in their surroundings. Such systems use multiple sensors to gather information regarding people and devices. However, when developing novel user experiences, researchers are left to building foundation code to support multiple network-connected sensors, a major hurdle to rapidly developing and testing new ideas. We introduce Creepy Tracker, an open-source toolkit to ease prototyping with multiple commodity depth cameras. It automatically selects the best sensor to follow each person, handling occlusions and maximizing interaction space, while providing full-body tracking in scalable and extensible manners. It also keeps position and orientation of stationary interactive surfaces while offering continuously updated point-cloud user representations combining both depth and color data. Our performance evaluation shows that, although slightly less precise than marker-based optical systems, Creepy Tracker provides reliable multi-joint tracking without any wearable markers or special devices. Furthermore, implemented representative scenarios show that Creepy Tracker is well suited for deploying spatial and context-aware interactive experiences. © 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

Abstract
This paper addresses different techniques for modelling electrochemical energy storage (ES) devices in insular power network applications supported on real data. The first contribution is a comprehensive performance study between a set of competing electrochemical energy storage technologies: Lithium-ion (Li-ion), Nickel-Cadmium (NiCd), Nickel-Metal Hydride (NiMH) and Lead Acid (PbA) batteries. As a second contribution, several key engineering parameters with regards to the PbA battery-based storage solution are examined, such as cell charge distribution, cell string configuration and battery capacity fade. Finally, as a third contribution, an ES system operating criterion is discussed and proposed to manage the inherent rapid aging of the batteries due to their cycling activity. The simulation results are supported on real data from two non-interconnected power grids, namely Crete (Greece) and Sao Miguel (Portugal) Islands, for demonstration and validation purposes.

Abstract
Analysis of automatically-generated alignments shows that ambiguous situations are quite common, preventing their application in scenarios demanding high quality and completeness, such ontology mediation (e.g. data transformation and information/knowledge integration). Even the best-performing alignment needs to be manually corrected, completed and verified before application. In this paper, we propose a decision support system (DSS) based in a general-purpose rule engine that assists the expert on improving and completing the automatically-generated alignments into fully-fledged alignments, balancing the precision and recall of the system with the user participation in the process. For that, the rules capture the preconditions (existing facts) and the actions to solve each (ambiguous) alignment scenario, in which the expert decision will be adopted in further automatic decisions. The evaluation of the proposed DSS shows the gain in reducing the need for expert's decisions while increasing the accuracy of the alignments. © 2017 Copyright is held by the owner/author(s).