Abstract
Worldwide microgrid capacity is expected to reach 7 GW and a market value of $35 billion dollars in the next few years. The decentralization of the generation dispatch role and different ownership models concerning microgrids, will contribute to increase the complexity of the future power systems. Analyzing new policies and strategies as well as evaluating those impacts is only possible with the use of sophisticated simulation tools. This paper presents a scalable computational simulation to address microgrid dispatch and the impact in the electricity market. Computational intelligence techniques are integrated to improve the effectiveness of the simulation tool. These techniques include CPLEX; differential search algorithm and quantum particle swaiin optimization. Each microgrid player is able to solve a day-ahead scheduling problem and submit bids to the electricity market agent (spot market), which calculates the market clearing price. The developed case study with a large number of players totaling about 150,000 consumers suggest the relevance of the developed computational framework.

Abstract
Imbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance of the most relevant cases for the user. This problem has been intensively studied for classification problems. Recently it was recognized that imbalanced domains occur in several other contexts and for a diversity of types of tasks. This paper focus on imbalanced regression tasks. Resampling strategies are among the most successful approaches to imbalanced domains. In this work we propose variants of existing resampling strategies that are able to take into account the information regarding the neighborhood of the examples. Instead of performing sampling uniformly, our proposals bias the strategies for reinforcing some regions of the data sets. In an extensive set of experiments we provide evidence of the advantage of introducing a neighborhood bias in the resampling strategies.

Abstract
To gain competitive advantage in today's mobile market, cellular network testing, monitoring and improving customer experience is crucial. Today independent benchmarking companies are hired by mobile operators to run drive tests in a certain geographical areas. The high cost for running these tests results in a low frequency of execution, typically this benchmarking is executed no more than two or three times per year, which is not sufficient to follow the dynamics of an LTE network in a dense urban area. The majority of the drive testing costs come from the car, driver, and the in-car technician. Another approach is to take advantage of existing transportation companies to carry on network benchmarking services to Mobile Network Operators. Unattended measurement nodes can be deployed in existing transportation fleets without the need for dedicated field personnel, reducing the cost of testing up to 70%. This demo uses nodes placed in buses, available in several cities in Europe, to create and validate an automatic LTE network benchmark. The tool allows an easy comparative analyses of mobile network quality of Service and quality of experience parameters based on the operators raw data.

Abstract
In this paper we design a model based method to locate a leakage and estimate its size in a gas network, using a linearised version of an hyperbolic PDE. To do this, the problem is reduced to two identical ODEs, allowing in this way for a representation of the pressure as well as the mass flow in terms of its system of fundamental solutions. Then using the available measurements at the grid boundary points, the correspondent coefficients can be determined. Assuming pressure continuity, we check for consistency of the coefficients in order to find faulty pipelines. Thence, the location of the leakage can be found either graphically or using a numerical method for a specific pipe. Next, its size can also be estimated. © Springer International Publishing Switzerland 2017.

Abstract

Abstract
The IEC 61499 standard provides means to specify distributed control systems in terms of function blocks. For the deployment, each device may hold one or many logical resources, each consisting of a function block network with service interface blocks at the edges. The execution model is event driven (asynchronous), where triggering events may be associated with data (and seen as messages). In this paper, we propose a low-complexity implementation technique allowing to assess end-to-end response times of event chains spanning over a set of networked devices. Based on a translation of IEC 61499 to RTFM1-tasks and resources, the response time for each task in the system at the device-level can be derived using established scheduling techniques. In this paper, we develop a holistic method to provide safe end-to-end response times taking both intra and interdevice delivery delays into account. The novelty of our approach is the accuracy of the system scheduling overhead characterization. While the device-level (RTFM) scheduling overhead was discussed in previous works, the network-level scheduling overhead for switched Ethernets is discussed in this paper. The approach is generally applicable to a wide range of commercial off-the-shelf Ethernet switches without a need for expensive custom solutions to provide hard real-time performance. A behavior characterization of the utilized switch determines the guaranteed response times. As a use case, we study the implementation onto (single-core) Advanced RISC Machine (ARM)-cortex-based devices communicating over a switched Ethernet network. For the analysis, we define a generic switch model and an experimental setup allowing us to study the impact of network topology as well as 802.1Q quality of service in a mixed critical setting. Our results indicate that safe sub millisecond end-to-end response times can be obtained using the proposed approach.