Abstract
A linear system is a fundamental building block for several mesh-based computer graphics applications such as simulation, shape deformation, virtual surgery, and fluid/smoke animation, among others. Nevertheless, such a system is most of the times seen as a black box and algorithms do not deal with its optimization. Depending on the number of unknowns, the linear system is often considered as an obstacle for real time application and as a building block for offline computations. We present in this paper, a neighboring-based methodology for representing a linear system. This new representation enables a compact storage of the set of equation, flexibility for ordering the unknowns and a rapid iterative solution, by means of an optimized matrix-vector multiplication. In addition, this representation facilitates the modification of part of the linear system without affecting its unchanged part and avoiding the complete rebuild of the system. This specially benefits applications dealing with dynamic meshes, where the geometry, the topology or both are constantly changed. We present the capabilities of our methodology in models with different sizes and for different operations, highlighting the dynamic characteristic of the mesh. We believe that several applications in computer graphics could benefit from our methodology, in order to improve their convergence and their performance, reducing the number of iterations and the computation time. © The Eurographics Association 2009.

Abstract
This paper presents a new architecture for MASCEM, a multi-agent electricity market simulator. The main focus is the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. VPPs can reinforce the importance of distributed generation technologies, mainly based on renewable energy sources, making them valuable in electricity markets. The new features are implemented in Prolog which is integrated in the JAVA program by using the LPA Win-Prolog Intelligence Server (IS) that provides a DLL interface between Win-Prolog and other applications. ©2009 IEEE.

Abstract
Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

Abstract
This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data. © 2009 IEEE.

Abstract

Abstract
Spatiotemporal databases provide effective means to represent, manage and query information evolving over time. However, the visualization of record sets that result from spatiotemporal queries through traditional visualization techniques can be of difficult interpretation or may lack the ability to meaningfully display several instants at the same time. We propose a Temporal Focus + Context visualization model to overcome issues from such techniques resorting to concepts from Information Visualization. In this model, Focus + Context is applied to time rather than, as more typically, to attributes or space, and allows large amounts of data from distinct periods of time and from several record sets to be compressed onto one. Underlying the proposed visualization technique is the calculation of a temporal degree of interest (TDOI) for each record driven by specific analysis, exploration or presentation goals and based on the record valid time attribute, as well as on user-defined temporal visualization requirements. In the mapping stage of the visualization pipeline, the TDOI for a record is used to control graphical properties, such as transparency and color. More complex rendering properties, such as sketch drawing edges or other non-photorealistic enhancement techniques, can also be used to convey the temporal aspects of data, replacing the original graphical features of the record data. By enhancing or dimming the representation of a data item, according to the corresponding degree of interest, it is possible to meaningfully compress information about distinct temporal states of data onto the same visualization display. The model has been applied to several test scenarios and proved appropriate and useful for a wide range of domains that require the display, exploration and analysis of spatial information discretely evolving over time. Information Visualization (2008) 7, 265-274. doi: 10.1057/palgrave.ivs.9500188