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Power Trading Coordination in Smart Grids Using Dynamic Learning and Coalitional Game Theory
No full textIn traditional power distribution models, consumers acquire power from the central distribution unit, while “micro-grids” in a smart power grid can also trade power between themselves. In this paper, we investigate the problem of power trading coordination among such micro-grids. Each micro-grid has a surplus or a deficit quantity of power to transfer or to acquire, respectively. A coalitional game theory based algorithm is devised to form a set of coalitions. The coordination among micro-grids determines the amount of power to transfer over each transmission line in order to serve all micro-grids in demand by the supplier micro-grids and the central distribution unit with the purpose of minimizing the amount of dissipated power during generation and transfer. We propose two dynamic learning processes: one to form a coalition structure and one to provide the formed coalitions with the highest power saving. Numerical results show that dissipated power in the proposed cooperative smart grid is only 10% of that in traditional power distribution networks
An efficient Self-Organizing Active Contour model for image segmentation
No full textActive Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, based on the evolution of an active contour. Among ACMs, supervised {ACMs} are able to exploit the information extracted from supervised examples to guide the contour evolution. However, their applicability is limited by the accuracy of the probability models they use. As a consequence, effectiveness and efficiency of supervised {ACMs} are among their main real challenges, especially when handling images containing regions characterized by intensity inhomogeneity. In this paper, to deal with such kinds of images, we propose a new supervised ACM, named Self-Organizing Active Contour (SOAC) model, which combines a variational level set method (a specific kind of ACM) with the weights of the neurons of two Self-Organizing Maps (SOMs). Its main contribution is the development of a new {ACM} energy functional optimized in such a way that the topological structure of the underlying image intensity distribution is preserved – using the two {SOMs} – in a parallel-processing and local way. The model has a supervised component since training pixels associated with different regions are assigned to different SOMs. Experimental results show the superior efficiency and effectiveness of {SOAC} versus several existing ACMs
Reconstructing topological properties of complex networks using the fitness model
No full textA major problem in the study of complex socioeconomic systems is represented by privacy issues—that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties of a complex network starting from limited information. This method uses the knowledge of an intrinsic property of the nodes (indicated as fitness), and the number of connections of only a limited subset of nodes, in order to generate an ensemble of exponential random graphs that are representative of the real systems and that can be used to estimate its topological properties. Here we focus in particular on reconstructing the most basic properties that are commonly used to describe a network: density of links, assortativity, clustering. We test the method on both benchmark synthetic networks and real economic and financial systems, finding a remarkable robustness with respect to the number of nodes used for calibration. The method thus represents a valuable tool for gaining insights on privacy-protected systems
A meshless adaptive multiscale method for fracture
No full textAbstract The paper presents a multiscale method for crack propagation. The coarse region is modelled by the differential reproducing kernel particle method. Fracture in the coarse scale region is modelled with the Phantom node method. A molecular statics approach is employed in the fine scale where crack propagation is modelled naturally by breaking of bonds. The triangular lattice corresponds to the lattice structure of the (111)plane of an {FCC} crystal in the fine scale region. The Lennard–Jones potential is used to model the atom–atom interactions. The coupling between the coarse scale and fine scale is realized through ghost atoms. The ghost atom positions are interpolated from the coarse scale solution and enforced as boundary conditions on the fine scale. The fine scale region is adaptively refined and coarsened as the crack propagates. The centro symmetry parameter is used to detect the crack tip location. The method is implemented in two dimensions. The results are compared to pure atomistic simulations and show excellent agreement
A dual gradient-projection algorithm for model predictive control in fixed-point arithmetic
No full textAlthough linear Model Predictive Control has gained increasing popularity for controlling dynamical systems subject to constraints, the main barrier that prevents its widespread use in embedded applications is the need to solve a Quadratic Program (QP) in real-time. This paper proposes a dual gradient projection (DGP) algorithm specifically tailored for implementation on fixed-point hardware. A detailed convergence rate analysis is presented in the presence of round-off errors due to fixed-point arithmetic. Based on these results, concrete guidelines are provided for selecting the minimum number of fractional and integer bits that guarantee convergence to a suboptimal solution within a pre-specified tolerance, therefore reducing the cost and power consumption of the hardware device
Organizing the Global Value Chain: a firm-level test
No full textIn this paper we study the organization of Global Value Chains on a sample of about 4,000 manufacturing parent
companies operating more than 90,000 affiliates on a global scale, which chose to integrate at least once in the
period 2004–2012. Assuming a technological sequence of production stages, a recent property rights framework
(Antràs and Chor, 2013; Alfaro et al., 2015) predicts that a choice of vertical integration is crucially based on both
the position of a supplier along the chain and on the relative size of demand elasticities faced by the final producer
and the supplier. We positively test whether, if final demand is sufficiently elastic (inelastic), producers of final
goods integrate production stages that are more proximate to (far from) the consumers. However, this is not valid
for cases of midstream parents, i.e. for producers of intermediate inputs that can integrate either backward or forward
along the chain. We document that midstream are at least as common as are downstream parent companies but that
existing theory neglects them. In these cases, we find that demand elasticities do not play a significant role in
integration choices. Interestingly, both midstream and downstream parents tend to integrate affiliates that are more
proximate in segments of a supply chain. Our findings point to a role for technological determinants that may be as
important as are contracting frictions in organizing Global Value Chains
Constructing regionalism in South America: the cases of sectoral cooperation on transport infrastructure and energy
No full textThis article contributes to the study of South American regionalism focusing on the emergence of sectoral cooperation starting in 2000. To do so, the article analyses two policy areas — transport infrastructure and energy integration — addressing two questions: Why has regional cooperation emerged despite the absence of economic interdependence and market-driven demand for economic integration? And why are policy outcomes evident in some areas (i.e. transport infrastructure) while limited in others (i.e. energy)? It is argued that the emergence of regional cooperation as well as the variation in policy outcomes between areas can be explained largely by the articulation of a regional leadership and its effect on the convergence of state preferences. The article shows how the Brazilian leadership, incentivised by the effects of the US-led Free Trade Area of the Americas negotiations and the financial crises that hit the region in the late 1990s, made state preferences converge towards a regionalist project encompassing all South American countries by making visible the mutual benefits of cooperation on transport infrastructure and energy. In the case of energy, however, the emergence of a second regional leadership project — pursued by Chávez’s Venezuela — and deep preference divergence led sectoral cooperation into a gridlock
Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines
No full textThis paper presents a method for an automated Parkinsonian disorders classification using Support Vector Machines (SVMs). Magnetic Resonance quantitative markers are used as features to train SVMs with the aim of automatically diagnosing patients with different Parkinsonian disorders. Binary and multi–class classification problems are investigated and applied with the aim of automatically distinguishing the subjects with different forms of disorders. A ranking feature selection method is also used as a preprocessing step in order to asses the significance of the different features in diagnosing Parkinsonian disorders. In particular, it turns out that the features selected as the most meaningful ones reflect the opinions of the clinicians as the most important markers in the diagnosis of these disorders. Concerning the results achieved in the classification phase, they are promising; in the two multi–class classification problems investigated, an average accuracy of 81% and 90% is obtained, while in the binary scenarios taken in consideration, the accuracy is never less than 88%
Debunking in a World of Tribes
No full textRecently a simple military exercise on the Internet was perceived as the beginning of a new civil war in the US. Social media aggregate people around common interests eliciting a collective framing of narratives and worldviews. However, the wide availability of user-provided content and the direct path between producers and consumers of information often foster confusion about causations, encouraging mistrust, rumors, and even conspiracy thinking. In order to contrast such a trend attempts to \textit{debunk} are often undertaken. Here, we examine the effectiveness of debunking through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users interact with proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook in the US. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. Both groups interact similarly with the information within their echo chamber. We examine 47,780 debunking posts and find that attempts at debunking are largely ineffective. For one, only a small fraction of usual consumers of unsubstantiated information interact with the posts. Furthermore, we show that those few are often the most committed conspiracy users and rather than internalizing debunking information, they often react to it negatively. Indeed, after interacting with debunking posts, users retain, or even increase, their engagement within the conspiracy echo chamber