The Experts below are selected from a list of 21558 Experts worldwide ranked by ideXlab platform
Arnaud Sangnier - One of the best experts on this subject based on the ideXlab platform.
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FoSSaCS - Playing with Probabilities in Reconfigurable Broadcast Networks
Lecture Notes in Computer Science, 2014Co-Authors: Nathalie Bertrand, Paulin Fournier, Arnaud SangnierAbstract:We study verification problems for a model of network with the following characteristics: the number of entities is parametric, communication is performed through broadcast with adjacent neighbors, entities can change their internal state probabilistically and reconfiguration of the communication Topology can happen at any time. The semantics of such a model is given in term of an infinite state system with both non deterministic and probabilistic choices. We are interested in qualitative problems like whether there exists an Initial Topology and a resolution of the non determinism such that a configuration exhibiting an error state is almost surely reached. We show that all the qualitative reachability problems are decidable and some proofs are based on solving a 2 player game played on the graphs of a reconfigurable network with broadcast with parity and safety objectives.
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Playing with probabilities in Reconfigurable Broadcast Networks
2014Co-Authors: Nathalie Bertrand, Paulin Fournier, Arnaud SangnierAbstract:We study verification problems for a model of network with the following characteristics: the number of entities is parametric, communication is performed through broadcast with adjacent neighbors, entities can change their internal state probabilistically and reconfiguration of the communication Topology can happen at any time. The semantics of such a model is given in term of an infinite state system with both non deterministic and probabilistic choices. We are interested in qualitative problems like whether there exists an Initial Topology and a resolution of the non determinism such that a configuration exhibiting an error state is almost surely reached. We show that all the qualitative reachability problems are decidable and some proofs are based on solving a 2 player game played on the graphs of a reconfigurable network with broadcast with parity and safety objectives.
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PACO - Parameterized Verification of Safety Properties in Ad Hoc Network Protocols
Electronic Proceedings in Theoretical Computer Science, 2011Co-Authors: Giorgio Delzanno, Arnaud Sangnier, Gianluigi ZavattaroAbstract:We summarize the main results proved in recent work on the parameterized verification of safetyproperties for ad hoc network protocols. We consider a model in which the communication Topologyof a network is represented as a graph. Nodes represent states of individual processes. Adjacentnodes represent single-hop neighbors. Processes are finite state automata that communicate via se-lective broadcast messages. Reception of a broadcast is restricted to single-hop neighbors. For thismodel we consider a decision problem that can be expressed as the verification of the existence ofan Initial Topology in which the execution of the protocol can lead to a configuration with at leastone node in a certain state. The decision problem is parametric both on the size and on the form ofthe communication Topology of the Initial configurations. We draw a complete picture of the decid-ability and complexity boundaries of this problem according to various assumptions on the possibletopologies.
Teruhisa Akashi - One of the best experts on this subject based on the ideXlab platform.
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Topology optimization using multistep mapping from 2D photomask to 3D structure for designing reinforcing rib
Sensors and Actuators A: Physical, 2014Co-Authors: Takashi Ozaki, Tsuyoshi Nomura, Norio Fujitsuka, Keiichi Shimaoka, Teruhisa AkashiAbstract:Abstract We developed a Topology optimization method for designing a two-dimensional (2D) photomask. The optimization employs multistep mapping compatible with micromachining processes to improve the characteristics of a three-dimensional (3D) MEMS fabricated by the photomask. We used this method in the development of an electrostatic micromirror to design the most effective reinforcing rib shape that reduces the deformation caused by internal residual stress. Employing a dot-array pattern as the Initial Topology, the optimization calculation converged after 300 iterations for predicted 79.1% reductions in maximum displacement. To verify this result, micromirror structures were fabricated and their deformations were experimentally measured. The experimental deformations were reduced by 87.1%. The calculated results agree well with the experimental results and demonstrate the effectiveness of the proposed method.
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Topology optimization method using multistep mapping from 2D photomask to 3D MEMS
2013 IEEE 26th International Conference on Micro Electro Mechanical Systems (MEMS), 2013Co-Authors: Takashi Ozaki, Tsuyoshi Nomura, Norio Fujitsuka, Keiichi Shimaoka, Teruhisa AkashiAbstract:We have developed a Topology optimization method for designing a two-dimensional (2D) photomask. It employs micromachining-process-like multistep mapping to analytically improve the characteristics of a three-dimensional (3D) MEMS fabricated by the photomask. We used this method to design reinforcing ribs for an electrostatic micromirror to reduce deformation caused by internal residual stress. Employing a dot-array pattern as the Initial Topology, the optimization calculation converged after 300 iterations. It predicts a 79.1% reduction in the maximum displacement. To verify this result, micromirror structures were fabricated and their deformations were measured. The deformation was reduced by 87.1% in this experiment. The calculation results agree well with the experimental results, demonstrating the effectiveness of the proposed method.
Enrique Castillo - One of the best experts on this subject based on the ideXlab platform.
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semi parametric nonlinear regression and transformation using functional networks
Computational Statistics & Data Analysis, 2008Co-Authors: Enrique Castillo, Ali S. Hadi, Beatriz Lacruz, Rosa Eva PrunedaAbstract:Functional networks are used to solve some nonlinear regression problems. One particular problem is how to find the optimal transformations of the response and/or the explanatory variables and obtain the best possible functional relation between the response and predictor variables. After a brief introduction to functional networks, two specific transformation models based on functional networks are proposed. Unlike in neural networks, where the selection of the network Topology is arbitrary, the selection of the Initial Topology of a functional network is problem driven. This important feature of functional networks is illustrated for each of the two proposed models. An equivalent, but simpler network may be obtained from the Initial Topology using functional equations. The resultant model is then checked for uniqueness of representation. When the functions specified by the transformations are unknown in form, families of linear independent functions are used as approximations. Two different parametric criteria are used for learning these functions: the constrained least squares and the maximum canonical correlation. Model selection criteria are used to avoid the problem of overfitting. Finally, performance of the proposed method are assessed and compared to other methods using a simulation study as well as several real-life data.
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Electricity Load Forecast using Functional Networks
2002Co-Authors: Enrique Castillo, Bertha Guijarro, Amparo AlonsoAbstract:In this paper a model using a functional network was employed to approach the problem of electricity load forecasting. Functional networks are generalised neural networks, that permit the specification of their Initial Topology using knowledge about the problem at hand. In this case, and after analysing the available data and their relations, an additive model was chosen, from which different alternatives were attempted until we developed three different models: one for the prediction of load for non-week-end days, another one for Saturdays and a third one for Sundays and weekends. The MAPE errors obtained were in the interval [1.857, 1.973] during training, and in the interval [3.430, 6.396] during testing.
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IWANN (1) - Optimal Transformations in Multiple Linear Regression Using Functional Networks
Connectionist Models of Neurons Learning Processes and Artificial Intelligence, 2001Co-Authors: Enrique Castillo, Ali S. Hadi, Beatriz LacruzAbstract:Functional networks are used to determine the optimal transformations to be applied to the response and the predictor variables in linear regression. The main steps required to build the functional network: selection of the Initial Topology, simplification of the Initial functional network, uniqueness of representation, and learning the parameters are discussed, and illustrated with some examples.
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SOME LEARNING METHODS IN FUNCTIONAL NETWORKS
Computer-Aided Civil and Infrastructure Engineering, 2000Co-Authors: Enrique Castillo, José M. Gutiérrez, Angel Cobo, Carmen CastilloAbstract:This article discusses some methods for learning functional networks. After a short introduction and motivation of functional networks using a CAD problem, 4 steps used in learning functional networks are described: 1) selection of the Initial Topology of the network, which is derived from the physical properties of the problem being modeled; 2) simplification of this Topology, using functional equations; 3) estimation of the parameters or weights, using least squares and minimax methods; and 4) selection of the subset of basic functions leading to the best fit to available data, using the minimum-description-length principle. Several examples are presented illustrating the learning procedure, including the use of a separable functional network to recover the missing data of the significant wave height records in 2 different locations, based on a complete record from a third location where the record is complete.
Nathalie Bertrand - One of the best experts on this subject based on the ideXlab platform.
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FoSSaCS - Playing with Probabilities in Reconfigurable Broadcast Networks
Lecture Notes in Computer Science, 2014Co-Authors: Nathalie Bertrand, Paulin Fournier, Arnaud SangnierAbstract:We study verification problems for a model of network with the following characteristics: the number of entities is parametric, communication is performed through broadcast with adjacent neighbors, entities can change their internal state probabilistically and reconfiguration of the communication Topology can happen at any time. The semantics of such a model is given in term of an infinite state system with both non deterministic and probabilistic choices. We are interested in qualitative problems like whether there exists an Initial Topology and a resolution of the non determinism such that a configuration exhibiting an error state is almost surely reached. We show that all the qualitative reachability problems are decidable and some proofs are based on solving a 2 player game played on the graphs of a reconfigurable network with broadcast with parity and safety objectives.
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Playing with probabilities in Reconfigurable Broadcast Networks
2014Co-Authors: Nathalie Bertrand, Paulin Fournier, Arnaud SangnierAbstract:We study verification problems for a model of network with the following characteristics: the number of entities is parametric, communication is performed through broadcast with adjacent neighbors, entities can change their internal state probabilistically and reconfiguration of the communication Topology can happen at any time. The semantics of such a model is given in term of an infinite state system with both non deterministic and probabilistic choices. We are interested in qualitative problems like whether there exists an Initial Topology and a resolution of the non determinism such that a configuration exhibiting an error state is almost surely reached. We show that all the qualitative reachability problems are decidable and some proofs are based on solving a 2 player game played on the graphs of a reconfigurable network with broadcast with parity and safety objectives.
Ningshen Zhang - One of the best experts on this subject based on the ideXlab platform.
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A Complex Network Approach to Topology Control Problem in Underwater Acoustic Sensor Networks
IEEE Transactions on Parallel and Distributed Systems, 2014Co-Authors: Linfeng Liu, Ye Liu, Ningshen ZhangAbstract:Underwater acoustic sensor networks (UASNs) have been developed for a set of underwater applications, including resource exploration, pollution monitoring, and tactical surveillance. Topology control techniques of UASNs are significantly different from those of terrestrial wireless sensor networks, due to the properties of underwater environments and acoustic communications. This research begins with a scale-free network model for calculating edge probability, which is used to generate Initial Topology randomly. Subsequently, a Topology control strategy based on complex network theory (TCSCN) is put forward to construct a double clustering structure, where there are two kinds of cluster-heads to ensure connectivity and coverage, respectively. The performance of TCSCN is analyzed through simulation experiments that indicate a well-constructed Topology, where (1, $\xi$ )-Coverage and (1, $\zeta$ )-Connectivity can be achieved while optimizing energy consumption and propagation delay as much as possible.