Traditional Earley parsers operate in two phases: first recognizing the input, then constructing the forest of parse trees. Practically speaking, this quirk makes it awkward to use in a compiler-compiler, because sema...
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Traditional Earley parsers operate in two phases: first recognizing the input, then constructing the forest of parse trees. Practically speaking, this quirk makes it awkward to use in a compiler-compiler, because semantic actions attached to rules are only executed after the fact. We address this problem by identifying safe Earley sets, points during the recognition phase at which partial parse trees can be constructed;this means that semantic actions may be executed on the fly. A secondary benefit is that Earley sets can be deleted during recognition, resulting in a substantial savings of both space and time.
Differential repetitive processes arise in the analysis and design of iterative learning control algorithms. They belong to a class of mathematical models whose dynamic properties are defined by two independent variab...
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Differential repetitive processes arise in the analysis and design of iterative learning control algorithms. They belong to a class of mathematical models whose dynamic properties are defined by two independent variables, such as a time and a spatial coordinate, also known as 2D systems in the literature. Moreover, standard stability analysis methods cannot be applied to such processes. This paper develops a vector Lyapunov function-based approach to the exponential stability analysis of differential repetitive processes and applies the resulting conditions to develop linear matrix inequality based iterative learning control law design algorithms in the presence of model uncertainty.
The present work explores, through a comprehensive sensitivity study, a new methodology to find a suitable artificial neural network architecture which improves its performances capabilities in predicting two signific...
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The present work explores, through a comprehensive sensitivity study, a new methodology to find a suitable artificial neural network architecture which improves its performances capabilities in predicting two significant parameters in safety assessment i.e. the multiplication factor k(eff) and the fuel powers peaks P-max of the benchmark 10 MW IAEA LEU core research reactor. The performances under consideration were the improvement of network predictions during the validation process and the speed up of computational time during the training phase. To reach this objective, we took benefit from Neural Network MATLAB Toolbox to carry out a widespread sensitivity study. Consequently, the speed up of several popular algorithms has been assessed during the training process. The comprehensive neural system was subsequently trained on different transfer functions, number of hidden neurons, levels of error and size of generalization corpus. Thus, using a personal computer with data created from preceding work, the final results obtained for the treated benchmark were improved in both network generalization phase and much more in computational time during the training process in comparison to the results obtained previously. (C) 2009 Elsevier B.V. All rights reserved.
In this paper, an image segmentation method is proposed that integrates fuzzy 2-partition into Yen's maximum correlation thresholding method. A fuzzy 2-partition of the image is obtained by transforming the image ...
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In this paper, an image segmentation method is proposed that integrates fuzzy 2-partition into Yen's maximum correlation thresholding method. A fuzzy 2-partition of the image is obtained by transforming the image into fuzzy domain by means of two parameterized membership functions. Fuzzy correlation is defined to measure the appropriateness of the fuzzy 2-partition. An ideal threshold is calculated from the optimal membership functions' parameters, which make the corresponding fuzzy 2-partition have maximum fuzzy correlation. In the process of searching the optimal parameters of membership functions, a fast recursive algorithm is presented in order to reduce the computation complexity. Experimental results on synthetic image, brain magnetic resonance (MR) images and casting images show that the proposed method has a satisfactory performance. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.
Mirroring and replication are common techniques for ensuring fault-tolerance and resiliency of client/server applications. Because such mirroring and replication procedures are not usually automated, they tend to be c...
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Mirroring and replication are common techniques for ensuring fault-tolerance and resiliency of client/server applications. Because such mirroring and replication procedures are not usually automated, they tend to be cumberson. In this paper, we present an architecture in which the identification of sites for replicated servers, and the generation of replicas, are both automated. The design is based on a self-configuring mesh of computers and a communication mechanism between nodes that operates on a rooted spanning tree. A query-search component uses Java(TM) language-based query capsules traveling along the branches of the spanning tree, and a caching scheme whereby the query and previous search results are cached at each node for improved efficiency. Furthermore, a security and anonymity component relies on one or more authentication servers and an anonymous communication scheme using link local addresses and indirect communication between the nodes via the spanning tree. The architecture also includes components for resource advertising and for application replication.
The current technology manifests a great demand for high precision and high positioning accuracy in many engineering applications that range from robot manipulators and high-speed flexible mechanisms to supercritical ...
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The current technology manifests a great demand for high precision and high positioning accuracy in many engineering applications that range from robot manipulators and high-speed flexible mechanisms to supercritical rotors and the space deployable structures. Active control is an important technique for suppressing vibrations in flexible mechanical systems where passive controllers may become either ineffective or impractical. A finite element model of the system dynamics in conjunction with modal reduction methods is introduced. The developed model accounts for the structural material damping. Pointwise observation and control are implemented using two sets of sensors and actuators, respectively. The developed computational active modal control algorithm is applied to the reduced order model of a double span elastic beam, and the dynamic responses of both the controlled and the residual frequency subsystems are numerically evaluated. (C) 2001 Elsevier Science B.V. All rights reserved.
A method of visual navigation by stereoimages for autonomous underwater vehicles is suggested. Two modifications of the method are considered. The first one is based on integration of measurement data of onboard navig...
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A method of visual navigation by stereoimages for autonomous underwater vehicles is suggested. Two modifications of the method are considered. The first one is based on integration of measurement data of onboard navigation system with visual data. The second modification relies on the use of a six-cloud computation scheme. Results of computational experiments with model data are presented.
Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models-mathe...
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Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models-mathematical and computational which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.
The interest in improving the efficiency of photovoltaic (PV) system has emerged because of increasing the number of home-based or small-scale PV power system. However, the home-based PV system is vulnerable to the no...
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The interest in improving the efficiency of photovoltaic (PV) system has emerged because of increasing the number of home-based or small-scale PV power system. However, the home-based PV system is vulnerable to the non-uniform operating conditions. Under such circumstances, multiple-local maximum power points (MPPs) occur on the power-voltage characteristics and an advanced control algorithm is required to track the global MPP. It is very difficult to provide a sophisticated control algorithm because of the non-linear characteristics of PV system. This study describes the potential to improve the efficiency of PV arrays under non-uniform operating conditions by using the conventional hill-climbing MPP tracking method in total cross tied (TCT) connected PV arrays, in which each group of series connected solar cells that belong to single bypass diode is interconnected. The various scenarios were tested and the results indicate that the efficiency of the proposed system is much higher than that of the same size of series-parallel (SP) PV array configuration.
The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global opti...
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The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
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