5,959 research outputs found

    On cost-effective reuse of components in the design of complex reconfigurable systems

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    Design strategies that benefit from the reuse of system components can reduce costs while maintaining or increasing dependability—we use the term dependability to tie together reliability and availability. D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) is a methodology that supports the design of complex systems with a focus on reconfiguration and component reuse. D3H2 systematizes the identification of heterogeneous redundancies and optimizes the design of fault detection and reconfiguration mechanisms, by enabling the analysis of design alternatives with respect to dependability and cost. In this paper, we extend D3H2 for application to repairable systems. The method is extended with analysis capabilities allowing dependability assessment of complex reconfigurable systems. Analysed scenarios include time-dependencies between failure events and the corresponding reconfiguration actions. We demonstrate how D3H2 can support decisions about fault detection and reconfiguration that seek to improve dependability while reducing costs via application to a realistic railway case study

    CONSTRAINTS: A PROGRAMMING PARADIGM AND A MODELLING METHODOLOGY

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    Constraints are often used as a formal approach to problems, because the very essence of the problem can be grasped by them. A lot of problems can be viewed as a set of variables and a set of relations on them. From this point of view the problem can be mapped naturally to a constraint network (the nodes of the network represent the variables; and the constraints in the network represent the relations between the variables of the problem); and this gives great significance to the research on constraints. An additional advantage is that they achieve global consistency through local computations. Constraints and the Constraint Satisfaction Problem (CSP) can be classified by various criteria. The most significant classification is based on the type of the values assigned to the nodes. Another possible classification of CSP is based on the kind of the required solution. Significant effort was invested in developing general constraint programming languages (CPL) to provide an environment where the only thing a user has to do is to declare what she/he wants, not bothering how it is done. Though these languages aimed at generality, due to the limited ability of data abstraction and higher order constraints they could not fully achieve their goal. If the main stress is on the efficiency, dedicated solutions claim their place with their unique data structures and specialised constraint satisfaction algorithms. The main goal of this paper is to give an overview of constraints as a flexible knowledge representation tool; to draw attention to the problems of representation and to methods of finding the solutions of the different types of constraint networks

    Data-free parameter pruning for Deep Neural Networks

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    Deep Neural nets (NNs) with millions of parameters are at the heart of many state-of-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85\% of the total parameters in an MNIST-trained network, and about 35\% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.Comment: BMVC 201
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