Measuring the survivability of core backbone communication networks to geographic correlated failures is important in network analysis and design. Different measures of network survivability have been used in the lite...
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Measuring the survivability of core backbone communication networks to geographic correlated failures is important in network analysis and design. Different measures of network survivability have been used in the literature, for example, Algebraic Connectivity, Network Criticality, Average Shortest Path, Network Diameter, Node Degree and Node Strength. This paper proposes a new performance measure Weighted Spectrum to evaluate network survivability regarding geographic correlated failures. Further we conduct a comparative analysis by finding the most vulnerable geographic cuts or nodes in the network though solving an optimization problem to determine the cut with the largest impact for each measure. Numerical results on several sample network topologies show that the worst-case geographic cuts depend on the measure used in an unweighted graph. The proposed Weighted Spectrum measure is shown to be more versatile than other measures in unweighted and weighted graphs.
Porous, nanostructured silver samples were produced using a direct-write method where a nanoparticle aerosol consisting of particles with a mean size of approximately 5 nm were accelerated to speeds of approximately 1...
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Porous, nanostructured silver samples were produced using a direct-write method where a nanoparticle aerosol consisting of particles with a mean size of approximately 5 nm were accelerated to speeds of approximately 1000 m/sec and impacted onto a translating substrate [1]. The impacting particles have sufficient energy to stick to the substrate, allowing patterned thick films to be directly written from the aerosol without a mask. Unlike other low temperature processing routes for achieving patterned films, no organics are added that can interfere with postdeposition processing. Typical films are 5- 100 μm thick, up to several centimeters long, and have an as-deposited relative densities as high as 70% of bulk Ag. Compression tests were carried out in steps at room temperature and at 150°C under constant displacement rates. Local strain and densification were measured by optical profilometry between each compression step. The results can be used as a starting point to better understand the mechanisms that govern plasticity, creep, and sintering in nanostructured, porous silver at low processing temperatures.
This paper presents a generation system based on the induction generator for wind energy application. The induction generator is connected to single-phase grid through an ac-dc-ac single-phase to three-phase converter...
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This paper presents a generation system based on the induction generator for wind energy application. The induction generator is connected to single-phase grid through an ac-dc-ac single-phase to three-phase converter. The single-phase converter is composed of two parallel single-phase half-bridge converters. Suitable modelling, including the circulation current, and control strategy are developed. The control of generator is based on the field oriented control. A pulse width-modulation (PWM) technique using a single and double carriers PWM is presented. Proposed topology permits to improve the harmonic distortion. Beside, it can reduce the power losses in the converter. Finally, simulation and experimental results are presented.
This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons th...
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This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons that offers an effective solution to modeling non-linear systems. As such, we propose the use of GMDH to approximate the forward and inverse dynamic behaviors of MR dampers. We also introduce two enhanced GMDH-based solutions. Firstly, a two-tier architecture is proposed whereby an enhanced GMD model is generated by the aid of a feedback scheme. Secondly, stepwise regression is used as a feature selection method prior to GMDH modeling. The proposed enhancements to GMDH are found to offer improved prediction results in terms of reducing the root-mean-squared error by around 40%.
The new trend in the process of data-intensive management indicates the importance of a distributed file system for both Internet large scale services and cloud computing environments. I/O latency and application buff...
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ISBN:
(纸本)9781479904051
The new trend in the process of data-intensive management indicates the importance of a distributed file system for both Internet large scale services and cloud computing environments. I/O latency and application buffering sizes are two of a number of issues that are essential to be analysed on different class of distributed file systems. In this paper, it is presented a research work comparing four different high performance distributed file systems. Those systems were employed to support a medical image server application in a private storage environment. Experimental results highlight the importance of an appropriate distributed file system to provide a differential level of performance considering application specific characteristics.
Multi-hole defect (MHD) photonic crystal cavities functionalized with in situ synthesized DNA bioreceptors are demonstrated for biosensing applications. By significantly increasing light interaction with target biomol...
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For an automatic chromosome classification, band resolution information is required in order to perform diagnosis on numerical and structural abnormalities. Metaphases with low band resolution are normal used for nume...
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For an automatic chromosome classification, band resolution information is required in order to perform diagnosis on numerical and structural abnormalities. Metaphases with low band resolution are normal used for numerical abnormality analysis while metaphases with high band resolution are used for structural abnormality analysis. In our work, we classify metaphases into low and high band resolution groups using chromosome shape. This band classification task can automatically perform without human intervention leading to faster the diagnosis process. The results showed that chromosome shape information is able to classify metaphases into low and high band resolution groups with the accuracy of 73.08% and 95.24%, respectively.
This work proposes a topology of compensator with the purpose of compensate harmonics and reactive loads or supply the load with balanced three-phase voltages, even when the grid voltage presents harmonics. The topolo...
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ISBN:
(纸本)9781479902712
This work proposes a topology of compensator with the purpose of compensate harmonics and reactive loads or supply the load with balanced three-phase voltages, even when the grid voltage presents harmonics. The topologies have two operation modes. Mode A: acting as shunt active power filter and mode B: acting as ac-dc-ac converter. The model of the system is derived and a suitable control strategy, including the PWM technique, is developed. Experimental results are presented, as well.
This paper presents an approach for speeding up the convergence of adaptive intelligent agents using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choi...
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ISBN:
(纸本)9789898565105
This paper presents an approach for speeding up the convergence of adaptive intelligent agents using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non-satisfactory policy. We have developed a technique for estimating policies which combines instance-based learning and reinforcement learning algorithms in Markovian environments. Experimental results in dynamic environments of different dimensions have shown that the proposed technique is able to speed up the convergence of the agents while achieving optimal action policies, avoiding problems of classical reinforcement learning approaches.
The amount of data generated in different knowledge areas has made it necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most imp...
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ISBN:
(纸本)9781479904532
The amount of data generated in different knowledge areas has made it necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. In this context, this paper aims to propose an adaptation of a bee-inspired optimization algorithm so that it is able to solve data clustering problems. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined.
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