811 resultados para Two-stage converter
Resumo:
In this paper, we study breakdown characteristics in shallow-trench isolation (STI)-type drain-extended MOSFETs (DeMOS) fabricated using a low-power 65-nm triple-well CMOS process with a thin gate oxide. Experimental data of p-type STI-DeMOS device showed distinct two-stage behavior in breakdown characteristics in both OFF-and ON-states, unlike the n-type device, causing a reduction in the breakdown voltage and safe operating area. The first-stage breakdown occurs due to punchthrough in the vertical structure formed by p-well, deep n-well, and p-substrate, whereas the second-stage breakdown occurs due to avalanche breakdown of lateral n-well/p-well junction. The breakdown characteristics are also compared with the STI-DeNMOS device structure. Using the experimental results and advanced TCAD simulations, a complete understanding of breakdown mechanisms is provided in this paper for STI-DeMOS devices in advanced CMOS processes.
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This paper presents the instrumentation and control architecture for a laboratory based two-stage 4-bed silica gel + water adsorption system. The system consists of primarily two fluids: refrigerant (water vapour) and heat transfer fluid (water) flowing through various components. Heat input to the system is simulated using multiple heaters and ambient air is used as the heat sink. The laboratory setup incorporates a real time National Instruments (NI) controller to control several digital and analog valves, heaters, pumps and fans along with simultaneous data acquisition from various flow, pressure and temperature sensors. The paper also presents in detail the various automated and manual tasks required for successful operation of the system. Finally the system pressure and temperature dynamics are reported and its performance evaluated for various cycle times. (C) 2015 Elsevier Ltd. All rights reserved.
Resumo:
Homogeneous temperature regions are necessary for use in hydrometeorological studies. The regions are often delineated by analysing statistics derived from time series of maximum, minimum or mean temperature, rather than attributes influencing temperature. This practice cannot yield meaningful regions in data-sparse areas. Further, independent validation of the delineated regions for homogeneity in temperature is not possible, as temperature records form the basis to arrive at the regions. To address these issues, a two-stage clustering approach is proposed in this study to delineate homogeneous temperature regions. First stage of the approach involves (1) determining correlation structure between observed temperature over the study area and possible predictors (large-scale atmospheric variables) influencing the temperature and (2) using the correlation structure as the basis to delineate sites in the study area into clusters. Second stage of the approach involves analysis on each of the clusters to (1) identify potential predictors (large-scale atmospheric variables) influencing temperature at sites in the cluster and (2) partition the cluster into homogeneous fuzzy temperature regions using the identified potential predictors. Application of the proposed approach to India yielded 28 homogeneous regions that were demonstrated to be effective when compared to an alternate set of 6 regions that were previously delineated over the study area. Intersite cross-correlations of monthly maximum and minimum temperatures in the existing regions were found to be weak and negative for several months, which is undesirable. This problem was not found in the case of regions delineated using the proposed approach. Utility of the proposed regions in arriving at estimates of potential evapotranspiration for ungauged locations in the study area is demonstrated.
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In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.
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We have developed a two-stage Ti:sapphire amplifier system which can produce 17-TW/23-fs pulses at a repetition rate 10 MHz. A birefringent plate is used in the regenerative amplifier to alleviate gain narrowing, while an all-reflective cylindrical-mirror-based pulse stretcher and an acousto-optic programmable dispersive filter (AOPDF) are used to compensate for the higher order dispersion of the system.
Two-stage, IV-based estimation and parametrization selection for linear multivariable identification
Resumo:
Chinese Academy of Sciences (ISCAS)