The Multiphase Latent Variable Model Predictive control (MLV-MPC) is developed based on the Principal component analysis (PCA) model. The proposed control methodology is capable of trajectory tracking as well as distu...
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ISBN:
(纸本)9781424445233
The Multiphase Latent Variable Model Predictive control (MLV-MPC) is developed based on the Principal component analysis (PCA) model. The proposed control methodology is capable of trajectory tracking as well as disturbance rejection. The model that is used in the course of MPC is a multiphase PCA model that is constructed based on the available data from the measurements on the process. Different data arrangements are studied and their effects on the performance of the control algorithm are evaluated.
Foreign oil dependence, increased cost of fuel, pollution, global warming are buzz words of today's era. Automobiles have a large impact on increasing energy demand, pollution and related issues. As a consequence,...
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ISBN:
(纸本)9781424445233
Foreign oil dependence, increased cost of fuel, pollution, global warming are buzz words of today's era. Automobiles have a large impact on increasing energy demand, pollution and related issues. As a consequence, many efforts are being concentrated on innovative systems for transportation that could replace petroleum with cleaner fuel, i.e. electricity from the power grid. The use of plug-in hybrid electric vehicles (PHEVs) can become a very important change in this direction, since such vehicles could benefit from the increasing availability of renewable energy. PHEVs requires new control and energy management algorithms, that are crucial for vehicle performance. This paper deals with evaluation of two modes, Electric Vehicle (EV) mode and Blended mode, for plug-in hybrid electric vehicles and their comparison with conventional and hybrid electric vehicle performance. In this paper two PHEV architectures are considered: through road parallel plug-in hybrid and series plug-in hybrid. Similar models have been developed to evaluate vehicle performance for conventional and hybrid architectures. Both PHEV architectures are analyzed with two different modes- EV and Blended;a modified version of ECMS (Equivalent Consumption Minimization Strategy) is used for both algorithms. Various standard as well as custom designed driving cycles are used in this analysis. The paper provides quantitative analysis of the control algorithms to analyze their effects on fuel economy, use of electric energy, cost of operation, etc.;these results are compared with the simulations for hybrid and conventional vehicles. Some important relationships between fuel economy, design architectures and control strategies are shown and can be useful in the design of the optimal control algorithms for PHEVs. As shown in the results, the control problem for PHEVs is not limited to fuel economy but it also involves external factors, such as price of electricity, energy market and regulations, charging ava
When both the variance and the N are unequal in a two-group design, the probability of a Type I error shifts from the nominal 5% error rate. The probability is too liberal when the small cell has the larger variance a...
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When both the variance and the N are unequal in a two-group design, the probability of a Type I error shifts from the nominal 5% error rate. The probability is too liberal when the small cell has the larger variance and too conservative when the large cell has the larger variance. We present an algorithm to circumvent the problem when the smaller group has the larger variance and show, by simulation, that the algorithm brings the error rate back to the nominal value without sacrificing the ability to detect true effects.
Computational models of lexical semantics, such as latent semantic analysis, can automatically generate semantic similarity measures between words from statistical redundancies in text. These measures are useful for e...
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Computational models of lexical semantics, such as latent semantic analysis, can automatically generate semantic similarity measures between words from statistical redundancies in text. These measures are useful for experimental stimulus selection and for evaluating a model's cognitive plausibility as a mechanism that people might use to organize meaning in memory. Although humans are exposed to enormous quantities of speech, practical constraints limit the amount of data that many current computational models can learn from. We follow up on previous work evaluating a simple metric of pointwise mutual information. Controlling for confounds in previous work, we demonstrate that this metric benefits from training on extremely large amounts of data and correlates more closely with human semantic similarity ratings than do publicly available implementations of several more complex models. We also present a simple tool for building simple and scalable models from large corpora quickly and efficiently.
The article discusses various reports published within the issue, including one on a dual-thread speculation system and another on a parallel version of the Tricluster algorithm.
The article discusses various reports published within the issue, including one on a dual-thread speculation system and another on a parallel version of the Tricluster algorithm.
In this paper, a distributed power control algorithm is proposed to perform adaptive spatial multiplexing for the relay enhanced orthogonal frequency division multiple access (OFDMA) cellular systems. As the optimal s...
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ISBN:
(纸本)9781424440757;9781424440740
In this paper, a distributed power control algorithm is proposed to perform adaptive spatial multiplexing for the relay enhanced orthogonal frequency division multiple access (OFDMA) cellular systems. As the optimal spatial multiplexing problem is non-convex, we utilize the distributed iteration method to achieve its near-optimal solution. By determining the restriction on the transmit power for each subcarrier, the individual power allocation at the base station (BS) and the relay station (RS) can always converge to some desirable Nash Equilibriums with satisfying performance. Simulation results show that the proposed distributed power control algorithm provides significant gain in system transmit rate over the traditional iterative water-filling scheme.
This paper presents the structural control results of shaking table tests for a steel frame structure in order to evaluate the performance of a number of proposed semi-active control algorithms using multiple magnetor...
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This paper presents the structural control results of shaking table tests for a steel frame structure in order to evaluate the performance of a number of proposed semi-active control algorithms using multiple magnetorheological (MR) dampers. The test structure is a six-story steel frame equipped with MR dampers. Four different cases of damper arrangement in the structure are selected for the control study. In experimental tests, the El Centro earthquake and Kobe earthquake ground motion data are used as excitations. Further, several decentralized sliding mode control algorithms are developed in this paper specifically for applications of MR dampers in building structures. Various control algorithms are used for the semi-active control studies, including the proposed decentralized sliding mode control (DSMC), LQR control, and passive-on and passive-off control. Each control algorithm is formulated specifically for the use of MR dampers installed in building structures. Additionally, each algorithm uses measurements of the device velocity and device drift for the determination of the control action to ensure that the algorithm can be implemented in a physical structure. The performance of each algorithm is evaluated based on the results of shaking table tests, and the advantages of each algorithm are compared and discussed. The reduction of story drifts and floor accelerations throughout the structure is examined.
The purpose of this article is to summarize a computational approach, which developed and matured over an extended period of time, and has been shown to be useful for performing large-eddy Simulation (LES) of flows wi...
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The purpose of this article is to summarize a computational approach, which developed and matured over an extended period of time, and has been shown to be useful for performing large-eddy Simulation (LES) of flows with active control. Because of the nature of active flow control, simulation of this class of problems typically cannot be carried out accurately by methods less sophisticated than LES. Active control flowfields are highly unsteady, and can be characterized by small-scale fluid structures which are produced by the control process, but may also be inherent in the original uncontrolled situation. The numerical scheme is predicated upon an implicit time-marching algorithm, and utilizes a high-order compact finite-difference approximation to represent spatial derivatives. Robustness of the scheme is maintained by employing a low-pass Pade-type nondispersive spatial filter, which also accounts for the fine-scale turbulent dissipation that otherwise is traditionally provided by an explicitly added subgrid-scale (SGS) stress model. Geometrically complex applications are accommodated by an overset grid technique, where spatial accuracy is preserved through use of high-order interpolation. Utility of the method is illustrated by specific computational examples, including suppression of acoustic resonance in supersonic cavity flow, leading-edge vortex control of a delta wing, efficiency enhancement of a transitional highly loaded low-pressure turbine blade, and separation control of a wall-mounted hump model. Control techniques represented in these examples are comprised of both steady and pulsed mass injection or removal, as well as plasma-based actuation. For each case, features of the flowfield are elucidated and the solutions are compared to the baseline situation where no control was enforced. Where available, comparisons are also made with experimental data. Published by Elsevier Ltd.
A class of multiplicative algorithms for computing D-optimal designs for regression models on a finite design space is discussed and a monotonicity result for a sequence of determinants obtained by the iterations is p...
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A class of multiplicative algorithms for computing D-optimal designs for regression models on a finite design space is discussed and a monotonicity result for a sequence of determinants obtained by the iterations is proved. As a consequence the convergence of the sequence of designs to the D-optimal design is established. The class of algorithms is indexed by a real parameter and contains two algorithms considered previously as special cases. Numerical results are provided to demonstrate the efficiency of the proposed methods. Finally, several extensions to other optimality criteria are discussed. (C) 2008 Elsevier B.V. All rights reserved.
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