Power system control in the next century

The present evolution of power systems is making operational control increasingly difficult and this paper identifies reactive power and voltage control as one of the prime culprits. Energy storage devices incorporating microprocessors and power electronics, coupled through a communications network provide an opportunity to enhance existing control measures. The continuing fast access to the energy storage will permit direct control measures for reducing the variations of consumer demand throughout the total grid. This approach has the potential of overcoming the growing technical and financial difficulties now facing large interconnected power systems. The projected introduction of electric vehicles in cities will be able to provide the battery energy storage capacity for overcoming the difficulties of power system control     

Extraction of Bistable-Percept-Related Features From Local Field Potential by Integration of Local Regression and Common Spatial Patterns

Bistable perception arises when an ambiguous stimulus under continuous view is perceived as an alternation of two mutually exclusive states. Such a stimulus provides a unique opportunity for understanding the neural basis of visual perception because it dissociates the perception from the visual input. In this paper, we focus on extracting the percept-related features from the local field potential (LFP) in monkey visual cortex for decoding its bistable structure-from-motion (SFM) perception. Our proposed feature extraction approach consists of two stages. First, we estimate and remove from each LFP trial the nonpercept-related stimulus-evoked activity via a local regression method called the locally weighted scatterplot smoothing because of the dissociation between the perception and the stimulus in our experimental paradigm. Second, we use the common spatial patterns approach to design spatial filters based on the residue signals of multiple channels to extract the percept-related features. We exploit a support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We apply the proposed approach to the multichannel intracortical LFP data collected from the middle temporal (MT) visual cortex in a macaque monkey performing an SFM task. We demonstrate that our approach is effective in extracting the discriminative features of the percept-related activity from LFP and achieves excellent decoding performance. We also find that the enhanced gamma band synchronization and reduced alpha and beta band desynchronization may be the underpinnings of the percept-related activity.     

An interval-amplitude algorithm for deinterleaving stochastic pulsetrain sources

We consider the deinterleaving of pulse trains transmitted by N independent sources. The deinterleaving problem considered has applications in spectral estimation, where N (known number) stochastic parameterized sources are sampled using a fast sensor recording the sign of the signal from each source. Due to communication constraints, the recorded signals-pulse trains or sequences of zeros and ones-are superimposed and transmitted through a single Gaussian communication channel. The aim of this paper is to estimate the parameters that characterize the sources and identify those sources that are responsible for the observed noisy pulses. Our proposed algorithm, subject to modeling assumptions, optimally combines hidden Markov model and binary time series estimation techniques and yields maximum likelihood parameter estimates of the sources     

Off-line quality control and ill-designed data

‘Bad’ data resulting from ill-designed experiments or in-production/after-production monitoring require a careful use of proper statistical techniques for their analysis. Prior exploration of the available data is of paramout importance especially if we wish to apply off-line quality control techniques such as the Taguchi method. A stepwise approach is proposed involving data analysis and straightforward significance tests, which can ensure statistically valid and useful conclusions.     

The role of data transformation in taguchi analysis

This paper reports on the usefulness of data transformation in a proper application of the ‘Taguchi method’ for off-line quality control. With the joint aim of establishing a meaningful measure of performance variability and reducing the risk of incorrectly assuming no interactions in the performance model, a simple method of choosing appropriate data transformations is presented. This provides the basis for a suggested general procedure for carrying out Taguchi analyses. The technique is assessed by considering how it performs on data taken from previously published applications of the Taguchi method.     

Stochastic sampling algorithms for state estimation of jump Markovlinear systems

Jump Markov linear systems are linear systems whose parameters evolve with time according to a finite-state Markov chain. Given a set of observations, our aim is to estimate the states of the finite-state Markov chain and the continuous (in space) states of the linear system. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain or the state of the jump Markov linear system grows exponentially in the number of observations. We present three globally convergent algorithms based on stochastic sampling methods for state estimation of jump Markov linear systems. The cost per iteration is linear in the data length. The first proposed algorithm is a data augmentation (DA) scheme that yields conditional mean state estimates. The second proposed scheme is a stochastic annealing (SA) version of DA that computes the joint MAP sequence estimate of the finite and continuous states. Finally, a Metropolis-Hastings DA scheme based on SA is designed to yield the MAP estimate of the finite-state Markov chain. Convergence results of the three above-mentioned stochastic algorithms are obtained. Computer simulations are carried out to evaluate the performances of the proposed algorithms. The problem of estimating a sparse signal developing from a neutron sensor based on a set of noisy data from a neutron sensor and the problem of narrow-band interference suppression in spread spectrum code-division multiple-access (CDMA) systems are considered     

Network planning based on virtual path bandwidth management

Periodical performance evaluation and adaptive resource assignment, already proposed as performance-oriented management, seems to be the most suitable strategy for network planning under demand uncertainty. In this paper, we exploit the inherent capability of ATM networks to rearrange dynamically the already installed resources, and propose performance-oriented management combined with virtual path bandwidth (VPB) control for the planning of the extensions of bandwidth capacities of virtual paths (VPs) and transmission links of the network. We define a large network optimization problem and solve it by a rigorous, analytical procedure. The optimization model comprises specific requirements of the network-planning problem and a bandwidth distribution scheme ensuring network reliability. We reveal the efficiency of the proposed scheme by applying it on a model network, considering two realistic case-studies of network-traffic evolution. We show that in the presence of VPB control: (a) the initial distribution of the total bandwidth to VPs is of no importance, since it can be adaptively rearranged according to the offered traffic, (b) the network is well used and bandwidth investment could be saved, and (c) whenever additional bandwidth must be installed in VPs which have an unanticipated bad grade-of-service, time savings result. We present the network performance in detail, in figures, and compare this with the performance of the network in the absence of VPB control.     

Iterative and recursive estimators for hidden Markoverrors-in-variables models

In this paper we propose maximum-likelihood (ML) estimation of errors in variables models with finite-state Markovian disturbances. Such models have applications in econometrics, speech processing, communication systems, and neurobiological signal processing. We derive the maximum likelihood (ML) model estimates using the expectation maximization (EM) algorithm. Then two recursive or “on-line” estimation schemes are derived for estimating such models. The first on-line algorithm is based on the EM algorithm and uses stochastic approximations to maximize the Kullback-Leibler (KL) information measure. The second on-line algorithm we propose is a gradient-based scheme and uses stochastic approximations to maximize the log likelihood     

Maximum likelihood parameter estimation from incomplete data viathe sensitivity equations: the continuous-time case

This paper deals with maximum likelihood (ML) parameter estimation of continuous-time nonlinear partially observed stochastic systems, via the expectation maximization (EM) algorithm. It is shown that the EM algorithm can be executed efficiently, provided the unnormalized conditional density of nonlinear filtering is either explicitly solvable or numerically implemented. The methodology exploits the relationships between incomplete and complete data, log-likelihood and its gradient