Online Computation of Hysteresis Boundary for Constant Switching Frequency Current-Error Space-Vector-Based Hysteresis Controller for VSI-Fed IM Drives

This paper proposes a current-error space-vector-based hysteresis controller with online computation of boundary for two-level inverter-fed induction motor (IM) drives. The proposed hysteresis controller has got all advantages of conventional current-error space-vector-based hysteresis controllers like quick transient response, simplicity, adjacent voltage vector switching, etc. Major advantage of the proposed controller-based voltage-source-inverters-fed drive is that phase voltage frequency spectrum produced is exactly similar to that of a constant switching frequency space-vector pulsewidth modulated (SVPWM) inverter. In this proposed hysteresis controller, stator voltages along alpha- and beta-axes are estimated during zero and active voltage vector periods using current errors along alpha- and beta-axes and steady-state model of IM. Online computation of hysteresis boundary is carried out using estimated stator voltages in the proposed hysteresis controller. The proposed scheme is simple and capable of taking inverter upto six-step-mode operation, if demanded by drive system. The proposed hysteresis-controller-based inverter-fed drive scheme is experimentally verified. The steady state and transient performance of the proposed scheme is extensively tested. The experimental results are giving constant frequency spectrum for phase voltage similar to that of constant frequency SVPWM inverter-fed drive.

Network congestion management by fuzzy inference using virtual flows

Effective network overload alleviation is very much essential in order to maintain security and integrity from the operational viewpoint of deregulated power systems. This paper aims at developing a methodology to reschedule the active power generation from the sources in order to manage the network congestion under normal/contingency conditions. An effective method has been proposed using fuzzy rule based inference system. Using virtual flows concept, which provides partial contributions/counter flows in the network elements is used as a basis in the proposed method to manage network congestions to the possible extent. The proposed method is illustrated on a sample 6 bus test system and on modified IEEE 39 bus system.

Lexicon-free, novel segmentation of online handwritten indic words

Research in the field of recognizing unlimited vocabulary, online handwritten Indic words is still in its infancy. Most of the focus so far has been in the area of isolated character recognition. In the context of lexicon-free recognition of words, one of the primary issues to be addressed is that of segmentation. As a preliminary attempt, this paper proposes a novel script-independent, lexicon-free method for segmenting online handwritten words to their constituent symbols. Feedback strategies, inspired from neuroscience studies, are proposed for improving the segmentation. The segmentation strategy has been tested on an exhaustive set of 10000 Tamil words collected from a large number of writers. The results show that better segmentation improves the overall recognition performance of the handwriting system.

Language models for online handwritten Tamil word recognition

N-gram language models and lexicon-based word-recognition are popular methods in the literature to improve recognition accuracies of online and offline handwritten data. However, there are very few works that deal with application of these techniques on online Tamil handwritten data. In this paper, we explore methods of developing symbol-level language models and a lexicon from a large Tamil text corpus and their application to improving symbol and word recognition accuracies. On a test database of around 2000 words, we find that bigram language models improve symbol (3%) and word recognition (8%) accuracies and while lexicon methods offer much greater improvements (30%) in terms of word recognition, there is a large dependency on choosing the right lexicon. For comparison to lexicon and language model based methods, we have also explored re-evaluation techniques which involve the use of expert classifiers to improve symbol and word recognition accuracies.

Hybrid online non-negative matrix factorization for clustering of documents

When document corpus is very large, we often need to reduce the number of features. But it is not possible to apply conventional Non-negative Matrix Factorization(NMF) on billion by million matrix as the matrix may not fit in memory. Here we present novel Online NMF algorithm. Using Online NMF, we reduced original high-dimensional space to low-dimensional space. Then we cluster all the documents in reduced dimension using k-means algorithm. We experimentally show that by processing small subsets of documents we will be able to achieve good performance. The method proposed outperforms existing algorithms.

Inference for the Component and System Lifetime Distribution of a k-unit Parallel System Based on System Data

In this paper, we consider the inference for the component and system lifetime distribution of a k-unit parallel system with independent components based on system data. The components are assumed to have identical Weibull distribution. We obtain the maximum likelihood estimates of the unknown parameters based on system data. The Fisher information matrix has been derived. We propose -expectation tolerance interval and -content -level tolerance interval for the life distribution of the system. Performance of the estimators and tolerance intervals is investigated via simulation study. A simulated dataset is analyzed for illustration.

Feature Search in the Grassmanian in Online Reinforcement Learning

We consider the problem of finding the best features for value function approximation in reinforcement learning and develop an online algorithm to optimize the mean square Bellman error objective. For any given feature value, our algorithm performs gradient search in the parameter space via a residual gradient scheme and, on a slower timescale, also performs gradient search in the Grassman manifold of features. We present a proof of convergence of our algorithm. We show empirical results using our algorithm as well as a similar algorithm that uses temporal difference learning in place of the residual gradient scheme for the faster timescale updates.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.

Nearly Constant Switching Frequency Hysteresis Current Controller with Fast Online Computation of Boundary for a 2-Level Induction Motor Drive

A nearly constant switching frequency current hysteresis controller for a 2-level inverter fed induction motor drive is proposed in this paper: The salient features of this controller are fast dynamics for the current, inherent protection against overloads and less switching frequency variation. The large variation of switching frequency as in the conventional hysteresis controller is avoided by defining a current-error boundary which is obtained from the current-error trajectory of the standard space vector PWM. The current-error boundary is computed at every sampling interval based on the induction machine parameters and from the estimated fundamental stator voltage. The stator currents are always monitored and when the current-error exceeds the boundary, voltage space vector is switched to reduce the current-error. The proposed boundary computation algorithm is applicable in linear and over-modulation region and it is simple to implement in any standard digital signal processor: Detailed experimental verification is done using a 7.5 kW induction motor and the results are given to show the performance of the drive at various operating conditions and validate the proposed advantages.

Hysteresis current controller for a general n-level inverter fed drive with online current error boundary computation and nearly constant switching frequency

A space vector-based hysteresis current controller for any general n-level three phase inverter fed induction motor drive is proposed in this study. It offers fast dynamics, inherent overload protection and low harmonic distortion for the phase voltages and currents. The controller performs online current error boundary calculations and a nearly constant switching frequency is obtained throughout the linear modulation range. The proposed scheme uses only the adjacent voltage vectors of the present sector, similar to space vector pulse-width modulation and exhibits fast dynamic behaviour under different transient conditions. The steps involved in the boundary calculation include the estimation of phase voltages from the current ripple, computation of switching time and voltage error vectors. Experimental results are given to show the performance of the drive at various speeds, effect of sudden change of the load, acceleration, speed reversal and validate the proposed advantages.

Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques

In this article, we aim at reducing the error rate of the online Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary support vector machine classifier. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants. Secondly, a dynamic time-warping method is proposed to automatically extract the discriminative regions for each set of confused characters. Class-specific features derived from these regions aid in reducing the degree of confusion. Thirdly, statistics of specific features are proposed for resolving any confusions in vowel modifiers. The reevaluation approaches are tested on two databases (a) the isolated Tamil symbols in the IWFHR test set, and (b) the symbols segmented from a set of 10,000 Tamil words. The recognition rate of the isolated test symbols of the IWFHR database improves by 1.9 %. For the word database, the incorporation of the reevaluation step improves the symbol recognition rate by 3.5 % (from 88.4 to 91.9 %). This, in turn, boosts the word recognition rate by 11.9 % (from 65.0 to 76.9 %). The reduction in the word error rate has been achieved using a generic approach, without the incorporation of language models.

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

Online Estimation of Evolving Human Visual Interest

Regions in video streams attracting human interest contribute significantly to human understanding of the video. Being able to predict salient and informative Regions of Interest (ROIs) through a sequence of eye movements is a challenging problem. Applications such as content-aware retargeting of videos to different aspect ratios while preserving informative regions and smart insertion of dialog (closed-caption text) into the video stream can significantly be improved using the predicted ROIs. We propose an interactive human-in-the-loop framework to model eye movements and predict visual saliency into yet-unseen frames. Eye tracking and video content are used to model visual attention in a manner that accounts for important eye-gaze characteristics such as temporal discontinuities due to sudden eye movements, noise, and behavioral artifacts. A novel statistical-and algorithm-based method gaze buffering is proposed for eye-gaze analysis and its fusion with content-based features. Our robust saliency prediction is instantiated for two challenging and exciting applications. The first application alters video aspect ratios on-the-fly using content-aware video retargeting, thus making them suitable for a variety of display sizes. The second application dynamically localizes active speakers and places dialog captions on-the-fly in the video stream. Our method ensures that dialogs are faithful to active speaker locations and do not interfere with salient content in the video stream. Our framework naturally accommodates personalisation of the application to suit biases and preferences of individual users.

De novo inference of protein function from coarse-grained dynamics

Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high-throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural dynamics match for a pair of segments from two dissimilar proteins, which may share even <10% sequence identity. To screen these matches, corresponding 1 mu s coarse-grained (CG) molecular dynamics trajectories were used to compute normalized root-mean-square-fluctuation graphs and select mobile segments, which were, thereafter, matched for all pairs using unweighted three-dimensional autocorrelation vectors. Our in-house custom-built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG dynamics trajectories. The test for correspondence of dynamics-signature of protein segments and function revealed 87% true positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof-of-principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom-made CG FF, useful to all. (C) 2014 Wiley Periodicals, Inc.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.