A passivity‐based simple adaptive synergetic control for a class of nonlinear systems

For a class of nonlinear systems with the representation {A(x), B(x), C} and where the system parameters and dynamics are unknown, a simple adaptive synergetic controller ensuring the asymptotic convergence of the system to a desired manifold is proposed based on the technique of simple adaptive control (SAC). It is well known that the design of the synergetic control (SC) law requires a thorough knowledge of the system parameters and dynamics. Such problem obstructs the synthesis of the SC law and the designer is prompted to pass through the estimation methods, which, in turn, poses a problem of increasing the computation time of the control algorithm. To cope with this problem, a solution is proposed by modifying the original SC law to develop an SAC‐like adaptive SC law without the need of prior knowledge of the system. The stability of the proposed adaptive controller is formally proven via the Lyapunov approach. Experimental application to a quadrotor system is given to validate the theoretical results.     

Distribution system reconfiguration using a modified Tabu Search algorithm

This article presents an efficient meta-heuristic method for reconfiguration of distribution systems. A modified Tabu Search (MTS) algorithm is used to reconfigure distribution systems so that active power losses are globally minimized with turning on/off sectionalizing switches. TS algorithm is introduced with some modifications such as using a tabu list with variable size according to the system size. Also, a random multiplicative move is used in the search process to diversify the search toward unexplored regions. The Kirchhoff algebraic method is adopted to check the radial topology of the system. A salient feature of the MTS method is that it can quickly provide a global optimal or near-optimal solution to the network reconfiguration problem. To verify the effectiveness of the proposed approach, the effect of load variation is taken into consideration and comparative studies are conducted on three test systems with rather encouraging results. The obtained results, using the proposed MTS approach, are compared with that obtained using other approaches in the previous work.     

Stabilization of controlled positive discrete‐time T‐S fuzzy systems by state feedback control

This paper deals with sufficient conditions of asymptotic stability and stabilization for nonlinear discrete‐time systems represented by a Takagi–Sugeno‐type fuzzy model whose state variables take only nonnegative values at all times t for any nonnegative initial state. This class of systems is called positive systems. The conditions of stabilizability are obtained with state feedback control. This work is based on multiple Lyapunov functions. The results are presented in linear matrix inequalities form. A real plant is studied to illustrate this technique. Copyright © 2010 John Wiley & Sons, Ltd.     

Pole assignment of multivariable systems using proportional-derivative state feedback

In this article, the parametric solution to the pole assignment problem for multivariable linear time-invariant systems controlled by proportional-derivative (PD) state feedback is developed. The new expressions for the PD gain controllers are derived which describe the available degrees of freedom offered by PD state feedback. The freedom provided by PD state gain matrices is utilised to obtain closed-loop systems with robust and small gain matrices. Two computational algorithms are introduced, and their effectiveness is demonstrated by two simulation examples.     

Positive sliding mode control for blood glucose regulation

Biological systems involving positive variables as concentrations are some examples of so-called positive systems. This is the case of the glycemia–insulinemia system considered in this paper. To cope with these physical constraints, it is shown that a positive sliding mode control (SMC) can be designed for glycemia regulation. The largest positive invariant set (PIS) is obtained for the insulinemia subsystem in open and closed loop. The existence of a positive SMC for glycemia regulation is shown here for the first time. Necessary conditions to design the sliding surface and the discontinuity gain are derived to guarantee a positive SMC for the insulin dynamics. SMC is designed to be positive everywhere in the largest closed-loop PIS of plasma insulin system. Two-stage SMC is employed; the last stage SMC2 block uses the glycemia error to design the desired insulin trajectory. Then the plasma insulin state is forced to track the reference via SMC1. The resulting desired insulin trajectory is the required virtual control input of the glycemia system to eliminate blood glucose (BG) error. The positive control is tested in silico on type-1 diabetic patients model derived from real-life clinical data.     

A large vocabulary system for Arabic online handwriting recognition

The success of using Hidden Markov Models (HMMs) for speech recognition application has motivated the adoption of these models for handwriting recognition especially the online handwriting that has large similarity with the speech signal as a sequential process. Some languages such as Arabic, Farsi and Urdo include large number of delayed strokes that are written above or below most letters and usually written delayed in time. These delayed strokes represent a modeling challenge for the conventional left-right HMM that is commonly used for Automatic Speech Recognition (ASR) systems. In this paper, we introduce a new approach for handling delayed strokes in Arabic online handwriting recognition using HMMs. We also show that several modeling approaches such as context based tri-grapheme models, speaker adaptive training and discriminative training that are currently used in most state-of-the-art ASR systems can provide similar performance improvement for Hand Writing Recognition (HWR) systems. Finally, we show that using a multi-pass decoder that use the computationally less expensive models in the early passes can provide an Arabic large vocabulary HWR system with practical decoding time. We evaluated the performance of our proposed Arabic HWR system using two databases of small and large lexicons. For the small lexicon data set, our system achieved competing results compared to the best reported state-of-the-art Arabic HWR systems. For the large lexicon, our system achieved promising results (accuracy and time) for a vocabulary size of 64k words with the possibility of adapting the models for specific writers to get even better results.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

Stabilisation of controlled positive delayed continuous-time systems

This article solves the stabilisation problem by output feedback control for linear continuous-time systems with delay. This technique can be applied for both positive systems and controlled positive systems (systems becoming positive in closed-loop). The synthesis of feedback controllers is solved in terms of linear programs, extending the solution also to stabilisation with bounded control and stabilisation by controllers with the memory of the delayed state. The proposed conditions do not involve the value of the delay. Some examples illustrate the proposed approach.     

A frame-based approach for capturing semantics from Arabic text for text-to-sign language MT

This paper describes the design and implementation of a computational model for Arabic natural language semantics, a semantic parser for capturing the deep semantic representation of Arabic text. The parser represents a major part of an Interlingua-based machine translation system for translating Arabic text into Sign Language. The parser follows a frame-based analysis to capture the overall meaning of Arabic text into a formal representation suitable for NLP applications that need for deep semantics representation, such as language generation and machine translation. We will show the representational power of this theory for the semantic analysis of texts in Arabic, a language which differs substantially from English in several ways. We will also show that the integration of WordNet and FrameNet in a single unified knowledge resource can improve disambiguation accuracy. Furthermore, we will propose a rule based algorithm to generate an equivalent Arabic FrameNet, using a lexical resource alignment of FrameNet1.3 LUs and WordNet3.0 synsets for English Language. A pilot study of motion and location verbs was carried out in order to test our system. Our corpus is made up of more than 2000 Arabic sentences in the domain of motion events collected from Algerian first level educational Arabic books and other relevant Arabic corpora.     

Electrocardiogram Signal Denoising Using Discrete Wavelet Transform

The most common noises in ECG （electrocardiogram） signal processing are BW （baseline wandering） and the 50 or 60 Hz PLI （power line interferences）. In order to remove these two major source of noises, we have used the recent powerful DWT （discrete wavelet transform） signal processing in ECG signals which are obtained from MIT-BIH Arrhythmia Database. The results indicate that DWT is a good method for filtering noises without changing the morphology of ECG, and can be applied to all types of ECG signals, whether normal or presenting arrhythmias.