The paper describes set-bounded parameters and state estimation component for Model Predictive control of integrated wastewater treatment plant at medium time scale purposes. This is one of the components within Intel...
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In this paper, a novel controller for parallel-connected online uninterruptible power supplies (UPS) without control interconnections based on the droop method is presented. The control approach consists in drop the f...
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In this paper, a novel controller for parallel-connected online uninterruptible power supplies (UPS) without control interconnections based on the droop method is presented. The control approach consists in drop the frequency of every module when its output power increases, resulting in an unavoidable nominal frequency deviation. Consequently, this method in its original form is only applicable to off-line or line-interactive UPS systems. As opposed to the conventional droop method, the proposed control scheme endows proper transient response, strictly frequency and phase synchronization with the AC mains, and excellent power sharing even for nonlinear loads. Hence, this controller is suitable for paralleled online UPS systems. Experimental results are obtained from two parallel-connected 1-kVA online UPS by using TMS320LF2407A DSP.
Optimising control of wastewater treatment systems (WWTS), allowing for cost savings while fulfilling the effluent discharge limits over long period requires application of advanced control techniques. Model Predictiv...
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The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a g...
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This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to z...
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The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image...
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The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image color content, which can serve as an image digital signature, allowing the efficient indexing and retrieval of large Internet-based multimedia databases. We applied the proposed method using the images from two Internet databases containing a collection of images of fine arts and a database of low resolution images, and show that the new method of image color representation is robust to image distorsions caused by resizing and compression and can be incorporated into existing web-based retrieval systems, that exploit the information on color content of digital images.
Digital watermarking has been researched extensively due to its potential use for data security and copyright protection. Much of the literature has focused on developing invisible watermarking algorithms. However, no...
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Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance th...
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Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance theory (ART), have shown promising results in this task. Most of the existing neural network based document clustering techniques rely on a "bag of words" document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features applied to our proposed document clustering technique, called hierarchical SOMART (HSOMART), which is a hierarchical network built up from independent SOM and ART neural networks. We describe a phrase grammar extraction technique, and the proposed HSOMART. The experimental results of clustering documents from the REUTERS corpus using the extracted phrases as features show an improvement in the clustering performance evaluated using the entropy and F-measure.
The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image...
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In this paper a novel class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter class is based on the nonparametric estimation of the density probability function i...
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