The paper deals with a model based fault diagnosis for a catalytic cracking converter process realized using artificial neural networks. Modelling of the considered process is carried out by using a locally recurrent ...
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The paper investigates approximation abilities of a special class of discrete-time dynamic neural networks. These networks are called locally recurrent globally feed-forward, because they are designed with dynamic neu...
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In this paper, an active FTC strategy is presented. First, it is developed in the context of linear systems and then it is extended to Takagi-Sugeno fuzzy systems. The key contribution of the proposed approach is an i...
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The paper provides a preview of some work in progress on the computer system to support breast cancer diagnosis. The approach is based on microscope images of the FNB (Fine Needle Biopsy) and assumes distinguishing ma...
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We investigate possibilities of choosing an activation policy of discrete scanning sensors in such a way as to maximize the accuracy of parameter estimation of a distributed system defined in a given multidimensional ...
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This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. As the auto...
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A new approach to identification of Wiener systems by using the instrumental variables method is presented. In this approach, an inverse characteristic of the nonlinear element is represented by a polynomial of a know...
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The paper deals with the problem of designing an unknown input filter for non-linear discrete-time stochastic systems. In particular, it is shown how to design an unknown input filter for a single (constant) unknown i...
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High performance computing is required in a number of data-intensive domains. CPU and GPU clusters are one of the most progressive branches in a field of parallel computing and data processing nowadays. Cloud computin...
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The paper presents a biometric recognition methodology based on hand thermal information. We start with a hardware presentation, specially designed for this research in a form of thermal sensor plate delivering hand t...
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ISBN:
(纸本)9781479903115
The paper presents a biometric recognition methodology based on hand thermal information. We start with a hardware presentation, specially designed for this research in a form of thermal sensor plate delivering hand thermal maps, which is a significantly cheaper alternative to thermal cameras. We use a heuristic feature selection technique employing mutual information (mRMR) and well known space transformation methods (PCA and its combination with the LDA) to develop optimal biometric features by selecting those parts of the hand, which deliver the most discriminating personal information. Two different classifiers (k-NN and SVM) are applied and evaluated with a database of band thermal maps captured for 50 different individuals in three sessions: two at the same day (enrollment attempts), and the third captured a week apart (verification attempt). We achieved 6.67% of an average equal error rate (EER), what suggests that temperature distribution of an inner part of human hand is individual. This may serve as e.g. supporting modality of two-modal biometric recognition (merged with hand geometry or palm print techniques), or may be a good candidate for hand liveness detection approach, as hand thermal maps are difficult to be copied and reconstructed on an artificial object imitating a human hand. To our best knowledge, this is the first work presenting the use of a human hand thermal maps as a direct source of biometric features.
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