Neural Networks and Micromechanics

Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

doi:10.1007/978-3-642-02535-8

Cited by 33 publications

(24 citation statements)

References 34 publications

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“…We have developed several paradigms of neural classifiers [37][38][39][40][41]. We used the proposed neural classifiers for image recognition and applied them to different tasks; for example, we used flat image recognition in the process of microdevice assembly [37] and handwritten digit recognition [38].…”

Section: Permutation Coding Neural Classifiermentioning

confidence: 99%

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Face recognition using a permutation coding neural classifier

Baidyk

Kussul

Monterrosas

et al. 2015

Neural Comput & Applic

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Face recognition is an important security task. We propose a high-level method to solve this problem: a permutation coding neural classifier (PCNC). A PCNC with a special feature extractor for face image recognition systems is a relatively new method that has been tested with good results to classify real environment images (such as larvae of various types and handmade elements). As baseline methods, a support vector machine (SVM) and the iterative closest point (ICP) method are selected for comparison. We applied these methods to gray-level images from the FRAV3D face database. Fifteen experiments were performed to examine a large set of training and testing conditions. As a general result, it was observed that errors are lower with the PCNC than with the SVM and the ICP classifier. We aggregated various distortions for the initial images to improve the PCNC. We analyze and discuss the obtained results.

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Section: Permutation Coding Neural Classifiermentioning

confidence: 99%

“…2, we describe the FRAV3D face image database. Section 3 is devoted to the description of the neural classifiers, especially the permutation coding neural classifier (PCNC), and their applications for face recognition [37][38][39][40][41]. The CMC error calculation [42] is discussed in Sect.…”

Section: Introductionmentioning

confidence: 99%

Face recognition using a permutation coding neural classifier

Baidyk

Kussul

Monterrosas

et al. 2015

Neural Comput & Applic

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“…To manufacture this micro channel recuperator it is possible to use Micro Equipment Technology [14][15][16].…”

Section: Microchannel Recuperatormentioning

confidence: 99%

Design of Ericsson Heat Engine with Micro Channel Recuperator

Kussul

Makeyev

Baidyk

et al. 2012

ISRN Renewable Energy

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Stirling cycle and Rankine cycle heat engines are used to transform the heat energy of solar concentrators to mechanical and electrical energy. The Rankine cycle is used for large-scale solar power plants. The Stirling cycle can be used for small-scale solar power plants. The Stirling cycle heat engine has many advantages such as high efficiencyand long service life. However, the Stirling cycle is good for high-temperature difference. It demands the use of expensive materials. Its efficiency depends on the efficiency of the heat regenerator. The design and manufacture of a heat regenerator are not a trivial problem because the regenerator has to be placed in the internal space of the engine. It is possible to avoid this problem if we place the regenerator out of the internal engine space. To realize this idea it is necessary to develop the Ericsson cycle heat engine. We propose theoretical model and design of this engine.

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“…The LIRA neural classifier is described in detail in [25] - [27]. Here we give only the main characteristics of the LIRA neural classifier.…”

Section: Lira Neural Classifiermentioning

confidence: 99%