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Time-domain structural analysis of speech

EKŠTEIN, K., MOUČEK, R. Time-domain structural analysis of speech. In Computational Linguistics and Intelligent Text Processing. Berlin : Springer , 2003. s. 506-510. ISBN: 3540005323
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This paper deals with an auxiliary speech signal parametrisation method based on structural analysis of speech signal in time domain. The method called TIDOSA grounds in analysing the "shape" of incoming waveform peaks.

Application of insurance methods in power engineering

VOJTÍŠKOVÁ, M., MAŠKOVÁ, H., NETRVALOVÁ, A., NOVÝ, P., VÁVRA, F., ZMRHAL, D. Application of insurance methods in power engineering. In APLIMAT 2003. Bratislava : Slovak University of Technology , 2003. s. 467-472. ISBN: 8022718130
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Our paper deals with the application of insurance methods in technical disciplines, mainly in power engineering and information technologies. Models used for the description of the cumulative claim are analogous to the probability description of the cumulative powerequipment outage duration. The same might be said about the cumulative costs of failure events clearance, their causes and consequences. We focus on the computational methods for model probabilities and we also present statistical methods of the estimations of model parameters. The counting process of failure events number and the compound point process of failure effects become grounds for our modelling.

Signature verifier based on self-organizing feature map

MAUTNER, P., MATOUŠEK, V., MARŠÁLEK, T. Signature verifier based on self-organizing feature map. In 5th Workshop on self-organizing maps. Paris: Sorbone University , 2005. s. 219-226.
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The Kohonen Self-organizing Feature Map (SOFM) has been developed for the clustering of input vectors and has been commonly used as unsupervised learned classifiers. In~this paper we describe the use of the SOFM neural network model for signature verification. The biometric data of all signatures were acquired by a special digital data acquisition pen and fast wavelet transformation was used for feature extraction. The part of authentic signature data was used for training the SOFM signature verifier. The architecture of the verifier and achieved results are discussed here and ideas for future research are also suggested.

Heuristic model of risk in insurance companies

VÁVRA, F., NOVÝ, P., VOJTÍŠKOVÁ, M., MAŠKOVÁ, H., ZMRHAL, D. Heuristic model of risk in insurance companies. In Modelování a řízení finančních rizik. Ostrava: Vysoká škola báňská - Technická univerzita , 2003. s. 228-233.
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This contribution should present one of the models which are applicable for insurance companies. It arises from an essential formula for a margin of an insurance company.

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