Wyniki wyszukiwania - BazTech

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Systemy mrówkowe w zastosowaniu do rozwiązania problemu problemu komiwojażera

Modrzejewski M.

Studia i Materiały Informatyki Stosowanej

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2009

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nr 1

65-76

PL

Artykuł porusza dwa zagadnienia. Pierwsze określane jest jako problem komiwojażera popularnie nazywanego TSP (z ang. Traveling Salesman Problem), oraz systemy mrówkowe (z ang. Ant Systems) jako przedstawiciel nowatorskiego podejścia do rozwiązywania problemów optymalizacyjnych z grupy NP-trudnych. Problem TSP jest zagadnieniem optymalizacyjnym polegającym na znalezieniu drogi o najmniejszym koszcie dla wyznaczonej przez komiwojażera trasy. Systemy mrówkowe są to algorytmy wzorujące się na świece przyrody, a konkretniej na sposobie organizacji kolonii mrówek w poszukiwaniu najkrótszej drogi z mrowiska do pokarmu i z powrotem. Artykuł ma za zadanie zapoznać czytelnika z dwoma zakreślonymi powyżej zagadnieniami, zaprezentować zastosowanie systemów mrówkowych do rozwiązania TSP, zbadać efektywność algorytmów mrówkowych oraz algorytmów klasycznych w poszukiwaniu optimum dla określonych problemów TSP oraz przedstawić otrzymane wyniki wraz z wnioskami końcowymi. Dodatkową częścią artykułu są kierunki dalszych badań, jakie są podejmowane przez naukowców, przy wykorzystaniu filozofii systemów mrówkowych.

EN

The paper discusses two problems. The first one is known as the Travelling Salesman Problem (TSP), whereas the second one is defined as the Ant Systems being the representative of innovative attitude to solving optimization problems belonging to the NPhard group. The TSP problem is an optimizing issue that consists in finding the lowest cost travelling way for the route specified by the n travelling salesman. The ant systems are algorithms pattered after the nature, more specifically, after the way an ant colony is organized is order to find the shortest way from the anthill to food and back. The aim of the paper is to familiarize readers with the above two problems, to present application of ant systems to solve the TSP, to examine efficiency of ant algorithms and classical algorithms when searching for the optimum for specific TSP problems as well as to present obtained results together with final conclusions. As an additional part of this paper, the author presented further directions of research undertaken by scientists using philosophy of ant systems

2

Study of effects of surgical treatment in the larynx area on the speech signal

Wszołek W., Kłaczyński M., Modrzejewski M.

Archives of Acoustics

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2008

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Vol. 33, No. 4

497-508

EN

The speech signal emitted by humans may be a source of useful diagnostic and prognostic information. The signal may become, indirectly by some selected parameters, an additional source of information concerning the condition of the patient's vocal tract anatomy, as well as physiology and pathology (deformation) of his/her other internal organs. The paper presents the next, consecutive stage of the authors' research, concerning the search for additional parameters, which could be used for objective detection and registration of pathological changes in the larynx and vocal tract area.

3

Application of new acoustic parameters in ANN-aided pathological speech diagnosis

Szaleniec J., Modrzejewski M., Szaleniec M., Wszołek W.

Archives of Acoustics

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2007

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Vol. 32, No. 1

177-186

EN

Most diseases of the vocal tract cause changes in the voice quality. Acoustic analysis of the speech signal is a widely used, noninvasive, objective and low-cost method of laryngeal pathology recognition and classification. There have been numerous attempts [1-3] to develop an automatic system which could aid the laryngological diagnosis. The goal of the presented research is to verify, whether the new approach to the acoustic analysis and parameters introduced in the Voice Analysis and Screening System (VASS 3.0 [4]) such as turbulence noise index (TNI) and normalized first harmonic energy (NFHE), can improve the effectiveness of automated diagnosis. The automated diagnosis was performed using Artificial Neural Networks (ANN). Multilayer perceptron and radial basis function neural networks of various architectures were trained to classify between pathologic and non-pathologic voices, while the parameters computed with VASS were used as input data. Preliminary results show that the Voice Analysis and Screening System coupled with ANN can be a highly effective tool for ANN-aided pathological speech diagnosis.