Prediction of the crash vehicle using machine learning methods

• Autorzy: Aleksander M. B. , Janisz K. , Karpinski M. , Yavorskyi A. , Yavorska N.

Warianty tytułu

PL

Metody uczenia maszynowego w przewidywaniach awarii pojazdu

• Języki publikacji: EN

Abstrakty

EN

The article refers to the prediction of failure of the vehicle based on an analysis of data from sensors installed on the vehicle.

PL

Artykuł dotyczy przewidywania awarii pojazdu na podstawie analizy danych z czujników zainstalowanych w pojeździe.

Słowa kluczowe

EN

failure of the vehicle; data analysis; sensors

PL

awaria pojazdu; analiza danych; czujniki

• Wydawca: Instytut Naukowo-Wydawniczy "SPATIUM". sp. z o.o.
• Czasopismo: Autobusy : technika, eksploatacja, systemy transportowe
• Rocznik: 2016
• Tom: R. 17, nr 6
• Strony: 734--739
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., pełen tekst na CD

Twórcy

autor

Aleksander M. B.

• State Higher Vocational School in Nowy Sącz

autor

Janisz K.

• State Higher Vocational School in Nowy Sącz

autor

Karpinski M.

• University of Bielsko-Biała

autor

Yavorskyi A.

• Ternopil Ivan Puluj National Technical University

autor

Yavorska N.

• Ternopil Ivan Puluj National Technical University

Bibliografia

• 1. Thomas Mitchell. Machine Learning. - McGraw-Hill, New York, 1997. - ISBN: 0070428077 9780070428072.
• 2. P. T. Spellman. Data analysis discussed by M. B. Eisen, P. T. Spellman, P. O. Brown, D. Botstein. - Proc. Natl. Acad. Sci. U.S.A, 2008. - 95, 14863.
• 3. M. Turmon, S, Mukhtar, J. Pap, in Proceedings of the Third Conference on Knowledge Discovery and Data Mining ( AAAI Press, Newport Beach, CA, 2003), pp. 267-270.
• 4. P. Townend: A High-Assurance Service-Oriented Grid Middleware System [Text] / P. Townend, J. Xu, N. Looker, D. Zhang // IEEE Computer Society. - 2008. - V. 41, No 8, - pp. 30 -37.
• 5. A. Avizienis. Dependability of computer systems: Fundamental concepts, terminology, and examples [Text] / A. Avizienis, J.C. Laprie, B. Randell // LAAS Report No., UCLA Report No., Newcastle No. - 2000.
• 6. John G. H., Kohavi R., Pfleger K. Irrelevant features and the subset selection problem / Proceedings of ICML-94, 11th International Conference on Machine Learning // New Brunswick, US. - 2009. - pp. 121-129.
• 7. Biebricher P., Fuhr N., Knorz G., Lustig G., Schwantner M. The automatic indexing system AIR/PHYS. From research to application. / Proceedings of SIGIR-88, 11th ACM International Conference on Research and Development in Information Retrieval // Grenoble, FR. - 2010. - pp. 333-342.
• 8. Kim Y.H., Hahn S.Y., Zhang B.T. Text filtering by boosting naive Bayes classifiers / Proceedings of SIGIR-00, 23rd ACM International Conference on Research and Development in Information Retrieval // Athens, GR. - 2000. - pp. 168-75.
• 9. Lewis D. D. Naive Bayes at forty: The independence assumption in information retrieval / Proceedings of ECML-98, 10th European Conference on Machine Learning // Chemnitz, DE. - 2009. - pp. 4-15.
• 10. Hua Yue (College of Information Science and Technology Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China) Xu Tao (College of Computer Science and Technology Civil Aviation University of China, Tianjin, 300300, China); Web Services Security Problem in Service-oriented Architecture[A];[C];2010.
• 11. OASIS Standard 200401. Web Services Security UsernameToken Profile 1.0. - http://docs.oasisopen.org/wss/2004/01/oasis-200401-wss-username-token-profile-1.0.
• 12. Chomsiri T. HTTPS Hacking Protection / Chomsiri T. // Advanced Information Networking and Applications Workshops. - 2007. - №21 - С. 77-80.
• 13. IBM Predictive Maintenance and Quality 2.0 Technical Overview.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.