Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

• Autorzy: Wodziński M. , Krzyżanowska A.

Identyfikatory

DOI

10.1515/mms-2017-0021

• Języki publikacji: EN

Abstrakty

EN

This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU- and GPU-based solutions.

Słowa kluczowe

EN

machine learning; shortest path; sequential data; quadrocopter; GPU; CUDA

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2017
• Tom: Vol. 24, nr 2
• Strony: 265--276
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wykr., wzory

Twórcy

autor

Wodziński M.

• AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics Computer Science and Biomedical Engineering, A. Mickiewicza 30, Cracow, Poland

autor

Krzyżanowska A.

• AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics Computer Science and Biomedical Engineering, A. Mickiewicza 30, Cracow, Poland

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).