Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Support Vector Machines (SVM)" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-6 z 6
    Tytuł:
    Wykrywanie uszkodzeń węzłów w modelu ramy stalowej na podstawie analizy inertancji
    Detection of defects connection between members of steel frame on the basis of FRF changes
    Autorzy:
    Ziaja, D.
    Miller, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/105271.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Rzeszowska im. Ignacego Łukasiewicza. Oficyna Wydawnicza
    Tematy:
    detekcja uszkodzeń
    SHM
    FRF
    Support Vector Machines
    SVM
    image detection
    Opis:
    W artykule przedstawiono możliwość detekcji uszkodzeń węzłów na podstawie analizy proporcji pomiędzy wytypowanymi fragmentami funkcji przejścia (FRF). W ramach zadania wykonano eksperyment na modelu laboratoryjnym dwukondygnacyjnej ramy portalowej, którą poddano testom dynamicznym i dla której określono model modalny. Funkcję przejścia odpowiadającą wybranym punktom układu potraktowano jako sygnał w dziedzinie częstotliwości. Wyznaczono odcięte środków ciężkości kwadratów sygnału wybranych fragmentów funkcji, które następnie potraktowano jako dane wejściowe w metodzie wektorów nośnych. Zaproponowane podejście umożliwia skuteczną detekcję uszkodzeń węzłów badanego modelu.
    The article presents the possibility of nodes failures detecting based on the analysis of the proportions between the selected intervals of FRF function. Within the scope of the task an experiment was performed on the laboratory model of a two-storey portal frame, which was subjected to dynamic tests and for which a modal model was defined. FRF function for selected system points was treated as a signal in the frequency domain. For the relevant fragments, the centers of gravity of the signal squares were determined, which were then used as input data in the Support Vector Machines (SVM) method. The proposed approach enables effective detection of connection damage in the tested structure.
    Źródło:
    Czasopismo Inżynierii Lądowej, Środowiska i Architektury; 2017, 64, 2/I; 247-255
    2300-5130
    2300-8903
    Pojawia się w:
    Czasopismo Inżynierii Lądowej, Środowiska i Architektury
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Parameter identification of ship maneuvering models using recursive least square method based on support vector machines
    Autorzy:
    Zhu, M.
    Hahn, A.
    Wen, Y.
    Bolles, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/116455.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    ship manoeuvering
    recursive least square method
    ship manoeuvering model
    ship maneuverability prediction
    Support Vector Machines (SVM)
    empirical mode decomposition (EMD)
    Computational Fluid Dynamics (CFD)
    Extended Kalman Filter (EKF)
    Opis:
    Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS), are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM), is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD) are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2017, 11, 1; 23-29
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adaptation of Evolutionary Algorithms for Decision Making on Building Construction Engineering (TSP Problem)
    Autorzy:
    Wazirali, R. A.
    Alzughaibi, A. D.
    Chaczko, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/226730.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    TSP
    genetic algorithms
    GA
    support vector machines
    SVM
    Opis:
    The report revolve on building construction engineering and management, in which there are a lot of requirements such as well supervision and accuracy and being in position to forecast uncertainties that may arise and mechanisms to solve them. It also focuses on the way the building and construction can minimise the cost of building and wastages of materials. The project will be based of heuristic methods of Artificial Intelligence (AI). There are various evolution methods, but report focus on two experiments Pattern Recognition and Travelling Salesman Problem (TSP). The Pattern Recognition focuses Evolutionary Support Vector Machine Inference System for Construction Management. The construction is very dynamic are has a lot of uncertainties, no exact data this implies that the inference should change according to the environment so that it can fit the reality, therefore there a need of Support Vector Machine Inference System to solve these problems. TSP focus on reducing cost of building construction engineering and also reduces material wastages, through its principals of finding the minimum cost path of the salesman.
    Źródło:
    International Journal of Electronics and Telecommunications; 2014, 60, 1; 125-128
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Symulacyjna ocena jakości zagregowanych modeli zbudowanych metodą wektorów nośnych
    Benchmarking Aggregated Support Vector Regression Models
    Autorzy:
    Trzęsiok, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/588038.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Metoda wektorów nośnych (SVM)
    Modele matematyczne
    Symulacja
    Mathematical models
    Simulation
    Support Vector Machines (SVM)
    Opis:
    Support Vector Machines (SVM) are a state-of-the-art classification method, but they are also suitable, after a special reformulation, to perform a regression task. Similarly to classification, for a nonlinear regression problem, SVMs use the kernel trick and map the input space into a high-dimensional feature space first, and then perform linear regression in the high-dimensional feature space. One can use the model ensemble approach to try to improve the prediction accuracy. The paper presents the comparison of a single SVM, aggregated SVM and other regression models (linear regression, Projection Pursuit Regression, Neural Networks, Regression Trees, Random Forest, Bagging) by the means of a mean squared test set error.
    Źródło:
    Studia Ekonomiczne; 2013, 132; 115-126
    2083-8611
    Pojawia się w:
    Studia Ekonomiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A two-layer neural system for reduced-reference visual quality assessment
    Autorzy:
    Redi, J.
    Gastaldo, P.
    Zunino, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/91584.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    reduced-reference system
    image
    visual quality assessmen
    luminance distribution
    visual distortions
    support vector machines
    SVM
    Opis:
    Real-time assessment of visual quality can be efficiently supported by reduced-refe-rence paradigms, which require a very limited amount of information on the original signal, easily embeddable in the signal itself. In this paper, a reduced-reference system for image quality assessment is proposed, based on a small sized numerical description of images encoding the luminance distribution and its variations due to visual distortions. The assessment paradigm is implemented exploiting machine learning tools and articulates in two phases: first, a Support Vector Machines-based classifier identifies the kind of distortion affecting the image; then, the actual quality level of the distorted image is computed by a specifically trained SVM regressor. The general validity of the approach is supported by experimental validations based on subjective quality data.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 27-41
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using multi-objective affinity model for mining the rules of revisits within 72 hours for emergency department patients
    Autorzy:
    Chao-Wen, Chen
    Yuh-Wen, Chen
    Moussa, Larbani
    Tzung-Hung, Li
    Powiązania:
    https://bibliotekanauki.pl/articles/578510.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Data Mining
    Leczenie
    Macierze
    Metoda wektorów nośnych (SVM)
    Placówki służby zdrowia
    Matrix
    Medical facilities
    Medical treatment
    Support Vector Machines (SVM)
    Opis:
    When patients return to the emergency department (ED) within 72 hours after their previous ED discharge, it is generally assumed that their initial evaluation or treatment had been somehow inadequate. Mining data related to unplanned ED revisits is one method to determine whether this problem can be overcome, and to generate useful guidelines in this regard. In this study, we use the receiver operating characteristic (ROC) curve to compare the data mining model by affinity set to other well known approaches. Some scholars have validated the affinity model for its simplicity and power in handling information systems especially when showing binary consequences. In experimental results, SVM showed the best performance, with the affinity model following only slightly behind. This study demonstrated that when patients visit the ED with normotensive status or smooth breath patterns, or when the physician-patient ratio is moderate, the frequency with which patients revisit the ED is significantly higher.
    Źródło:
    Multiple Criteria Decision Making; 2015, 10; 5-31
    2084-1531
    Pojawia się w:
    Multiple Criteria Decision Making
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies