Informacja

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

Wyszukujesz frazę "Mahalanobis's method" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
The definition of the area of felling forests by high resolution satellite images
Autorzy:
Burshtynska, K.
Polishchuk, B.
Madyar, J.
Powiązania:
https://bibliotekanauki.pl/articles/100258.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Rolniczy im. Hugona Kołłątaja w Krakowie
Tematy:
remote sensing of the Earth
space image
forest monitoring
classification
method of maximum probability
Mahalanobis's method
method of minimum distance
deforestation
teledetekcja Ziemi
klasyfikacja
monitoring lasu
metoda maksymalnego prawdopodobieństwa
Metoda Mahalanobisa
wylesienie
Opis:
The paper presents a hybrid classification method based on the determination of the optimal number of classes according to uncontrolled classification followed by image processing techniques of controlled classification. A criterion for determining the optimal number of classes is proposed based on the definition of averaged values differences of average spectral brightness among the classes. Space images from satellites Ikonos (2002, 2007) and QuickBird (2010) were used to study different time cuttings in the forests of the Carpathian region. A significant amount of ground observation was held for getting test information. A Hybrid Classification Method is used for different time cuttings by QuickBird satellite images and implemented in a software environment of ERDAS Imagine. In order to obtain acreage of cuttings made for the period of 2002-2007 and 2007-2010, a comparative analysis of cuttings is introduced in these time intervals and their area is determined on the basis of the digital images of polygons in the ArcGIS software environment.
Źródło:
Geomatics, Landmanagement and Landscape; 2014, 3; 43-54
2300-1496
Pojawia się w:
Geomatics, Landmanagement and Landscape
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An approach to identify the interactions between the control factors in a Mahalanobis-Taguchi System
Autorzy:
Labidi, Amal
Tanabe, Ikuo
Tanabe, Yuko
Isobe, Hiromi
Powiązania:
https://bibliotekanauki.pl/articles/2052189.pdf
Data publikacji:
2022
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
MT system
Mahalanobis-Taguchi system
RT method
control
Mahalanobis distance
design of experiment
Opis:
The Mahalanobis-Taguchi System (MTS) is, today, widely used to define the optimal conditions for the design stage of product development especially, in the field of Artificial Intelligence (AI) considering the non-linear properties and non-digital data. In this paper, an approach to identify the several interactions in a MTS is proposed. The MTS contains four methods; Mahalanobis-Taguchi (MT) method, Mahalanobis Taguchi Adjoint (MTA) method, Recognition Taguchi (RT) method and Taguchi (T) method. The method to use for the analysis is selected based on the system’s properties. For the case of study used in this research, the unit space is created through the RT method and used to calculate the Mahalanobis-Taguchi distances (MTD). For the method proposed in this paper, the relationships between control factors and MTDs were firstly clarified by MTS (RT), then the same relationships were clarified using a modified design of experiments method, and the several interactions between control factors in MTS (RT) were finally identified by comparing the two relationships. Then effectiveness of the proposed method was evaluated by using a mathematical model.
Źródło:
Journal of Machine Engineering; 2022, 22, 1; 96-110
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Monte Carlo investigation of two distance measures between statistical populations and their application to cluster analysis
Miary odległości pomiędzy populacjami statystycznymi i ich zastosowanie w analizie skupień - Badanie Monte Carlo
Autorzy:
Rossa, Agnieszka
Powiązania:
https://bibliotekanauki.pl/articles/904614.pdf
Data publikacji:
1997
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
hierarchical cluster analysis methods
robustness of the nearest neighbour method
the Mahalanobis distance
the Kullback-Leibler divergence
the Marczewski-Steinhaus distance measure
Opis:
The paper deals with a simulation study of one of the well-known hierarchical cluster analysis methods applied to classifying the statistical populations. In particular, the problem of clustering the univariate normal populations is studied. Two measures of the distance between statistical populations are considered: the Mahalanobis distance measure which is defined for normally distributed populations under assumption that the covariance matrices are equal and the Kullback-Leibler divergence (the so called Generalized Mahalanobis Distance) the use of which is extended on populations of any distribution. The simulation study is concerned with the set of 15 univariate normal populations, variances of which are chanched during successive steps. The aim is to study robustness of the nearest neighbour method to departure from the variance equality assumption when the Mahalanobis distance formula is applied. The differences between two cluster families, obtained for the same set of populations but with the different distance matrices applied, are studied. The distance between both final cluster sets is measured by means of the Marczewski-Steinhaus distance.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 1997, 141
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

    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