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Wyszukujesz frazę "unsupervised methods" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
An unsupervised approach to leak detection and location in water distribution networks
Autorzy:
Quiñones-Grueiro, M.
Verde, C.
Prieto-Moreno, A.
Llanes-Santiago, O.
Powiązania:
https://bibliotekanauki.pl/articles/330518.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
water distribution network
leak location
unsupervised methods
principal component analysis
demand model
sieć wodociągowa
lokalizacja wycieku
analiza składników głównych
model popytu
Opis:
The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods have shown very promising results mainly because they can deal with uncertain data and the variability of models better than model-based methods. The main contribution of this work is an unsupervised approach to leak detection and location in water distribution networks. This approach is based on a zone division of the network, and it only requires data from a normal operation scenario of the pipe network. The proposition combines a periodic transformation and a data vector extension together with principal component analysis of leak detection. A reconstruction-based contribution index is used for determining the leak zone location. The Hanoi distribution network is employed as the case study for illustrating the feasibility of the proposal. Single leaks are emulated with varying outflow magnitudes at all nodes that represent less than 2.5% of the total demand of the network and between 3% and 25% of the node’s demand. All leaks can be detected within the time interval of a day, and the average classification rate obtained is 85.28% by using only data from three pressure sensors.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2018, 28, 2; 283-295
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Good and bad sociology: does topic modelling make a difference?
Autorzy:
BARANOWSKI, MARIUSZ
CICHOCKI, PIOTR
Powiązania:
https://bibliotekanauki.pl/articles/2028162.pdf
Data publikacji:
2021-12-31
Wydawca:
Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:
unsupervised text analysis
LDA
topic modelling
sociological methods
big data sociology
Opis:
The changing social reality, which is increasingly digitally networked, requires new research methods capable of analysing large bodies of data (including textual data). This development poses a challenge for sociology, whose ambition is primarily to describe and explain social reality. As traditional sociological research methods focus on analysing relatively small data, the existential challenge of today involves the need to embrace new methods and techniques, which enable valuable insights into big volumes of data at speed. One such emerging area of investigation involves the application of Natural Language Processing and Machine-Learning to text mining, which allows for swift analyses of vast bodies of textual content. The paper’s main aim is to probe whether such a novel approach, namely, topic modelling based on Latent Dirichlet Allocation (LDA) algorithm, can find meaningful applications within sociology and whether its adaptation makes sociology perform its tasks better. In order to outline the context of the applicability of LDA in the social sciences and humanities, an analysis of abstracts of articles published in journals indexed in Elsevier’s Scopus database on topic modelling was conducted. This study, based on 1,149 abstracts, showed not only the diversity of topics undertaken by researchers but helped to answer the question of whether sociology using topic modelling is “good” sociology in the sense that it provides opportunities for exploration of topic areas and data that would not otherwise be undertaken.
Źródło:
Society Register; 2021, 5, 4; 7-22
2544-5502
Pojawia się w:
Society Register
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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