Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 4

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: big data analysis

Sortuj według:

Ogranicz wyniki do:

Research on intelligent avoidance method of shipwreck based on bigdata analysis

100%

Li W. , Huang Q.

tom S 3

113--120

In order to solve the problem that current avoidance method of shipwreck has the problem of low success rate of avoidance, this paper proposes a method of intelligent avoidance of shipwreck based on big data analysis. Firstly,our method used big data analysis to calculate the safe distance of approach of ship under the head-on situation, the crossing situation and the overtaking situation.On this basis, by calculating the risk-degree of collision of ships,our research determined the degree of immediate danger of ships.Finally, we calculated the three kinds of evaluation function of ship navigation, and used genetic algorithm to realize the intelligent avoidance of shipwreck.Experimental result shows that compared the proposed method with the traditional method in two in a recent meeting when the distance to closest point of approach between two ships is 0.13nmile, they can effectively evade.The success rate of avoidance is high.

Understanding the Impact of COVID–19 on Global Financial Network Using Graph Based Algorithm: Minimum Spanning Tree Approach

75%

Hatipoğlu V. F.

tom Vol. 46, No. 1

111--123

In this paper effects of COVID–19 pandemic on stock market network are analyzed by an application of operational research with a mathematical approach. For this purpose two minimum spanning trees for each time period namely before and during COVID–19 pandemic are constructed. Dynamic time warping algorithm is used to measure the similarity between each time series of the investigated stock markets. Then, clusters of investigated stock markets are constructed. Numerical values of the topology evaluation for each cluster and time period is computed.

Fenomén #scicomm: využití big data a jejich analýzy při sledování komunikace vědy na internetových stránkách českých výzkumných organizací

75%

Raudenská P. , Topinková R.

nr 4

387-415

This study focused on science communication on the websites of Czech research institutions. Particularly, we inquired to what extent Czech science is shared with the public on the Internet and what differences can be found between the websites of social and natural science institutions. Textual analysis revealed that on the scientific websites, terms like ‘science’ and ‘popularization’ occurred together with references to scientific institutions, study, and research. In the case of natural sciences, the term ‘popularization’ was more often linked to receiving science awards for science popularization and promotion. Structural web analysis showed that most scientific webs contained hyperlinks to social media such as Facebook, Twitter, YouTube, Instagram, and LinkedIn. Similarly, they often referred to online news outlets such as ceskatelevize.cz, novinky.cz, lidovky.cz, and rozhlas.cz. On the other side, they much less often referred to institutional and government websites. The results suggested that Czech science communication can be characterized as more interactive than canonical.

An effective data reduction model for machine emergency state detection from big data tree topology structures

51%

Iaremko I. , Senkerik R. , Jasek R. , Lukastik P.

tom Vol. 31, no. 4

601--611

This work presents an original model for detecting machine tool anomalies and emergency states through operation data processing. The paper is focused on an elastic hierarchical system for effective data reduction and classification, which encompasses several modules. Firstly, principal component analysis (PCA) is used to perform data reduction of many input signals from big data tree topology structures into two signals representing all of them. Then the technique for segmentation of operating machine data based on dynamic time distortion and hierarchical clustering is used to calculate signal accident characteristics using classifiers such as the maximum level change, a signal trend, the variance of residuals, and others. Data segmentation and analysis techniques enable effective and robust detection of operating machine tool anomalies and emergency states due to almost real-time data collection from strategically placed sensors and results collected from previous production cycles. The emergency state detection model described in this paper could be beneficial for improving the production process, increasing production efficiency by detecting and minimizing machine tool error conditions, as well as improving product quality and overall equipment productivity. The proposed model was tested on H-630 and H-50 machine tools in a real production environment of the Tajmac-ZPS company.