Fuzzy Quantification-Based Linguistic Summaries in Data Cubes with Hierarchical Fuzzy Partition of Time Dimension

Author(s):  
Rita Castillo-Ortega ◽  
Nicolás Marín ◽  
Daniel Sánchez
2011 ◽  
Vol 26 (10) ◽  
pp. 1002-1021 ◽  
Author(s):  
Rita Castillo-Ortega ◽  
Nicolás Marín ◽  
Daniel Sánchez

1994 ◽  
Vol 39 (2) ◽  
pp. 158-159
Author(s):  
Roy F. Baumeister

Author(s):  
S. Chef ◽  
C. T. Chua ◽  
C. L. Gan

Abstract Limited spatial resolution and low signal to noise ratio are some of the main challenges in optical signal observation, especially for photon emission microscopy. As dynamic emission signals are generated in a 3D space, the use of the time dimension in addition to space enables a better localization of switching events. It can actually be used to infer information with a precision above the resolution limits of the acquired signals. Taking advantage of this property, we report on a post-acquisition processing scheme to generate emission images with a better image resolution than the initial acquisition.


Author(s):  
E. E. Akimkina

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.


2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.


Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 626
Author(s):  
Ramya Gupta ◽  
Abhishek Prasad ◽  
Suresh Babu ◽  
Gitanjali Yadav

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both scientific and social standpoints. Despite several documented similarities, the coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20,000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. Unlike the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic to study changes in thematic foci and societal pressures dictating research over the course of a crisis.


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