Application of Object—Attribute Space Segmentation in Bidding Activities

LISS 2020 ◽  
2021 ◽  
pp. 649-663
Author(s):  
Yijie Yin ◽  
Yuwen Huo ◽  
Yaoyu Hu
enadakultura ◽  
2021 ◽  
Author(s):  
Eliso Koridze

The class of modern German prepositions is not a closed system. In it the old linguistic units are constantly disappearing and new ones are emerging. The preposition in the row of speech parts is presented next to orher, auxiliary and unchangable word classes. Unlike them, the preposition has the abilikty to manage. The pokisemy of prepositions determaines the siversity of their meaning and functioning. This issue es especially interesting at the syntax level. The proposal is not an independent member, but is always part of any member of the proposal. It can appear with object, attribute and adverbial modifier. But in thid case the decisive role is playes by the factor belongigng to the pre-existing old, new named unit. The management of the prepositions is conditioned not onle by the turnover but by the preposition itself, which id directly related to the distribution. In this case it is related to both autosemnatic and synesemantic words, but the actualization of the preposition is mainly influenced by the full-meaning word – verb.


Vestnik MEI ◽  
2021 ◽  
pp. 100-107
Author(s):  
Yuliya S. Aleksandrova ◽  
◽  
Dmitriy A. Balarev ◽  
Oleg S. Kolosov ◽  
Anna V. Ovivyan ◽  
...  

The technology of testing dynamically and structurally similar aircraft models for flutter in subsonic wind tunnels using information and The article addresses techniques for setting up the attribute space of informative features of periodic signals recorded at the output of a dynamic object with an unknown structure in response to rectangular testing signals of different frequencies applied to the object input. The attribute space is used in developing expert systems for diagnosing the current state of an operating dynamic object. With a great variety of possible developing faults, the simplest practical techniques involving the use of characteristic points of change in the observed time dependencies yield a limited number of features with large mutual intersection domains. To expand the attribute space, it is proposed to use the expansion of input and output signals into a Fourier series for setting up a base of additional features. The proposed features characterize, depending on the testing conditions, the object’s transferring properties in the frequency domain from changes in its amplitude and phase characteristics. The test pulse frequency and duration serve as such conditions. For the convenience of comparing the object’s frequency responses variation pattern, two special procedures are used. The first procedure allows the observed time dependencies to be reduced to a single pseudo frequency of the test signals. The second procedure uses specially formed windows for subjecting individual fragments of the observed time dependencies to a spectral analysis. It is shown that, depending on the type of the frequency responses being analyzed, the techniques for their polynomial approximation, as well as integral estimates of frequency response individual domains can be useful. The polynomial approximation makes it possible to use the coefficients of the approximating polynomials as additional features, and the integration of individual characteristic domains of the frequency responses makes it possible to introduce dimensionless relative indicators that characterize the degree of change in the frequency responses depending on the experimental conditions. The considered techniques open the possibility to select additional features that can help distinguish both separate groups of faults and individual faults in operating objects. The study results are illustrated by the examples of analyzing the changes in electroretinograms that record changes in the eye retina biopotential in response to light flashes of different frequencies.


2020 ◽  
Vol 106 ◽  
pp. 107467
Author(s):  
M. Babai ◽  
N. Kalantar-Nayestanaki ◽  
J.G. Messchendorp ◽  
M.H.F. Wilkinson
Keyword(s):  

2014 ◽  
Vol 551 ◽  
pp. 302-308
Author(s):  
Tao Guo ◽  
Gui Yang Li

In multi-label classification, each training example is associated with a set of labels and the task for classification is to predict the proper label set for each unseen instance. Recently, multi-label classification methods mainly focus on exploiting the label correlations to improve the accuracy of individual multi-label learner. In this paper, an improved method derived from binary relevance named double layer classifier chaining (DCC) is proposed. This algorithm decomposes the multi-label classification problem into two stages classification process to generate classifier chain. Each classifier in the chain is responsible for learning and predicting the binary association of the label given the attribute space, augmented by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. Experiments on benchmark datasets validate the effectiveness of proposed approach comparing with other well-established methods.


Sign in / Sign up

Export Citation Format

Share Document