Zobrazeno 1 - 10
of 766
pro vyhledávání: '"Space transformation"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved c
Externí odkaz:
https://doaj.org/article/e084530f151c4ef8a5c18fc9fa8ae85e
Publikováno v:
Fashion and Textiles, Vol 11, Iss 1, Pp 1-21 (2024)
Abstract To realize the three-dimensional structure simulation and dress simulation of the fully-fashioned knitted skirt, based on the study of the structural characteristics of the fully-fashioned knitted skirt, the mathematical model of the knitted
Externí odkaz:
https://doaj.org/article/79ed1269432d444abbe815d081dd7b2e
Autor:
A. A. Salnikova, T. V. Afanasieva
Publikováno v:
Ученые записки Казанского университета: Серия Гуманитарные науки, Vol 166, Iss 2, Pp 147-161 (2024)
This article explores how «Vechernyaya Kazan’» (‘Evening Kazan’), a socio-political newspaper specializing in urban affairs, shaped the celebratory image of Kazan for its 1000th anniversary by publishing a variety of multi-genre materials in
Externí odkaz:
https://doaj.org/article/7b1593cda67f42f99979b6e3f2e0e99f
Publikováno v:
IEEE Access, Vol 12, Pp 107944-107958 (2024)
Machine fault diagnosis for expensive high-capacity machines may be challenging as it is infeasible to induce faults and collect data. In this work, we propose a scalable fault modeling approach to address this challenge. The proposed method utilizes
Externí odkaz:
https://doaj.org/article/200d7156df6e457b8f19f7793f815a3f
Autor:
Veronica Zelli, Andrea Manno, Chiara Compagnoni, Rasheed Oyewole Ibraheem, Francesca Zazzeroni, Edoardo Alesse, Fabrizio Rossi, Claudio Arbib, Alessandra Tessitore
Publikováno v:
Journal of Translational Medicine, Vol 21, Iss 1, Pp 1-14 (2023)
Abstract Background Machine learning (ML) represents a powerful tool to capture relationships between molecular alterations and cancer types and to extract biological information. Here, we developed a plain ML model aimed at distinguishing cancer typ
Externí odkaz:
https://doaj.org/article/288ef954cc5d4dd28645dbf634c63cbe
Publikováno v:
SoftwareX, Vol 25, Iss , Pp 101623- (2024)
The goal of the linear law-based feature space transformation (LLT) algorithm is to assist with the classification of univariate and multivariate time series. The presented R package, called LLT, implements this algorithm in a flexible yet user-frien
Externí odkaz:
https://doaj.org/article/dbabf758b4af4056bf54976a7a214f45
Analysis and Research on Color Encoding and K-means Clustering Algorithm in Lingnan Landscape Design
Autor:
Tan Hongyan
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
As a prominent visual phenomenon, the color environment influences the landscape design of the Lingnan region in three aspects: shape, color, and texture. The purpose of this paper is to examine the landscape nature of color by analyzing the psycholo
Externí odkaz:
https://doaj.org/article/f2c986f43497421fadfebcea1b4c2fee
Autor:
Pratt, Geraldine1
Publikováno v:
Geographical Review. Oct2017, Vol. 107 Issue 4, pe58-e60. 3p.
Autor:
Viktor Stovba, Oleksandr Zhmud
Publikováno v:
Кібернетика та комп'ютерні технології, Iss 4, Pp 45-55 (2022)
Introduction. Minimization of ravine convex functions, both smooth and non-smooth, arises in many problems of planning, control, stability analysis of dynamic systems, artificial intelligence, and machine learning. Therefore, the development of new a
Externí odkaz:
https://doaj.org/article/8014d1d6d2264210b4c2efe2dbe110f7
Publikováno v:
Al-Mustansiriyah Journal of Science, Vol 34, Iss 3 (2023)
This work focuses on fused color images resulting from motion blur (left and right) with a blur block size of 11 pixels. The color conversion process was performed from RGB color space (Red, Green, Blue) to HSV (Hue, Saturation, Value), L*a*b*, and Y
Externí odkaz:
https://doaj.org/article/f228d750dcfa4819a95611b60001e01a