scholarly journals Composite Method Employing Pseudopotential at CCSD(T) Level

10.5772/35591 ◽  
2012 ◽  
Author(s):  
Nelson Henrique
Keyword(s):  
Composite Method

Composite method for precise freeform optical beam shaping

2015 ◽  
Vol 54 (31) ◽  
pp. 9364 ◽  
Author(s):  
Zexin Feng ◽  
Brittany D. Froese ◽  
Rongguang Liang
Keyword(s):  
Beam Shaping ◽  
Optical Beam ◽  
Composite Method

Fabrication of X-ray absorption grating by thermal composite method

2021 ◽  
Author(s):  
wu Hao ◽  
Yang Jun ◽  
Zong Fang Ke ◽  
Luo Kun Hao ◽  
Zhen Jing Biao ◽  
...  
Keyword(s):  
X Ray ◽  
Composite Method ◽  
X Ray Absorption

scholarly journals 224 Automatic image feature extraction for genetic analysis in cattle

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 47-47
Author(s):  
Jessica Nye ◽  
Laura Zingaretti ◽  
Miguel Perez-Enciso
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Genetic Analysis ◽  
Coat Color ◽  
Large Data ◽  
Image Feature ◽  
Background Information ◽  
Pedigree Data ◽  
Require Time ◽  
Composite Method

Abstract Image analysis has increasingly become an important tool for increasing productivity in many industries, yet its application in breeding programs is under utilized. With coat color patterns from dairy bull images, we explore automatic image analysis that extracts features which can be used in genetic analysis. In order to remove the unnecessary background information, the current methods require time consuming human inspection. Here, we present and compare a composite method that creates a mask (i.e., removes the background portion of the image) and calculates the proportion of dark and light coloration in bulls (n = 657) from the breeds Holstein and Ayrshire in dynamic backgrounds (e.g., forest, grass, hay, snow, etc.). This composite method combines the supervised algorithm MASK-RCNN, an unsupervised image segmentation approach, and k-means color clustering. The first step identifies the region of interest removing the majority of the background noise, while the second and third steps optimize the identification of the bull and segments the color patterning. We find a very low discrepancy between the proportion of white and dark between the manual curation and the composite method (+/- 1.40%); with an immense reduction in data collection time. This automatic composite method greatly improves the efficiency of complex image segmentation and analysis without compromising the quality of the data extracted, making analysis computationally feasible for large data sets. The next step is to calculate genetic parameters from these extracted phenotypes with genomic and/or pedigree data.

Template-Free Fabrication of Silicon Micropillar Structures by a One-Step Masking/MACE Composite Method

2020 ◽  
Vol 3 (10) ◽  
pp. 9889-9896
Author(s):  
Siqi Yu ◽  
Zihua Wu ◽  
Fen Feng ◽  
Fuyuan Luan ◽  
Yuanyuan Wang ◽  
...  
Keyword(s):  
Composite Method ◽  
One Step ◽  
Template Free

scholarly journals A Composite Method for Predicting Local Accuracies in Remotely Sensed Land-Cover Change Using Largely Non-Collocated Sample Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2818
Author(s):  
Yingying Mei ◽  
Jingxiong Zhang ◽  
Wangle Zhang ◽  
Fengzhu Liu
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Cross Correlation ◽  
Cross Validation ◽  
Reference Sample ◽  
Alternative Methods ◽  
Multiplication Operators ◽  
Product Rule ◽  
Composite Method ◽  
Sample Data

As in conventional error matrix-based accuracy assessments, collocated reference sample data are often used for characterizing per-pixel (local) accuracies in land-cover change maps so that local accuracy predictions can be made using direct methods. In that way, correctness in “from-to” change categorization at sample pixels is assessed and modeled directly. To circumvent the issue of reference sample data being non-collocated, as is often the case for sample data collected independently for mono-temporal reference land-cover labeling or those added necessarily to reflect landscape changes, the PXCOV (Product rule with adjustment for cross-COVariance between single-date classification correctness) method was developed previously. However, the use of PXCOV becomes complicated when few or no collocated sample data are available and cross-validation cokriging, a procedure involving non-trivial geostatistical modeling, has to be incurred for estimation of cross-correlation. To overcome PXCOV’s lack of practicality when using mostly non-collocated sample data, this paper presents a simple alternative. It is furnished through stratified approximation of cross-correlation and features combined use of minimum and multiplication operators. Specifically, in this composite method (named Fuzzy+Product), minimum operator (resembling fuzzy set “min” operator and thus named Fuzzy) is applied over no-change pixels stratum where maximum correlation is assumed, while multiplication operator (i.e., product rule named Product) is applied for change pixels stratum where cross-correlation is assumed negligible (i.e., minimum correlation), without having to run cross-validation cokriging as in PXCOV. Studies were undertaken to test the proposed method based on datasets collected previously concerning GlobeLand30 2000 and 2010 land-cover at five sites in China. For each site, five model-training samples (being mostly non-collocated) of equal sizes and one independent model-testing sample (collocated) were used. Logistic regression models fitted with relevant sample data were applied to predict local accuracies in single-date classifications using selected map class occurrence pattern indices quantified in optimized moving windows. The area under the curve (AUC) of the receiver operating characteristic was used for evaluating alternative methods. Empirical results confirmed that method Fuzzy+Product is more accurate than both Fuzzy and Product in general and there are no statistically significant differences between it and PXCOV. This indicates Fuzzy+Product being a method of relative simplicity but reasonable accuracy when reference data are non-collocated or mostly so. Its value is likely best manifested when local and global accuracy characterization in multi-temporal change information (discrete and fractional) is concerned.

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