Construction of mapping functions for nonsymmetric defects with different kinds of angular reentry points on a contour

In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. We parameterize the volumetric mapping using a convolutional network and train it in an unsupervised way by leveraging the spatial transformer to minimize the gap between the warped volumetric image and the template grid. Instead of learning the unidirectional map, we learn the nonlinear mapping functions for both forward and backward transformations. We introduce the combinational inverse constraints for the volumetric one-to-one maps, where the pairwise and triple constraints are utilized to learn the cycle-consistent correspondence maps between volumes. Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques.

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Evaluation of Wet Mapping Functions Used in Modeling Tropospheric Propagation Delay Effect on GPS Measurements

The Open Atmospheric Science Journal ◽

10.2174/1874282301711010001 ◽

2017 ◽

Vol 11 (1) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Sobhy Abdel-Monam Younes

Keyword(s):

A Priori ◽

Mapping Function ◽

Propagation Delay ◽

Radiosonde Data ◽

Tropospheric Delay ◽

Delay Effect ◽

Gps Measurements ◽

Mapping Functions ◽

Significant Bias ◽

Mapping Techniques

Background:The author compares several methods to map the a priori wet tropospheric delay of GNSS signals in Egypt from the zenith direction to lower elevations.Methods and Materials:The author compared the following mapping techniques against ray-traced delays computed for radiosonde profiles under the assumption of spherical symmetry: Saastamoinen, Hopfield, Black, Chao, Ifadis, Herring, Niell, Moffett, Black and Eisner and UNBabc mapping functions. Radiosonde data were computed from radiosonde stations at the Egyptian stations; in the south of Egypt, near the Mediterranean Sea, and near the Red Sea over a period of 5 years (2000-2005), most of the stations launched radiosonde twice daily, every day of the year. Moreover, data is received from the Egyptian Meteorology Authority.Results and Conclusion:The results indicate that currently, the saastamoinen mapping function should be used for all geodetic applications in Egypt, and if necessary, the Chao and Moffett mapping functions can serve as an acceptable replacement without introducing a significant bias into the station position.

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PELABELAN TOTAL TITIK AJAIB PADA GRAF PETERSEN

Mathematics Education Journal ◽

10.22219/mej.v1i1.4549 ◽

2017 ◽

Vol 1 (1) ◽

pp. 44

Author(s):

Chusnul Noeriansyah Poetri

Keyword(s):

Mapping Functions ◽

Vertex Set ◽

Positive Integers

Suppose a graph G with vertex set V(G) and the edge set E(G) where each vertex V(G) and edge E(G) is given a one - one function and on the mapping functions using positive integers {1,2, … ,

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TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v7.i6.2019.761 ◽

2019 ◽

Vol 7 (6) ◽

pp. 77-83 ◽

Cited By ~ 1

Author(s):

Dr. C. Arunabala ◽

P. Jwalitha ◽

Soniya Nuthalapati

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Sentiment Analysis ◽

Expressive Power ◽

Sentiment Classification ◽

Experimental Results ◽

Analysis Method ◽

Mapping Functions ◽

Generalization Ability ◽

Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

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