Thermodynamic characterization of high-temperature superconductors in the yttrium-barium-copper-oxygen system. The Y123 solid solution (Technical Report)

2000 ◽  
Vol 72 (3) ◽  
pp. 463-477 ◽  
Author(s):  
G. F. Voronin
Keyword(s):  
Solid Solution ◽  
Gibbs Energy ◽  
Thermodynamic Data ◽  
High Temperature Superconductors ◽  
Gibbs Energy Function ◽  
Data Set ◽  
Y123 Phase ◽  
Barium Copper ◽  
Self Consistent ◽  
Consistent Thermodynamic Data

The aim of this report is to inform the chemical community about a self-consistent thermodynamic data set for the YBa2 Cu3 O6+z (1 ≥ z ≥ 0) solid solution, that is well known as the Y123 phase and possesses superconducting properties at z~1 and low temperatures. About 3300 experimental points obtained in 240 miscellaneous experiments published in 78 papers have been processed simultaneously in order to obtain the most reliable Gibbs energy function of the Y123 phase in the temperature range from 250 to 1300 K and pressures up to 100 kbar. A function is recommended for approximation of the Gibbs energy, which has 16 adjustable parameters. All other thermodynamic properties of the Y123 solution, including the conditions for its internal stability, can be derived from the assessed Gibbs energy. Brief descriptions of the thermodynamic model, experimental and data assessment methods as well as examples of self-consistent thermodynamic data applications are given.

Real-time unstructured road detection based on CNN and Gibbs energy function

2021 ◽  
pp. 1-19
Author(s):  
Mingzhou Liu ◽  
Xin Xu ◽  
Jing Hu ◽  
Qiannan Jiang
Keyword(s):  
Gibbs Energy ◽  
Energy Function ◽  
Gaussian Mixture Models ◽  
Texture Features ◽  
Gaussian Mixture ◽  
Gibbs Energy Function ◽  
Road Detection ◽  
The Road ◽  
Detection Algorithms ◽  
Random Sample Consensus

Road detection algorithms with high robustness as well as timeliness are the basis for developing intelligent assisted driving systems. To improve the robustness as well as the timeliness of unstructured road detection, a new algorithm is proposed in this paper. First, for the first frame in the video, the homography matrix H is estimated based on the improved random sample consensus (RANSAC) algorithm for different regions in the image, and the features of H are automatically extracted using convolutional neural network (CNN), which in turn enables road detection. Secondly, in order to improve the rate of subsequent similar frame detection, the color as well as texture features of the road are extracted from the detection results of the first frame, and the corresponding Gaussian mixture models (GMMs) are constructed based on Orchard-Bouman, and then the Gibbs energy function is used to achieve road detection in subsequent frames. Finally, the above algorithm is verified in a real unstructured road scene, and the experimental results show that the algorithm is 98.4% accurate and can process 58 frames per second with 1024×960 pixels.

scholarly journals N and Fe-Peak Elemental Abundances from IUE Co-Added Spectra of Hg-Mn and Normal Stars

1993 ◽  
Vol 138 ◽  
pp. 154-160
Author(s):  
Scott W. Roby ◽  
Saul J. Adelman ◽  
David S. Leckrone ◽  
Charles Cowley ◽  
Glenn M. Wahlgren
Keyword(s):  
Optical Spectra ◽  
High Quality ◽  
Term Study ◽  
Data Set ◽  
B Stars ◽  
Elemental Abundances ◽  
Long Term Study ◽  
Self Consistent ◽  
Good Agreement

AbstractLeckrone and Adelman have established an IUE observing strategy that has yielded co-added spectra with enhanced S/N ratios for several A and B stars. New observations by Roby and Adelman using the same technique have added two new Hg-Mn stars into this sample. We have begun a long-term study of elemental abundances in this uniform, high-quality set of IUE spectra for 13 stars. We report on the first stages of this project: abundances for N, Cr, Mn, Fe, Co, and Ni. The study of the Fe- peak elements show that our data set can provide accurate abundances and that abundances obtained from UV and optical spectra often are in good agreement. This study provides the groundwork for self-consistent abundance analyses of more exotic elements in our long term project.

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