Musical Structure Analysis with Object Detection Networks

2021 ◽  
Author(s):  
Christopher Uzokwe
Keyword(s):  
Object Detection ◽  
Structure Analysis ◽  
Musical Structure

A New Corpus for Computational Music Research andA Novel Method for Musical Structure Analysis

2021 ◽  
Author(s):  
Filippo Carnovalini ◽  
Antonio Rodà ◽  
Nicholas Harley ◽  
Steven T. Homer ◽  
Geraint A. Wiggins
Keyword(s):  
Structure Analysis ◽  
Musical Structure ◽  
Music Research ◽  
Novel Method

200kv superconducting cryo electron microscope

1987 ◽  
Vol 45 ◽  
pp. 642-643
Author(s):  
M. Iwatsuki ◽  
Y. Kokubo ◽  
Y. Harada ◽  
J. Lehman
Keyword(s):  
Electron Microscope ◽  
Structure Analysis ◽  
Organic Materials ◽  
Strong Interactions ◽  
Specimen Preparation ◽  
Great Effort ◽  
Electron Microscopic ◽  
Crystal Fields ◽  
Special Skill ◽  
Long Time

In recent years, the electron microscope has been significantly improved in resolution and we can obtain routinely atomic-level high resolution images without any special skill. With this improvement, the structure analysis of organic materials has become one of the interesting targets in the biological and polymer crystal fields.Up to now, X-ray structure analysis has been mainly used for such materials. With this method, however, great effort and a long time are required for specimen preparation because of the need for larger crystals. This method can analyze average crystal structure but is insufficient for interpreting it on the atomic or molecular level. The electron microscopic method for organic materials has not only the advantage of specimen preparation but also the capability of providing various information from extremely small specimen regions, using strong interactions between electrons and the substance. On the other hand, however, this strong interaction has a big disadvantage in high radiation damage.

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

RETRIEVE – a system for XRPD phase and structure analysis

2009 ◽  
Vol 2009 (30) ◽  
pp. 209-214 ◽  
Author(s):  
P. S. Dubinin ◽  
I. S. Yakimov ◽  
O. E. Piksina ◽  
Y. I. Yakimov ◽  
A. N. Zaloga
Keyword(s):  
Structure Analysis
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