Application of Neural Network in Predicting Forging Hardness

2020 ◽  
Vol 305 ◽  
pp. 169-177
Author(s):  
Yuan Ping Luh ◽  
Chang Hung Tu ◽  
Huang Li Wang ◽  
Chien Jung Chiu
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
User Interface ◽  
Graphical User Interface ◽  
Cold Forging ◽  
High Quality ◽  
Simple Program ◽  
Experimental Parameters ◽  
Neck Diameter ◽  
The Relationship

This study used cold forging to produce car components with the desired hardness without using postprocessing methods such as heat treatment, surface blasting, and polishing. Material diameter, mold neck diameter, and hardness of the material after annealing were used as parameters, and nine sets of experimental parameters were obtained using the Taguchi method. Under the premise of high-quality forging, this study determined the optimal hardness and the relationship between formation parameters and forging hardness. By inputting the hardness data obtained from the Taguchi L9 orthogonal array into a Matlab-implemented neural network, this study determined a hardness formula. Finally, using the inbuilt graphical user interface software of Matlab, a simple program was written that can be used to predict hardness and that could extensively reduce the costs and time associated with forging development.

scholarly journals AUTOMATIC IDENTIFICATION OF MINERALS IN IMAGES OF POLISHED SECTIONS

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 113-118
Author(s):  
A. V. Khvostikov ◽  
D. M. Korshunov ◽  
A. S. Krylov ◽  
M. A. Boguslavskiy
Keyword(s):  
Neural Network ◽  
User Interface ◽  
Graphical User Interface ◽  
Software Tool ◽  
Semantic Segmentation ◽  
Automatic Analysis ◽  
Automatic Identification ◽  
Polished Section ◽  
High Quality ◽  
Mineral Associations

Abstract. Automatic identification of minerals in images of polished section is highly demanded in exploratory geology as it can provide a significant reduction in time spent in the study of ores and eliminate the factor of misdiagnosis of minerals. The development of algorithms for automatic analysis of images of polished sections makes it possible to create of a universal tool for comparing ores from different deposits, which is also much in demand. The main contribution of this paper can be summed up in three parts: i) creation of LumenStone dataset (https://imaging.cs.msu.ru/en/research/geology/lumenstone) which unites high-quality geological images of different mineral associations and provides pixel-level semantic segmentation masks, ii) development of CNN-based neural network for automatic identification of minerals in images of polished sections, iii) implementation of software tool with graphical user interface that can be used by expert geologists to perform an automatic analysis of polished sections images.

Teaching Java™

2011 ◽  
pp. 185-201
Author(s):  
Henry H. Emurian
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Test Performance ◽  
Self Efficacy ◽  
Learning Processes ◽  
Instructional Materials ◽  
Classroom Observations ◽  
Simple Program ◽  
Hands On ◽  
Hands On Learning

Information systems students in a graduate section and an undergraduate section of an introductory Java graphical user interface course completed the following initial assignments to learn a simple program: (1) automated programmed instruction tutoring, (2) hands-on learning with a lecture, and (3) collaborative peer tutoring. Tests of knowledge transfer and software self-efficacy were administered before students began the first assignment and following completion of each one. The results showed progressive improvement in rule test performance and software self-efficacy across the several instructional events. Taken together, the results of these classroom observations extend the generality of previous work to an updated set of instructional materials and assignments, and that outcome shows the reliability of the learning processes with new groups of students. Students who are new to Java had the privilege of exposure to an initial repertoire of teaching tactics that are synergistic and cumulative.

scholarly journals Cell-Free DNA Pattern Identification and its Classification using Convolution Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5362-5364
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
User Interface ◽  
Graphical User Interface ◽  
Probabilistic Method ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Free Dna ◽  
Dna Pattern

Circulating cell DNA (cfDNA) design identification assumes a cardinal job in fetal drug, transplantation and oncology. Be that as it may, it has additionally demonstrated to be a biomarker for different maladies. There are numerous order strategies by which the acknowledgment and arrangement should be possible. So as to have a superior time unpredictability and improve the precision further, this strategy targets distinguishing and arranging the general DNA examples and ailments related with them utilizing cfDNA Images in a Convolution Neural Network. A probabilistic method is used for cfDNA image feature extraction, fragmentation and interpretation. Graphical User Interface is the platform where this method is employed since it uses visual indicators in place of text-based interface. The aftereffects of this test demonstrate that the Convolution Neural Network calculation can perceive cfDNA successions accurately and successfully with no dubiety. Prepared with examples, the CNN can effectively characterize the picture surrendered to coordinated and unparalleled examples with numerical highlights.

scholarly journals The antiSMASH database version 3: increased taxonomic coverage and new query features for modular enzymes

2020 ◽  
Vol 49 (D1) ◽  
pp. D639-D643 ◽  
Author(s):  
Kai Blin ◽  
Simon Shaw ◽  
Satria A Kautsar ◽  
Marnix H Medema ◽  
Tilmann Weber
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
High Quality ◽  
Microbial Genomes ◽  
Fungal Genomes ◽  
Interactive Graphical User Interface

Abstract Microorganisms produce natural products that are frequently used in the development of antibacterial, antiviral, and anticancer drugs, pesticides, herbicides, or fungicides. In recent years, genome mining has evolved into a prominent method to access this potential. antiSMASH is one of the most popular tools for this task. Here, we present version 3 of the antiSMASH database, providing a means to access and query precomputed antiSMASH-5.2-detected biosynthetic gene clusters from representative, publicly available, high-quality microbial genomes via an interactive graphical user interface. In version 3, the database contains 147 517 high quality BGC regions from 388 archaeal, 25 236 bacterial and 177 fungal genomes and is available at https://antismash-db.secondarymetabolites.org/.

X-Seed 4: updates to a program for small-molecule supramolecular crystallography

2020 ◽  
Vol 53 (4) ◽  
pp. 1141-1146 ◽  
Author(s):  
Leonard J. Barbour
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Crystal Packing ◽  
Current Version ◽  
Molecular Graphics ◽  
High Quality ◽  
The Past ◽  
Structure Solution ◽  
User Friendly ◽  
Microsoft Windows

X-Seed is a native Microsoft Windows program with three primary functions: (i) to serve as a graphical user interface to the SHELX suite of programs, (ii) to facilitate exploration of crystal packing and intermolecular interactions, and (iii) to generate high-quality molecular graphics artwork suitable for publication and presentation. Development of X-Seed Version 1.0 began in 1998, when point-and-click crystallographic software was still limited in scope and power. Considerable enhancements have been implemented within X-Seed over the past two decades. Of particular importance are support for the SHELX2019 programs (SHELXS, SHELXD, SHELXT and SHELXL) for structure solution and refinement, and MSRoll for rendering void spaces in crystal structures. The current version (i.e. Version 4) of X-Seed has a new interface designed to be more interactive and user friendly, and the software can be downloaded and used free of charge.

scholarly journals Polo: an open-source graphical user interface for crystallization screening

2021 ◽  
Vol 54 (2) ◽  
Author(s):  
Ethan T. Holleman ◽  
Erica Duguid ◽  
Lisa J. Keefe ◽  
Sarah E. J. Bowman
Keyword(s):  
Neural Network ◽  
Crystal Growth ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Graphical Interface ◽  
Commercial Use ◽  
Crystallization Experiments ◽  
Crystallization Conditions ◽  
General Public License

Polo is a Python-based graphical user interface designed to streamline viewing and analysis of images to monitor crystal growth, with a specific target to enable users of the High-Throughput Crystallization Screening Center at Hauptman-Woodward Medical Research Institute (HWI) to efficiently inspect their crystallization experiments. Polo aims to increase efficiency, reducing time spent manually reviewing crystallization images, and to improve the potential of identifying positive crystallization conditions. Polo provides a streamlined one-click graphical interface for the Machine Recognition of Crystallization Outcomes (MARCO) convolutional neural network for automated image classification, as well as powerful tools to view and score crystallization images, to compare crystallization conditions, and to facilitate collaborative review of crystallization screening results. Crystallization images need not have been captured at HWI to utilize Polo's basic functionality. Polo is free to use and modify for both academic and commercial use under the terms of the copyleft GNU General Public License v3.0.

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