BABIČ, Matej ;SKALA, Karolj ;KUMAR, Dookhitram ;ŠTURM, Roman . New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 64, n.6, p. 393-400, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/new-hybrid-system-of-machine-learning-and-statistical-pattern-recognition-for-a-3d-visibility-network/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2017.5189.
Babič, M., Skala, K., Kumar, D., & Šturm, R. (2018). New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network. Strojniški vestnik - Journal of Mechanical Engineering, 64(6), 393-400. doi:http://dx.doi.org/10.5545/sv-jme.2017.5189
@article{sv-jmesv-jme.2017.5189,
author = {Matej Babič and Karolj Skala and Dookhitram Kumar and Roman Šturm},
title = {New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {64},
number = {6},
year = {2018},
keywords = {fractal geometry; hybrid system; laser hardened specimens; visibility network; statistical pattern recognition},
abstract = {Intelligent systems are an excellent tool to use for solving complex problems in the field of industrial applications. We use the mathematical method of fractal geometry and network theory when laser-hardening techniques are applied. The microstructure of the robot-laser-hardened specimens is very complex; however, we can present it by using a 3D visibility network. We convert the scanning electron microscope (SEM) images of the microstructure to a 3D graph and calculate the density of the visibility network of these 3D networks. We have analyzed the topographical properties of the hardened specimens by using the algorithm for the construction of a visibility network in a 3D space. We develop a new hybrid system of machine learning for predicting carbide content of the hardened specimens by using multiple regression, neural networks, and a genetic algorithm. We find the statistical significance of the relationship between attributes of the hardened specimens, the topological properties of visibility graphs, and carbide content of the hardened specimens.},
issn = {0039-2480}, pages = {393-400}, doi = {10.5545/sv-jme.2017.5189},
url = {https://www.sv-jme.eu/sl/article/new-hybrid-system-of-machine-learning-and-statistical-pattern-recognition-for-a-3d-visibility-network/}
}
Babič, M.,Skala, K.,Kumar, D.,Šturm, R. 2018 June 64. New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 64:6
%A Babič, Matej %A Skala, Karolj %A Kumar, Dookhitram %A Šturm, Roman %D 2018 %T New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network %B 2018 %9 fractal geometry; hybrid system; laser hardened specimens; visibility network; statistical pattern recognition %! New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network %K fractal geometry; hybrid system; laser hardened specimens; visibility network; statistical pattern recognition %X Intelligent systems are an excellent tool to use for solving complex problems in the field of industrial applications. We use the mathematical method of fractal geometry and network theory when laser-hardening techniques are applied. The microstructure of the robot-laser-hardened specimens is very complex; however, we can present it by using a 3D visibility network. We convert the scanning electron microscope (SEM) images of the microstructure to a 3D graph and calculate the density of the visibility network of these 3D networks. We have analyzed the topographical properties of the hardened specimens by using the algorithm for the construction of a visibility network in a 3D space. We develop a new hybrid system of machine learning for predicting carbide content of the hardened specimens by using multiple regression, neural networks, and a genetic algorithm. We find the statistical significance of the relationship between attributes of the hardened specimens, the topological properties of visibility graphs, and carbide content of the hardened specimens. %U https://www.sv-jme.eu/sl/article/new-hybrid-system-of-machine-learning-and-statistical-pattern-recognition-for-a-3d-visibility-network/ %0 Journal Article %R 10.5545/sv-jme.2017.5189 %& 393 %P 8 %J Strojniški vestnik - Journal of Mechanical Engineering %V 64 %N 6 %@ 0039-2480 %8 2018-06-26 %7 2018-06-26
Babič, Matej, Karolj Skala, Dookhitram Kumar, & Roman Šturm. "New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network." Strojniški vestnik - Journal of Mechanical Engineering [Online], 64.6 (2018): 393-400. Web. 19 Nov. 2024
TY - JOUR AU - Babič, Matej AU - Skala, Karolj AU - Kumar, Dookhitram AU - Šturm, Roman PY - 2018 TI - New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2017.5189 KW - fractal geometry; hybrid system; laser hardened specimens; visibility network; statistical pattern recognition N2 - Intelligent systems are an excellent tool to use for solving complex problems in the field of industrial applications. We use the mathematical method of fractal geometry and network theory when laser-hardening techniques are applied. The microstructure of the robot-laser-hardened specimens is very complex; however, we can present it by using a 3D visibility network. We convert the scanning electron microscope (SEM) images of the microstructure to a 3D graph and calculate the density of the visibility network of these 3D networks. We have analyzed the topographical properties of the hardened specimens by using the algorithm for the construction of a visibility network in a 3D space. We develop a new hybrid system of machine learning for predicting carbide content of the hardened specimens by using multiple regression, neural networks, and a genetic algorithm. We find the statistical significance of the relationship between attributes of the hardened specimens, the topological properties of visibility graphs, and carbide content of the hardened specimens. UR - https://www.sv-jme.eu/sl/article/new-hybrid-system-of-machine-learning-and-statistical-pattern-recognition-for-a-3d-visibility-network/
@article{{sv-jme}{sv-jme.2017.5189},
author = {Babič, M., Skala, K., Kumar, D., Šturm, R.},
title = {New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {64},
number = {6},
year = {2018},
doi = {10.5545/sv-jme.2017.5189},
url = {https://www.sv-jme.eu/sl/article/new-hybrid-system-of-machine-learning-and-statistical-pattern-recognition-for-a-3d-visibility-network/}
}
TY - JOUR AU - Babič, Matej AU - Skala, Karolj AU - Kumar, Dookhitram AU - Šturm, Roman PY - 2018/06/26 TI - New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 64, No 6 (2018): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2017.5189 KW - fractal geometry, hybrid system, laser hardened specimens, visibility network, statistical pattern recognition N2 - Intelligent systems are an excellent tool to use for solving complex problems in the field of industrial applications. We use the mathematical method of fractal geometry and network theory when laser-hardening techniques are applied. The microstructure of the robot-laser-hardened specimens is very complex; however, we can present it by using a 3D visibility network. We convert the scanning electron microscope (SEM) images of the microstructure to a 3D graph and calculate the density of the visibility network of these 3D networks. We have analyzed the topographical properties of the hardened specimens by using the algorithm for the construction of a visibility network in a 3D space. We develop a new hybrid system of machine learning for predicting carbide content of the hardened specimens by using multiple regression, neural networks, and a genetic algorithm. We find the statistical significance of the relationship between attributes of the hardened specimens, the topological properties of visibility graphs, and carbide content of the hardened specimens. UR - https://www.sv-jme.eu/sl/article/new-hybrid-system-of-machine-learning-and-statistical-pattern-recognition-for-a-3d-visibility-network/
Babič, Matej, Skala, Karolj, Kumar, Dookhitram, AND Šturm, Roman. "New Hybrid System of Machine Learning and Statistical Pattern Recognition for a 3D Visibility Network" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 64 Number 6 (26 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 64(2018)6, 393-400
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Intelligent systems are an excellent tool to use for solving complex problems in the field of industrial applications. We use the mathematical method of fractal geometry and network theory when laser-hardening techniques are applied. The microstructure of the robot-laser-hardened specimens is very complex; however, we can present it by using a 3D visibility network. We convert the scanning electron microscope (SEM) images of the microstructure to a 3D graph and calculate the density of the visibility network of these 3D networks. We have analyzed the topographical properties of the hardened specimens by using the algorithm for the construction of a visibility network in a 3D space. We develop a new hybrid system of machine learning for predicting carbide content of the hardened specimens by using multiple regression, neural networks, and a genetic algorithm. We find the statistical significance of the relationship between attributes of the hardened specimens, the topological properties of visibility graphs, and carbide content of the hardened specimens.