Suchergebnisse - "Okada, Natsuo"

Improving Hokkai shrimp tracking accuracy using YOLOv8 with reflection and parallax correction.

Autoren: Okada, Natsuo Kubo, Serina Zhu, Yanhui et al.

Quelle: Scientific Reports; 8/12/2025, Vol. 15 Issue 1, p1-14, 14p

Schlagwörter: SHRIMPS, OBJECT recognition (Computer vision), AQUATIC ecology, OPTICAL distortion, SEAGRASS restoration, BIODIVERSITY

APiS (AI powered intelligence spectrum analyser): hyperspectral imaging machine learning analyser app for mineral processing.

Autoren: Okada, Natsuo Owada, Narihiro Takizawa, Kaito et al.

Quelle: International Journal of Mining, Reclamation & Environment; Dec2025, Vol. 39 Issue 10, p713-739, 27p

Schlagwörter: MINERAL processing, MACHINE learning, HYPERSPECTRAL imaging systems, USER interfaces, DATA analysis, DATA visualization

Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging.

Autoren: Maniteja, Mujigela Samanta, Gopinath Gebretsadik, Angesom et al.

Quelle: Minerals (2075-163X); Feb2025, Vol. 15 Issue 2, p131, 33p

Schlagwörter: IRON ores, MINES & mineral resources, ORE deposits, GEOLOGY databases, SUPPORT vector machines

Artificial Intelligence-Based Hyperspectral Classification of Rare Earth Element-Related Heavy Mineral Sand.

Autoren: Muacanhia, Okhala Okada, Natsuo Ohtomo, Yoko et al.

Quelle: Minerals (2075-163X); Oct2025, Vol. 15 Issue 10, p1015, 20p

Schlagwörter: ARTIFICIAL intelligence, HEAVY minerals, RARE earth metals, SUPPORT vector machines, HYPERSPECTRAL imaging systems, MINERAL analysis, CONVOLUTIONAL neural networks, ARTIFICIAL neural networks

Integrated spectral image registration protocol for enhanced land pollution assessment and management: bridging satellite and drone data for high-resolution mapping.

Autoren: Okada, Natsuo Toriya, Hisatoshi Gebretsadik, Angesom et al.

Quelle: Journal of Material Cycles & Waste Management; Mar2025, Vol. 27 Issue 2, p685-698, 14p

Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning.

Autoren: Okada, Natsuo Bino Sinaice, Brian Kim, Jaewon et al.

Quelle: International Journal of Mining, Reclamation & Environment; Dec2024, Vol. 38 Issue 10, p851-871, 21p

Schlagwörter: INDUSTRIAL minerals, GEOLOGICAL surveys, ELECTRONIC data processing, MACHINE learning, MINERAL processing

Advancing arsenic contamination detection in boring cores using hyperspectral imaging and convolutional neural networks.

Autoren: Takizawa, Kaito Okada, Natsuo Owada, Narihiro et al.

Quelle: Green & Smart Mining Engineering; Sep2025, Vol. 2 Issue 3, p246-258, 13p

Schlagwörter: HYPERSPECTRAL imaging systems, CONVOLUTIONAL neural networks, ARSENIC poisoning, ENVIRONMENTAL monitoring, DRILL core analysis, X-ray fluorescence, CONSTRUCTION industry safety, GEOLOGICAL formations

Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM.

Autoren: Nakazawa, Yuna Okada, Natsuo Sasaki, Jo et al.

Quelle: Discover Applied Sciences; Apr2025, Vol. 7 Issue 4, p1-17, 17p

An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection.

Autoren: Manjate, Elsa Pansilvania Andre Okada, Natsuo Ohtomo, Yoko et al.

Quelle: Mining; Dec2024, Vol. 4 Issue 4, p747-765, 19p

Schlagwörter: ARTIFICIAL neural networks, MINES & mineral resources, ARTIFICIAL intelligence, MINING methodology, SUPERVISED learning

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection.

Autoren: Okada, Natsuo Nozaki, Hiromasa Nakamura, Shinichiro et al.

Quelle: Scientific Reports; 5/21/2024, Vol. 11 Issue 1, p1-21, 21p

Schlagwörter: ARSENIC, MACHINE learning, COPPER ores, SULFIDE ores, MINERALS, PROCESS capability, ARSENIC compounds

A system of the granite weathering degree assessment using hyperspectral image and CNN.

Autoren: Kim, Jaewon Kawamura, Youhei Nishikawa, Osamu et al.

Quelle: International Journal of Mining, Reclamation & Environment; Jun2022, Vol. 36 Issue 5, p368-380, 13p

Schlagwörter: WEATHERING, CONVOLUTIONAL neural networks, GRANITE, DEEP learning, ELECTROMAGNETIC radiation

Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing.

Autoren: Okada, Natsuo Maekawa, Yohei Owada, Narihiro et al.

Quelle: Minerals (2075-163X); Sep2020, Vol. 10 Issue 9, p809, 1p

Schlagwörter: MINERAL processing, DEEP learning, GRAIN size, SPECTRAL imaging, MINERALS, AUTOMATIC identification

The capacity-building and science-enabling activities of the IUGONET for the solar-terrestrial research community.

Autoren: Yatagai, Akiyo Sato, Yuka Shinbori, Atsuki et al.

Quelle: Earth, Planets & Space; 1/8/2015, Vol. 67 Issue 1, p1-1, 1p

Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019

Autoren: Erkan Topal

Resource Type: eBook.

Schlagworte: Sustainability, Mineralogy, Metals

Categories: SCIENCE / Environmental Science, SCIENCE / Earth Sciences / Mineralogy, TECHNOLOGY & ENGINEERING / Materials Science / Metals & Alloys