1 |
Experimental methods in chemical engineering: Transmission electron microscopy-TEM Braidy N, Bechu A, Terra JCD, Patience GS Canadian Journal of Chemical Engineering, 98(3), 2020 |
2 |
Variation in the mineral composition of wine produced using different winemaking techniques Shimizu H, Akamatsu F, Kamada A, Koyama K, Iwashita K, Goto-Yamamoto N Journal of Bioscience and Bioengineering, 130(2), 166, 2020 |
3 |
Challenges in sample preparation for measuring nanoparticles size by scanning electron microscopy from suspensions, powder form and complex media Ghomrasni NB, Chivas-Joly C, Devoille L, Hochepied JF, Feltin N Powder Technology, 359, 226, 2020 |
4 |
Next-generation virtual metrology for semiconductor manufacturing: A feature-based framework Suthar K, Shah D, Wang J, He QP Computers & Chemical Engineering, 127, 140, 2019 |
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A series of microscope objective lenses combined with an interferometer for individual nanoparticles detection Ibrahim DGA Current Applied Physics, 19(7), 822, 2019 |
6 |
Characteristics of a plasma information variable in phenomenology-based, statistically-tuned virtual metrology to predict silicon dioxide etching depth Jang YC, Roh HJ, Park S, Jeong S, Ryu S, Kwon JW, Kim NK, Kim GH Current Applied Physics, 19(10), 1068, 2019 |
7 |
Ultrasonic parameter measurement as a means of assessing the quality of biodiesel production Baesso RM, Costa-Felix RPB, Miloro P, Zeqiri B Fuel, 241, 155, 2019 |
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Measurement challenges for hydrogen vehicles Murugan A, de Huu M, Bacquart T, van Wijk J, Arrhenius K, te Ronde I, Hemfrey D International Journal of Hydrogen Energy, 44(35), 19326, 2019 |
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DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology Maggipinto M, Beghi A, McLoone S, Susto GA Journal of Process Control, 84, 24, 2019 |
10 |
DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology Maggipinto M, Beghi A, McLoone S, Susto GA Journal of Process Control, 84, 24, 2019 |