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SCEJ 53rd Autumn Meeting (Nagano, 2022)

Program search result : 予測 : 27 programs

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Title (J) field includes “予測”; 27 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.

TimePaper
ID
Title / AuthorsKeywordsTopic codeAck.
number
Day 1
9:009:20
CA101Prediction of regeneration energies for non-aqueous amine CO2 absorbents
(Waseda U.) *(Stu)Takahara Masaki, (Reg)Furukawa Yukio
CO2 capture
organic solvents
NMR
SY-5195
Day 1
9:009:20
FA101Short term electricity demand forecasting from high dimensional data on microgrid with gradient boosting decision tree model
(Tokyo Tech) *(Stu)Lee Hyojae, (Stu)Tsuda Shunsaku, (Stu)Iijima Taiki, (Reg)Manzhos Sergei, (Reg)Ihara Manabu
Electrical demand forecasting
microgrid
high dimensional data
ST-22226
Day 1
9:0010:00
PB114Construction of Sequence-based prediction model for high expression VHH clones using machine learning
(Kyoto Inst. Tech.) *(Stu)Takahashi Asuka, Hamamoto Yuri, (Stu)Numata Tatsunori, Mihara Akihiro, (Reg)Horiuchi Jun-ichi, (Reg)Kumada Yoichi
VHH
machine learning
production
SY-67733
Day 1
10:3011:30
PB133Prediction of culture phase of Saccharomyces cerevisiae using image analysis technology
(Kitami Inst. Tech.) *(Stu)Nakakura Ryoya, (Stu)Watanabe Kazuki, Chiou Tai-Ying, (Reg)Konishi Masaaki
Saccharomyces cerevisiae
Image analysis
microscope
SY-67637
Day 1
11:2011:40
DB108Simulation of molten glass flow from a nozzle to predict conditions at the bottom of a glass melter
(Saitama U.) *(Stu)Koyama Ryoya, (Reg)Homma Shunji, (IHI) (Cor)Miyasaka Iku, (Cor)Ikai Hiroshi
molten glass
glass melter
CFD
SY-52282
Day 1
11:4012:00
BA109[Featured presentation] Prediction of a feed factor of two-component powder in a twin screw feeder
(Kyoto U.) *(Stu·PCEF)Kobayashi Yuki, (TUAT) (Reg)Kim Sanghong, (Powrex) (Reg)Nagato Takuya, Uchida Kazuhiro, (Reg)Oishi Takuya, (Kyoto U.) (Reg)Kano Manabu
Process modeling
Feed factor
Multi-component powder
SY-64517
Day 1
13:0013:40
DJ113[Review lecture] Improvement of prediction accuracy of W-ALD deposition rate by machine learning
(TTS) *Aita Michitaka, Yamasaki Hideaki, Hotta Takanobu, Kawaguchi Takuya, Narushima Kensaku, Kubo Atsushi, Mochizuki Seiichiro, Takagi Toshio
Machine Learning
ALD
Simulation
ST-24221
Day 1
14:2014:40
DJ117Study of estimating vapor pressure of metal complex with COSMO-SAC method
(U. Tokyo) *(Reg)Sato Noboru, (Stu)Yamaguchi Jun, (Stu)Wu Yuxuan, Deura Momoko, (Reg)Momose Takeshi, (Reg)Shimogaki Yukihiro
vapor pressure
metal complex
COSMO-SAC
ST-24686
Day 1
15:0016:20
PB133Prediction of culture phase of Saccharomyces cerevisiae using image analysis technology
(Kitami Inst. Tech.) *(Stu)Nakakura Ryoya, (Stu)Watanabe Kazuki, Chiou Tai-Ying, (Reg)Konishi Masaaki
Saccharomyces cerevisiae
Image analysis
microscope
SY-67637
Day 1
15:4016:00
CA121Application to predict the extraction amount of naturally derived components using Hansen solubility parameter
(Kansai U.) *(Stu·PCEF)Hiraoka Koki, (Reg·PCE)Yamamoto Hideki
Cohesive energy density
Solid liquid extraction
Vanillin
SY-51403
Day 1
15:4016:00
CB121Prediction of molecular weight distribution of protein decomposition in subcritical water
(Shinshu U.) *(Stu)Shintani Kenta, (Reg)Shimada Iori, (Reg)Fukunaga Hiroshi, (Reg)Takahashi Nobuhide, (Reg)Osada Mitsumasa
subcritical water
protein
Molecular weight distribution
SY-73690
Day 1
15:4016:00
DD121Study on the prediction formula about pressure drop of air filter consisting of two diameter types of fiber
(Gunma U.) *(Stu)Sekiguchi Yusuke, Toyama Ryoma, Zama Yoshio
Air filter
Fiber diameter
Pressure drop
SY-53275
Day 1
16:2017:40
PB114Construction of Sequence-based prediction model for high expression VHH clones using machine learning
(Kyoto Inst. Tech.) *(Stu)Takahashi Asuka, Hamamoto Yuri, (Stu)Numata Tatsunori, Mihara Akihiro, (Reg)Horiuchi Jun-ichi, (Reg)Kumada Yoichi
VHH
machine learning
production
SY-67733
Day 2
9:009:20
BB201Performance prediction of CO2 capture simulating change in solution properties of degraded amine solution
(Waseda U.) *(Stu)Saito Takumi, (Stu)Isogai Hirotaka, Isihara Manami, (Reg)Nakagaki Takao
chemical absorption
process simulation
reaction rate
SY-82211
Day 2
10:0010:20
CA204Prediction of thermal conductivity for polymer-based composite materials using machine learning
(Kanazawa U.) *(Stu)Tsukamura Keita, (Reg)Haruki Masashi
Thermal conductivity
Machine learning
Polymer-based composite material
SY-5191
Day 2
10:4011:00
BB206Prediction of pellet density of Japanese cedar from pelletizing conditions
(Shinshu U.) *(Stu)Mori Kouki, (Reg)Shimada Iori, (Reg)Osada Mitsumasa, (Reg)Fukunaga Hiroshi, (Reg)Takahashi Nobuhide
pelletizing pressure
moisture
regression analysis
SY-82794
Day 2
14:0014:20
AB216A simplified simulation model of Marangoni deformation of a solution droplet during drying
(Kyushu U.) *(Stu)Miyazaki Ranri, (Reg)Hironaka Shuji, (Reg)Fukai Jun
film formation
Marangoni convection
modeling
SY-55340
Day 2
14:4015:00
DD218Freeze-drying process control for quality preservation based on prediction of microcollapse development
(Kyoto U.) *(Stu)Shimbo Atsushi, (Reg)Nakagawa Kyuya, (Reg)Suzuki Tetsuo, (Reg)Sano Noriaki
freeze-drying
collapse
SY-58110
Day 2
15:4016:00
CB221Modeling of solute elution behavior in supercritical fluid chromatography based on distribution coefficient
(Tohoku U.) *(Stu·PCEF)Matsuda Shuta, (Reg)Ota Masaki, (Reg)Hiraga Yuya, (Reg)Inomata Hiroshi, (Reg)Watanabe Masaru
supercritical CO2
ethanol
partition coefficient
SY-73740
Day 3
9:009:20
AB301Adsorption Rate Prediction of Granular Activated Carbon Packed Bed using CFD
(Shizuoka U./Toclas) *(Stu)Kasai Yuma, (Shizuoka U.) (Reg)Jinbo Yoshinori, (Toclas) Kamikawa Hideya, (Shizuoka U.) (Reg)Sanada Toshiyuki
Adsorption Rate
CFD
Activated Carbon
SY-5533
Day 3
9:0010:30
PA305Prediction of agglomeration behavior in fuel cell catalyst ink by simulation
(Kyushu U.) *(Stu)Saito Y., So M., (Reg)Inoue G.
fuel cell
simulation
particle agglomeration
ST-2399
Day 3
9:0010:30
PA313Study and analysis of clustering methods for high-dimensional "Energy Data" for building power forecasting models
(Tokyo Tech) *(Stu)Iijima Taiki, (Stu)Lee Hyojae, (Stu)Tsuda Shunsaku, (Reg)Manzhos Sergei, (Reg)Ihara Manabu
energy system
machine learning
data science
ST-23544
Day 3
9:4010:20
BB303[Invited lecture] Water related future projection and adaptation options in IPCC AR6
(Shibaura Inst. Tech.) Hirabayashi Yukiko
water resources
flood
adaptation to climate change
SY-8386
Day 3
10:2011:20
PB308A research for descriptors that contribute to the prediction accuracy of ion solvation extractability to Au(III) by machine learning.
(U. Miyazaki) *(Stu)Iwakiri Yuhi, (Reg)Inada Asuka, (Reg)Ohe Kaoru, (Reg)Oshima Tatsuya
machine learning
solvent extraction
gold
SY-57259
Day 3
10:3012:00
PA312Analysis of high-dimensional energy data and application of regression models toward generalized electricity demand forecasting
(Tokyo Tech) *(Stu)Tsuda Shunsaku, (Stu)Okubo Tatsuya, (Stu)Lee Hyojae, (Stu)Iijima Taiki, (Stu)Otoshi Natsuki, (Reg)Manzhos Sergei, (Reg)Ihara Manabu
energy system
machine learning
data science
ST-23265
Day 3
16:0016:20
AC322Prediction method combining CFD and PBM of droplet size distribution in a turbulent liquid-liquid stirred vessel
(Yokohama Nat. U.) *(Stu)Takahashi Rei, (Reg)Misumi Ryuta
mixing
PBM
liquid-liquid dispersion
SY-54468
Day 3
16:0016:20
DC322Effect of physics-based feature engineering in predicting product yields of catalytic cracking reactions
(Shinshu U.) *(Stu)Yasuike Shun, (Reg)Osada Mitsumasa, (Reg)Shimada Iori
catalytic cracking
machine learning
feature engineering
ST-21101

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SCEJ 53rd Autumn Meeting (Nagano, 2022)


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