明治大院理工 - Program search result - SCEJ 55th Autumn Meeting (Sapporo, 2024)

`SCEJ 55th Autumn Meeting (Sapporo, 2024)`

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Program search result : 明治大院理工 : 19 programs

The preprints are now open. These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants and invited persons are required.

Affillations (J) field begins with “明治大院理工”, “明治大院理”, “明治大理”, “Meiji U.”; 19 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Day 1 9:00– 12:00	YB130	Construction of Prediction Model for the Yield of Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Ni Catalysts and Proposal of Novel Polymeric Ni Catalysts (Meiji U.) *(Stu)Takaoka Sho, (Riken) Zhang Zhenzhong, Yamada Yoichi M. A., (Meiji U.) (Reg)Kaneko Hiromasa	Machine Learning Polymeric Ni Catalysts Chemical Reactions	SY-63	271
Day 1 9:00– 12:00	YB158	Energy-saving design of ethylbenzene production process using machine learning and Bayesian optimization (Meiji U.) *(Stu)Ishikawa Eri, (Reg)Kaneko Hiromasa	Machine learning Bayesian optimization Process design	SY-63	76
Day 1 11:00– 11:12	YA189	Investigation of the use of information from olfactory receptors for molecular odor prediction (Meiji U.) *(Stu)Wakutsu Yuta, (Reg)Kaneko Hiromasa	Odor Protein Machine Learning	SY-68	165
Day 1 12:40– 14:00	YA189	Investigation of the use of information from olfactory receptors for molecular odor prediction (Meiji U.) *(Stu)Wakutsu Yuta, (Reg)Kaneko Hiromasa	Odor Protein Machine Learning	SY-68	165
Day 1 13:00– 13:20	L113	Machine learning model estimating blood glucose monitoring from mid-infrared spectra measured by the photothermal monitoring method (Meiji U.) *(Stu)Takami Yuta, (Mitsubishi Electric) Miyagawa Keita, Tsuda Yuki, Akiyama Koichi, (Meiji U.) (Reg)Kaneko Hiromasa	mid-infrared spectra noninvasive blood glucose monitoring machine learning	SY-66	102
Day 1 13:40– 14:00	L115	Development of a soft sensor model that takes into account the dynamic characteristics of the process and two similar qualities (Meiji U.) *(Stu)Ohkuma Ayami, (Mitsubishi Chemical) (Cor)Yamauchi Yoshihito, (Cor)Yamada Nobuhito, (Cor)Ooyama Satoshi, (Meiji U.) (Reg)Kaneko Hiromasa	machine learning soft sensor time delay	SY-66	260
Day 1 16:40– 17:40	YB130	Construction of Prediction Model for the Yield of Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Ni Catalysts and Proposal of Novel Polymeric Ni Catalysts (Meiji U.) *(Stu)Takaoka Sho, (Riken) Zhang Zhenzhong, Yamada Yoichi M. A., (Meiji U.) (Reg)Kaneko Hiromasa	Machine Learning Polymeric Ni Catalysts Chemical Reactions	SY-63	271
Day 1 16:40– 17:40	YB158	Energy-saving design of ethylbenzene production process using machine learning and Bayesian optimization (Meiji U.) *(Stu)Ishikawa Eri, (Reg)Kaneko Hiromasa	Machine learning Bayesian optimization Process design	SY-63	76
Day 2 9:30– 11:00	YA206	Constructing model for predicting pesticide activity using scaffolds by machine learning and visualizing the basis for prediction (Meiji U.) *(Stu)Sakai Yuta, (Reg)Kaneko Hiromasa	Machine Learning Quantitative Structure-Activity Relationship Pesticide	SY-78	86
Day 2 9:30– 11:00	YA214	Use of Bayesian optimization in improving emulsion stability (Kagoshima U.) *(Stu·PCEF)Sono Nao, (Meiji U.) (Reg)Kaneko Hiromasa, (NIT Miyakonojo) (Reg)Kiyoyama Shiro, (U. Miyazaki) (Reg)Shiomori Koichiro, (Kagoshima U.) (Reg)Yoshida Masahiro, (Reg)Takei Takayuki	machine learning emulsion surfactant	SY-78	729
Day 2 9:30– 11:00	YA232	Construction of predictive model for biodegradability as an alternative to biodegradability test using machine learning (Meiji U.) *(Stu)Ochiai Haruki, (Reg)Kaneko Hiromasa	Machine Learning Biodegradation plastic	SY-78	90
Day 2 10:30– 12:00	YA223	Research to Improving the Accuracy of Machine Learning Models for Predicting Reorganization Energy (Meiji U.) *(Stu)Nakanishi Yamato, (Panasonic Ind.) Matsuzawa Nobuyuki N, Maeshima Hiroyuki, Ando Tatsuhito, (Meiji U.) (Reg)Kaneko Hiromasa	Machine learning Organic semiconductor Graph Convolutional Network	SY-78	272
Day 2 10:30– 12:00	YA285	Definition of the domain in chemical space that discriminates the stability of a molecular structure (Meiji U.) *(Stu)Kosakai Soma, (Reg)Kaneko Hiromasa	Machine learning Stability Molecule design	SY-78	194
Day 2 13:00– 13:20	L213	Optimization of property prediction model in dynamic manufacturing process for carbon materials using a genetic algorithm (Meiji U.) *(Stu)Matsubara Masayoshi, (Mitsubishi Chemical) (Cor)Sasaki Ryo, (Cor)Takahara Jun, (Cor)Moritake Shinji, (Cor)Harada Yasuyuki, (Meiji U.) (Reg)Kaneko Hiromasa	Machine learning Genetic-algorithm-based process variables and dynamics selection Multi-objective optimization	SY-66	273
Day 2 13:00– 13:40	R213	[Review lecture] Property prediction with machine learning models and designs of molecules, materials and processes through inverse analysis of models (Meiji U.) (Reg)Kaneko Hiromasa	Machine learning Material design Process design	SY-51	431
Day 2 13:00– 14:30	YB245	Machine learning model predicting properties from monomer structures of alkyl sulfonated polyimides (Meiji U.) *(Stu)Ando Ruka, (JAIST) Aoki Kentaro, Nagao Yuki, (Meiji U.) (Reg)Kaneko Hiromasa	alkyl sulfonated polyimide machine learning fuel cell	SY-75	82
Day 2 14:30– 16:00	YB236	Mass transport analysis of the different active materials in a redox flow battery (Gunma U.) *(Stu)Ueyanagi Kota, (Meiji U.) (Reg)Ishitobi Hirokazu, (AIST) Ohira Akihiro, Funaki Takashi, Sato Yukari, (Gunma U.) Nakagawa Nobuyoshi	Redox flow battery Mass transport analysis Limiting current	SY-75	623
Day 2 15:00– 15:20	L219	Molecular design with direct inverse analysis of autoencoder-based QSAR/QSPR model (Meiji U.) *(Stu)Shino Yuto, (Reg)Kaneko Hiromasa	Machine learning Inverse analysis Molecular design	SY-66	140
Day 3 11:40– 12:00	J309	Electrode activity and active material transport in porous carbon electrode for redox flow battery (Meiji U.) *(Reg)Ishitobi Hirokazu, (Gunma U.) Shiraishi Soshi, (Aion) Tsukada Hidehiko, (Gunma U.) Doki Honoka, Ide Tomoki, (Reg)Nakagawa Nobuyoshi	Redox flow battery Electrode activity Active material transport	SY-79	125

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SCEJ 55th Autumn Meeting (Sapporo, 2024)

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