Kaneko Hiromasa - Program search result - SCEJ 55th Autumn Meeting (Sapporo, 2024)

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

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Program search result : Kaneko Hiromasa : 18 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.

Authors field exact matches “Kaneko Hiromasa”; 18 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 15:00– 16:00	Chair: Kaneko Hiromasa
	L119	Digital Utilization of P&ID: Challenges in Using Topology Data from PFD to P&ID and 3D (JGC) *(Cor)Iwamuro Norito, (Cor)Koito Hiroyuki, (Cor)Yamada Yoshinori, (Cor)Kokubo Hiroki, (Cor)Kaida Sachiko, (Cor)Hori Reo	Piping and Instrument Diagram Plant Design Topology	SY-66	521
	L120	Elucidation on suction effect for powder filling in a rotary tablet press (U. Tokyo) *(Stu)Hashimoto Arata, (Reg)Sakai Mikio	multi-physics simulation discrete element method powder die filling	SY-66	192
	L121	A method for estimating the reaction rates by inverse analysis of elementary reaction and transport model simulating a denitrification system (Toshiba Energy Systems & Solutions) *(Reg)Nakamura Kotaro, Takeyama Daiki, Tsukada Keisuke, Fukuta Masato	Kinetic model Surface reaction Optimization	SY-66	326
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 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

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SCEJ 55th Autumn Meeting (Sapporo, 2024)

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