研究者詳細 - 北　栄輔

論文 - 北　栄輔

分割表示 500 件中 61 - 80 件目／全件表示 >>

The Application of Sequential Generative Adversarial Networks for Stock Price Prediction 査読有り

He, BT; Kita, E

REVIEW OF SOCIONETWORK STRATEGIES 15 巻 ( 2 ) 頁： 455 - 470 2021年12月

Successive Future Image Generation of a Walking Pedestrian Using Generative Adversarial Networks 査読有り

He, BT; Kita, E

REVIEW OF SOCIONETWORK STRATEGIES 15 巻 ( 2 ) 頁： 309 - 325 2021年12月

Application of Machine Learning for Growth Environment Prediction in Agriculture 招待有り査読有り

Momoko Fujita, Akari Yamada, Mao Susuki, Hiroya Makino, Eisuke Kita

2021 IEEE International Conference on Data Minig Workshop (IEEE ICDMW2021) 2021年11月

Stochastic Schemata Exploiter-Based Hyper-Parameter Optimization of XGBoost 招待有り査読有り

Hiroya Makino, Eisuke Kita

2021 IEEE International Conference on Data Minig Workshop (IEEE ICDMW2021) 2021年11月

隊列走行の開発動向招待有り査読有り

北　栄輔

高速道路と自動車 64 巻 ( 11 ) 頁： 5 - 8 2021年11月

Application of Machine Learning for Growth Environment Prediction in Agriculture 招待有り査読有り

Momoko Fujita, Akari Yamada, Mao Susuki, Hiroya Makino, Eisuke Kita

2021 IEEE International Conference on Data Minig Workshop (IEEE ICDMW2021) 2021年11月

Control Program Design of Autonomous Vehicle Robot using Grammatical Evolution 招待有り査読有り

Shuya Yamada, Ryoma Sato, Tatsuhiro Tamaki, Eisuke Kita

6th International Conference on Robotics and Automation Engineering (ICRAE-2021) 2021年11月

トマト栽培における飽差推定とその応用招待有り査読有り

藤田桃子，山田明里，鈴木麻央，北栄輔

日本機械学会第31回設計工学システム部門講演会論文集 2021年9月

ベイジアンネットワークによる景気動向予測について

中村典雅, 北田宙士, 北栄輔

日本機械学会第31回設計工学システム部門講演会 2021年9月

Genetic Algorithm-based Optimization of Deep Neural Network Ensemble 招待有り査読有り

Feng, XN; Zhao, JN; Kita, E

REVIEW OF SOCIONETWORK STRATEGIES 15 巻 ( 1 ) 頁： 27 - 47 2021年6月

隊列走行車両群と単独車両の合流挙動シミュレーション査読有り

大野颯汰,山田修也,玉城龍洋,北栄輔

計算工学講演会論文集 2021年5月

Genetic Algorithm based Optimization of Deep Neural Network Ensemble for Personal Identification in Pedestrians Behaviors 招待有り査読有り

Xuanang Feng, Jianing Zhao, Eisuke Kita

The Review of Socionetwork Strategies 14 巻 ( 2 ) 頁： -- 2021年2月

Yield and Quality Prediction of Rice Cultivation Using Machine Learning 招待有り査読有り

Akari Yamada, Yuichiro Maeda, Eisuke Kita

Proceedings of World Congress on Computational Mechanics 2021年1月

Stochastic Schemata Exploiter-Based AutoML 査読有り

Makino, H; Kita, E

21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS ICDMW 2021 2021-December 巻頁： 238 - 245 2021年

担当区分：最終著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE International Conference on Data Mining Workshops, ICDMW

Machine Learning (ML) models are often determined by relying on knowledge and experience. In recent years, many automatic building methods are proposed, but there are some problems related to accuracy, computation cost, and explainability. We propose the Stochastic Schemata Exploiter-Based AutoML. The Stochastic Schemata Exploiter (SSE) is one of the Evolutionary Algorithms. In each generation, SSE calculates fitness of the individuals, defines the best subsets according to their average fitness, generates individuals based on the subsets, and applies a mutation operator. Since the original SSE uses a binary string representation, we have to modify the SSE algorithm in the following points for parameter optimization: the initialization method, the schema extraction method, the new individual generation method, the mutation method, and the generation update method. In this paper, we propose a genetic representation of the stacking model and optimize the stacking model using SSE. Compared with the Genetic Algorithm, the Tree-structured Parzen Estimator, the Covariance Matrix Adaptation-Evolution Strategy, and Random Search, SSE shows an interesting feature: a better accuracy for combinatorial optimization problems with categorical, discrete, and continuous variables, such as hyper-parameter optimization. In addition, we propose the visualization of the process of SSE. The visualization helps us to understand the process, which is another advantage of the SSE-based optimization (SSEopt).

DOI： 10.1109/ICDMW53433.2021.00037

Web of Science

Scopus

Control Program Design of Autonomous Vehicle Robot Using Grammatical Evolution 査読有り

Yamada S., Sato R., Tamaki T., Kita E.

2021 6th International Conference on Robotics and Automation Engineering, ICRAE 2021 頁： 308 - 312 2021年

担当区分：最終著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：2021 6th International Conference on Robotics and Automation Engineering, ICRAE 2021

The automatic design of the control program of the vehicle robot is presented in this study. LEGO MINDSTORM EV3 and RVW Level Builder are used for this study. The control program of EV3 is designed automatically by using Grammatical Evolution (GE), which is one of the evolutionary algorithms. The generated program is applied for controlling EV3. The results show that the robot behavior in the experiment of EV3 was roughly equivalent to it in simulator. Therefore, the proposed algorithm based on GE can generate the control program of robot automatically.

DOI： 10.1109/ICRAE53653.2021.9657773

Scopus

Application of Machine Learning for Growth Environment Prediction in Agriculture 査読有り

Fujita, M; Yamada, A; Susuki, M; Makino, H; Kita, E

21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS ICDMW 2021 2021-December 巻頁： 208 - 213 2021年

It is very important to control the value of saturation in the adequate range in tomato cultivation in greenhouse. The prediction of the saturation value in greenhouse are discussed in this study. The mathematical model for prediction of saturation is defined by regression analysis of Ridge regression, Lasso regression, Random Forest and XGBoost. The results show that Random Forest and XGBoost has better accuracy than the others and that Random Forest is the best among them. The variable importance analysis shows that the indoor temperature, temperature inside greenhouse, is the most effective for the saturation. The prediction model is applied for controlling the saturation in the adequate range by changing the temperature.

DOI： 10.1109/ICDMW53433.2021.00033

Web of Science

Scopus

隊列走行車両群の分流走行挙動シミュレーション査読有り

北　栄輔，高上寛斗，玉城龍洋，村井希

日本計算数理工学論文集 20 巻頁： 95 - 100 2020年12月