Daiki Shimizu

National Institute of Technology, Kisarazu College

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Haemi Park

Sophia University

Mária Szikszay

Universidade de São Paulo

Wataru Takeuchi

The University of Tokyo

Raphael Hypólito

Águas de Portugal (Portugal)

Shinichi Oeda

National Institute of Technology, Kisarazu College

Yuuya Fukumoto

Tokyo University of Technology

Chihiro Shibata

Tokyo University of Technology

Miho Yonezawa

The University of Tokyo

Eiji Hato

The University of Tokyo

Cooperative Institutions

The University of Tokyo

Universidade de São Paulo

Instituto de Geociencias

Universidad San Pedro

Tokyo University of Technology

Tohoku University

Sophia University

Japan Aerospace Exploration Agency

Ulsan National Institute of Science and Technology

Kyung Hee University

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Development of evaluation approach for local congestion in city transport using spatiotemporal scheduling model

Journal of the City Planning Institute of Japan (2019)

Miho Yonezawa Daiki Shimizu Eiji Hato

交通空間の混雑緩和施策の実施検討は喫緊の課題である．従前の交通施策評価では交通需要そのものがODトリップ単位に固定化されたまま分析が行われるケースが多いため，急激な都市開発などによって時空間的に変化するトリップチェインを考慮した交通施策評価手法が求められている．本研究では時空間ネットワーク上で一般化RLモデルを援用し，スケジューリング問題を直接決定する動学最適化問題としてモデルを構築した．さらに実際のデータを用いて推定したパラメータ群によって，複数の条件組み合わせからなる交通施策シナリオを作成し，施策によって変化する局所的な空間の混雑緩和効果をDEA（Data Environment Analysis）を用いて総合的に評価するための枠組みを提案した．現実の都市空間におけるスケジューリングを，提案モデルは精度よく再現できていることを確認した．さらに，ネットワーク整備施策やTDM施策を検討する場合において，複数条件を組み合わせた施策パッケージを複数の指標から相対的に総合評価し，最適施策群が解曲面として判定されることを確認することができた．

10.11361/journalcpij.54.734

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Study of mobility of Zn, Co, Cu and Pb in the unsaturated zone in tropical climate

Mária Szikszay Raphael Hypólito Daiki Shimizu

Tropical climate

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Generation of Character Illustrations from Stick Figures Using a Modification of Generative Adversarial Network

Yuuya Fukumoto Daiki Shimizu Chihiro Shibata

We propose a modification of generative adversarial networks (GANs) that generate illustrations of human figures from given poses represented by stick figures. In recent years, while various methods that generate images of characters using GANs have been proposed, it is not yet possible for users to freely designate poses of human figures. When generating an image of a character, the pose of the character takes is an important component of its composition. Thus it is necessary fora user who wants to create an illustration to be able to specify the pose easily. We collected a set of illustrations of human figures from the internet, and for each illustration, a simple line drawing that specifies the pose was drawn manually. We constructed a GAN that takes a line drawing as its input and creates an illustration of a person in a pose that matches the line drawing. These networks are learned using the data set we prepared. In this paper, we propose a new network architecture. After constructing two networks both of which have almost the same structure as pix2pix, which is a variant model of GANs, we stack up those networks based on the idea of stack GAN. The experimental results show that, from stick figures representing common poses such as a standing pose, our methods was able to successfully generate images of characters. However, in the case of stick figures having rare poses that were not in the dataset, such as figures raising a hand or lying down, the generated images were blurred and not of a high-quality but still had the desired shapes. By expanding the dataset to include various poses, it is possible to generate diverse poses more precisely.

Line (geometry)

Component (thermodynamics)

10.1109/compsac.2018.10225

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Verification of Usefulness of Student Modeling with Real Educational Data using Convex Factorization Machines

Procedia Computer Science (2021)

Shinichi Oeda Daiki Shimizu

Extracting useful information generated from educational settings involves the application of data mining, machine learning, and statistics to the large amount of electronic data collected by educational systems. To generate better higher learning outcomes using an intelligent tutoring system, such as an e-learning system, it is necessary to more accurately understand the state of student knowledge. The purpose of student modeling is to estimate the students' skills from log data, such as examination results, and to predict whether or not a student will be able to solve a problem. In this study, we propose a student performance prediction method using convex factorization machines. Factorization machines offer a combination of the advantages of support vector machines and factorization models such as matrix factorization. The results of conventional methods, which predict student performance using factorization machines, exhibited better results than they have before. However, because factorization machines are not convex optimizations, they acquire local minima, which is a disadvantage. Therefore, we used convex factorization machines in order to improve the performance of student modeling predictions.

10.1016/j.procs.2021.08.083

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Drainage Canal Detection using Machine Learning Algorithm in Tropical Peatlands

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2020)

Haemi Park Daiki Shimizu Wataru Takeuchi

Tropical peatlands have been experienced human-induced disturbances including drainage constructions, oil palm plantations, and wildfires. Since peat soils consist of large organic matter, it is important for the carbon cycle in the tropical area. This study aimed at the delineating of the distribution of drainage canals using microwave images. As the result of validation, overall accuracy was 56.3 % and about 10 % of the peatland areas detected as the drainage canals. For further study, a Keetch Byram Drought Index-based groundwater table will be revised applying the decreasing effect by drainage canals.

10.1109/igarss39084.2020.9323901

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