研究者業績

深澤 佑介

フカザワ ユウスケ  (Yusuke Fukazawa)

基本情報

所属
上智大学 応用データサイエンス学位プログラム 准教授 (Associate Professor)

研究者番号
80776617
J-GLOBAL ID
202001012805218090
researchmap会員ID
R000003283

外部リンク

論文

 94
  • 渡邉真, 深澤佑介
    人工知能学会論文誌 40(5) 2025年9月  査読有り最終著者責任著者
  • Taeko Sato, Yusuke Fukazawa
    International Journal of Data Science and Analytics, Springer 2025年6月16日  査読有り最終著者責任著者
  • Shin Nakamoto, Yusuke Fukazawa
    International Journal of Data Science and Analytics, Springer 2025年  査読有り最終著者責任著者
  • Masahiro Suzuki, Yusuke Fukazawa
    Journal of Information Processing 2025年  査読有り最終著者責任著者
  • 三村 知洋, 石黒 慎, 鈴木 喬, 山田 曉, 深澤 佑介
    情報処理学会論文誌 65(6) 1058-1070 2024年6月15日  査読有り最終著者責任著者
    近年,バイクシェアサービス(Bicycle Sharing Services; BSS)に注目が集まっている.BSSの利用が増えることで,専用駐輪場(ポート)の自転車溢れ・不足の問題が顕在化している.この問題の解決方法の1つとして自転車が溢れているポートから自転車が不足しているポートへ自転車を移動する再配置がある.再配置業務をより効率的に行うためには正確な自転車の需要を予測することが重要である.そこで,本研究ではBSSの需要をポート・時間ごとに予測する機械学習モデルを提案する.提案手法は,変分オートエンコーダーに時系列生成モデルを組み合わせたモデルである.我々はこの手法を“Conditional Variational Autoencoders considering Partial Future data”(CVAE-PF)と名付けた.我々はオフライン実験・オンライン実験を通じて提案手法の評価を行った.オフライン実験ではRoot Mean Square Error(RMSE)を用いた評価を行い,提案手法の予測精度が比較手法に比べて高いことを示した.また,オンライン実験では従来の再配置方法に比べ,ポートごとの自転車の溢れ・不足を減少させることができることを確認した. In recent years, Bicycle Sharing Services (bike-shares) have been established worldwide. One important aspect of bike-share management is to periodically rebalance the positions of the available bikes. Because the bike demand varies by and over time, the number of bikes at each bike-port tends to become unbalanced. To efficiently rebalance a bike-share system, it is essential to predicting the number of bikes in each bike-port. In this paper, we propose a method to predicting bike demand and the number of bike pickups and drop offs at each bike-port every hour, up to 24 hours in advance. To predict demand, we used a time series generation model based on the Variational Autoencoders model and the Attention based Sequence to Sequence learning model. We named this method “Conditional Variational Autoencoders considering Partial Future data” (CVAE-PF). In the experiment, our proposed method showed higher prediction accuracy in root mean square error (RMSE) compared to conventional methods. In addition, the constructed demand model of bicycle sharing services was used for rebalance, and it was confirmed that rebalance could be carried out more efficiently than with conventional methods.

MISC

 1

講演・口頭発表等

 63
  • 伊藤 拓, 深澤 佑介, 沖村 宰, 山下 祐一, 前田 貴記, 太田 順
    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 2016年5月26日 電子情報通信学会
  • 落合桂一, 落合桂一, 山田渉, 深澤佑介, 菊地悠, 松尾豊
    情報処理学会論文誌トランザクション データベース(Web) 2016年
  • 高橋柊, 菊地悠, 深澤佑介
    電子情報通信学会技術研究報告 2016年
  • 伊藤拓, ZHU Dandan, 深澤佑介, 太田順
    電子情報通信学会技術研究報告 2015年
  • Takuya Shinmura, Yusuke Fukazawa, Dandan Zhu, Jun Ota
    研究報告モバイルコンピューティングとユビキタス通信(MBL) 2013年11月7日 一般社団法人情報処理学会
    we propose a method of predicting human's activity, including the location and purpose, by using Twitter posts with location information. The proposed method predicts target users' activities based on the location transition and tweet of users in the database. Concretely, we adopt both the similarity of current location and interest, and the similarity of long term interest and location to select the base user and tweet. And then, we can utilize these two baselines to predict target users' activities. We evaluate the proposed method by the following two points: one is the error range of the distance, and the other is the similarity of tweet contents. We used three months of Twitter data with location information (almost 40 mil.) as the database. The experiment results demonstrate that the prediction accuracy of the proposed method is superior to the two control groups which only consider one of the similarity of current location and interest and the similarity of long term interest and location.we propose a method of predicting human's activity, including the location and purpose, by using Twitter posts with location information. The proposed method predicts target users' activities based on the location transition and tweet of users in the database. Concretely, we adopt both the similarity of current location and interest, and the similarity of long term interest and location to select the base user and tweet. And then, we can utilize these two baselines to predict target users' activities. We evaluate the proposed method by the following two points: one is the error range of the distance, and the other is the similarity of tweet contents. We used three months of Twitter data with location information (almost 40 mil.) as the database. The experiment results demonstrate that the prediction accuracy of the proposed method is superior to the two control groups which only consider one of the similarity of current location and interest and the similarity of long term interest and location.
  • 深澤佑介, 深澤佑介, 太田順
    情報処理学会研究報告(CD-ROM) 2012年
  • Karapetsas Eleftherios, Yusuke Fukazawa, Jun Ota
    研究報告モバイルコンピューティングとユビキタス通信(MBL) 2011年8月29日
    The web is thriving with websites containing How-To articles and DIY guides. In websites like that information about activities and guides how to accomplish them are located. An activity is defined as any action that a person can accomplish. Our research focuses on Retrieving activities from the Web related to a user's search query. To accomplish that we have created a system that performs meta-search in multiple How-To websites using semantic query expansion. An overview of the activities retrieval system is presented along with the explanation of the query expansion algorithm which utilizes ConceptNet. Finally experimental results gathered from a user study are given in order to evaluate the performance of our system.The web is thriving with websites containing How-To articles and DIY guides. In websites like that information about activities and guides how to accomplish them are located. An activity is defined as any action that a person can accomplish. Our research focuses on Retrieving activities from the Web related to a user's search query. To accomplish that we have created a system that performs meta-search in multiple How-To websites using semantic query expansion. An overview of the activities retrieval system is presented along with the explanation of the query expansion algorithm which utilizes ConceptNet. Finally experimental results gathered from a user study are given in order to evaluate the performance of our system.
  • Yusuke Fukazawa, Jun Ota
    International Journal of Knowledge-Based and Intelligent Engineering Systems 2011年
  • 深澤 佑介, 太田 順
    研究報告モバイルコンピューティングとユビキタス通信(MBL) 2010年11月4日 情報処理学会
    本稿では,ユーザの実世界行動モデルの構築を軽減するため,クラスタリング手法の適用可能性を検証する.検証実験の結果,マニュアルで構築したモデルと 50% 程度重複するモデルを構築することができた.現状,実世界行動モデルをマニュアルで構築するには 1 ヶ月程度かかることから,クラスタリングとマニュアルを組み合わせることで構築に要する時間を劇的に削減できると考えられる.To construct user's real world activity model in an automatic way, in this paper, the possibility to use the clustering method is verified. As the experimental result, the taxonomic overlap between model constructed with clustering and manually constructed reference model becomes almost 50%. Considering that we need one month to construct the real world activity model buy manual, this result indicates that the time required constructing the model will be reduced dramatically by using the clustering with manual way of constructing the model.
  • 深澤 佑介, 太田 順
    人工知能学会全国大会論文集 2010年 人工知能学会
  • 笹嶋宗彦, 古谷孝一郎, 來村徳信, 深澤佑介, 長沼武史, 倉掛正治, 溝口理一郎
    人工知能学会全国大会論文集(CD-ROM) 2009年
  • 深澤佑介, 長沼武史, 倉掛正治
    電子情報通信学会技術研究報告 2008年
  • 深澤佑介, 長沼武史, 町田基宏, 倉掛正治
    電子情報通信学会大会講演論文集 2007年

所属学協会

 4

共同研究・競争的資金等の研究課題

 4

産業財産権

 274