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情報数理学専攻 令和4年度情報数理学セミナー

第5回

日時: 6月9日(木) 13:30~
会場: 情報科学研究科A棟 A110
内容: 博士論文公聴会
講演者: RATANAKUAKANGWAN SUDLOP(森田研)
講演題目: Efficiency Measurement of Energy Planning under Uncertainties
概要: This study proposes a framework that combines the concepts of an efficiency measurement and a multi-objective optimization model in order to determine the most efficient energy mix considering the multi-dimensional aspects of energy requirements and various uncertainty scenarios. Extending the focus beyond the Energy Trilemma (i.e., energy affordability, energy security, and environmental protection), the proposed model incorporates aspects of social impact and social benefit. Uncertainties in future projections include future demand, technological advancements in renewable energy power plants, cost reductions in renewable energy, social impact fluctuations and reliable capacity. Unlike other optimization models that tend to focus exclusively on either scenario-based or worst-case scenario realization, the proposed approach takes both uncertainties into account based on their practical condition. Various multi-objective functions were then appended in order to include some of the broader aspects of energy planning. A slacks-based measure of efficiency methodology was then applied to determine the best energy mix from the set of results produced by the proposed model. The empirical results from the study provide quantitative support for policy makers seeking to determine an efficient energy policy that maximizes the satisfaction of multiple requirements, while taking into account various scenarios of future uncertainties.

第4回

日時: 6月2日(木) 13:30~15:00
会場: 情報科学研究科A棟 A110
講演者: 来嶋 秀治(滋賀大学 データサイエンス学部 教授)
講演題目: マルコフ連鎖モンテカルロ(MCMC)法と完璧サンプリング
概要: マルコフ連鎖モンテカルロ(MCMC)法は、所望の定常分布をもつマルコフ連鎖を設計し、その極限分布(=定常分布)からサンプリングを行う素朴なアルゴリズムです。通常のMCMC法では、極限分布とみなせるまで、マルコフ連鎖を十分な回数推移させてサンプリングを行いますが、有限回の推移で打ち切ると、厳密には打ち切りによる誤差が生じます。過去からのカップリング(coupling from the past)法はマルコフ連鎖のシミュレーションを工夫することで、厳密に定常分布からサンプリングするアルゴリズムです。本発表では、話者の過去の研究から、2行分割表の一様サンプリングを題材に、過去からのカップリング法を紹介します。

第3回

日時: 5月19日(木) 15:10~16:10
会場: P1-311
担当: 小西 毅准教授、木村 吉秀准教授
講演題目: 安全教育講習会Ⅱ Lectures for safety education 2
目的: 博士前期・後期課程の教育・研究において、またその後の社会活動においても重要な事柄である「安全」について、その概略を講義する。
The lectures are delivered for understanding the guideline of "safety" which is important for social activities following the education and research of the master and doctoral course.
内容:
  1. 15:10~15:40 小西 毅「電気、電子機器、電磁波、赤外・紫外光、レーザー光」
  2. 15:40~16:10 木村 吉秀「各種機械・工具・工作機械、放射線、粒子線」」

第2回

日時: 5月12日(木) 13:30~14:30
会場: 情報科学研究科A棟 A210/A212
内容: 博士後期課程中間発表会
講演者: ZHANG ZHICHENG(藤崎研)
講演題目: Probabilistic Guarantees in Sparse Robust Optimization: Theory and Applications
概要: Sparse robust optimization is a general sparse optimization with uncertain constraints that minimizes the decision variables associated with L_0 cost to promote the sparsity as well as hedges against the uncertainty to satisfy the robustness. A crucial step for robust analysis of uncertainty in this study is probabilistic relaxation on feasibility, that is, uncertain variable is modeled as a finite number of i.i.d. randomly sampled "scenarios" from a probability distribution. Besides, the challenge of sparse optimization is to perform the L_0 norm program, namely, calculate the support or cardinality of decision variable set, which is an non-convex optimization and hence, it is extremely difficult to solve. In this study, we introduce convex relaxation in cost function and shows that the relaxed L_1 norm still induces the sparse decision. Therefore, the original sparse robust optimization converts into a convex L_1 norm scenario optimization problem, and the obtained sparse scenario solution is with a high probabilistic robustness for uncertainty. Finally, we illustrate the theoretical results on sparse robust control in uncertain discrete-time systems. Numerical results suggest that the proposed method provides a high probabilistic robustness guarantee for the obtained sparse control input to practical control system applications.
講演者: ASAVANANT TISSAWAT (森田研)
講演題目: Forecastability analysis for passenger rail network Origin-Destination matrix: Statistically distributed variability approach
概要: Static OD-matrix Estimation problem gives seed matrix representation of the demand which is then uses as input for dynamic extension given correlation to additional information. In rail network, seed matrix statistically represents the average value explaining the variability of the demand and is the deciding factor for scheduling problem. Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest of multiple models using varieties of data, the relationships between features (e.g, temporal dependence or time slices, trend, linearity features) and actual variability (evaluation of estimated seed matrix and actual demand) are scarcely analyzed in the literature. In this study, multiple observations of entering time-based OD-matrices of rail networks are used to developed systematic distribution forecastability analysis framework to fill the gap in literature. First, a multinomial distributed data is assumed, and parameter estimation is conducted using maximum likelihood estimator (MLE) from multivariate discrete data and multivariate transformed sampling proportions. Secondly, linearly defined homogenous scaling factor is used to classify ODM components by critical condition derived from related distributions attributing to the evaluation of the seed matrix. Lastly, forecastability evaluation criteria are investigated by sub-components of the OD-matrix. The proposed method was tested using real data of 2 subway networks (P002 and P502) of Bangkok, Thailand. Our proposed framework demonstrated the forecastability of the network depending on factors (i.e., time-slices and calendar classification, users' behavior analysis and stability, proportionality approach and individual discrete bin, and classification-based forecasting criteria).

第1回

日時: 4月28日(木) 15:10~16:10
会場: P1-311
担当: 松﨑 賢寿助教、馬越 貴之講師
講演題目: 安全教育講演Ⅰ Lectures for safety education 1
目的: 博士前期・後期課程の教育・研究において、またその後の社会活動においても重要な事柄である「安全」について、その概略を講義する。
The lectures are delivered for understanding the guideline of "safety" which is important for social activities following the education and research of the master and doctoral course.
内容:
  1. 15:10~15:40 松﨑 賢寿「研究倫理、CITI Japanプログラムのe-learning教材、および化学・生物実験に関する安全講習」
  2. 15:40~16:10 馬越 貴之「コンピュータセキュリティ」