慶應義塾大学 · 工学
中村大輔教授の研究室では、流体力学と機械学習の融合を柱とした先端的研究を推進しています。主に乱流や三次元複雑な流れの低次元モデル化に注力し、深層学習を用いた流動状態推定や、直接時分解シミュレーション(DNS)データを活用した次元削減手法の開発を進めています。特に、畳み込みニューラルネットワークとLSTMを組み合わせた機械学習ベースの低次元モデル(ML-ROM)の構築が中心であり、流体工学分野における知的予測・制御の基盤を構築することを目的としています。
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We investigate the applicability of the machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at the friction Reynolds number of Reτ=110 in a minimum domain, which can maintain coherent structures of turbulence. Training datasets are prepared by direct numerical simulation (DNS). The present ML-ROM is constructed by combining a three-dimensional convolutional neural network autoencoder (CNN-AE) and a long short
This article is concerned with what we will call determiner-headed free relatives (DHFRs), e.g. this is the what you do against the Rams. We argue, based on the raising analysis of relative clauses, that this type of relative is a variant of headed relatives whose invisible nominal head is derived from (i) decomposing wh-words into [D wh-] and [N -at], (ii) raising the latter as the nominal head, and (iii) pronouncing the original copy. This analysis attributes their headed-relative-like propert
Neural networks (NNs) and linear stochastic estimation (LSE) have widely been utilized as powerful tools for fluid-flow regressions. We investigate fundamental differences between them considering two canonical fluid-flow problems: (1) the estimation of high-order proper orthogonal decomposition coefficients from low-order their counterparts for a flow around a two-dimensional cylinder, and (2) the state estimation from wall characteristics in a turbulent channel flow. In the first problem, we c
In this paper, we propose a method for use in requirements engineering education in a university, and based on 3 principles. (1) A type of expert system, which accumulates business domain knowledge related to the customer's business field, and answers learners' question on behalf of the customer is necessary. (2) Group-work role-play training is essential to elicit customer's real requirements and analyze them from various points of view. (3) A software agent system to monitor learner's behavior
Role-play exercise is an effective method of teaching project management, in which each student concentrates on playing the role of a stakeholder defined in a virtual project to solve problems from the perspective of his/her role, sharing information in a timely fashion. This paper proposes the introduction of a software agent in on-line role-play exercises, to act as a mentor and provide appropriate hints to students. The analysis of the log data collected from role-play exercises based on the
State estimation from limited measurements can widely be found in various fields such as engineering, biology, and economics. In fluid mechanics, it has also contributed for flow control and experimental data processing. However, its application to fluid flows is particularly difficult due to their nonlinearities and high-degrees of freedom in space and time. Neural networks (NNs) have recently been recognized as a powerful tool to tackle the aforementioned challenges. In this presentation, we e
Fingerprint image enhancement is an important process for improving the matching performance of low quality fingerprints. In order to eliminate high contrast noise lines such as deep wrinkles, which are prevalent in the low quality fingerprint images, we focus on the fact that true ridges have multiple paralleling neighbors. In this report, we propose the parallel ridge filtering method which can strongly suppress non-parallel noise lines by utilizing the parallelism of ridges. We also show the
Role-play training is an effective method for project management education. We have provided a role-play training environment based on online - group-work training to develop student's human elated skills: communication, negotiation and leadership. However students could not share the required information adequately with other students to make a decision about a trouble encountered in a virtual project. In order to cope with this problem, we have developed a software agent system which can play
This squib argues against Merchant’s (2008) analysis of voice mismatches in VP-deletion and pseudogapping by pointing out that it makes a false prediction when extended to sentences in which more than one VP-deletion operation applies.Merchant (2008) argues that while the target of pseudogapping is VoiceP, whose head encodes the active/passive voices, VP-deletion targets VP, the complement of the Voice head.1 The targets of (i) VP-deletion and (ii) pseudogapping are schematically represented as
A role-play training system for enhancing the efficiency of education in project management is proposed and its effectiveness evaluated. The system is essentially a Web application that runs an interactive virtual project, in which each learner assumes the role of one of its stakeholders. It is a non-intelligent system driven by a scenario that defines the scope of the virtual project, gives information about the stakeholders, and issues a series of directions to the participants. An institution
This paper proposes the use of agents to involve learners in supporting online group work for the study of project management. Role-play training may be an appropriate tool for universities to use as an efficient alternative to on-the-job training (OJT) for project management education. However, we have found that because of the way that role-play exercises using an on-line group work training system in the project management course have operated, students have not been able to share the require
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