Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
Robust Vehicle Localization in Urban Environments Using Probabilistic MapsKitsukawa Yuki
研究室ゼミでの論文紹介資料です。
Robust Vehicle Localization in Urban Environments Using Probabilistic Maps
Jesse Levinson, Sebastian Thrun
International Conference on Robotics and Automation (ICRA), 2010
Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
Robust Vehicle Localization in Urban Environments Using Probabilistic MapsKitsukawa Yuki
研究室ゼミでの論文紹介資料です。
Robust Vehicle Localization in Urban Environments Using Probabilistic Maps
Jesse Levinson, Sebastian Thrun
International Conference on Robotics and Automation (ICRA), 2010
2013年7月16日にシンガポールで開催された第一回アジア組み合わせテストワークショップ(1st Asian Workshop on Combinatorial Testing for Complex Computer Systems)で発表した"Combinatorial Testing in Japan"のスライドを日本語にしました(だいぶ遅くなりましたが)。
英語版はこちら
https://www.slideshare.net/Bugler/combinatorial-testing-injapan20130616
Dynamic Time Warping を用いた高頻度取引データのLead-Lag 効果の推定Katsuya Ito
This paper investigates the Lead-Lag relationships in high-frequency data.
We propose Multinomial Dynamic Time Warping (MDTW) that deals with non-synchronous observation, vast data, and time-varying Lead-Lag.
MDTW directly estimates the Lead-Lags without lag candidates. Its computational complexity is linear with respect to the number of observation and it does not depend on the number of lag candidates.
The experiments adopting artificial data and market data illustrate the effectiveness of our method compared to the existing methods.
2013年7月16日にシンガポールで開催された第一回アジア組み合わせテストワークショップ(1st Asian Workshop on Combinatorial Testing for Complex Computer Systems)で発表した"Combinatorial Testing in Japan"のスライドを日本語にしました(だいぶ遅くなりましたが)。
英語版はこちら
https://www.slideshare.net/Bugler/combinatorial-testing-injapan20130616
Dynamic Time Warping を用いた高頻度取引データのLead-Lag 効果の推定Katsuya Ito
This paper investigates the Lead-Lag relationships in high-frequency data.
We propose Multinomial Dynamic Time Warping (MDTW) that deals with non-synchronous observation, vast data, and time-varying Lead-Lag.
MDTW directly estimates the Lead-Lags without lag candidates. Its computational complexity is linear with respect to the number of observation and it does not depend on the number of lag candidates.
The experiments adopting artificial data and market data illustrate the effectiveness of our method compared to the existing methods.
The document summarizes research on simulating hydrogen dispersion using the ADVENTURE_sFlow solver. It describes modeling hydrogen dispersion as an analogy to thermal convection problems. Two models are analyzed: a hallway model and a car garage model. The hallway model analyzes hydrogen dispersion from inlet, door, and roof vents in an empty volume. The car garage model analyzes hydrogen leakage from a fuel cell car in a full-scale garage. The objective is to demonstrate the feasibility of using the ADVENTURE_sFlow solver, which uses a hierarchical domain decomposition method, to efficiently solve large-scale problems like hydrogen dispersion in engineering facilities.
Stationary Incompressible Viscous Flow Analysis by a Domain Decomposition MethodADVENTURE Project
This document describes an iterative domain decomposition method for analyzing large-scale stationary incompressible viscous flow problems using finite element analysis. The method decomposes the domain into subdomains and solves the inner degrees of freedom using a skyline solver. Interface degrees of freedom are solved using preconditioned BiCGSTAB or GPBiCG iterative solvers. Numerical examples are provided to demonstrate the method on problems with over 1 million degrees of freedom and compare results to a monolithic finite element method solver.
16. 流体解析 流体解析
時刻 t 時刻 t+Δt
構造解析 構造解析
圧力 圧力
t
p tt
p
連成解析法の分類
片方向連成(流体 → 構造)/Off-line vs. On-line
姜玉雁,吉村忍,今井隆太,桂裕之,吉田哲也,加藤千幸,”多段遠心ポンプの流体誘起固体伝播
音の高精度計算法と騒音発生機構の解明”,日本機械学会論文集C編,72-719 (2006), 2065-2072 16
17. 流体解析 流体解析
時刻 t 時刻 t+Δt
構造解析 構造解析
構造予測子
構造予測子
圧力 圧力
変位
tt
d
ttttt
dtdtdd 2
)(
2
1
t
p tt
p
連成解析法の分類
双方向連成(流体 → 構造):単純互い違い法の場合
17
18. )1()()(
)1(
ititit
ddd
流体解析 流体解析
時刻 t 時刻 t+Δt
構造解析 構造解析
圧力 圧力
変位
)(it
d
)1( it
p
)1( itt
p
変位
)(itt
d
反復計算
連成解析法の分類
双方向連成(流体 ⇔ 構造):分離反復法の場合
T. Yamada, S. Yoshimura, “Line Search Partitioned Approach for Fluid-Structure Interaction Analysis of
Flapping Wing”, Computer Modeling in Engineering and Sciences, 24-1 (2008), 51-60
S. Minami, S. Yoshimura, “Performance Evaluation on Nonlinear Algorithms with Line-Search for
Partitioned Coupling Techniques for Fluid-Structure Interactions”, International Journal for Numerical
Methods in Fluids, 64(10-12) (2010), 1129-1147 18
19. ADVENTURE
Coupler
ADVENTURE_Couplerによる分離反復型連成解析
11
nn
F dp
)( 11
nn
S pd
tn-1 tn
d p d p d p
構造解析
流体解析
ADVENTURE_Solid
収束判定
更新量の計算
(Broyden法など)
物理量マッピング
d
ADVENTURE_Thermal
物理量マッピング
p
構造解析ソルバー 音響流体用ポアソン解析ソルバー
反復
19
S.Kataoka, S.Minami, H.Kawai, T.Yamada, S.Yoshimura,“A Parallel Iterative Partitioned
Coupling Analysis System for Large-Scale Acoustic Fluid-Structure Interactions”,
Computational Mechanics, Vol.53, No.6, pp.1299-1310, (2014)
20. 連成反復アルゴリズムの選択
■ 種々の連成アルゴリズムが提案されてきた。
■ その中から、次の判断基準に基づき、アルゴリズムを選定した。
(1) ロバストな収束
(2)各ソルバをブラックボックス的に活用
(3) 容易かつ効率的な並列化
Requires matrix &
Jacobian
Newton’s
method
Inexact
Newton
Ability to treat each solver as a black box
Gauss
Seidel
Matrix free
Newton-Klyrov
Quasi-Newton
(Broyden
Method)
S. Minami, S. Yoshimura, “Performance Evaluation on Nonlinear Algorithms with Line-Search for
Partitioned Coupling Techniques for Fluid-Structure Interactions”,
International Journal for Numerical Methods in Fluids, 64(10-12) (2010), 1129-1147. 20
38. Disp. BC at Leading-edge
Flexible
(elastic)
Rigid
Prescribed Rotation of
Flapping at Wing Root
Flapping Motion
翼長, 翼幅, 翼厚 5.36, 1.00, 0.075
翼幅方向の弾性部の長さ 0.25
石原らの実験*を参考に、
翼サイズを設定した。
*D.Ishihara, Y.Yamashita, T. Horie, S. Yoshida and T. Niho, “Passive Maintenance of High
Angle of Attack and Its Lift Generation during Flapping Translation in Crane Fly Wing”, The
Journal of Experimental Biology, 212 (2009), 3882-3891.
y
z x
Ishihara’s Experimental Model
38
弾性羽ばたき翼のパラメトリックスタディー
ガガンボ