This document discusses non-structured data analysis, focusing on image data. It defines structured and non-structured data, with images, text, and audio given as examples of non-structured data. Images are described as high-dimensional vectors that are generated from analog to digital conversion via sampling and quantization. Various types of image data and analysis tasks are introduced, including image recognition, computer vision, feature extraction and image compression. Image processing techniques like filtering and binarization are also briefly covered.
This document discusses non-structured data analysis, focusing on image data. It defines structured and non-structured data, with images, text, and audio given as examples of non-structured data. Images are described as high-dimensional vectors that are generated from analog to digital conversion via sampling and quantization. Various types of image data and analysis tasks are introduced, including image recognition, computer vision, feature extraction and image compression. Image processing techniques like filtering and binarization are also briefly covered.
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.
14. DEPARTMENT OF
SYSTEMS INNOVATION
工学系研究科 システム創成学専攻
ADVENTURE_Matesの処理フロー (1)
14
開始
ファイル読み込み
マップ生成
終了条件?
終了
ステップ数
加算
Yes
No
描画/車両情報出力
ドライバモデル処理
車両移動モデル処理
車両発生処理
for each OD-node
(車両発生処理)
予定された
車両発生時刻?
目的地決定,経路選択
車両生成
発生待機列に追加
発生待機列に
車両が存在?
次の車両発生時刻を算出
発生点前方に
十分な空き?
発生待機列から車両を取り出して
道路上に配置
for each car agent
15. DEPARTMENT OF
SYSTEMS INNOVATION
工学系研究科 システム創成学専攻
ADVENTURE_Matesの処理フロー (2)
15
開始
ファイル読み込み
マップ生成
終了条件?
終了
ステップ数
加算
Yes
No
描画/車両情報出力
ドライバモデル処理
車両移動モデル処理
車両発生処理
for each OD-node
for each car agent
(ドライバモデル,車両移動処理)
情報取得
「仮想先行車」生成
速度決定要因抽出
速度更新
車線を移る?
希望加速度決定
車線変更が
必要?
車線変更開始
車線移動処理
位置更新
18. DEPARTMENT OF
SYSTEMS INNOVATION
工学系研究科 システム創成学専攻
自動車エージェントの機能 (1)
エージェントの経路選択機構
経路はO(Origin: 出発地)とD(Destination: 目的地)とを結ぶ交差点の
リストとして個々のエージェントが保持
経路選択にはA*アルゴリズムを用いる
[Hart, et al., A Formal Basis for the Heuristic Determination of Minimum Cost Paths,
IEEE, Transactions on Systems Science and Cybernetics,, Vol. 4, No. 2, pp. 100-107, 1968]
以下の6つの要因を考慮して各エージェントの好みの経路を選択する
1. 経路の長さ
2. 予想旅行時間
3. 交差点での直進回数
4. 交差点での左折回数
5. 交差点での右折回数
6. 通過する単路部の幅の逆数 … 値が小さい方が高効用
走行中の状況により予定された経路から外れた場合には再探索する
18