Chapter 1: Types of Underwater Vehicles
Chapter 2: A Brief History of Underwater Vehicle Development
Chapter 3: Technology Review of ROVs/AUVs
Chapter 4: Coordinate Transformation
Chapter 5: Modelling of Underwater Vehicles
To be continued ...
This course aims to provide the students a basic knowledge about various types of underwater vehicles. Another objective of this course is to provide the students the basic theory behind the design and development of underwater vehicles. The course is intended for students who are either new to the underwater vehicle community or those who desire an overview of the subject matter.
This course provides the students a broad overview of underwater vehicle technology. Following topics are included: (1) Underwater vehicles and their development; (2) Underwater Vehicle Systems, Capabilities, and Technologies; (3) Kinematics and Dynamics of Underwater Vehicles; (4) Positioning of Underwater Vehicles; (5) Navigation of Underwater Vehicles.
Week 1 Introduction and Course Overview
Week 2 Types of Underwater Vehicles (1): Underwater vehicle defined; Manned Underwater vehicles
Week 3 Types of Underwater Vehicles (2): Remotely operated vehicles (ROVs); Autonomous underwater vehicles (AUVs); Deep-towed vehicles (DTV)
Week 4 A Brief History of Underwater vehicles (1): Development of manned submersibles
Week 5 A Brief History of Underwater vehicles (2): Development of ROVs; Development of AUVs
Week 6 Technology Review of ROVs/AUVs (1)
Week 7 Technology Review of ROVs/AUVs (2)
Week 8 Concepts of Coordinate Transformation
Week 9 Modeling of Underwater Vehicles (1): Kinematics; Newtonian and Lagrangian Mechanics
Week 10 Modeling of Underwater Vehicles (2): Rigid-body dynamics; Hydrodynamic forces and moments; Equation of motion
Week 11 Modeling of Underwater Vehicles (3): Hydrodynamic parameter identification
Week 12 Project Midterm Oral Presentation
Week 13 Underwater Positioning (1): Methods of underwater positioning; Ray tracing
Week 14 Underwater Positioning (2): Sensor misalignment calibration
Week 15 Underwater Positioning (3): GPS/Acoustic Geodetics
Week 16 Vehicle Navigation (1): Sensors; Kalman Filter
Week 17 Vehicle Navigation (2): Implementation of Kalman Filter
Week 18 Project Final Oral Presentation
NUMERICAL METHODS WITH MATLAB
Introduction to Matlab
Chapter 0: Numerical Computing and Computers
Chapter 1: Solving Nonlinear Equations
Chapter 2: Solving Sets of Equations
Chapter 3: Interpolation and Curve Fitting
Chapter 4: Approximation of Functions
Chapter 5: Numerical Differentiation and Integration
Chapter 6: Initial Value Problems for Ordinary Differential Equations
Chapter 7: Boundary Value Problems for Ordinary Differential Equations
Chapter 8: Solution of Matrix Eigenvalue Problem
精通MATLAB程式語言寫作方法
善用MATLAB之Toolbox求解工程問題
了解各種數值分析的原理與技巧
數值分析簡介
非線性方程式數值求解
聯立方程式數值求解
內插法與曲線擬合法
函數近似法
數值微分與數值積分
常微分方程式數值求解
邊界值問題數值求解
矩陣特徵值求解
課程簡介、數值方法簡介
非線性方程式求解 (Linear interpolation methods, Newton's method, Muller's method, Fixed-point iteration, Synthetic division method, Multiple roots.)
聯立方程式求解 (Applications, Matrix notation, Gaussian Elimination, Gauss-Jorden method, LU Decomposition, Iterative methods, The Relaxation method, Systems of nonlinear equations. )
內插與曲線擬合 (Lagrangian polynomials, Neville's method, Divided differences, Cubic spline, Bezier curves, B-spline curves.)
函數近似 (Discrete and continuous least squares approximation, Orthogonal polynomials, Chebyshev polynomials, Economized power series, Rational function, Fourier series. )
數值微分與積分 (Central difference, Backward difference, Forward difference, Trapezoidal rule, Simpson's rules, Gaussian quadrature. )
常微分方程求解 (The Taylor-series method, Euler method, Midpoint method, Runge-Kutta methods.)
非線性常微分方程求解 (The Taylor-series method, Runge-Kutta-Fehlberg methods.)
邊界值問題求解 (Shooting method, Finite difference method, Collocation methods, Galerkin methods.)
矩陣特徵值求解 (Power method, Inverse Power method, Jacobi method)
期末考試
NUMERICAL OPTIMIZATION METHODS
Chapter 1: Basic Concepts
Chapter 2: Functions of One Variable
Chapter 3: Unconstrained Functions of N Variables: Zero Order Methods
Chapter 4: Unconstrained Functions of N Variables: First and Second Methods
Chapter 5: Unconstrained Functions of N Variables: Linear Programming
Chapter 6: Constrained Functions of N variables: Sequential Unconstrained Minimization Techniques
Chapter 7: Genetic Algorithms
Chapter 8: A Brief Introduction to MatlOptimization Toolbox
Chapter 9: Applied Optimization Examples
Projects
課程簡介
最佳化基本概念 (Optimization problems, Gradient vector, Hessian matrix, Taylor series expansion, Iterative optimization procedures, Existence and uniqueness of an optimum solution)
單變數最佳化 (Problem definition, Equal interval search, Fibonacci method, Golden section method, Polynomial interpolation, Zero of a function)
無限制條件最佳化：Zero Order Methods (Random search, Powell’s method, Rosenbrock’s method, The simplex method)
無限制條件最佳化：First and Second Order Methods (The steepest descent method, The conjugate gradient method, Newton’s method, Quasi-Newton method, The Davidon-Fletcher-Powell (DFP) method)
MATLAB Optimization toolbox 介紹
Project I 期中報告 （題目自訂）
線性程式最佳化 (Linear programming (LP), LP terminology, The simplex method, The two-phase approach, The big-M method)
具限制條件最佳化 (Exterior penalty function method, Interior penalty function method, Extended interior penalty function method, The augmented Lagrange multiplier (ALM) method)
基因演算法 (Selection, Crossover, Mutation, The Traveling Salesman Problem (TSP))
整數與離散問題之最佳化 (zero-one programming、branch and bound algorithm, Gomory cut method, Farak’s emthod)
Project I 期末報告
Project II: TSP Performance tests
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