| １． |
Outline |
|
"System" means a whole set of interacting components. Ecological systems in natural world consist of interacting living creatures. Network-connected computer systems consist of computers communicating with each other. "Systems engineering" deals with investigation, analysis, planning, and operation of various systems. It has become more important as economic developments and advancing technologies have more impact on natural environment and make artificial systems more complicated and influential.
This course consists of lectures in the classroom with exercises of practical problems, and laboratory works in the computer laboratory (CL).
Important contents to be learned in this course are:
1. Introduction, modeling principles
2. Outline of mathematical and graphical models
4. Techniques of mathematical modeling
5. Simulations of deterministic systems
6. Probability distribution, simulation of a queuing system
7. Optimization 1: mathematical programming, linear/nonlinear programming
8. Optimization 2: genetic algorithm
Through this course, students are expected to acquire the knowledge and technical methods with respect to Diploma Policy 1.
|
| ２． |
Objectives |
|
In this course, students are expected to learn and understand the principles and important techniques of systems engineering. Lectures are given in such a way that even students without basic knowledge of systems engineering can understand the lectures.
The first objective is to learn and understand the basic knowledge and techniques of modelling-- how to express a system with mathematic equations and/or graphical diagrams, and to be able to make a model for a system of small size.
The second objective is to learn the concepts and techniques of computer simulation, and to be able to obtain system features and behaviors through simulations with various different settings or conditions.
The third objective is to learn and understand the concepts and techniques of system optimization, and to be able to optimize a well-defined system by running an optimization software.
|
| ３． |
Grading Policy |
|
Grading policy is based on the results of exercise answers (50%) and reports on laboratory works (50%).
|
| ４． |
Textbook and Reference |
|
Textbook: H. Tamura, et al., "Systems engineering", Ohm publishing Co. (in Japanese)
[田村 坦之 編著：「システム工学」 オーム社, (1999), ISBN-13: 978-4274131677, \1,824 ]
|
| ５． |
Requirements (Assignments) |
|
This course puts an emphasis on basic and fundamental things of systems engineering so that students without any knowledge beforehand can follow the lecture. Students with enough knowledge in this field are not necessary to take this course.
Various techniques in systems engineering require mathematical knowledge. When a student does not have enough knowledge and understanding in mathematics, for example, in differentials and integrals, vectors and matrices, exponential and logarithmic functions, etc., he/she is required to review and study these fields beforehand.
Before each class, students should prepare for the class by studying with the textbook or with related materials，and write down the things hard to understand in a notebook.
Sometimes at the end of the class, exercise questions useful for review are given to students. After the class, students should review the things learned and write down exercise answers on an answer sheet. The answer sheet should be submitted at the beginning of the next class. After submission, the answers are explained in the class and students should understand and write down the procedure to reach correct answers.
Before and after the class together, students should spend at least two hours in average for the above-mentioned preparation, review and exercise work, and in this course, students should spend at least 30 hours in total.
|
| ６． |
Note |
|
As mentioned above, this course is intended to give lectures which are understandable to students without any knowledge beforehand in systems engineering. Students of various different technological fields are welcome to this course.
|
| ７． |
Schedule |
|
| １. Introduction to systems engineering |
| ２. Systems description—mathematical models 1: differential equation, transfer function |
| ３. Systems description—mathematical models 2: least squares method, multiple regression |
| ４. Laboratory work 1: multiple regression |
| ５. Systems description—graphical models: state transition, adjacency matrix |
| ６. Differential equation and simulation: Euler's method, Runge-Kutta method |
| ７. Laboratory work 2: simulation of differential equations |
| ８. Probability distribution 1: uniform/normal distribution, random number generation |
| ９. Probability distribution 2: Poison/exponential distribution |
| 10. Stochastic systems and simulation, queuing system |
| 11. Laboratory work 3: queuing system |
| 12. System optimization 1: linear programming (LP) |
| 13. System optimization 2: nonlinear programming (NLP) |
| 14. Laboratory work 4: linear and nonlinear programming |
| 15. Genetic algorithm, reviews and exercises |
|
| １． |
Outline |
|
"System" means a whole set of interacting components. Ecological systems in natural world consist of interacting living creatures. Network-connected computer systems consist of computers communicating with each other. "Systems engineering" deals with investigation, analysis, planning, and operation of various systems. It has become more important as economic developments and advancing technologies have more impact on natural environment and make artificial systems more complicated and influential.
This course consists of lectures in the classroom with exercises of practical problems, and laboratory works in the computer laboratory (CL).
Important contents to be learned in this course are:
1. Introduction, modeling principles
2. Outline of mathematical and graphical models
4. Techniques of mathematical modeling
5. Simulations of deterministic systems
6. Probability distribution, simulation of a queuing system
7. Optimization 1: mathematical programming, linear/nonlinear programming
8. Optimization 2: genetic algorithm
Through this course, students are expected to acquire the knowledge and technical methods with respect to Diploma Policy 1.
|
| ２． |
Objectives |
|
In this course, students are expected to learn and understand the principles and important techniques of systems engineering. Lectures are given in such a way that even students without basic knowledge of systems engineering can understand the lectures.
The first objective is to learn and understand the basic knowledge and techniques of modelling-- how to express a system with mathematic equations and/or graphical diagrams, and to be able to make a model for a system of small size.
The second objective is to learn the concepts and techniques of computer simulation, and to be able to obtain system features and behaviors through simulations with various different settings or conditions.
The third objective is to learn and understand the concepts and techniques of system optimization, and to be able to optimize a well-defined system by running an optimization software.
|
| ３． |
Grading Policy |
|
Grading policy is based on the results of exercise answers (50%) and reports on laboratory works (50%).
|
| ４． |
Textbook and Reference |
|
Textbook: H. Tamura, et al., "Systems engineering", Ohm publishing Co. (in Japanese)
[田村 坦之 編著：「システム工学」 オーム社, (1999), ISBN-13: 978-4274131677, \1,824 ]
|
| ５． |
Requirements (Assignments) |
|
This course puts an emphasis on basic and fundamental things of systems engineering so that students without any knowledge beforehand can follow the lecture. Students with enough knowledge in this field are not necessary to take this course.
Various techniques in systems engineering require mathematical knowledge. When a student does not have enough knowledge and understanding in mathematics, for example, in differentials and integrals, vectors and matrices, exponential and logarithmic functions, etc., he/she is required to review and study these fields beforehand.
Before each class, students should prepare for the class by studying with the textbook or with related materials，and write down the things hard to understand in a notebook.
Sometimes at the end of the class, exercise questions useful for review are given to students. After the class, students should review the things learned and write down exercise answers on an answer sheet. The answer sheet should be submitted at the beginning of the next class. After submission, the answers are explained in the class and students should understand and write down the procedure to reach correct answers.
Before and after the class together, students should spend at least two hours in average for the above-mentioned preparation, review and exercise work, and in this course, students should spend at least 30 hours in total.
|
| ６． |
Note |
|
As mentioned above, this course is intended to give lectures which are understandable to students without any knowledge beforehand in systems engineering. Students of various different technological fields are welcome to this course.
|
| ７． |
Schedule |
|
| １. Introduction to systems engineering |
| ２. Systems description—mathematical models 1: differential equation, transfer function |
| ３. Systems description—mathematical models 2: least squares method, multiple regression |
| ４. Laboratory work 1: multiple regression |
| ５. Systems description—graphical models: state transition, adjacency matrix |
| ６. Differential equation and simulation: Euler's method, Runge-Kutta method |
| ７. Laboratory work 2: simulation of differential equations |
| ８. Probability distribution 1: uniform/normal distribution, random number generation |
| ９. Probability distribution 2: Poison/exponential distribution |
| 10. Stochastic systems and simulation, queuing system |
| 11. Laboratory work 3: queuing system |
| 12. System optimization 1: linear programming (LP) |
| 13. System optimization 2: nonlinear programming (NLP) |
| 14. Laboratory work 4: linear and nonlinear programming |
| 15. Genetic algorithm, reviews and exercises |
|
|