| １． |
Outline |
|
"System" means a whole set of interacting components. Ecological systems in the natural world consist of interacting living creatures, and network-connected computer systems consist of computers communicating to 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 the 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 work in the computer laboratory (CL). Important contents to be learned in this course are:
1. Introduction to system engineering, modeling principles
2. Mathematical models: static/dynamic, deterministic/stochastic, linear/nonlinear, etc.
3. Graphical models: block diagrams, state transition diagrams
4. Mathematical modeling 1: differential equations, state equations, transfer functions,
5. Mathematical modeling 2: multiple regression, least square method
6. Simulation of differential equations
7. System optimization: linear/nonlinear programming
Students are expected to acquire the knowledge and techniques of DP1.
<Comments>
"System" means a whole set of interacting components. Ecological systems in the natural world consist of interacting living creatures, and network-connected computer systems consist of computers communicating to 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 the 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 to system engineering, modeling principles
2. Mathematical models: static/dynamic, deterministic/stochastic, linear/nonlinear, etc.
3. Graphical models: block diagrams, state transition diagrams
4. Mathematical modeling 1: differential equations, state equations, transfer functions,
5. Mathematical modeling 2: multiple regression, least square method
6. Simulation of differential equations
7. System optimization: linear/nonlinear programming
Students are expected to acquire the knowledge and techniques of DP1.
|
| ２． |
Objectives |
|
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 final examination (60%), on exercise answers at each class (15%), and on the reports of laboratory work (25%).
|
| ４． |
Textbook and Reference |
|
Hiroyuki Tamura: Systems Engineering, Ohm publishing Co. (in Japanese), ISBN-13: 978-4274131677
|
| ５． |
Requirements (Assignments) |
|
In this course, lectures include basic and fundamental things so that students without any knowledge beforehand in systems engineering 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.
At the end of each 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 |
|
The techniques of systems engineering are widely used in every field of technology, and their importance is increasing along with the progress of computer software. The basics of systems engineering are helpful in studying various subjects of aerospace engineering.
|
| ７． |
Schedule |
|
| １. Introduction to systems engineering, how to express a system, purposes and procedures of modelling |
| ２. Mathematical models: static/dynamic, deterministic/probabilistic, linear/nonlinear, constant/discrete, micro/macro model |
| ３. Graphical models: signal model -- block diagram, signal-flow diagram, flow model--network model, flow chart, discrete-event model, state transition diagram |
| ４. How to develop mathematical models (1): modelling with differential equations and with transfer functions, Laplace transform |
| ５. How to develop mathematical models (2): modelling with input and output data--multiple regression analysis and least squares method |
| ６. Laboratory work on multiple regression analysis (1) |
| ７. Laboratory work on multiple regression analysis (2) |
| ８. Principles of computer simulations, modelling of an ecological system with differential equations |
| ９. Numerical methods to solve differential equations, Euler's method, application to an ecological system simulation |
| 10. Laboratory work of simulation on an ecological system |
| 11. System optimization: concepts and expressions, objective function and constraints, linear programming (LP) and its example (mixing of livestock feeds) |
| 12. Nonlinear programming (NLP), search method for the optimal point |
| 13. Laboratory work: linear and nonlinear programming |
| 14. Nonlinear programming with constraints, optimization with penalty in the objective function |
| 15. Review and exercises, examination |
|
| １． |
Outline |
|
"System" means a whole set of interacting components. Ecological systems in the natural world consist of interacting living creatures, and network-connected computer systems consist of computers communicating to 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 the 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 work in the computer laboratory (CL). Important contents to be learned in this course are:
1. Introduction to system engineering, modeling principles
2. Mathematical models: static/dynamic, deterministic/stochastic, linear/nonlinear, etc.
3. Graphical models: block diagrams, state transition diagrams
4. Mathematical modeling 1: differential equations, state equations, transfer functions,
5. Mathematical modeling 2: multiple regression, least square method
6. Simulation of differential equations
7. System optimization: linear/nonlinear programming
Students are expected to acquire the knowledge and techniques of DP1.
<Comments>
"System" means a whole set of interacting components. Ecological systems in the natural world consist of interacting living creatures, and network-connected computer systems consist of computers communicating to 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 the 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 to system engineering, modeling principles
2. Mathematical models: static/dynamic, deterministic/stochastic, linear/nonlinear, etc.
3. Graphical models: block diagrams, state transition diagrams
4. Mathematical modeling 1: differential equations, state equations, transfer functions,
5. Mathematical modeling 2: multiple regression, least square method
6. Simulation of differential equations
7. System optimization: linear/nonlinear programming
Students are expected to acquire the knowledge and techniques of DP1.
|
| ２． |
Objectives |
|
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 final examination (60%), on exercise answers at each class (15%), and on the reports of laboratory work (25%).
|
| ４． |
Textbook and Reference |
|
Hiroyuki Tamura: Systems Engineering, Ohm publishing Co. (in Japanese), ISBN-13: 978-4274131677
|
| ５． |
Requirements (Assignments) |
|
In this course, lectures include basic and fundamental things so that students without any knowledge beforehand in systems engineering 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.
At the end of each 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 |
|
The techniques of systems engineering are widely used in every field of technology, and their importance is increasing along with the progress of computer software. The basics of systems engineering are helpful in studying various subjects of aerospace engineering.
|
| ７． |
Schedule |
|
| １. Introduction to systems engineering, how to express a system, purposes and procedures of modelling |
| ２. Mathematical models: static/dynamic, deterministic/probabilistic, linear/nonlinear, constant/discrete, micro/macro model |
| ３. Graphical models: signal model -- block diagram, signal-flow diagram, flow model--network model, flow chart, discrete-event model, state transition diagram |
| ４. How to develop mathematical models (1): modelling with differential equations and with transfer functions, Laplace transform |
| ５. How to develop mathematical models (2): modelling with input and output data--multiple regression analysis and least squares method |
| ６. Laboratory work on multiple regression analysis (1) |
| ７. Laboratory work on multiple regression analysis (2) |
| ８. Principles of computer simulations, modelling of an ecological system with differential equations |
| ９. Numerical methods to solve differential equations, Euler's method, application to an ecological system simulation |
| 10. Laboratory work of simulation on an ecological system |
| 11. System optimization: concepts and expressions, objective function and constraints, linear programming (LP) and its example (mixing of livestock feeds) |
| 12. Nonlinear programming (NLP), search method for the optimal point |
| 13. Laboratory work: linear and nonlinear programming |
| 14. Nonlinear programming with constraints, optimization with penalty in the objective function |
| 15. Review and exercises, examination |
|
|