2025 (Current Year) Faculty Courses School of Engineering First-Year Courses
Engineering Literacy I g
- Academic unit or major
- First-Year Courses
- Instructor(s)
- Hideyuki Tsukagoshi / Hiraku Sakamoto / Mitsuji Sampei / Yuki Onishi / Akisue Kuramoto / Daisuke Matsuura / Wakako Araki / Katsuko Furukawa / Ming Jiang / Kotaro Hoshiba
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue
- Class
- g
- Course Code
- XEG.B101
- Number of credits
- 0.50.50
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Apr 3, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course aims to become a bridge between general education at high school and specialized education in the subjects of No.200 or higher. This course also aims to cultivate not only basic knowledge of engineering but also sense and attitude for problem solving so that the freshmen of School of Engineering can learn actively specialized subjects after sophomore.
By taking all Engineering literacy I-IV, the students experience the following all seven subjects in Engineering Literacy.
【Water Rocket Development and Control】
【Gliding Locomotion Robot "Gyotaro-IIIa"】
【AI-Drone (Machine Learning and Motion Control)】
【Control】
【Wireless electric car with microcomputer】
【Communication, Computation, and Intelligent Information Processing】
【Industrial Engineering and Economics (Macroeconomics and Data Analysis)】
Course description and aims
By completing this course, students will be able to:
【Control】
1) understand the basic principle of measurement by sensors.
2) understand the basic principle of feedback control.
3) master the design method of control systems.
【AI-Drone (Machine Learning and Motion Control)】
1) Understand the basic principles of model generation by machine learning.
2) Understand the basic principles of feedback control through drones.
3) Understand the importance of real objects and hands-on experience through autonomous drone flight.
Keywords
【Control】
Measurement, Control, System
【AI Drone (Machine Learning)】
Machine learning, Artificial Intelligence, Deep learning, Neural Network, Drone, Control
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
【Control】
This course introduces the basic principle of feedback control using the signal measured by sensors, and holds a competition to help them to understand the effectiveness using the line tracing car.
【AI Drone (Machine Learning)】
Build a drone that flies autonomously by estimating markers in images with models generated from machine learning. Through the experience with these real objects, students will learn the importance of real-life experience and verification.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction: Aim of the course, notice for students, setup of computer systems. | A student should have an overlook of the course and set up his/her computer system for the course. |
| Class 2 | Measurement, sensor, and collision avoidance | Understand how sensors work and how collision can be avoided. |
| Class 3 | Design control system for line tracing | Carry out the design of control system for line tracing. |
| Class 4 | Competition: Evaluation and Award Ceremony | Evaluate the performance of the control through the line tracing competition. |
| Class 5 | Machine learning | Understand the basic principles of machine learning and how to build models. |
| Class 6 | Control of a drone | Understand feedback control using drones. |
| Class 7 | Verification of autonomous drone flight | Understand the importance of real-world verification through image inference and autonomous drone flight. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required
Reference books, course materials, etc.
Documents will be distributed
Evaluation methods and criteria
【Control】
Evaluate the report and the result of the competition.
【AI drone (machine learning)】
Evaluate the report and the result of the autonomous drone flight.
Related courses
- XEG.B101 ： Engineering Literacy I
- XEG.B102 ： Engineering Literacy II
- XEG.B103 ： Engineering Literacy III
- XEG.B104 ： Engineering Literacy IV
Prerequisites
This lecture is only for the freshmen of School of Engineering.
Students are strongly recommended to take all Engineering literacy I-IV to experience all subjects in Engineering Literacy.