Course Objective:   Gaining knowledge about: the main characteristics of the Cyber-Physical Systems, their application areas, components selection rules, programming methodology, specific aspects related to different measured physical parameters, data storage, reporting and communications.

Learning Outcomes: The students on the completion of this course would be able to:

• • • • Identify links between industrial engineering knowledge and methods, on one side, and the design, modeling and management activities related to CPIS, on the other side (Analyze)
      • Implement smart production and co-created product design & development concepts in CPIS related activities (Create)
      • Identify use cases of big data and real time data analytics applied for CPIS, for supporting smart production, product design & development and advanced manufacturing process (Evaluate)
      • Exploit the CPIS online connectivity for strengthening business capability (Apply)
      • Applying CPIS related  knowledge and competences for improving sustainability (Apply)

Prerequisite:  None

Course Outline:

Module 1:  Sensors and Transducers Basics

1. 1. 1. 1. Introduction – concept of CPS, basics, model
         2. Identifying the physical quantities to be measured or the datasets to be acquired and computed
         3. Types of transducers, characteristics, application fields, selection criteria
         4. Choosing or designing the sensors and/or the transducers for measuring or for data collection

Module 2:  Data Acquisition Basics

1. 1. 1. 1. Signal conditioning basics
         2. Data acquisition basics
         3. Choosing or designing the needed electronics
         4. Connecting the CPS components
         5. Testing the CPS assembly

Module 3:  Programming for Data Acquisition, Processing and Communication

1. 1. 1. 1. Data acquisition programming basics
         2. Developing the CPS data acquisition software components
         3. Data processing basics
         4. Developing the CPS data processing software components
         5. Data communication basics
         6. IoT communication protocols basics
         7. Developing the CPS data communication software components

Module 4:  Advanced CPIS Topics

1. 1. 1. 1. Cloud computing and artificial intelligence basics
         2. Feeding artificial intelligence component with experimental data

Week	Topic	Workshop	Learning material	Teaching material
1	Introduction – concept of CPS, basics, model		01+MSIE-10-L-M1S1part1(แก้ไขแล้ว)
2	Establishing the projects’ subjects and forming the teams			MSIE-10-T-M1S1-W01
2	Identify the physical quantities to be measured or the datasets to be acquired and computed			MSIE-10-T-M1S1-W02
3	Types of transducers, characteristics, application fields, selection criteria		M04+MSIE-10-L-M1S2แก้ไขแล้ว
4	Choose or design the sensors and/or the transducers for measuring or for data collection			MSIE-10-T-M1S2
5	Signal conditioning basics		06+MSIE-10-L-M2S1-01(แก้ไขแล้ว)
5	Data acquisition basics		07+MSIE-10-L-M2S1-02(แก้ไขแล้ว)
6	Choose or design the needed electronics (power supplies, signal conditioning, analog to digital converters, multiplexers, communication subsystems)			MSIE-10-T-M2S1-W01
7	Connect the CPS components	MSIE-10-L-M2S1-W02
8	Test the CPS assembly	MSIE-10-L-M2S1-W03
9	Data acquisition programming basics		11 MSIE-10-L-M3S1_thai_finish
9	Develop the CPS data acquisition software components	MSIE-10-L-M3S1-W01
10	Data processing basics		13+MSIE-10-L-M3S2(แก้ไขแล้ว)
10	Develop the CPS data processing software components			MSIE-10-T-M3S2
11	Data communication basics		15+MSIE-10-L-M3S3-01(แก้ไขแล้ว)
11	IoT communication protocols basics		16+MSIE-10-L-M3S3-02(แก้ไขแล้ว)
12	Develop the CPS data communication software components	MSIE-10-L-M3S3-W01
13	Cloud computing and artificial intelligence basics		18+MSIE-10-L-M4S1(แก้ไขแล้ว)
14	Feed artificial intelligence component with experimental data	MSIE-10-L-M4S1-W01
15	Final project presentation

Learning resources:

Textbooks: No designated textbook, but class notes and handouts will be provided.

Reference books:

1. Wang, L. and Wang, X.V. (2018). Cloud-Based Cyber-Physical Systems in Manufacturing. Springer
2. Markwedel, P. (2018). Embedded System Design: Embedded Systems, Foundations of Cyber-Physical Systems, and the Internet of Things. Springer.
3. Brown, P. (Ed.) (2016). Sensors and Actuators: Technology and Applications. Library Press.
4. Morris, A.S. and Langari, R. (2017). Measurement and Instrumentation: Theory and Application (Second Edition). Elsevier.
5. Boyer, S.A. (2009). SCADA: Supervisory Control and Data Acquisition. ISA The Instrumentation, Systems and Automation Society
6. Buyya, R. and Dastjerdi, A.V. (Eds.) (2016). Internet of Things: Principles and Paradigms. Morgan Kaufmann

Teaching and Learning Methods

The teaching / learning methodology is mainly student-centered (active learning) rather than teacher-centered. The course comprises lectures and laboratory sessions (for projects’ development). The lectures, besides the expositive part (teacher-centered), incorporate elements of active learning (e.g. small tasks to be solved individually or by teams in 5-10 minutes). The laboratory sessions adopt the project-based learning (PBL) approach. The projects are developed by teams and incorporate project management skills (e.g. time management and tasks’ distribution), problem solving, hands-on work (learning by doing), communications skills (project presentation and discussion) and peer assessment.

Time Distribution and Study Load:

• Lectures: 15 hours
• Laboratory sessions: 45 hours
• Autonomous work (self-study): 60 hours

Organisational topics

• One semester course
• 15 – 20 students in a group, 3 – 4 students in a team
• Different project for each team

Assessment

During lectures

• Presence is compulsory
• Students are graded according to their answers to questions addressed during the lecture

During teamwork lab activities

• Each student continuously assessed during the lab works, individually graded every week regarding:
  • solutions correctness
  • volume of needed support
  • adopted approach
  • innovative solutions
• Each student peer assessed, by the teammates, regarding:
  • contribution to the overall project objective achievement
  • Innovative solutions
• Team graded every week regarding the alignment to the project plan and milestones achievement

During the final project presentation:

• Each student individually graded regarding:
  • Solutions
  • Presentation skills (also peer assessed by other teams)
• Team graded regarding:
  • Technical solutions (also peer assessed by questions from other teams)
  • Quality of the technical report
  • Quality of teamwork
  • Questions asked to other teams

Evaluation Scheme

The final grade will be computed according to the following weight distribution:

• Assessment during lectures: 10 %
• Assessment during teamwork lab activities:
  • Individuall student grade: 50 %
  • Peer assessment by teammates: 10 %
  • Team grading: 10 %
• Assessment during final project presentation:
  • Individually: 10 %
  • Team assessment: 10 %