Postgraduate Course: Measurement and Sensors (IDCORE) (PGEE11256)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course gives students skills to collect large ORE datasets, check quality & interpret measurements. It introduces Python as a tool which will be used throughout IDCORE (assuming no prior experience). Measuring the environment at ORE sites requires a wide suite of instruments (including wave rider buoys, acoustic Doppler current profilers & acoustic anemometers), which often use unique data formats. Students gain practical experience of handling and interpreting data sets including using the Quality Assurance and Quality Control of Real-Time Oceanographic Data (QARTOD) standard. The course supports both Resource Assessment and later experimental work. |
| Course description |
An overview of ORE instrumentation and why it is important. Industrial use cases for data. Types of instrument (wave gauges, ADVs, ADCPs, mechanical sensors etc.) Fundamentals of what data is: spatial and temporal resolution, sample rates. Typical laboratory data methods. Practical considerations associated with field data.
Showing how this links to the resource assessment course.
Practical data collection exercise in a laboratory and analysis of data. This is likely to be collection of ADV and wave gauge data from a wave current flume. Flume to contain a source of turbulence and something that interacts with the waves.
Introduction to python. Use of Jupyter notebooks. All IDCORE students will be supplied with a laptop, relevant software will be installed. Hours given here are for introductory learning and time to analyse the data gathered in the lab experiment.
Interrogate information from a field instrument (ADCP). Assessment through an analysis of one particular dataset. Ideally, each student will be given a different data set.
Mention issues e.g. instantaneous E,N,U velocities & compare to calculations from along-beam velocities.
Invited talk from instrument supplier and/or industrial partner.
Interpreting data sets including using the Quality Assurance and Quality Control of Real-Time Oceanographic Data (QARTOD) standard. Focus will be on field data sets. Linking data collection to IEC standards for e.g. resource assessment or performance.
Write up and synthesis of learnings into final report. Students will collaborate on preparation and comparative analysis of different treatments of sensor data, and produce a report summarising their findings.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 14,
Supervised Practical/Workshop/Studio Hours 12,
Summative Assessment Hours 22,
Other Study Hours 50,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
0 )
|
| Additional Information (Learning and Teaching) |
Self study
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% Coursework
Write up and data analysis of lab experiment. (40%) (end of week 1)
Analysis of field data. Reflection on types of data and methodologies encountered. (60%) |
| Feedback |
Feedback on assignment 1 will be given during week 2. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Students will demonstrate basic understanding of Python for interpreting and analysing data from real sensors (M1 ¿ applying knowledge of coding techniques and resource analysis methods; M2 & M3 ¿ formulating the assessment problem in a way that can be solved).
- Students will use the learned skills to access data from a range of sensors (M13 ¿ selection of suitable methods for different instruments)
- Students will collaborate on preparation and comparative analysis of different treatments of sensor data, and produce a report summarising their findings (M14 ¿ quality control of data; M16 ¿ individual and team performance; M17 ¿ effective communication).
|
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Data,python,measurement |
Contacts
| Course organiser | Dr Brian Sellar
Tel: (0131 6)51 3557
Email: brian.sellar@ed.ac.uk |
Course secretary | Dr Katrina Tait
Tel: (0131 6)51 9023
Email: k.tait@ed.ac.uk |
|
|
University Homepage