PHBA8240 Complex stimulus control - research and application Course description

Master-s level knowledge of behavior analysis

Students can:

• describe the research strategies and main findings of the initial research on Complex stimulus control describe and discuss the evolution of the field of equivalence research, with emphasis on theoretical explanation
• describe the main theoretical explanatory models for stimulus equivalence, and discuss the supporting evidence for each of them
• describe the experimental procedures in equivalence research, and discuss the findings from selected experiments using the different training procedures
• design and execute experiments

The course consists of 1 introductory meeting, and 6 - 12 seminars of 4 x 45 minutes, and time for discussion and a final seminar (4 x 45 minutes). The introductory meeting takes place 2 weeks ahead of the main part of the course. At this meeting, the structure, content and purpose of the course are presented. The students are asked to give short presentations of their Ph.D. projects, and describe how the course is relevant to it. The main purpose of this early session is to help the students to start systematic work with the course readings.

The seminars target central themes from the course readings for discussion and reflection. A high level of student participation is expected. During this time, students will produce several reaction papers (3 - 4 pages double spaced), and a final presentation of one central theme from the course. Themes are assigned by the lecturers.

The final seminar consists of a discussion of the presentations, which are distributed in advance as papers not to exceed 10 pages double spaced, and introduced by each candidate in a short (3 minute) session. Course teachers mediate the discussions.

• All papers approved,
• attendance in at least 80 % of the seminars, and
• approved paper presentation in final seminar.

Portfolio.

Portfolio requirements: 4 reaction papers

This course covers contemporary topics in smart energy systems such as smart power grid, smart buildings, vehicle-to-grid (V2G) and communication technologies for and network security in smart energy systems, including emerging approaches towards energy intelligence such as machine learning and blockchain.

The course will be offered once a year, provided 5 or more students sign up for the course. If less than 5 students sign up for a course, the course will be cancelled for that year

None.

Knowledge

On successful completion of the course, the student:

• is at the forefront of knowledge about smart energy systems, both at the system level and at the specific component/application level.
• understands what different technologies can be used at what level in energy generation, transmission, distribution and consumption networks.
• knows about communication technologies and their performance limits for enabling energy intelligence in smart energy systems.

Skills

On successful completion of the course, the student can:

• solve resource optimisation problems for the energy information network.
• apply optimisation techniques and machine learning-based approaches for residential demand response management and vehicle-to-grid.

General competence

On successful completion of the course, the student can:

• communicate and collaborate with experts from other disciplines on larger interdisciplinary and multidisciplinary research projects.
• Recognise and assess a project's potential and value
• participate in debates and communicate results through recognised international channels, such as academic conferences.
• can construct and develop relevant models and discuss the model's validity.
• Disseminate knowledge to broader audiences