DISA

When? Friday May 31 14-16
Where? Onsite: B1006 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link by May 29st https://forms.gle/DVCYhx4aeiU87xmS8 (especially important if you plan on attending onsite so we have fika for everyone)

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: AI and data literacy: integration of related knowledge in K-12 education? – Johanna Velander, PhD-student in Computer Science and Media Technology
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Performance and Condition Monitoring of Drilling Machinery. – Rammohan Kodakadath Premachandran, PhD-student in Mecanical Engineering
15.50 -16.00 Sum up and plan for our next seminar on September 6th

Abstracts

AI and data literacy: integration of related knowledge in K-12 education? Johanna Velander
Uncovering patterns and trends in vast quantities of data has been enabled by different machine learning methods and techniques used in many of the applications that we use in our daily lives. Permeating many aspects of our lives and influencing our choices, development in this field continues to advance and increasingly impacts us as individuals and our society. The risks and unintended effects such as bias from input data or algorithm design have recently stirred discourse about how to inform and teach about AI in K-12 education. As AI is a new topic not only for pupils in K-12 but also for teachers, new skill sets are required that enable critical engagement with AI.

In this presentation, I will talk about my PhD project which sits at the intersection of computer science and teacher education. In a recent study deploying a Learning Analytics plugin at some LNU courses students’ thoughts, attitudes, and emotions were investigated when engaging with their data (collected by the LMS Moodle). Results revealed a low awareness of data collection and data-driven practices and worries about how this data could be used and who could access it. Following these insights and according to my PhD affiliation with UPGRADE and WASP-HS I have continued to investigate how awareness and knowledge of AI concepts, applications, and potential ethical concerns often referred to as AI literacy can be taught at a K-12 level to inspire future data scientists and to enable equal participation in a digital data-driven society according to a critical literacy perspective that empowers learners to act on and find alternatives to issues present in current AI practice.

Performance and Condition Monitoring of Drilling Machinery. – Rammohan Kodakadath Premachandran, PhD-student in Mechanical Engineering
Abstract: The drilling industry is steadily moving towards automation. To have a better control over the drilling operation and to optimize the drilling performance, it is necessary to have a good under-standing of the physics involved in the process.

Today drilling operations mainly relies on the experience of the operators. Experienced drillers observe the vibrations or sound produced by the drilling machinery and in general, base their judgments on their intuition. An efficient drill monitoring system, providing reliable and robust information on the performance of the drilling process, is likely to provide experienced drillers with additional information, to further improve the drilling process. Such a system is also likely to provide guidance to inexperienced drillers to improve their drilling performance concerning quality and efficiency. To enable monitoring of drilling performance in down the hole (DTH) rock drilling, simultaneous measurements of vibrations, with the aid of accelerometers mounted at specific locations on the drill rig, and other quantities such as different line pressures controlling different movements of the drill string are considered in combination with suitable signal processing methods. A number of properties, e.g. spectral properties, of the vibration signals and the pressure line signals under good drilling and bad drilling conditions have to be analyzed.

A representative simulation model can provide insights into various phenomena that appear during drilling for different drilling conditions. Such a model is also likely to be of assistance in preparing various measurements. Issues such as choosing the type of sensors, their positions and which quantities to measure are supported by a model. A well calibrated model in combination with information extracted from measured data are hence likely to assist in selecting control strategies for optimized drilling performance.

This seminar would discuss the evolution of simulation modeling process i.e what to model, what to include and exclude from a simulation model. General strategy and considerations for making a simulation model.

The various measurement and signal processing strategy employed as a part of this work and how simulations aid measurement work.