Short courses

Course Overview

The evolution of Systems Engineering into using model-based tools has resulted in a range of benefits from greater traceability of information within a project to better informed decisions.  Model-based Systems Engineering (MBSE) is a refinement of the traditional Systems Engineering practice to bring the information into a common model.  To support MBSE, the Object Management Group (OMG) and the International Council of Systems Engineers (INCOSE) collaborated to develop the Systems Modelling Language (SysML). 

This course will introduce the MBSE concepts utilising SysML as the modelling paradigm.  The course subsequently expands upon the understanding of SysML to see how broader and more complicated concepts are captured.

Introductory course: introducing concepts, methods or tools to relevant students or professionals

Course Outline

21 hours over 6 sessions

Course Benefits By the end of this course you will appreciate:

• What is MBSE and how it differs from traditional SE
• How to read SysML and the nuances inside the language
• How to utilise the tools to capture more complicated concepts such as risk and human factors

Course Pre-Requisite A knowledge of Systems Engineering principles is preferred but not required.

Session 1 + 2 – Introduction and Physical Modelling

• Overview of MBSE and SysML
• Knowledge Management Support for Systems Engineering
• Physical Structure Modelling

Session 3 + 4 – Behaviour and Requirements

• Activity and State Modelling
• Dedicated Views and Model Usability
• Requirements Representations
• Allocation between the Pillars of SysML

Session 5 + 6 – Broadening the Scope

• Introduction to Architecture Frameworks
• Risk and Safety Modelling
• Modelling Human Factors

Course Conveners

Dr William Scott is a Chartered Engineer with a PhD in Systems Engineering as well as being internationally certified both as a Certified Systems Engineering Professional (CSEP) through INCOSE and as an Advanced Systems Modeller (OCSMP- Advanced) through the Object Management Group (OMG). Since the 1990s, he has been engaged in modelling and simulation activities that aim to enhance system engineering activities largely in Australian Defence and heavy rail as well as other areas such as hospital operations. He has extensive experience in tailoring and utilizing best practice to aid industry and develop new concepts to ensure that models meet the stakeholder needs.

Grace Kennedy is an Associate Research Fellow in the SMART Infrastructure Facility at UOW.  She has a MEng in Systems Engineering from Loughborough University and specialises in Human Factors, Organisational Systems & Modelling. Grace is a CPEng with specialization in the area of SE and holds CSEP through INCOSE. Prior to emigrating to Australia, she worked in the UK Defence industry.  She is currently undertaking her PhD looking at applications of MBSE on Socio Technical Systems for organisational change.

Dr Farid Shirvani is a research fellow and lecturer in SMART Infrastructure Facility at University of Wollongong. He has a PhD in Model Based Systems Engineering specialises in procurement of complex infrastructure systems. He is OMG Certified Expert in System Modelling (OCSMP) as well as a Microsoft Certified Systems Engineer. Farid has been involved in a variety of systems modelling and systems analysis projects for Transport for NSW, Sydney Trains and ACRI (Australasian Centre of Rail Innovations). His research and teaching interest are model based systems engineering, metamodeling, domain specific languages, and knowledge and information management.

Enrol Now

• Course dat: TBC
• This course will run over two weeks on Monday, Wednesday and Friday from 1.30pm to 5pm
• Delivered online in real time with recordings available after each session
• Cost $1800

This introductory course on data science covers the following topics: data manipulation, data analysis with statistic and machine learning, data visualisation and how to work with large data sets. These concepts will be illustrated using programming languages often used and freely available, namely R, Python and SQL. The course presents in a practical way multivariate statistical analysis methods such as Regression, Clustering, Principal Component Analysis, Factor Analysis and ANOVA.

Infrastructure systems are sociotechnical systems within an organisational environment. The presence of social and organisational aspects increases complexity and influences these systems throughout their life cycle, from conception and planning, engineering, operation, upgrades and final disposal. Infrastructure systems will be examined as ‘System of Systems’ (SoS). Various approaches for System of Systems Engineering (SoSE) will be presented and discussed. ‘Systems Thinking’, considered the most adequate approach to deal with the complexity of sociotechnical SoS, will be presented and illustrated with practical examples. Designing for Adaptability and evolution in System of Systems Engineering (DANSE) methodology will be introduced. The course will address the fundamentals of modelling and simulation considered to be of great importance for Infrastructure SoSE.

If computer models are to be faithful representations of real-world systems, how can we possibly build them without input from the people who actually interact with and form part of systems in reality? This course introduces the use of participatory or collaborative model building to empower audiences to become architects of what would otherwise
be a purely scientific modelling process occurring behind closed doors. Participatory modelling serves as the ‘glue’ for stakeholders to collectively explore the implications of their actions and decisions on social, economic, and environmental outcomes of concern, particularly in those cases where responsibilities and burdens are unclear.

This introductory course will provide researchers, government and industry professionals with the basic knowledge and skills to facilitate collaborative modelling in interdisciplinary and cross-cultural settings. Attendees will gain access to a rich methodological toolbox that can help groups navigate through complex problems, engage in constructive dialogue towards common goals, and identify leverage points for building sustainability and resilience in the systems they need to manage or are part of. Participants will learn how to capture the salient features of a complex system into a coherent and simple but elegant simulation model. 

Today’s world is producing an ever increasing amount of data. Businesses then need data analysis to provide forward-looking guidance that yields better, more-informed decisions. This subject introduces quantitative methods to optimise the decisions to be made in the context of supply chain and logistic systems. Each method will be illustrated with real world case studies. As such, participants will learn to verify and enhance existing operating models.

The course starts with an introduction to supply chain and logistics and, some representative problems with several real-life applications. Effective tools for tackling these problems such as standard mathematical techniques or Linear Programming are explained and their implementation in Microsoft Excel is emphasised. The course is concluded by introducing some advanced metaheuristics, and their implementation in Excel VBA. 

Societies, modern cities, and urban infrastructure systems are becoming more complex, interconnected, difficult to optimise, control, and manage. Agent-based modelling (ABM) offers a new lens to understand and steer the functioning of these systems by conducting experiments on artificial societies of computer agents.

The course will begin by introducing fundamental principles of complexity and the dynamics of complex adaptive systems. A structured process to conceptualise, design, build, analyse and validate ABMs will then be explained and illustrated using real-world examples. The course will draw on applications in a wide variety of social, urban, and infrastructure problems, to help illustrate the power of ABM as an effective and accessible tool to understand why systems don’t always behave as expected, and what can be done to improve them.  

Course Overview

With more than 30 Billion connected devices expected by the end of 2020, the Internet of Things (IoT) is radically changing the technological landscapes. Applications opportunities are endless: home automation, healthcare, predictive maintenance, agriculture, energy management, or transportation are only some of those use cases. However, the Internet of Things is more than just sensors, it’s a process ranging from remote data collection to data analytics in order to grasp the full potential of your data. This course offers an introduction to the IoT covering not only the theoretical background and current usages, but also providing practical knowledge through hands-on tutorials and workshops. Students will gain expertise on the whole IoT process.

Course Outline

This 5-day (21 hours, 6 sessions) course will be delivered face-to-face.

Session 1 (1:00pm-4.30pm, Thursday, 5 May 2022) — What is the Internet of Things and why should we care? 

• Introduction 
• Defining the Internet of Things: history, technologies, trends, impacts, and business opportunities 
• IoT networks, protocols, and interoperability 

Session 2 (1:00pm-4.30pm, Friday, 6 May 2022) — Workshop 

• Introduction to sensors 
• Introduction to IoT development kits: Arduino, LoPy, Raspberry Pi 
• A first experiment: Building your first sensor and visualize data 

Session 3 (1:00pm-4.30pm, Thursday, 12 May 2022) — LoRaWAN and The Things Network 

• Real-world applications (SMART Pedestrians, SMART Storm waterways management) 
• Achieving Long Range and Low Power data transmission 
• LoRaWAN Architecture 
• The Tings Network – A free to use and open LoRaWAN network 

Session 4 (1:00pm-4.30pm, Friday, 13 May 2022) — Workshop 

• Tutorial/Workshop: Connecting your sensors to The Things Network 
• Managing payloads: Encoding and decoding messages 
• Tutorial: Connecting your sensor to the cloud and building your first dashboard with Cayenne 

Session 5 (9:00pm-12:30pm, Thursday, 19 May 2022) — Dashboards and building advanced applications 

• Publishing data: MQTT 
• Introduction to Node-Red 
• Interfacing Arduino with Node-RED 
• LoPy4 MQTT – Publish & Subscribe 

Session 6 (1:00pm-4.30pm, Thursday, 19 May 2022) — Small project / Assignment 

• Graphically build your IoT Application with Node-Red 

Course Benefits

By the end of this course, you will be able to:

• Understand the Internet of Things and its applications
• Discover hardware for the Internet of Things
• Know the different network protocols for the Internet of things
• Have extended knowledge on LoRaWAN
• Deploy and connect sensors to the Things Network
• Build IoT applications

Course Type

Introductory course: introducing concepts, methods or tools to relevant students or professionals.

Course Pre-Requisite

Basic knowledge of Computer Science and programming is preferred but not required.

Assessment

A small IoT project will be given, and individual report needs to be submitted upon completion of this course. The goal of this assignment is to detail at a high-level an original IoT solution to solve a real-world problem.

Course Conveners

Dr. Yan Li

Dr Yan Li is a research fellow at SMART. She completed her PhD in the Department of Electrical and Electronic Engineering, University of Melbourne. Her research interests include wireless sensor networks, mobile computing, indoor localization, Internet of Things (IoT) applications.

Dr. Adrian Xiao

Dr. Adrian Xiao received his PhD degree in Electronic Engineering from The University of Queensland. Dr. Xiao joined SMART as a research fellow since 2022. Prior to that, he was a FPGA hardware engineer working for industry for 4 years. He has broad experience in nano-satellite design and communication, interface/protocol architecture design, video algorithm implementation, hardware acceleration and FPGA based architecture optimization.

Download the course outline

Fees and Discounts

The standard registration fee for this course is $1,800. However, discount codes are available for the following:

• 10% discount for UOW staff, students and alumni
• 10% discount for 2 or more course enrolments from the same person
• 10%, 15%, 20% discount for group booking with 5-9, 10-15 and 15+ enrolments, respectively, from the same organisation.

Location

Computer Lab, Ray Cleary building, the University of Wollongong Shoalhaven Campus.

Contact 

For any inquiry please contact: Bobby Du
Coordinator of SMART Teaching Program
Email: bdu@uow.edu.au