Engineers and scientists often learn to deal with complex systems through individual knowledge or collective wisdom. However, with the advancement of science and mechatronic manufacturing, the possibility of frequent change exponentially increases the difficulty of understanding, developing, and simulating complex systems.
In this presentation, Stéphane Marouani, discusses how companies and universities are using MATLAB and Simulink to explore new ways to accelerate the pace of innovation while managing change and complexity.
Stéphane Marouani, MathWorks
Between blackbox models, that rely solely on data, and whitebox models, that are purely theoretical, are greybox models that rely on both theory and data. In fact, almost all models are greybox since some theory is used and some data is needed. The theory may be only that some smoothness of response is needed, or at the other extreme the theory may be complete and need only some parameter values, originating from data, to be supplied.
The theoretical part of a model can generally be specified as mathematical or computational relations involving some parameters that likely vary with operating or experimental conditions. The model inversion technique allows relations for nonlinear parameters expressed as functions of the operating conditions to be investigated using efficient linear regression techniques. This can be used to test the possibility of improving the model or to improve the model by selecting relevant terms in the relations between parameters and operating conditions.
The functions greyboxeval for testing if a model is complete and greyboxbuild for completing a greybox model framework are available from the MATLAB File Exchange. greyboxbuild includes a new default linear regression method, linfitregsel, that combines regularisation and term selection. Further work on linear regression is proposed.
Only basic MATLAB was used in this project. The efficiency of development in MATLAB made this possible as a retirement project. Some of the supporting functions, including linfitregsel, have also been submitted to the MATLAB File Exchange.
Bill Whiten, The University of Queensland
Winning a world championship in the extremely competitive world of Formula 1 requires continuous development to stay one step ahead of the competition. It is a real challenge to fit this in between two seasons and even between two races, which are normally every other week. The team has very little time to test new developments in a car on the racetrack as car testing is limited by FIA regulations. This requires a methodology where new strategies are fully developed and tested before they are used on the track.
This presentation shows how the Renault F1 team used MATLAB and Simulink in its development cycle to achieve the very fast development process with reliable results.
Peter Wezenbeek, Wingmate
The cosmetic integrity of Zeiss products –ophthalmic lenses and coatings– is currently assessed visually by many inspectors worldwide. The disadvantages of such a QA/QC process are obvious. It is slow, subjective, and often results in “borderline” ratings that require further and/or repeated assessment. This presentation discusses a project aimed at designing a machine vision system using MATLAB and the Image Processing Toolbox. The goal is to capture and analyse images of lenses and automatically identify and quantify relevant defects. Such a system will assist the inspector make a “pass/fail” decision by providing “hard data” of location, area, severity, and type of defect.
Dennis Palms, Carl Zeiss Vision Australia
Super Conducting Quantum Interference Devices, or SQUIDs, have been in use in laboratories for the last 40 years and since the discovery of high temperature superconductors are now seeing new applications in industrial settings. This presentation briefly introduces the SQUID as an ultra sensitive magnetic field detector as well as some of the difficulties presented when using the device in the field. It also looks at the application of MATLAB and Simulink as well as Xilinx System Generator to the design of a new type of SQUID signal readout system that has recently been developed by the CSIRO. Finally, the presentation examines a small selection of applications for these remarkable devices.
Chris Lewis, CSIRO
Natural gas use as an alternative fuel is accelerating in many markets, in particular North America, China, and India, due to its lower operating cost, reduced air pollution, and globally abundant supply. As the global leader in natural gas engines and gaseous fuel technologies, Westport supplies a wide range of natural gas engines into the market, including Westport™ HPDI, Cummins Westport spark-ignited systems, and Westport WiNG™ Ford products.
With a product development and application presence spanning the globe, it is essential to implement effective, efficient, and rapid engineering development processes. To meet program timings while maintaining the highest level of quality, Westport utilises MATLAB, Simulink, and Embedded Coder® as integral components of its engine management system development tool set.
This presentation outlines the approach taken by the Westport Perth team to develop a model-based software development platform for rapid commercialisation of Westport’s spark-ignited CNG engine controller.
Terran Barber, Westport
The Institute for Telecommunications Research is leading a consortium to develop a global sensor network architecture for remote sensing and communication. With $5 million in support from the Australian Space Research Program, this work is an integral part of a $12.5 million program running from 2011 to 2013, with partners COM DEV (Canada), DSTO, SAGE Automation, CSIRO, and the Australian Institute of Marine Science.
The program has developed novel techniques for highly efficient one- and two-way data communication with large numbers of remotely located sensors and devices. The system includes new architectures and waveform designs and makes innovative use of software-defined radio (SDR) for both ground and space segments. The end result is a cost-effective, scalable, and flexible system that is able to support very large numbers of users while requiring only a small amount of radio bandwidth.
A key component of the program has been to transfer the fundamental research that enables the system into a working implementation using SDR hardware. The development process was driven from a living MATLAB reference model that evolved as research transformed into implementation. MATLAB based postprocessing and visualisation tools were developed and used to analyse performance and provide fast turnaround for system tuning.
This presentation describes the methodology that transformed advanced satellite communications research outcomes into a field trial–proven system.
David Haley, University of South Australia
Undergraduate students at the University of Technology Sydney undertake a year-long capstone project in which they apply the skills and knowledge acquired in their coursework to a practical project. It is an opportunity for students to demonstrate that they can meet the levels of performance expected of a professional engineer. Ideas for the capstone projects can be suggested by academic supervisors, industry, or the students themselves.
MATLAB can be a highly effective tool for training my students in the geotechnical engineering discipline because it makes the study of complex concepts more interesting. Each semester, I organize a number of intensive training sessions to familiarise capstone students with the main features of MATLAB such as programming, graphics, optimisation tools, and graphical user interface.
Accordingly, capstone students can quickly generate results then plot or animate their results via an interactive interface, without being inundated with low-level programming details. The project presented is one example of many capstone projects that students have completed using MATLAB to generate useful recommendations for professional and practising civil engineers.
Hadi Khabbaz, University of Technology Sydney
Transpower owns and operates the electricity grid in New Zealand, keeping energy flowing to 4 million residents. The New Zealand power system is long and skinny, with the major generation centres in the bottom of the South Island and the major load centre at the top of the North Island. As such, the system is very susceptible to frequency deviations and oscillations when a generator or large load unexpectedly trips off.
Transpower uses MATLAB to calculate how much spinning reserve and interruptible load must be scheduled during each half hour trading period to ensure that, in the event of a large disturbance, the system frequency doesn’t deviate to the point where it could cause cascading outages, voltage stability issues, or blackout. Detailed models of each generator and governor are built in Simulink, along with models of the HVDC link between the two islands and some basic load models. All of the detailed models are combined into a large Simulink model of the New Zealand power system. Run in conjunction with other MATLAB code files, this tool, called the Reserve Management Tool or RMT, calculates how much spinning reserve is required under a variety of conditions.
During every half hour period, RMT uses MATLAB to calculate what the largest risk to the system will be (a large generator or the HVDC link), how fast the frequency will fall if the calculated unit trips, how much load will be shed under certain contingencies due to the last-resort Automatic Under-Frequency Load Shedding scheme, what the expected governor response will be from dispatched generators, and the system inertia. It uses these parameters to calculate a final figure of how much spinning reserve and interruptible load must be scheduled for the given conditions.
Heidi Heath, Transpower
Climate change related phenomena like higher temperatures, increased carbon dioxide concentration in the atmosphere, and more frequent and intensive climatic anomalies, such as heat waves and floods, have placed great pressure on agricultural production around the world. In this scenario, agriculture research and production requires more intensive spatial and temporal monitoring of critical variables to assess the effects of climate change on plant physiology, growth, and fruit quality. Image analysis is becoming an important component in modern agriculture and horticulture. It allows the use of inexpensive devices to acquire meaningful information on crop growth, water status, and quality. In the past, these kinds of technology and analysis were too expensive and required specific know how, which was not readily available to growers. This presentation describes the tools used to solve this problem, such as automated analysis of RGB images and video of plant material, scanned images, and infrared thermal images of canopies to assess plant growth and canopy architectural parameters, leaves and fruit development and plant water status. Results from proposed analysis tools have shown similar outcomes in accuracy and robustness compared to more established techniques. The presenter has developed automated image and video analysis codes using the following MATLAB tools: Image Acquisition Toolbox™, Image Analysis Toolbox™, and Statistical Toolbox™.
Sigfredo Fuentes, University of Melbourne
Many people die from heart failure, a disease that can be treated by heart transplant or mechanical assist devices. Due to a lack of donors, medical devices are an excellent alternative for treatment. Third generation mechanical assist devices combine a rotary motor and a magnetic bearing. Some are clinically applied and others are under development, but there are currently no examples of a miniaturized microcontroller design to improve implantability for some blood pumps under development.
The aim of this project is to implement a control algorithm into a suitable microcontroller to improve the implantability of these devices under development and provide an improved, miniaturised, blood pump for the treatment of end-stage heart failure. Generally, magnetic levitated rotary blood pumps have two systems, including a permanent synchronous motor (PMSM) and a magnetic bearing. This presentation will explain the process of modeling, simulating, and testing the motor controller.
David Morales, Queensland University of Technology
Lurking somewhere in every undergraduate engineering course is the Laplace Transform, and it is a curious beast. Students don’t like it: They battle the algebra, lose their tables, question its relevance, and wonder why teachers carefully distinguish between the Laplace variable s and the differential operator d/dt. It is no wonder the Laplace transform is rarely pulled out of the toolbox when the professional engineer is out in the real world.
It does not need to be this way. Computational tools, such as MATLAB, and dynamic simulators, such as Simulink, have taken the pain out of building dynamic systems. This presentation highlights some interesting, non-obvious features of dynamic systems that should stimulate curiosity in ways partial fractions don’t. These include unstable zeros, systems with nonlinearities such as relay feedback, and the usefulness of the frequency response in system identification.
David will also cover some consulting “war stories”—the industrial problems that have a sting, but most can be solved by strong maths, a good prototyping environment, and a cautious (even skeptical) attitude. For those who really, really want to solve their Laplace transform, we can do that too, both symbolically and numerically. The real question is, “Why bother?”
David Wilson, Auckland University of Technology
Discover the new features in MATLAB R2013a and see how you can use the streamlined MATLAB desktop interface to increase your productivity. In this session, see how you can quickly and interactively import data, analyse it, build a graphical user interface, and then package and share your work with colleagues as a MATLAB app.
Have you considered ways to bring your course material closer to real life? Are you concerned that students are losing interest in engineering science? Project-based learning is an effective teaching method because students can see, hear, and touch what would otherwise be abstract. In this session, we show how MATLAB and Simulink can easily interface with a broad range of affordable hardware.
Coorous Mohtadi, MathWorks
Parallel Computing Toolbox™ enables you to perform large-scale computations using multicore desktops, GPUs, clusters, and grids. In this session, we demonstrate how you can quickly and easily take advantage of a wide range of high-performance computing resources to accelerate large-scale simulations and data processing tasks.
With prices in many commodities trading at historical highs in recent years, miners have been bringing new supplies on stream as quickly as possible. Now that margins are falling and as cost pressures increase, mining organisations need to look at how to be disciplined in their operations and generate a greater return from existing assets.
This presentation covers the different ways the mining industry can identify bottlenecks, increase efficiency, and streamline processes using MATLAB and Simulink. This brings discipline to capital and operational expenditure without overinvesting in their IT infrastructure.
Simon De Rosa, MathWorks
MATLAB and Image Processing Toolbox™ provide a flexible environment to explore design ideas and create unique solutions for imaging systems. Attend this session to learn how you can rapidly develop image processing algorithms.
Simulink is a block-based development environment used to model and simulate real-world systems and operations. Attend this session to learn about the capabilities that help you model these complex dynamic systems.
Machine learning techniques help to quickly detect patterns and build accurate predictive models from large data sets. They include neural networks, decision trees, fuzzy logic, K-means clustering, discriminant analysis, and linear, logistic, and nonlinear regression. In this session, see how you can easily compare and evaluate the performance of MATLAB algorithms for machine learning in applications.
Ever wondered about the balance between theory, practice, and the use of technology in your courses? What does industry need and expect to remain competitive? How do I leverage computing in my research? Technical computing is defined as the union of math and programming, and by having these skills students are better prepared for upper-level courses, research, and industry. In this session, we discuss the significance and broad application of technical computing in education.
Coorous Mohtadi, MathWorks
Recent updates to the Simulink development environment have made modelling and simulation easier. Attend this session to explore new capabilities that improve the way you model and simulate complex systems.