Proceedings

Carl Wouters, Atlas Copco

Atlas Copco is constantly looking for ways to improve their products and services throughout the whole product lifecycle of their machines in all aspects.  To reduce cost of ownership, they integrate simulation and data analytics from engineering to production, sales, and services. One of their objectives is to enable thousands of sales engineers to do reliable performance simulations during customer engagements while interacting with customers. Another objective is to allow the services division to set up different customer-specific maintenance strategies based on real-time data collection from more than 100,000 machines in the field.

Connecting colleagues, customers, and the supply chain using a single source of truth results in shorter manufacturing throughput times and improved workplace safety. This is achieved by establishing an enterprise-wide data acquisition and analytics platform. The importance of a model-based engineering approach is key.

This presentation will showcase how this approach leads to better products and services throughout the whole product lifecycle.

Jason Ghidella, MathWorks

Insight. Implementation. Integration.

AI, or artificial intelligence, is transforming the products we build and the way we do business. It also presents new challenges for those who need to build AI into their systems. Creating an “AI-driven” system requires more than developing intelligent algorithms. It also requires:

• Insights from domain experts to generate the tests, models, and scenarios required to build confidence in the overall system
• Implementation details including data preparation, compute-platform selection, modeling and simulation, and automatic code generation
• Integration into the final engineered system

Jason Ghidella demonstrates how engineers and scientists are using MATLAB^® and Simulink^® to successfully design and incorporate AI into the next generation of smart, connected systems.

Arjo van der Ham, Lightyear

Lightyear One is a remarkable innovation: It is an all-wheel drive, five-seater electric car charged by the sun. The key challenge of this innovation is matching the energy required while driving, with the limited and fluctuating energy yield of the solar panels. MathWorks solutions are used by Lightyear engineers as the “Swiss Army knife” to address this challenge, optimizing the energy flow from the solar panel and its accompanying electronics to the vehicle. This presentation provides an overview of Lightyear’s accomplishments to date, and its ongoing mission: clean mobility for everyone.

Mohamed Anas, MathWorks

Learn about new capabilities in the MATLAB^® and Simulink^® product families to support your research, design, and development workflows. This talk highlights features for deep learning, wireless communications, automated driving, and other application areas. You will see new tools for defining software and system architectures, and modeling, simulating, and verifying designs.

Ronald Grootelaar, 3T

Products and systems increasingly use advanced motor drives to control cars, pumps, medical equipment, robots, and industrial automation. 3T has developed scalable amplifier platforms called VIPER and PEPPER. Together with the MINT system-on-chip (SoC) platform they can be used for a wide range of applications. These platforms are used to kick-start customer projects and reduce development cost and time to market.

VIPER is a cost-efficient solution for most power control applications. PEPPER provides increased power density and bandwidth. Applications for both platforms are modelled in MATLAB^® and Simulink^®, including power electronics and plant dynamics. The complete control loop is closed in a multidisciplinary simulation environment to rapidly develop control algorithms without the need for physical hardware and prove feasibility early in the design phase.

This talk focuses on PEPPER applications developed and deployed using MathWorks SoC design flows including C and HDL code generation. Ronald Grootelaar of 3T discusses a high-speed sensorless brushless DC motor drive running a field-oriented control algorithm at 50 kHz and a solenoid actuator control loop suppressing machine frame vibrations.

Paola Jaramillo, MathWorks

Developing predictive models for signal, time-series, and text data using artificial intelligence (AI) techniques is growing in popularity across a variety of applications and industries, including speech classification, radar target classification, physiological signal recognition, and sentiment analysis.

In this talk, you will learn how MATLAB^® empowers engineers and scientists to apply deep learning beyond the well-established vision applications. You will see demonstrations of advanced signal and audio processing techniques such as automated feature extraction using wavelet scattering and expanded support for ground truth labelling. The talk also shows how MATLAB covers other key elements of the AI workflow:

• Use of signal preprocessing techniques and apps to improve the accuracy of predictive models
• Use of transfer learning and wavelet analysis for radar target and ECG classification
• Interoperability with other deep learning frameworks through importers and ONNX™ converter for collaboration in the AI ecosystem
• Scalability of computations with GPUs, multi-GPUs, or on the cloud
  

John van Tol, ASML

ASML is the world's leading manufacturer of lithography systems for the semiconductor industry. These complex machines are critical for producing integrated circuits or chips.

Quality and time to market are the two main drivers for this business. At first sight, these interests appear to be conflicting, but by making the right choices, the outcome for both factors can be favorable.

The focus of this presentation is twofold. First, the adoption of HDL Coder™, which automatically generates HDL source code and test bench code into the ASML FPGA design flow. John van Tol of ASML will show which steps need to be taken to implement HDL Coder in a customized development environment. Secondly, the reduction of the gap between system architect and engineer. John will then discuss the use of Model-Based Design by the system architect and FPGA designer and the major benefits of this approach. Applying these techniques will ensure that the quality improves, and the introduction time of new functionality diminishes.

Francesco Vittori, KPMG Luxembourg

KPMG Luxembourg started the development of its risk reporting solution more than eight years ago.

The platform supports the computation of different risk figures including value-at-risk (VaR), conditional VaR, sensitivity analysis, stress testing, and other quantitative measures.

The constantly increasing requirements in terms of new financial, mathematical, and statistical models to be integrated in the platform can be satisfied only by having a flexible, efficient, and safe development lifecycle.

KPMG will present the risk platform architecture and how the quantitative team is integrating new models in the existing architecture.

As a practical example, KPMG will showcase how MATLAB^®, together with a microservice architecture, can be used to build high-performing, scalable software embedding advanced financial and statistical techniques.

Paul Lambrechts, MathWorks

Industry 4.0 has brought the rise of connected devices that stream information and optimize operational behavior over the course of a device’s lifetime. This presentation covers how to develop and deploy MATLAB^® algorithms and Simulink^® models as digital twin and IoT components on assets, edge devices, or cloud for anomaly detection, control optimization, and other applications. It includes an introduction to how assets, edge, and OT/IT components are connected. The talk features customer use cases starting from design to final operation, the underlying technology, and results.

Beril Sirmacek, University of Twente

Beril Sirmacek of University of Twente has been working with her research group on developing handheld devices for giving patients the opportunity to observe the developments of their skin burns, cancer tissues, or diabetic wounds. These devices benefit from simultaneous localization and mapping (SLAM) and artificial intelligence algorithms. The self-observation process can be very helpful in giving patients early warnings, allowing them to visit their doctors on time. It can also reduce the doctor’s stress by helping them classify the patient’s emergency accurately and provide an appropriate appointment schedule.

Because of Beril’s interest in projection mapping and augmented reality, she has put effort into texturing the human skin with projected visuals from the handheld devices. These projections can show what is not seen with the naked eye and can make the skin development process more observable both for the patient’s and doctor’s understanding.

In this talk, Beril introduces how MATLAB^® provides a single platform for all aspects of her project including computer vision, SLAM, artificial intelligence, and GPU implementation.

Stephan van Beek, MathWorks
Tadele Shiferaw, MathWorks

Using a buck-boost power converter example, this master class explains how Simulink^® and Simscape Electrical™ are used to develop, simulate, and implement a controller that maintains desired output voltage in the presence of input voltage variations and load changes to achieve fast and stable response.

The presentation covers:

• Modeling passive circuit elements, power semiconductors, and varying power sources and loads
• Simulating the converter in continuous and discontinuous conduction modes
• Determining power losses and simulating thermal behavior of the converter
• Tuning the controller to meet design requirements such as rise time, overshoot, and settling time
• Generating C code from the controller model for implementation on a Texas Instruments™ C2000™ microcontroller
• Performing Hardware-in-the-Loop simulations leveraging FPGA performance
  

Paola Jaramillo, MathWorks
Toon Weyens, MathWorks

Engineers and scientists increasingly adopt practices from software development to write programs that are easy to debug, verify, and maintain. In this talk, you will learn how to integrate MATLAB^® with source control systems like GitHub™ and integration servers like Jenkins, which also facilitates Agile development. You will additionally learn how to test code with the MATLAB Unit Test Framework and manage code with projects.

Giorgia Zucchelli, MathWorks

As part of the Women in Tech initiative, MathWorks will be hosting a Women in Tech lunch during this year’s MATLAB EXPO Benelux, intended for female delegates and presenters. Join the lunch to hear from leading technical experts and to discuss your experiences, and use this opportunity to meet and network with other female industry peers.