Proceedings
Featured Presentations
AI, or artificial intelligence, is transforming the products we build and the way we do business but creating an “AI-driven” system requires more than developing intelligent algorithms. It also requires insights from domain experts, implementation details and integration into the final engineered system. Join us to learn how engineers and scientists are using MATLAB® and Simulink® to successfully design and incorporate AI into the next generation of smart, connected systems.
What’s New in MATLAB and Simulink
Learn about new capabilities in the MATLAB® and Simulink® product families to support your research, design, and development workflows. You will see new tools for defining software and system architectures, and modeling, simulating, and verifying designs.
Keynote Presentation
AI, or artificial intelligence, is transforming the products we build and the way we do business but creating an “AI-driven” system requires more than developing intelligent algorithms. It also requires insights from domain experts, implementation details and integration into the final engineered system. Join us to learn how engineers and scientists are using MATLAB® and Simulink® to successfully design and incorporate AI into the next generation of smart, connected systems.
Customer Presentations - Tampere, Finland
Experiences of using Model-Based Design in Development of Control Software for Electric Drives
Tapani Hyvämäki, Danfoss
Danfoss has been using Model-Based Design in the development of motor and grid converter control software for more than 15 years. This presentation will cover how simulation of power electronics systems has been the back-bones of the development and also how other capbilities such as production code generation, HIL, unit testing, system testing and version control have been adopted to maintain a fluent and agile development process.
Model-Based Design in Industrial Automation
Tommi Latva, Wärtsilä
Wärtsilä has made a step towards autonomous power plants where human interactions are coming more and more insignificant. With this conscious choice Model-Based Design is playing significant role by providing methods to build a system which is tested at the earliest stage and prevents design errors and ensures flawless operations from day one. Standardisation and modularisation of plants is a key success factor and a development environment based on MATLAB® and Simulink® supports this work approach.
Customer Presentations - Stockholm, Sweden
Deep Learning for Sonar Applications
Karl Thomas Hjelmervik, FFI
Conventionally, high false alarm rates within operation of anti-submarine warfare (ASW) sonars have been handled by increasing the threshold applied in the sonar detection algorithm or by employing classification algorithms. In recent years, for related applications, deep neural networks applied directly on raw data have been shown to outperform conventional machine learning algorithms that are fed with features. In this presentation you will be guided on employing deep learning algorithm on a sonar data set.
Model Based Design using MathWorks Toolchain at Getinge in Solna
Magnus Nord, Getinge Group, Maquet
At the Getinge Group in Solna we invent, develop and manufacture high-end ventilators and anesthesia machines including state of the art functions such as NAVA (Neurally Adjusted Ventilator Assist) and AGC (Automatic Gas Control). This presentation covers areas were the MathWorks toolchain is used, including system identification, physical modeling, external mode, control design, logic design , unit tests, PIL and production code delivery and will be flavored with lessons learned.
Customer Presentations - Aarhus, Denmark
System Simulation of Linear Actuator System
Peter Gravesen, LINAK A/S
LINAK Linear electrical actuators are mechatronic systems developed for a wide range of applications including medical beds, couches and patient lifters, beds for home, furniture and a wide range of industrial applications for eg. agriculture machinery. This presentation will focus on the methods and “tricks” used to make a complete model without considering all geometrical details but rather focus on functional elements built with Simulink/Simscape library elements.
A Journey with Model-Based Design
Per Hagen Nielsen, Vestas Wind Systems A/S
A practical path for a full Model-Based Design approach at Plant Control Engineering at Vestas over 5 years. The presentation will cover challenges before that approach, pre-study, reasons for the decision, ambitions, unforeseen challenges, education, new roles, MathWorks involvement and what is up next.
Master Classes
Adopting Model-Based Design for FPGA, ASIC, and SoC Development
The competing demands of functional innovation, aggressive schedules, and product quality have significantly strained traditional FPGA, ASIC, and SoC development workflows. This Master Class will guide you though Model-Based Design with MATLAB® and Simulink® for algorithm- and system-level design and verification including refining, and also prototyping and verification on hardware.
Predictive Maintenance with MATLAB
Predictive maintenance reduces operational costs for organizations running and manufacturing expensive equipment by predicting failures from sensor data. In this talk, you will learn how MATLAB® combines machine learning with traditional model-based and signal processing techniques to create hybrid approaches for predicting and isolating failures as well as integrating validated predicitve mainatenance algorithms with your embedded devices and enterprise IT/OT platforms.
MathWorks Technical Presentations
Power Electronics HIL with Simscape to HDL Conversion
Hardware-in-the-loop (HIL) testing is an accepted real-time simulation practice for validating controllers for power electronics systems. However, controller validation for power electronics can require running simulations at challenging sampling frequencies. In this talk, you’ll learn how to create circuit models and deploy them to FPGAs in a real-time system that can be used for testing a digital controller.
Simplifying Requirements Based Verification with Model-Based Design
With Model-Based Design, informal textual requirements can be modeled and simulated to verify behavior earlier, and then, be automatically generated into code for an embedded target. This talk shows you how you can translate informal text requirements into unambiguous assessments with clear, defined semantics that can identify inconsistencies.
Developing Digital Control for Power Converters
Using a buck-boost power converter example, this talk 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.
Deploying AI/ML for Near Real-Time Manufacturing Decisions
With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science associated with challenges such as modleing of assets, training predictive models and deploying these at scale. This talk will focus on building a system to address these challenges using MATLAB, Simulink®, Apache Kafka, and Microsoft® Azure®.
Becoming a Data-Centric Engineering Team: Catching up to the Data Deluge
Engineering teams have more data available to them than ever before as data from tests, operations, production, and other sources present opportunities for data-based design decisions and new data-based products and services. See how MATLAB tools enable engineering teams to grow from ad-hoc data analysis to building centralized tools for organization-wide use, laying a foundation for production apps and analytics.
What’s New in MATLAB and Simulink
Learn about new capabilities in the MATLAB® and Simulink® product families to support your research, design, and development workflows. You will see new tools for defining software and system architectures, and modeling, simulating, and verifying designs.
Deep Learning and Reinforcement Learning Workflows in AI
Two new workflows in AI, deep learning and reinforcement learning, are transforming industries and improving applications such as diagnosing medical conditions, driving autonomous vehicles, and controlling robots. This talk dives into how MATLAB® supports these workflows including labeling, interoperability with open source, training deep neural networks, tuning and generating multi-trget code for e.g. NVIDIA.