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
Join us as 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.
The future of Internet of Things (IOT) will be driven by the applications we choose, and this will create new engineering challenges for the future.
With the advent of 5G or LoRaWAN communications, the problem of connectivity seems like it is about to be solved, and the vision of an IOT spanning all industrial and consumer applications seems to be almost a reality. Simultaneously, the availability of low-cost, low-power sensing and computing platforms means that it is feasible for nearly anyone to create an IoT device. However, market acceptance depends on creating something that enhances the quality of life for those using the system and can be used sustainably. To achieve this, there is a need for new approaches to the design, engineering, and maintenance of IoT systems, which, in turn, must be driven by a deep understanding of the real-world context of the application.
iHomeLab has been applying IoT technology for smart energy management and active and assisted living for over 10 years. In this talk, Andrew Paice highlights the key factors for the acceptance of technology, and reflects on the resulting implications for the design, development, and implementation of IoT systems. This leads to a vision of human- or application- centered IoT design for the future.
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.
Machine tools remain the major vehicle driving technological progress today. This industry segment is highly influenced by rapid evolution in Industry 4.0 technologies, including machine connectivity, Internet of Things (IoT), manufacturing process simulation, production flow modelling, and cloud-based analytics for predictive maintenance.
Artificial intelligence and machine learning technologies have a special place in the industrial IoT. Driven by increasing manufacturing performance needs together with growing technology complexity on one side and the reducing base of skilled operators combined with shrinking product cycle times and time to market on the other, machine tool makers have to respond in a smart way to meet expectations in productivity gains. This expected change applies both to end users, such as machine operators and workshop managers, and to product development teams of machine tool builders.
As a major industrial player, GF Machining Solutions is at the forefront of integrating machine learning technologies into its products. In this presentation, Dr. Sergei Schurov discusses several applications of such algorithms to the GF Solutions portfolio and will explore the benefits and challenges of bringing them into new system designs while remaining competitive in the market.
As part of the Women in Tech initiative, MathWorks will be hosting a Women in Tech lunch during this year’s MATLAB EXPO Switzerland, intended for female delegates. Join the lunch to hear from leading technical experts and to discuss your experiences. Use this opportunity to meet and network with other female industry peers.
Autonomous vehicles will play a key role in delivery operations in the future, particularly in hybrid indoor/outdoor and street/walkway operation. Kyburz Switzerland AG—a well-established manufacturer of electric delivery vehicles—has joined this competitive market space with its own brand of robotic delivery vehicles based on its proven eTrolley chassis. An important differentiator from its competitors is Kyburz’s emphasis on applying functional safety norms to its electromechanical systems development.
This presentation illustrates how a workflow for Model-Based Design built around MATLAB®, Simulink®, and Stateflow® can greatly expedite the development process. A variety of examples to highlight the benefits of this approach will be provided, with an emphasis on control challenges related to developing safe self-driving systems. Safety-conformant code is automatically generated from models eliminating the error-prone manual translation into handwritten code, significantly improving the productivity and software quality.
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
Real-time industrial fabric inspection systems face challenges of a great number of pattern variations, fast and easy training processes, strongly imbalanced datasets, and even more, lack of samples from certain classes at the beginning of inspection. To solve these problems, Stäubli Sargans AG has developed an incremental model that combines machine learning and deep learning techniques.
Their system consists of two interoperating models: a “base” that models general fabric characteristics, and a second (“update”) model that is iteratively retrained during the deployed inspection process. To improve robustness and accuracy, the update-model is iteratively trained by using more samples during subsequent inspection processes.
Key advantages of this iterative approach include feasibility, applicability, resource efficiency, fast training with fewer samples, and incremental improvement required by industrial products. To enable fast implementation and verification of the algorithm on system-on-chip platforms, Stäubli has chosen Model-Based Design from model creation through to hardware-software-co-simulation, ensuring continuous and rapid improvement, conforming to changing requirements.
The availability of quality weather data has improved dramatically over the past decade. At the same time, the number of big data analytics businesses delivering sector-specific solutions and business insights has also grown accordingly. However, timely access to such quality weather data, suited to specific business requirements and delivered in formats that users can apply seamlessly to in-house systems, has remained a challenge.
Meteomatics is a commercial weather data provider that is working collaboratively with national meteorological services and scientific communities. The company brings together historical, nowcast, and forecast weather data from global models such as the UK Met Office, ECMWF IFS model, satellite operations, station data, and their in-house developed Meteodrone system. By applying hyper-local modelling and downscaling capabilities, Meteomatics is able to deliver weather data for any location and time period for use in third-party models via an industrial-scale robust weather API. In this talk, the API is introduced and demonstrated.
Weather API data enables insights that are relevant across all sectors, both public and private. Energy companies, both in the traditional and renewable sectors, are extensively using these solutions to forecast demand and power output, inform energy trading, protect themselves against unfavorable seasons, and safeguard revenues. Also, water utilities are enhancing demand and leakage models, better management of system capacity, and regulatory compliance through weather-enabled automation of alarms, catchment modelling, and enhanced workforce management.
In summary, simple API access to quality weather data is extending the understanding of weather risk for a broad range of sectors. The speed of development of new products and services underpinned by quality weather data, indices, benchmarks, and parametric triggers is growing rapidly.
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.
Elevator designs are increasingly unique for each customer and are assembled on-site in very diverse environments, from skyscrapers in the desert to large airports as well as cruise ships. This conservative industry still relies on expensive and time-consuming physical testing. In particular, the software testing is a huge challenge because of the variability of the product and the late integration with the mechanical system in an elevator shaft.
Schindler Elevator Ltd. has recognized the benefits of a model-based validation workflow and actively drives its introduction in the development process.
The in-house modelling and simulation environment EDEn (Elevator Dynamics Environment) enables engineers to test system behavior early in the development process. EDEn provides various tools developed in MATLAB®, Simulink®, and Simscape™ to cover offline simulations using web-based applications as well as hardware-in-the-loop tests with Speedgoat real-time systems.
As a result, a complete software testing campaign has been reduced from three to four weeks with physical prototypes to a single night of automated simulation runs, greatly reducing commissioning costs and risks whilst establishing this model-centric development as one of the key enablers for digital transformation of the whole organization.
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
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.