
Jim Tung
MathWorks
Jim Tung, MathWorks
Joachim Levelt, MathWorks
Adam Pietrzyk – Inżynier Aplikacji, ONT
Valerie Leung, Christoph Stockhammer, MathWorks
Gabriele Bunkheila, MathWorks
Shankar Abhinav, Sonja Krzok, MathWorks
Maria Beer – Inżynier Aplikacji, ONT
Konrad Kolski – Inżynier Aplikacji, ONT
13.00-13.30 | Keynote : Pragmatic Digital Transformation Through the Systematic Use of Data and Models Jim Tung, MathWorks |
13.30-14.00 | Challenges and perspectives in Engineering education: building a computational learning environment for the engineer of the future Joachim Levelt – Regional Education Sales Manager EMEA, MathWorks |
14.00-14.10 | Pytania i odpowiedzi |
14.10-14.45 | Integer Linear Programming Enables Optimal Decision Making in Supply Chain Management Operations Witold Pawlus, Nokia |
14.45-15.20 | Innovative system of flight stabilisation – Real-Time and HIL methods |
13.00-13.30 | Co nowego w wydaniu R2020a oprogramowania MATLAB i Simulink? Adam Pietrzyk – Inżynier Aplikacji, ONT |
13.30-13.40 | Pytania i odpowiedzi |
13.40-14.06 | Reinforcement Learning Workflows for AI Valerie Leung, Christoph Stockhammer, MathWorks |
14.06-14.15 | Pytania i odpowiedzi |
14.15-14.45 | The Key Role of Data in Modern AI-Driven Systems: Spotting Voice Keywords and Beyond Gabriele Bunkheila, MathWorks |
14.45-14.55 | Pytania i odpowiedzi |
13.00-13.20 | Real-Time Prototyping and Testing for ADAS: Lane Keeping and Following Assist Systems Shankar Abhinav, Sonja Krzok, MathWorks |
13.20-13.30 | Pytania i odpowiedzi |
13.30-14.00 | Automated Driving Development in MATLAB and Simulink Maria Beer – Inżynier Aplikacji, ONT |
14.00-14.10 | Pytania i odpowiedzi |
14.10-14.40 | Designing and Deploying Embedded Algorithms on PLCs and Other Industrial Controllers Konrad Kolski – Inżynier Aplikacji, ONT |
14.40-14.50 | Pytania i odpowiedzi |
Organizations with digital transformation initiatives are making the transition from visionary ambitions to practical projects. These organizations have defined their high-level digital transformation objectives, and are now looking to their engineers and scientists to achieve them by learning new technologies, collaborating with unfamiliar groups, and proposing new products and services.
To meet this challenge, technical organizations must master how to systematically use data and models, not only during the research and development stages, but also across groups throughout the lifecycle of the offering. An effective digital transformation plan needs to consider changes in people’s skills, processes, and technology.
Join us as [insert presenter] describes this pragmatic approach to digital transformation and demonstrates how engineering and scientific teams are leveraging data and models to achieve their transformative objectives. Powróć do programu konferencji.
We will discuss trends and generic demands in the workplace for future engineers.
An interconnected and inter-disciplinary industry that is more and more relying on computational tools has a different expectation.
How will universities respond to this and how can MathWorks support this? Powróć do programu konferencji.
Effective supply chain management is one of the key business processes of any manufacturing company. The need for a systematic decision making framework in supply chain applications is especially pronounced when available supply does not meet the demand and it becomes necessary to prioritize deliveries among customers and manufacturing among products.
To address this challenge, the production planning task is formulated as an integer linear programming problem (ILP). This allows to optimally allocate the available raw materials in a considered time horizon according to a selected financial objective (such as net sales maximization) and under real-world constraints (e.g. manufacturing capacities of factories).
The developed approach is demonstrated on two practical use cases: 1) verification whether an ad-hoc increase in components supply at a premium cost translates into an increased production output and is thus to commercial benefit; and 2) evaluation of commercial implications of prioritizing deliveries to selected customers.
The proposed optimization algorithm is a key enabler of a systematic supply chain decision-making framework, enabling quick response to challenging supply situations thanks to combining optimization techniques with data-driven business paradigm. Powróć do programu konferencji.
W ramach projektu badawczego ISSLOT opracowywany jest demonstrator innowacyjnego układu stabilizacji lotu za pomocą trymerów samolotu PZL-130 Orlik. Regulator układu stabilizacji projektowany jest z wykorzystaniem narzędzi Simulink w metodyce Model Based Design. Wygenerowany kod jest zaimplementowany na platformie sprzętowej demonstratora i testowany w symulowanym locie metodą Hardware in the Loop z wykorzystaniem komputera czasu rzeczywistego Speedgoat i modelu środowiska w Simulinku. Zastosowanie tego podejścia i narzędzi to pozwoliło m.in. na rozpoczęcie testowania we wczesnej fazie projektu oraz wyeliminowało konieczność budowy czasochłonnych i kosztownych dedykowanych testerów. Powróć do programu konferencji.
Reinforcement learning allows you to solve control problems using deep learning but without using labeled data. Instead, learning occurs through multiple simulations of the system of interest. This simulation data is used to train a policy represented by a deep neural network that would then replace a traditional controller or decision-making system.
In this session, you will learn how to apply reinforcement learning using MATLAB® and Simulink® products, including how to set up environment models, define the policy structure, and scale training through parallel computing to improve performance. Powróć do programu konferencji.
Datasets are essential to AI models. They provide the truth by which we train AI models and the tests by which we measure AI success. While researchers tend to reuse well-known datasets, engineers building real-world systems must create datasets that represent all scenarios in which the AI model is expected to operate. This is often an iterative process that requires application-specific resources, tools, and expertise.
In this session, we will explore a well-known practical example: waking up voice-enabled devices using trigger phrases like „Hey Siri” or „OK Google.” We will cover a number of data-specific best practices focused on data labeling and annotation, data ingestion, data synthesis and augmentation, feature extraction, and domain transformations. This practical example provides general considerations that can be applied to a wide range of applications. Powróć do programu konferencji.
Automated driving spans a wide range of automation levels, from advanced driver assistance systems (ADAS) to fully autonomous driving. As the level of automation increases, the use scenarios become less restricted and the testing requirements increase, making the need for modeling and simulation more critical. In this session, you will learn and how MATLAB® and Simulink® support engineers building automated driving systems with increased levels of automation. You will learn about new features in R2019b and R2020a to:
In this session, we will show how industrial systems engineers can use desktop simulation to design and test control logic and predictive algorithms without the need for a physical prototype.
Through automatic generation of C/C++ code and code compliant with the IEC 61131-3 standard, you can accelerate deployment of embedded algorithms onto industrial controllers like PLCs, and stay hardware platform independent.
We show how to leverage simulation models of industrial systems using Model-Based Design to develop control logic and condition monitoring algorithms, automatically generate code for PLCs, and perform real-time testing. Powróć do programu konferencji.
Sonja Krzok posiada tytuł magistra elektrotechniki w zakresie technologii automatyzacji uzyskany na Politechnice w Deggendorf. Zanim w lipcu 2019 roku dołączyła do MathWorks jako inżynier aplikacji, pracowała dla in-tech GmbH jako Product Manager. Obecnie jest kierownikiem zespołu i projektu w dziedzinie zastosowania Simulink Real-Time (SLRT) w technologii HiL. Ma duże doświadczenie w modelowaniu, symulowaniu i wdrażaniu systemów czasu rzeczywistego, głównie w przemyśle motoryzacyjnym.