Celosvětová akce, na které přední odborníci představí své technologické vize a zkušenosti s využitím nástrojů MATLAB® a Simulink®,
poprvé v České republice.
Pragmatic Digital Transformation Through the Systematic Use of Data and Models
Chris Hayhurst, MathWorks
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 Chris describes this pragmatic approach to digital transformation and demonstrates how engineering and scientific teams are leveraging data and models to achieve their transformative objectives.
BIO: Chris Hayhurst leads the MathWorks Consulting Services organization in Europe, managing a group of highly experienced engineers who cover many industries and every aspect of MATLAB and Simulink product capability. Chris works with automotive, aerospace, electrical machine, and industrial equipment companies to assess and optimize their development processes and adoption of Model-Based Design. Before coming to MathWorks, Chris was involved in the design of flight control systems for helicopters, working with Simulink to model future helicopter dynamics and control strategies. He holds a degree in electrical engineering from Cambridge University and is also active in the Institute of Engineering and Technology and in encouraging engineering and computing education in schools.
Machine Learning: Proven Applications and New Features
Matthew Elliott, MathWorks
While many organizations get excited about adopting machine learning techniques, success does not come easy. Come to this talk to learn about applications where machine learning generates considerable ROI, including fleet data analysis, energy forecasting, and smart manufacturing. We will also demonstrate how engineers are integrating machine learning techniques with their controls and signal processing workflows to improve system performance.
Throughout the presentation we will highlight new features in MATLAB® that accelerate deploying machine learning. This includes applying automation techniques to feature selection, model selection, and hyperparameter optimization (AutoML). We will also cover new ways for integrating machine learning models with production workflows such as updating deployed models and C/C++ code generation.
Come to this talk to learn how your peers have applied machine learning, and to get inspiration for how machine learning could be applied to your own work.
Engineering Design Tools for Advanced Embedded Control
Vladimír Havlena, Honeywell
BIO: Vladimir Havlena is a Senior Fellow in Honeywell Intl., leading the HBT (Honeywell Building Technology) Innovation & Architecture Control Science team. He has over 25 years professional experience with industrial automation, advanced control and real-time optimization. Vladimir has a Ph.D. in control engineering from the Czech Technical University in Prague and is still a part time professor at the Department of Control Engineering, lecturing Modern Control Theory and Estimation and Filtering M.Sc. courses. He is an enthusiastic MATLAB user switching between industrial, academic and teaching applications.
MATLAB Supported Model Based Radar Design
Pavel Šedivý, Retia
MATLAB supports radar system in RETIA over whole design and production phase of life cycle.
In concept phase is MATLAB and several toolboxes used for modeling of expected performance. Modelling is performed from high level to detailed (subsystem). Models are modular to allow change of input to real data (records) and/or comparison of output with real implementation.
Activities includes phased array antenna design and analysis, pulse compression filter design, Doppler processing, adaptive detection algorithms design, target coordinates extraction, target tracking and non coopereating targets recognition. All these models are use for analysis of real data records and algorithms parameter optimization. These analysis include processing of additional data sources (height profile, vector map data sets, flight logs, ect.).
Additional use of MATLAB capabilities is within testing (design vewrification) and production (control of measurement equipment, data analysis, report generation). Within these two phases are utilised MATLAB compiler generated applications.
Model-Based Design in Safety Applications: Theory and Practice
Pavel Kučera, Eaton Elektrotechnika s.r.o.
Model-Based Design is today an integral part of safety critical software development in various industries. Main standards used are ISO 26262, IEC 61508 and DO-178B. Theoretical aspects and advantages of modern SW development, verification and validation are field-tested when they are applied in complete product development lifecycle. In this talk, we would like to share our practical experience with integration of various MBD tools in SW development process. Following topics will be covered in our contribution: relationship of industrial safety standards and MBD, complexity of today's embedded SW, tools maturity and their integration with established development processes, human aspects, development cost.
BIO: Pavel Kucera is an engineering specialist in Eaton European Innovation Center (EEIC) in Prague, Czech Republic. Pavel has almost 20 years background in development of fault tolerant and safety applications in various industries (steel casting, forging and milling, automotive, turbomachinery, hydraulics, molding, ...). In the last 6 years he is responsible for applying and integration of MBD tools into development processes with respect to various business needs in automotive, eMobility, hydraulics and aerospace. Pavel holds a Ph.D. degree in Cybernetics from Brno University of Technology.
Bridging the Gap Between Systems Engineers' Architecture Models and Model-Based Design
Jan Houška, Humusoft
Systems engineering is a challenging problem, and often the tools used to tackle these challenges do not connect well to the other tools used throughout the design process. MathWorks systems engineering tools combine with MATLAB® and Simulink® to create a unified modeling environment, enabling the use of a single platform throughout systems engineering, design, implementation, and verification processes tools.
In this talk, we present a workflow for systems engineering and architectural modeling with a tight connection to Model-Based Design.
- Building a bridge between early architecture work and downstream design
- Creating architecture models and extending the language through stereotypes and profiles
- Analyzing architectures
- Moving to design and implementation
Reinforcement Learning Workflows for AI
Jaroslav Jirkovský, Humusoft s.r.o.
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.
Master Class: 21 MATLAB Features You Need Now
Jaroslav Jirkovský, Humusoft s.r.o.
Are you getting the most out of MATLAB®, or are you still using it just the way you were taught your first year in university? With over 2,000 people working year-round to design, build, test, and document MathWorks products, it is a safe bet that there are more than a few useful features you don’t know.
This fast-paced talk will introduce at least 21 features you can start using today to make your use of MATLAB more efficient, more effective, and more fun. Some features will be very new, while others may be 5, 10, or maybe even more than 15 years old. How many of them will be new to you?
What’s New in MATLAB and Simulink R2020a
Jaroslav Jirkovský, Michal Blaho, Humusoft s.r.o.
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 preprocessing and analyzing data; developing motor control algorithms; creating interactive apps; packaging and sharing simulations; and modeling, simulating, and verifying designs.
BIO: Jaroslav Jirkovský je zaměstnancem společnosti Humusoft, kde pracuje desátým rokem na pozici aplikačního inženýra. Je absolventem magisterského studia oboru Přístrojová a řídicí technika a doktorského studia oboru Technická kybernetika, obojí na Fakultě strojní ČVUT v Praze. S prostředím MATLAB a Simulink aktivně pracuje od roku 2003.
Sensor Fusion and Navigation for Autonomous Systems
Michal Blaho, Humusoft s.r.o.
In order for autonomous systems to move within their environment, engineers need to design, simulate, test, and deploy algorithms that perceive the environment, keep track of moving objects, and plan a course of movement for the system itself. This workflow is critical for a wide range of systems including self-driving cars, warehouse robots, and unmanned aerial vehicles (UAVs). In this talk, you will learn how to use MATLAB® and Simulink® to develop perception, sensor fusion, localization, multi-object tracking, and motion planning algorithms. Some of the topics that will be covered include:
- Perception algorithm design using deep learning
- Fusing sensor data (cameras, lidar, and radar) to maintain situational awareness
- Mapping the environment and localizing the vehicle using SLAM algorithms
- Path planning with obstacle avoidance
- Path following and control design
BIO: Michal Blaho pracuje v spoločnosti Humusoft ako aplikačný inžinier pre Slovenskú republiku. Je absolventom inžinierskeho a doktorandského štúdia v odbore Automatizácia na Fakulte elektrotechniky a informatiky STU v Bratislave. Medzi jeho hlavné odborné záujmy patrí modelovanie systémov, riadenie systémov, low-cost hardvér a generovanie kódu.