Sự Kiện

This session demonstrates an end-to-end MATLAB workflow for developing anomaly detection models in the context of a pill production quality control data set comprising a large collection of images. The objective is to verify the quality of pills using automated visual inspection techniques.

This presentation considers the alternative construction of the design space based on experiment data and a grey-box model of the reactions. The models are subsequently used to optimize the production process by changing the process variables. In addition, the effect of uncertainty and variability of the parameters on the process performance is also examined with a Monte-Carlo simulation.

Learn how to use MATLAB for hyperspectral imaging and aerial lidar data processing for terrain classification and vegetation detection in agricultural applications.

Deep Learning and Machine Learning are powerful tools to build applications for signals and time-series data across a broad range of industries. These applications range from predictive maintenance and health monitoring to financial portfolio forecasting and advanced driver assistance systems.

MATLAB® and Simulink® with deep learning frameworks, TensorFlow and PyTorch, provide enhanced capabilities for building and training your machine learning models. Via interoperability, you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

In this webinar, we will show how MATLAB and Simulink can be used to validate renewable energy models to help address regulatory requirements for commissioning, such as voltage-ride through requirements. As a result of the increasing number of utility-scale solar, wind, and energy storage systems, regulatory requirements have recently been expanded to include many renewable energy installations.

Unmanned aerial vehicles are changing the way we create content, perform land surveys, and move goods. As we develop more and more aerial vehicles, speeding up their development is more crucial. In this session, you will learn how MATLAB is used in UAV development, from component design to AI algorithm development to deployment simulations.

In this webinar, we will perform system-level simulation of a microgrid system to demonstrate system optimization and validation of an industrial-scale distributed control system. The webinar will show how designing with simulation helps minimize time for testing of new safety critical algorithms and improves equipment uptime in the field.

Signal processing and biomedical applications are becoming increasingly complex and computationally intensive. With the increasing adoption of machine learning and deep learning techniques, powerful hardware like multicore CPUs, GPUs, and High-Performance Computing clusters/cloud are common. With Parallel Computing Toolbox™, MATLAB® helps you take advantage of your hardware to speed up your applications without having to rewrite code. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming.