Why Time Delay Matters | Control Systems in Practice
Time delays are inherent to dynamic systems. If you’re building a controller for a dynamic system, it’s going to have to account for delay in some way. Time-Delay Systems: Analysis and Design with MATLAB and Simulink: http://bit.ly/2C354yp Time delays exist in two varieties: signal dist
From playlist Control Systems in Practice
A Better Way to Think About a Notch Filter | Control Systems in Practice
Check out the other videos in the series: Part 1 - What Does a Control Engineer Do? https://youtu.be/ApMz1-MK9IQ Part 2 - What is Gain Scheduling? https://youtu.be/YiUjAV1bhKs Part 3 - What is Feedforward Control? https://youtu.be/FW_ay7K4jPE Part 4 - Why Time Delay Matters https://youtu.b
From playlist Control Systems in Practice
See also https://youtu.be/BYTi0RWp494 and https://youtu.be/vV_vIFL3LKU
From playlist gradient_descent
4 Ways to Implement a Transfer Function in Code | Control Systems in Practice
Check out the other videos in the series: Part 1 - What Does a Controls Engineer Do? https://youtu.be/ApMz1-MK9IQ Part 2 - What Is Gain Scheduling? https://youtu.be/YiUjAV1bhKs Part 3 - What Is Feedforward Control? https://youtu.be/FW_ay7K4jPE Part 4 - Why Time Delay Matters https://youtu
From playlist Control Systems in Practice
Mini Batch Gradient Descent | Deep Learning | with Stochastic Gradient Descent
Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini Batch Gradient Descent updates weight parameters after assessing the small batch of the datase
From playlist Optimizers in Machine Learning
Introduction to Frequency Selective Filtering
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Separation of signals based on frequency content using lowpass, highpass, bandpass, etc filters. Filter g
From playlist Introduction to Filter Design
Example of a system with time delays in the denominator
I analyse a tank system with delayed recycle which leads to a transfer function with time delay exponentials in the denominator
From playlist Laplace
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Noncausal filtering of stored data to obtain zero-phase response using the time-reversal property of the DFT, as implemented by the "filtfilt" comma
From playlist Introduction to Filter Design
Lecture 10 | Convex Optimization II (Stanford)
Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd introduces a new topic, Decomposition Applications. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentral
From playlist Lecture Collection | Convex Optimization
AI Weekly Update #9 October 20th, 2019
3:24 OpenAI Robotic Hand Rubik’s Cube Solver https://openai.com/blog/solving-rubiks-cube/ 11:10 GoogleAI Massively Multilingual, Massive Neural Machine Translation https://ai.googleblog.com/2019/10/exploring-massively-multilingual.html 12:45 GoogleAI Video Architecture Search https://ai.go
From playlist AI Research Weekly Updates
Lec 13 | MIT 6.033 Computer System Engineering, Spring 2005
Congestion Control View the complete course at: http://ocw.mit.edu/6-033S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.033 Computer System Engineering, Spring 2005
Stanford Seminar - BitTorrent Live: A Low Latency Live P2P Video Streaming Protocol
"BitTorrent Live: A Low Latency Live P2P Video Streaming Protocol" -Bram Cohen, BitTorrent Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operatin
From playlist Engineering
DEFCON 16: New ideas for old practices - Port-Scanning improved
Speakers: Fabian "fabs" Yamaguchi, Recurity Labs GmbH, Berlin, Germany FX, Head of Recurity Labs How fast a port-scan can be is largely dependent on the performance of the network in question. Nonetheless, it is clear that choosing the most efficient scanning-speed is only possible based
From playlist DEFCON 16
Optimising flow within mobility systems with AI: Neil Walton and Damon Wischik
This workshop is held in collaboration with the Toyota Mobility Foundation, who are sponsoring the Turing’s research into Optimising flow within mobility systems with AI. It forms part of the Turing’s research programme on AI. The workshop is aimed at identifying solutions for future urb
From playlist AI for traffic control
Speakers: Fabian Yamaguch, FX of Phenoelit Port-Scanning large networks can take ages. Asking yourself how much of this time is really necessary and how much you can blame on the port-scanner, you may find yourself integrating your own scanner into the linux-kernel. Or at least we did
From playlist 24C3: Full steam ahead
Guilherme Mazanti: "Second-order local minimal-time mean field games"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop III: Mean Field Games and Applications "Second-order local minimal-time mean field games" Guilherme Mazanti - CentraleSupélec Abstract: Motivated by the problem of proposing mean field game models for crowd motion, this talk considers a
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Bertrand Maury - Transport optimal et mouvements de foules sous contrainte de congestion (Part 1)
Transport optimal et mouvements de foules sous contrainte de congestion (Part 1)
From playlist Inter’actions en mathématiques 2015
Math: Partial Differential Eqn. - Ch.1: Introduction (11 of 42) What is the Gradient Operator?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a gradient operator. The gradient operator indicates how much the function is changing when moving a small distance in each of the 3 directions. I will write an example of the gradient
From playlist PARTIAL DIFFERENTIAL EQNS CH1 INTRODUCTION