SuperBru Predictions with AI
Congrats to the Boks! For the recent Rugby World Cup a few members of the Opti-Num team participated in a sports prediction game using the SuperBru platform. Points were awarded in the Rugby World Cup for: Predicting the correct winning team Predicting the correct margin of winning Many of us used our rugby knowledge, patriotism […]
AI in Industry 4.0: I have all this sensor data… Now what?
Predictive maintenance uses data to monitor the condition of equipment. Using fourth industrial revolution techniques means reduced failures, less machine downtime, reduced maintenance costs, and so much more. Most industrial plants collect large amounts of sensor data from their equipment. The sheer volume of data may leave most people thinking “What now?”. Using this data to inform smart business decisions requires a rare combination of both domain and statistical expertise.
Accessing Equity Data using a MATLAB Web App
Timely and accurate access to data is a staple in the world of financial analysis. See how Vacation Work students Grace and Monaheng access equity data via a web app originally developed by Application Engineer, Verushen Coopoo.
Smart Manufacturing: From Simulation to Implementation
This paper, which was written by Opti-Num consultants (Richard Fisher and Jason Miskin) in collaboration with John Thompson (a division of ACTOM), was presented at the IFAC MMM (Mining, Minerals and Metal Processing) 2019 conference. It discusses a practical end-to-end implementation of a Smart Manufacturing technique, namely Model-Predictive Control (MPC) in search for efficiency gains.
Constructing the Optimal Portfolio with MATLAB and Smart Beta
In this script, we demonstrate the strength of MATLAB’s optimisation capabilities to construct a portfolio driven by smart beta investment techniques. We do not advocate for the use of any particular investment or trading strategy, but rather demonstrate how to import your own data, implement a custom-made optimisation routine, and backtest the strategy: all in one place. We supply the code in the form of this live script for you to get started right away.
Drones: Reaching New Heights in Education
Are you looking to make your classes more relevant and engaging? Drones have increased functionality and have decreased in size, making it easier to bring into classrooms. They do not cost a lot and have significant benefits, making teaching and learning so much more fun and enriching. It is a modern dynamic educational model that […]
AI in Industry 4.0: Neural Networks for Time Series Modelling in MATLAB
An essential aspect of the mining process is the froth flotation process. This removes impurities from minerals, such as silica from iron ore, which ultimately determines the quality of the product. In this article we focus on how neural networks can be applied to the mineral extraction process, ensuring products of a higher quality. The […]
AI in Finance: Using Deep Learning for RegTech
When deep learning and image processing come together, they can solve the huge problem of meeting regulations in the financial services industry. Take a look at this video where vacation work students Monaheng and Grace take you through the RegTech app, developed by previous vacation work students Christiaan and Zanele.
Real-Time Simulation and Testing: Four Part Series
Learn about Real-Time Simulation and Testing with this short 4-part video series created by our Vacation Work students Andrew and James. Here we will explore Real-Time Simulations, Rapid Control Prototyping, Hardware-in-the-Loop and Speedgoat Real-Time Target machines.
AI in Industry 4.0: Reinforcement Learning for control design
Watch our two-part video series created by our Vacation Work Students Daniel and Vicky to find out more about how reinforcement learning works and how to implement a practical controller with a reinforcement learning algorithm in MATLAB.
Optimisation in Finance: Building the Optimal Trading Strategy
In a previous example, it was shown how MATLAB can be used to backtest a simple trading strategy in 8 lines of code, where the trading strategy was developed based on the Relative Strength (RS) index. In this article, I take Kawee’s work a step further and investigate if we can optimise the buy and sell RS index values to maximise our returns. The buy and sell signals are originally chosen at an RS Index of 40 and 70 respectively. Over a six-year period, this gave approximately 18% return-on-equity. Can this be improved?
AI in Industry 4.0: Getting started with Machine Learning
Machine learning has made its mark! From medical diagnostic tools, speech recognition (think Siri and Alexa) to movie recommendations and predictive maintenance; machine learning techniques are being used to make critical business and life decisions.