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Tech Corner

26 Nov 2019

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.

26 Nov 2019

Accessing Equity Data using a MATLAB Web App

Timely and convenient access to data is crucial in the world of finance to enable rapid and informed decision-making.  To address this need, we have developed a web app in MATLAB that demonstrates how data access and custom analytics can be made accessible across an organisation. The app shows a very simplistic workflow that allows […]

26 Nov 2019

Smart Manufacturing: From Simulation to Implementation

More and more manufacturers are adopting a new approach to process optimisation by integrating practical advances in technology into their day-to-day operations. The concept is built around developing a plant-wide collaborative process that results in efficiency gains that would otherwise have been missed. Many global tech and industry experts believe that this approach is the […]

26 Nov 2019

Constructing the Optimal Portfolio with MATLAB and Smart Beta

Verushen Coopoo, Application Engineer Opti-Num Solutions (2019) 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 […]

26 Nov 2019

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 […]

26 Nov 2019

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 […]

26 Nov 2019

AI in Finance: Using Deep Learning for RegTech

Regulatory Technology (RegTech) refers to the use of new technology such as Artificial Intelligence to improve how companies adhere to regulatory requirements. The RegTech app, developed in MATLAB, aims to accelerate and automate tedious regulatory data extraction processes. Data capturers at financial institutions constantly capture data from IDs, payslips and proofs-of-address. By using deep learning […]

05 Aug 2019

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.

31 Jul 2019

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.

17 Jul 2019

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?