Prof Zenghui Wang from the College of Science, Engineering and Technology was presented with one of the 2019 Chancellor’s Awards for Excellence in Research during the 2020 Research & Innovation Awards. The awards celebrate a community of Unisa researchers who are at the helm of knowledge production in the institution.
Wang’s research on intelligent systems is fundamental to the sustainable development of our country. As the world is centrally moving towards the Fourth Industrial Revolution (4IR), an intelligent system is a machine with an embedded, Internet-connected computer that has the capacity to gather and analyse data and communicate with other systems. Other criteria for intelligent systems include the capacity to learn from experience, security, connectivity, the ability to adapt according to current data and the capacity for remote monitoring and management.
Explaining his research on waste management using intelligent systems, Wang says that "the accumulation of solid waste in the urban area is becoming a great concern, and over time it will result in environmental pollution and may be hazardous to human health if it is not properly managed. It is therefore important to have an advanced/intelligent waste management system to manage a variety of waste materials."
One of the most important steps of waste management is the separation of the waste into the different components; this process is normally done manually by hand-picking. To simplify the process, Wang proposes an intelligent waste material classification system that is developed by using the 50-layer residual net pre-train (ResNet-50) Convolutional Neural Network model, which is a machine learning tool and serves as the extractor, and Support Vector Machine (SVM), which is used to classify the waste into different groups/types such as glass, metal, paper and plastic.
"The proposed system is tested on the trash image dataset which was developed by Gary Thung and Mindy Yang, and is able to achieve an accuracy of 87% on the dataset. The separation process of the waste will be faster and intelligent using the proposed waste material classification system without or reducing human involvement," he adds.
Wang’s calibre of research aims to contribute purposeful and socially relevant research. He explains that his objective is always focused on the premise of quality research that can be applied to solve societal problems. In this regard, his research field is in Electrical Engineering and Computer Science, with a specific focus on control theory and control engineering, engineering optimisation and its applications (deep learning), neural networks, chaotic systems and image processing, among other interests.
Expressing joy over winning this award, Wang said, "winning this award has encouraged me to work on more contributions in socially relevant research areas and to ensure that I achieve high-quality research outputs. I strongly feel propelled to do more." Wang has an envied reputation among his peers; some of his research highlights include publishing 34 papers, including 18 ISI master-indexed journal papers in the past two years.
* By Tshimangadzo Mphaphuli, Senior Journalist, Department of Institutional Advancement
Publish date: 2020-04-06 00:00:00.0