Promising Technologies Predicted by Deep Learning-based Model

6Feb - by aiuniverse - 0 - In Deep Learning


The Korea Institute of Science and Technology Information (KISTI) and the Data Science Lab of Myongji University have selected the 10 most promising technological fields by using big data and artificial intelligence. The fields expected to show a very rapid growth until the mid-2020s include autonomous driving, energy, machine vision, biotechnology and robotics. The prediction is based on their deep learning-based future prediction model with an accuracy of over 86 percent.

They used 16 million pieces of data published worldwide for the past 12 years in developing the prediction model. The data was classified into 4,500 subject categories and AI and deep learning techniques were employed for quantification by category of network structure data, research content and research fields.

The 10 fields include renewable energy storage and conversion for hydrogen energy utilization. This technique for using hydrogen in fuel cells by producing it from water electrolyzed by renewable energy is expected to contribute to renewable energy storage and greenhouse gas emissions reduction.

The other fields include the development of advanced and eco-friendly air conditioning and heating system materials. Examples of the materials expected to contribute to greenhouse gas emissions reduction include nano-adsorbents for use in adsorption air conditioners and heaters, which are predicted to replace electric air conditioners and heaters.

Carbon dioxide capture and utilization, in the meantime, is to capture carbon dioxide and turn it into resources for use in biofuels, chemical products, construction materials, and so on. It can result in added value creation in various forms as well as carbon reduction.

Vehicle control technology development for autonomous driving improvement is to better control vehicle behaviors and ensure safety by recognizing fast-changing traffic situations with more accuracy and precision. It is data processing performance enhancement and intellectualization that are key to the development.

AI-based machine vision can be defined as automated decision making based on image acquisition and processing. These days, the scope of application of this technology is expanding very rapidly with the development of deep learning-based image processing and classification techniques and Industry 4.0 technologies such as smart factory operation.

Ultra-high-performance concrete development is to improve the salt resistance and durability of concrete and better prevent its carbonation so that buildings and structures can be used for extended periods.

Biodiversity research is a field comprehensively covering species exploration, research on interactions between organisms in the same habitats, research on genetic variations related to genes and individual organisms, etc.

High-voltage direct current transmission is to convert produced AC power into DC power, transmit it at a high voltage, and then supply electric power after reconversion into AC power. It is an advanced power transmission technique ensuring stability and a decrease in power loss and the demand for it is soaring with regard to cross-border power grid construction, renewable energy system linkage, etc.

Humanoid robot development is to work on controllable humanoids, including two-legged robots, so that they can do various jobs in place of humans. In this field, intellectualization is in rapid progress as to incident recognition, determination and prediction, hazard avoidance, and so on.

Lastly, hyperspectral imaging is to allow an object or a substance to be distinguished or detected with greater ease by acquiring spectrum data on a fragmented band by image pixel. Nowadays, it is developing at a rapid pace in combination with ultraspectral imaging, machine learning-based big data analysis, micro image sensors, and the like.

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