By applying basic artificial intelligence algorithms, the phases of array antennas are optimized to obtain desired radiation patterns.

AI algorithms such as DNN, CNN, MDN, and GAN can be utilized.

This research is being extended to optimize metasurface structures using AI.

In the future, it is expected that by merging with DOA algorithms in the 6G field, real-time metasurface optimization will help eliminate coverage holes. 

Since ultra-high-speed hyperTube trains operating at subsonic speeds use linear motors as their propulsion system, it is necessary to minimize phase errors over distance for precise control.

To achieve this, ground coils in the form of inductive cross loops are used.

Research is being conducted on methods to optimize the shapes of both ground and onboard coils to reduce phase errors, as well as to secure cost-effectiveness by reducing the number of ground coils.

In the future, it is expected that both absolute and relative position detection systems will be developed at lower cost. 

Wireless Power Transfer systems for railways handle high power, and thus require optimization research that considers issues such as dielectric breakdown.

It is also necessary to analyze induced power on nearby conductors and identify heat generation factors.

Simulation methods are applied to predict performance and environmental impacts.

Furthermore, reviews are conducted on requirements that may be needed for future railway technical standards and regulations. 

Design tasks for antennas required in various industrial fields are carried out, along with electromagnetic wave analysis.