Abstract: Provided are a drone taxi system based on multi-agent reinforcement learning and a drone taxi operation method using the same. The drone taxi system includes a plurality of drone taxies configured to receive call information including departure point information and destination information from passenger terminals present within a certain range and a control server configured to receive call information of passengers from each drone taxi, select a candidate passenger depending on whether a passenger is present, generate travel route information of each drone taxi from drone state information of the plurality of drone taxies through multi-agent reinforcement learning, and transmit the travel route information to the drone taxi.

Abstract: Provided is a surveillance system employing a plurality of unmanned aerial vehicles (UAVs), the surveillance system showing improved surveillance performance while optimizing common energy consumption for computing of all the UAVs and also providing a stable visual monitoring service using autonomous mobility of the plurality of UAVs regardless of movement of an object to be monitored and action uncertainty of an adjacent UAV.

Abstract: The present invention relates to a quantum federated learning system that performs federated learning on the basis of at least one observation value input from a single-hop offloading environment, and the system includes: a global server for initializing parameters of a quantum slimmable neural network (QSNN) model and transmitting the initialized quantum slimmable neural network model to at least one local device; and the at least one local device for inputting the at least one observation value into the initialized quantum slimmable neural network model to train the quantum slimmable neural network model, and transmitting the parameters of the trained quantum slimmable neural network model to the global server side. Through the system, the environmental epidemiology problems of the federated learning performed in conventional computing, such as communication channel conditions and energy limitations over time can be solved.

Abstract: Provided is a surveillance system employing a plurality of unmanned aerial vehicles (UAVs), the surveillance system showing improved surveillance performance while optimizing common energy consumption for computing of all the UAVs and also providing a stable visual monitoring service using autonomous mobility of the plurality of UAVs regardless of movement of an object to be monitored and action uncertainty of an adjacent UAV.

Abstract: A method for matching between high altitude platform (HAP) and unmanned aerial vehicle (UAV) in space-air-ground integrated network includes determining if there is an unmatched UAV in the space-air-ground integrated network including at least one UAV and at least one HAP; requesting matching to an unmatched m-th (where m is a natural number) UAV from an n-th (where n is a natural number) HAP having an unconnected antenna in a presence of the unmatched UAV; determining if the m-th UAV is matched with another HAP; comparing a priority of the n-th HAP with a priority of the matched HAP, in case of the m-th UAV being matched with another HAP; and matching the unconnected antenna of the n-th HAP with the m-th UAV, in case of the priority of the n-th HAP being higher than the priority of the matched HAP.

Abstract: A processor-implemented method with virtual object rendering includes: determining a plurality of predictive trajectories of a first object according to a Gaussian random path based on a high-level model that is trained by hierarchical reinforcement learning; determining direction information of a second object according to subgoals corresponding to the predictive trajectories based on a low-level model that is trained by hierarchical reinforcement learning; determining direction information of the second object according to a subgoal corresponding to one of the predictive trajectories based on an actual trajectory of the first object; and rendering the second object, which is a virtual object, based on the determined direction information.

Abstract: Provided are a drone taxi system based on multi-agent reinforcement learning and a drone taxi operation method using the same. The drone taxi system includes a plurality of drone taxies configured to receive call information including departure point information and destination information from passenger terminals present within a certain range and a control server configured to receive call information of passengers from each drone taxi, select a candidate passenger depending on whether a passenger is present, generate travel route information of each drone taxi from drone state information of the plurality of drone taxies through multi-agent reinforcement learning, and transmit the travel route information to the drone taxi.