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公开(公告)号：US20220363279A1
公开(公告)日：2022-11-17
申请号：US17535567
申请日：2021-11-24
Inventor： Minhae Kwon , Dongsu Lee
Abstract: A method for combating a stop-and-go wave problem using deep reinforcement learning based autonomous vehicles includes selecting one of a plurality of deep reinforcement learning algorithms for training an autonomous vehicle and a reward function in a roundabout environment in which autonomous vehicles and non-autonomous vehicles are driving, determining a deep neural network architecture according to the selected deep reinforcement learning algorithm, learning a policy which enables the autonomous vehicle to drive at a closest velocity to a constant velocity based on state information including a velocity of the autonomous vehicle and a relative velocity and a relative position between the autonomous vehicle and an observable vehicle by the autonomous vehicle at a preset time interval and reward information, using the selected deep reinforcement learning algorithm, and driving the autonomous vehicle based on the learned policy to determine an action of the autonomous vehicle.
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公开(公告)号：US12091025B2
公开(公告)日：2024-09-17
申请号：US17535567
申请日：2021-11-24
Inventor： Minhae Kwon , Dongsu Lee
CPC classification number: B60W50/06 , B60W60/001 , G06N3/08
Abstract: A method for combating a stop-and-go wave problem using deep reinforcement learning based autonomous vehicles includes selecting one of a plurality of deep reinforcement learning algorithms for training an autonomous vehicle and a reward function in a roundabout environment in which autonomous vehicles and non-autonomous vehicles are driving, determining a deep neural network architecture according to the selected deep reinforcement learning algorithm, learning a policy which enables the autonomous vehicle to drive at a closest velocity to a constant velocity based on state information including a velocity of the autonomous vehicle and a relative velocity and a relative position between the autonomous vehicle and an observable vehicle by the autonomous vehicle at a preset time interval and reward information, using the selected deep reinforcement learning algorithm, and driving the autonomous vehicle based on the learned policy to determine an action of the autonomous vehicle.
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公开(公告)号：US12294868B2
公开(公告)日：2025-05-06
申请号：US17662570
申请日：2022-05-09
Applicant: Ewha University—Industry Collaboration Foundation , Foundation of Soongsil University-Industry Cooperation
Inventor： Hyunggon Park , Nayoung Kim , Minhae Kwon
Abstract: A method of building an ad-hoc network of a wireless relay node and an ad-hoc network system are disclosed. The method includes verifying state information representing a relative distance and angle to a neighboring node capable of receiving data when each of relay nodes transmits the data, determining an action representing a change over time of each relay node, determining, based on an amount of change in a network throughput determined according to the state information and an amount of energy consumption according to the action, a reward corresponding to the action, and building a network including a source node, a destination node, and a plurality of relay nodes by generating, based on a reward of each of the relay nodes, a policy that allows a cumulative reward to be maximized.
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公开(公告)号：US11909751B2
公开(公告)日：2024-02-20
申请号：US17528203
申请日：2021-11-17
Inventor： Minhae Kwon , Hyoseon Kye
CPC classification number: H04L63/1425 , H04L43/16 , H04L67/12
Abstract: An anomaly detection method includes searching for one principal component axis by analyzing a normal data set collected in time series from a plurality of IoT devices by using a principal component analysis technique, setting a center point of the principal component, receiving a currently measured measurement data set from the plurality of IoT devices, acquiring a linear transformation data set having a plurality of projection points as elements by projecting a plurality of measurement data which is each element in the measurement data set onto the principal component axis, calculating a Mahalanobis distance between the projection point and the central point, and detecting whether or not data of the IoT devices is abnormal by comparing the Mahalanobis distance calculated for each element with a threshold.
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