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公开(公告)号：US20230406327A1
公开(公告)日：2023-12-21
申请号：US17959515
申请日：2022-10-04
Inventor： Minhae KWON , Dongsu LEE
CPC classification number: B60W50/00 , G08G1/0116 , G08G1/0133 , G08G1/052 , G05B13/0265 , B60W60/001
Abstract: The present disclosure relates to a driving characteristic inference apparatus and method of a vehicle. According to an exemplary embodiment of the present disclosure, a driving characteristic inference method includes observing a target driving vehicle which is an observation target to generate driving data including behavior information of the target driving vehicle, by an observation module; performing reinforcement learning on an artificial intelligence model using learning data including a first driving characteristic coefficient, by a learning module; generating sampled inference behavior data with the driving data and the first driving character coefficient as an input of the artificial intelligence model, by a model utilization module; and comparing the generated inference behavior data with measured actual behavior data to determine the second driving characteristic coefficient, by an inference module.
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Patent Agency Ranking
公开(公告)号：US20220159021A1
公开(公告)日：2022-05-19
申请号：US17528203
申请日：2021-11-17
Inventor： Minhae KWON , Hyoseon KYE
IPC: H04L29/06
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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公开(公告)号：US20240239350A1
公开(公告)日：2024-07-18
申请号：US18139602
申请日：2023-04-26
Inventor： Minhae KWON , Dong Su LEE
CPC classification number: B60W40/09 , B60W60/001 , B60W2554/80 , B60W2556/45
Abstract: Provided is a method and an apparatus of inferring a stochastic driving characteristic of a driving vehicle. The driving characteristic inferring apparatus may include a model training unit which trains a plural driving characteristic model and an inference model using learning driving data of a learning driving vehicle, and a driving characteristic inferring unit which infers a driving characteristic coefficient representing a driving characteristic of a target driving vehicle with driving data of the target driving vehicle as an input of the inference model.
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公开(公告)号：US20240047079A1
公开(公告)日：2024-02-08
申请号：US17960849
申请日：2022-10-06
Inventor： Minhae KWON , Sujin AHN
IPC: G16H50/80
CPC classification number: G16H50/80
Abstract: A system for estimating for infectious disease transmission includes: a parameter receiving unit that constructs a discrete-time Markov chain model indicating a state and a state transition probability, and receives a parameter indicating status information according to the infectious disease transmission at a time point after t days have elapsed from a start of infection spread; a calculation unit that calculates the number of hidden infectious states through backward reasoning, and calculates the number of hidden infectious states through forward reasoning using the received parameter; an extraction unit that extracts an inverse scale coefficient using the calculated number of infection states and calculates a reproduction factor using the extracted inverse scale coefficient; and a prediction unit that updates infectious disease status information using the calculated reproduction factor, and predicts the number of hidden infectious persons using the updated infectious disease status information.
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5.发明公开公开(公告)号：US20230406350A1
公开(公告)日：2023-12-21
申请号：US17959550
申请日：2022-10-04
Inventor： Minhae KWON , Dongsu LEE
CPC classification number: B60W60/0011 , B60W40/04 , G06N5/04 , G06N3/08 , G06F17/11 , B60W2554/4045 , B60W2554/4046
Abstract: The present disclosure relates to an apparatus and a method for deciding a behavior of an agent, and more particularly, to an apparatus and a method for deciding a behavior of a single agent using an episodic future thinking mechanism. The decision-making method according to an exemplary embodiment of the present disclosure includes collecting observation information and behavior information of a surrounding agent, by a first information collecting unit; inferring a character coefficient of a surrounding agent using data of the first information collecting unit, by a character inferring unit, collecting observation information of a main agent and the surrounding agent at a first time point, by a second information collecting unit; predicting a behavior of the surrounding agent based on the observation information and the character coefficient of the surrounding agent, by a behavior predicting unit, inferring expected observation information of the environment state and the surrounding agent at a second time point corresponding to the behavior prediction result of the surrounding agent, by a state inferring unit; and deciding a behavior of the main agent at the first time point based on the expected observation information of the environment state and the surrounding agent at a second time point, by a decision-making unit.
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6.发明公开公开(公告)号：US20230351198A1
公开(公告)日：2023-11-02
申请号：US17979728
申请日：2022-11-02
Inventor： Minhae KWON , Hyoseon KYE , Miru KIM
IPC: G06N3/091 , H04L9/40 , G06N3/0455
CPC classification number: G06N3/091 , H04L63/1425 , G06N3/0455
Abstract: The present disclosure provides a hierarchical network intrusion detection method including preprocessing normal data for training, outputting reconstruction data by inputting the preprocessed normal data for training into an autoencoder, calculating a reconstruction error by using the preprocessed normal data for training and the reconstruction data, training the autoencoder to minimize a reconstruction error, extracting hierarchical information of the autoencoder, setting a threshold value by using latent vector for the normal data for training, the reconstruction data, and an output value of each of L hidden layers included in an encoder, calculating anomaly scores of the latent vector for the network data, the reconstruction data, and an output value of each of the L hidden layers in a state in which a target network data is input to the autoencoder, and determining whether an intrusion into the network data is detected by using the threshold value and the anomaly scores.
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