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1.发明公开公开(公告)号：US20230187069A1
公开(公告)日：2023-06-15
申请号：US17938012
申请日：2022-10-04
Inventor： Jae Hun CHOI , Do Hyeun KIM , Hwin Dol PARK
Abstract: Disclosed is an artificial intelligence apparatus, which includes an episode conversion module that receives an electronic medical record (EMR) of a patient and converts the received EMR into an episode including a condition of the patient, a treatment method, and a treatment history, a patient condition predictive intelligence deep learning module that trains a patient condition predictive intelligence for predicting a following condition of the patient after applying the treatment method, a local policy intelligence reinforcement learning module that performs reinforcement learning of a policy intelligence for planning an optimized treatment path for the patient based on the episode, an optimized treatment path exploration module that plans the optimized treatment path for the patient by using the policy intelligence, and a global policy intelligence management module that updates a global policy intelligence for planning and exploring the optimized treatment path based on the policy intelligence.
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公开(公告)号：US20220207297A1
公开(公告)日：2022-06-30
申请号：US17551820
申请日：2021-12-15
Inventor： Youngwoong HAN , Hwin Dol PARK , Jae Hun CHOI
IPC: G06K9/62
Abstract: Disclosed is a data processing device that processes unbalanced data, which includes a preprocessor that calculates a reference value based on a plurality of training data and target data, and a learner that applies the plurality of training data to a first weight model to generate first prediction data, calculates a loss value based on a first distance between the target data and the reference value and a second distance between the target data and the first prediction data, and updates the first weight model based on the calculated loss value, and the plurality of training data and the target data have an unbalanced distribution.
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3.发明公开公开(公告)号：US20240192187A1
公开(公告)日：2024-06-13
申请号：US18509735
申请日：2023-11-15
Inventor： Jae Hun CHOI , Do Hyeun KIM , Hwin Dol PARK , Seunghwan KIM , Hyung Wook NOH , Chang-Geun ANH , YongWon JANG , Kwang Hyo CHUNG
IPC: G01N33/00
CPC classification number: G01N33/0034 , G01N33/0011 , G01N33/0062 , G01N2033/0019
Abstract: Disclosed is an artificial intelligence apparatus for detecting a target gas, which includes a mixed gas measurement unit that measures a mixed gas collected in a plurality of domains through a sensor array to generate sensing data including heterogeneous domain measurement data measured from the mixed gas collected in a domain different from the target gas and target domain measurement data measured from the mixed gas collected from the same domain as the target gas, a heterogeneous intelligence model deep learning unit that receives the heterogeneous domain measurement data to train a heterogeneous intelligence model, a target intelligence model deep learning unit that receives the heterogeneous intelligence model and the target domain measurement data to train a target intelligence model, and a target gas detection unit that determines whether an environmental gas includes the target gas using the target intelligence model.
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4.发明公开公开(公告)号：US20230229915A1
公开(公告)日：2023-07-20
申请号：US18155471
申请日：2023-01-17
Inventor： Hwin Dol PARK , Jae Hun CHOI , Young Woong HAN
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Disclosed herein is a method and apparatus for predicting a future state and reliability based on time series data. In the method and the apparatus, a future state is predicted by preprocessing past state data and executing an algorithm based on the preprocessed past state data to generate a trained model, followed by preprocessing current state data and executing an algorithm based on the created trained model, the preprocessed current state data, and the preprocessed past state data.
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公开(公告)号：US20230176594A1
公开(公告)日：2023-06-08
申请号：US17716889
申请日：2022-04-08
Inventor： Kwang Hyo CHUNG , Chang Geun AHN , Do Hyun KIM , Seung Hwan KIM , Hyung Wook NOH , Hwin Dol PARK , Yong Won JANG , Jae Hun CHOI
CPC classification number: G05D7/0664 , F16L41/008 , F16L41/03
Abstract: Provided is a multi-port gas flow rate control apparatus. The multi-port gas flow rate control apparatus includes a gas supply chamber configured to supply a measurement gas input through one gas inflow channel while allowing the measurement gas to diverge into a plurality of flows, a plurality of gas divergence flow channels each having one side connected to the gas supply chamber and configured to transfer the measurement gas flowing through the gas supply chamber to a plurality of gas sensors, respectively, and a gas measurement chamber configured to accommodate the plurality of gas sensors, including the plurality of gas divergence flow channels configured to connect to the gas supply chamber to the plurality of gas sensors to transfer a gas outflow diverging through the gas supply chamber to the plurality of accommodated gas sensors, and configured to discharge the gas outflow sensed by the plurality of gas sensors.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20250137979A1
公开(公告)日：2025-05-01
申请号：US18927774
申请日：2024-10-25
Inventor： Hyung Wook NOH , Do Hyeun KIM , Hwin Dol PARK , Chang Geun AHN , Yong Won JANG , Jae Hun CHOI
IPC: G01N33/00
Abstract: The present disclosure relates to a method and apparatus for performing sensor drift compensation based on double cycling measurement. A method for performing drift correction according to gas sensor measurement according to an embodiment of the present disclosure may comprise: obtaining first measurement data for a reference gas for each sensor using one or more sensors in a first cycle; obtaining second measurement data for a target gas for each sensor using the one or more sensors in a second cycle; and generating a drift-corrected feature for each sensor based on a ratio calculated by dividing the second measurement data by the first measurement data.
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7.发明公开公开(公告)号：US20240193417A1
公开(公告)日：2024-06-13
申请号：US18504214
申请日：2023-11-08
Inventor： Hwin Dol PARK , Do Hyeun KIM , Jae Hun CHOI
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Disclosed is an apparatus, which includes a preprocessor that generates raw data, generates preprocessed time series data, and generates preprocessed learning data, and a learner that receives the preprocessed learning data as input data and trains a prediction model such that the similarity between a first future state predicted using the input data and a second future state predicted using data included in the same cluster as the input data increases and such that the similarity between the first future state and a third future state predicted using data included in a different cluster from the input data decreases, and the prediction model is a machine learning model for predicting a future state of the time series data at an arbitrary time point.
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8.发明申请公开(公告)号：US20210319341A1
公开(公告)日：2021-10-14
申请号：US17229606
申请日：2021-04-13
Inventor： Youngwoong HAN , Hwin Dol PARK , Jae Hun CHOI
Abstract: Disclosed is a time-series data processing device that includes a preprocessor, a learner, and a predictor. The preprocessor generates time-series interval data based on a time interval of time-series data, generates feature interval data based on a time interval of each of features of the time-series data, and preprocesses the time-series data. The learner generates a weight group of a prediction model for generating a prediction result based on the time-series interval data, the feature interval data, and the preprocessed time-series data. The predictor generates a time-series weight, which depends on a feature weight of each of the features and a time flow of the time-series data, based on the time-series interval data, the feature interval data, and the preprocessed time-series data and generates a prediction result based on the feature weight and the time-series weight.
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9.发明申请公开(公告)号：US20220343160A1
公开(公告)日：2022-10-27
申请号：US17725172
申请日：2022-04-20
Inventor： Hwin Dol PARK , Jae Hun CHOI , Youngwoong HAN
IPC: G06N3/08
Abstract: Disclosed is a time series data processing device which includes a pre-processor that performs pre-processing on time series data to generate pre-processing data, and a learner that creates or updates a feature model through machine learning for the pre-processing data. The learner includes a time series irregularity learning model that learns time series irregularity of the pre-processing data, and a feature irregularity learning model that learns feature irregularity of the pre-processing data.
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公开(公告)号：US20220187262A1
公开(公告)日：2022-06-16
申请号：US17513567
申请日：2021-10-28
Inventor： YongWon JANG , Hwin Dol PARK , CHANG-GEUN AHN , Do Hyeun KIM , Seunghwan KIM , Hyung Wook NOH , Kwang Hyo CHUNG , Jae Hun CHOI
Abstract: Disclosed are a device and a method for anomaly detection of a gas sensor. The device includes a measuring unit that extracts a characteristic of a gas supplied from the outside, generates data based on the extracted characteristic, and outputs the data, and a data processing unit that receives the data, determines whether an error occurs in the data, and outputs an anomaly detection result based on a result of determining whether the error occurs in the data. The measuring unit performs a calibration operation or an environment adjusting operation before extracting the characteristic, and the data processing unit determines whether the error occurs in the data, based on machine learning.
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