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1.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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2.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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3.发明公开公开(公告)号：US20230316156A1
公开(公告)日：2023-10-05
申请号：US18057080
申请日：2022-11-18
Inventor： Do Hyeun KIM , Myung Eun LIM , Jae Hun CHOI
IPC: G06N20/20
CPC classification number: G06N20/20
Abstract: Disclosed herein a method and apparatus for learning a multi-label ensemble based on multi-center prediction accuracy. According to an embodiment of the present disclosure, there is provided a multi-label ensemble learning method comprising: collecting a prediction value for learning data for each of a plurality of prediction models; calculating a prediction error of each of the prediction models using the prediction value of each of the prediction models and a correct answer prediction value; generating a weight label for each of the prediction models based on the prediction error; and learning an ensemble weight prediction model for predicting a weight of each of the prediction models using the weight label.
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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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公开(公告)号：US20240221940A1
公开(公告)日：2024-07-04
申请号：US18345709
申请日：2023-06-30
Inventor： Do Hyeun KIM , Hwin Dol PARK , Jae Hun CHOI
Abstract: Disclosed is an apparatus for exploring an optimized treatment pathway of a target patient, which includes an episode sampling module that receives a virtual electronic medical record (EMR) episode, calculates a similarity between a first current state of the target patient, which corresponds to the received virtual EMR episode, and a second current state of a patient, which corresponds to each of a plurality of EMR episodes, extracts an EMR episode, and outputs a pair of the virtual EMR episode and the extracted EMR episode, a state value evaluation module that predicts an expected value of a reward, a treatment method learning module that predicts an optimized treatment method and optimized timing of treatment and provides an external prediction model with the current state of the target patient and the treatment method, and a virtual episode generation module that generates a new virtual EMR episode.
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Patent Agency Ranking
公开(公告)号：US20220170900A1
公开(公告)日：2022-06-02
申请号：US17401449
申请日：2021-08-13
Inventor： Jae Hun CHOI , Hwin Dol PARK , Chang-Geun AHN , Do Hyeun KIM , Seunghwan KIM , Hyung Wook NOH , YongWon JANG , Kwang Hyo CHUNG
Abstract: Disclosed are a gas detection intelligence training system and an operating method thereof. The gas detection intelligence training system includes a mixing gas measuring device that collects an environmental gas from a surrounding environment, generates a mixing gas based on the collected environmental gas and a target gas, senses the mixing gas by using a first sensor array and a second sensor array under a first sensing condition and a second sensing condition, respectively, and generates measurement data based on the sensed results of the first sensor array and the second sensor array, and a detection intelligence training device including a processor that generates an ensemble prediction model based on the measurement data.
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7.发明公开公开(公告)号：US20230297895A1
公开(公告)日：2023-09-21
申请号：US18121763
申请日：2023-03-15
Inventor： Myung Eun LIM , Do Hyeun KIM , Jae Hun CHOI
IPC: G06N20/20
CPC classification number: G06N20/20
Abstract: Disclosed are a method and apparatus for selective ensemble prediction based on dynamic model combination. The method of ensemble prediction according to an embodiment of the present disclosure includes: collecting prediction values for input data of each of the prediction models; calculating a model weight of each of the prediction models using a pre-trained ensemble model that uses the prediction value as an input; selecting at least some model weights from the model weights using a predetermined optimal model combination parameter; and calculating an ensemble prediction value for the input data based on the selected model weight and a prediction value of a prediction model corresponding to the selected model weight.
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8.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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9.发明申请公开(公告)号：US20220359082A1
公开(公告)日：2022-11-10
申请号：US17735320
申请日：2022-05-03
Inventor： Myung-eun LIM , Do Hyeun KIM , Jae Hun CHOI
Abstract: Disclosed is an operation method of a health state prediction system which includes an ensemble prediction model. The operation method includes sending a prediction result request for health time-series data to a plurality of external medical support systems, receiving a plurality of external prediction results associated with the health time-series data from the plurality of external medical support systems, generating long-term time-series data and short-term time-series data for each of the health time-series data, and the plurality of external prediction results, extracting a plurality of long-term trends based on the long-term time-series data, extracting a plurality of short-term trends based on the short-term time-series data, calculating external prediction goodness-of-fit based on the plurality of long-term trends and the plurality of short-term trends, and generating an ensemble prediction result based on the external prediction goodness-of-fit and the plurality of external prediction results.
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10.发明申请公开(公告)号：US20210174229A1
公开(公告)日：2021-06-10
申请号：US17115373
申请日：2020-12-08
Inventor： Myung-eun LIM , Do Hyeun KIM , Jae Hun CHOI
Abstract: Disclosed is a device which includes a data manager, a learner, and a predictor. The data manager generates output data based on time-series data, receives device prediction results corresponding to the output data from the prediction devices, and calculates device errors based on the difference between device prediction results and time-series data. The learner may adjust a parameter group of a prediction model for generating device weights, based on device prediction results and device errors. The predictor generates the ensemble result of first and second device prediction results based on device weights.
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