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11.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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13.发明申请公开(公告)号：US20180150609A1
公开(公告)日：2018-05-31
申请号：US15812540
申请日：2017-11-14
Inventor： Minho KIM , YoungWon KIM , Donghun LEE , Jae Hun CHOI , Dae Hee KIM , Myung-eun LIM , Ho-Youl JUNG , Youngwoong HAN , Seunghwan KIM
CPC classification number: G16H50/20 , G06N3/04 , G06N5/048 , G06N20/00 , G16H10/60 , G16H50/50 , Y02A90/26
Abstract: The present disclosure herein relates to a future health trend forecasting system and a method thereof through a similar case cluster-based prediction model, and more specifically, to a server and a method thereof for extracting multiple associated feature similar case clusters that match a prediction query for the user's health information through a class prediction model and a future value prediction model for health features of a similar case cluster generated by cyclically clustering the target feature that is a health feature for personal health information and an associated feature of the target feature, predicting future health trends for each associated feature using multiple prediction models based on corresponding similar case clusters, and combining and outputting the prediction results.
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14.发明申请公开(公告)号：US20150261990A1
公开(公告)日：2015-09-17
申请号：US14480216
申请日：2014-09-08
Inventor： Dae Hee KIM , Ho Youl JUNG , Min Ho KIM , Myung Eun LIM , Jae Hun CHOI
CPC classification number: H03M7/70 , G06T9/00 , G06T2207/30072 , H03M7/40
Abstract: Provided are a method and apparatus for compressing DNA data based on a binary image. The method for compressing DNA data based on a binary image includes splitting DNA data including adenine (A), thymine (T), guanine (G), cytosine (C), and an indefinite base (N) into a plurality of binary images, determining a coding mode of each of the binary images according to characteristics of each of the binary images, and first coding each of the binary images based on the determined coding mode.
Abstract translation: 提供了一种基于二进制图像压缩DNA数据的方法和装置。 基于二值图像压缩DNA数据的方法包括将包括腺嘌呤（A），胸腺嘧啶（T），鸟嘌呤（G），胞嘧啶（C））和不定碱（N）的DNA数据分解成多个二值图像， 根据每个二进制图像的特性确定每个二进制图像的编码模式，并且基于所确定的编码模式对每个二进制图像进行编码。
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公开(公告)号：US20140100789A1
公开(公告)日：2014-04-10
申请号：US14038456
申请日：2013-09-26
Inventor： Jae Hun CHOI , Minho KIM , Myung-eun LIM , Ho-Youl JUNG , Soo Jun PARK
IPC: G06F19/22
CPC classification number: G16B30/00
Abstract: Provided is a nucleic reads aligning method. More particularly, the present invention relates to a nucleic reads aligning method using a many-core process. A nucleic reads aligning device aligning a set of nucleic reads of a sequence to be analyzed with a reference sequence according to the present invention includes a main memory storing the reference sequence and the set of nucleic reads, a main processor splitting the reference sequence to produce first and second reference sequence fragments, and a many-core module aligning the set of nucleic reads with each of the first and second reference sequence fragments in parallel. The nucleic reads aligning device and method according to the present invention split a reference sequence and quickly align nucleic reads in a many-core environment.
Abstract translation: 提供核酸读取对准方法。 更具体地，本发明涉及使用多核方法的核读取对准方法。 核酸读取对准装置将待分析序列的一组核酸读取与根据本发明的参考序列对准，包括存储参考序列和该组核酸读取的主存储器，分割参考序列以产生 第一和第二参考序列片段，以及多核心模块，其将所述一组核酸读取与所述第一和第二参考序列片段中的每一个平行对准。 核心读取根据本发明的对准装置和方法分割参考序列并在多核环境中快速对准核读。
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Patent Agency Ranking
16.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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17.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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18.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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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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20.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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