-
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。
-
12.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.
-
13.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.
-
14.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.
-
15.发明申请公开(公告)号：US20200184284A1
公开(公告)日：2020-06-11
申请号：US16699060
申请日：2019-11-28
Inventor： Myung-Eun LIM , Jae Hun CHOI , Youngwoong HAN
Abstract: Provided is a device for ensembling data received from prediction devices and a method of operating the same. The device includes a data manager, a learner, and a predictor. The data manager receives first and second device prediction results from first and second prediction devices, respectively. The learner may adjust a weight group of a prediction model for generating first and second item weights, first and second device weights, based on the first and second device prediction results. The first and second item weights depend on first and second item values, respectively, of the first and second device prediction results. The first device weight corresponds to the first prediction device, and the second device weight corresponds to the second prediction device. The predictor generates an ensemble result of the first and second device prediction results, based on the first and second item weights and the first and second device weights.
-
Patent Agency Ranking
16.发明申请公开(公告)号：US20170147753A1
公开(公告)日：2017-05-25
申请号：US15349703
申请日：2016-11-11
Inventor： Youngwoong HAN , Ho-Youl JUNG , Jae Hun CHOI , Minho KIM , YoungWon KIM , Myung-eun LIM , Dae Hee KIM , Seunghwan KIM
CPC classification number: G16H10/60 , G06F16/285 , G06F16/9535 , G06N5/022 , G06N20/00 , G16H50/50 , G16H50/70
Abstract: Provided are a search method and device in which, in order to search for health data having a multivariate (multi-dimensional) time-series characteristic with high calculation complexity for a search, a format of the health data is converted and a dimension of the health data is reduced through feature extraction to which a learning model is applied, so that the calculation complexity for the search may be remarkably reduced and the similar case search may be performed efficiently.
-
17.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.
-
18.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.
-
19.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.
-
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.
-
-
-
-
-
-
-
-
-
Search Results
31
records
(took 0.023 seconds)
