Abstract: An apparatus according to an embodiment includes a first training module configured to perform a first training on a text detection model which receives a document image and outputs a text score map and a text mask for the document image by using first training data including text detection ground truth (GT) and text enhancement GT, and a second training module configured to perform a second training on the text detection model by using second training data including only the text detection GT.

Abstract: A time series data analysis method according to an embodiment of the inventive concept is performed by a computing device. The method includes inputting, for each of a plurality of units, into which the time series data is split on a time axis, a feature of each of the units to an intermediate neural network, obtaining m-dimensional intermediate output data (m is a natural number of 2 or more) from the intermediate neural network, inputting the intermediate output data of a plurality of units immediately adjacent in time to a final neural network to obtain final output data output from the final neural network, and generating an analysis result of the time series data using the final output data.

Abstract: An electrocardiogram analysis apparatus according to an embodiment of the present invention includes an inputter configured to receive an electrocardiogram of a subject, a beat analyzer configured to first detect and classify one or more normal beats among a plurality of beats included in the received electrocardiogram, request beat classification for remaining beats except for the one or more normal beats from a remote diagnosis server, and label each of the plurality of beats according to a normal beat detection result and a beat classification result received from the remote diagnosis server, and a rhythm analyzer configured to perform rhythm analysis on the electrocardiogram based on a labeling result.

Abstract: Provided are a method for detecting an anomaly based on behavior-analysis.

Abstract: Provided are a method for detecting an anomaly based on behavior-analysis.

Abstract: Provided are a data clustering apparatus and method, which can rapidly and accurately cluster data. The data clustering apparatus includes an index discriminating unit discriminating an index corresponding to an input position of new data input to a space for data clustering, including a lattice-type segmented space having lattice unit spaces set with different indexes, and a clustering unit creating a new cluster in the discriminated index using the input new data as a representative value when a cluster is not created at the discriminated index.

Abstract: The present invention relates to a heterogeneous data cluster generation apparatus and method and a data clustering method and apparatus, and more particularly, to a data clustering method and apparatus which cluster data measured by different sensors into a number of groups. Aspects of the present invention provide an apparatus and method for generating clusters by putting together heterogeneous data which are values measured by different types of sensors. Aspects of the present invention also provide an apparatus and method for generating clusters by setting indices in order to effectively cluster multi-dimensional data, massive data, or scattered data.

Abstract: The present invention relates to a heterogeneous data cluster generation apparatus and method and a data clustering method and apparatus, and more particularly, to a data clustering method and apparatus which cluster data measured by different sensors into a number of groups. Aspects of the present invention provide an apparatus and method for generating clusters by putting together heterogeneous data which are values measured by different types of sensors. Aspects of the present invention also provide an apparatus and method for generating clusters by setting indices in order to effectively cluster multi-dimensional data, massive data, or scattered data.

Abstract: The present invention relates to a heterogeneous data cluster generation apparatus and method and a data clustering method and apparatus, and more particularly, to a data clustering method and apparatus which cluster data measured by different sensors into a number of groups. Aspects of the present invention provide an apparatus and method for generating clusters by putting together heterogeneous data which are values measured by different types of sensors. Aspects of the present invention also provide an apparatus and method for generating clusters by setting indices in order to effectively cluster multi-dimensional data, massive data, or scattered data.

Abstract: There is provided an apparatus for monitoring the target system. The apparatus includes: a data collection unit configured to collect a first data set acquired from a target system at a first time point and determined to represent a status of the target system and a second data set acquired from the target system at a second time point subsequent to the first time point; and a calculation unit configured to calculate at least one index associated with the status of the system based on the first data set and the second data set, wherein at least one of the data collection unit and the calculation unit are implemented via at least one central processing unit or at least one hardware processor.

Abstract: Provided are a method and an apparatus for controlling streams to receive data in parallel. The method includes receiving data in parallel using a plurality of connected streams, calculating a network congestion rate using a change in a transmission speed of data received through each of the plurality of streams, adjusting a number of connections of the plurality of streams based on the calculated network congestion rate, and receiving the data in parallel using the adjusted number of streams.

Abstract: Disclosed are an apparatus and method for managing data clusters. The data cluster management apparatus may include: a cluster selection unit configured to calculate a similarity of each of the data clusters with respect to input data, and select, based on the similarity, a data cluster from among the data clusters; and a cluster update unit configured to determine, based on the selected data cluster and the input data, whether the input data is included in the selected data cluster, and use the input data in accordance with the determination to create a new data cluster or update the selected data cluster.

Abstract: Provided are a data clustering apparatus and method, which can rapidly and accurately cluster data. The data clustering apparatus includes an index discriminating unit discriminating an index corresponding to an input position of new data input to a space for data clustering, including a lattice-type segmented space having lattice unit spaces set with different indexes, and a clustering unit creating a new cluster in the discriminated index using the input new data as a representative value when a cluster is not created at the discriminated index.

Abstract: The present invention relates to a heterogeneous data cluster generation apparatus and method and a data clustering method and apparatus, and more particularly, to a data clustering method and apparatus which cluster data measured by different sensors into a number of groups. Aspects of the present invention provide an apparatus and method for generating clusters by putting together heterogeneous data which are values measured by different types of sensors. Aspects of the present invention also provide an apparatus and method for generating clusters by setting indices in order to effectively cluster multi-dimensional data, massive data, or scattered data.

Abstract: Provided are a method and an apparatus for controlling streams to receive data in parallel. The method includes receiving data in parallel using a plurality of connected streams, calculating a network congestion rate using a change in a transmission speed of data received through each of the plurality of streams, adjusting a number of connections of the plurality of streams based on the calculated network congestion rate, and receiving the data in parallel using the adjusted number of streams.