Abstract: According to an embodiment of the present disclosure, there is provided a method and apparatus for transmitting/receiving a digital broadcast. The method for transmitting a digital broadcast, the method comprising: configuring AV data; configuring broadcast data; configuring application signaling information (ASI) by considering at least one of the AV data and the broadcast data or combination thereof; configuring an MPEG-based transport stream by multiplexing the AV data, the broadcast data and the ASI; and transmitting the MPEG-based transport stream, wherein the ASI includes data for driving an application executed by a broadcast receiver.

Abstract: A multi-layered knowledge base system and a processing method thereof are provided. In the system, semantic space learning of converting learning data into a semantic vector, which is a vector of semantic space, by learning a transformation function based on a plurality of learning data is performed. Then, relational knowledge learning of acquiring a relation between the semantic vectors by learning a relation function based on the semantic vectors obtained by the semantic space learning is performed. The acquired relation is converted into graph knowledge. The graph knowledge uses the relation as an edge and the semantic vector corresponding to the relation as a node.

Abstract: The present invention relates to an apparatus and method for deep learning-based coreference resolution using a dependency relation. An apparatus for deep learning-based coreference resolution according to the present invention includes a training data generation module that extracts one or more natural language sentences from a natural language paragraph and performs dependency parsing on the natural language sentences to generate dependency relation data of the natural language sentences, an embedding module that generates an integrated embedding vector for the natural language paragraph based on the natural language sentence and the dependency relation data, and a coreference resolution module that trains a deep learning neural network based on the integrated embedding vector and a first coreference mention preset for the natural language paragraph to generate a coreference resolution model.

Abstract: Method of recognizing a SPO tuple relationship based on deep learning includes reading a natural language sentence stored in a first storage in which a natural language sentence and an SPO tuple are configured in pairs and stored, generating dependent syntax relationship information by parsing a dependent syntax from the natural language sentence, reading the SPO tuple paired with the natural language sentence stored in the first storage, generating dependent-syntax-based first SPO tuple relationship information for training a deep learning model based on the SPO tuple, the natural language sentence, and the dependent syntax relationship information, performing embedding on the dependent syntax relation information, recognizing second SPO tuple relationship information by inputting the embedded dependent syntax relationship information to a multi-layered bidirectional time series neural network model, and training the deep learning model by comparing the first and second SPO tuple relationship information.

Abstract: An adaptive multi-view streaming processing method and apparatus for a multi-view stream is disclosed. Specifically, disclosed herein is a method and apparatus for adaptively processing or transmitting adaptive streaming by reflecting network states and changes in transmission environments in a multi-view streaming service environment where several or more multiple videos are simultaneously transmitted at an arbitrary moment. An adaptive streaming processing method for a multi-view stream may comprise selecting and downloading an i-th segment of a main view stream with a first quality level, inferring a size of an i-th segment of downloadable neighboring view streams based on a download time and transfer rate of the i-th segment of the main view stream, and selecting a second quality level corresponding to the inferred size of the i-th segment of the neighboring view streams and downloading the i-th segment of the neighboring view streams based on the selected second quality level.

Abstract: Disclosed herein is a method for receiving adaptive media streaming. The method may include calculating segment download time for each video quality level based on network throughput, a segment bitrate for the video quality level, segment duration, and a standard deviation of segment bitrates, determining a segment bitrate for maximizing the quality of viewing experience of a user based on the segment download time for each video quality level and a buffer level received from a playback buffer, making a request for the determined segment bitrate, transferring the request to a server, and transferring a corresponding video segment received in response to the request to the playback buffer.

Abstract: Disclosed herein are a hybrid download method and apparatus for multiview streaming. The hybrid download method includes requesting a group download for an n?1-th segment from a server, playing an n?3-th segment at a viewpoint VP1 of a viewer, when a group download request for the n?1-th segment is completed, making a group download request for an n-th segment, when a change in a viewpoint of the viewer occurs at a time at which an n?2-th segment is played, playing a low-quality n?2-th segment at a viewpoint VP3, when the group download for the n-th segment is completed, requesting a single download for a high-quality n?1-th segment corresponding to the viewpoint VP3 from the server, and when the single download for the high-quality n?1-th segment corresponding to the viewpoint VP3 is completed, playing the high-quality n?1-th segment at the viewpoint VP3.

Abstract: An adaptive multi-view streaming processing method and apparatus for a multi-view stream is disclosed. Specifically, disclosed herein is a method and apparatus for adaptively processing or transmitting adaptive streaming by reflecting network states and changes in transmission environments in a multi-view streaming service environment where several or more multiple videos are simultaneously transmitted at an arbitrary moment. An adaptive streaming processing method for a multi-view stream may comprise selecting and downloading an i-th segment of a main view stream with a first quality level, inferring a size of an i-th segment of downloadable neighboring view streams based on a download time and transfer rate of the i-th segment of the main view stream, and selecting a second quality level corresponding to the inferred size of the i-th segment of the neighboring view streams and downloading the i-th segment of the neighboring view streams based on the selected second quality level.

Abstract: According to an embodiment of the present disclosure, there is provided a method and apparatus for transmitting/receiving a digital broadcast. The method for transmitting a digital broadcast, the method comprising: configuring AV data; configuring broadcast data; configuring application signaling information (ASI) by considering at least one of the AV data and the broadcast data or combination thereof; configuring an MPEG-based transport stream by multiplexing the AV data, the broadcast data and the ASI; and transmitting the MPEG-based transport stream, wherein the ASI includes data for driving an application executed by a broadcast receiver.

Abstract: Method of recognizing a SPO tuple relationship based on deep learning includes reading a natural language sentence stored in a first storage in which a natural language sentence and an SPO tuple are configured in pairs and stored, generating dependent syntax relationship information by parsing a dependent syntax from the natural language sentence, reading the SPO tuple paired with the natural language sentence stored in the first storage, generating dependent-syntax-based first SPO tuple relationship information for training a deep learning model based on the SPO tuple, the natural language sentence, and the dependent syntax relationship information, performing embedding on the dependent syntax relation information, recognizing second SPO tuple relationship information by inputting the embedded dependent syntax relationship information to a multi-layered bidirectional time series neural network model, and training the deep learning model by comparing the first and second SPO tuple relationship information.

Abstract: Disclosed herein are a system and a method for volitional electromyography (vEMG) signal detection from an EMG signal when functional electrical stimulation (FES) is applied. The system for vEMG signal detection includes a receiver for receiving data from an EMG electrode when FES is applied, a memory for storing a program for detecting a vEMG signal using the received data, and a processor for executing the program, wherein the processor cuts the data received from the EMG electrode disposed at a predetermined position, calculates a difference between data for previous FES and data for current FES, and detects the vEMG signal using the calculated difference.

Abstract: An image receiving method includes receiving, from a plenoptic server, a high-quality image stream of a first viewpoint; requesting, from the plenoptic server, information on a second viewpoint to be shifted when a user's view is shifted, and requesting, from an edge server, a medium-quality image stream of the shifted second viewpoint; receiving, from the edge server, the medium-quality image stream of the second viewpoint and playing the received medium-quality image stream; and receiving information on the second viewpoint and subsequently playing a high-quality image stream of the second viewpoint received from the plenoptic server.

Abstract: Disclosed herein are an apparatus and method for removing a self-interference signal. The apparatus includes one or more processors and executable memory for storing at least one program executed by the one or more processors. The at least one program receives an upstream signal for removing self-interference, generates a downstream signal for channel estimation, replicates a self-interference signal in a time domain and a self-interference signal in a frequency domain using the upstream signal and the downstream signal, and removes the self-interference signal from the upstream signal using the replicated self-interference signal in the time domain and the replicated self-interference signal in the frequency domain.

Abstract: Disclosed are a method and a device for transmitting and receiving a signal, the method including: identifying a downlink signal-only transmission section in which a first electronic device transmits a downlink signal to a second electronic device, and transmitting the signal in the downlink signal-only transmission section; detecting at least one symbol that has a power value relatively higher than a predetermined threshold value; estimating, on the basis of the detected at least one symbol, an interference channel in which a self-interference signal occurs; detecting the self-interference signal; and identifying an FDX transmission section in which an uplink signal is received from the second electronic device and the downlink signal is transmitted simultaneously, and processing transmission of the downlink signal and reception of the uplink signal in the FDX transmission section, wherein the reception of the uplink signal is processed by removing the self-interference signal occurring in the interference c

Abstract: A self-evolving agent-based simulation system generates model evolution strategy for applying a difference between real-data and a simulation resulting value to a simulation model, and reconstructing components included in the simulation module using the model evolution strategy to evolve the simulation model when the difference between the real-data and the simulation resulting value of the agent-based simulation model does not satisfy a value in a predetermined error range.

Abstract: Disclosed herein are a synchronization apparatus and method for a upstream system. The synchronization apparatus for a upstream system includes one or more processors, and execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to receive a signal and calculate a first channel estimation value for the received signal using a predefined pilot, and calculate a second channel estimation value using a predefined complementary pilot and the first channel estimation value.

Abstract: Disclosed herein are an apparatus and method for removing a self-interference signal. The apparatus includes one or more processors and executable memory for storing at least one program executed by the one or more processors. The at least one program receives an upstream signal for removing self-interference, generates a downstream signal for channel estimation, replicates a self-interference signal in a time domain and a self-interference signal in a frequency domain using the upstream signal and the downstream signal, and removes the self-interference signal from the upstream signal using the replicated self-interference signal in the time domain and the replicated self-interference signal in the frequency domain.

Abstract: Disclosed herein are a synchronization apparatus and method for a upstream system. The synchronization apparatus for a upstream system includes one or more processors, and execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to receive a signal and calculate a first channel estimation value for the received signal using a predefined pilot, and calculate a second channel estimation value using a predefined complementary pilot and the first channel estimation value.

Abstract: Disclosed herein are a signal-multiplexing apparatus and method based on machine learning. The signal-multiplexing method based on machine learning, performed by a signal-multiplexing apparatus based on machine learning, including training a transmitted signal using a machine-learning technique, performing complex mapping to transmit the transmitted signal as multiple signals, receiving the multiple signals and regularizing the multiple signals based on a preset rule corresponding to the machine-learning technique, outputting an estimated value for the transmitted signal by training regularized multiple signals using the machine-learning technique, calculating a difference between the transmitted signal and the estimated value for the transmitted signal, and detecting the transmitted signal from an output value when the difference is less than or equal to a preset value.

Abstract: A multi-layered knowledge base system and a processing method thereof are provided. In the system, semantic space learning of converting learning data into a semantic vector, which is a vector of semantic space, by learning a transformation function based on a plurality of learning data is performed. Then, relational knowledge learning of acquiring a relation between the semantic vectors by learning a relation function based on the semantic vectors obtained by the semantic space learning is performed. The acquired relation is converted into graph knowledge. The graph knowledge uses the relation as an edge and the semantic vector corresponding to the relation as a node.