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: The present invention relates to an apparatus and method for automatically generating machine reading comprehension training data, and more particularly, to an apparatus and method for automatically generating and managing machine reading comprehension training data based on text semantic analysis. The apparatus for automatically generating machine reading comprehension training data according to the present invention includes a domain selection text collection unit configured to collect pieces of text data according to domains and subjects, a paragraph selection unit configured to select a paragraph using the pieces of collected text data and determine whether questions and correct answers are generatable, and a question and correct answer generation unit configured to generate questions and correct answers from the selected paragraph.

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: A method of preparing a carbon fiber including: preparing a precursor fiber for preparing a carbon fiber; and stabilizing the precursor fiber. The stabilization of the precursor fiber includes a first stabilization phase, a second stabilization phase, a third stabilization phase, and a fourth stabilization phase, which are set at four different temperatures between a temperature at which heat starts to be generated from the stabilization reaction of the precursor fiber and a temperature at which the generation of heat is maximized. Ozone gas is input while at least one phase of the third stabilization phase and the fourth stabilization phase is carried out.

Abstract: The present invention includes obtaining block type identification information on a partition of the current macroblock when the current macroblock is intra-mixed specifying a bock type of the current macroblock based on the block type identification information, obtaining an intra prediction mode of the current macroblock according to the block type of the partition of the current macroblock, and predicting the current macroblock using the intra prediction mode and a pixel value of a neighboring block.

Abstract: A method for hand pose identification in an automated system includes providing map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Abstract: A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Abstract: A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Abstract: A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Abstract: A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Abstract: A method for training a hierarchy of trained neural networks for hand pose detection includes training a first neural network to generate a first plurality of activation features that classify an input depth map data corresponding to a hand based on a wrist angle of the hand, the training using a plurality of depth maps of a hand with predetermined wrist angles as inputs to the first neural network during the training, and storing the first neural network in a memory after the training for use in classifying an additional depth map corresponding to a hand based on an angle of a wrist of the hand in the additional depth map.

Abstract: A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Abstract: A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Abstract: A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.