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: A method of controlling an EOP of a powertrain may include determining, by a controller electrically connected to the EOP, whether an oil sloshing phenomenon in which it is difficult for oil to return to a space where an oil intake port of the EOP is positioned may occur while a vehicle is running; and reducing, by the controller, the revolutions per minute (RPM) of the EOP by a predetermined reduced RPM when it is determined that the oil sloshing phenomenon may occur.

Abstract: A display device includes a display panel which includes a display area and a non-display area, a plurality of signal wirings arranged in the display area, a plurality of fanout wirings arranged in the non-display area and electrically connected to the signal wirings, and a plurality of connecting wirings connecting the signal wirings to the fanout wirings, where some of the plurality of fanout wirings intersect and are overlapped with each other in a plan view.

Abstract: The interaction system includes: an infrastructure image sensor-based 3D cloud restoration module configured to create a 3D cloud point using a multi-view image received from an infrastructure sensor; a real-time 3D map creation and learning update module configured to generate subset data composed of point clouds into a 3D map using 3D point information received from the infrastructure image sensor-based 3D cloud restoration module; an object estimation module in a 3D space configured to recognize and track an object existing in a 3D space based on the 3D map received from the real-time 3D map creation and learning update module; and a dangerous situation recognition and interaction module in space configured to receive information on an object existing in the 3D space from the object estimation module in the 3D space, recognize a dangerous situation, and provide dangerous situation related information.

Abstract: The present disclosure relates to a display device, and the display device according to an embodiment includes: a substrate including a display area and a peripheral area; a plurality of light-emitting elements positioned in a display area; a plurality of pixel circuits connected to a plurality of light-emitting elements, respectively; a sensor positioned on the peripheral area of the substrate; a leakage current detecting circuit connected to the sensor; and a light emitting wiring connected to the leakage current detecting circuit and connected to at least one of the plurality of light-emitting elements, wherein the sensor includes a first sensor and the first sensor includes a first sensing transistor, and a first conductive pattern spaced apart from the channel of the first sensing transistor, and the first conductive pattern is positioned in the same layer as at least one of a plurality of layers configuring the transistor.

Abstract: Apparatuses and methods for image processing are provided. The image processing apparatus performs area classification and object detection in an image, and includes a feature map generator configured to generate the feature map of the input image using the neural network, and an image processor configured to classify the areas and to detect the objects in the image using the generated feature map.

Abstract: Disclosed is an apparatus and method to train an autonomous driving model. The apparatus includes a driver information collection processor configured to collect driver information while a vehicle is being driven. The apparatus also includes a sensor information collection processor configured to collect sensor information from a sensor installed in the vehicle while the vehicle is being driven, and a model training processor configured to train the autonomous driving model based on the driver information and the sensor information.

Abstract: An electronic device comprises a camera; a sensor configured to sense a motion; and one or more processors, wherein the one or more processors are configured to: sequentially acquire a plurality of images using the camera and acquire motion information of the plurality of images using the sensor, during a specified time; create a panoramic image with a first image sub-set comprising a portion of the plurality of images and a second image sub-set comprising another portion of the plurality of images, and moving picture data related to the panoramic image; and determine whether to correct at least a partial section of the moving picture data using a particular image from the second image sub-set that corresponds to the at least the partial section of the moving picture data, based on the frame rate of the at least the partial section of the moving picture data.

Abstract: Apparatuses and methods for image processing are provided. The image processing apparatus performs area classification and object detection in an image, and includes a feature map generator configured to generate the feature map of the input image using the neural network, and an image processor configured to classify the areas and to detect the objects in the image using the generated feature map.

Abstract: Apparatuses and methods for image processing are provided. The image processing apparatus performs area classification and object detection in an image, and includes a feature map generator configured to generate the feature map of the input image using the neural network, and an image processor configured to classify the areas and to detect the objects in the image using the generated feature map.

Abstract: A tomography method for generating a computed tomography (CT) image, including generating a first tomography image based on first raw data corresponding to a received X-ray comprising acquired photons; determining second raw data by generating a second tomography image having an increased resolution in comparison with the first tomography image and performing forward projection on the second tomography image; determining third raw data based on a first parameter, the first raw data, and the second raw data; and generating a third tomography image based on the third raw data, wherein the determining of the third raw data may be based on information about a distribution of the acquired photons, the information being included in at least one from among the first raw data and the second raw data.

Abstract: Disclosed are methods and apparatuses for object recognition using an artificial neural network. An object recognition apparatus includes an adjacent pixel setter configured to set the neighboring pixel pairs in the image frame, each neighboring pixel pair including first pixel and one or more second pixels adjacent to the first pixel, a labeler configured to label the first pixel using deep neural network-based model based on probability density function value of the neighboring pixel pairs, and an object recognizer configured to recognize an object based on the labeled first pixel.

Abstract: Disclosed is an apparatus and method to train an autonomous driving model. The apparatus includes a driver information collection processor configured to collect driver information while a vehicle is being driven. The apparatus also includes a sensor information collection processor configured to collect sensor information from a sensor installed in the vehicle while the vehicle is being driven, and a model training processor configured to train the autonomous driving model based on the driver information and the sensor information.

Abstract: An electronic device comprises a camera; a sensor configured to sense a motion; and one or more processors, wherein the one or more processors are configured to: sequentially acquire a plurality of images using the camera and acquire motion information of the plurality of images using the sensor, during a specified time; create a panoramic image with a first image sub-set comprising a portion of the plurality of images and a second image sub-set comprising another portion of the plurality of images, and moving picture data related to the panoramic image; and determine whether to correct at least a partial section of the moving picture data using a particular image from the second image sub-set that corresponds to the at least the partial section of the moving picture data, based on the frame rate of the at least the partial section of the moving picture data.

Abstract: Disclosed is an apparatus and method to train an autonomous driving model. The apparatus includes a driver information collection processor configured to collect driver information while a vehicle is being driven. The apparatus also includes a sensor information collection processor configured to collect sensor information from a sensor installed in the vehicle while the vehicle is being driven, and a model training processor configured to train the autonomous driving model based on the driver information and the sensor information.

Abstract: A tomography method for generating a computed tomography (CT) image, including generating a first tomography image based on first raw data corresponding to a received X-ray comprising acquired photons; determining second raw data by generating a second tomography image having an increased resolution in comparison with the first tomography image and performing forward projection on the second tomography image; determining third raw data based on a first parameter, the first raw data, and the second raw data; and generating a third tomography image based on the third raw data, wherein the determining of the third raw data may be based on information about a distribution of the acquired photons, the information being included in at least one from among the first raw data and the second raw data.

Abstract: Apparatuses and methods for image processing are provided. The image processing apparatus performs area classification and object detection in an image, and includes a feature map generator configured to generate the feature map of the input image using the neural network, and an image processor configured to classify the areas and to detect the objects in the image using the generated feature map.

Abstract: Disclosed is an apparatus and method to train an autonomous driving model. The apparatus includes a driver information collection processor configured to collect driver information while a vehicle is being driven. The apparatus also includes a sensor information collection processor configured to collect sensor information from a sensor installed in the vehicle while the vehicle is being driven, and a model training processor configured to train the autonomous driving model based on the driver information and the sensor information.

Abstract: Disclosed are methods and apparatuses for object recognition using an artificial neural network. An object recognition apparatus includes an adjacent pixel setter configured to set the neighboring pixel pairs in the image frame, each neighboring pixel pair including first pixel and one or more second pixels adjacent to the first pixel, a labeler configured to label the first pixel using deep neural network-based model based on probability density function value of the neighboring pixel pairs, and an object recognizer configured to recognize an object based on the labeled first pixel.

Abstract: A nonvolatile memory device, including a first latch unit and a nonvolatile memory cell, and a method of writing data in a nonvolatile memory device are provided. The method includes receiving a first writing command or a second writing command from outside of the nonvolatile memory device, and writing first data stored in the first latch unit in the nonvolatile memory cell in response to the first or second writing command. The first data is retained in the first latch unit until the writing of the first data stored in the first latch unit in the nonvolatile memory cell is completed.