Abstract: A vehicle includes a camera provided to obtain image data, radar provided to obtain radar data, Lidar provided to obtain Lidar data, and a controller configured to process the image data, the radar data, and the Lidar data to generate a first sensor fusion track, where the controller calculates reliability of at least one sensor in which an event does not occur among a plurality of sensors including the camera, the radar and the Lidar when the event for the sensor is detected, and changes from the first sensor fusion track to a second sensor fusion track based on the at least one sensor when the reliability is greater than or equal to a predetermined threshold, and where the controller is configured to control a braking amount or a deceleration amount of the vehicle based on the first sensor fusion track or the second sensor fusion track.

Abstract: A display apparatus includes a liquid crystal panel disposed on a back-light unit; and a viewing angle control unit disposed between the back-light unit and the liquid crystal panel, wherein the viewing angle control unit includes light-blocking patterns having a light-blocking material and variable patterns disposed between a first control electrode and a second control electrode, wherein the variable patterns are extended in a direction perpendicular to the light-blocking patterns, and wherein transmittance of each variable pattern is changed according to a voltage applied to the first control electrode and the second control electrode, thereby preventing a distraction of people around a user by selectively sharing a realized image.

Abstract: A vehicle is equipped with a lidar installed on the vehicle to secure an external field of view of the vehicle and configured to obtain lidar data for detecting an object in the external field of view. The vehicle includes a controller including at least one processor configured to process the lidar data and perform an avoidance control or a warning control based on the processed results. In particular, the controller is further configured to generate a plurality of virtual lines in a moving direction of the vehicle within a full width of the vehicle, obtain a plurality of contour points of the object by processing the lidar data, and determine a collision risk for the avoidance control or the warning control based on the number of crossing points between the plurality of virtual lines and the plurality of contour points.

Abstract: A vehicle includes a camera configured to capture an outside of the vehicle, a radar configured to detect an object outside the vehicle, and a controller configured to acquire a virtual path of the vehicle based on image data output from the camera and radar data output from the radar and perform forward collision-avoidance braking based on maintaining a time in which the object intersects the virtual path of the vehicle.

Abstract: A vehicle may include a camera configured to obtain image data associated with an image. The image may include a trailer being in proximity to the vehicle in an external field of view. The vehicle may further include a radar associated with a second external field of view of the vehicle and configured to obtain radar data associated with the trailer in the second external field of view, and a controller. The controller may be configured to determine whether the trailer is coupled to the vehicle by processing the image data and the radar data, perform at least one of: generating a warning signal or controlling a braking device based on a forward collision-avoidance assist (FCA) function, and change at least one of: a timing of the generating the warning signal or an operation of the controlling the braking device based on the trailer being coupled to the vehicle.

Abstract: A method for an image-based eye disease diagnosis according to an embodiment includes receiving a fundus image, determining whether the fundus image is a deep diagnosis target using a deep diagnosis target classification model, and determining whether the fundus image has a disease through a preset deep diagnosis process when a result of the determination shows that the fundus image is the deep diagnosis target, or determining whether the fundus image has a disease by applying the fundus image to a general diagnosis model when the result of the determination shows that the fundus image is not the deep diagnosis target.

Abstract: An advanced driver assistance system (ADAS) and a vehicle including the same include a camera; a plurality of distance detectors; a braking device; and a processor configured to recognize a fusion track and a plurality of single tracks based on obstacle information recognized by the camera and obstacle information recognized by at least one of the plurality of distance detectors, upon determining that the fusion track is present, obtain a cluster area in a stationary state and a cluster area in a moving state based on movement information and reference position information of the fusion track and movement information and position information of each of the single tracks, determine a possibility of collision based on the obtained cluster area, and control the braking device in response to the determined possibility of collision.

Abstract: The present invention relates to a method of manufacturing collagen for restoring cartilage tissue usable for a joint in an injection manner, and a usage method thereof. The present invention provides a method of obtaining a high-concentration collagen-solution injection by aseptically filling an injection container with collagen separated from pig skin tissue, and a method of using the high-viscosity collagen loaded into the injection container for the purpose of cartilage restoration. Cartilage tissue regeneration is effectively induced when tissue restoration is implemented with respect to a cartilage-deficient portion using collagen, which is a biocompatible material, in a form that is capable of being injected into an application site with an injection needle without a surgical incision. Accordingly, restoration and regeneration of cartilage are easily and quickly induced in an animal, excluding a human, while relieving the burden related to surgery.

Abstract: The present invention relates to a conductive lift pin used in an electrostatic chuck, an electrostatic chuck including same, and a semiconductor manufacturing method using same. Provided in the present invention are a conductive electrostatic chuck lift pin and an electrostatic chuck having the lift pin mounted therein, the lift pin including AlTiC, which has a TiC phase dispersed on an Al2O3 phase, and having electrical resistivity of 2.5×10-5 to 3.5×10-5 ?·m. The electrostatic chuck lift pin of the present invention can solve the problem of defects induced by static electricity and electric reaction due to contact between the components and insulating ceramic when using existing insulating ceramic, and is conductive and thus can release residual charge between a semiconductor wafer and the electrostatic chuck even without the presence of plasma. Therefore, since the lift pin can be raised immediately after de-chucking, treatment time per wafer sheet is reduced.

Abstract: A joint connector includes a housing comprising a plurality of insertion holes. A first isolation terminal is inserted into a first insertion hole of the plurality of insertion holes. The first isolation terminal is configured to block current flow. A connection terminal is inserted into a second insertion hole of the plurality of insertion holes. The second insertion hole is adjacent to the first insertion hole and is electrically connected to the first isolation terminal.

Abstract: An apparatus for training an image classification model according to an embodiment disclosed includes a first trainer that trains a model body and a first head through supervised learning based on a labeled data set subjected to type 1 labeling, a second trainer that trains the model body, the first head, and a second head through multi-task learning based on the labeled data set and an unlabeled data set, and a third trainer that trains a plurality of third heads through supervised learning based on the labeled data set subjected to type 2 labeling while freezing the model body.

Abstract: An image segmentation method according to an embodiment of the present invention is performed in a computing device having one or more processors and memory for storing one or more programs executed by means of the one or more processors, and includes the steps of: (a) receiving the input of an image; (b) generating a first-generation image segment set by dividing the input image in an overlapped manner; and (c) generating a second or higher-generation image segment set from the first-generation image segment set, wherein a subsequent-generation image segment set is generated by dividing in an overlapped manner at least one of a plurality of image segments included in the previous-generation image segment set.

Abstract: A vehicle includes a front camera, a front radar, a corner radar, a corner LiDAR, and a controller configured to generate a first fusion mode by processing image data and radar data or to generate a second fusion mode by processing radar data and LiDAR data, wherein the controller changes the first fusion mode to the second fusion mode when the controller detects an abnormality of the front camera while performing avoidance control of the vehicle based on the first fusion mode, and performs the avoidance control based on the second fusion mode for a predetermined time period.

Abstract: A vehicle includes a camera obtaining image data for detecting a target in the external field of view, a radar obtaining radar data for detecting the target, and a controller configured to process the image data and the radar data, and controlling at least one of braking and steering devices based on a result of the processing, wherein the controller is configured to detect a target in a lane adjacent to the vehicle, and obtain a first index based on a lateral velocity of the target, a second index based on a heading angle of the target, and a third index based on a possibility of collision of the vehicle with the target, and when each of the first to third indexes corresponds to a predetermined output value, detect a cut-in attempt of the target, and control at least one of the braking and steering devices for adjusting an avoidance control timing for the target.

Abstract: A washing machine having a plurality of washing apparatuses is disclosed. By providing the plurality of washing apparatuses, it is possible to separately wash the laundry as needed. In addition, by controlling the plurality of washing apparatuses using one power button and an additional option button, it is possible to implement the control panel that is easy to operate and convenient to use.

Abstract: A mobile terminal including: a memory having a plurality of applications stored therein; an application management module configured to receive application information corresponding to the respective applications, and generate status information of the applications, corresponding to the application information; and a controller configured to determine execution history information of the applications through the status information provided from the application management module, wherein the application management module includes: an application information collection unit configured to collect cache data size information of the respective applications at preset time intervals; and a comparison unit configured to generate the status information of the applications by comparing the cache data size information of the applications, collected by the application information collection unit, to reference values corresponding to the respective applications.

Abstract: An unsupervised domain adaptation method for adapting an artificial neural network trained with source data to target data, the unsupervised domain adaptation method includes calculating an average and a standard deviation of a prediction value for a mini-batch of the target data with respect to a first generator, except for a classifier included in the artificial neural network, generating a second generator by training the first generator through a first loss function based on the average and a variance, training a second generator based on a second loss function, based on a difference between a prediction value of the first generator and a prediction value of the second generator for the mini-batch, and calculating a final weight by weighted summing a first weight for minimizing the first loss function and a second weight for minimizing the second loss function.