Abstract: A frame structure for a vehicle includes: an inner panel connected to a side member and defining an inner side of a rear part of the side member; an outer panel connected to a rear side of the side member and coupled to an outer side of the inner panel defining a closed cross-section together with the inner panel and defining an outer side of the rear part of the side member; an opening portion formed below the inner and outer panels partially opening the closed cross-section so that a front end of a rear suspension arm can be inserted into the opening portion; and a reinforcing member disposed in the opening portion defining the closed cross-section together with the outer panel and the inner panel and configured to close an internal space in the outer and inner panels.

Abstract: An image encoding/decoding method is provided. An image decoding method of the present invention may comprise deriving an intra-prediction mode of a current luma block, deriving an intra-prediction mode of a current chroma block based on the intra-prediction mode of the current luma block, generating a prediction block of the current chroma block based on the intra-prediction mode of the current chroma block, and the deriving of an intra-prediction mode of a current chroma block may comprise determining whether or not CCLM (Cross-Component Linear Mode) can be performed for the current chroma block.

Abstract: Disclosed is a method of decoding an image and a method of encoding an image. The method of decoding an image includes: obtaining motion-constrained tile set information; determining, on the basis of the motion-constrained tile set information, a first boundary region of a collocated tile set within a reference picture, which corresponds to a motion-constrained tile set; padding a second boundary region corresponding to the first boundary region; and performing inter prediction on the motion-constrained tile set by using a collocated tile set that includes the padded second boundary region.

Abstract: An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

Abstract: Disclosed herein are an apparatus and method for task-adaptive neural network retrieval based on meta-contrastive learning. The apparatus for task-adaptive neural network retrieval based on meta-contrastive learning includes: memory configured to store a database including a learning model pool consisting of a plurality of datasets and neural networks pre-trained on the datasets and also store a program for task-adaptive neural network retrieval based on meta-contrastive learning; and a controller configured to perform task-adaptive neural network retrieval based on meta-contrastive learning by executing the program. In this case, the controller learns a cross-modal latent space for datasets and neural networks trained on the datasets by calculating the similarity between each dataset and a neural network trained on the dataset while considering constraints included in any one task previously selected from the database, thereby retrieving an optimal neural network.

Abstract: An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

Abstract: Provided is a positive electrode active material for a lithium ion secondary battery including layer-structured lithium metal oxide expressed by the following Chemical Formula 1 and at least one selected from the group consisting of spinel-structured lithium metal oxides and olivine-structured lithium metal oxides. xLiMO2·(1?x)Li2MnO3??(1) (where M is one or more selected from the group consisting of nickel (Ni), cobalt (Co), and manganese (Mn), and 0<x<1.

Abstract: Provided are a positive electrode active material, a method of preparing the same, and a lithium secondary battery using the positive electrode active material, and more particularly, a positive electrode active material in which a surface of layer-structured lithium transition metal composite oxide is coated with one or more indium-based compounds selected from the group consisting of indium oxides and alloys including indium, a method of preparing the positive electrode active material, and a lithium secondary battery using the positive electrode active material. According to the present disclosure, degradation of cycle characteristics according to repetitive discharge of a battery may be prevented and thermal stability and rate characteristics may be improved.