Abstract:
Provided are a hardmask composition, a method of preparing the same, and a method of forming a patterned layer using the hardmask composition. The hardmask composition may include graphene quantum dots, a metal compound, and a solvent. The metal compound may be chemically bonded (e.g., covalently bonded) to the graphene quantum dots. The metal compound may include a metal oxide. The metal oxide may include at least one of zirconium (Zr) oxide, titanium (Ti) oxide, tungsten (W) oxide, or aluminum (Al) oxide. The graphene quantum dots may be bonded to the metal compound by an M-O—C bond or an M-C bond, where M is a metal element, O is oxygen, and C is carbon.
Abstract:
Example embodiments relate to electrode materials, secondary batteries including the electrode materials, and methods of manufacturing the electrode materials and the secondary batteries. An electrode material may include a foam structure having a plurality of pores and a plurality of nanostructures disposed in the plurality of pores. The foam structure may include a graphene foam structure. The plurality of nanostructures may include at least one of a nanoparticle and a nanorod. The plurality of nanostructures may include a material capable of accommodating/discharging ions. The electrode material may be used as an anode material of a secondary battery.
Abstract:
A method of manufacturing an X-ray detector includes: applying a mask having an opening on a substrate on which a plurality of charge detection units are positioned; filling the opening with a paste including a photoelectric conversion material that absorbs X-rays to generate charges; and forming a photoconductive layer from the paste by separating the mask from the substrate. A thickness of the paste within the opening is thicker in an area adjacent to at least one edge among edges of the opening than in areas around other edges.
Abstract:
An X-ray detector may include: a thin film transistor (TFT) unit; and/or a capacitor unit. The capacitor unit may include two or more storage capacitors. The TFT unit may include: a gate electrode on one region of a substrate; a gate insulating layer on the gate electrode; an active layer on the gate insulating layer; and/or a source electrode and a drain electrode respectively on sides of the active layer.
Abstract:
A data transmission method is provided that can increase an amount of time that an electronic device has been in use and minimize the side effects. An electronic device adapted to the method is also provided. The data transmission method includes: recognizing a data transmission request of at least one first application; determining whether a first timer according to the data transmission request is within a second timer (a data detecting timer) where a data request of a second application required for network access is detected; and transmitting, when the first timer is within the second timer, request data related to the second application and user data of the first application by using at least part of the second timer and the first timer.
Abstract:
A method and apparatus with a feature-level ensemble model are provided. A method of operating an ensemble model based on feature-level consolidation includes: obtaining queries by inputting a same input data item to respective transformer models, the transformer models generating respective queries from the input data item; forming an ensemble query corresponding to the queries; and generating a predicted value of the input data item by applying the ensemble query to a prediction model that includes a transformer decoder, the prediction model inferring the predicted value from the ensemble query.
Abstract:
A processor-implemented method with object recognition includes obtaining sensor data comprising points representing a surrounding environment of a sensor, detecting, from the sensor data, a shaded region in which the points are not generated due to occlusion by a surrounding object, generating feature data using the shaded region, and performing object recognition for the surrounding environment of the sensor based on the feature data.
Abstract:
Provided are an object detection method and apparatus for an autonomous vehicle. A method of controlling a vehicle includes: detecting a surrounding environment using pieces of data on a driving environment of the vehicle and generating an indication of the surrounding environment; determining, among trained visual prompts received via a network from a server, a target visual prompt corresponding to the pieces of data; generating a merged image by combining a driving image of the autonomous vehicle with the target visual prompt using a predetermined operation; and performing object detection by inputting the merged image into a neural network model of the vehicle, the neural network model configured to infer objects from images inputted thereto.
Abstract:
A method performed by one or more processors of an electronic device includes: processing an input image and point cloud data corresponding to the input image; projecting the point cloud data to generate a first depth map and adding new depth values to the first depth map based on the input image; obtaining a second depth map by inputting the input image to a depth estimation model configured to infer depth maps from input images; and training the depth estimation model based on a loss difference between the first depth map and the second depth map.
Abstract:
A processor-implemented method with object tracking includes: performing, using a first template, forward object tracking on first image frames in a first sequence group; determining a template candidate of a second template for second image frames in a second sequence group; performing backward object tracking on the first image frames using the template candidate; determining a confidence of the template candidate using a result of comparing a first tracking result determined by the forward object tracking performed on the first image frames and a second tracking result determined by the backward object tracking performed on the first image frames; determining the second template based on the confidence of the template candidate; and performing forward object tracking on the second image frames using the second template.