Samsung Electronics Co., Ltd.

Seoul National University

Hamamatsu Photonics Korea Co., Ltd.

Taejun Park

Sangjo Lee

Sangkwon Na

Daeeun Kim

Bongjoe Kim

Kyuha Choi

Seung-Hoon Park

Jihoon Kim

Jungsoo Jung

Kangjin Yoon

Sunghyun Choi

Byounghoon Jung

Daesung Cho

Doohyun Kim

Minkyu Kim

Jaehun Lee

Younghoon Jeong

Soodol Park

Minyong Sung

Duhwa Jung

Daeyoung Ryu

Sihyeon Ryu

Jaehong Yl

Junnyeon Jeong

Seokyung Kim

Sanghun Lim

Kyoonwoo Kim

Seunggyun Kim

Sunwook Hwang

An electronic apparatus is provided. The electronic apparatus includes: a communication interface; a user interface; and a processor configured to control the electronic apparatus to: receive a content and metadata corresponding to the content from a server through the communication interface; obtain an input lag allowance time corresponding to the content based on any one or any combination of image information corresponding to the content, the metadata, or a pattern of a user command received through the user interface; identify at least one image processing method, among a plurality of image processing methods, based on the input lag allowance time; and perform image processing on the content based on the at least one image processing method.

Electronic apparatus and control method thereof

The disclosure relates to an artificial intelligence (AI) system that uses a machine learning algorithm and an application thereof. A method for controlling an electronic apparatus according to the disclosure includes receiving image data and information associated with a filter set that is applied to an artificial intelligence model for upscaling the image data from an external server; decoding the image data; upscaling the decoded image data using a first artificial intelligence model that is obtained based on the information associated with the filter set; and providing the upscaled image data for output.

Apparatus and method with artificial intelligence for scaling image data

A refrigeration cycle device includes a compressor. The compressor includes a casing, a driving unit disposed in the casing, a compression unit coupled to the driving unit and configured to compress a refrigerant, and a valve configured to control a flow of the refrigerant in the casing. The valve includes a valve chamber including a main flow path in which the refrigerant is to flow, the main flow path including a refrigerant inlet and a refrigerant outlet. The valve also includes a floating body disposed in the valve chamber to open or close the main flow path, and a bypass flow path formed in the valve chamber and which is to be opened or closed by the floating body. When the driving unit is stopped and the bypass flow path is opened by the floating body, the refrigerant is to be detoured to the bypass flow path.

Compressor and refrigeration cycle device having the same

An artificial intelligence (AI) encoding apparatus includes at least one processor configured to: determine a downscaling target, based on a target resolution for a first image, obtain the first image by AI-downscaling an original image using an AI-downscaling neural network corresponding to the downscaling target, generate image data by encoding the first image, select AI-upscaling neural network set identification information, based on the target resolution of the first image, characteristic information of the original image, and a target detail intensity, generate AI data including the target resolution of the first image, bit depth information of the first image, the AI-upscaling neural network set identification information, and encoding control information, and generate AI encoding data including the image data and the AI data; and a communication interface configured to transmit the AI encoding data to an AI decoding apparatus, wherein the AI data includes information about an AI-upscaling neural network corresponding to the AI-downscaling neural network.

AI encoding apparatus and operation method of the same, and AI decoding apparatus and operation method of the same

An artificial intelligence (AI) processor includes at least one memory; a plurality of neural network operators comprising circuitry configured to process an image; and a controller configured to control the at least one memory and the plurality of neural network operators. The controller controls input image data of an image to be stored in the at least one memory and controls at least one of the plurality of neural network operators to perform a neural network operation on image data split based on a size of the image and data processing capabilities of the plurality of neural network operators, and output upscaled image data.

Artificial intelligence processor and method of performing neural network operation thereof

A substrate testing apparatus configured to perform a hot electron analysis (HEA) test for analyzing a stand-by failure in a substrate includes a heating chuck having a first surface configured to support the substrate and a second surface opposite to the first surface. The heating chuck is configured to heat the substrate and has an aperture passing through the first surface and the second surface. A substrate moving device moves the substrate on the heating chuck in a lateral direction. A camera is under the heating chuck and photographs the substrate, which is exposed by the heating chuck aperture.

Substrate testing apparatus

An electronic apparatus is disclosed. The electronic apparatus includes: a memory configured to store a downscaling network of a first artificial intelligence model, a communication interface comprising communication circuitry, and a processor connected to the memory and the communication interface and configured to control the electronic apparatus, wherein the processor is configured to: obtain an output image in which an input image is downscaled by inputting the input image the downscaling network, control the communication interface to transmit the output image to another electronic apparatus, and wherein the first artificial intelligence model is configured to be learned based on: a sample image, a first intermediate image obtained by inputting the sample image to the downscaling network, a first final image obtained by inputting the first intermediate image to an upscaling network of the first artificial intelligence model, a second intermediate image in which the sample image is downscaled by a legacy scaler, and a second final image in which the first intermediate image is upscaled by the legacy scaler.

Electronic apparatus, control method thereof and electronic system

A method for controlling an electronic apparatus according to the disclosure includes receiving image data and information associated with a filter set that is applied to an artificial intelligence model for upscaling the image data from an external server; decoding the image data; upscaling the decoded image data using a first artificial intelligence model that is obtained based on the information associated with the filter set; and providing the upscaled image data for output.

Electronic apparatus and method for upscaling a down-scaled image by selecting an improved filter set for an artificial intelligence model

Disclosed is a 5th generation (5G) or pre-5G communication system for supporting a data transmission rate higher than that of a 4th generation (4G) communication system such as long-term evolution (LTE). According to various embodiments of the present disclosure, a terminal operation method in a wireless communication system comprises the steps of: identifying a primary anchor carrier among a plurality of anchor carriers used by a base station; and receiving an information signal (IS) from the base station through the identified primary anchor carrier, wherein the primary anchor carrier can be continuously maintained in order to receive the IS from the base station.

Apparatus and method for using multiple carriers in wireless communication system

Disclosed is an electronic device including: a communication circuit; a processor; and a memory, wherein the memory stores instructions which, when executed, cause the processor to control the electronic device to: acquire first information on types of objects disposed within a first space and second information on locations of the objects in a first direction, select a target object, generate first name of the target object based on information on a type of the target object in the first information, generate second name of at least one counterpart object based on information on a type of the counterpart object disposed around the target object in the first information, determine a relative location relation between the target object and the at least one counterpart object based on the second information, and generate third name of the target object based on the first name, the second name, and the relative location relation.

Method of controlling external electronic device and electronic device for supporting same

Growing community of inventors

Suwon-si, South Korea

Taejun Park