Samsung Electronics Co., Ltd.

Jaegon Kim

Sanghyuck Ha

Byeoungsu Kim

Sangsoo Ko

Kyoungyoung Kim

Changgwun Lee

Jiyoon Park

Yongju Lee

Nari Im

Hyunhee Park

Ildo Kim

Youngjo Kim

Sungjun Lim

Changsu Han

Hongseok Yang

Sangjin Lee

Hyeyun Jung

Seunghye Chyung

Kihuk Lee

Kwangtai Kim

Jonghoon Won

Taewoo Kim

Jonghun Won

Mincheol Hwang

Seungho Ryu

The present disclosure relates to a communication method and system for converging a 5-Generation (5G) communication system for supporting higher data rates beyond a 4-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. According to the present disclosure, communication is performed more efficiently in a software defined network (SDN) environment.

Method and device for performing communication in software defined network system

An operation method of a processor configured to operate based on an artificial neural network including a first neural network and a second neural network is provided. The operation method includes: analyzing similarity of a structure of the first neural network and a structure of the second neural network, the first neural network including a plurality of first neural network layers and the second neural network including a plurality of second neural network layers; selecting, from the plurality of first neural network layers and the plurality of second neural network layers, sharing layers capable of being commonly used, based on a result of the analysis; and reconstructing the structure of the first neural network or the structure of the second neural network based on the sharing layers.

Processor for reconstructing artificial neural network, electrical device including the same, and operating method of processor

The inventive concept relates to a neural network device, an operation method of the neural network device, and a neural network system including the neural network device, the operation method of the neural network device includes, receiving raw data from a sensor, converting the raw data to a feature map suitable for a first hidden layer among a plurality of hidden layers of a target neural network using a sensor response network adaptively trained to the sensor, inputting the feature map to the first hidden layer of the target neural network, and driving the neural network on a basis of the feature map.

Neural network device, operation method thereof, and neural network system including the same

An advanced driver assist system (ADAS) includes a processing circuit and a memory storing instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to: obtain, from a vehicle, a video sequence including a plurality of frames captured while driving the vehicle, where each of the frames corresponds to a stereo image including a first viewpoint image and a second viewpoint image; determine depth information in the stereo image based on reflected signals received while driving the vehicle; fuse the stereo image and the depth information to generated fused information, and detect at least one object included in the stereo image based on the fused information.

Advanced driver assist system, method of calibrating the same, and method of detecting object in the same

A semiconductor memory device is provided. The semiconductor memory device includes a memory core including a plurality of memory cells configured to store a plurality of data received from an external processor; and a statistical feature extractor disposed on a data path between the external processor and the memory core, the statistical feature extractor being configured to analyze statistical characteristics of the plurality of data, identify at least one statistical feature value associated with the statistical characteristics, store the at least one statistical feature value and transmit the at least one statistical feature value to the external processor.

Semiconductor memory device and electronic system the same

A computing device for training an artificial neural network model includes: a model analyzer configured to receive a first artificial neural network model and split the first artificial neural network model into a plurality of layers; a training logic configured to calculate first sensitivity data varying as the first artificial neural network model is pruned, calculate a target sensitivity corresponding to a target pruning rate based on the first sensitivity data, calculate second sensitivity data varying as each of the plurality of layers is pruned, and output, based on the second sensitivity data, an optimal pruning rate of each of the plurality of layers, the optimal pruning rate corresponding to the target pruning rate; and a model updater configured to prune the first artificial neural network model based on the optimal pruning rate to obtain a second artificial neural network model, and output the second artificial neural network model.

Computing device for training artificial neural network model, method of training the artificial neural network model, and memory system for storing the same

An electronic device comprises a first camera capable of photographing a designated direction, a second camera capable of photographing a direction different from the designated direction, a memory, and a processor, wherein the processor can be configured to acquire first images by using the first camera and second images by using the second camera, confirm at least one reflective object having a reflective attribute related to the light reflection in the first images, and synthesize, on the basis of the reflective attribute, at least a part of the second images with at least a part of an area corresponding to the at least one reflective object.

Method for synthesizing image on reflective object on basis of attribute of reflective object included in different image, and electronic device

A neural network system includes a processor and a memory. The processor is configured to perform learning including multiple learning iterations on multiple layers, to determine at least one layer in which the learning is interrupted among the multiple layers. The determination of the at least one layer in which the learning is interrupted is based on a result of comparing for each of the multiple layers a distribution of first weight values resulting from a first learning iteration with a distribution of second weight values resulting from a second learning iteration. The processor is also configured to perform a third learning iteration in layers except the at least one layer for which interruption of the learning has been determined. The memory stores first distribution information of the first weight values and second distribution information of the second weight values and is configured to provide the first distribution information and the second distribution information to the processor when the second learning iteration is completed.

Neural network system for performing learning, learning method thereof, and transfer learning method of neural network processor

According to various embodiments, an electronic device comprises a communication module, a memory, and a processor functionally connected with the communication module and the memory, wherein the processor may be configured to: receive a request for revision information corresponding to a first image from an external electronic device via the communication module; generate, on the basis of image recognition, first revision information by using a raw image corresponding to the first image; determine, on the basis of a comparison between the first image and the first revision information, whether a feature of the first revision information satisfies a predetermined condition or not; transmit, when the feature of the first revision information does not satisfy the predetermined condition, the first revision information to the external device to enable the external electronic device to generate a second image, that is, the revised first image by using the first revision information; generate, when the feature of the first revision information satisfies the predetermined condition, second revision information having a feature which is at least partially different from the first revision information, by using the first image and the first revision information; and transmit the second revision information to the external device to allow the external electronic device to generate a third image, that is, the revised first image by using the second revision information. Other embodiments are possible.

Electronic device and method for processing image by electronic device

An electronic device is provided. The electronic device includes a communication module, a memory, a display, and a processor, wherein the processor may confirm a user input related to selection of an image corrected by using a first image processing scheme, transmit attribute information related to the first image processing scheme to an external electronic device through the communication module, based on the user input, receive, from the external electronic device through the communication module, the image corrected by using a second image processing scheme selected based on the attribute information in the external electronic device, and display, through the display, the image corrected by using the second image processing scheme.

Electronic device and method for correcting image corrected in first image processing scheme in external electronic device in second image processing scheme

An advanced driver assist system (ADAS) may obtain a video sequence including a plurality of frames captured at the vehicle, each frame corresponding to a separate stereo image including a first viewpoint image and a second viewpoint image; generate disparity information associated with a stereo image; obtain depth information associated with an object included in the stereo image based on reflected electromagnetic waves captured at the vehicle; calculate correlation information between the depth information and the disparity information based on the stereo image, the depth information and the disparity information; and correct depth values associated with the stereo image based on the disparity information and the correlation information to generate a depth image with respect to the stereo image. The ADAS may detecting the at least one object in the stereo image, based on the depth image, and may generate an output signal based on the detection.

Advanced driver assist systems and methods of detecting objects in the same

According to embodiments, an electronic device comprising a processor configured to receive an image including one or more objects, acquire a first one or more of the received one or more objects, acquire one or more reliability measures associated with the acquired one or more first objects, receive an input including information having one or more words, acquire one or more second objects corresponding to at least a part of the one or more words, when there is at least one object corresponding to the one or more second objects among the one or more first object, adjust at least one reliability measure corresponding to the at least one object among the acquired one or more reliability measures, and recognize the one or more first objects by using an image recognition scheme at least based on the one or more reliabilities including the adjusted at least one reliability.

Electronic device and method for reliability-based object recognition

An electronic device according to embodiments may include: a communication module; a first processor configured to correct images, and a second processor operatively connected to the communication module and the first processor. The second processor is configured to control the first processor to correct a first image set obtained from a database, select at least one image from the first image set based at least on a correction result of the first image set, and transmit feature information on the selected at least one image to the first processor. The first processor is configured to obtain a second image set from the database based on the feature information, and generate a third image set by correcting the second image set. Various other embodiments may be possible.

Growing community of inventors

Gyeonggi-do, South Korea

Jaegon Kim