Samsung Electronics Co., Ltd.

Samsung Sdi Co., Inc.

Ji-man Kim

Chan-ho Pak

Hyun-woo Lee

Do-Jun Yang

Chan-jong Park

Hyuk Jae Chang

Jeong-kuk Shon

Sang-hoon Joo

Hyung-Ik Lee

Hyung-ik Lee

Han-su Kim

Seung-sik Hwang

Jae-man Choi

Moon-seok Kwon

Min-sang Song

Dae-jong Yoo

Gwi-ok Park

Myung-hoon Kim

Sang Hoon Joo

Soo-sung Kong

Jin-hoe Kim

Jeong-kuk Sohn

Disclosed is a data recognition model construction apparatus. The data recognition model construction apparatus includes a video inputter configured to receive a video, an image composition unit configured to, based on a common area included in each of a plurality of images that form at least a portion of the video, generate a composition image by overlaying at least a portion of the plurality of images, a learning data inputter configured to receive the generated composition image, a model learning unit configured to make a data recognition model learn using the generated composition image, and a model storage configured to store the learnt data recognition model.

Data recognition model construction apparatus and method for constructing data recognition model thereof, and data recognition apparatus and method for recognizing data thereof

Disclosed are an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. The present disclosure provides an electronic device comprising: an input unit for receiving content data; a memory for storing information on the content data; an audio output unit for outputting the content data; and a processor, which acquires a plurality of data keywords by analyzing the inputted content data, matches and stores time stamps, of the content data, respectively corresponding to the plurality of acquired keywords, based on a user command being inputted, searches for a data keyword corresponding to the inputted user command among the stored data keywords, and plays the content data based on the time stamp corresponding to the searched data keyword.

Electronic device and control method

The disclosure relates to an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. In particular, an electronic apparatus, a document displaying method thereof, and a non-transitory computer readable recording medium are provided. An electronic apparatus according to an embodiment of the disclosure includes a display unit displaying a document, a microphone receiving a user voice, and a processor configured to acquire at least one topic from contents included in a plurality of pages constituting the document, recognize a voice input through the microphone, match the recognized voice with one of the acquired at least one topic, and control the display unit to display a page including the matched topic.

Electronic apparatus, document displaying method thereof and non-transitory computer readable recording medium

An electronic device, a warning message providing method therefor, and a non-transitory computer-readable recording medium are provided. Disclosed is an artificial intelligence (AI) system using a machine learning algorithm such as deep learning and an application thereof. Disclosed, according to one embodiment, is an electronic device which can comprise: a position determination unit for determining a current position of the electronic device; a communication unit for receiving accident data and a driving situation; an output unit for outputting a warning message; and a processor for learning the received accident data to establish a plurality of accident prediction models, selecting an accident prediction model to be applied from among the plurality of accident prediction models based on the determined current position, determining possibility of accident occurrence by using the selected accident prediction model, and controlling the output unit such that the output unit provides a warning message based on determining that the possibility of accident occurrence is greater than or equal to a preset value.

Electronic device, warning message providing method therefor, and non-transitory computer-readable recording medium

Provided is an Artificial Intelligence (AI) system for simulating a human brain's functions, such as recognition, decision, etc., by using a machine learning algorithm such as deep learning, etc. and applications of the AI system. Provided is an electronic device including: a camera configured to capture an outside image of a vehicle, and a processor configured to execute one or more instructions stored in a memory, wherein the processor executes the one or more instructions to: determine, from the captured image, at least one object for estimating lane information; estimate, from the image, lane information of a road on which the vehicle is traveling, based on a distance between the determined at least one object and the vehicle and a vanishing point of the image; and output guide information for guiding driving of the vehicle based on the estimated lane information.

Method for estimating lane information, and electronic device

Provided herein is a negative active material including an ordered porous manganese oxide, wherein pores of the ordered porous manganese oxide have a bimodal size distribution. Provided herein is a method of preparing a negative active material that includes the ordered porous manganese oxide. The invention also includes a negative electrode which includes the negative active material and a lithium battery which includes the negative electrode.

Negative active material including manganese oxides, negative electrode including the same, lithium battery including negative electrode and method of preparing negative active material

An electrode active material, an electrode including the electrode active material, a lithium battery including the electrode, and a method of preparing the electrode active material. The electrode active material includes a core having at least one of a metal or a metal oxide that enables intercalation and deintercalation of lithium ions and a crystalline carbon thin film that is formed on at least a portion of a surface of the core. The electrode active material has a nano-structure.

Electrode active material, electrode comprising the same, lithium battery comprising the electrode, and method of preparing the electrode active material

A composite support including: an ordered mesoporous carbon including mesopores having an average diameter of about 2 nanometers to about 8 nanometers; and silicon carbide dispersed in the ordered mesoporous carbon.

Composite support, method of preparing the same, electrode catalyst including the composite support, and membrane-electrode assembly and fuel cell each including the electrode catalyst

An anode active material including a porous transition metal oxide; an anode including the anode active material; a lithium battery including the anode; and a method of preparing the anode active material.

Anode active material comprising a porous transition metal oxide, anode comprising the anode active material, lithium battery comprising the anode, and method of preparing the anode active material

An ordered mesoporous carbon (OMC) composite catalyst includes an OMC; and metal particles and at least one component selected from a group consisting of nitrogen and sulfur included in the OMC. The ordered mesoporous carbon composite catalyst may be formed by impregnating an ordered mesoporous silica with a mixture of at least one selected from the group consisting of a nitrogen-containing carbon precursor, and a sulfur-containing carbon precursor, a metal precursor, and a solvent; drying and heat-treating the impregnated OMS; carbonizing the dried and heat-treated OMS to obtain a carbon-OMS composite; and removing the OMS from the carbon-OMS composite. A fuel cell may contain the OMC composite catalyst.

Ordered mesoporous carbon composite catalyst, method of manufacturing the same, and fuel cell using the same

Provided are a CNT-mesoporous silica composite, a CNT-mesoporous carbon composite, a supported catalyst using the CNT-mesoporous carbon composite as a support, and a fuel cell using the supported catalyst as the anode, cathode, or both anode and cathode. The CNT-mesoporous carbon composite is prepared using the CNT-mesoporous silica composite. The CNT-mesoporous carbon composite has a high electrical conductivity due to the CNTs contained therein, and thus, when the CNT-mesoporous carbon composite is used in an electrode of a fuel cell, the fuel cell has a remarkably improved performance relative to the conventional catalyst support which does not contain CNTs.

Growing community of inventors

Suwon-si, South Korea

Ji-man Kim