Samsung Electronics Co., Ltd.

Hyundai Motor Company

Kia Motors Corporation

Kia Motor Corporation

Jungwook Kim

Juyong Song

Hyunjoo Jung

Dong Pil Yoon

Jaehong Kim

Jaedeok Kim

Howon Seo

Sungjin Kim

Sunghyun Choi

Sangkyu Kim

Hyunjung Lee

Kyu Hwan Jo

Chiyoun Park

Sunho Kim

Tae Soo Kim

Jaewook Shin

Kwang Hun Choi

Jonglae Park

Junho Rim

Jeamun Lee

Aeran Lim

HoSun Yoo

Ilgu Kang

Sungwook Jung

Dongjae Lim

Joohee Kim

Hyunju Kang

An electronic apparatus includes at least one memory configured to store at least one instruction and a first neural network model; a communicator comprising communication circuitry; and at least one processor configured to execute the at least one instruction to: receive, from an external electronic device, information on a second neural network model stored in the external electronic device through the communicator; compare the first neural network model with the second neural network model based on the information on the second neural network model; and control the communicator to transmit, to the external electronic device, information on a weight between nodes of the first neural network model based on comparison between the second neural network model and the first neural network model.

Electronic apparatus for applying personalized artificial intelligence model to another model

An electronic device and a controlling method of an electronic device are provided. The electronic device includes identifying whether each of one or more neural network models included in a first external device is suitable for hardware of the electronic device and whether each of the one or more neural network models identified as suitable for the hardware of the electronic device is suitable to replace the neural network models included in the electronic device, based on first device information on a hardware specifications of the electronic device, second device information on a hardware specification of the first external device, first model information on the one or more neural network models included in the first external device, and second model information on the one or more neural network models included in the electronic device.

Electronic device and controlling method for installing neural network models suitable for hardware of the electronic device

A fastening automation apparatus for an upper electrode of an etching facility includes a ring, and a plurality of fastening modules movably secured to the ring and configured to be movable in a radial direction on the ring. Each fastening module includes a first frame that is movable in a radial direction on the ring, a driving source installed below the first frame, a driving shaft that transmits a driving force from the driving source, a power transmission unit connected to the driving shaft, and a fastening bit connected to the power transmission unit and configured to be rotated. The plurality of fastening modules are configured to operate simultaneously to install the upper electrode in the etching facility.

Fastening automation apparatus for upper electrode

An electronic device and a method for controlling thereof is provided. The electronic device includes a memory including a neural network model, a display, a communicator including circuitry, and a processor configured to identify, based on a user command to transmit a first image to an external device being input, whether private information of a user is included in the first image by inputting the first image in the neural network model, based on identifying that private information of the user is included in the first image, display a first user interface (UI) asking whether to process at least one private information based on the private information included in the first image, and based on a user command input through the first UI, process the first image and control the communicator to transmit the processed to the external device.

Electronic device and method for controlling thereof

Electronic device and controlling method of electronic device for identifying transition suitability of a neural network model included in an external device

The electronic device includes a camera, a non-volatile memory storing at least one instruction and a plurality of object recognition models, a volatile memory, and a processor, connected to the non-volatile memory, the volatile memory, and the camera, and configured to control the electronic device. The processor, by executing the at least one instruction, is configured to, based on an operation mode of the electronic device, load, to the volatile memory, a hierarchical object recognition model having a hierarchical structure corresponding to the operation mode, the hierarchical object recognition model including objection recognition models among the plurality of object recognition models, obtain information on an object by inputting an object image obtained through the camera to the hierarchical object recognition model, and determine an operation of the electronic device based on the information on the object.

Electronic device and method for controlling the electronic device

There is provided a method of controlling performance boosting of a semiconductor device. According to the method, input of a user is monitored. A performance of the semiconductor device is boosted by consecutively executing a plurality of boosting policies associated with a plurality of macros based on an input event associated with the input of the user and available energy during a boosting interval. Boosting level in each of the boosting policies may be adaptively determined based on the boosting level and the amount of usage of the semiconductor device used in the previous boosting policy and the boosting policies are consecutively executed. Accordingly, improved and/or optimal performance boosting can be provided to the semiconductor device and at the same time, a waste of power can be mitigated and/or prevented.

Method of controlling performance boosting of semiconductor device based on at least user input and feedback from previous boosting policies and semiconductor device performing the method

A vehicle and a control method of the vehicle are provided. The vehicle includes an engine management system to drive an engine to adjust a driving torque of the engine to accelerate the vehicle, a communicator to receive road gradient information, and a controller configured to control the engine management system such that running speed of the vehicle follows a target speed. The controller predicts an increase amount of speed of a next downhill section based on the road gradient information, and determines a start point of coasting control of a current section for maximizing a coasting distance based on the predicted speed increase amount.

Vehicle and control method thereof

A cruise control system, the vehicle including the same, and the method of controlling the cruise control system are provided to improve fuel efficiency by changing control target vehicle speeds by continuously predicting vehicle speeds and variations in required driving forces on roads with various slope variations. Additionally, driving performance is improved by using kinetic energies and preventing unnecessary acceleration and deceleration in comparison with conventional vehicles. Driver convenience and satisfaction is also improved by preventing an unintended operation stop of the cruise control system caused by frequent acceleration and deceleration on roads with substantial slope variations in advance and by preventing unintended acceleration/deceleration on roads with frequent slope variations.

Cruise control system, vehicle including the same, method of controlling the cruise control system

A method and an apparatus for controlling a hybrid electric vehicle are provided. The apparatus includes a navigation device that provides information regarding a gradient, a speed limit, and a traffic speed of a road. An accelerator pedal position detector detects a position of an accelerator pedal and a brake pedal position detector detects a position of a brake pedal. A vehicle speed detector detects a vehicle speed, a state of charge (SOC) detector detects an SOC of a battery, and a gear stage detector detects a gear stage that is currently engaged. A controller operates the hybrid vehicle based on signals of the navigation device, the accelerator pedal position detector, the brake pedal position detector, the vehicle speed detector, the SOC detector, and the gear stage detector.

Method and apparatus for controlling hybrid electric vehicle

Growing community of inventors

Suwon-si, South Korea

Jungwook Kim