Other

Lg Electronics Inc.

Daewoo Electronics Co., Ltd.

Industry-University Cooperation Foundation Hanyang University

Il Hong Suh

Yong Zu Kim

Sang Woo Park

Taewon Kim

Yong Koo Kang

Hong Woo Lee

Kye-Jung Shin

Sung Gil Park

Goonjin Kim

Yong Ho Chung

Sang Joo Lee

Dong Kin Kim

Yeseong Park

Janghee Lee

Woo Sok Chang

Byeonggil Yoo

Bong Suk Ko

Hyun-Taek Choi

Sungeun Chung

Youngbin Park

Jae Doo Huh

Disclosed herein are a control server and method for controlling a robot using an artificial neural network, and a robot implementing the same. The robot includes a driver, a processor controlling the driver using an artificial neural network-based algorithm model, and a learning processor learning the algorithm model, where the algorithm model includes a first GAN (generative adversarial network) and a second GAN, the processor generates a plurality of real-like-fake images using a first generator included in a learned first GAN, the learning processor learns a second GAN based on a plurality of random texture rendered images and the plurality of real-like-fake images, and the processor generates a canonical image by inputting a real image to a second generator included in a learned second GAN, and controls the driver using the canonical image.

Control server and method for controlling robot using artificial neural network, and robot implementing the same

The present invention relates to carbapenem derivatives of Formula I, wherein X is carbonyl or sulfonyl, and a preparation method thereof. The carbapenem derivatives of the invention have excellent antibacterial properties and are thus useful as antibiotics.

Carbapenem derivatives and a preparation method thereof

A fuzzy controller controls a track seeking operation and a track following operation for an actuator in accordance with a position error between a present position value and a reference value. When the actuator is in track seeking operation, the actuator is accelerated or decelerated based on a velocity error value between an actual velocity value of the actuator and a target velocity value determined from a velocity profile according to the position error. In a deceleration control of the actuator, a fuzzy inference value generated based on the velocity error value and its differentiated velocity error value is used for a control of the actuator, thereby reducing an occurrence of overshooting at a target track.

Fuzzy controller for an actuator and the controlling method thereof

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Seoul, South Korea

Il Hong Suh