Industry-University Cooperation Foundation Hanyang University

Bitsensing Inc.

Sk Innovation Co., Ltd.

Lg Energy Solution, Ltd.

Lg Hausys, Ltd.

Jae-Eun Lee

Jin-Hak Kong

Je-Jun Lee

Jae-Won Lim

Duk-Hyun Ryu

Kyu-Hyun Choi

Jong-Sik Park

Kwan-Hee Lee

Kwang-Su Hwangbo

Byoung-Gu Lee

Young Moo Lee

Min-Ki Jo

Su-Ji Choi

Yu-Sung Choe

Geon-Woo Min

Hak-Kyun Kim

Jae-Woong Kim

Bo-Hyun Kang

Hae-Jin Lim

Soon-O Lee

Ji-Min Jung

Chul-Ho Jung

Seongwook Lee

Ho-bum Park

Sang-Hoon Han

Hye-Jin Kim

Pil-kyu Park

Young-Moo Lee

Do-Gyun Kim

Haesueng Lim

Hae-Seung Lim

Ho-Bum Park

Won-Yeob Kim

Chul-Soon Moon

Ho-Chul Yoon

Jong-Nam Ahn

Youngjune Park

In-Suk Yang

Jae-Wong Kim

Jong-Chul Lee

Facial identification of subjects using a millimeter wave radar sensor, including: transmitting a radar signal onto faces of subjects using the millimeter wave radar sensor; receiving and accumulating reflected radar data, wherein the reflected radar data is received from channels of multiple receiving antennas; converting the reflected radar data into an image form suitable as an input to a convolutional neural network; and performing the facial identification on the image form using a classifier trained by the convolutional neural network.

Face identification using millimeter-wave radar sensor data

Extracting target information from signals of a radar and measurement environments, including: transmitting frequency-modulated continuous wave transmit signal using the radar; receiving reflected signal reflected from a human face at multiple antenna elements; multiplying the transmit signal with the reflected signal using a mixer to produce a mixed signal; passing the mixed signal through a low pass filter to produce a baseband signal including sinusoidal signals; extracting a frequency of each sinusoidal signal from the baseband signal to produce extracted frequencies; and measuring a distance between the human face and the radar using the extracted frequencies.

DNN-based human face classification

The present invention relates to a composition for forming a hard coating layer, which may form a hard coating layer having significantly improved hardness as well as excellent flexibility such that curling is minimized.

Composition for making hard coating layer

Disclosed herein are a polyimide-polybenzoxazole copolymer, a method for preparing thereof and a gas separation membrane comprising the same. More specifically, provided are a polyimide-polybenzoxazole copolymer simply prepared through thermal-rearrangement performed by thermally treating a polyimide-poly (hydroxyimide) copolymer as a precursor, a method for preparing the same, and a gas separation membrane comprising the same. The copolymer shows superior gas permeability and gas selectivity, thus being suitable for use in gas separation membranes in various forms such as films, fibers or hollow fibers. The gas separation membrane thus prepared can advantageously endure even harsh conditions such as long operation time acidic conditions and high humidity due to the rigid polymer backbone present in the copolymer.

Polyimide-co-polybenzoxazole copolymer, preparation method thereof, and gas separation membrane comprising the same

Disclosed is a method of preparing porous polybenzimidazole. The method includes providing polyaminoimide by reacting aromatic amine including at least two ortho-positioned amino groups and acid dianhydride, obtaining polypyrrolone from the polyaminoimide, subjecting the polypyrrolone to alkaline treatment, and subjecting the alkaline-treated polypyrrolone to heat treatment. The polybenzimidazole shows permeability and selectivity for various gases due to a fractional free volume and well-connected picopores.

Preparation method of polybenzimidazole

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Seoul, South Korea

Jae-Eun Lee