상세 보기

손기민 프로필 사진

손기민

Sohn, Kee Min

공과대학

도시시스템공학

연구분야

연도별 연구 활동 현황

전체 73건 중 11번부터 20번까지의 결과를 표시합니다.

2022

Article

Area-wide traffic signal control based on a deep graph Q-Network (DGQN) trained in an asynchronous manner

Article

Special issue on smart cities and its applications

2021

Article

Variational embedding of a hidden Markov model to generate human activity sequences

Article

Non-Anchor-Based Vehicle Detection for Traffic Surveillance Using Bounding Ellipses

Article

Transferability of a convolutional neural network (Cnn) to measure traffic density

Article

Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm

Article

Predicting Short-Term Traffic Speed Using a Deep Neural Network to Accommodate Citywide Spatiooral Correlations

2020

Patent

동영상을 이용한 다중 교차로 교통 신호 제어 방법 및 그 장치

Article

Measuring traffic volumes using an autoencoder with no need to tag images with labels

Article

Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)

상세 보기

손기민

연구분야

자료유형

발행연도

키워드

언어

Area-wide traffic signal control based on a deep graph Q-Network (DGQN) trained in an asynchronous manner

Special issue on smart cities and its applications

Variational embedding of a hidden Markov model to generate human activity sequences

Non-Anchor-Based Vehicle Detection for Traffic Surveillance Using Bounding Ellipses

Transferability of a convolutional neural network (Cnn) to measure traffic density

Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm

Predicting Short-Term Traffic Speed Using a Deep Neural Network to Accommodate Citywide Spatiooral Correlations

동영상을 이용한 다중 교차로 교통 신호 제어 방법 및 그 장치

Measuring traffic volumes using an autoencoder with no need to tag images with labels

Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)