Sungjoo Hwang

Emailhwangsjewha.ackr

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51 Article
10 Conference contribution
1 Paper

Analysis of masonry work activity recognition accuracy using a spatiotemporal graph convolutional network across different camera angles
Yun, S., Hong, S., Hwang, S., Lee, D. & Kim, H., Jul 2025, In: Automation in Construction. 175, 106178.
Research output: Contribution to journal › Article › peer-review
6 Scopus citations
Assessing human emotional experience in pedestrian environments using wearable sensing and machine learning with anomaly detection
Kim, T., Kim, S., Lee, M., Kang, Y. & Hwang, S., Feb 2025, In: Transportation Research Part F: Traffic Psychology and Behaviour. 109, p. 540-555 16 p.
Research output: Contribution to journal › Article › peer-review
8 Scopus citations
Assessing human responses to construction noise using EEG and EDA signal features with Consideration of individual sensitivity
Hwang, S., Lee, S., Lee, M., Lee, S. & Choi, M., 5 Jun 2025, In: Applied Acoustics. 236, 110717.
Research output: Contribution to journal › Article › peer-review
3 Scopus citations
Assessing the efficiency of specialty contractors in the construction industry using DEA and Tobit regression
Jeong, D. W., Kim, K. R., Hwang, S., Ryu, J. H. & Choi, B., 2025, (Accepted/In press) In: Journal of Asian Architecture and Building Engineering.
Research output: Contribution to journal › Article › peer-review

Open Access
1 Scopus citations
Automatic method to predict visual pleasantness and unpleasantness of streetscapes and identify key microscale components for improving pedestrian environments
Lee, M., Choi, B. & Hwang, S., Apr 2025, In: Developments in the Built Environment. 22, 100652.
Research output: Contribution to journal › Article › peer-review

Open Access
1 Scopus citations

Sungjoo Hwang

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Collaborations and top research areas from the last five years

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Analysis of masonry work activity recognition accuracy using a spatiotemporal graph convolutional network across different camera angles

Assessing human emotional experience in pedestrian environments using wearable sensing and machine learning with anomaly detection

Assessing human responses to construction noise using EEG and EDA signal features with Consideration of individual sensitivity

Assessing the efficiency of specialty contractors in the construction industry using DEA and Tobit regression

Automatic method to predict visual pleasantness and unpleasantness of streetscapes and identify key microscale components for improving pedestrian environments

Sungjoo Hwang

Fingerprint

Collaborations and top research areas from the last five years

Research output

Analysis of masonry work activity recognition accuracy using a spatiotemporal graph convolutional network across different camera angles

Assessing human emotional experience in pedestrian environments using wearable sensing and machine learning with anomaly detection

Assessing human responses to construction noise using EEG and EDA signal features with Consideration of individual sensitivity

Assessing the efficiency of specialty contractors in the construction industry using DEA and Tobit regression

Automatic method to predict visual pleasantness and unpleasantness of streetscapes and identify key microscale components for improving pedestrian environments