Study of the Construction of a Coastal Disaster Prevention System using Deep Learning

Kim, Yeon-Joong; Kim, Tae Woo; Yoon, Joonsung; Kim, Myong-Kyu

doi:10.26748/ksoe.2019.066

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…A number of studies on changes in beaches and coastlines have been conducted in South Korea. However, most of the studies have analyzed mid-to long-term changes in the coastline over several years or decades Jang, Kim, and Yoon, 2010;Kim, 2013;Kim, Yoon, and Lee, 2013), or have focused on changes in the coastline due to coastal development, maintenance projects, and installation of artificial structures (Choi, Oh, and Lee, 2009;Jang, Kim, and Baek, 2012;Kim and Park, 2008;Kim and Song, 2012;Kim and Yoon, 2010;. There are also many studies that have analyzed seasonal changes in the coast (Bang and Lee, 2011;Kim and Jang, 2011;Woo and Jang, 2010;Youn and Kim, 2011).…”

Section: Methodsmentioning

confidence: 99%

The Impact of Coastal Erosion and Shoreline Changes on Beach Leisure Activities: Evaluating the Importance of Maritime Police in Maintaining Public Safety

Moon

2024

Journal of Coastal Research

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Section: Methodsmentioning

confidence: 99%

The Impact of Coastal Erosion and Shoreline Changes on Beach Leisure Activities: Evaluating the Importance of Maritime Police in Maintaining Public Safety

Moon

2024

Journal of Coastal Research

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The Application of Deep Learning Model in Recruitment Decision

Yang

Zhu

2022

Wireless Communications and Mobile Computing

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With the rapid development of social economy, the competition of human resources is becoming more and more fierce. Recruitment, as the main way for enterprises to obtain talents, determines the future development of enterprises to a great extent. Compared with Western advanced countries, the research on recruitment in China started late, the overall research level is relatively backward, and most of the relevant technical means and analytical methods are introduced from advanced countries. The existing literature is still relatively scattered, which is not conducive to the rapid development of recruitment direction research, and is not conducive to specific applications. Starting with the existing deep learning, from four models, that is, based on the traditional machine learning model, conditional random field (CRF), deep learning models Bi-LSTM-CRF, BERT, and BERT-Bi-LSTM-CRF identify and automatically extract recruitment entities and study recruitment accordingly; BERT-Bi-LSTM-CRF-BERT and BERT-BiLSTM-CRF are the models with the worst recognition effect. Although they have stronger text feature extraction ability and context information capture ability, they are limited by the small scale of information science recruitment corpus and the small number of entities, so their performance under this task is not brought into full play. Although CRF is relatively traditional, it can still achieve excellent results on some small-scale sparse datasets.

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Storm Wave Characteristics during Typhoons Maysak and Haishen on the East and South Coasts of Korea

Seo

Kim

Hwang

et al. 2022

Journal of Coastal Research

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Study of the Construction of a Coastal Disaster Prevention System using Deep Learning

Cited by 6 publications

References 4 publications

The Impact of Coastal Erosion and Shoreline Changes on Beach Leisure Activities: Evaluating the Importance of Maritime Police in Maintaining Public Safety

The Impact of Coastal Erosion and Shoreline Changes on Beach Leisure Activities: Evaluating the Importance of Maritime Police in Maintaining Public Safety

The Application of Deep Learning Model in Recruitment Decision

Storm Wave Characteristics during Typhoons Maysak and Haishen on the East and South Coasts of Korea

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