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* { | ||
margin: 0; | ||
padding: 0; | ||
box-sizing: border-box; | ||
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body { | ||
font-family: 'Arial', sans-serif; | ||
line-height: 1.6; | ||
background-color: #f9f9f9; | ||
color: #333; | ||
} | ||
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.container { | ||
width: 80%; | ||
max-width: 1200px; | ||
margin: 0 auto; | ||
padding: 20px; | ||
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.header { | ||
background-color: #333; | ||
color: #fff; | ||
padding: 20px 0; | ||
text-align: center; | ||
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section ul li { | ||
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main { | ||
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border-radius: 8px; | ||
box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1); | ||
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footer { | ||
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<!DOCTYPE html> | ||
<html lang="en"> | ||
<head> | ||
<meta charset="UTF-8"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | ||
<meta http-equiv="X-UA-Compatible" content="ie=edge"> | ||
<title>HAIL Lab Projects</title> | ||
<link rel="stylesheet" href="../css/styles.css"> | ||
</head> | ||
<body> | ||
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<header class="header"> | ||
<div class="container"> | ||
<h1>HAIL Lab Projects</h1> | ||
<p>At HAIL, we work at the forward edge of artificial intelligence and machine learning, aiming to leverage cutting-edge technologies to improve everyday life.</p> | ||
</div> | ||
</header> | ||
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<main class="container"> | ||
<section> | ||
<h2>ML Method Development</h2> | ||
<p>HAIL is a research group focused on machine learning-driven AI. We generalize real-world problems and propose solutions grounded in modern mathematical and statistical approaches.</p> | ||
<ul> | ||
<li>Prediction with graph data</li> | ||
<li>Outlier detection using Transformer</li> | ||
<li>Learning with multiple modalities</li> | ||
</ul> | ||
</section> | ||
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<section> | ||
<h2>Medical & Clinical AI</h2> | ||
<p>HAIL is dedicated to applying AI technologies in healthcare to enhance the quality of medical services and increase access to care for more people. Currently, we are involved in the following projects:</p> | ||
<ul> | ||
<li>Development of liver disease support software (with Dr. Answer 2.0 Liver Disease Team, Ajou University Hospital)</li> | ||
<li>AI algorithm for fecal image recognition for health monitoring (with HEM Pharma)</li> | ||
<li>Risk model and analysis for pancreatic cancer (with Herings)</li> | ||
</ul> | ||
</section> | ||
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<section> | ||
<h2>Anomalies in Surveillance Video</h2> | ||
<p>The proliferation of surveillance cameras has led to a massive amount of footage that still relies on human operators for analysis during incidents. In this project, we aim to develop automated methods to detect critical segments in surveillance videos using advanced video processing techniques.</p> | ||
<ul> | ||
<li>Video anomaly detection with variational autoencoder (with GMDSOFT)</li> | ||
<li>Video Anomaly Transformer for Monitoring Accidents and Nefariousness (VATMAN, with GMDSOFT)</li> | ||
<li>Video Indexing for Rapid Surveillance Footage Summarization and Review (VIDEX, with GMDSOFT)</li> | ||
<li>Driving habit analysis using dashcam videos (with AI-CAR)</li> | ||
</ul> | ||
</section> | ||
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<section> | ||
<h2>Battery Remaining Useful Life</h2> | ||
<p>Electric vehicles have become a part of daily life and represent the future of transportation. Through analysis of data collected from EV Battery Management Systems (BMS), we are building the foundations for technology that helps users predictively manage their vehicles.</p> | ||
<ul> | ||
<li>Range prediction</li> | ||
<li>Future remaining useful life prediction</li> | ||
</ul> | ||
</section> | ||
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<section> | ||
<h2>Korean Unification Big Data Center (KUBiC)</h2> | ||
<p>Korea remains the world’s last divided nation. At HAIL, we are actively developing IT-based methodologies and case studies to accelerate reconciliation between North and South Korea.</p> | ||
<ul> | ||
<li>Development of the KUBiC search engine</li> | ||
<li>Development of a South-North Korean parallel corpus and translation model</li> | ||
<li>Bias analysis of LLMs on North Korean and unification issues</li> | ||
</ul> | ||
</section> | ||
</main> | ||
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<footer class="footer"> | ||
<div class="container"> | ||
<p>© 2024 HAIL Lab, Handong Global University. All Rights Reserved.</p> | ||
</div> | ||
</footer> | ||
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</body> | ||
</html> |