Yonsei University · 工学
Professor Ha Young Kim's research lab specializes in interdisciplinary artificial intelligence applications, focusing on multimodal learning, computer vision, and natural language processing with real-world impact. The lab develops advanced deep learning models—such as hybrid LSTM-CNN architectures and vision-language frameworks—for tasks ranging from financial time series forecasting and stock price prediction to image-to-music retrieval and autonomous vehicle perception. A key research direction involves enhancing AI systems with explainability and human-in-the-loop design, particularly in creative domains like art authentication and medical diagnosis using low-resource, imbalanced data. The lab also explores generative AI for information recovery in safety-critical environments, such as restoring obscured road signs for autonomous driving.
Figures are computed from collected data and may differ slightly.
Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. We propose a model, called the feature fusion long short-term memory-convolutional neural network (LSTM-CNN) model, that combines features learned from different representations of the same data, namely, stock time series and stock chart images, to predict stock prices. The proposed model is composed of LSTM and a CNN, which are utilized for extracti
The implant survival rate of mandibular overdentures seemed to be high regardless attachment systems. The prosthetic maintenance and complications may be influenced by attachment systems. However patient satisfaction may be independent of the attachment system.
Among all diseases affecting rice production, rice blast disease has the greatest impact. Thus, monitoring and precise prediction of the occurrence of this disease are important; early prediction of the disease would be especially helpful for prevention. Here, we propose an artificial-intelligence-based model for rice blast disease prediction. Historical data on rice blast occurrence in representative areas of rice production in South Korea and historical climatic data are used to develop a regi
Compressive strength is a critical indicator of concrete quality for ensuring the safety of existing concrete structures. As an alternative to existing nondestructive testing methods, image-based concrete compressive strength estimation models using three deep convolutional neural networks (DCNNs), namely AlexNet, GoogLeNet, and ResNet, were developed for this study. Images of the surfaces of specially produced specimens were obtained using a portable digital microscope, after which the samples
Total Healthcare costs of osteoporotic fractures in South Koreans ≥50-year-of-age increased between 2008 and 2011. This trend will likely continue, which is an important health problem in the elderly population and economically.
Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market‐neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs‐trading strategy using deep reinforcement learning—particularly with the deep Q‐network—utilizing various trading and stop‐loss boundaries. More specifically, if s
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