J
Jeonghun Kim
Researcher at Pohang University of Science and Technology
Publications - 4
Citations - 18
Jeonghun Kim is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Artificial neural network & Convolutional neural network. The author has an hindex of 2, co-authored 4 publications receiving 13 citations.
Papers
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Journal ArticleDOI
Selective Deep Convolutional Neural Network for Low Cost Distorted Image Classification
TL;DR: The proposed selective DCNN scores up to $2.18\times $ higher than the state-of-the-art DCNN model when evaluated using NetScore, a comprehensive metric that considers both CNN performance and hardware cost.
Proceedings ArticleDOI
Low-Complexity Dynamic Channel Scaling of Noise-Resilient CNN for Intelligent Edge Devices
TL;DR: A novel channel scaling scheme for convolutional neural networks (CNNs) that can improve the recognition accuracy for the practical distorted images without increasing the network complexity is presented.
Proceedings ArticleDOI
Hierarchical Approximate Memory for Deep Neural Network Applications
TL;DR: In this article, the use of hierarchical approximate memory for deep neural networks (DNNs) is studied and modeled, and it is shown that the overall memory power consumption can be reduced by up to 43.38% by using the proposed model to optimally divide up the available error budget.
Algorithms for Causality Evaluation of Adverse Events from Health/Functional Foods
Kyung-Jin Lee,Kyoung Sik Park,Jeonghun Kim,Youngjoo Lee,Taehyung Yoon,Ki-Mi No,Mi-Sun Park,Donggil Leem,Chang Yong Yoon,Jayoung Jeong +9 more
TL;DR: The Korea Food & Drug Administration has developed a new algorithm tool to reflect the characteristics of dietary supplements in the causality analysis, but additional work will be required to confirm if the newly developed algorithm tool has reasonable sensitivity and not to generate an unacceptable number of false positives signals.