Výsledky vyhledávání

Report

Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem

Autor: Lee, Jaejin, Kee, Seho, Janakiram, Mani, Runger, George

Job shop scheduling problems represent a significant and complex facet of combinatorial optimization problems, which have traditionally been addressed through either exact or approximate solution methodologies. However, the practical application of t

Externí odkaz: http://arxiv.org/abs/2401.16580

Zobrazit plný text záznamu

Akademický článek

Operational Dst index prediction model based on combination of artificial neural network and empirical model

Autor: Park Wooyeon, Lee Jaejin, Kim Kyung-Chan, Lee JongKil, Park Keunchan, Miyashita Yukinaga, Sohn Jongdae, Park Jaeheung, Kwak Young-Sil, Hwang Junga, Frias Alexander, Kim Jiyoung, Yi Yu

Publikováno v: Journal of Space Weather and Space Climate, Vol 11, p 38 (2021)

In this paper, an operational Dst index prediction model is developed by combining empirical and Artificial Neural Network (ANN) models. ANN algorithms are widely used to predict space weather conditions. While they require a large amount of data for

Externí odkaz: https://doaj.org/article/06584567789743f98be81ec67584aecf

Zobrazit plný text záznamu

Report

Lightweight Soft Error Resilience for In-Order Cores

Autor: Zeng, Jianping, Kim, Hongjune, Lee, Jaejin, Jung, Changhee

Acoustic-sensor-based soft error resilience is particularly promising, since it can verify the absence of soft errors and eliminate silent data corruptions at a low hardware cost. However, the state-of-the-art work incurs a significant performance ov

Externí odkaz: http://arxiv.org/abs/2202.09444

Zobrazit plný text záznamu

Akademický článek

Water dimer in CCl4 investigated by in-situ infrared spectroscopy and computational analysis

Autor: Lee, Jaejin, Turner, Adam H., Ryu, Soo Ryeon, Kim, Yung Sam, Kim, Doseok

Publikováno v: In Vibrational Spectroscopy March 2024 131

Zobrazit plný text záznamu

Report

CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers

Autor: Kim, Jinpyo, Jung, Wooekun, Kim, Hyungmo, Lee, Jaejin

Deep Convolutional Neural Networks (CNNs) are empirically known to be invariant to moderate translation but not to rotation in image classification. This paper proposes a deep CNN model, called CyCNN, which exploits polar mapping of input images to c

Externí odkaz: http://arxiv.org/abs/2007.10588

Zobrazit plný text záznamu