• Proceedings of the Korean Society of Laser Processing Conference
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• 1998.05a
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• pp.56-57
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• 1998

• Journal of Welding and Joining
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• v.14 no.3
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• pp.1-1
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• 1996

• Magazine of the Korea Concrete Institute
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• v.25 no.4
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• pp.24-28
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• 2013

• Transportation Technology and Policy
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• v.8 no.1
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• pp.133-136
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• 2011

• Computational Structural Engineering
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• v.23 no.3
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• pp.87-96
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• 2010

• 한국도로학회:학술대회논문집
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• 2008.10a
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• pp.145-152
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• 2008

• Wind and Structures
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• v.22 no.1
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• pp.1-16
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• 2016

The paper presents the state-of-the-art in aerodynamic performance of the modern horizontal axis wind turbine. The study examines the different complexities involved with wind turbine blade aerodynamic performance in open atmosphere and turbine wakes, and highlights the issues which require further investigations. Additionally, the latest concept of smart blades and frequently used wind turbine design analysis tools have also been discussed. The investigation made through this literature survey shows significant progress towards wind turbine aerodynamic performance improvements in general. However, still there are several parameters whose behavior and specific role in regulating the performance of the blades is yet to be elucidated clearly; in particular, the wind turbulence, rotational effects, coupled effect of turbulence and rotation, extreme wind events, formation and life time of the wakes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• Clean Technology
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• v.11 no.1
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• pp.29-39
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• 2005

Polychlorinated biphenyls, (PCBs) are a group of highly toxic chlorinated industrial chemicals used as dielectrics, coolants and lubricants in electrical transformers. This article reviewed the state-of-the-art on technologies for decomposition of Polychlorinated biphenyls (PCBs), one of the persistent organic materials (POPs). The purpose of this study was to evaluate the feasibility of decontaminating PCBs contaminated pollutants using treatment technologies such as chemical dechlorination, photodegradation and biological transformation.