• Journal of the Korean Vacuum Society
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• v.13 no.1
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• pp.39-45
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• 2004

Ultraviolet-nanoimprint lithography (UV-NIL) is a promising method for cost-effectively defining nanoscale structures at room temperature and low pressure. A 5${\times}$5${\times}$0.09 in. quartz stamp is fabricated using the etch process in which a Cr film was employed as a hard mask for transferring nanostructures onto the quartz plate. FAS(Fluoroalkanesilane) is used as a material for anti-adhesion surface treatment on the stamp and a thin organic film to improve adhesion on a wafer is formed by spin-coating. The low viscosity resin droplets with a nanometer scale volume are dispensed on the whole area of the coated wafer. The UV-NIL experiments have been performed using the EVG620-NIL. 370 nm - 1 m features on the stamp have been transferred to the thin resin layer on the wafer using the multi-dispensing method and UV-NIL process. We have measured the imprinted patterns and residual layer using SEM and AFM to evaluate the potential of the process.

• International Journal of Highway Engineering
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• v.8 no.1 s.27
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• pp.139-152
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• 2006

Many experimental and numerical approaches have been developed to evaluate paving materials and to predict pavement response and distress. Micromechanical simulation modeling is a technology that can reduce the number of physical tests required in material formulation and design and that can provide more details, e.g., the internal stress and strain state, and energy evolution and dissipation in simulated specimens with realistic microstructural features. A clustered distinct element modeling (DEM) approach was implemented In the two-dimensional particle flow software package (PFC-2D) to study the complex behavior observed in asphalt mixture fracturing. The relationship between continuous and discontinuous material properties was defined based on the potential energy approach. The theoretical relationship was validated with the uniform axial compression and cantilever beam model using two-dimensional plane strain and plane stress models. A bilinear cohesive displacement-softening model was implemented as an intrinsic interface and applied for both homogeneous and heterogeneous fracture modeling in order to simulate behavior in the fracture process zone and to simulate crack propagation. A disk-shaped compact tension test (DC(T)) with heterogeneous microstructure was simulated and compared with the experimental fracture test results to study Mode I fracture. The realistic arbitrary crack propagation including crack deflection, microcracking, crack face sliding, crack branching, and crack tip blunting could be represented in the fracture models. This micromechanical modeling approach represents the early developmental stages towards a 'virtual asphalt laboratory,' where simulations of laboratory tests and eventually field response and distress predictions can be made to enhance our understanding of pavement distress mechanisms, such its thermal fracture, reflective cracking, and fatigue crack growth.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.85-91
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• 2014

The objective of the research is to automatically recognize railway objects from MLS data in which 9 key objects including terrain, track, bed, vegetation, platform, barrier, posts, attachments, powerlines are targeted. The proposed method can be divided into two main sub-steps. First, multi-scale contextual features are extracted to take the advantage of characterizing objects of interest from different geometric levels such as point, line, volumetric and vertical profile. Second, by considering contextual interactions amongst object labels, a contextual classifier is utilized to make a prediction with local coherence. In here, the Conditional Random Field (CRF) is used to incorporate the object context. By maximizing the object label agreement in the local neighborhood, CRF model could compensate the local inconsistency prediction resulting from other local classifiers. The performance of proposed method was evaluated based on the analysis of commission and omission error and shows promising results for the practical use.

• Land and Housing Review
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• v.3 no.2
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• pp.187-193
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• 2012

A technology that newly attract attention in the area of energy-related study is the energy harvesting(or scavenging) technology. In this paper, the performance of power generation for the energy harvesting block with a combination of piezoelectric technology and electromagnetic technology among various energy harvesting technologies was investigated. The goal of this study is to evaluate on the applicability of our developed energy harvesting block into the field of urban & housing. First, we evaluated the performance of power generation for the multi-layer energy harvester at laboratory scale. Second, we described the features of our developed prototype module that includes amplification technologies to improve power density per module and evaluated the performance of power generation for the energy harvesting block in a variety of ways. From the test results, the developed product increased the performance of power generation up to 255% or 505% compared to the existing product and its superiority were shown. Finally, we suggested the direction for the improvement of the energy harvesting block module.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.29-37
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• 2021

In this paper, we propose a deep learning-based retinal vessel segmentation model for handling multi-scale information of fundus images. we integrate the selective kernel convolution into U-Net-based convolutional neural network. The proposed model extracts and segment features information with various shapes and sizes of retinal blood vessels, which is important information for diagnosing eye-related diseases from fundus images. The proposed model consists of standard convolutions and selective kernel convolutions. While the standard convolutional layer extracts information through the same size kernel size, The selective kernel convolution extracts information from branches with various kernel sizes and combines them by adaptively adjusting them through split-attention. To evaluate the performance of the proposed model, we used the DRIVE and CHASE DB1 datasets and the proposed model showed F1 score of 82.91% and 81.71% on both datasets respectively, confirming that the proposed model is effective in segmenting retinal blood vessels.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.13-20
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• 2018

This research is to analyze the public cases of information facilities in terms of central circulations in multi level volumes such as atrium or court which provide visual intervention between different spaces and physical connections such as bridges. Hunt Library design balances the understood pre-existing needs with the University's emerging needs to create a forward-thinking learning environment. While clearly a contemporary structure within a traditional context of the NCSU campus, the Hunt Library provides a positive platform for influencing its surroundings. Both technical and programmatic innovations are celebrated as part of the learning experience and provide a versatile and stimulating environment for students. Public library as open spaces connecting to an interactive social domain over communities can provide variety of learning environments, or technology based labs. There are many cases of the public information spaces with dynamic networks where participants can play their roles in physical space as well as in the intellectual stimulation. In the research, new public projects provide typologies of information spaces with user oriented media. The research is to address a creative transition between the reading space and the experimental links of the integration of state-of-the-art technology is highly visible in the building's design. The user-friendly browsing system that replaces the traditional browsing with the virtual shelves classified and archived by their form, is to reduce the storage space of the public library and it is to allow more space for collaborative learning. In addition to the intelligent robot of information storages, innovative features is the large-scale visualization space that supports team experiments to carry out collaborative online works and therefore the public library's various programs is to provide visitors with more efficient participatory environment.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.14-25
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• 2021

Change detection, which finds changes in remote sensing images of the same location captured at different times, is very important because it is used in various applications. However, registration errors, building displacement errors, and shadow errors cause false positives. To solve these problems, we propose a novle deep convolutional network called CADNet (Change Attention Dense Siamese Network). CADNet uses FPN (Feature Pyramid Network) to detect multi-scale changes, applies a Change Attention Module that attends to the changes, and uses DenseNet as a feature extractor to use feature maps that contain both low-level and high-level features for change detection. CADNet performance measured from the Precision, Recall, F1 side is 98.44%, 98.47%, 98.46% for WHU datasets and 90.72%, 91.89%, 91.30% for LEVIR-CD datasets. The results of this experiment show that CADNet can offer better performance than any other traditional change detection method.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1577-1589
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• 2023

Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.369-377
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• 2023

In the industrial manufacturing sector, quality control is pivotal for minimizing defect rates; inadequate management can result in additional costs and production delays. This study underscores the significance of detecting texture defects in manufactured goods and proposes a more precise defect detection technique. While the DFR(Deep Feature Reconstruction) model adopted an approach based on feature map amalgamation and reconstruction, it had inherent limitations. Consequently, we incorporated a new loss function using statistical methodologies, integrated a skip connection structure, and conducted parameter tuning to overcome constraints. When this enhanced model was applied to the texture category of the MVTec-AD dataset, it recorded a 2.3% higher Defect Segmentation AUC compared to previous methods, and the overall defect detection performance was improved. These findings attest to the significant contribution of the proposed method in defect detection through the reconstruction of feature map combinations.