• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.119-120
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• 2007

반도체 공정에서 사용되는 ILD막질 중 oxy-nitrde(SiON) film은 contact etch stopper, photo공정을 위한 ARL(anti-reflection lay떠 그리고, 후속공정의 plasma damage에 대한 blocking layer로서의 역할을 담당하며 많은 공정에 널리 사용되고 있다. 그러나 막질 자체의 불완전성 (trap site, dangling bond)에 의해 cell current instability(CCI) 특성을 악화 시킬 수 있어 이에 대한 원인규명 및 대책이 요구되었다. 본 연구는 미국 S사(社) super flash memory에서 oxy-nitride 막질 증착 시의 gas flow량에 따른 CCI 특성변화를 연구하고 최적의 공정조건을 제시하고자 한다.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• Computers and Concrete
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• v.25 no.4
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• pp.293-302
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• 2020

Connections play a significant role in strength of structures against earthquake-induced loads. According to the post-seismic reports, connection failure is a cause of overall failure in reinforced concrete (RC) structures. Connection failure results in a sudden increase in inter-story drift, followed by early and progressive failure across the entire structure. This article investigated the cyclic performance and behavioral improvement of shape-memory alloy-based connections (SMA-based connections). The novelty of the present work is focused on the effect of shape memory alloy bars is damage reduction, strain recoverability, and cracking distribution of the stated material in RC moment frames under seismic loads using 3D nonlinear static analyses. The present numerical study was verified using two experimental connections. Then, the performance of connections was studied using 14 models with different reinforcement details on a scale of 3:4. The response parameters under study included moment-rotation, secant stiffness, energy dissipation, strain of bar, and moment-curvature of the connection. The connections were simulated using LS-DYNA environment. The models with longitudinal SMA-based bars, as the main bars, could eliminate residual plastic rotations and thus reduce the demand for post-earthquake structural repairs. The flag-shaped stress-strain curve of SMA-based materials resulted in a very slight residual drift in such connections.

• Journal of the Korean Magnetics Society
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• v.19 no.1
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• pp.22-27
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• 2009

Reduction of the critical current density ($J_c$) for STT magnetization switching is most important issue of magnetic tunnel junctions (MTJs) based MRAM. This report describes how to decrease the Jc and will introduce the recent research progresses of STT-MRAM devices with material engineering and structural improvement, respectively.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.295-302
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• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

• Earthquakes and Structures
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• v.22 no.3
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• pp.231-243
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• 2022

Beam-column joints (BCJs) are recognized among the most crucial zones in reinforced concrete structures, as they are the critical elements subjected to a complex state of forces during a severe earthquake. Under such conditions, BCJs exhibit behaviors with impacts that extend to the whole structure and significantly influence its ductility and capability of dissipating energy. The focus of this paper is to investigate the effect of undamaged transverse beam (secondary beams) on the ductility of concrete BCJs reinforced with conventional steel and shape memory alloys bars using pushover analysis at tip of beam under different axial load levels at the column using a nonlinear finite element model in ABAQUS environment. A numerical model of a BCJ was constructed and the analysis outcomes were verified by comparing them to those obtained from previous experiments found in the literature. The comparison evidenced the capability of the calibrated model to predict the load capacity response of the joint. Results proved the ability of undamaged secondary beams to provide a noticeable improvement to the ductility of reinforced concrete joints, with a very negligible loss in load capacity. However, the effect of secondary beams can become less significant if the beams are damaged due to seismic effects. In addition, the axial load was found to significantly enhance the performance of BCJs, where the increase in axial load magnified the capacity of the joint. However, higher values of axial load resulted in greater initial stiffness of the BCJ.

• Journal of IKEEE
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• v.28 no.1
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• pp.1-5
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• 2024

To improve the performance of DRAM, it is essential to reduce sensing failures caused by mismatch in SA. Unlike flip failures, delay failures can be degraded, especially when high-speed operation is required, making it a critical consideration in the design of next-generation memory. While conventional SA operates with all transistors starting amplification simultaneously, SDSA selectively activates only two transistors that output BLB, thus alleviating offset. In this paper, we validate the superior performance of SDSA in mitigating delay failures through simulations. It was confirmed that SDSA exhibits approximately a 90 % reduction in delay failures compared to conventional SA.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.3-17
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• 2004

With the rapid growth of the Internet geographic information services through the WWW such as a location-based service and so on. Web GISs (Geographic Information Systems) have also come to be a cluster-based architecture like most other information systems. That is, in order to guarntee high quality of geographic information service without regard to the rapid growth of the number of users, web GISs need cluster-based architecture that will be cost-effective and have high availability and scalability. This paper proposes the design of the cluster-based web GIS with high availability and scalability. For this, each node within a cluster-based web GIS consists of main memory spatial databases which accomplish role of caching by using data declustering and the locality of spatial query. Not only simple region queries but also the proposed system processed spatial join queries effectively. Compare to the existing method. Parallel R-tree spatial join for a shared-Nothing architecture, the result of simulation experiments represents that the proposed spatial join method achieves improvement of performance respectively 23% and 30% as data quantity and nodes of cluster become large.