• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 하계학술대회 논문집 Vol.8
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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.

• 한국멀티미디어학회논문지
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• 제20권5호
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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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• 제25권4호
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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.

• 한국자기학회지
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• 제19권1호
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• pp.22-27
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• 2009

자기터널접합 기반의 MRAM(Magnetoresistive Random Access Memory)의 상용화를 위해서 가장 중요한 이슈는 쓰기 과정(writing operation)에서의 자화반전에 필요한 자화반전전류를 감소시키는 것이다. 본고에서는 나노자기소자 기술의 중요한 분야인 MRAM의 기술발전방향과 특히 스핀전달토크(Spin Transfer Torque, STT)를 이용한 자화반전전류의 저감기술 개발동향을 재료기술, 구조기술 등으로 살펴보았다.

• Journal of Information Processing Systems
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• 제15권3호
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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.

• 한국인터넷방송통신학회논문지
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• 제19권2호
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• pp.127-133
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• 2019

지금까지의 빈발 항목 추출에서는 FP-Tree에 대한 순회와 패턴의 탐색이 필수적인 과정이기 때문에 마이닝 데이터를 트리에 저장하는데 공간이 필요하고 탐색하는데 CPU시간이 필요하기 마련이다. 이러한 단점을 극복하기 위하여 본 논문에서는 조건부 FP-Tree의 의존하지 않고 트랜잭션 데이터의 각 항목들의 위치 정보를 부여하여 트랜잭션 데이터를 2차원의 위치정보 Look-Up테이블로 변환하여 시간과 공간적인 접근성을 용이하게 한다. 또한 항목과 항목의 위치에 대한 매핑배열을 병행하여 시간 복잡도를 줄이는 방법을 고려하는 알고리즘을 제안한다. 실험 결과를 통하여 제안된 방법은 FIMI 저장소 웹 사이트에서 얻은 데이터 세트를 기반으로 많은 실행 시간과 메모리 사용을 줄일 수 있음을 보였다.

• 한국해양공학회지
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• 제36권5호
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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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• 제22권3호
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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.

• 전기전자학회논문지
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• 제28권1호
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• pp.1-5
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• 2024

DRAM의 성능 개선을 위해 센스앰프의 미스매치로 인한 센싱페일을 감소시켜야 한다. 플립페일과 달리 딜레이페일은 고속 동작이 요구될 때 더 심화될 수 있어 차세대 메모리 설계 시 면밀히 고려되어야 할 문제이다. Conventional SA는 증폭 시작 시 모든 트랜지스터가 동시에 동작하는 반면, SDSA는 BLB를 출력으로 하는 트랜지스터 2개만 먼저 동작시켜 오프셋을 완화할 수 있다. 본 논문에서는 SDSA의 딜레이페일에 대한 우수성을 시뮬레이션을 통해 검증하였다. Conventional SA에 비해 약 90%의 딜레이 페일 감소 효과를 갖고 있음을 확인했다.

• 한국공간정보시스템학회 논문지
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• 제6권1호
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• pp.3-17
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• 2004

웹을 통해 위치기반 서비스 등과 같은 다양한 지리정보 서비스를 사용하려는 사용자가 급격하게 증가하면서, 웹 지리정보시스템도 많은 다른 인터넷 정보시스템들과 같이 클러스터 기반 아키텍쳐로의 변화가 요구되고 있다. 즉, 사용자의 수에 상관없이 양질의 지리정보 서비스를 지속적이며 빠르게 제공하기 위해서는 비용대비 효율, 가용성과 확장성이 높은 클러스터 기반의 웹 지리정보시스템이 필요하다. 본 논문에서는 가용성과 확장성이 높은 클러스터 기반의 웹 지리정보시스템을 설계한다. 이를 위해 메모리 상주 공간 데이터베이스들을 클러스터의 각 노드로 구성하고 전체 데이터 영역 중 일부만을 복제 처리함으로써, 각 노드가 공간 질의에 대해 공간적 근접성을 이용한 캐시 역할을 수행하도록 한다. 또한, 제안된 시스템은 단순 영역 질의외에 연산 비용이 큰 공간 조인 연산을 효율적으로 처리한다. 본 논문에서는 성능평가를 통해 제안된 기법이 기존 기법에 비해 데이터 양이 많고, 클러스터의 노드 수가 증가할수록 각각 약 23%, 30%의 향상된 성능을 갖음을 보인다.