• 대한원격탐사학회지
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• 제38권6_4호
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• Advances in nano research
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• 제12권5호
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• pp.529-547
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• 2022

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (V_i) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (E_p^*) of the impacted nanostructures and residual velocity (V_r) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

• Smart Structures and Systems
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• 제30권6호
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• 정보보호학회논문지
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• 제32권6호
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• pp.1081-1090
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• 2022

얼굴 인식 모델은 스마트폰의 신원 인식에 활용되는 등 많은 사용자에게 편의를 제공하고 있다. 이에 따라 DNN 모델의 보안성 검토가 중요해지고 있는데 DNN 모델의 잘 알려진 취약점으로 적대적 공격이 존재한다. 적대적 공격은 현재 DNN 모델의 인식 결과만을 이용하여 공격을 수행하는 의사결정 공격기법까지 발전하였다. 그러나 기존 의사결정 기반 공격기법[14]은 적대적 예제 생성 시 많은 질의 수가 필요한 문제점이 있다. 특히, 기울기를 근사하는데 많은 질의 수가 소모되는데 정확한 기울기를 구할 수 없는 문제가 존재한다. 따라서 본 논문에서는 기존 의사결정 공격기법의 기울기를 근사할 때 소모되는 질의 수 낭비를 막기 위해서 직교 공간 샘플링과 차원 축소 샘플링 방법을 제안한다. 실험 결과 섭동의 크기가 L2 distance 기준 약 2.4 적은 적대적 예제를 생성할 수 있었고 공격 성공률의 경우 약 14% 향상할 수 있었다. 실험 결과를 통해 본 논문에서 제안한 적대적 예제 생성방법의 같은 질의 수 대비 공격 성능이 우수함을 입증한다.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.143-149
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• 2022

특허문서는 연구 개발된 기술에 대한 상세한 결과를 포함하고 있기 때문에 효과적인 기술분석을 위한 다양한 특허분석 방법에 대한 연구가 진행되고 있다. 특히 통계학과 머신러닝 알고리즘에 의한 정량적인 특허분석에 대한 연구가 최근 활발하게 이루어지고 있다. 정량적 특허분석에서 가장 많이 사용되는 특허 데이터는 기술 키워드이다. 기술 키워드 데이터를 분석하는 기존의 방법은 대부분 음의 무한대부터 양의 무한대까지 실수 공간 전체를 확률변수의 값으로 갖는 가우시안 확률분포에 기반한 모형이었다. 본 논문에서는 이론적으로 0부터 양의 무한대까지의 값을 갖는 특허 키워드의 빈도 데이터를 분석하기 위하여 감마 확률분포를 활용한 모형을 제안한다. 또한 감마 회귀모형의 회귀방정식을 결정하기 위하여 키워드 간의 기술 연관성을 시각화하는 2-모드 네트워크를 구축한다. 제안 방법과 기존의 가우시안 기반의 분석모형 간의 성능평가를 위하여 실제 특허 데이터를 수집하여 분석한다.

• Journal of Positioning, Navigation, and Timing
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• 제11권3호
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• pp.217-227
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• 2022

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.

• 한국문헌정보학회지
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• 제57권3호
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• pp.119-141
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• 2023

코로나19 팬데믹은 근대 이래로 지구촌 공공도서관이 수행해 왔던 지식정보서비스 지형을 크게 변화시켰다. 특히 사회적 거리두기가 일상화되면서 문화활동의 위축과 온라인 플랫폼으로의 이동은 도서관 방문·이용서비스에 부정적인 영향을 주었고 대출과 열람, 공간과 시설 이용, 상호대차서비스, 프로그램 운영, 아웃리치서비스 등을 크게 위축시켰다. 이에 본 연구는 코로나19를 계기로 국내외 공공도서관이 제공하는 서비스 현황을 조사·분석하고, 사서의 인식을 설문조사한 결과를 반영하여 코로나19에 대응하는 실제적인 서비스 개선 및 확대 방안을 제안하였다. 구체적으로 살펴보면, 열람 및 대출서비스를 위한 온라인 예약시스템 개선 및 다양한 아웃리치서비스 개발·제공, 전자자료 확보 및 서비스 확대, 온라인 프로그램 서비스(독서, 문화, 평생학습 등) 확대, 지식정보 취약계층을 위한 도서관서비스 강화, 신종 감염병 관련 정보 포털서비스 제공, 시설과 공간 제공서비스 강화, 감염병 대응지침 마련과 사서 역량강화를 위한 교육·훈련 제공 등이 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 스마트미디어저널
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• 제11권1호
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• pp.46-57
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• 2022

본 논문에서는 딥러닝을 기반으로 입력영상의 옵티컬 플로우(optical flow)와 그래디언트(gradient)를 이용하여 종단간 행동인식이 가능한 다중영역 기반 방사성 GCN(MRGCN: Multi-region based Radial Graph Convolutional Network) 알고리즘에 대해 기술한다. 이 방법은 데이터 취득이 어렵고 계산이 복잡한 스켈레톤 정보를 사용하지 않기 때문에 카메라만을 주로 사용하는 일반 CCTV 환경에도 활용이 가능하다. MRGCN의 특징은 입력영상의 옵티컬플로우와 그래디언트를 방향성 히스토그램으로 표현한 후 계산량 축소를 위해 6개의 특징 벡터로 변환하여 사용한다는 것과 시공간 영역에서 인체의 움직임과 형상변화를 계층적으로 전파시키기 위해 새롭게 고안한 방사형 구조의 네트워크 모델을 사용한다는 것이다. 또 데이터 입력 영역을 서로 겹치도록 배치하여 각 노드 간에 공간적으로 단절이 없는 정보를 입력으로 사용한 것도 중요한 특징이다. 30가지의 행동에 대해 성능평가 실험을 수행한 결과 스켈레톤 데이터를 입력으로 사용한 기존의 GCN기반 행동인식과 동등한 84.78%의 Top-1 정확도를 얻을 수 있었다. 이 결과로부터 취득이 어려운 스켈레톤 정보를 사용하지 않는 MRGCN이 복잡한 행동인식이 필요한 실제 상황에서 더욱 실용적인 방법임을 알 수 있었다.