• 한국정보시스템학회지:정보시스템연구
• /
• 제31권1호
• /
• pp.147-173
• /
• 2022

Purpose As the use of social network services has become common, it has become possible to freely communicate and establish relationships with other people anytime, anywhere for communication and information sharing. Influencers who have a strong influence on consumers' perceptions and attitudes through their own opinions and stories have appeared on various social media channels such as YouTube. Recently, companies utilize influencers with a large number of followers to check interactions with customers to understand customer attitudes and opinions about products in real time. Start-ups with insufficient resources need to quickly examine customer responses to reduce the probability of failure after product planning. The Lean process of creating an MVP and quickly confirming and learning the market response should be repeated over and over again. Findings In this paper, we try to suggest that the YouTube platform can play a sufficient role as a customer experiment space through examples. The case company is a company that has successfully commercialized products by continuously interacting with customers through the YouTube platform for the first four months of its founding. This paper is expected to be helpful in the experimental process for prospective founders and early founders to examine customer responses to reduce the probability of market failure before commercialization. Design/methodology/approach This paper analyzed the YouTube channel data of case companies based on the netnography methodology and presented the contents of the lean process management carried out in the experimental stage and the post-production stage through interview research.

• Nutrition Research and Practice
• /
• 제16권2호
• /
• pp.173-193
• /
• 2022

BACKGROUND/OBJECTIVES: Alzheimer's disease (AD) is one of the most representative neurodegenerative disease mainly caused by the excessive production of amyloid beta (Aβ). Several studies on the antioxidant activity and protective effects of Populus tomentiglandulosa (PT) against cerebral ischemia-induced neuronal damage have been reported. Based on this background, the present study investigated the protective effects of PT against cognitive impairment in AD. MATERIALS/METHODS: We orally administered PT (50 and 100 mg/kg/day) for 14 days in an Aβ_25-35-induced mouse model and conducted behavioral experiments to test cognitive ability. In addition, we evaluated the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in serum and measured the production of lipid peroxide, nitric oxide (NO), and reactive oxygen species (ROS) in tissues. RESULTS: PT treatment improved the space perceptive ability in the T-maze test, object cognitive ability in the novel object recognition test, and spatial learning/long-term memory in the Morris water-maze test. Moreover, the levels of AST and ALT were not significantly different among the groups, indicating that PT did not show liver toxicity. Furthermore, administration of PT significantly inhibited the production of lipid peroxide, NO, and ROS in the brain, liver, and kidney, suggesting that PT protected against oxidative stress. CONCLUSIONS: Our study demonstrated that administration of PT improved Aβ_25-35-induced cognitive impairment by regulating oxidative stress. Therefore, we propose that PT could be used as a natural agent for AD improvement.

• 한국컴퓨터정보학회논문지
• /
• 제27권7호
• /
• pp.75-82
• /
• 2022

우리 주위에 다양한 전시관, 쇼핑몰, 테마파크 등이 있으며 실제 전시하고 있는 전시물, 콘텐츠에 대한 관심도, 흥미도에 대한 분석은 설문 정도로만 이루어지고 있다. 이러한 설문은 주로 피설문자의 주관적인 기억에 의존하고 있어서 잘못된 통계 결과를 얻을 수 있는 문제가 있다. 따라서 방문객의 동선 추적과 수를 카운팅 하여 흥미가 떨어지는 전시 공간 파악이 가능하며 이를 통해 교체가 필요한 전시물에 대해 정량적 자료로 사용이 가능하다. 본 논문에서는 딥러닝 기반의 인공지능 알고리즘을 이용하여 방문객을 인식하고, 인식된 방문객에 아이디를 할당하여 이를 지속적으로 추적하는 방식으로 동선을 파악한다. 이때 방문객이 카운팅 라인을 통과하게 되면 그 수를 카운팅 하고, 데이터는 서버에 전송하여 통합 관리할 수 있도록 시스템을 설계하였다.

• 한국콘텐츠학회논문지
• /
• 제22권7호
• /
• pp.540-547
• /
• 2022

온라인 교육은 미래 교육 변혁의 큰 흐름으로, 2020년 이래의 코로나19 사태는 이 변혁의 진행 과정을 더욱 가속화 시켰다. 온라인 교육은 안정성과 연속성 측면에서 현대 교육시스템의 중요한 부분으로 자리 잡았다. 그러나 전통적인 온라인 교육은 오프라인 교육에 비하여 상호작용과 학습 몰입감에 대한 문제점이 지적되고 있다. 또한 예술실기 수업이면 사용자의 체험감이 떨어지기 때문에 온라인 교육을 업그레이드하는 것이 미래 온라인 교육의 관건으로 대두되고 있다. 메타버스 플랫폼을 기반으로 하는 온라인 교육은 온라인 교육의 발생 공간을 재정의하고, 온라인 교육의 교수 모델과 학습 및 평가 방식을 변화시켜 발전 잠재력을 보여 주고 있다. 본 연구에서는 먼저 메타버스의 특징과 그 기술 발전에 대한 현황분석, 예술교육 중심으로 메타버스에 기초한 스마트 온라인 교육환경 구축 방안을 제시하고자 한다.

• 대한원격탐사학회지
• /
• 제38권6_4호
• /
• pp.1911-1923
• /
• 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
• /
• 제12권5호
• /
• pp.529-547
• /
• 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
• /
• 제30권6호
• /
• pp.601-612
• /
• 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)
• /
• 제16권12호
• /
• pp.4008-4023
• /
• 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
• /
• 제32권6호
• /
• pp.1081-1090
• /
• 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
• /
• 제27권8호
• /
• pp.143-149
• /
• 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.