• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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• pp.119-136
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• 2023

Purpose We introduce a groundbreaking surveillance system explicitly designed to overcome the limitations typically associated with conventional surveillance systems, which often focus primarily on object-centric behavior analysis. Design/methodology/approach The study introduces an innovative approach to risk assessment in surveillance, employing image captioning to generate descriptive captions that effectively encapsulate the interactions among objects, actions, and spatial elements within observed scenes. To support our methodology, we developed a distinctive dataset comprising pairs of [image-caption-danger score] for training purposes. We fine-tuned the BLIP-2 model using this dataset and utilized BERT to decipher the semantic content of the generated captions for assessing risk levels. Findings In a series of experiments conducted with our self-constructed datasets, we illustrate that these datasets offer a wealth of information for risk assessment and display outstanding performance in this area. In comparison to models pre-trained on established datasets, our generated captions thoroughly encompass the necessary object attributes, behaviors, and spatial context crucial for the surveillance system. Additionally, they showcase adaptability to novel sentence structures, ensuring their versatility across a range of contexts.

• Smart Structures and Systems
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• 제33권1호
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.89-94
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• 2024

Indian subcontinent is a birthplace of multilingual people where documents such as job application form, passport, number plate identification, and so forth is composed of text contents written in different languages/scripts. These scripts may be in the form of different indic numerals in a single document page. Due to this reason, building a generic recognizer that is capable of recognizing handwritten indic digits written by diverse writers is needed. Also, a lot of work has been done for various non-Indic numerals particularly, in case of Roman, but, in case of Indic digits, the research is limited. Moreover, most of the research focuses with only on MNIST datasets or with only single datasets, either because of time restraints or because the model is tailored to a specific task. In this work, a hybrid model is proposed to recognize all available indic handwritten digit images using the existing benchmark datasets. The proposed method bridges the automatically learnt features of Capsule Network with hand crafted Bag of Feature (BoF) extraction method. Along the way, we analyze (1) the successes (2) explore whether this method will perform well on more difficult conditions i.e. noise, color, affine transformations, intra-class variation, natural scenes. Experimental results show that the hybrid method gives better accuracy in comparison with Capsule Network.

• 한국문헌정보학회지
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• 제56권4호
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• pp.5-28
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• 2022

이 연구에서는 해외 도서관의 LD 구축 사례를 발행 데이터세트, 재사용 어휘집, 인터링킹 외부 데이터세트를 중심으로 분석하고, 분석 결과를 토대로 국내도서관의 LD 구축 방안에 대한 기초적인 데이터를 확보하였다. 21개 해외 도서관 사례 분석 결과, 해외 도서관은 충실한 전거 LD를 구축하였고, 발행 LD를 활용한 새로운 서비스를 진행하였다. 이를 위해 해외 도서관은 도서관의 주도하에 다른 도서관과 문화기관들과 지역 내에서, 국가 내에서, 국가적으로 협력하였고, 이러한 협력을 바탕으로 특성화된 데이터세트를 발행하였다. 해외 도서관은 발행 LD의 가시성을 높이기 위해 Schema.org를 사용하였고, 기술의 세분화를 위해 BIBFRAME 등을 사용하여 다양한 개체를 정의하고, 정의된 개체에 기반하여 LD를 구축하였다. 해외 도서관은 다양한 개체를 관련 정보 연계와 검색 결과 디스플레이, 브라우징, 대량 다운로드에 활용하였다. 해외 도서관은 인터링킹 외부 데이터세트를 지속해서 현행화하였고, 외부 데이터를 직접적으로 활용하여 목록정보를 보강하였다. 이 연구에서는 도출된 시사점을 토대로 국내도서관의 LD 구축 시 고려할 점을 제안하였다. 연구 결과는 향후 국내도서관이 LD 서비스를 계획하거나 기존 서비스를 고도화할 때 기초자료로 활용될 수 있을 것이다.

• 경영정보학연구
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• 제24권4호
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• pp.23-40
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• 2022

Support Vector Machine(SVM)은 기업부실 예측문제 등 다양한 분야에서 성공적으로 활용되어 왔으나 범주 불균형 문제가 존재하는 경우 다수 범주의 경계영역은 확장되는 반면, 소수 범주의 경계영역은 축소되고 분류 경계선이 소수 범주로 편향되어 분류 성과에 부정적인 영향을 미치는 것으로 보고되고 있다. 본 연구는 범주 불균형 문제에 대한 대칭 마진 SVM(EMSVM)의 한계점을 개선하기 위하여 비대칭 마진 SVM(UMSVM)과 임계점 이동 기법을 결합한 최적화 비대칭 마진 SVM인 OPT-UMSVM을 제안한다. OPT-UMSVM은 소수 범주 방향으로 치우진 분류 경계선을 다수 범주로 재이동함으로써 소수 범주의 민감도를 개선하고 최적화된 분류 성과를 산출함으로써 SVM의 일반화 능력을 향상시키는 장점을 가진다. OPT-UMSVM의 성과 개선 효과를 검증하기 위하여 불균형 비율이 상이한 5개의 표본군을 구성하여 10-fold 교차타당성 검증을 수행한 결과는 다음과 같다. 첫째, 범주 불균형이 미미한 표본에서 UMSVM은 EMSVM의 성과 개선 효과가 미약한 반면, 범주 불균형이 심화된 표본에서 UMSVM은 EMSVM의 성과개선에 크게 공헌하고 있다. 둘째, OPT-UMSVM은 EMSVM 및 기존의 UMSVM과 비교하여 범주 균형 및 범주 불균형 표본 모두에서 보다 우수한 성과를 가지고 있으며, 특히 범주 불균형이 심화된 표본에서 유의적인 성과 차이를 보였다.

• IMMUNE NETWORK
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• 제22권3호
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• pp.22.1-22.25
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• 2022

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndromecoronavirus-2 (SARS-CoV-2), has spread over the world causing a pandemic which is still ongoing since its emergence in late 2019. A great amount of effort has been devoted to understanding the pathogenesis of COVID-19 with the hope of developing better therapeutic strategies. Transcriptome analysis using technologies such as RNA sequencing became a commonly used approach in study of host immune responses to SARS-CoV-2. Although substantial amount of information can be gathered from transcriptome analysis, different analysis tools used in these studies may lead to conclusions that differ dramatically from each other. Here, we re-analyzed four RNA-sequencing datasets of COVID-19 samples including human bronchoalveolar lavage fluid, nasopharyngeal swabs, lung biopsy and hACE2 transgenic mice using the same standardized method. The results showed that common features of COVID-19 include upregulation of chemokines including CCL2, CXCL1, and CXCL10, inflammatory cytokine IL-1β and alarmin S100A8/S100A9, which are associated with dysregulated innate immunity marked by abundant neutrophil and mast cell accumulation. Downregulation of chemokine receptor genes that are associated with impaired adaptive immunity such as lymphopenia is another common feather of COVID-19 observed. In addition, a few interferon-stimulated genes but no type I IFN genes were identified to be enriched in COVID-19 samples compared to their respective control in these datasets. These features are in line with results from single-cell RNA sequencing studies in the field. Therefore, our re-analysis of the RNA-seq datasets revealed common features of dysregulated immune responses to SARS-CoV-2 and shed light to the pathogenesis of COVID-19.

• Clinical and Experimental Pediatrics
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• 제64권9호
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• pp.468-470
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• 2021

• Ocean Science Journal
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• 제43권4호
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• pp.165-173
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• 2008

In this article, we describe a simple yet effective method for insertion of observational datasets in a mesoscale atmospheric model used in one-dimensional configuration through Nudging. To demonstrate the effectiveness of this technique, vertical profiles of meteorological parameters obtained from GLASS Sonde launches from a tiny island of Kaashidhoo in the Republic of Maldives are injected in a mesoscale atmospheric model - Advanced Regional Prediction System (ARPS), and model simulated parameters are compared with the available observational datasets. Analysis of one-time nudging in the model simulations over Kaashidhoo show that incorporation of this technique reasonably improves the model simulations within a time domain of +6 to +12 Hrs, while its impact on +18 Hrs simulations and beyond becomes literally null.

• ETRI Journal
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• 제38권4호
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• pp.714-723
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• 2016

Considerable attention has been given to processing graph data in recent years. An efficient method for computing the node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large-scale, mutable datasets and user queries, top-k query processing has gained significant interest. This paper presents a novel method to find top-k answers in a node proximity search based on the well-known measure, Personalized PageRank (PPR). First, we introduce a distribution state transition graph (DSTG) to depict iterative steps for solving the PPR equation. Second, we propose a weight distribution model of a DSTG to capture the states of intermediate PPR scores and their distribution. Using a DSTG, we can selectively follow and compare multiple random paths with different lengths to find the most promising nodes. Moreover, we prove that the results of our method are equivalent to the PPR results. Comparative performance studies using two real datasets clearly show that our method is practical and accurate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2619-2643
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• 2018

In this paper, a new clustering algorithm based on the connected region generation (CRG-clustering) is proposed. It is an effective and robust approach to clustering on the basis of the connectivity of the points and their neighbors. In the new algorithm, a connected region generating (CRG) algorithm is developed to obtain the connected regions and an isolated point set. Each connected region corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. Then, a region expansion strategy and a consensus criterion are used to deal with the points in the isolated point set. Experimental results on the synthetic datasets and the real world datasets show that the proposed algorithm has high performance and is insensitive to noise.