• 제목/요약/키워드: Analysis of means

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Permanent Means of Access 강도 평가 방법에 대한 연구 (A Procedure for a Strength Assessment of Permanent Means of Access Structure)

  • 장범선;정성욱;고대은;전민성;김지영
    • 대한조선학회논문집
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    • 제46권1호
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    • pp.31-42
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    • 2009
  • Common structural rule (CSR) doesn' t provide any other specific regulations for permanent means of access (PMA) platform structure in a cargo oil tank. The PMA platform is recommended to comply with scantling requirement of local support member. However, it leads to too conservative scantlings compared with actual loads imposed on the platform. This paper proposes a strength assessment procedure for the PMA structure based on a nonlinear ultimate strength. The ultimate strength is evaluated in a sufficiently conservative way. The first linear buckling mode is used as an initial imperfection shape and its magnitude is determined using the definitions of DNV PULS. Since the same imperfection mode as the failure mode of the ultimate limit state is assumed, it can accelerate the failure. Au ultimate strength capacity curve obtained from a series of nonlinear FE analysis is compared with actual stresses calculated by CSR cargo hold analysis.

그리드 기반 표본의 무게중심을 이용한 케이-평균군집화 (K-means clustering using a center of gravity for grid-based sample)

  • 이선명;박희창
    • Journal of the Korean Data and Information Science Society
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    • 제21권1호
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    • pp.121-128
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    • 2010
  • 케이-평균 군집분석은 데이터들을 k개의 군집으로 임의로 분할을 하여 군집의 평균을 대푯값으로 분할해 나가는 방법으로 데이터들을 유사성을 바탕으로 재배치를 하는 방법이다. 이러한 케이-평균 군집분석은 시장조사, 패턴분석 및 인식, 그리고 이미지 처리 분야 등에서 폭넓게 응용되고 있다. 그러나 대용량의 데이터베이스를 분석대상으로 하므로 그 만큼 데이터 처리 시간이 많이 소요되는 것이 문제 중의 하나이다. 특히 웹이 보편화된 현재 사용자들의 다양한 패턴을 분석하기 위한 데이터 마이닝 방법이 사용되어지고 있는데 처리 속도 문제는 더욱 중요하게 생각하고 있다. 이러한 속도 문제를 해결하기 위해 본 논문에서는 분할 군집법에서 가장 일반적으로 사용되고 있는 케이-평균 알고리즘에 대해 그리드를 기반으로 한 무게중심 알고리즘을 제안하고자 한다.

영상 잡음 제거를 위한 주성분 분석 기반 비 지역적 평균 알고리즘의 효율적인 공분산 행렬 계산 방법 (An Efficient Method to Compute a Covariance Matrix of the Non-local Means Algorithm for Image Denoising with the Principal Component Analysis)

  • 김정환;정제창
    • 방송공학회논문지
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    • 제21권1호
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    • pp.60-65
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    • 2016
  • 본 논문에서는 영상에 존재하는 잡음 (noise) 들을 제거하는 방법 중 하나인 비 지역적 평균 (non-local means, NLM) 알고리즘을 먼저 소개하고 비 지역적 평균 알고리즘의 개선된 방법 중 하나인 주성분 분석 (principal component analysis, PCA) 기반의 알고리즘에 대해서도 소개한다. 주성분 분석을 활용하기 위해서는 선행적으로 공분산 행렬 (covariance matrix)을 구해야 하는데, 영상의 모든 픽셀들을 대상으로 하였을 때 이 공분산 행렬을 구하기 위해서는 큰 크기를 가지는 행렬 곱 연산이 필요하다. 만약 비 지역적 평균 알고리즘의 영상 패치 (neighborhood patch) 의 크기를 S × S = S2, 영상 전체의 픽셀 수를 Q라고 한다면 공분산 행렬을 구하기 위해서는 S2 × Q 크기의 행렬 곱 연산이 필요하게 된다. 이는 영상의 특성을 고려하면 비효율적인 연산이다. 따라서 본 논문에서는 공분산 행렬을 효율적으로 구하기 위해, 영상 패치들간의 일정 간격을 유지하면서 샘플링을 하는 방법을 제안하고자 한다. 최종적으로, 샘플링 후에는 S2 × floor (Width/l) × (Height/l) 크기를 가진 행렬의 곱 연산으로 공분산 행렬을 구할 수 있다.

데이터 클러스터링을 위한 혼합 시뮬레이티드 어닐링 (Hybrid Simulated Annealing for Data Clustering)

  • 김성수;백준영;강범수
    • 산업경영시스템학회지
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    • 제40권2호
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    • pp.92-98
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    • 2017
  • Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

Fault Tree Analysis을 활용한 집진기(Bag Filter) 고장의 체계적 분석 (A System Approach to A Bag Filter Failure using Fault Tree Analysis)

  • 이근희;이동형
    • 산업경영시스템학회지
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    • 제12권20호
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    • pp.1-24
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    • 1989
  • This paper takes aim at the reliability evaluation by application of Fault Tree Analysis and its computerization. FTA is one of the methods for evaluation of system reliability and safety analysis. The important characteristic of this paper is that computer program is written in the package program(dBaseIII+) by 16Bit/AT personal computer. The program consists of three program segments. (1) The minimal cut sets of the system fault tree are obtained by means of "Fault tree reduction algorithm" (2) The minimal path sets are obtained by inversion of the minimal cut sets determined from fault tree (3) The importance of the basic events which are presented in the minimal cut sets is obtained by means of structural importance analysis. In this paper, a Fault Tree Analysis is applied to a BAG FILTER which is a kind of dust collector.collector.

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도시하수도망에 대한 유출모형의 남용과 유출해석 (Runoff Analysis and Application of Runoff Model of Urban Storm Drainage Network)

  • 박성천;이관수
    • 한국환경보건학회지
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    • 제22권4호
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    • pp.33-42
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    • 1996
  • This research is to show the application of runoff model and runoff analysis of urban storm drainage network. the runoff models that were used for this research were RRL, ILLUDAS, and SWMM applicative object basin were Geucknak-chun and Sangmu drainage basin located in Seo-Gu, Kwangju. The runoff analysis employed the design storm that distributed the rainfall intensity according to the return period after the huff's method. The result from the comparative analysis of the three runoff models was as follows The difference of peak runoff by return period was 20-30% at Sangmu drainage area of $3.17 Km^2$, while less than 10% at Geucknak-chun drainage area of $12.7 Km^2$. The peak runoff were similar to all models. At the runoff hydrograph the times between rising and descending points were in the sequence of RRL, ILLUDAS and SWMM, but the peak times were similar to all models. The conveyance coefficient to examine the conveyance of the existing drainage network was 0.94-1.37, which means insecure, in Geucknak-chun drainage basin and 0.69-1.16, which means secure, in sangmu drainage basin.

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머신러닝을 활용한 행위 및 스크립트 유사도 기반 크립토재킹 탐지 프레임워크 (Behavior and Script Similarity-Based Cryptojacking Detection Framework Using Machine Learning)

  • 임은지;이은영;이일구
    • 정보보호학회논문지
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    • 제31권6호
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    • pp.1105-1114
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    • 2021
  • 최근 급상승한 암호 화폐의 인기로 인해 암호 화폐 채굴 악성코드인 크립토재킹 위협이 증가하고 있다. 특히 웹 기반 크립토재킹은 피해자가 웹 사이트에 접속만 하여도 피해자의 PC 자원을 사용해 암호 화폐를 채굴할 수 있으며 간단하게 채굴 스크립트만 추가하면 되기 때문에 공격이 쉽고 성능 열화와 고장의 원인이 된다. 크립토재킹은 피해자가 피해 상황을 인지하기 어렵기 때문에 크립토재킹을 효율적으로 탐지하고 차단할 수 있는 연구가 필요하다. 본 연구에서는 크립토재킹의 대표적인 감염 증상과 스크립트를 지표로 활용하여 효과적으로 크립토재킹을 탐지하는 프레임워크를 제안하고 평가한다. 제안한 크립토재킹 탐지 프레임워크에서 행위 기반 동적 분석 기법으로 컴퓨터 성능 지표를 학습한 K-Nearest Neighbors(KNN) 모델을 활용했고, 스크립트 유사도 기반 정적 분석 기법은 악성 스크립트 단어 빈도수를 학습한 K-means 모델을 크립토재킹 탐지에 활용했다. 실험 결과에 따르면 KNN 모델은 99.6%의 정확도를 보였고, K-means 모델은 정상 군집의 실루엣 계수가 0.61인 것을 확인하였다.

픽토리얼 타이포그래피가 사용된 인쇄 광고의 커뮤니케이션 효과 연구 (Communication Effects of Print Ad Having Pictorial Typography)

  • 이광숙;곽보선
    • 한국인쇄학회지
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    • 제30권2호
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    • pp.13-22
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    • 2012
  • This research attempts to analyze communication effects of print ad having pictorial typography. 150 Questionnaires were distributed to respondents staying Daejeun City and 148 copies were retreated for five days from April 22nd to 26th, 2012. Frequency analysis, factor analysis, Cronbach's alpha for reliability analysis were utilized for data analysis with SPSS 12.0. For testing hypothesis, regression analysis was used. As result of testing hypothesis, 'informative, beneficial, creative, reliable' were partially significant to attitude towards print ad having pictorial typography. That means 'creative' and 'reliable' were insignificant, while 'informative' and 'beneficial' are significant. Variable of the most influencing on attitude towards advertising is 'informative.' 'Informative, beneficial, creative, and reliable' were partially significant to brand attitude, too. That means 'beneficial' and 'creative' were insignificant, while 'informative' and 'reliable' were significant. Variable of the most influencing on brand attitude was 'reliable.' Therefore, to enhance communication effect of print ad having pictorial typography, 'informative' and 'reliable' are most significant variables.

Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm

  • Kim, Kyeong-Seop
    • 한국컴퓨터정보학회논문지
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    • 제22권5호
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    • pp.65-72
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    • 2017
  • Premature Ventricular Contraction(PVC) arrhythmia is most common abnormal-heart rhythm that may increase mortal risk of a cardiac patient. Thus, it is very important issue to identify the specular portraits of PVC pattern especially from the patient. In this paper, we propose a new method to extract the characteristics of PVC pattern by applying K-means machine learning algorithm on Heart Rate Variability depicted in Poinecare plot. For the quantitative analysis to distinguish the trend of cluster patterns between normal sinus rhythm and PVC beat, the Euclidean distance measure was sought between the clusters. Experimental simulations on MIT-BIH arrhythmia database draw the fact that the distance measure on the cluster is valid for differentiating the pattern-traits of PVC beats. Therefore, we proposed a method that can offer the simple remedy to identify the attributes of PVC beats in terms of K-means clusters especially in the long-period Electrocardiogram(ECG).

COUNTING OF FLOWERS BASED ON K-MEANS CLUSTERING AND WATERSHED SEGMENTATION

  • PAN ZHAO;BYEONG-CHUN SHIN
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • 제27권2호
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    • pp.146-159
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    • 2023
  • This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.