• Journal of applied mathematics & informatics
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• 제26권5_6호
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• pp.1169-1183
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• 2008

In this paper, a method to find the weighted possibilistic variance and moments about the mean value of fuzzy numbers via applying a difuzzification using minimizer of the weighted distance between two fuzzy numbers is introduced. In this way, we obtain the nearest weighted point with respect to a fuzzy number, this main result is a new and interesting alternative justification to define of weighted mean of a fuzzy number. Considering this point and the weighted distance quantity, we introduce the weighted possibilistic mean (WPM) value and the weighted possibilistic variance(WPV) of fuzzy numbers. This paper shows that WPM is the nearest weighted point to fuzzy number and the WPV of fuzzy number is preserved more properties of variance in probability theory so that it can simply introduce the possibilistic moments about the mean of fuzzy numbers without problem. The moments of fuzzy numbers play an important role to estimate of parameters, skewness, kurtosis in many of fuzzy times series models.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1055-1067
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• 2019

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initialize-expand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.

• 전자공학회논문지B
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• 제32B권10호
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• pp.1294-1304
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• 1995

In this paper, a method has been proposed for the fully automatic detection of left ventricular endocardial boundary in B-mode short axis echocardiography without manual intervention by human operator. The proposed method makes use of the weighted model that approximates to endocardium and incomplete edge information for echocardiography. Therefore, this method is more effective than boundary detection by only edge information. The implementation of this method is as follows. First, the proposed algorithms are used in order to detect the approximate boundary, then a weighted model with the approximate boundary is constructed. Finally, the cavity center of the left ventricle performing the Hough transform with the weighted model and edge image can be found automatically, and then the endocardial boundary using detected center, original image, weighted model, and edge image can be detected. validations of this method with experimental results on echo image of dog's heart and clinical echocardiography is verified.

• 한국건설관리학회논문집
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• 제15권5호
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• pp.49-60
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• 2014

전기공사비지수는 실적공사단가의 합리적인 시간차 보정과 물가변동 등에 따른 계약단가조정을 위한 자료로 활용된다. 전기공사비지수는 전기공사업계 산업구조의 변화, 신기술공법의 변화, 새로운 상품등장 및 퇴장 등을 반영하여야 한다. 그러나 현행 고정방식 전기공사비지수는 이러한 변화를 즉각적으로 반영에는 가중치 및 기준연도 가격지수의 장기간 고정으로 인한 이론적으로 한계가 있다. 그리고 전기공사업 특성상 노임비중이 높아 특정 월을 중심으로 주기적으로 급격한 변동이 발생되는 문제점이 있었다. 이러한 문제점과 이론적 한계를 보완하는 대안으로 연쇄방식 전기공사비지수를 개발하여, 이를 건설공사비지수, 고정방식 전기공사비지수와 비교분석을 하였다. 그 결과에 의하면, 연쇄방식 전기공사비지수는 이론적 특성상 고정방식에 비하여 현실 상황을 잘 반영하며, 특히 전기공사지수의 활용취지 등에 부합하는 것을 알 수 있다. 따라서 연쇄방식 전기공사비지수는 고정방식에 비하여 상대적으로 우월하고는 말할 수 없지만, 최소한 고정방식의 문제점을 해결할 수 있는 하나의 대안으로 생각하여 볼 수 있다.

• Structural Engineering and Mechanics
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• 제91권2호
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• pp.227-238
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• 2024

Sparse regularization methods have proven effective in addressing the ill-posed equations encountered in moving force identification (MFI). However, the complexity of vehicle loads is often ignored in existing studies aiming at enhancing MFI accuracy. To tackle this issue, a double 𝑙₁ regularization method is proposed for MFI based on a response spectrum-based weighted dictionary in this study. Firstly, the relationship between vehicle-induced responses and moving vehicle loads (MVL) is established. The structural responses are then expanded in the frequency domain to obtain the prior knowledge related to MVL and to further construct a response spectrum-based weighted dictionary for MFI with a higher accuracy. Secondly, with the utilization of this weighted dictionary, a double 𝑙₁ regularization framework is presented for identifying the static and dynamic components of MVL by the alternating direction method of multipliers (ADMM) method successively. To assess the performance of the proposed method, two different types of MVL, such as composed of trigonometric functions and driven from a 1/4 bridge-vehicle model, are adopted to conduct numerical simulations. Furthermore, a series of MFI experimental verifications are carried out in laboratory. The results shows that the proposed method's higher accuracy and strong robustness to noises compared with other traditional regularization methods.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.651-659
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• 2001

Preference map is a widely used graphical method for the preference data set which is frequently encountered in the field of marketing research. This provides joint configuration usually in two dimensional space between "products" and their "attributes". Whereas the classical preference map adopts the ordinary least squares method in deriving map, the present article suggests the weighted least squares approach providing the better graphical display and interpretation compared to the classical one. Internet search engine data in Korea are analysed for illustration.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.821-822
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• 2008

This paper presents a new demosaicking method based on weighted sum in the wavelet domain. In our method, the missing wavelet coefficients in lowest frequency subband are obtained by weighted sum. Since detail coefficients have large values at the edge region, these values are used as weighting factors. Detail coefficients are replaced by the coefficients in the corresponding subbands. Experimental results show that the proposed method generates good performance.

• 전기학회논문지
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• 제57권4호
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• pp.715-717
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• 2008

The connectivity weighted Hough transform is a useful method for detecting well-connected short lines without generating false lines yet requires extensive computation. This letter describes a method that reduces the computation of the connectivity weighted Hough transform by removing unnecessary weight calculations using the gradient angles of feature points. In simulations with synthetic images and experiments with liquid crystal display panel images, the proposed method showed significantly improved speed without compromising detectability.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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• pp.369-376
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• 2002

The stochastic analysis of semi-infinite domain is presented using the weighted integral method, which is improved to include the higher order terms in expanding the displacement vector. To improve the weighted integral method, the Lagrangian remainder is taken into account in the expansion of the status variable with respect to the mean value of the random variables. In the resulting formulae only the 'proportionality coefficients' are introduced in the resulting equation, therefore no additional computation time and memory requirement is needed. The equations are applied in analyzing the semi-infinite domain. The results obtained by the improved weighted integral method are reasonable and are in good agreement with those of the Monte Carlo simulation. To model the semi-infinite domain, the Bettess's infinite element is adopted, where the theoretical decomposition of the strain-displacement matrix to calculate the deviatoric stiffness of the semi-infinite domains is introduced. The calculated value of mean and the covariance of the displacement are revealed to be larger than those given by the finite domain assumptions which is thought to be rational and should be considered in the design of structures on semi-infinite domains.