• 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.

• 한국소프트웨어감정평가학회 논문지
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• 제15권1호
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• pp.25-35
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• 2019

우리나라에서 컴퓨터프로그램의 실질적 유사성 여부 판단은 정량적인 유사도를 산출하여 그 결과를 활용하는 방식이 일반적으로 이용된다. 실질적 유사성은 유사한 부분의 양과 질을 고려하여 판단되어야 하는데, 실무에서는 정량적인 유사도 계산 과정에서 가중치를 곱함으로써 유사한 부분의 질을 고려하는 모습을 보인다. 그런데 실질적 유사성 판단과 관련하여 유사한 부분의 양적, 질적인 고려는 동일한 지위에서 순차적으로 이루어져야 한다는 본질적 특징을 고려할 때, 현재와 같은 실무 방식은 적절하다고 할 수 없다. 이에 이와 같은 가중치 활용의 문제를 지적하고, 실질적 유사성 판단을 위한 유사 부분의 질적 평가는 정량적 유사도 판단에 후행하여 그와 동일한 지위에서 이루어져야 함을 제시 및 이를 위한 적절한 실무적 방안을 제언하였다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1995년도 춘계공동학술대회논문집; 전남대학교; 28-29 Apr. 1995
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• pp.122-129
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• 1995

This paper presents a similarity coefficient based approach to the problem of machine-part grouping for cellular manufacturing. The method uses relevant production data such as part type, production volume, routing sequence to make machine cells and part families for cell formation. A new similarity coefficient using weighted factors is introduced and an algorithm for formation of machine cells and part families is developed. A comparative study of two similarity coefficients - Gupta and seifoddini's method and proposed method - is conducted. A software program using TURBO C has been developed to verify the implementation.

• 대한산업공학회지
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• 제22권1호
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• pp.141-154
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• 1996

This paper presents a similarity coefficient based approach to the problem of machine-part grouping for cellular manufacturing. The method uses relevant production data such as part type, production volume, routing sequence to make machine cells and part families for cell formation. A new similarity coefficient using weighted factors is introduced and an algorithm for formation of machine cells and part families is developed. A comparative study of two similarity coefficient methods, Gupta and Seifoddini's method and the proposed method, is conducted.

• 한국멀티미디어학회논문지
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• 제21권7호
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• pp.772-778
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• 2018

We have previously proposed a preliminary cut-off indoor positioning scheme considering the reference point with the same signal similarity. This scheme estimates the position using the relative rank of the peak of received signal strength from the beacons around user. However, this scheme has a weak point with lower accuracy when there are more than one nearest reference points having the same signal similarity. In order to tackle this, we propose a weighted preliminary cut-off indoor positioning scheme. Firstly, if the above problem occurs, the similarity to the peak of signal strength is considered as well as the relative rank. Next, weights are assigned to the nearest reference points using the similarity to the peak of the received signal strength. Finally, the user's position is estimated by applying the weights. As a result, the weighted preliminary cut-off scheme improves the positioning accuracy by about 7.9% compared to the previous scheme.

• Journal of applied mathematics & informatics
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• 제40권5_6호
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• pp.897-914
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• 2022

Pythagorean fuzzy sets (PFSs) are capable of modelling information with more uncertainties in decision-making problems. The essential feature of PFSs is that they are described by three parameters: membership function, non-membership function and hesitant margin, with the total of the squares of each parameter equal to one. The purpose of this article is to suggest some new similarity measures and weighted similarity measures for PFSs. Numerical computations have been carried out to validate our proposed measures. Applications of these measures have been applied to some real-life decision-making problems of pattern detection and medicinal investigations. Moreover, a descriptive illustration is employed to compare the results of the proposed measures with the existing analogous similarity measures to show their effectiveness.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1407-1423
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• 2020

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1059-1068
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• 2022

Recently, research using image-based deep learning is being conducted to determine similar fonts or recommend fonts. In order to increase the accuracy in judging the similarity of Hangul fonts, a previous study was conducted to calculate the similarity according to the combination of stroke elements. In this study, we tried to solve this problem by designing an integrated model that reflects the weights for each stroke element. By comparing the results of the user's font similarity calculation conducted in the previous study and the weighted model, it was confirmed that there was no difference in the ranking of the influence of the stroke elements. However, as a result of comparison by letter sizes, it was confirmed that there was a difference in the ranking of the influence of stroke elements. Accordingly, we proposed a weighted model set separately for each font size.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1960-1969
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• 2016

In this paper, we propose an automatic hierarchical organ segmentation method on abdominal CT images. First, similar atlases are selected using bone-based similarity registration and similarity of liver, kidney, and pancreas area. Second, each abdominal organ is roughly segmented using image-based similarity registration and intensity-based locally weighted voting. Finally, the segmented abdominal organ is refined using mask-based affine registration and intensity-based locally weighted voting. Especially, gallbladder and pancreas are hierarchically refined using location information of neighbor organs such as liver, left kidney and spleen. Our method was tested on a dataset of 12 portal-venous phase CT data. The average DSC of total organs was $90.47{\pm}1.70%$. Our method can be used for patient-specific abdominal organ segmentation for rehearsal of laparoscopic surgery.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.184-200
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• 2020

Similarity searching is a basic issue in information processing because of the large size of formal contexts and their complicated derivation operators. Recently, some researchers have focused on knowledge reduction methods by using granular computing. In this process, suitable information granules are vital to characterizing the quantities of attributes and objects. To address this problem, a novel approach to obtain an entropy-weighted concept lattice with inclusion degree and similarity distance (ECLisd) has been proposed. The approach aims to compute the combined weights by merging the inclusion degree and entropy degree between two concepts. In addition, another method is utilized to measure the hierarchical distance by considering the different degrees of importance of each attribute. Finally, the rationality of the ECLisd is validated via a comparative analysis.