• 한국수학교육학회지시리즈D:수학교육연구
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• 제26권3호
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• pp.147-166
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• 2023

Mathematics is a science of pattern. Mathematics is a subject of inquiring which aims at discovering the models hidden behind the world. Pattern is abstraction and generalization of the model. Mathematical pattern is a higher level of mathematical model. Mathematics patterns are often hidden in pattern similarity. Creation of mathematics lies largely in discovering the pattern similarity among the various components of mathematics. Inquiring is the core and soul of mathematics teaching. It is very important for students to study mathematics like mathematicians' exploring and discovering mathematics based on pattern similarity. The author describes an example about how to guide students to carry out mathematics inquiring based on pattern similarity in classroom.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Design & Manufacturing
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• 제17권3호
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• pp.34-40
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• 2023

In this study, real-time collection of mold vibration signals during injection molding processes was achieved through IoT devices installed on the mold surface. To analyze changes in the collected vibration signals, injection molding was performed under six different process conditions. Analysis of the mold vibration signals according to process conditions revealed distinct trends and patterns. Based on this result, cosine similarity was applied to compare pattern changes in the mold vibration signals. The similarity in time and acceleration vector space between the collected data was analyzed. The results showed that under identical conditions for all six process settings, the cosine similarity remained around 0.92±0.07. However, when different process conditions were applied, the cosine similarity decreased to the range of 0.47±0.07. Based on these results, a cosine similarity threshold of 0.60~0.70 was established. When applied to the analysis of mold vibration signals, it was possible to determine whether the molding process was stable or whether variations had occurred due to changes in process conditions. This establishes the potential use of cosine similarity based on mold vibration signals in future applications for real-time monitoring of molding process changes and anomaly detection.

• ETRI Journal
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• 제38권3호
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• pp.540-550
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• 2016

Spectral clustering is a powerful tool for exploratory data analysis. Many existing spectral clustering algorithms typically measure the similarity by using a Gaussian kernel function or an undirected k-nearest neighbor (kNN) graph, which cannot reveal the real clusters when the data are not well separated. In this paper, to improve the spectral clustering, we consider a robust similarity measure based on the shared nearest neighbors in a directed kNN graph. We propose two novel algorithms for spectral clustering: one based on the number of shared nearest neighbors, and one based on their closeness. The proposed algorithms are able to explore the underlying similarity relationships between data points, and are robust to datasets that are not well separated. Moreover, the proposed algorithms have only one parameter, k. We evaluated the proposed algorithms using synthetic and real-world datasets. The experimental results demonstrate that the proposed algorithms not only achieve a good level of performance, they also outperform the traditional spectral clustering algorithms.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1449-1461
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• 2019

Face sketch synthesis plays an important role in public security and digital entertainment. In this paper, we present a novel face sketch synthesis method via local similarity and nonlocal similarity regularization terms. The local similarity can overcome the technological bottlenecks of the patch representation scheme in traditional learning-based methods. It improves the quality of synthesized sketches by penalizing the dissimilar training patches (thus have very small weights or are discarded). In addition, taking the redundancy of image patches into account, a global nonlocal similarity regularization is employed to restrain the generation of the noise and maintain primitive facial features during the synthesized process. More robust synthesized results can be obtained. Extensive experiments on the public databases validate the generality, effectiveness, and robustness of the proposed algorithm.

• 복식
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• 제64권7호
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• pp.97-113
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• 2014

The study aims to determine the significance and characteristics of self-similarity inherent in natural objects or phenomena, the existence of self-similarity in design created by fashion designers, and the traits and internal significance implied in self-similarity and their effects on fashion. The subject of the study is Viktor & Rolf, and the scope of the study is the collections created from 2001 to 2014, which include designs implemented in their early years and those unveiled in the media. Self-similarity means attributes of a fractal structure appearing without change in the original form, even after modification of scale or direction in terms of shape or phenomena. As self-similarity is applied to the arts and design sectors, it leads people to pay attention to fundamental characteristics and intrinsic forms as a factor of expressing a unique creative world. Analysis of Viktor & Rolf collections generated ribbons, overlapping/juxtaposition, side decorations and exaggerated design elements as basic units of self-similarity. These factors had self-similarity rates as high as 84%. Self-similarity was established as design elements formed in the incipient stage were repeated in a certain form, and continued for a long period of time. It served as an element that recognizes design and a fashion designer at the same time. Characteristics of self-similarity appearing in Viktor & Rolf collections can be summarized as homeostasis based on an equivalent relationship, balance based on self-organization, reducibility into essential elements, and uniqueness based on odd shapes. These characteristics influenced the pursuit of consistent brand image, the maintenance of a fashion designer's creative world, the formation of styles and the expression of a fashion designer's identity.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1324-1329
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• 2004

In this article we have tested stability of classifier based on fuzzy similarity in generalized Lukasiewicz structure. Two different tests for stability was made:In on test stability was checked respect to weight parameters and other test was carried out for idealvectors. Tests have made with three different classification problems.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제20권12호
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• pp.711-716
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• 2014

최근 논문 표절 논란과 지능형 텍스트 검색서비스에 대한 관심이 증가하면서 문장 유사도 측정의 필요성이 증가하고 있다. n-gram, 편집거리, LSA 등 기존의 다양한 방향으로 선행 연구가 있었지만 각 기법마다 장단점이 존재한다. 본 논문에서는 집합 기반 POI 검색 기법을 이용한 새로운 방향의 문장 유사도 측정 기법을 제안한다. 집합 기반 POI 검색 기법은 하드매칭에 비해 단어의 도치, 누락, 삽입, 변경에 현저한 성능 향상을 보인다. 이 기법을 이용하면 보다 정확하고 빠른 문장 유사도 측정이 가능하다. 제안하는 기법은 기존 집합 기반 POI 검색 기법의 데이터 로딩 알고리즘과 텍스트 검색 알고리즘을 변형하고 어절 연산 알고리즘을 추가하여 두 문장의 유사도를 백분율로 표현한다. 실험을 통해 본 논문에서 제시하는 기법이 정확도와 속도에서 n-gram과 기존 집합 기반 POI 검색 기법에 비해 우수함을 확인하였다.

• 인터넷정보학회논문지
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• 제12권5호
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• pp.113-125
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• 2011

정보의 출처와 형식이 다양해지고 정보의 양 또한 많아짐에 따라 소셜 웹에서의 맞춤형 지식 생성은 더욱 어려워지고 있다. RSS(Really Simple Syndication)가 정보 수집 방법의 개선에 일조했으나, 웹에 산재된 정보를 찾아 필요한 정보들만으로 구성된 맞춤형 지식을 생성하는 것은 여전히 사용자들의 몫으로 남아 있다. 본 논문에서는 맞춤형 지식 생성의 용이성을 제고하기 위해 상황 기반 유사도를 이용한 맞춤형 지식생성 프레임워크를 제안하였다. 본 프레임워크는 기본적으로 사례 기반추론의 절차를 따르지만, 기존 사례 기반의 유사도 계산 방식이 문법적 추론에 기반했던 것과 달리, 온톨로지를 활용한 의미적 유사도를 이용한 사례 기반 추론을 활용한다. 또한 사용자 요구를 만족하는 유사사례의 보정을 위해 온톨로지를 활용한 정보 집적도 기반의 유사도 방법론을 제안하였다. 본 프레임워크에서는 첫째 비구조적인 웹 정보를 사례 형태의 구조적 정보로 변환하고, 둘째 사용자의 요구에 적합한 의미론적 유사사례를 찾은 후 셋째, 선택된 유사사례의 정보 집적도를 고려한 보정을 통해 맞춤형 지식을 생성하는 과정을 거친다. 본 논문에서는 유사도 계산에 일반적으로 활용되는 여러 방법론들과 비교를 통하여 제안한 온톨로지 기반 의미적 유사도 계산 방법론의 타당성을 입증하였다.

• 한국CDE학회논문집
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• 제9권1호
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• pp.51-61
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes.. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the second one of the two companion papers, describes the similarity assessment method using NFD.