• Journal of the Korean Society of Visualization
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• v.6 no.2
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• pp.28-32
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• 2008

Contrary to the flow rate in fully filled pipe flows, the flow rate in partially filled pipe flows is significantly influenced by the variation of water level, channel slop, and so on. The major difference in these two flows results from the existence of a free surface. To make it clear, in the present study, a similarity of the velocity profile in a partially filled circular pipe has been investigated according to the water level. A particle image velocimetry (PIV) technique was applied to measure the three-dimensional velocity profiles. As a result, there is found a similarity of the velocity profile near the central region. However, near the side wall, the similarity is broken due to the interaction between the wall and the free surface.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.76-81
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• 1999

The degree of relative similarity between 2D patterns is obtained using Open-Ball Scheme. Open-Ball Scheme employs a method of transforming the geometrical information on 3D objects or 2D patterns into the features to measure the relative similarity for object(patten) recognition, with invariance on scale, rotation, and translation. The feature of an object is used to obtain the relative similarity and mapped into [0, 1] the interval of real line. For decades, Moment-Invariant Method has been used as one of the excellent methods for pattern classification and object recognition. Open-Ball Scheme uses the geometrical structure of patterns while Moment Invariant Method uses the statistical characteristics. Open-Ball Scheme is compared to Moment Invariant Method with respect to the way that it interprets two-dimensional patten classification, especially the paradigms are compared by the degree of closeness to human's intuitive understanding. Finally the effectiveness of the proposed Open-Ball Scheme is illustrated through simulations.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.60-83
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• 2014

This paper aims to propose a research framework of analyzing voting activities of a national assembly on the basis of member-level voting similarity and provides a case study in the $18^{th}$ national assembly in South Korea. First, we propose a bill contentiousness measure that gives a higher score to bills for which ayes and noes are more diversified in both conservative and progressive parties. Based on the bill contentiousness measure, the top 5%, 10%, and 20% bills were identified and used for further analyses. Moreover, we propose a member-level voting similarity measure that compensates for the lower frequency of noes, and evaluate the pair-wise voting similarities for all lawmakers. Then, voting similarity differences to the affiliated/non-affiliated parties were analyzed for the members in the two major parties according to some internal/external key factors. Finally, similar voting groups were identified and their affiliations were investigated based on the multi-dimensional scaling (MDS) and network analysis techniques. A case study on the $18^{th}$ national assembly of South Korea showed that the cohesion of the members in the 'Hanara' party becomes higher than that of the 'Minju' party as the bill contentiousness increases, whereas the number of elected, local constituency versus proportional representation, and the competition intensity in a local constituency were found to be partially influential to the voting activities of lawmakers. In addition, MDS and network analysis showed that there is a distinctive difference between two parties when all bills are analyzed, whereas the diversity of parties increases in the same group as the bill contentiousness increases.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.119-124
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• 2002

Clustering of data is of a great interest in many data mining applications. In the field of document clustering, a document is represented as a data in a high dimensional space. Therefore, the document clustering can be accomplished with a general data clustering techniques. In this paper, we introduce a document clustering system based on similarity among documents. The developed system consists of three functions: 1) gatherings documents utilizing a search agent; 2) determining similarity coefficients between any two documents from term frequencies; 3) clustering documents with similarity coefficients. Especially, the document clustering is accomplished by a hybrid algorithm utilizing genetic and K-Means methods.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1270-1281
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• 1999

피라미드 기법 1 은 d-차원의 공간을 2d개의 피라미드들로 분할하는 특별한 공간 분할 방식을 이용하여 고차원 데이타를 효율적으로 색인할 수 있는 새로운 색인 방법으로 제안되었다. 피라미드 기법은 고차원 사각형 형태의 영역 질의에는 효율적이나, 유사성 검색에 많이 사용되는 고차원 구형태의 영역 질의에는 비효율적인 면이 존재한다. 본 논문에서는 고차원 데이타를 많이 사용하는 유사성 검색에 효율적인 새로운 색인 기법으로 구형 피라미드 기법을 제안한다. 구형 피라미드 기법은 먼저 d-차원의 공간을 2d개의 구형 피라미드로 분할하고, 각 단일 구형 피라미드를 다시 구형태의 조각으로 분할하는 특별한 공간 분할 방법에 기반하고 있다. 이러한 공간 분할 방식은 피라미드 기법과 마찬가지로 d-차원 공간을 1-차원 공간으로 변환할 수 있다. 따라서, 변환된 1-차원 데이타를 다루기 위하여 B+-트리를 사용할 수 있다. 본 논문에서는 이렇게 분할된 공간에서 고차원 구형태의 영역 질의를 효율적으로 처리할 수 있는 알고리즘을 제안한다. 마지막으로, 인위적 데이타와 실제 데이타를 사용한 다양한 실험을 통하여 구형 피라미드 기법이 구형태의 영역 질의를 처리하는데 있어서 기존의 피라미드 기법보다 효율적임을 보인다.Abstract The Pyramid-Technique 1 was proposed as a new indexing method for high- dimensional data spaces using a special partitioning strategy that divides d-dimensional space into 2d pyramids. It is efficient for hypercube range query, but is not efficient for hypersphere range query which is frequently used in similarity search. In this paper, we propose the Spherical Pyramid-Technique, an efficient indexing method for similarity search in high-dimensional space. The Spherical Pyramid-Technique is based on a special partitioning strategy, which is to divide the d-dimensional data space first into 2d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into 1-dimensional space as the Pyramid-Technique does. Thus, we are able to use a B+-tree to manage the transformed 1-dimensional data. We also propose the algorithm of processing hypersphere range query on the space partitioned by this partitioning strategy. Finally, we show that the Spherical Pyramid-Technique clearly outperforms the Pyramid-Technique in processing hypersphere range queries through various experiments using synthetic and real data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.5 no.2
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• pp.107-120
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• 1993

This paper presents a new modal solution of linear three-dimensional hydrodynamic equations using similarity transform technique. The governing equations are first separated into external and internal mode equations. The solution of the internal mode equation then proceeds as in previous modal models using the Galerkin method but with expansion of arbitrary basis functions. Application of similarity transform to resulting full matrix equations gives rise to a set of uncoupled partial differential equations of which the unknowns are coefficients of mode vector. Using the transform technique a computationally efficient time integration is possible. In present from the model use Chebyshev polynomials for Galerkin solution of internal mode equations. To examine model performance the model is applied to a homogeneous, rectangular basin of constant depth under steady, uniform wind field.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.2
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• pp.174-185
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• 1994

This paper presents a modal solution of linear three-dimensional hydrodynamic equations using similarity transform technique. The solution over the vertical space domain is obtained using the Galerkin method with linear shape funtions (Galerkin-FEM model). Application of similarity transform to resulting tri-diagonal matrix equations gives rise 掠 a set of uncoupled partial differential equations of which the unknowns are coefficients of mode shape vectors. The proposed method.

• Journal of Fashion Business
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• v.22 no.1
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• pp.135-147
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• 2018

Since the 20th century, there has been a growing interest in the new concept of fractals, a combination of mathematics and art, and the attempt to study the creative spatial aspects of the concept is being made. The purpose of this research is to examine artistic characteristics of fractal dimension and then analyze the types of fractal dimensions expressed in the fashion. Previous literature on fractals and dimension, and visual data on art and fashion collected over the Internet were used for analysis. Fractal dimension refers to the spatial concept of structural dimension of geometrical self-similarity. An analysis of the types of fractals seen in fashion revealed spatial expansion, the repetition in continual figures, superposition accordant to different sizes, and shades of different shapes. The aesthetic characteristics of fractal dimension appearing in fashions were examined based on analyses of fractal dimension types; the inherent characteristics of self-similarity, superimposition, and atypicality were found. Results obtained from this study are expected to be used as basic materials for the application of the design of fractal dimension into various perspectives of fashion.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.2
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• pp.174-184
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• 1998

In order to accumulate fundamental. data for control of fishes’ behavior at the real fishing ground, the fitness of the numerical model for describing the behavior of fishes was examined by the marine fish. Mullet, Mugil cephalus were used as experimental fishes. The numerical model of fishes’ behavior presented in our earlier paper was modified on the vertical movement of fish school. For the comparision of parameters of the modified numerical model between mullet and rainbow trout, the estimated values of parameters were identified with dimension. The fitness of the modified numerical model was examined by the comparision between experiment and simulation on the several indexes represented by fishes’ swimming characteristics. The obtained result are summarized a follows : 1. The non-dimensional parameter a’ of propulsive force and kb’ of interactive force by the experiment without model net showed a similarity, but the non-dimensional parameter k sub(c’) of schooling force for rainbow trout was lager than one for mullet and the non-dimensional parameter k sub(w’) of repulsive force for mullet was lager than one for rainbow trout. 2. The non-dimensional parameter a’ and k sub(b’) for rainbow trout by the experiment with model net were a little lager than ones for mullet, but non-dimensional parameter k sub(c’) and k sub(w’) for mullet were lager than ones for rainbow trout. 3. The non-dimensional parameter k sub(c’) and k sub(b’) showed the largest and the smallest value among the non-dimensional parameters for rainbow trout and mullet, respectively. 4. The fitness of the modified numerical model was confirmed by means of the compulsion between experiment and simulation on the swimming trajectory of fishes, the mean distance of individual from wall, the mean swimming speed, the mean swimming depth and the mean distance between the nearest individuals. Especially, the similarity of mean swimming depth was improved by using the modified numerical model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.