• Journal of Information Processing Systems
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• v.20 no.2
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• pp.185-199
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• 2024

In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms-K-means, fuzzy C-means, TSC, EIGENGAP, DBSCAN, and density peak-were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.

• The Journal of Fisheries Business Administration
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• v.14 no.2
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• pp.1-14
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• 1983

This paper considers a classification of the 88 islands located at Kyong-nam area in Korea, using by examples of 12 components of the islands. By means of principal component analysis 2 principle components were extracted, which explained a total of 73.7% of the variance. Using an eigen variable criterion (λ>1), no further principle components were discussed. Principal component 1 and 2 explained 63.4% and 10.3% of the total variance respectively, The representation of the unrelated factor scores along the first and second principal axes produced a new information with respect to the classification of the islands. Based upon the representation, 88 islands were classified into 6 groups i. e. A, B, C, D, E, and F according to similarity of the components among them in this paper. The "Group F" belongs to a miscellaneous assortment that does not fit into the logical category. category.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.27
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• pp.83-89
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• 1993

In order to model the Group Technology Problem three formulations are used, that is, generally the following formulations are used: (1) Matrix formulation, (2) Mathematical programming formulation, (3) Graph formulation. In the case of Matrix formulation, it is difficult to discribe the situation using the multi-job machine. But this paper proposed the model of Group Technology using the multi-job machin, taking the method of making practical application of principle of similarity coefficient.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.53-56
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• 2003

In this paper, we propose a new method which generates the trajectory of the mouth shape for the characters by the user inputs. It is based on the character at a basis syllable and can be suitable to the mouth shape generation. In this paper, we understand the principle of the Korean language creation and find the similarity for the form of the mouth shape and select it as a basic syllable. We also consider the articulation of this phoneme for it and create a new mouth shape trajectory and apply at face of an 3D avatar.

• Molecules and Cells
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• v.25 no.2
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• pp.163-171
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• 2008

The genus Cynodon comprises ten species. The objective of this study was to evaluate the genetic diversity of Korean bermudagrasses at the morphological, cytological and molecular levels. Morphological parameters, the nuclear DNA content and ploidy levels were observed in 43 bermudagrass ecotypes. AFLP markers were evaluated to define the genetic diversity, and chromosome counts were made to confirm the inferred cytotypes. Nuclear DNA contents were in the ranges 1.42-1.56, 1.94-2.19, 2.54, and 2.77-2.85 pg/2C for the triploid, tetraploid, pentaploid, and hexaploid accessions, respectively. The inferred cytotypes were triploid (2n = 3x = 27), tetraploid (2n = 4x = 36), pentaploid (2n = 5x = 45), and hexaploid (2n = 6x = 54), but the majority of the collections were tetraploid (81%). Mitotic chromosome counts verified the corresponding ploidy levels. The fast growing fine-textured ecotypes had lower ploidy levels, while the pentaploids and hexaploids were coarse types. The genetic similarity ranged from 0.42 to 0.94 with an average of 0.64. UPGMA cluster analysis and principle coordinate analysis separated the ecotypes into 6 distinct groups. The genetic similarity suggests natural hybridization between the different cytotypes, which could be useful resources for future breeding and genetic studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.257-271
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• 2016

The Structural SIMilarity (SSIM) index is one of the most widely-used methods for perceptual image quality assessment (IQA). It is based on the principle that the human visual system (HVS) is sensitive to the overall structure of an image. However, it has been reported that indices predicted by SSIM tend to be biased depending on the type of distortion, which increases the deviation from the main regression curve. Consequently, SSIM can result in serious performance degradation. In this study, we investigate the aforementioned phenomenon from a new perspective and review a constant that plays a big role within the SSIM metric but has been overlooked thus far. Through an experimental study on the influence of this constant in evaluating images with SSIM, we are able to propose a new solution that resolves this issue. In the proposed IQA method, we first design a system to classify different types of distortion, and then match an optimal constant to each type. In addition, we supplement the proposed method by adding color perception-based structural information. For a comprehensive assessment, we compare the proposed method with 15 existing IQA methods. The experimental results show that the proposed method is more consistent with the HVS than the other methods.

• KCID journal
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• v.17 no.2
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.14-25
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• 2020

This research proposes a methodology to evaluate the rationality of passengers' route choice who make trips within Seoul metropolitan subway based on smart card data. The rationality of user route choice is divided into the degree of determinacy and similarity concepts as basic principle. Determinacy is the degree to which the route selected by the passenger is identical to the system optimal path. Similarity indicates the degree to which the route is similar to the system optimal path. The K-path search method is used for path enumeration, which allows for measurement of determinacy. To assess determinacy within similarity, transfer tag data of private operators is used. Consequently, the concept of similarity applied to the model is such that the passenger's path choice is identical to the path taken using the tag reader. Results show that the determinacy of appearance of the shortest path (K=1) is 90.4%, while the similarity of appearance as K=(2-10) is 7.9%, summing to 98.3%. This indicates that trips on the metropolitan subway network are being rationally explained. 1.7% of irrational trips are attributed to the unexplainable error term that occurs due to the diversity of passengers.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.4
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• pp.62-71
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• 2009

In this paper, we propose a fault diagnosis algorithm for BLDC motors by principle component analysis (PCA) and implement a real-time fault diagnosis system for BLDC motors. To verify the proposed diagnosis algorithm, various faulty data are acquired by Lab VIEW program from experimental system. We extract a fault feature using principle component analysis after preprocessing and then finally the fault diagnosis is performed by Euclidean similarity. Also, we embed the PCA algorithm and k-NN classification algorithm into a digital signal processor. From various experiments, we found that the proposed algorithm can be used as a powerful technique to classify the several fault signals acquired from BLDC motors.

• Journal of Fashion Business
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• v.16 no.2
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• pp.124-137
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• 2012

This study aims to identify the meaning and formation cause of a style, and the essential elements of style formation, through psychobiological research as well as an analysis of the designs of Iris Van Herpen, a fashion designer, who in just 6 years has developed into a world-renowned new designer. As a result, it has been found that the psychobiological causes to form a style stem from the action of 'long-term memory', which is consolidated by 'selective attention', 'perceptional subjectivity', the principle of the 'neuron's connection specificity and invariance', and the principle of a 'neuronal signal's unilateral flow'. With such action, Herpen could develop her own original composition techniques. The formative shapes created by such composition techniques are characterized by enumeration, superposition, and hanging. The study has also found that the essential elements for a designer to be able to form his/her own style include 'aesthetic originality' in which the designer views the property of a thing from his/her inherent perspective, and finds the uniqueness from the thing that only he/she can express, 'technical differences' that are creative and original, and 'formative specificity' that is summarized into one property through an impressive shape.