• 복식
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• 제64권3호
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• pp.62-76
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• 2014

The purpose of this study is to classify the facial images and analyze shape characteristics of Korean women in their 20s. Previous research and survey were used for the study, the surveys targeted 220 university students in their 20s. The subjects of the experiment were 20-24 year-old Korean women. SPSS 12.0 statistics program was used to analyze the results, and factor analysis, Cronbach's ${\alpha}$ reliability analysis, and multidimensional scaling(MDS) were executed. The results of the study are as follows: First, the facial image types of Korean women in their 20s were classified into 4 categories as 'Youthfulness', 'Classiness', 'Friendliness', and 'Activeness'. Second, the multi-dimensional scaling method was performed and two orthogonal dimensions for the facial image of the Korean women were suggested: strong - soft and classy-friendly. Third, by analyzing the basic statistics concerning the structural characteristics of facial image of Korean women, there were differences in structural characteristics that form the facial images. Especially, significant difference appeared in items related forehead, eyebrows, eyes and jaw.

• 대한인간공학회지
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• 제23권4호
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• pp.23-34
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• 2004

The importance of the industrial safety has been growing gradually as well as the prevention activities on industrial accidents. Industrial accident rates have been decreasing by the prevention activities. However, the fatal industrial accidents such as the death tend to increase and risk per accident has increased. The previous studies on the industrial accidents focus on the entire accidents. However, these studies are lacking for the fatal industrial accidents such as the death. The purpose of this paper is to analyze the characteristics and trend of death which occurred by industrial accident, based on the real data of deaths collected last 5 years from 1999 to 2003 in korea. This paper suggests a analysis method using MDS(MultiDimensional Scaling) that considers accidents variables and properties simultaneously. We evaluate MDPREF (Multidimensional Analysis of Preference Data), one of the MDS analysis, to know the relations between the type of industry and region as well as the type of industry and occupation. This paper finds the type of industry which has high possibilities of death by regional groups. In addition, we find the type of occupation which has high possibilities of death by the type of industry. These findings indicate that industrial classification should be differently controled according to type of occupation and region.

• 동의생리병리학회지
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• 제21권2호
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• pp.548-553
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• 2007

In general, cancer pain is neither fully recognized nor adequately treated. The inadequate pain control is due to failure of accurate assessment of cancer pain. This study was aimed to understand various characters of cancer pain and investigate available assessment scales which have been designed for, or frequently used with people with cancer pain. A total of 23 articles were selected from two different databases. The selected articles were analyzed according to three aspects of initial assessment, patient self-report and assessment of the outcomes of pain management. The characters of cancer pain is complex and includes physical, psychosocial, and spiritual dimension. Also, subjective pain can be classified into at least four specific factors, such as pain intensity, pain affect, pain relief, and pain quality. Based on various classification, the pain assessment scales can be divided into unidimensional or multidimensional. Among the more commonly used clinical tools are numeric rating scales, verbal rating scales, visual analog scales, and picture scales. Above all, in order to assess cancer pain objectively, the clinician must select appropriate assessment instruments which reflect pain definition and clinical purpose.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• 생태와환경
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• 제41권spc호
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• pp.1-10
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• 2008

We patterned water quality of agricultural reservoirs according to the differences of six physico-chemical environmental factors (TN, TP, DO, BOD, COD, and SS) using four different ordination methods: Principal Components Analysis (PCA), Detrended Correspondence Analysis (DCA), Nonmetric Multidimensional Scaling (NMS), and Isometric Feature Mapping (Isomap). The data set was obtained from the water quality monitoring networks operated by the Ministry of Agriculture and Forestry and the Ministry of Environments. Chlorophyll-${\alpha}$ displayed the highest correlation with COD, followed by TP, BOD, SS, and TN (p<0.01), while negatively correlated with altitude and bank height of the reservoirs (p<0.01). Although four different ordination methods similarly patterned the reservoirs according to the gradient of nutrient concentration, PCA and NMS appeared to be the most efficient methods to pattern water quality of reservoirs based on the explanation power. Considering variable scores in the ordination map, the concentration of nutrients was positively correlated with Chl-${\alpha}$, while negatively correlated with altitude and bank height. These ordination methods may help to pattern agricultural reservoirs according to their water quality characteristics.

• 대한원격탐사학회지
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• 제1권1호
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• 한국컴퓨터정보학회논문지
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• 제20권2호
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• pp.21-28
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• 2015

본 논문에서는 최근접 이웃 규칙을 이용한 프로토타입 선택 기반 분류 학습을 제안하였다. 각 훈련 데이터가 대표하는 클래스 영역을 구(sphere)로 분할하는데 최근접 이웃 규칙을 적용시키며, 구의 내부는 동일 클래스 데이터들만 포함하도록 한다. 프로토타입은 구의 중심점이며 프로토타입의 반지름은 가장 인접한 다른 클래스 데이터와 가장 먼 동일 클래스 데이터의 중간 거리 값으로 결정한다. 그리고 전체 훈련 데이터를 대표하는 최소의 프로토타입 집합을 선택하기 위해 집합 덮개 최적화를 이용하여 프로토타입 선택 문제를 변형시켰다. 제안하는 프로토타입 선택 방법은 클래스 별 적용이 가능한 그리디 알고리즘으로 설계되었다. 제안하는 방법은 계산 복잡도가 높지 않으며, 대규모 훈련 데이터에 대한 병렬처리의 가능성이 높다. 프로토타입 기반 분류 학습은 선택된 프로토타입 집합을 새로운 훈련 데이터 집합으로 사용하고 최근접 이웃 규칙을 적용하여 테스트 데이터의 클래스를 예측한다. 실험에서 제안하는 프로토타입 기반 분류기는 최근접 이웃 학습, 베이지안 분류 학습과 다른 프로토타입 분류기에 비해 일반화 성능이 우수하였다.

• 감성과학
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• 제26권4호
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• pp.3-14
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• 2023

최근 소셜미디어 이용이 심리적 웰빙에 미치는 영향이 부각되고 있으나 어떤 요소가 소셜미디어 상에서의 관계의 질을 예측할 수 있는지에 대한 연구는 상대적으로 드물다. 본 연구는 머신러닝 기법을 이용하여 COVID-19로 인한 자가격리 동안 인스타그램 활동과 외로움, 우울 등의 심리 상태가 소셜미디어 상에서의 관계의 질을 예측할 수 있는지 알아보고자 하였다. 성인 95명을 대상으로 자가격리 중과 자가격리 해제 후 시점에서 외로움, 인스타그램 활동, 소셜미디어 상에서의 관계, 우울 등에 대해 자기보고식 설문에 응답하도록 하였다. 그 후, 다차원 척도법과 표상유사성분석, 분류분석을 각 시점에 대해 수행하였다. 다차원척도법 결과, 1차원에서 인스타그램 이용 시간과 우울이 다른 변인들과 구별되었으며, 2차원에서 외로움과 수동적 이용이 다른 변인들과 구별되었다. 그 후 소셜미디어 상에서의 관계의 질의 고,저 집단에 대해 표상유사성분석을 실시한 결과, 소셜미디어 상에서의 관계의 질이 높은 집단은 낮은 집단보다 자가격리의 영향을 더 많이 받는 것으로 나타났다. 분류분석 결과에서도 소셜미디어 상에서의 관계의 질 예측 변인이 사회적 고립의 여부에 따라 달라지는 것으로 나타났다. 따라서 본 연구의 결과는 사람들이 사회적 고립 상황에 있지 않을 때 인스타그램 이용 변인과 심리적 변인이 소셜미디어 상에서의 관계를 더 잘 예측할 수 있음을 시사한다.

• 복식문화연구
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• 제23권4호
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• pp.554-568
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• 2015

Technological change leads to a value shift in human society. Various cultural experiences through the digital paradigm influence the expression of fashion. This article considers fashion film as a new form for presenting fashion and explores the distinctiveness of expression in digital fashion film. For the methodology, a literature review was conducted to examine the concepts and features of digital fashion film and metareality. Empirical research was also performed by drawing from Nick Knight's digital films, "Sans Couture", "#asif", and "The Elegant Universe" and by specifically analyzing the classification of the themes, visuals, and auditory expression. The results are as follows. The proliferation of fashion film has accelerated in the internet environment. New media in the digital era allows images to become more realistic and variable through immaterial conversion. Metareality is the notion of a reality beyond existence. A metarealistic image maintains the metaphysical nature of an object and transcends empirical appearance. It possesses immaterial, transboundary, and multidimensional features, and the image is realized by digital technology. The expression analysis identifies the metareality expressed in contemporary fashion film appearing as atypical forms, irrational combinations, and the playfulness of motion. It shows a positive attitude, transcending the immaterial limit of reality toward fashion. This study indicates how fashion as products challenges the metaphysical transformation in the digital era. The exploration of metareality in digital fashion film promotes a wider perspective and understanding of the concept of fashion.

• 전자공학회논문지
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• 제52권10호
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• pp.73-81
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• 2015

이 논문은 프로토타입 선택 방법을 제안하고, 편의-분산 분해를 이용하여 최근접 이웃 알고리즘과 프로토타입 기반 분류 학습의 일반화 성능 비교 평가에 있다. 제안하는 프로토타입 분류기는 클래스 영역 내에서 가변 반지름을 이용한 다차원 구를 정의하고, 적은 수의 프로토타입으로 구성된 새로운 훈련 데이터 집합을 생성한다. 최근접 이웃 분류기는 새 훈련 집합을 이용하여 테스트 데이터의 클래스를 예측한다. 평균 기대 오류의 편의와 분산 요소를 분해하여 최근접 이웃 규칙, 베이지안 분류기, 고정 반지름을 이용한 프로토타입 선택 방법, 제안하는 프로토타입 선택 방법의 일반화 성능을 비교한다. 실험에서 제안하는 프로토타입 분류기의 편의-분산 변화 추세는 모든 훈련 데이터를 사용하는 최근접 이웃 알고리즘과 비슷한 편의-분산 추세를 보였으며, 프로토타입 선택 비율은 전체 데이터의 평균 약 27.0% 이하로 나타났다.