• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

• Science of Emotion and Sensibility
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• v.9 no.2
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• pp.119-132
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• 2006

The dimensions of affection structure from hand movements was developed for the purpose of understanding relationship between affective words and physical factors to apply it to computing environment. To analyze hand movements, three dimensions -direction, time, weight- were found through reconstructing sub-properties of Laban Movement Analysis. The direction dimension has five freedoms of movement (horizontal, vertical, sagittal, circular, shaking) while the time and weight dimensions both have two sub categories each, (sudden, sustained), (light, strong) respectively. By factorial design using the three dimensions, twenty movement were videotaped. Participants rated a list of fifty korean affective words on each twenty movements. The results were studied by nonlinear principal component analysis. The results suggested that time and weight dimensions are closely related with arousal level dimension of affection. Strong and sudden movements associated with highly aroused affection, while light and sustained movements associated with the opposite affection. The direction sub-dimensions were found to be associated with the kinds of affection. Linear movements like horizontal, vortical and sagittal direction were correlated to highly aroused negative affection. Circular movements were found to correlate closely by fun and delight on the graph, while shaking movements were correlated to anxiety and impatience. These results imply that the dimensions of affection structure and sub-properties of hand movements are closely connected with each other.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.121-130
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• 2009

In this paper, feature extraction methods, which is one field of reducing dimensions of high-dimensional data, are empirically investigated. We selected the traditional PCA(Principal Component Analysis), ICA(Independent Component Analysis), NMF(Non-negative Matrix Factorization), and sNMF(Sparse NMF) for comparisons. ICA has a similar feature with the simple cell of V1. NMF implemented a "parts-based representation in the brain" and sNMF is a improved version of NMF. In order to visually investigate the extracted features, handwritten digits are handled. Also, the extracted features are used to train multi-layer perceptrons for recognition test. The characteristic of each feature extraction method will be useful when applying feature extraction methods to many real-world problems.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.196-199
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• 2008

Lineament is defined generally as a linear feature or pattern on interpretation of a satellite image and indicates the geological structures such as faults and fractures. For this reason, a lineament extraction and analysis using remote sensing images have been widely used for mapping large areas. The Gyeong-gang Fault is a NNE trending structure located in Gangwon-do and Kyeonggi-do district. However, a few geological researches on that fault have been carried out and its trace or continuity is ambiguous. In this study, we investigate the geologic features at Gyeong-gang Fault Zone using LANDSAT ETM+ satellite image and SRTM digital elevation model. In order to extract the characteristics of geologic features effectively, we transform the LANDSAT ETM+ image using Principal Component Analysis (PCA) and create a shade relief from SRTM data with various illumination angles. The results show that it is possible to identify the dimensions and orientations of the geologic features at Gyeong-gang Fault Zone using remote sensing data. An aerial photograph interpretation and a field work will be future tasks for more accurate analysis in this area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• Transactions of Materials Processing
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• v.11 no.8
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• pp.668-675
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• 2002

The circles deform into various shape during deformation, the major and minor axes of which indicate the direction of the major and minor principal strains. Likewise, the measured dimensions are used to determine the major and minor principal strain magnitudes. This circular grid technique of measuring strains can be used to diagnose the causes of necking and fracture in industrial practice and to investigate whether these defects were caused by material property variation, changes in lubrication, of incorrect press settings. In non-axisymmetric deep drawing, three modes of forming regimes are found: draw, stretch, plane strain. The stretch mode for non-axisymmetric deep drawing could be defined when the major and minor strains are positive. The draw mode could be defined when the major strain is positive and minor strain is negative, and plane strain mode could be defined when the major strain is positive and minor strain is zero. Through experiments the draw mode was shown on the wall and flange are one of a drawn cup, while the plane strain and the stretch mode were on the punch head and the punch corner area respectively, We observed that the punch load of elliptical deep drawing was decreased according to increase of die corner radius and the thickness deformation of minor side was more large than major side.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.2 s.140
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• pp.151-158
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• 2005

The concept of recent cruise ship design is changing rapidly according to the expansion of cruise fleet sizes, emphasis on passenger safety and tightened requirements for ecotourism. In this view point, this study focuses on investigative analysis for the recent trends in cruise ship design and construction. Based on the shipyard production logs and the cruise industry's annual news, the data for principal dimensions of newly built cruise ships, their hull forms and propulsion devices and the characteristics of cabin and public spaces are collected and analysed. As expected, it is found that the size of cruise ships is growing and the design concept is becoming more leisure-oriented for all ages rather than lust sightseeing. For producing a greater ton/pax ratio, the adoption of podded electric propulsion system, outside cabins and balcony spaces is a common trend in recent cruise ship design.

• Journal of Distribution Science
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• v.19 no.1
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• pp.37-47
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• 2021

Purpose: This study aims to review existing research on the definition, conceptualization, and measurement of relationship values to guide future research. This study specifically measures the relationship value between principals and retailers, which is still not widely discussed in previous research. Besides, to broaden our knowledge of the value-based determinants of competitive advantage, satisfaction, and retailer loyalty in business-to-business relationships. Research design, data, and methodology: This research assessed three alternative models of the relationship value construct's dimension structure and then tested for reliability, validity, and confirmatory factor analysis. The research sample is 185 retailers-data analysis using Structural Equation Modeling (SEM). Results: The results showed that product-based values and relationship-based values positively influenced competitive customer advantage, satisfaction, and loyalty. Conclusions: Relationship value construct can be measured and consist of five dimensions: product quality, delivery performance, customer orientation, service support, and personal interaction. They are key factors that maintain the relationship between principal and retailers. This study indicates that principals must invest more time and effort in building valuable relationships with their retailers. Finally, the value of relationships is a determinant of retailer performance: satisfaction and loyalty.

• International Journal of Contents
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• v.3 no.2
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• pp.1-5
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• 2007

This paper proposes a technique for principal component analysis (PCA) to raise the recognition rate of a front face in a low dimension by hierarchical image and parallel processing structure. The conventional PCA shows a recognition rate of less than 50% in a low dimension (dimensions 1 to 6) when used for facial recognition. In this paper, a face is formed as images of 3 fixed-size levels: the 1st being a region around the nose, the 2nd level a region including the eyes, nose, and mouth, and the 3rd level image is the whole face. PCA of the 3-level images is treated by parallel processing structure, and finally their similarities are combined for high recognition rate in a low dimension. The proposed method under went experimental feasibility study with ORL face database for evaluation of the face recognition function. The experimental demonstration has been done by PCA and the proposed method according to each level. The proposed method showed high recognition of over 50% from dimensions 1 to 6.

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.99-104
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• 2014

Sensor arrays based on chemical sensors produce multidimensional patterns of data that may be used discriminate between different chemicals. For the human observer, visualization of multidimensional data is difficult, since the eye and brain process visual information in two or three dimensions. To devise a simple means of data inspection from the response of sensor arrays, PCA (Principal Component Analysis) or Sammon's nonlinear mapping technique can be applied. The PCA, which is a well-known statistical method and widely used in data analysis, has disadvantages including data distortion and the axes for plotting the dimensionally reduced data have no physical meaning in terms of how different one cluster is from another. In this paper, we have investigated two techniques and proposed a combination technique of PCA and nonlinear Sammom mapping for visualization of multidimensional patterns to two dimensions using data sets from odor sensing system. We conclude the combination technique has shown more advantages comparing with the PCA and Sammon nonlinear technique individually.