• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Korean Journal of Computational Design and Engineering
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• v.4 no.1
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• pp.43-51
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• 1999

Producing the relevant information (features) from the CAD models of CAM, called feature recognition or extraction, is the essential stage for the integration of CAD and CAM. Most feature recognition methods, however, have problems in the recognition of intersecting features because they do not handle the intersection geometry properly. In this paper, we propose a machining feature recognition algorithm, which has a solid model consisting of orthogonal primitives as input. The algorithm calculates candidate features and constitutes the Intersection Geometry Matrix which is necessary to represent the spatial relation of candidate features. Finally, it recognizes machining features from the proposed candidate features dividing and growing systems using half space and Boolean operation. The algorithm has the following characteristics: Though the geometry of part is complex due to the intersections of design primitives, it can recognize the necessary machining features. In addition, it creates the Maximal Feature Volumes independent of the machining sequences at the feature recognition stage so that it can easily accommodate the change of decision criteria of machining orders.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.151-153
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• 2001

Feature Simplification is an essential method for multiple representations of spatial features in GIS. However, spatial features re various, complex and a alrge size. Among spatial features which describe spatial information. linear feature is the msot common. Therefore, an efficient linear feature simplification method is most critical for spatial feature simplification in GIS. This paper propose an original method, by which the problem of linear feature simplification is mapped into the signal processing field. This method avoids conventional geometric computing in existing methods and exploits the advantageous properties of wavelet transform. Experimental results are presented to show that the proposed method outperforms the existing methods and achieves the time complexity of O(n), where n is the number of points of a linear feature. Furthermore, this method is not bound to two-dimension but can be extended to high-dimension space.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.277-282
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• 2003

In this paper we propose Using Higher Order Neuron on the Supervised Learning Machine of the Kohonen Feature Map. The architecture of proposed model adopts the higher order neuron in the input layer of Kohonen Feature Map as a Supervised Learning Machine. It is able to estimate boundary on input pattern space because or the higher order neuron. However, it suffers from a problem that the number of neuron weight increases because of the higher order neuron in the input layer. In this time, we solved this problem by placing the second order neuron among the higher order neuron. The feature of the higher order neuron can be mapped similar inputs on the Kohonen Feature Map. It also is the network with topological mapping. We have simulated the proposed model in respect of the recognition rate by XOR problem, discrimination of 20 alphabet patterns, Mirror Symmetry problem, and numerical letters Pattern Problem.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.808-816
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• 2004

This paper proposes the method of implementation of practical online face verification system based on multiple feature combination and a similarity space. The main issue in face verification is to deal with the variability in appearance. It seems difficult to solve this issue by using a single feature. Therefore, combination of mutually complementary features is necessary to cope with various changes in appearance. From this point of view, we describe the feature extraction approaches based on multiple principal component analysis and edge distribution. These features are projected on a new intra-person/extra-person similarity space that consists of several simple similarity measures, and are finally evaluated by a support vector machine. From the experiments on a realistic and large database, an equal error rate of 0.029 is achieved, which is a sufficiently practical level for many real- world applications.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.13-24
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• 2011

This paper deals with a 3D-pose (orientation and position) estimation problem of a circular object in 3D-space. Circular features can be found with many objects in real world, and provide crucial cues in vision-based object recognition and location. In general, as a circular feature in 3D space is perspectively projected when imaged by a camera, it is difficult to recover fully three-dimensional orientation and position parameters from the projected curve information. This paper therefore proposes a 3D pose estimation method of a circular feature using a coplanar point. We first interpret a circular feature with a coplanar point in both the projective space and 3D space. A procedure for estimating 3D orientation/position parameters is then described. The proposed method is verified by a numerical example, and evaluated by a series of experiments for analyzing accuracy and sensitivity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1946-1956
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• 2001

In this paper, we propose a novel 2-D partial matching algorithm based on model-based stochastic analysis of feature correspondences in a relation vector space, which is quite robust to shape variations as well as invariant to geometric transformations. We represent an object using the ARG (Attributed Relational Graph) model with features of a set of relation vectors. In addition, we statistically model the partial occlusion or noise as the distortion of the relation vector distribution in the relation vector space. Our partial matching algorithm consists of two-phases. First, a finite number of candidate sets areselected by using logical constraint embedding local and structural consistency Second, the feature loss detection is done iteratively by error detection and voting scheme thorough the error analysis of relation vector space. Experimental results on real images demonstrate that the proposed algorithm is quite robust to noise and localize target objects correctly even inseverely noisy and occluded scenes.

• Korean Institute of Interior Design Journal
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• v.14 no.3 s.50
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• pp.114-121
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• 2005

Louis 1. Kahn was a wise architect who learned from history. He developed his own unique architecture by combining his creative sense with design principles and vocabularies that can be found in historical architecture. When restricting a space, he surrounded the space with thick walls as it had been done in historical buildings. The interior space encompassed by this method became a center-oriented and stable space. The objective of this study is to find the characteristics of Kahn's interior spaces by analyzing his projects in terms of space, form, daylight and materials. For this purpose, five works that are considered to have significance from the aspect of interior design were selected and analyzed. The characteristics realized through this study are as follows. A) Spatial features: 1) Generally speaking, each required space has been arranged symmetrically. 2) Being clearly defined as the main space, the subsidiary space, or the service space, each space also was placed very functionally. 3) The space encompassed by thick walls became a center-oriented, stable space. And in most case, it was characterized as a dark space. B) Formative features: 4) The space was defined as a basic solid such as a cylinder, a hexahedron, and an octagonal box, and was developed into a complex shape by the recessed windows. 5) Historical vocabularies such as an arch, a vault, and a dome were reinterpreted in new ways by kahn's own eyes. 6) Haying diverse shapes, the skylights enrich the space in terms of form. C) Daylight feature: 7) The vertical light entering through the skylights creates a solemn and mysterious atmosphere. 8) Given the shadows from the windows that change according to time, the interior space becomes a very vivid space. D) Material feature: 9) Harmonized with cold and smooth materials such as exposed concrete, metal, and glass, the interior space provides a modern atmosphere. 10) Warm appearing wood was used for furniture and part of walls or floors. The effective use of wood takes on a role that is quite complementary to the cold ambience of the smooth and cold materials. 11) With flexibility In building shapes, the concrete becomes the form-endowing materials.

• Journal of the Korean Home Economics Association
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• v.23 no.2
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• pp.55-66
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• 1985

This study is intended to compare the quality of housing envirionments between single family house and apartments. To be specific, firstly, it is to be examined as to whether there exists any differences between residents of single family house and those of highrise apartments in terms of their perception of the quality of housing environment. Secondly, the major factors of the perception of the quality of housing environment may be linked to the level of housing satisfaction are to be explored in this study. The perception of the quality housing environment is composed of four factors such as living space, noise, neighbor environment, and structural feature. For the purpose, questionnaires were adinistered to 125 home makers living in single family house and 125 home makers in high-rise apartments in Kwangju. The data were analyzed with factor analysis, analysis of variance, and multiple regression analysis.The following conclusions are derived from the data analysis in thi study: 1) Resjdents of apartments tended to be more satisfied with structural feature of housing unit and less satisfied with noise than those of single family house. There are negligible differences between two housing types in perception of the quality of living space, and neighbor environment. 2) According to the singhle family house group, it is found that structural feature, neighbor environment, and living space predict most of the variance in the level of housing unit satisfaction. It is also turned out that neighbor environment, noise, and structural feature have impact on the level of neighborhood statisfaction. 3) the apartments group shows that structural feature is the only predictor having impact on housing unit satisfaction. It is found that neighbor environment factor predicted the level of neighborhood satisfaction.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.