• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.55-65
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• 2000

This paper presents a linear programming based algorithm for pattern classification. Pattern classification is being considered to be critical in the area of artificial intelligence and business applications. Previous methods employing linear programming have been aimed at two-group discrimination with one or more linear discriminant functions. Therefore, there are some limitations in applying available linear programming formulations directly to general multi-class classification problems. The algorithm proposed in this manuscript is based on quadratic or polynomial discriminant functions, which allow more flexibility in covering the class regions in the N-dimensional space. The proposed algorithm is compared with other competitive methods of pattern classification in experimental results and is shown to be competitive enough for a general purpose classifier.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• Korean Journal of Weed Science
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• v.10 no.3
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• pp.192-196
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• 1990

Eleven herbicides were treated in soybean fields of Dukso and Yeoncheon in Gyeonggi province. These herbicides were classified by two-dimensional ordination analysis based on important values of the weed flora which were obtained after application of herbicides. Weed community types were E. crus-galli(56%)-D. adscendens (14%) -C. album (10%)-P. oleracea (8%) and P. oleracea (58%)-E. crus-galli (29%) -A. mangostanus (5%) -D. adscendens (3%), respectively. From soybean field at Dukso, 11 weed community types or 11 herbicide groups were obtained. And at Yeoncheon, 9 weed community types or 9 herbicide groups were classified. At treated blocks with clomazone and bentazon, C. amuricus and E. crus-galli dominated respectively. And at treated blocks with quizalofop, haloxifop and alloxydium, P. oleracea dominated remarkably. The herbicides classification by the two-dimensional ordination analysis could be used more effectively to selecting herbicides for reciprocal and systematic weed control than by similarity analysis.

• Bulletin of the Korean Mathematical Society
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• v.52 no.6
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• pp.2035-2045
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• 2015

Meridian surfaces in the Euclidean 4-space are two-dimensional surfaces which are one-parameter systems of meridians of a standard rotational hypersurface. On the base of our invariant theory of surfaces we study meridian surfaces with special invariants. In the present paper we give the complete classification of Chen meridian surfaces and meridian surfaces with parallel normal bundle.

• Journal of the Korean Data and Information Science Society
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• v.5 no.1
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• pp.1-10
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• 1994

Projection pursuit(PP) is a computer-intensive method which seeks out interesting linear projections of multivariate data onto a lower dimension space by machine. By working with lower dimensional projections, projection pursuit avoids the sparseness of high dimensional data. We show through simulation that two projection pursuit discriminant mothods proposed by Chen(1989) and Huber(1985) do not improve very much the error rate than the existing methods and compare several classification procedures.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1636-1645
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• 1996

In this paper we proposed a new scheme for conditional two dimensional (2-D)moment-based classification of patterns on the basis of Gibbs random fields which are will suited for representing spatial continuity that is the characteristic of the most images. This implementation contains two parts: feature extraction and pattern classification. First of all, we extract feature vector which consists of conditional 2-D moments on the basis of estimated Gibbs parameter. Note that the extracted feature vectors are invariant under translation, rotation, size of patterns the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on 486 66Mhz PC. Experiments reveal that the proposed scheme has high classification rate over 94%.

• Journal of Environmental Science International
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• v.17 no.9
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• pp.957-968
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• 2008

The purpose of this study is to analyze the characteristics of hydraulic behavior of the natural channel flow according to the temporal classification mode, and thus propose the hydraulic analysis method for future channel design. For analysis, the temporal flow characteristics of the channel section was divided into the steady flow and the unsteady flow. For hydraulic analysis, the HEC-RAS model, which is a one-dimensional numerical analysis model, and the SMS-RAM2 model, which is a two-dimensional model, were used and the factors used for analysis of hydraulic characteristics were flood elevation and flow rate. The flow state was analyzed on the basis of the one-dimensional steady flow and unsteady flow for review. In the unsteady flow analysis the flow rate changed by $(-)0.16%{\sim}(+)0.26%$, and the flood elevation varied by $(-)0.35%{\sim}(+)0.51%$ as compared to the values in the steady flow analysis. Given these results, in the one-dimensional flow analysis based on the unsteady flow the flood elevation and flow rate were greater than when the analysis was done on the basis of the steady flow. The flow state was analyzed on the basis of the two-dimensional steady flow and unsteady flow. In the unsteady flow analysis the flow rate varied by $(-)0.16%{\sim}(+)1.08%$, and the flood elevation changed by $(-)0.24%{\sim}(+)0.41%$ as compared to the values in the steady flow analysis. Given these analysis results, in the two dimensional flow analysis based on the unsteady flow, the flood elevation and flow rate were greater than when the analysis was done on the basis of the steady flow.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.296-302
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• 2021

This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1179-1189
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• 2017

In this study, we propose an alternative method for discriminant analysis using a multivariate empirical distribution function to express multivariate data as a simple one-dimensional statistic. This method turns to be the estimation process of the optimal threshold based on classification accuracy measures and an empirical distribution function of data composed of classes. This can also be visually represented on a two-dimensional plane and discussed with some measures in ROC curves, surfaces, and manifolds. In order to explore the usefulness of this method for discriminant analysis in the study, we conducted comparisons between the proposed method and the existing methods through simulations and illustrative examples. It is found that the proposed method may have better performances for some cases.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.421-427
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• 2001

In this paper, a method for selection of the optimal feature vectors is proposed for the classification of closed 2D shapes using the bispectrum of a contour sequence. The bispectrum based on third order cumulants is applied to the contour sequences of the images to extract feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images, but there is no certain criterion on the selection of the feature vectors for optimal classification of closed 2D images. In this paper, a new method for selecting the optimal bispectral feature vectors based on the variances of the feature vectors. The experimental results are presented using eight different shapes of aircraft images, the feature vectors of the bispectrum from five to fifteen and an weighted mean fuzzy classifier.