• East Asian mathematical journal
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• v.39 no.1
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• pp.11-21
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• 2023

Non-trivial automorphisms of the unit disc in the complex plane can be classified by three classes; elliptic, parabolic and hyperbolic automorphisms. This classification is due to a representation in the projective special linear group of the real field, or in terms of fixed points on the closure of the unit disc. In this paper, we will characterize this classification by the distance function of the Poincaré metric on the interior of the unit disc.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• Journal of Environmental Impact Assessment
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• v.22 no.1
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• pp.65-78
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• 2013

Since pursuing the pleasant life for people, there is an increase of desire to appreciate outstanding scenery with the difference in certain level for perception and understanding of human on landscaping, However, the quality of landscaping has become artificial with the pleasance to be declining due to the urbanization. This study was applied at the site around High One Resort area in Gohan-eup, Jeongseon-gun Gangwon-do for analyzing the areas sensitive to the landscaping change as well as degree of requirement for landscape management for forest landscape management with the focus on presenting the zoning method and the management class classification method. Even if the forest is the same, the function of it is different depending on land use or what resource is placed that the forestry function is found out to present the management plan for each forestry function in the subject site and the result of the management grade classification is analyzed in overlapping to the forestry function level. As a result, from the landscaping management requirement and visual absorption analysis, the result formulated for upper, middle and lower zones to classify the final forestry landscape management degree into 1-4 grades and the management plan is presented on the respective 1-4 grade area for each forestry function. By applying the technique to set the management grade, it was possible to formulate the result to provide the means for integrated management in consideration of the forestry function and management of forestry landscape and resources.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.10-17
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• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.56-64
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• 2018

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.137-143
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• 2013

Recent investigation of acoustic correlates of Korean stop manner contrasts has reported a diachronic transition in Korean stops: young Seoul speakers are relatively more dependent on the F0 characteristics of the stops than on the VOT characteristics in aspirated and lenis stop distinction. This finding has been examined against tonal dialects of Korean and the results suggested that the speakers of tonal dialects are not sharing the transition. These results also suggested that F0 function for segmental stop classification interferes with the function for lexical tone classification in their tonal speech. The current study investigated these findings in terms of perception. Perceptual behavior of Seoul and Busan speakers of Korean was examined in a comparative manner through the measurement of perceptual cue weight of F0 and VOT in particular. The results from regression and correlation analyses revealed that Busan speakers are closer to older Seoul speakers than to younger Seoul speakers in that the cue weight for VOT and F0 were comparable in the aspirated-lenis stop distinction. This result was in contrast to the perceptual behavior of younger Seoul speakers who showed clear dominance of F0 over VOT for the same distinction. These findings provided perceptual evidence of the dual function of F0 for segmental and lexical distinctions in tonal dialects of Korean.

• Journal of the Korean housing association
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• v.19 no.5
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• pp.37-46
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• 2008

The purpose of this study is to derive and adapt the Valuation Factor of urban regeneration scientifically and synthetically, which is the basis of developing a rational plan for urban revitalization. For this, we have selected 37 factors relating to urban regeneration as outlined in preceding studies and inquiry about importance of factors. we analysed he Valuation factors influencing he importance of urban revitalization through a questionnaire which was completed by inhabitants and expert groups in Gwangju Metropolitan City. From he results of he Factor analysis using SPSSWIN(VER.14.0), it was found that the factors which influence the importance of urban regeneration are Environment, Function, Resources and Policy Factors. Environment Factor comprises amenity, culture, beauty and convenience, The while the Function Factor comprises interchange, information, complexity and security. This classification has credibility because of the high factor loading through the Varimax Factor Analysis and is due to a high Cronbach's coefficient. There is a strong correlation between the classified factors through correlation analysis. Finally, through AMOS (Analysis of Moment Structure) 16.0 it was found that the upper classification is credible because main suitability index confirms recommending an admission standard.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.817-821
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• 2001

In this paper, a new learning methodology for kernel methods that results in a sparse representation of kernel space from the training patterns for classification problems is suggested. Among the traditional algorithms of linear discriminant function, this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epoches. For sequential learning of kernel methods, extended SVM and kernel discriminant function are defined. Systematic derivation of learning algorithm is introduced. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• ETRI Journal
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• v.39 no.5
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.