• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1894-1896
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• 2004

전력을 공급하는 변전소 등의 주요 위치에 시설되는 GIS는 사고를 미연에 방지하기 위해 여러 가지 진단방법을 이용하여 이상여부를 판별한다. 이러한 진단 방법 중 현재 국내외적으로 각광을 받고 있는 방법이 UHF센서를 이용한 부분방전 검출방법이다. 따라서, 본 논문에서는 부분방전을 발생시키기 위한 인공결함을 제작하여 GIS 내부에 삽입하고 부분 방전을 발생시켰으며, 이때 발생된 부분방전 신호를 UHF센서를 이용하여 검출하였다. 검출된 부분방전 신호는 phi-f-q 방법으로 분석 하였으며, 그 결과 발생된 파라메터를 인공신경망에 적용하여 각각의 결함에 따른 인식률에 대하여 알아보았다.

• The Journal of Natural Sciences
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• v.17 no.1
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• pp.65-77
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• 2006

This study aims at providing suitable outdoor spaces for physical, social, and psychological condition of Korea elderly, examining design characteristics and presenting design guideline of outdoor space for elderly. To do this, this study examined previous research papers and scholastic writings, related to elderly housing, and analyzed existing outdoor space for elderly. Additionally evaluation of usage pattern and physical trace carried out four silver towns. As a result, first, the study showd that characteristics of outdoor space are related to safety, amenity, recognition and efficiency. Second, types of outdoor space in elderly related facilities are very limited in verity, just facilitating with rest space, sport space, and garden space. Third, from a standpoint of barrier-free-design, four silver town cases are not carefully concerning about outdoor space for elderly. These spaces should be designed for both neighborhood and elderly people.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.281-286
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• 2011

Station structures, one of important infrastructures, which have been being operated since the 1970s, are especially vulnerable to even the medium-level earthquake and they could be damaged by long-term internal or external vibrations such as ambient vibrations. Recently, much attention has been paid to real-time monitoring of the fatal defect or long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. In this study, a structural health monitoring methodology using acceleration responses is proposed to evaluate the health-state of the station structures and to detect initial damage-stage. A damage index is developed using the acceleration data and it is applied to outlier analysis, one of unsupervised learning based pattern recognition methods. A threshold value for the outlier analysis is determined based on confidence level of the probabilistic distribution of the acceleration data. The probabilistic distribution is selected according to the feature of the collected data.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.191-197
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• 2012

One-class classification (OCC) is one of the recent growing areas in data mining and pattern recognition. In the present study we examine a k-nearest neighbors data description (kNNDD) algorithm, one of the OCC algorithms widely used. In particular, we propose to use nonparametric estimation methods to determine the threshold of the kNNDD algorithm. A simulation study has been conducted to explore the characteristics of the proposed approach and compare it with the existing approach that determines the threshold. The results demonstrate the usefulness and flexibility of the proposed approach.

• ETRI Journal
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• v.10 no.3
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• pp.125-140
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• 1988

Computational geometry has wide applications in pattern recognition, image processing, VLSI design, and computer graphics. Voronoi diagrams in computational geometry possess many important properites which are related to other geometric structures of a set of point. In this pater the design of systolic algorithms for the static and the dynamic Voronoi diagrams is considered. The major motivation for developing the systolic architecture is for VLSI implementation. A new systematic transform technique for designing systolic arrays, in particular, for the problem in computational geometry has been proposed. Following this procedure, a type T systolic array architecture and associated systolic algorithms have been designed for constructing Voronoi diagrams. The functions of the cells in the array are also specified. The resulting systolic array achieves the maximal throughput with O(n) computational complexity.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.430-433
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• 1996

The shape of an object plays a very important role in pattern analysis and classification. Roughly, the researches on this topic can be classified into three fields, i.e. (i) edge detection, (ii) dominant points extraction, and (iii) shape recognition and classification. Many works have been done in these three fields. However, it is very seldom to see the research that discusses the connection relationship of objects. This problem is very important in robot assembly systems. Therefore, here we focus on this problem and discuss how to recover the connection relationship of planar objects. Our method is based on the partial curve identification algorithm. The experiment results show the efficiency and validity of this method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.682-685
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• 2004

This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.

• Journal of Korean Academy of Nursing
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• v.34 no.6
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• pp.913-923
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• 2004

Purpose: This study was to explore health experiences of victims of sexual violence reflected in Newman's health as expanding consciousness theory. Method: Nurse as researcher use Newman's praxis methodology because it is good for showing the process of interaction between researcher and sexual abuse victims. Results: The significant characteristics of early health experiences during negative situations were a lot of suffering. However, after a turning point in health experience, the health experiences of most of the participants evolved as expanding consciousness. Conclusion: The study has provided support for Newman's theory of health. Most of the participants recognised meanings in their patterns and trustful caring relationships with the nurse as researcher. Pattern recognition as a nursing practice was a meaningful transforming process in the participant-nurse partnership.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2895-2898
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• 2003

In this study, our primary goal is to classify the EMG(electromyographic) signals including the specific patterns related to hand motions in an arm. To do this, the EMG recognition method based on the LP coefficients and delay between multi-channels obtained by cross-correlation function is presented. The study consists of three functional parts, which are pans for obtaining the EMG signals from am muscle, analyzing LP coefficients and delay parameter obtained by cross-correlation function, and recognizing specific patterns. In the experiment, the result of the present method is compared with the results of the conventional methods. We expect that the results of this study is very effective in the mobile computer and wearable computer environment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.77-85
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• 1993

In this paper, we present a method to solve a convexly combined fuzzy relational equation with generalized connectives. For this, we propose a neural network whose structure represents the fuzzy relational equation. Then we derive a learning algorithm by using the concept of back-propagation learning. Since the proposed method can be used for a general form of fuzzy relational equations, such fuzzy max-min or min-max relational equations can be treated as its special cases. Moreover, the relational structure adopted in the proposed neurocomputational approach can work in a highly parallel manner so that real-time applications of fuzzy sets are possibles as in fuzzy logic controllers, knowledge-based systems, and pattern recognition systems.