• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.17-26
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• 1993

Process fault diagnosis is a complicated matter because quality control problems can result from a variety of causes. These causes include problems with electrical components, mechanical components, human errors, job justification errors, and air conditioning influences. In order to make the system run smoothly with minimum delay, it is necessary to suggest heuristic remedies for the detected faults. Hence, this paper describes a heuristic methodology of fault diagnosis that is performed using statistical patterns generated by quality characteristics The proposed methodology is described briefly as follows: If a sample pattern generated by random variables is similar to the number of prototype patterns, the sample pattern may be matched by any prototype pattern among them to be resembled. This concept is based on the similarity between a sample pattern and the matched prototype pattern. The similarity is calculated as the weighted average of squared deviation, which is expressed as the difference between the relative values of standard normal distribution to be transformed by the observed values of quality characteristics in a sample pattern and the critical values of the corresponding ones in a matched prototype pattern.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.193-217
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• 2001

Despite of the rich literature in discriminant analysis, this complicated subject remains much to be explored. In this article, we study the theoretical foundation that supports Fisher's linear discriminant analysis (LDA) by setting up the classification problem under the dimension reduction framework as in Li(1991) for introducing sliced inverse regression(SIR). Through the connection between SIR and LDA, our theory helps identify sources of strength and weakness in using CRIMCOORDS(Gnanadesikan 1977) as a graphical tool for displaying group separation patterns. This connection also leads to several ways of generalizing LDA for better exploration and exploitation of nonlinear data patterns.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.17 no.5
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• pp.590-597
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• 1988

Characteristics of flow regime transitions in inclined upwards gas-liquid two-phase flow have been investigated based upon a statistical analysis of instantaneous pressure drop curves through an orifice. The probability density functions of the curves indicate distinct patterns depending upon two-phase flow regime, which are very similar to those of horizontal two-phase. The dimensionless intensity of fluctuations of the pressure drops sharply change as the flow transitions such as plug-slug, pseudo slug-slug and annular-slug take place. The effects of inclination angle on the flow regime transitions have been also investigated. The results show that the method to identify the flow pattern based upon the statistical analysis of instantaneous pressure drops is suitable for inclined flow as well as horizontal flow.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.70.2-70.2
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• 2019

Recent observational works have reported spiral wave patterns (SWPs) in sunspots, but there is a lack of samples to derive the physical properties. In this presentation, we suggest the automatic method to detect the SWPs in observational data and present their statistical properties. From our method, we find more than 1000 SWPs observed by the Atmospheric Imaging Assembly onboard in the Solar Dynamic Observatory from 2013 to 2018. From our samples, more than half of the SWPs has the one spiral arm. The predominant oscillation period is 2 to 3 minutes. The rotating direction of the spiral arms does not depend on the latitude and the polarity of the sunspots. Our statistical results support the physical model suggested by Kang et al. (2019) that explain the generation of SWPs as the depth of the wave driving source and azimuthal modes in the straight vertical magnetic flux tube.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.469-482
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• 2002

This paper suggests a new method of compensating for wave nonresponse in panel survey, which combines weighting adjustment and imputation. By deleting less frequent nonresponse patterns, we can get simplicity. A new mean estimator under the new combining method is provided and a limited simulation study employing a real data is conducted.

• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.1-14
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• 1990

This paper proposes an approach to the identification of trnasfer function models. A strategy for the identification of the model structure is based on R and S arrays constructed by the impulse response function of the model. Theoretical patterns of the arrays associated with the model are investigated, and the practical implementation method of the suggested approach is also discussed. Finally two published samples are employed to demonstrate the practicability of the approach.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.1
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• pp.19-23
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• 2000

Partial discharges(PD)in air insulated electric power apparatus often lead to deterioration of solid insulation by electron bombardments and electrochemical reaction. The PD caused to reduce the life time of power apparatus and to increase power losses. Thus understanding and classification of PD patterns in air are very important to discern sources of PD. In this paper, PD in air by using statistical methods was investigated. We classified air discharges, corona, surface discharges and cavity discharges by Kohonen network. For classification of PD patterns, we used statistical operators and parameters such as skewness$(S^+,\; S^-),\; kurtosis(K^+, K^-),\; mean phase(AP^+, AP^-)$, cross-correlation factor(CC) and asymmetry derived from the mean pulse-height phase distribution$(H_{avg}(\phi))$, the max pulse-height phase distribution $(H_{qmax}(\phi))$, the pulse count phase distribution $(H_n(\phi))$ and the pulse height vs. Repetition rate $(H_q(n))$.

• Architectural research
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• v.8 no.2
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• pp.1-10
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• 2006

This research has acquired preliminary information about the existing conditions and understanding of members regarding time management for members in the architectural field. The goal of this research is to construct a basis for the time management education framework in architectural field in the future. This research mainly focuses on following questions based on limited objectives: Do groups of academics and professionals have understanding and knowledge of time management? Can the level of an individual's scheduling techniques be correlated to the patterns of knowledge and understanding of time management principles and practices? Can the time management practice status in individual's working environment be correlated to the patterns of knowledge and understanding of time management principles and practices? Can an individual's self-confidence level be correlated to the patterns of knowledge and understanding of time management principles and practices? Data have been collected through comprehensive questionnaires given to academics and professionals in United States. By means of statistical analysis, the hidden patterns, deficiencies and relationships in attitudes about time management have been revealed. The statistical analysis has produced conclusions that, among several subdivisions, self-discipline and planning have strong relationships and confidence, personal organization, control, and information gathering subdivisions have certain relationships with objectives of time management education in this research.