• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.71-79
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• 2009

Currently much research is being done on host based intrusion detection using system calls which is a portion of kernel based data. Sequence based and frequency based preprocessing methods are mostly used in research for intrusion detection using system calls. Due to the large amount of data and system call types, it requires a significant amount of preprocessing time. Therefore, it is difficult to implement real-time intrusion detection systems. Despite this disadvantage, the frequency based method which requires a relatively small amount of preprocessing time is usually used. This paper proposes an effective method for detecting denial of service attacks using the frequency based method. Principal Component Analysis(PCA) will be used to select the principle system calls and a bayesian network will be composed and the bayesian classifier will be used for the classification.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.3
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• pp.312-318
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• 2014

We evaluated the morphological characteristics of 156 maize inbred lines, which were developed to breeding normal maize variety at Maize Experiment Station, Gangwon Agricultural Research and Extension Services, by examining 11 quantitative and three qualitative characteristics. On the evaluation of three qualitative traits for 156 maize inbred lines, most inbred lines showed yellow (85 and 84 inbred lines) at tassel color (QL1) and silk color (QL2), and showed semi erect (105 inbred lines) at plant type (QL3). While, the evaluation of 11 quantitative traits for 156 maize inbred lines, they showed the morphological variation in days of tasseling (QN1, 56.5 to 76.0 days), days of silking (QN2, 59.0 to 85.5 days), stem thickness (QN3, 12.7 to 42.9 mm), plant height (QN4, 111.8 to 239.8 cm), ear height (QN5, 48.2 to 126.5 cm), 100 kernel weight (QN6, 14.9 to 36.4 g), ear length (QN7, 10.0 to 79.0 cm), setted kernel length (QN8, 8.0 to 70.5 cm), ear thickness (QN9, 4.0 to 22.0 cm), total kernel weight (QN10, 22.0 to 490.0 kg) and water content (QN11, 9.3 to 11.9%), respectively. As a result, 11 inbred lines (00hf3, 00hf19, 00hf30, 00hf36, 02S8069, 02S8072, 02S8090, 02S8099, 05S10011, 06S8085-6, 07S8011) in the 156 normal maize inbred lines have showed comparatively high values. While, the results of PCA (principal component analysis) indicated that the ear length (QN7), setted kernel length (QN8), ear thickness (QN9) and total kernel weight (QN10) greatly contributed in positive direction on the first principal components. And also, days of tasseling (QN1), days of silking (QN2), plant height (QN4) and ear height (QN5) contributed in negative direction on the second principal component. Thus these morphological characters, which were greatly contributed in the first and second principal components, might be considered to be useful for discrimination among 156 normal inbred lines. Specifically, this study's assessment of morphological characteristics of 156 normal inbred lines will be helpful useful for normal maize breeding programs such activities as planning crosses for hybrid and line development at Maize Experiment Station, Gangwon Agricultural Research and Extension Services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.25-44
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• 2010

With the increasing popularity of mobile devices, it has become necessary to protect private information and content in these devices. Face recognition has been favored over conventional passwords or security keys, because it can be easily implemented using a built-in camera, while providing user convenience. However, because mobile devices can be used both indoors and outdoors, there can be many illumination changes, which can reduce the accuracy of face recognition. Therefore, we propose a new face recognition method on a mobile device robust to illumination variations. This research makes the following four original contributions. First, we compared the performance of face recognition with illumination variations on mobile devices for several illumination normalization procedures suitable for mobile devices with low processing power. These include the Retinex filter, histogram equalization and histogram stretching. Second, we compared the performance for global and local methods of face recognition such as PCA (Principal Component Analysis), LNMF (Local Non-negative Matrix Factorization) and LBP (Local Binary Pattern) using an integer-based kernel suitable for mobile devices having low processing power. Third, the characteristics of each method according to the illumination va iations are analyzed. Fourth, we use two matching scores for several methods of illumination normalization, Retinex and histogram stretching, which show the best and $2^{nd}$ best performances, respectively. These are used as the inputs of an SVM (Support Vector Machine) classifier, which can increase the accuracy of face recognition. Experimental results with two databases (data collected by a mobile device and the AR database) showed that the accuracy of face recognition achieved by the proposed method was superior to that of other methods.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.681-687
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• 2007

Face recognition technique is very popular for a personal information security and user identification in recent years. However, the face recognition system is very hard to be implemented due to the difficulty where change in illumination, pose and facial expression. In this paper, we consider that an illumination change causing the variety of face appearance, virtual image data is generated and added to the D-LDA which was selected as the most suitable feature extractor. A less sensitive recognition system in illumination is represented in this paper. This way that consider nature of several illumination directions generate the virtual training image data that considered an illumination effect of the directions and the change of illumination density. As result of experiences, D-LDA has a less sensitive property in an illumination through ORL, Yale University and Pohang University face database.

• Korean Journal of Plant Resources
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• v.19 no.1
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• pp.105-111
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• 2006

The objectives of this study, an analysis of fruit and leaf morphological characteristics among the five Zizyphus cultivars could be used for the investigation of cultivars classification and could provide information to make out the UPOV TG(Test Guidelines). ANOVA tests showed that there were statistically significant differences in all fruit and leaf morphological characteristics among the five Zizyphus cultivars at 1% level. But, for kernel characteristics, differences were statistically non-significant among the cultivars. Approximately, the Wolchul and Boeun cultivars showed larger and smaller values in overall characteristics and cultivars, respectively. The results of principal component analysis(PCA) for the fruit and leaf morphological characteristics showed that the first for principal components(PC's) explained about 65.3% of the total variation. The first PC was correlated with those characteristics that were mainly related to the terminal leaf length(TLL), leaf length(LL), fruit length(FL), terminal leaf width(TLW), and leaf petiole length(LPL). The second and third PC was mainly correlated with the terminal leaf morphological index(TLMI). Therefore, these characteristics were important to analysis of the fruit and leaf morphological characteristics and classification among the five Zizyphus cultivars. Cluster analysis using UPGMA method based on principal components showed that five Zizyphus cultivars could be clustered into two groups. Group I comprises Mudung, Wolchul, and Bokjo and Geumsung cultivars, Group II is Boeun cultivar. These results well similar to that of principal component analysis.