• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.376-383
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• 2008

Generally a return current plane(RCP) of high speed digital and analog part is partitioned. This is achieved in order to decrease the noise interference between subsystem in PCBs(Printed Circuit Boards). However, when the connected signal line exists between each subsystem, this partition will cause unwanted effects. In a EMI(Electromagnetic Interference) point of view, the partition of the return current plane becomes a primary factor to increase the radiated emission. Component bridge(CB) is used for the way of maintaining radiated emission, still specific user's guide doesn't give sufficient principle. In a view point of EMI, design principle of multi-CB using method will be analyzed by measurement. And design principle of noise mitigation will be provided. Generally interval of multi-CB is ${\lambda}/20$ ferrite bead. In this study, When multi-CB connection is applied, design principle of ferrite bead and chip resistor is proved by measurement. Multi-connected chip resistance$(0{\Omega})$ is proved to be more effective design method in the point of EMI.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.366-375
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• 2008

Generally a return current plane(RCP) of high speed digital and analog part is partitioned. This is achieved in order to decrease the noise interference between subsystem in PCBs(Printed Circuit Boards). However, when the connected signal line exists between each sub system, this partition will cause unwanted effects. In a circuital point of view, RCP partition has a bad influence upon signal integrity. In a EMI(Electromagnetic Interference) point of view, the partition of the return current plane becomes a primary factor to increase the radiated emission. Component bridge(CB) is usecl for the way of maintaining signal integrity, still specific user's guide doesn't give sufficient principle. In a view point of signal integrity, design principle of multi-CB using method will be analyzed by measurement and simulation. And design principle of noise mitigation will be provided. Generally interval of CB is ${\lambda}/20$ ferrite bead. In this study. When multi-CB connection is applied, design principle of ferrite bead and chip resistor is proved by measurement and simulation. Multi-connected chip resistance$(0{\Omega})$ is proved to be more effective design method in the point of signal integrity.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.254-257
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• 2012

Hedge funds can be established in Korea after the deregulation about setting up private equity funds on September, 2011. Although the variety of asset allocation strategies is the strength of hedge funds, most of Korean hedge funds uses only the equity long/short strategy. Therefore, it is need to introduce other strategies into Korea hedge funds, however all strategies can not be adopted at once because of the infrastructure of Korea financial market. In this paper, we find the optimal introductive order of strategies for Korea hedge fund in view of individual or institutional investors. For this analysis, HFRI data are used for the historical return of each hedge fund strategy and three methods (network visualization, principle component analysis and efficient frontier optimization) are used for finding the optimal order.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.454-460
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• 2003

In this paper, we propose an efficient method for recognizing human lips. Based on Point Distribution Model, a lip shape is represented as a set of points. We calculate a lip model and the distribution of shape parameters using Principle Component Analysis and Gaussian mixture, respectively. The Expectation Maximization algorithm is used to determine the maximum likelihood parameter of Gaussian mixture. The lip contour model is derived by using the gray value changes at each point and in regions around the point and used to search the lip shape in a image. The experiments have been performed for many images, and show very encouraging result.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.370-373
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• 2005

We have developed Hybrid algorithm for yellow sand detection. Hybrid algorithm is composed of three methods using infrared bands. The first method used the differential absorption in brightness temperature difference between $11\mu m\;and\;12\mu m$ (BID _1), through which help distinguish the yellow sand from various meteorological clouds. The second method uses the brightness temperature difference between $3.7\mu m\;and\;11\mu m$ (BID_2). The technique would be most sensitive to dust loading during the day when the BID _2 is enhanced by reflection of $3.7\mu m$ solar radiation. The third one is a newly developed algorithm from our research, the so-called surface temperature variation method (STY). We have applied the three methods to MODIS for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. PCI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between PCI and MODIS aerosols optical depth (AOD) shows remarkable good correlations during daytime and relatively good correlations over the land.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.4
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• pp.62-71
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• 2009

In this paper, we propose a fault diagnosis algorithm for BLDC motors by principle component analysis (PCA) and implement a real-time fault diagnosis system for BLDC motors. To verify the proposed diagnosis algorithm, various faulty data are acquired by Lab VIEW program from experimental system. We extract a fault feature using principle component analysis after preprocessing and then finally the fault diagnosis is performed by Euclidean similarity. Also, we embed the PCA algorithm and k-NN classification algorithm into a digital signal processor. From various experiments, we found that the proposed algorithm can be used as a powerful technique to classify the several fault signals acquired from BLDC motors.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• Environmental Engineering Research
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• v.15 no.2
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• pp.93-98
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• 2010

This study aims to determine the chemical composition and seasonal variation of atmospheric acid deposition in order to identify possible sources contributing to precipitation. Sampling and analysis of 132 wet deposition samples were carried out from January to December 2008 at Mae Hia Research Center, Chiang Mai University, Chiang Mai Province. Total precipitation was 1,286.7 mm. Mean electro-conductivity and pH values were 0.94 mS/m and 6.27, respectively. Major cations ($Na^+$, ${NH_4}^+$, $K^+$, $Ca^{2+}$, and $Mg^{2+}$) and major anions ($HCOO^-$, $CH_3COO^-$, $Cl^-$, ${NO_3}^-$, and ${SO_4}^{2-}$) were determined by Ion Chromatography. The relative volume weight mean concentrations of anions, in descending order, were ${SO_4}^{2-}$ > ${NO_3}^-$ > $Cl^-$ > $CH_3COO^-$ > $HCOO^-$ and those of cations were $NH_4^+$ > $Ca^{2+}$ > $Mg^{2+}$ > $K^{+}$ > $Na^+$. Results of a principle component analysis highlighted the influence of various possible sources of ions such as agricultural activity, fuel combustion, marine sources, soil resuspension, and biomass burning.