• The Korean Journal of Food And Nutrition
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• v.30 no.2
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• pp.363-370
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• 2017

This study investigated the chemical composition of headspace gas from white-flowered lotus (Nelumbo nucifera Gaertner). Volatile flavor compositions of headspace from white-flowered lotus (floral leaf, stamen, flower stalk, stem) were investigated through the solid-phase microextraction method using polydimethylsiloxane-divinylbenzene fiber. The headspace was directly transferred to a gas chromatography-mass spectrometry. Sixty-three volatile flavor constituents were detected in the headspace of lotus floral leaves, and undecanoic acid (7.81%) was the most abundant component. Fifty-three volatile flavor constituents were detected in the headspace of lotus stamina, and isobutylidene phthalide (7.94%) was the most abundant component. Forty-four volatile flavor constituents were detected in the headspace of lotus flower stalks, and 3-butyl dihydrophthalide (11.23%) was the most abundant component. Fifty-nine volatile flavor constituents were detected in the headspace of lotus stems, and ligustilide (16.15%) was the most abundant component. The content of phthalides was higher in the headspace of flower stalks and stems, while alcohols and acids were the predominant compounds in lotus floral leaves.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.41-44
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• 2002

Alamouti proposes a two branch transmit diver-sity scheme that provides the same diversity order as maximal ratio combining at the receiver. It has many advantages of no bandwidth expansion, not requiring channel information at the transmitter and simple maximum likelihood decoding at the receiver. Papadias and Foschini extend this sch-eme to four transmit antennas and suggest several schemes to decrease the interference component and allow the attainment of the open-loop capacity. This paper shows the performance of ZF and MM-SE schemes comparing with ideal case on 4xl sy-stem over BER and 10% outage capacity.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.897-902
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• 2002

Because of rapid evolution of software technique, numerous software professionals have been concerned with component based development methodologies. However, it is hard to find out a systematic technique for the selection of COTS (Commercial Off The Shelf) component in consumer position. Up to date, the major of component quality evaluation is object-oriented metric based evaluation methodology. But this paper present four step process and evaluation criteria based on MCDM (Multiple Criteria Decision Making) technique for optimal COTS component selection in consumer position. We considered funtionality, efficiency, usability based on IS0/IEC 9126 for Quality measurement and executed practical analysis about commercial EJB component in internet. This paper show that the proposed selection technique is applicable to optimal COTS component selection.

• Journal of the Korean Society of Clothing and Textiles
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• v.14 no.3 s.35
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• pp.208-215
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• 1990

The purpose of this study is to provide the fundamental data of a dummy design for more suitable ready made clothing by making a pattern of somatic types and analyzing their morphological characteristics in accordance with different pattern of somatic types. The side view silhouettes of 90 junior high school girls of age $13\~16$ in seoul urban area were measured by means of the plan photographing and the low data were examined by principal component analysis, while the principal component analysis was applied and three components were extracted and then interpreted to explain to variation of the form of the body. Using three components respectively the cluster analysis was carried out and the subject classified into 4 cluster The following outcomes are obtained. . The results of principal component analysis of this study would be turned out the three; 1) The first principal component shows the degree of erectness or stoop of the figure. 2) The second principal component was a stature length or a growth rate. 3) The third principal component was the obesity component. 2. The results of cluster analysis by using three principal component analysis would be turned out the four cluser; 1) Cluster 1 ($29\%$ of the total) is characterized with lower stature. 2) Cluster 2 ($21\%$ of the total) is characterized with backward somatotype, and the highest leg. 3) Cluster 3 ($23\%$ of the total) is thicked back of neck. 4) Cluster 4 ($27\%$ of the total) is characterized with forward somatotype, and highest stature, height.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.105-118
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• 2008

There are many debates on the topic of the relationship between oil prices and economic growth. Through the repeated processes of conformations and contractions on the subject, two main issues are developed; one is how to define and drive oil shocks from oil prices, and the other is how to specify an econometric model to reflect the asymmetric relations between oil prices and output growth. The study, thus, introduces the unobserved component model to pick up the oil shocks and a first-order Markov switching model to reflect the asymmetric features. We finally employ unique oil shock variables from the stochastic trend components of oil prices and adapt four lags of the mean growth Markov Switching model. The results indicate that oil shocks exert more impact to recessionary state than expansionary state and the supply-side oil shocks are more persistent and significant than the demand-side shocks.

• Journal of KSNVE
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• v.7 no.6
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• pp.1025-1030
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• 1997

This paper introduces the experimental results of ride values assessed for several passenger cars. The experiment was conducted at four vehicles on two roads for three persons by measuring the acceleration in the 12-axis of human. The results include the comparison of the ride values, such as the component ride values, overall ride value, and seat effective amplitude transmissibiity. It is proved that acceleration between 1 and 15Hz is the most significant in evaluating the ride quality. The contribution of the acceleration in each measurement axis is quantified from the component ride value. SEAT value shows a relatively low sensitivity for the road condition and human mass.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.598-605
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• 2003

In this paper, we propose the method that separates PPG signal and motion artifact signal from two input signals using new independent component analysis algorithm in time domain. In order to eliminate the large level artifact efficiently, block interleaving. lowpass time filtering and innovation processing technique were applied in ICA preprocessing, and FastICA algorithm were applicable. Experiments are made with the numerical simulation and the real PPG signal including four kinds of motion artifact pattern. Our results show that ICA can effectively detect, separate and remove motion artifact in input signals. Then from the separated signals we restore the original PPG signal and propose a new method which computes SpO$_2$ using ICA mixing matrix.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.90-101
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• 1996

Determination of assembly sequence of components is a key issue in assembly operation. Although a number of articles dealing with assembly sequence determination have appeared, an efficient and general methodology for complex products has yet to appear. The objective of this paper is to present the problems and models used to generate assembly sequence from design data. An essential idea of this research is to acquire a finite number of voxels from any complex geometric entity, such as 3D planar polygons, hollow spheres, cylinders. cones, tori, etc. In order to find a feasible assembly sequence, the following four steps are needed: (1) The components composing of an assembly product are identified and then the geometric entities of each component are extracted. (2) The geometric entities extracted in the first step are translated into a number of voxels. (3) All the mating or coupling relations between components are found by considering relations between voxels. (4) The components to be disassembled are determined using CCGs (Component Coupling Graph).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.790-795
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• 1994

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item, component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introducd to Hierarchical Artificial Neural Network(HANN) for this purpose. HANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification,forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trainined by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing HANN with multitasking and message transfer between processes in SUN workstation. We tested HANN in reactor system.