• International Commerce and Information Review
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• v.9 no.2
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• pp.65-86
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• 2007

This paper reviews the properties and application methods of widely used types of risk measures, identifies the rationale and business-side effects of hedging, derives the theoretical formula of optimal hedging ratio, and analyzes the various functional aspects of VaR(Value-at-risk) as a risk measure and a hedging tool. Especially this paper focuses on the characteristics of VaR compared with other risk measures in terms of their own principal determinants and identifies its stronger aspects in the dimension of hedging strategy tools. As well, this paper provides the detailed processes deriving the optimal hedge ratios based on the distributional parameters and risk factors. In addition, this paper presents the detailed and substantial processes of estimating the minimum variance hedge ratio and minimum-VaR hedge ratio using the actual data and shows that the minimum variance hedge ratio proves helpful for many cases although it is not appropriate for the non-linear portfolio including the option contracts. We demonstrate the trade-off relationship between the minimum variance hedge strategy and the minimum-VaR hedge strategy in their hedging costs and performances through calculation of the respective VaRs and variances of unhedged and hedged portfolios and the optimal hedge ratio and hedging effectiveness values for the given long position in US Dollar with the short position in Euro.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.687-692
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• 2006

In a conventional ICA(Independent Component Analysis) based face recognition method, PCA(Principal Component Analysis) first is used for feature extraction, ICA learning method then is applied for feature enhancement in the reduced dimension. It is not considered that a necessary component can be located in the discarded feature space. In the new ICA(NICA), learning extracts features using the magnitude of kurtosis (4-th order central moment or cumulant). But, the pure ICA method can not discard noise effectively. The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. Namely, PCA does whitening and noise filtering. ICA performs feature extraction. Experiment results show the effectiveness of the new ICA method compared to the conventional ICA approach.

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.99-104
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• 2014

Sensor arrays based on chemical sensors produce multidimensional patterns of data that may be used discriminate between different chemicals. For the human observer, visualization of multidimensional data is difficult, since the eye and brain process visual information in two or three dimensions. To devise a simple means of data inspection from the response of sensor arrays, PCA (Principal Component Analysis) or Sammon's nonlinear mapping technique can be applied. The PCA, which is a well-known statistical method and widely used in data analysis, has disadvantages including data distortion and the axes for plotting the dimensionally reduced data have no physical meaning in terms of how different one cluster is from another. In this paper, we have investigated two techniques and proposed a combination technique of PCA and nonlinear Sammom mapping for visualization of multidimensional patterns to two dimensions using data sets from odor sensing system. We conclude the combination technique has shown more advantages comparing with the PCA and Sammon nonlinear technique individually.

• Journal of Families and Better Life
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• v.19 no.5
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• pp.51-66
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• 2001

This study attempted to examine the changing patterns of marital love constructs among married men and women in Korea. It is based on our prior research that showed each of the 3 dimensions of marital love(intimacy, passion, and commitment derived from Stermberg’s Triangular Theory of Love) shaped U pattern as the marriage continued. We analyzed 1687 respondents’answers by principal axis factoring with contextual point of view including individual, familial, and socio-cultural development. The results are as follows. First, the love constructs of Korean men and women in their marriage show different qualitative patterns as the marriage continued. Second, intimacy is the most powerful indicator of love, coming out the first factor in all the stages of marital relationships. Third we can see marital stability from the locus of commitment with other components of love and it could be apply to marital educational program for promoting marital stability. Forth, passion is found separated behavioral from perceived dimension except the first marital stage. As conclusion, it need to analyze with family life cycle. birth and marriage cohort groups for examining closely the causes of this qualitative changes in marital relationships.

• Journal of the Korean Chemical Society
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• v.9 no.4
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• pp.174-178
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• 1965

p-Phenylenediamine dihydrobromide and p-phenylenediamine dihydrochloride are found to be isomorphous. p-Phenylenediamine dihydrobromide is triclinic with lattice parameters, $a=4.52{\pm}0.02{\AA}\;b=6.13{\pm}0.02{\AA},c=8.88{\pm}0.03{\AA},\;{\alpha}=111{\pm}1^{\circ},\;{\beta}=97{\pm}1^{\circ},\;{\gamma}=101{\pm}1^{\circ}.$ It belongs to space group $P\bar{1}$, and there is one molecule in the unit cell. The crystal structure is determined according to the method of Fourier synthesis from the electron density projections in three principal crystallographic axes. The crystal structure, thus determined is refined by the method of two-dimensional difference Fourier synthesis.

• IE interfaces
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• v.25 no.2
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• Cross-Cultural Studies
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• v.26
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• pp.297-319
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• 2012

We all know how beautiful our wild and it's importance to our living planet Earth. But did you realise the speed at which man himself is damaging it's unique natural habitat. We are well on our way to destroy our forests, plants, wetlands. We are polluting our oceans and seas. This way, we're driving numerous animal species, plant species and many others into extinction. Everyone should be aware of the importance of our natural environment. We live in the period of echocide. Why we need nature to survive and how we can deal with the environmental problems we face. This paper has the purpose to reform city's environment. Because, Metropolis and megalopolis are the principal cause of environmental disruption. To reform the city is needed to consider digital technology in our age. In the face of economic and cultural globalization, many have argued that we live an increasingly placeless world. However, as a growing number of cities participate and compete in key marketplaces of advanced capitalism, the spectacle of the city is more than ever a significant medium of communication in its own right. In doing so, this work is focused specifically on the dimension of city's media environment. To that end, the paper examined U-City and U-Eco city. In this study, we will introduce the study on model of U-Eco City as one way for the eco-freindly future city.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Journal of Korea Water Resources Association
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• v.44 no.4
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• pp.295-304
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• 2011

For effective water quality management, it is necessary to secure reliable water quality information. There are many variables that need to be included in a comprehensive practical monitoring network : representative sampling locations, suitable sampling frequencies, water quality variable selection, and budgetary and logistical constraints are examples, especially sampling location is considered to be the most important issues. Until now, monitoring network design for water quality management was set according to the qualitative judgments, which is a problem of representativeness. In this paper, we propose network design system for optimal water quality monitoring using the scientific statistical techniques. Network design system is made based on the SAS program of version 9.2 and configured with simple input system and user friendly outputs considering the convenience of users. It applies to Excel data format for ease to use and all data of sampling location is distinguished to sheet base. In this system, time plots, dendrogram, and scatter plots are shown as follows: Time plots of water quality variables are graphed for identifying variables to classify sampling locations significantly. Similarities of sampling locations are calculated using euclidean distances of principal component variables and dimension coordinate of multidimensional scaling method are calculated and dendrogram by clustering analysis is represented and used for users to choose an appropriate number of clusters. Scatter plots of principle component variables are shown for clustering information with sampling locations and representative location.