• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.81-86
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• 2004

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.639-642
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• 1999

There exist geometrically invariant relations in single-view images under a specific geometrical structure. This invariance may be utilized for 3D object recognition. Two types of invariants are compared in terms of the robustness to the variation of the feature points. Deviation of the invariant relations are measured by adding random noise to the feature point location. Zhu’s invariant requires six points on adjacent planes having two sets of four coplanar points, whereas the Kaist method requires four coplanar points and two non-coplanar points. Experimental results show that the latter method has the advantage in choosing feature points while suffering from weak robustness to the noise.

• Journal of the Korean Chemical Society
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• v.58 no.6
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• Journal of The Korean Astronomical Society
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• v.36 no.2
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• pp.61-66
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• 2003

We have examined the relationship between the speeds of coronal mass ejections (CMEs) and the GOES X-ray peak fluxes of associated flares. Noting that previous studies were possibly affected by projection effects and random association effects, we have considered two sets of carefully selected CME-flare events: four homologous events and four well-observed limb events. In the respective samples, good correlations are found between the CME speeds and the GOES X-ray peak fluxes of the associated flares. A similarly good correlation is found for all eight events of both samples when the CME speeds of the homologous events are corrected for projection effect. Our results suggest that a close relationship possibly exists between CME kinematics and flaring processes.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.27-30
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• 2003

The recent technology developments introduce new difficulties into the test process by the increased complexity of the chip. Most widely used method for testing high complexity and embedded systems is built-in self-test(BIST). In this paper, we describe 5-stage pipeline system as circuit under testing(CUT) and proposed a BIST scheme for the hazard detection unit of the pipeline system. The proposed BIST scheme can generate sequential instruction sets by pseudo-random pattern generator that can detect all hazard issues and compare the expected hazard signals with those of the pipelined system. Although BIST schemes require additional area in the system, it proves to provide a low-cost test solution and significantly reduce the test time.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1103-1111
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• 2000

The hyper-geometric distribution software reliability growth model (HGDM) usually assumes that all the software faults detected are perfectly removed without introducing new faults. However, since new faults can be introduced during the test-and-debug phase, the perfect debugging assumption should be relaxed. In this context, Hou, Kuo and Chang [7] developed a modified HGDM for imperfect debugging environment, assuming tat the learning factor is constant. In this paper we extend the existing imperfect debugging HGDM for tow respects: introduction of random sensitivity factor and allowance of variable learning factor. Then the statistical characteristics of he suggested model are studied and its applications to two real data sets are demonstrated.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.191-194
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• 2001

In general, the imputation problems which are caused from survey nonresponse have been studied for being based on ignorable cases. However the model based approach can be applied to survey with nonresponse suspected of being nonignorable. Here in this study, we will make the nonresponse for nonignorable into ignorable cell using adjustment cell approach, then we can applied the ignorable nonresponse method. For data sets of each nonresponse cells are simulated from normal distribution.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.336-346
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• 1995

In many problems concerned with statistical inferences, it will be of interest to compute tail areas rather than densities. But, it is often hard to calculate the exact tail probability. Saddlepoint approximation formula to the tail probability of a smooth function of random cector is developed by DiCiccio and Martin(1991). Applications of this method to stress-strength model are considered in this paper. To obtain the generalized p-values suggested by Tsui and Weerahandi(1989), we need to calculate complicated multiple integration. However, DiCiccio and Martin's(1991) results offer a convenient method to approximate these very accurately. For many artificial data sets, we access the accuracy of DiCiccio and Martin's by comparing the approximate value with the exact one.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.385-396
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• 2018

Competing risks are commonly encountered in biomedical research. Regression models for competing risks data can be developed based on data routinely collected in hospitals or general practices. However, these data sets usually contain the covariate missing values. To overcome this problem, multiple imputation is often used to fit regression models under a MAR assumption. Here, we introduce a multivariate imputation in a chained equations algorithm to deal with competing risks survival data. Using pseudo-observations, we make use of the available outcome information by accommodating the competing risk structure. Lastly, we illustrate the practical advantages of our approach using simulations and two data examples from a coronary artery disease data and hepatocellular carcinoma data.

• Journal of the Korean Home Economics Association
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• v.23 no.3
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• pp.149-167
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• 1985

This paper aims at measuring the quality of life in the context of social well being. To meet this purpose, several objective and subjective indicators of the quality of life were selected them the recent theoretical studies in thisfield. Then, a zuestionnaire survey was made on random sampled urban failies to find out the relationship among the two sets of indicators and the quality of family life. The data obtained from the survey were analyzed using Pearson's correlation analysis, multiple regression methods and One-way ANOVA. RESULTS : 1. The quality of life of the high-income earners is higher than that of the low-income earners. 2. The quality of life tends to be high in the family with spouse'high educational background. 3. The degree of coctribution of the objective and subjective indicator to the quality of life was very high. 4. The objective indicator and the subjective indicator concerning the quality of life was closely related.