• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.105-110
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• 2008

This paper presents a control method to maintain stable mold level for unsteady bulging in high speed casting. The stabilizing mold level is an important factor to get high quality products. But, for various reasons, there are several disturbance factors which make mold level unstable. Bulging is one of disturbance factors and considered for the current stage of study. We design a disturbance observer to attenuate the effect of bulging which is periodic mold level disturbance. Since the period of bulging varies with the casting speed, the parameters of disturbance observer are changed by the casting speed. Simulation is illustrated to demonstrate stable performance of the proposed method in comparison with a previous method in a variable operating frequency.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.11
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• pp.587-596
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• 1999

The use of Switching elements in power systems causes the current or voltage to involve harmonic waves. Harmonics bring about registration errors of the equipment for measuring power. In case the induction watthour meter designed on sinusoidal source is used in the measurement of power with harmonics, the precise measurement of power has many problems because harmonics cause a decrease of power factor and vibration by the unstable driving force on the aluminum disc. In this paper, analysis and test results on the harmonic sensitivity of an induction watthour meter is reported when the input voltage and current with harmonics were supplied to single-phase watthour meter.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.3
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• pp.182-188
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• 2006

Contactless power system is base on power transmission by magnetic force. The transformer loss is large because it separated with the gap. Also the system has unstable factor, since the parameters in the secondary can vary with the system movement. This paper proposes light train power transmission system using contactless transformer with multiple primary winding. To increase the system efficiency and to obtain the stable power transmission to the dynamic load, a resonant inverter is adopted. The proposed system was verified by the simulation using Spice and Maxwell. The designed contactless power transmission system is implemented for 5[kW] class and experimental results are discussed.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.265-275
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• 1998

In testing the partial association between two variables after controlling for the S levels of a third factor, the Mantel and Haenszel (1959) statistic is often used. Since the statistic is based on the asymptotic distribution of the sum X of S hypergeometric variates, a guideline for the minimum requirements for the application of the statistic is useful. Mantel and Fleiss (1980) developed a criterion based on the guideline for the Pearson's $X^2$ statistic. The criterion requires the distance from the expected value to the closer bound of X to be at least five. The Mantel-Fleiss (MF) criterion was studied through a simulation using the hypergeometric sampling scheme. The criterion is not satisfactory. The size of statistic exceeded nominal 0.05 level nearly 1/5 of the cases even when the criteion is met. However, the results show that the statistic is much more unstable and conservative when the criterion is not met.

• International Journal of Automotive Technology
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• v.8 no.2
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• pp.219-224
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• 2007

Recently, atmospheric contaminations has become worse due to the increased number of automobile. The PCV (Positive Crankcase Ventilation) valve acts as a flow control to allow re-combustion of blow-by gas by having it flow from a crankcase to an inlet manifold suction tube. Also, during the fabrication of the PCV valve, micro cracks may occur in the valve body and be extended under operation. The excessive stress distribution and crack initiation on the PCV valve body would bring an unstable blow-by gas flow rate control and would cause valve failure. The purpose of this study is to examine the crack affects on the stress and strain variations on the PCV valve according to the inlet and outlet manifold under differential pressures. From the results, we can explain the behavior of the crack extension for a safe condition of PCV valve.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.67-68
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• 2006

Organic light-emitting devices (OLEDs) with the high efficiency and long lifetime are of growing interest in next-generation displays. Among the factors influencing OLEDs properties, one of unstable factor is $Alq_3$ cationic species caused by the excess holes resided in $Alq_3$ layer. Therefore, we suppressed the accumulation of excess holes by using the mixed-hole transporting layer (MHTL) of NPB and CBP in multilayer green OLEDs. The devices with MHTL showed improved characteristics in the luminance efficiency and lifetime. More characteristics and the carrier transport mechanism will be discussed.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.44-54
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• 1998

In this study a new arc sensor algorithm for automatic weld seam tracking was proposed, which uses the relative welding current variation according to the tip-to-workpiece distance in GMA welding. Since the new developed arc sensor algorithm is not sensitive to unstable factors of arc signal, the system is expected to get rid of the problems of already existing arc sensor system which include the difficulty of modeling the process for various welding conditions and limitation of application to thick plate welding. Thus the system is applicable not only to thick plate welding but also to thin plate welding. To implement the new arc sensor algorithm the system parameters which include sampling time, averaging range, weighting factor of moving averaging, basic compensation time, and basic compensation distance were determined by experimental analysis. Consequently this system has shown the successful tracking capability for the various welding conditions.

• Archives of Pharmacal Research
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• v.22 no.3
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• pp.274-278
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• 1999

Expression of the active urease of the enterobacterium, Klebsiella aerogens, requires the presence of the accessory genes (ureD, ureE, ureF, and ureG) in addition to the three structural genes (ureA, ureB, and ureC). These accessory genes are involved in functional assembly of the nickel-metallocenter for the enzyme. Characterization of ureF gene has been hindered, however, since the UreF protein is produced in only minute amount compared to other urease gene products. In order to overexpress the ureF gene, a recombinant pMAL-UreF plasmid was constructed from which the UreF was produced as a fusion with maltose-binding protein. The MBP-UreF fusion protein was purified by using an amylose-affinity column chromatography followed by an anion exchange column chromatography. Polyclonal antibodies raised against the fusion protein were purified and shown to specifically recognize both MBP and UreF peptides. The UreF protein was shown to be unstable when separated from MBP by digestion with factor Xa.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.431-436
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• 2008

T-burner tests of an Al/HTPB propellant in conjunction with a Pulsed DB/AB Method were conducted to find an acoustic amplification factor. Aluminum-free and aluminum-heavy propellants were examined. Instant surface ignition was successfully made by the use of a supplementary propellant of fractionally higher reaction rate. With the presence of higher aluminum concentration in the propellants, the pressure perturbations were promptly damped down and the pressure fluctuations were no longer dispersive. Addition of aluminum particles into the propellant was advantageous for stabilizing pressure-coupled unstable waves.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.