• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.4
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• pp.1275-1289
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• 1996

The plane elasticity problem of collinear cracks in a layered medium is investigated. The medium is modeled as bonded structure constituted from a surface layer and a semi-infinite substrate. Along the bond line between the two dissimilar homegeneous constituents, it is assumed that as interfacial zone having the functionally graded, nonhomogeneous elastic modulus exists. The layered medium contains three collinear cracks, one in each constituent material oriented perpendicular to the nominal interfaces. The stiffness matrix formulation is utilized and a set of homogeneous conditions relevant to the given problem is readily satisfied. The proposed mixed boundary value problem is then represented in the form of a system of integral equations with Cauchy-type singular kernels. The stress intensity factors are defined from the crack-tip stress fields possessing the standard square-root singular behavior. The resulting values of stress intensity factors mainly address the interactions among the cracks for various crack sizes and material combinations.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.865-872
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• 2008

Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.586-591
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• 2001

This research investigated the feasibility of the accelerated carbonation of cement waste forms with carbon dioxide in a supercritical state. Hydraulic cement has been used as a main solidification matrix for the immobilization of radioactive and/or hazardous wastes. As a result of the hydration reaction for major compounds of portland cement, portlandite (Ca(OH)$_2$) is present in the hydrated cement waste form. The chemical durability of a cement form is expected to increase by converting portlandite to the less soluble calcite (CaCO$_3$). For a faster reaction of portlandite with carbon dioxide, SCCD (supercritical carbon dioxide) rather than gaseous $CO_2$, in ambient pressure is used. The cement forms fabricated with an addition of slated lime or Na-bentonite were cured under ambient conditions for 28days and then treated with SCCD in an autoclave maintained at 34$^{\circ}C$ and 80atm. After SCCD treatment, the physicochemical properties of cement matrices were analyzed to evaluate the effectiveness of accelerated carbonation reaction. Conversion of parts of portlandite to calcite by the carbonation reaction with SCCD was verified by XRD (X-ray diffraction) analysis and the composition of portlandite and calcite was estimated using thermogravimetric (TG) data. After SCCD treatment, tile cement density slightly increased by about 1.5% regardless of the SCCD treatment time. The leaching behavior of cement, tested in accordance with an ISO leach test method at 7$0^{\circ}C$ for over 300 days, showed a proportional relationship to the square root of the leaching time, so the major leaching mechanism of cement matrix was diffusion controlled. The cumulative fraction leached (CFL) of calcium decreased by more than 50% after SCCD treatment. It might be concluded that the enhancement of the characteristics of a cement matrix by an accelerated carbonation reaction with SCCD is possible to some extent.

• Journal of Pharmaceutical Investigation
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• v.17 no.3
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• pp.111-126
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• 1987

Methyl methacrylate-butyl methacrylate copolymer (MMBM)-dibutyl phthalate (DBP) films were investigated as a potential topical drug delivery system for the controlled release of nitrofurazone. The kinetic analysis of release data indicated that drug release followed a diffusion-controlled granular matrix model, where the quantity released per unit area is proportional to the square root of time. DBP of several hydrophobic plasticizers selected was found to give the highest release of nitrofurazone. However, hydrophilic plasticizers such as propylene glycol and polyethylene glycol 400 had no controlled release properties and acceptable film formation. The effects of changes in film composition, drug concentration, film thickness, pH of release medium, and temperature on the in vitro release of nitrofurazone were analyzed both theoretically and experimentally. The release rate constant (k') was found to be proportional to DBP content, pH, and the temperature of release medium, but independent of film thickness, and drug concentration in a range of 0.1-0.4% by weight. The linear relationship was found to exist between the log k' and DBP content. The release of nitrofurazone from MMBM-DBP (8:2) films was found to be an energy-linked process. Two energy terms were calculated ; the activation energy for matrix diffusion was 13.45 kcal/mole, and the heat of drug crystal solvation was 27.26-29.34 kcal/mole. Observation of scanning electron micrographs and microscopic photographs showed that the incorporation of DBP in films increased markedly the particle size of nitrofurazone dispersed in the film matrix, comparing with the fine dispersion of nitrofurazone in pure MMBM film alone.

• Genomics & Informatics
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• v.21 no.1
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• pp.9.1-9.13
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• 2023

Matrix metalloproteinase-9 (MMP-9) is a zinc and calcium-dependent proteolytic enzyme involved in extracellular matrix degradation. Overexpression of MMP-9 has been confirmed in several disorders, including cancers, Alzheimer's disease, autoimmune diseases, cardiovascular diseases, and dental caries. Therefore, MMP-9 inhibition is recommended as a therapeutic strategy for combating various diseases. Cinnamic acid derivatives have shown therapeutic effects in different cancers, Alzheimer's disease, cardiovascular diseases, and dental caries. A computational drug discovery approach was performed to evaluate the binding affinity of selected cinnamic acid derivatives to the MMP-9 active site. The stability of docked poses for top-ranked compounds was also examined. Twelve herbal cinnamic acid derivatives were tested for possible MMP-9 inhibition using the AutoDock 4.0 tool. The stability of the docked poses for the most potent MMP-9 inhibitors was assessed by molecular dynamics (MD) in 10 nanosecond simulations. Interactions between the best MMP-9 inhibitors in this study and residues incorporated in the MMP-9 active site were studied before and after MD simulations. Cynarin, chlorogenic acid, and rosmarinic acid revealed a considerable binding affinity to the MMP-9 catalytic domain (ΔG_binding < -10 kcal/ mol). The inhibition constant value for cynarin and chlorogenic acid were calculated at the picomolar scale and assigned as the most potent MMP-9 inhibitor from the cinnamic acid derivatives. The root-mean-square deviations for cynarin and chlorogenic acid were below 2 Å in the 10 ns simulation. Cynarin, chlorogenic acid, and rosmarinic acid might be considered drug candidates for MMP-9 inhibition.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.193-197
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• 2003

Radar target identification can be achieved by using various radar signatures, such as one-dimensional(1-D) range profile, 2-D radar images, and 1-D or 2-D scattering centers on a target. In this letter, five 1-D scattering center extraction methods are discussed - TLS(Total Least Square)-Prony, Fast Root-MUSIC (Multiple Signal Classification), Matrix-Pencil, GEESE(GEneralized Eigenvalues utilizing Signal-subspace Eigenvalues), TLS-ESPRIT(Total Least Squares - Estimation of Signal Parameters via Rotational Invariance Technique), These methods are compared in the context of estimation accuracy as well as a computational efficiency using a noisy data. Finally these methods are applied to the target classification experiment with the measured data in the POSTECH compact range facility.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• Bulletin of the Korean Chemical Society
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• v.29 no.4
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• pp.833-841
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• 2008

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.53-54
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• 2015

The maritime industry is expanding at an alarming rate and as such there is a perpetual need to improve situation awareness in the maritime environment using new and emerging technology. Tracking is one of the numerous ways of enhancing situation awareness by providing information that may be useful to the operator. The tracking system described herein comprises determining existing states of own ship, state prediction and state compensation caused by random noise. The purpose of this paper is to analyze the process of tracking and develop a tracking algorithm by using ${\alpha}-{\beta}-{\gamma}$ tracking filter under a random noise or irregular motion for use in a warship. The algorithm involves initializing the input parameters of position, velocity and course. The actual positions are then computed for each time interval. In addition, a weighted difference of the observed and predicted position at the nth observation is added to the predicted position to obtain the smoothed position. This estimation is subsequently employed to determine the predicted position at (n+1). The smoothed values, predicted values and the observed values are used to compute the twice distance root mean square (2drms) error as a measure of accuracy of the tracking module.