• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.210-218
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• 2004

Poly(ADP-ribose)polymerase-1 (PARP-1) is a nuclear enzyme involved in various physical functions related to genomic repair, and PARP inhibitors have therapeutic application in a variety of neurological diseases. Docking and the QSAR (quantitative structure-activity relationships) studies for 52 PARP-1 inhibitors were conducted using FlexX algorithm, comparative molecular field analysis (CoMFA), and hologram quantitative structure-activity relationship analysis (HQSAR). The resultant FlexX model showed a reasonable correlation (r$^{2}$ = 0.701) between predicted activity and observed activity. Partial least squares analysis produced statistically significant models with q$^{2}$ values of 0.795 (SDEP=0.690, r$^{2}$=0.940, s=0.367) and 0.796 (SDEP=0.678, r$^{2}$ = 0.919, s=0.427) for CoMFA and HQSAR, respectively. The models for the entire inhibitor set were validated by prediction test and scrambling in both QSAR methods. In this work, combination of docking, CoMFA with 3D descriptors and HQSAR based on molecular fragments provided an improved understanding in the interaction between the inhibitors and the PARP. This can be utilized for virtual screening to design novel PARP-1 inhibitors.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.9-16
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• 2015

In this paper, selection criterions on selection diversity are researched. The diversity is applied to the multiple antenna system based on wireless access in vehicular environment (WAVE) standard for rapid varying channel. Least squares (LS) based decision feedback equalizer (DFE) are used for channel equalization. Received signal is regenerated by means of the decision feedback path. In the selection diversity, the regenerated signal as well as the received signal is selected according to selection criterion. The decision feedback algorithm can follow the fast speed of WAVE fading channel. To control the tracking speed of the time-varying channel, simple low pass filter is used. Finally, the estimated channel value recovers the distorted payloads. Signal power before automatic gain control (AGC) in analog stage can be used as a selection criterion. In the digital stage, signal power after AGC, noise power after AGC, signal to noise ratio after AGC and cross-correlation method can be used as selection criterions. According to the simulation results, the performance of the selection diversity is improved in comparison with that of the combining diversity for the WAVE fading channel.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.663-668
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• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2498-2504
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• 2009

The fundamental research for the mobile robot navigation using the numerical optimization method is presented. We propose an image-based visual servo navigation algorithm for a wheeled mobile robot utilizing a ceiling mounted camera. For the image-based visual servoing, we define the composite image Jacobian which represents the relationship between the speed of wheels of a mobile robot and the robot's overall speed in the image plane. The rotational speed of wheels of a mobile robot can be directly related to the overall speed of a mobile robot in the image plane using the composite image Jacobian. We define the mobile robot navigation problem as an unconstrained optimization problem to minimize the cost function with the image error between the goal position and the position of a mobile robot. In order to avoid the obstacle, the modified cost function is proposed which is composed of the image error between the position of a mobile robot and the goal position and the distance between the position of a mobile robot and the position of the obstacle. The performance was evaluated using the simulation.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.27-36
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• 2014

This paper presents an adaptive wide-area damping controller (WADC) based on generalized predictive control (GPC) and model identification for damping the inter-area low frequency oscillations in large-scale inter-connected power system. A recursive least-squares algorithm (RLSA) with a varying forgetting factor is applied to identify online the reduced-order linearlized model which contains dominant inter-area low frequency oscillations. Based on this linearlized model, the generalized predictive control scheme considering control output constraints is employed to obtain the optimal control signal in each sampling interval. Case studies are undertaken on a two-area four-machine power system and the New England 10-machine 39-bus power system, respectively. Simulation results show that the proposed adaptive WADC not only can damp the inter-area oscillations effectively under a wide range of operation conditions and different disturbances, but also has better robustness against to the time delay existing in the remote signals. The comparison studies with the conventional lead-lag WADC are also provided.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• YAKHAK HOEJI
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• v.48 no.1
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• pp.6-12
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• 2004

Near-infrared spectra of methanol-water mixtures and acetonitrile-water mixtures were acquired to find interactions between solvents widely used for reverse-phase liquid chromatography. Mixtures were prepared to give a series of increasing mole fractions of methanol or acetonitrile in water. Data matrices of acquired spectra were analyzed to determine the proper number of principal components of each mixture system using Malinowski's factor indicator function. Initial guess of score matrix and loading matrix were calculated by nonlinear iterative partial least squares (NIPALS) algorithm for faster computation. Iterative target transform factor analysis (ITTFA) was applied to convert the initial estimation of score matrix to true concentration profile and loading matrix to pure spectra of pure components of the mixtures. In case of methanol-water the number of principal components was found to be 4 and those initial guess of factors were converted to the pure spectra of water methanol and two kinds of complexes. In case of acetonitrile-water the number of pure components of the mixtures was found to be 3 and the pure spectrum of acetonitrile-water complex was found. The nonlinear characteristics of concentration profiles of complexes in the solvent mixtures may give a good criteria in understanding their elution characteristics in reverse-phase liquid chromatogrsphy.

• Nuclear Engineering and Technology
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• v.33 no.1
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• pp.25-33
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• 2001

To determine the pin power distribution without disassembling, HANARO fuel assemblies are gamma-scanned and then the distribution is reconstructed tv using the tomography method. The iterative least squares method (ILSM and the wavelet singular value decomposition method (WSVD) are chosen to solve the problem. An optimal convergence criterion is used to stop the iteration algorithm to overcome the potential divergence in ILSM. WSVD gives better results than ILSM , and the average values from the two methods give the best results. The RMSE (root mean square errors) to the reference data are 5.1, 6.6, 5.0, 6.5, and 6.4% and the maximum relative errors are 10.2, 13.7, 12.2, 13.6, and 14.3%, respectively. It is found that the effect of random positions of the pins is important. Although the effect can be accommodated by the iterative calculations simulating the random positions, the use of experimental equipment with a slit covering the whole range of the assembly horizontally is recommended to obtain more accurate results. We made a new apparatus using the results of this study and are conducting an experiment in order to obtain more accurate results.