• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.7-14
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• 2015

To implement an automatic picking system (APS) in distribution center with high precision and high dynamics, this paper presents a high gain observer-based robust speed controller design for a linear induction motor (LIM) drive. The force disturbance as well as the mechanical parameter variations such as the mass and friction coefficient gives a direct influence on the speed control performance of APS. To guarantee a robust control performance, the system uncertainty caused by the force disturbance and mechanical parameter variations is estimated through a high gain disturbance observer and compensated by a feedforward manner. While a time-varying disturbance due to the mass variation can not be effectively compensated by using the conventional disturbance observer, the proposed scheme shows a robust performance in the presence of such uncertainty. A Simulink library has been developed for the LIM model from the state equation. Through comparative simulations based on Matlab - Simulink, it is proved that the proposed scheme has a robust control nature and is most suitable for APS.

• Proceedings of the Korea Association of Crystal Growth Conference
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• 1996.06b
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• pp.285-309
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• 1996

Non oxide ceramics such as nitrides of transition metals have shown significant potential for future economic impact, in diverse applications in ceramic, aerospace and electronic industries, as refractory products, abrasives and cutting tools, aircraft components, and semi-conductor substrates amid others. Combustion synthesis has become an attractive alternative to the conventional furnace technology to produce these materials cheaply, faster and at a higher level of purity. However he process os highly exothermic and manifests complex dynamics due to its strongly non-linear nature. In order to develop an understanding of this process and to study the effect of operational parameters on the final outcome, numerical modeling is necessary, which would generated essential knowledge to help scale-up the process. the model is based on a system of parabolic-hyperbolic partial differential equations representing the heat, mass and momentum conservation relations. The model also takes into account structural change due to sintering and volumetric expansion, and their effect on the transport properties of the system. The solutions of these equations exhibit steep moving spatial gradients in the form of reaction fronts, propagating in space with variable velocity, which gives rise to varying time scales. To cope with the possibility of extremely abrupt changes in the values of the solution over very short distances, adaptive mesh techniques can be applied to resolve the high activity regions by ordering grid points in appropriate places. To avoid a control volume formulation of the solution of partial differential equations, a simple orthogonal, adaptive-mesh technique is employed. This involves separate adaptation in the x and y directions. Through simple analysis and numerical examples, the adaptive mesh is shown to give significant increase in accuracy in the computations.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.1
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• pp.39-45
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• 2016

Thermal management of a fuel cell is important to satisfy the requirements of durability and efficiency under varying load conditions. In this study, a linear state feedback controller was designed to maintain the temperature within operating conditions. Due to the nonlinearity of automotive fuel cell system, the state feedback controller results in marginal stable under load condition from $0.5A/cm^2$ to $0.7A/cm^2$. A PWM (Pulse Width Modulation) and the modified state feedback controller are applied to control the temperature under the load condition from $0.5A/cm^2$ to $0.7A/cm^2$. The cooling system model is composed of a reservoir, radiator, bypass valve, fan, and a water pump. The performance of the control algorithm was evaluated in terms of the integral time weighted absolute error (ITAE). Additionally, MATLAB/SIMULINK$^{(R)}$ was used for the development of the system models and controllers. The modified state feedback controller was found to be more effective for controlling temperature than other algorithms when tested under low load conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1287-1294
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• 2006

In many current wireless packet data system, the short-burst transmissions are used, and training overhead is very significant for such short burst formats. So, the availability of the short training sequence and the fast converging algorithm is essential in the adaptive equalizer. In this paper, the new equalizer algorithm is proposed to improve the performance of a MTLMS (multiple-training least mean square) based DFE (decision feedback equalizer)using the short training sequence. In the proposed method, the output of the DFE is fed back to the LMS (least mean square) based adaptive DEF loop iteratively and used as an extended training sequence. Instead of the block operation using ML (maximum likelihood) estimator, the low-complexity adaptive LMS operation is used for overall processing. Simulation results show that the perfonnance of the proposed equalizer is improved with a linear computational increase as the iterations parameter in creases and can give the more robustness to the time-varying fading.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3498-3511
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• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• Wind and Structures
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• v.26 no.5
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• pp.331-341
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• 2018

A numerical study on the flow over a square cylinder in the vicinity of a wall is conducted for different Couette-Poiseuille-based non-uniform flow with the non-dimensional pressure gradient P varying from 0 to 5. The non-dimensional gap ratio L (=$H^{\ast}/a^{\ast}$) is changed from 0.1 to 2, where $H^{\ast}$ is gap height between the cylinder and wall, and $a^{\ast}$ is the cylinder width. The governing equations are solved numerically through finite volume method based on SIMPLE algorithm on a staggered grid system. Both P and L have a substantial influence on the flow structure, time-mean drag coefficient ${\bar{C}}_D$, fluctuating (rms) lift coefficient ($C_L{^{\prime}}$), and Strouhal number St. The changes in P and L leads to four distinct flow regimes (I, II, III and IV). Following the flow structure change, the ${\bar{C}}_D$, $C_L{^{\prime}}$, and St all vary greatly with the change in L and/or P. The ${\bar{C}}_D$ and $C_L{^{\prime}}$ both grow with increasing P and/or L. The St increases with P for a given L, being less sensitive to L for a smaller P (< 2) and more sensitive to L for a larger P (> 2). A strong relationship is observed between the flow regimes and the values of ${\bar{C}}_D$, $C_L{^{\prime}}$ and St. An increase in P affects the pressure distribution more on the top surface than on bottom surface while an increase in L does the opposite.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.61-68
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• 2003

The guided wave has been widely employed to characterize thin plates and layered media. The dispersion curves of phase and group velocities are essential for the quantitative application of guided waves. In the present work, a fully automated system for the measurement of backward radiation of LLW has been developed. The specimen moves in two dimensional plane as well as in angular rotation. The signals of backward radiation of LLW were measured from an elastic plate in which specific modes of Lamb wave were strongly generated. Phase velocity of the corresponding modes was determined from the incident angle. The generated Lamb waves propagated forward and backward with the leakage of energy into water. Backward radiated LLW was detected by the same transducer and its frequency components were analyzed to extract the related information to the dispersion curves. The dispersion curves of phase velocity were measured by varying the incident angle. Moving the specimen in the linear direction of LLW propagation, group velocity was determined by measuring the transit time shift in the ultrasonic waveform.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Macromolecular Research
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• v.11 no.4
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• pp.207-223
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• 2003

For last five years, we are developing the novel local drug delivery devices using biodegradable polymers, especially polylactide (PLA) and poly(D,L-lactide-co-glycolide) (PLGA) due to its relatively good biocompatibility, easily controlled biodegradability, good processability and only FDA approved synthetic degradable polymers. The relationship between various kinds of drug [water soluble small molecule drugs: gentamicin sulfate (GS), fentanyl citrate (FC), BCNU, azidothymidine (AZT), pamidronate (ADP), $1,25(OH)_2$ vitamin $D_3$, water insoluble small molecule drugs: fentanyl, ipriflavone (IP) and nifedipine, and water soluble large peptide molecule drug: nerve growth factor (NGF), and Japanese encephalitis virus (JEV)], different types of geometrical devices [microspheres (MSs), microcapsule, nanoparticle, wafers, pellet, beads, multiple-layered beads, implants, fiber, scaffolds, and films], and pharmacological activity are proposed and discussed for the application of pharmaceutics and tissue engineering. Also, local drug delivery devices proposed in this work are introduced in view of preparation method, drug release behavior, biocompatibility, pharmacological effect, and animal studies. In conclusion, we can control the drug release profiles varying with the preparation, formulation and geometrical parameters. Moreover, any types of drug were successfully applicable to achieve linear sustained release from short period ($1{\sim}3$ days) to long period (over 2 months). It is very important to design a suitable formulation for the wanting period of bioactive molecules loaded in biodegradable polymers for the local delivery of drug. The drug release is affected by many factors such as hydrophilicity of drug, electric charge of drug, drug loading amount, polymer molecular weight, the monomer composition, the size of implants, the applied fabrication techniques, and so on. It is well known that the commercialization of new drug needs a lot of cost of money (average: over 10 million US dollar per one drug) and time (average: above 9 years) whereas the development of DDS and high effective generic drug might be need relatively low investment with a short time period. Also, one core technology of DDS can be applicable to many drugs for the market needs. From these reasons, the DDS research on potent generic drugs might be suitable for less risk and high return.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.