• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.11
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• pp.1191-1197
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• 2003

In the multi-carrier OFDM communication system, the inter-carrier-interference(ICI) produced by phase noise in the transceiver local oscillator makes a severe influence on the system performance. In this paper, a new ICI self-cancellation scheme in the data-conversion type is proposed to reduce effectively the ICI. Also, the common phase error(CPE), ICI and carrier to interference power ratio(CIR) are found by the linear approximation of the phase noise. Then, the proposed method is compared with the conventional OFDM to analyze the efficiency of system performance improvement. When the number of subcarriers is 64, there are respectively the SNR gain of 0.6 ㏈ in the phase noise variance of 0.3 with QPSK and 1.5 ㏈ in the phase noise variance of 0.1 with 16 QAM at BER=10$\^$-3/. As a result, the performance degradation by ICI can be effectively lowered in the proposed system with ICI self. cancellation scheme, compared with the conventional OFDM system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.2
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• pp.355-365
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• 1994

An analytical model of return flow is presented outside the surf zone. The governing equation is derived from the Navier-Stokes equation and the continuity. Each term of the governing equation is evaluated by the ordering analysis. Then the infinitesimal terms, i.e. the turbulent normal stress, the squared vertical velocity of water particle and the streaming velocity, are neglected. The driving forces of return flow are calculated using the linear wave theory for the shallow water approximation. Especially, the space derivative of local wave heights is described considering a shoaling coefficient. The vertical distribution of eddy viscosity is discussed to the customary types which are the constant, the linear function and the exponential function. Each coefficient of the eddy viscosities which sensitively affect the precision of solutions is uniquely decided from the additional boundary condition which the velocity becomes zero at the wave trough level. Also the boundary conditions at the bottom and the continuity relation are used in the integration of the governing equation. The theoretical solutions of present model are compared with the various experimental results. The solutions show a good agreement with the experimental results in the case of constant or exponential function type eddy viscosity.

• 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.