• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• 한국정보통신학회논문지
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• 제5권4호
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• pp.777-790
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• 2001

광통신용 광섬유의 최저손실 파장영역인 1.55w에서 고출력으로 안정하게 동작하는 SLD를 설계하기 위하여 이론적인 해석을 수행하였다. 활성영역과 SCH층의 재료는 Int-xGaxAsyPl-y를 이용하였다. 활성영역의 측방향과 횡방향 모드해석으로부터 단일모드 고출력 동작을 위한 광전력분포와 광가둠계수를 구하였으며, 이들 계산으로부터 최대 광가둠계수를 얻기 위한 SCH층의 조성과 두께를 계산하였다. 낮은 반사도를 얻기 위하여 후면 에 윈도우 영역을 두었고 활성영역과 윈도우 영역의 계면이 측방향으로 각도를 가지게 하였으며 가우시안빔 근사와 모드해석으로부터 반사도를 계산하였다. $1.3\mum$ InGaAsP를 SCH층으로 하였을 때 최대의 광가둠계수를 얻기 위한 SCH층의 두께는$0.08\mum$정도이었다. 10-4정도의 반사도를 얻기 위해서는 활성층의 두께를 $0.2\mum$, SCH 층의 두께를 $0.08\mum$ 로 하였을 때 무반사코팅을 하지 않을 경우 윈도우 영역의 길이는 $100\mum$ 정도이고, 반사도 1% 정도의 무반사 코팅을 할 경우 $10\mum$ 정도가 된다. 측면 경사각이 $10~15^{\circ}$이면 반사도는 10-3정도가 된다. 이들 결과로부터 AR코팅을 하지 않고도 윈도우 영역의 길이와 측면 경사각을 적당히 조절한다면 안정적으로 동작하는 SLD의 제작이 가능하다는 것을 알 수 있다.

• 한국재료학회지
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• 제8권10호
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• pp.961-968
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• 1998

상변화형 광디스크의 보호막으로 사용되는 $ZnS-SiO_2$ 유전체막을 RF magnetron 스퍼트링방법에 의하여 제조하는 경우에 기판 바이어스전압의 영향을 조사하기 위하여, 알곤가스 분위기에서 ZnS(80mol%)-$SiO_2$(20mol%)타겟을 사용하여 Si Wafer와 Corning flass 위에 박막을 증착시켰다. 본 실험에서는 여러 실험 변수를 효과적으로 조절하면서 실험의 양을 줄이고 도시의 산포를 동시에 만족시키는 최적조건으로 타겟 RF 출력 200W, 기판 RF 출력 20W, 아르곤 압력 5mTorr과 증착시간 20분을 얻을 수 있었으며, 신뢰구간 95%에서 확인실험을 수행하였다. 증착된 박막의 열적 저항성을 측정하기 위해 $300^{\circ}C$와 $600^{\circ}C$에서 열처리시험을 수행하였고, Spectroscopic Ellipsometry 측정을 통한 광학적 데이터를 바탕으로 Bruggeman EMA(Effective Medium Approximation)방법을 이용하여 기공(void)분률을 측정하였다. 본 연구결과에 의하면 특성치 굴절률에 대하여 기판 바이어스인자와 증착시간 사이에는 서로 교호작용이 강하게 존재함을 확인할 수 있었다. TEM분석과 XRD 분석 결과에 의하면 기판 바이어스를 가한 최적조건에서 증착된 미세조직은 기존의 바이어스를 가하지 않을 조건에서 증착시킨 박막보다 미세한 구조를 가지며, 또한 과도한 바이어스전압은 결정구조의 조대화를 야기시켰다. 그리고 적절한 바이어스전압은 박막의 밀도를 증가시키며, 기공분률을 약 3.7%정도 감소시킴을 확인할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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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.