• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7A
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• pp.702-708
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• 2006

본 논문에는 초광대역 통신시스템을 위한 주파수 영역의 통계학적 채널 모델을 서술하고 있다. 채널 모렐링은 3개의 사무실 환경, 46개의 송, 수신 위치에서 얻어진 23,000개의 채널응답함수로 부터 얻어졌다. 측정실험을 통해 얻어진 데이터를 바탕으로 주파수 변화에 따른 경로감쇄지수 변화에 대해 서술한 후 전파환경과 가시경로의 존재여부에 따른 수신신호의 확률분포모델을 연구하였다. 마지막으로는 수신된 주파수 톤에 해당하는 수신파워의 표준편차와 같은 통계적 특성들을 고찰하였는데, 가시경로가 존재하는 경우에는 송, 수신기 사이의 거리가 멀어지면서 표준편차 값이 커지고 그에 따라 수신 주파수 톤의 파워가 평균 수신파워에서 일정한 범위 안에 들어올 확률이 떨어지는 것을 알 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.765-768
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• 2014

본 연구에서는 비대칭 이중게이트 MOSFET의 상하단 게이트 산화막 두께 비에 대한 문턱전압 및 전도중심의 변화에 대하여 분석하고자한다. 비대칭 이중게이트 MOSFET는 상하단 게이트 산화막의 두께를 다르게 제작할 수 있어 문턱전압이하 영역에서 전류를 제어할 수 있는 요소가 증가하는 장점이 있다. 상하단 게이트 산화막 두께 비에 대한 문턱전압 및 전도중심을 분석하기 위하여 포아송방정식을 이용하여 해석학적 전위분포를 구하였다. 이때 전하분포는 가우스분포함수를 이용하였다. 하단게이트 전압, 채널길이, 채널두께, 이온주입범위 및 분포편차를 파라미터로 하여 문턱전압 및 전도중심의 변화를 관찰한 결과, 문턱전압은 상하단 게이트 산화막 두께 비에 따라 큰 변화를 나타냈다. 특히 채널길이 및 채널두께의 절대값보다 비에 따라 문턱전압이 변하였으며 전도중심이 상단 게이트로 이동할 때 문턱전압은 증가하였다. 또한 분포편차보단 이온주입범위에 따라 문턱전압 및 전도중심이 크게 변화하였다.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.204-210
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• 2002

The self-organizing feature map of Kohonen has disadvantage that needs too much input patterns in order to converge into the equilibrium state when it trains. In this paper we proposed the method of improving the convergence speed and rate of self-organizing feature map converting the interaction set into Dynamic Gaussian function. The proposed method Provides us with dynamic Properties that the deviation and width of Gaussian function used as an interaction function are narrowed in proportion to learning times and learning rates that varies according to topological position from the winner neuron. In this Paper. we proposed the method of improving the convergence rate and the degree of self-organizing feature map.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.957-963
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• 2015

The autocorrelation of an alternative binary offset carrier (AltBOC) signal provides an improved positioning accuracy because of its narrow main-peak. However, The AltBOC signal has a disadvantage that the autocorrelation of the AltBOC signal has multiple side-peaks which incur a severe positioning error. In this paper, we propose a generating method of an unambiguous correlation function for AltBOC signal tracking. Specifically, we first obtain symmetric partial correlation functions, and subsequently, we obtain an unambiguous correlation function by combining them. In numerical results, it is confirmed that the proposed correlation function provides better tracking error standard devation (TESD) performances comparing with the conventional correlation functions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.75-81
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• 2022

Autonomous vehicles, which can dramatically solve the lack of parking spaces, are making great progress through deep reinforcement learning. Activation functions are used for deep reinforcement learning, and various activation functions have been proposed, but their performance deviations were large depending on the application environment. Therefore, finding the optimal activation function depending on the environment is important for effective learning. This paper analyzes 12 functions mainly used in reinforcement learning to compare and evaluate which activation function is most effective when autonomous vehicles use deep reinforcement learning to learn parking. To this end, a performance evaluation environment was established, and the average reward of each activation function was compared with the success rate, episode length, and vehicle speed. As a result, the highest reward was the case of using GELU, and the ELU was the lowest. The reward difference between the two activation functions was 35.2%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.716-718
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• 2012

This paper have presented the change for subthreshold characteristics for double gate(DG) MOSFET based on scaling theory and the shape of Gaussian function. To obtain the analytical solution of Poisson's equation, Gaussian function been used as carrier distribution and consequently potential distributions have been analyzed closely for experimental results, and the subthreshold characteristics have been analyzed for the shape parameters of Gaussian function such as projected range and standard projected deviation. Since this potential model has been verified in the previous papers, we have used this model to analyze the subthreshold chatacteristics. The scaling theory is to sustain constant outputs for the change of device parameters. As a result to apply the scaling theory for DGMOSFET, we know the subthreshold characteristics have been greatly changed, and the change of threshold voltage is bigger relatively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1260-1265
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• 2012

This paper have presented the change for subthreshold characteristics for double gate(DG) MOSFET based on scaling theory and the shape of Gaussian function. To obtain the analytical solution of Poisson's equation, Gaussian function been used as carrier distribution and consequently potential distributions have been analyzed closely for experimental results, and the subthreshold characteristics have been analyzed for the shape parameters of Gaussian function such as projected range and standard projected deviation. Since this potential model has been verified in the previous papers, we have used this model to analyze the subthreshold chatacteristics. The scaling theory is to sustain constant outputs for the change of device parameters. As a result to apply the scaling theory for DGMOSFET, we know the subthreshold characteristics have been greatly changed, and the change of threshold voltage is bigger relatively.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.527-540
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• 2020

While analyzing data, researching outliers, which are out of the main tendency, is as important as researching data that follow the general tendency. In this study we discuss the influence function for outlier discrimination. We derive sample influence functions of sample mean, sample variance, and sample standard deviation, which were not directly derived in previous research. The results enable us to mathematically examine the relationship between the empirical influence function and sample influence function. We can also consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between the approximated sample influence function and the empirical influence function is also verified by the simulation of random sampled data in normal distribution. As the result of a simulation, both the relationship between the two influence functions, sample and empirical, and the method of approximating the sample influence function through the emperical influence function were verified. This research has significance in proposing a method that reduces errors in the approximation of the empirical influence function and in proposing an effective and practical method that proceeds from previous research that approximates the sample influence function directly through empirical influence function by constant revision.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.997-1012
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• 2006

We have presented a nonparametric stochastic approach for the SOI(Southern Oscillation Index) series that used nonlinear methodology called Nonlinear AutoRegressive(NAR) based on conditional kernel density function and CAFPE(Corrected Asymptotic Final Prediction Error) lag selection. The fitted linear AR model represents heteroscedasticity, and besides, a BDS(Brock - Dechert - Sheinkman) statistics is rejected. Hence, we applied NAR model to the SOI series. We can identify the lags 1, 2 and 4 are appropriate one, and estimated conditional mean function. There is no autocorrelation of residuals in the Portmanteau Test. However, the null hypothesis of normality and no heteroscedasticity is rejected in the Jarque-Bera Test and ARCH-LM Test, respectively. Moreover, the lag selection for conditional standard deviation function with CAFPE provides lags 3, 8 and 9. As the results of conditional standard deviation analysis, all I.I.D assumptions of the residuals are accepted. Particularly, the BDS statistics is accepted at the 95% and 99% significance level. Finally, we split the SOI set into a sample for estimating themodel and a sample for out-of-sample prediction, that is, we conduct the one-step ahead forecasts for the last 97 values (15%). The NAR model shows a MSEP of 0.5464 that is 7% lower than those of the linear model. Hence, the relevance of the NAR model may be proved in these results, and the nonparametric NAR model is encouraging rather than a linear one to reflect the nonlinearity of SOI series.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.8 no.2
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• pp.123-133
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• 2010

A statistical evaluation methodology was developed to determine the compliance of candidate waste stream with clearance criteria based upon distribution of radionuclide in a waste stream at a certain confidence level. For the cases where any information on the radionuclide distribution is not available, the relation between arithmetic mean of radioactivity concentration and its acceptable maximum standard deviation was demonstrated by applying widely-known Markov Inequality and One-side Chebyshev Inequality. The relations between arithmetic mean and its acceptable maximum standard deviation were newly derived for normally or lognormally distributed radionuclide in a waste stream, using probability density function, cumulative density function, and other statistical relations. The evaluation methodology was tested for a representative case at 95% of confidence level and 100 Bq/g of clearance level of radioactivity concentration, and then the acceptable range of standard deviation at a given arithmetic mean was quantitatively shown and compared, by varying the type of radionuclide distribution. Furthermore, it was statistically demonstrated that the allowable range of clearance can be expanded, even at the same confidence level, if information on the radionuclide distribution is available.