• Journal of Communications and Networks
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• v.8 no.3
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• pp.267-274
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• 2006

Self-spreading (SSP) is a spread spectrum technique where the spreading sequence is generated from data bits. Although SSP allows communications with low probability of interception by unintended receivers, despreading by the intended receiver is prone to error propagation. In this paper, we propose both a new transmitter and a new receiver based on SSP with the aim to a) reduce error propagation and b) increase the concealment of the transmission. We first describe a new technique for the generation of SSP spreading sequence, which generalizes SSPs of existing literature. We include also coding at the transmitter, in order to further reduce the effects of error propagation at the receiver. For the receiver, we propose a turbo architecture based on the exchange of information between a soft despreader and a soft-input soft-output decoder. We design the despreader in order to fully exploit the information provided by the decoder. Lastly, we propose a chip decoder that extracts the information on data bits contained in the spreading sequence from the received signal. The performance of the proposed scheme is evaluated and compared with existing spread-spectrum systems.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.239-250
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• 1994

In this paper, we prove a strong large deviations theorem for the ratio of independent randoem variables with error rate of $O(n^{-1})$. To obtain our results we use the inversion formula for the tail probability and apply the Chaganty and Sethuraman's (1985) approach.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.834-838
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• 1992

Tracking performance depends on the quantity of the measurement data. In the Kalman-Bucy filter and other trackers, this dependence is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the value of measurement inputs. In this paper, we derived approximated error covariance matrix to evaluate the dependence of target detection probability and false alarm probability in the presence of uncertainty of measurement origin.

• Communications of the Korean Mathematical Society
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• v.23 no.2
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• pp.285-294
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• 2008

A new Monte Carlo method is presented to compute the prices of barrier options on stocks. The key idea of the new method is to use an exit probability and uniformly distributed random numbers in order to efficiently estimate the first hitting time of barriers. It is numerically shown that the first hitting time error of the new Monte Carlo method decreases much faster than that of standard Monte Carlo methods.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.4
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• pp.85-92
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• 2002

To investigate the safety of the in-use vehicles emission against the tail-pipe emission regulation, in-use vehicles emission trend according to vehicle mileage should be known. But it is impossible to collect all vehicles emission data In order to know that. Therefore, it is necessary to establish a statistically meaningful inference method that can be used generally to estimate in-use vehicles emissions distribution according to the vehicle mileage with relatively less in-use vehicles emission data. To do this, a linear regression model that solved the problems of data normality and common variance of error was studied. As a way that can secure the data normality, In(emission) instead of emission itself was used as a sampled data. And a reciprocal of mileage was suggested as a factor to secure common variance of error. As an example, 36 data of FTP-75 test were handled in this study. As a result, using average value and standard deviation at each mileage which were inferred from a linear regression model, probability density distribution and cumulative distribution of emissions according to the vehicle mileage were obtained and it was possible to predict the deterioration factor through full useful life mileage and also possible to decide whether those in-use vehicles will meet the tail-pipe emission regulations or not.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.69-78
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• 2010

The irreversible 9/7 JPEG2000, which is one of sub-band coding techniques, has a problem of severe picture quality distortion at the edge and the background caused by the quantization error below 0.15bpp. In this paper, to solve such problems we propose a VQ(Visual Quantizer) based on L-pdf(Laplace probability density function) statistical characteristics of high frequency sub-bands. The proposed VQ is designed by visual parameter for improving the subjective quality and weighting parameter for increasing the compression ratio. A proposed method, based on 9/7 JPEG2000 scheme, gives the high subjective quality to reconstructed images below 0.15bpp and provides minimum MSE(Mean-Squared Error) regardless of the compression ratio.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.56-60
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• 2005

To calculate the probability of bit error of UWB communication systems, the exact expression of multiple access interference is essential. So far, in many researches, MAI has been modeled by the Gaussian Approximation, which leads to the huge errors. And there are some tries to obtain the exact model fot the MAI but they have some problems such as long calculation time. We introduce the simple expression to calculate the probability of error of an UWB-TH system with BPSK. The multiple access interference is explained by the characteristic function method combined with the Gaussian approximation. It allows us to easily and fast calculate the bit error rate of an UWB-TH system.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.21-30
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• 2007

A practical method to design the input shaping which generates control command is proposed in this paper, We suggest an experimental technique considering human operator's target tracking error to improve aiming accuracy which significantly affects hit probability. It is known that stabilization performance is one of the most important factors for ground combat vehicle system. In particular, stabilization error of the manual target tracking system mounted on moving vehicle directly affects hit probability. To reduce this error, we applied input command shaping method using preprocessing filtering and functional curve fitting. First of all, we construct the human operator model to consider effects of human operator on our system. Input shaping curve is divided into several regions to get rid of the above problems and to improve the system performance. At example design part, we chose three steps of functional command curve and determine the parameters of the function by the proposed design method. In order to verify the proposed design method, we carried out the experiments with real plant of a fighting vehicle.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.233-237
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• 2003

An asymptotic property of the ordinary least squares estimator of the disturbance variance is considered in the regression model with correlated errors. It is shown that the convergence in probability of S$^2$ is equivalent to the asymptotic unbiasedness. Beyond the assumption on the design matrix or the variance-covariance matrix of disturbances error, the result is quite general and simplify the earlier results.