• Korean Journal of Optics and Photonics
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• v.14 no.3
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• pp.230-235
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• 2003

A fiber optic intrusion sensor system using the variations of speckle patterns was developed. The intrusion sensor system consisted of an analog unit, a digital control unit, and a DSP unit. Some experiments were carried out using a 500 m length optical fiber sensor. The system detected intruders without missing, and distinguished between cars and persons. When the intruders were cars, the discrimination probability was 100% and when the intruders were persons, the discrimination probability was 90%.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.9-15
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• 2018

The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1311-1327
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• 2016

We propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the 'illness-death model', which composes three states, such as 'healthy', 'diseased', and 'dead'. The state of 'diseased' can be considered as an intermediate event. Two more states are added into the illness-death model to describe missing events caused by a loss of follow-up before the end of the study. One of them is a state of 'LTF', representing a lost-to-follow-up, and the other is an unobservable state that represents the intermediate event experienced after LTF occurred. Given covariates, we employ the Cox proportional hazards model with a normal frailty and construct a full likelihood to estimate transition intensities between states in the multi-state model. Marginalization of the full likelihood is completed using the adaptive Gaussian quadrature, and the optimal solution of the regression parameters is achieved through the iterative Newton-Raphson algorithm. Simulation studies are carried out to investigate the finite-sample performance of the proposed estimation procedure in terms of the empirical coverage probability of the true regression parameter. Our proposed method is also illustrated with the dataset adapted from Helmer et al. (2001).

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1459-1473
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• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2561-2576
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• 2013

The ship ad-hoc network (SANET) extends the coverage of the high data-rate terrestrial communications to the ships with the reduced cost in maritime communications. Cognitive radio (CR) has the ability of sensing the radio environment and dynamically reconfiguring the operating parameters, which can make SANET utilize the spectrum efficiently. However, due to the dynamic topology nature and no central entity for data fusion in SANET, the interference brought into the primary network caused by the hidden primary user requires to be carefully managed by a sort of decentralized cooperative spectrum sensing schemes. In this paper, we propose a self-weighted decentralized cooperative spectrum sensing (SWDCSS) scheme to solve such a problem. The analytical and simulation results show that the proposed SWDCSS scheme is reliable to detect the primary user in SANET. As a result, secondary network can efficiently utilize the spectrum band of primary network with little interference to primary network. Referring the complementary receiver operating characteristic (ROC) curves, we observe that with a given false alarm probability, our proposed algorithm reduces the missing probability by 27% than the traditional embedded spectrally agile radio protocol for evacuation (ESCAPE) algorithm in the best condition.

• Water for future
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• v.28 no.5
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• pp.191-203
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• 1995

In this study, the past flood levels of Goan station, which is one of major gaging stations and located at downstream of Paldang dam, were converted based on the 1994's cross section and the flood quantiles were estimated from flood frequency analysis. The recently established rating curve was used to convert flood levels. And the parameters of the several probability distributions commonly used in hydrologic analysis were estimated based on the method of probability weighted moments and the goodness of fit tests were applied to those distributions. As a result, the gamma-2 and gamma-3 distributions were selected as the appropriate models. The flood lovels and quantiles for selected return periods were calculated based on those distributions. Furthermore, frequency analysis using historical flood information was performed to overcome the misleading caused by missing data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4C
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• pp.358-363
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• 2010

In IEEE 802.15.4a IR-UWB (Impulse Radio Ultra Wideband) systems, it is crucial to acquire initial carrier/timing synchronization and estimate channel response by exploiting the SYNC symbols embedded in each packet. On the other hand, it is also crucial to detect the SFD pattern followed by the header and data symbols to reliably extract the information contained in the packet. In this paper, we propose a reliable SFD detection scheme utilizing some surplus SYNC symbols in addition to SFD symbols to improve the SFD detection performance.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3124-3130
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• 1999

A duplex system enhances reliability by tolerating faults through spatial redundancy. Faults can be detected by duplicating identical tasks in pairs of modules. However, this kind of systems cannot even detect the fault if it occurs coincidently due to either malfunctions of common component such as power supply and clock or due to such environmental disruption as EMI. In the paper, we propose a method to reduce those effects of coincident faults in the duplex controller computer. Specifically, a duplex system tolerates coincident faults by using a sophistication sequencing of scheduling technique with certain timing redundancy. In particular when all tasks should be completed in the sense of real-time, the suggested scheduling method works properly to minimize the probability of faulty tasks due to coincident fault without missing the timing constraints.