• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.563-572
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• 2011

Reliability is an essential factor in a navigation system. Therefore, an integrity monitoring system is considered one of the most important parts in an avionic navigation system. A fault due to systematic malfunctioning definitely requires integrity reinforcement through systematic analysis. In this paper, we propose a method to detect faults of the GPS signal by using a distributed nonlinear filter based probability test. In order to detect faults, consistency is examined through a likelihood ratio between the main and auxiliary particle filters (PFs). Specifically, the main PF which includes all the measurements and the auxiliary PFs which only do partial measurements are used in the process of consistency testing. Through GPS measurement and the application of the autonomous integrity monitoring system, the current study illustrates the performance of the proposed fault detection algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.943-951
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• 2012

In this paper, we propose a novel spectrum sensing scheme based on the order statistic for cooperative cognitive radio network in non-Gaussian noise environments. Specifically, we model the ambient noise as the bivariate isotropic symmetric ${\alpha}$-stable random variable, and then, propose a cooperative spectrum sensing scheme based on the order of observations and the generalized likelihood ratio test. From numerical results, it is confirmed that the proposed scheme offers a substantial performance improvement over the conventional scheme in non-Gaussian noise environments.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.15-22
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• 2013

In clinical study or epidemic research, the $2{\times}2$ frequency table is useful to present a summary statistic The values of four cells in $2{\times}2$ table use to calculate the effect measures such as risk ratio, relative ris ratio or odds ratio. In this paper, we suggest that the improved visualization method using a radar diagram supported by MS-office Excel from the $2{\times}2$ frequency table is able to understand and draw easily betweendiagnosti measures such as sensitivity, specificity, predictivity, and likelihood ratio. We use some numerical example in order to show the usage of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.116-121
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• 2006

In this paper, we propose the dimension reduction method of multi-dimension speech feature vector for real-time adaptation procedure in various noisy environments. This method which reduces dimensions non-linearly to map the likelihood of speech feature vector and noise feature vector. The LRT(Likelihood Ratio Test) is used for classifying speech and non-speech. The results of implementation are similar to multi-dimensional speech feature vector. The results of speech recognition implementation of detected speech data are also similar to multi-dimensional(10-order dimensional MFCC(Mel-Frequency Cepstral Coefficient)) speech feature vector.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.117-122
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• 2006

In this paper, we present a simple and general soft bit decision expression for a Gray coded PAM signal over additive white Gaussian noise(AWGN) channel with the log likelihood ratio(LLR). In order to reduce the complexity of the LLR calculation, we make the bitwise LLR expression simple by replacing the mathematical max functions of the conventional Max-Log-MAP expression with simple arithmetic functions associated with some deterministic parameters, such as a received value and distance between symbols on a signal space. Taking the implementation issues, like the area of silicon, the power consumption, the timing latency, and so on, into consideration, we submit that the proposed method is a promising alternative way to conventional methods for reconfigurable systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1987.10a
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• pp.35-35
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• 1987

Consider a job shop that is modelled as an open queueing network of the Jackson(l957) type. All work stations in the shop have the same number of parallel servers. Two problems are studied : the loading of stations and the assignment of servers, which are represented by loading and assingment vectors, respectively. Ma jorization and arrangement orderings are established to order, respectively, the loading and the assignment vectors. It is shown that reducing the loading vector under ma jorizat ion or increasing the assignment vector under arrangement ordering will reduce the congestion in the shop in terms of reducing the total number of jobs(in the sense of likelihood ratio ordering), the maximum queue length(in the sense of stochastic ordering), and the queue-length vector( in the sense of stochastic majorization). The results can be used to supprot production planning in certain job shops, and to aid the desing of storage capacity. (OPEN QUEUEING NETWORK; WJORIZATION; ARRANGEMENT ORDERINC; LIKELIHOOD RATIO ORDERINC; STOCHASTIC ORDERING)

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.71-84
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• 2005

The Fieller-Creasy problem involves statistical inference about the ratio of two independent normal means. It is difficult problem from either a frequentist or a likelihood perspective. As an alternatives, a Bayesian analysis with noninformative priors may provide a solution to this problem. In this paper, we extend the results of Yin and Ghosh (2001) to unbalanced sample case. We find various noninformative priors such as first and second order matching priors, reference and Jeffreys' priors. The posterior propriety under the proposed noninformative priors will be given. Using real data, we provide illustrative examples. Through simulation study, we compute the frequentist coverage probabilities for probability matching and reference priors. Some simulation results will be given.

• Speech Sciences
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• v.10 no.3
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• pp.225-239
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• 2003

In this paper, we develop a keyword spotting system using vocabulary-independent speech recognition technique, and investigate several non-keyword modeling and post-processing methods to improve its performance. In order to model non-keyword speech segments, monophone clustering and Gaussian Mixture Model (GMM) are considered. We employ likelihood ratio scoring method for the post-processing schemes to verify the recognition results, and filler models, anti-subword models and N-best decoding results are considered as an alternative hypothesis for likelihood ratio scoring. We also examine different methods to construct anti-subword models. We evaluate the performance of our system on the automatic telephone exchange service task. The results show that GMM-based non-keyword modeling yields better performance than that using monophone clustering. According to the post-processing experiment, the method using anti-keyword model based on Kullback-Leibler distance and N-best decoding method show better performance than other methods, and we could reduce more than 50% of keyword recognition errors with keyword rejection rate of 5%.

• ETRI Journal
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• v.28 no.3
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• pp.291-300
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• 2006

A simple and general bit log-likelihood ratio (LLR) expression is provided for Gray-coded rectangular quadrature amplitude modulation (R-QAM) signals. The characteristics of Gray code mapping such as symmetries and repeated formats of the bit assignment in a symbol among bit groups are applied effectively for the simplification of the LLR expression. In order to reduce the complexity of the max-log-MAP algorithm for LLR calculation, we replace the mathematical max or min function of the conventional LLR expression with simple arithmetic functions. In addition, we propose an implementation algorithm of this expression. Because the proposed expression is very simple and constructive with some parameters reflecting the characteristic of the Gray code mapping result, it can easily be implemented, providing an efficient symbol de-mapping structure for various wireless applications.

• International Journal of Reliability and Applications
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• v.15 no.1
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• pp.65-76
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• 2014

In this article, a new model based on Lomax distribution is introduced. This new model is both useful and practical in areas such as economic, reliability and life testing. Some statistical properties of this model are presented including moments, hazard rate, reversed hazard rate, mean residual life and mean inactivity time functions, among others. It is also shown that the distributions of the new model are ordered with respect to the strongest likelihood ratio ordering. The method of moment and maximum likelihood estimation are used to estimates the unknown parameters. Simulation is utilized to calculate the unknown shape parameter and to study its properties. Finally, to illustrate the concepts, the appropriateness of the new model for real data sets are included.