• Journal of Communications and Networks
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• v.7 no.4
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• pp.489-498
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• 2005

For better performance over a noisy channel, mobile wireless networks transmit packets with forward error correction (FEC) code to recover corrupt bits without retransmission. The static determination of the FEC code size, however, degrades their performance since the evaluation of the underlying channel state is hardly accurate and even widely varied. Our measurements over a wireless sensor network, for example, show that the average bit error rate (BER) per second or per minute continuously changes from 0 up to $10^{-3}$. Under this environment, wireless networks waste their bandwidth since they can't deterministically select the appropriate size of FEC code matching to the fluctuating channel BER. This paper proposes an adaptive FEC technique called adaptive FEC code control (AFECCC), which dynamically tunes the amount of FEC code per packet based on the arrival of acknowl­edgement packets without any specific information such as signal to noise ratio (SNR) or BER from receivers. Our simulation experiments indicate that AFECCC performs better than any static FEC algorithm and some conventional dynamic hybrid FEC/ARQ algorithms when wireless channels are modeled with two-state Markov chain, chaotic map, and traces collected from real sensor networks. Finally, AFECCC implemented in sensor motes achieves better performance than any static FEC algorithm.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.709-720
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• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.

• ETRI Journal
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• v.38 no.3
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• pp.444-454
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• 2016

Voice service is very demanding in cognitive radio networks (CRNs). The available spectrum in a CRN for CR users varies owing to the presence of licensed users. On the other hand, voice packets are delay sensitive and can tolerate a limited amount of delay. This makes the support of voice traffic in a CRN a complicated task that can be achieved by devising necessary considerations regarding the various network functionalities. In this paper, the support of secondary voice users in a CRN is investigated. First, a novel packet scheduling scheme that can provide the required quality of service (QoS) to voice users is proposed. The proposed scheme utilizes the maximum packet transmission rate for secondary voice users by assigning each secondary user the channel with the best level of quality. Furthermore, an analytical framework developed for a performance analysis of the system, is described in which the effect of erroneous spectrum sensing on the performance of secondary voice users is also taken into account. The QoS parameters of secondary voice users, which were obtained analytically, are also detailed. The analytical results were verified through the simulation, and will provide helpful insight in supporting voice services in a CRN.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.441-447
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• 2003

Recently there is a growing Interest In mobile cellula r network providing multimedia service. However, the link bandwidth of mobile cellular network is not sufficient enough to provide satisfactory services to use rs. To overcome this problem, an adaptive framework has been proposed in this study, we propose a new method of estimating DPR(degradation period ratio) in an adaptive multimedia network where the bandwidth of ongoing call can be dynamically adjusted during its lifetime. DPR is a QoS(quality of service) parameter which represents the ratio of allocated bandwidth below a pre-defined target to the whole service time of a call. We improve estimation method of DPR using DTMC(discrete time Markov chain) model. We also calculate mean degradation period and degradation probability more precisely than in existing studies. Under Threshold CAC(call admission control) algorithm, we present analytically how to guarantee QoS to users and illustrate the method by numerical examples. The proposed method is expected to be used as one of CAC schemes in guaranteeing predefined QoS level of DPR

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5905-5926
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• 2017

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.725-740
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• 2008

In this study a multi-site daily precipitation generator which generates the precipitation with similar spatial correlation, and at the same time, with conserving statistical properties of the observed data is developed. The proposed generator is intended to be a tool for down-scaling the data obtained from GCMs or RCMs into local scales. The occurrences of precipitation are simultaneously modeled in multi-sites by 2-parameter first-order Markov chain using random variables of spatially correlated while temporally independent, and then, the amount of precipitation is simulated by 3-parameter mixed exponential probability density function that resolves the issue of maintaining intermittence of precipitation field. This approach is applied to the Nakdong river basin and the observed data are daily precipitation data of 19 locations. The results show that spatial correlations of precipitation series are relatively well simulated and statistical properties of observed precipitation series are simulated properly.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1069-1076
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• 2014

Rainfall weather patterns have changed due to global warming and sudden heavy rainfalls have become more frequent. Economic loss due to heavy rainfall has increased. We study the generalized Pareto distribution for modelling rainfall in Seoul based on data from 1973 to 2008. We use several priors including Jeffrey's noninformative prior and Gibbs sampling method to derive Bayesian posterior predictive distributions. The probability of heavy rainfall has increased over the last ten years based on estimated posterior predictive distribution.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.959-973
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• 2014

Asset pricing models that account for asymmetric volatility in asset prices have been recently proposed. This article presents an efficient Bayesian method to analyze asset-pricing models. The method is developed by devising a partially collapsed Gibbs sampler that capitalizes on the functional incompatibility of conditional distributions without complicating the updates of model components. The proposed method is illustrated using simulated data and applied to daily S&P 500 data observed from September 1980 to August 2014.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.12
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• pp.9-24
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• 1995

In this paper, we proposed and analysed two kinds of priority control mechanism to archive the cell loss rate requirement and the delay requirement of each class. The service classes of our concern are the high time priority class(class 1) and the high loss priority class(class 2). Two kinds of priority control mechanism is divided by the method of storing the arriving class 2 cell in buffer on case of buffer full. The first one is the method which discarding the arriving class 2 cell, the second one is the mothod which storing the arriving class 2 cell on behalf of pushing out the class 1 cell in buffer. In the proposed priority schemes, one cell of the class 1 is transmitted whenever the maximum K cells of the class 2 is transmitted on case of transmitting the class 1 cell and the class 2 cell sequentially. In this paper, we analysed the cell loss rate and the mean cell delay for each class of the proposed priority scheme by using the Markov chain. The analytical results show that the characteristic of the mean cell delay becomes better for the class 1 cell and that of the cell loss rate becomes better for the class 2 cell by selecting properly the cell transfer ratio according to the condition of input traffic.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.8
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• pp.35-43
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• 2004

An AAL2 transmission scheme is used to deliver voice and data traffic between Node-B and RNC on 3G WCDMA network. To predict performance of AAL2 multiplexing precisely, we derived analytically bandwidth gain and cell packing density using discrete-time Markov chain model for voice service and validated these results with simulation. We also performed detailed simulation for AAL2 multiplexing in a concentrator. Based on the analytical result, we propose the engineering guideline to select the optimal Timer_CU in a Node-B. We found that there is no major benefit of additional AAL2 multiplexing in a concentrator and the benefit of AAL2 switching in tub for data services is much less than that for voice service.