• KDI Journal of Economic Policy
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• v.37 no.sup
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• pp.33-63
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• 2015

This paper attempts to investigate the Korean households' inflation expectations with particular attention to information rigidity. For this purpose, we derive an empirical model from a sticky information model $\acute{a}$ la Mankiw and Reis (2002) and estimate it. In addition, it is also examined whether the expectation formation is state-dependent on macroeconomic conditions. The main findings of this paper are as follows. First, it turns out that the information rigidity in Korean households' inflation expectations is very high. In a month, most of the households simply keep their inflation expectations the same as before instead of updating them based on newly arrived information. Furthermore, when updating their expectations, the households tend to rely on the backward-looking information such as actual inflation rates in the past rather than on the forward-looking forecasts by experts. Second, it is found that the expectation formation is varying as inflation rate changes. Specifically, when the inflation is high, the sensitivity of the households' inflation expectations to actual inflation increases and the gap between inflation expectations and actual inflation shrinks. It implies that Korean households update their expectations more frequently when the inflation matters than not.

• The Journal of the Acoustical Society of Korea
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• v.23 no.6
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• pp.462-472
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• 2004

In this paper a method for compensating for thp performance degradation or automatic speech recognition (ASR) is proposed. which is mainly caused by speaking rate variation. Before the new method is proposed. quantitative analysis of the performance of an HMM-based ASR system according to speaking rate is first performed. From this analysis, significant performance degradation was often observed in the rapidly speaking speech signals. A quantitative measure is then introduced, which is able to represent speaking rate. Time scale modification (TSM) is employed to compensate the speaking rate difference between input speech signals and training speech signals. Finally, a method for compensating the performance degradation caused by speaking rate variation is proposed, in which TSM is selectively employed according to speaking rate. By the results from the ASR experiments devised for the 10-digits mobile phone number, it is confirmed that the error rate was reduced by 15.5% when the proposed method is applied to the high speaking rate speech signals.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.42-47
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• 2003

This paper proposes the method to generate new test data using the sample shift of the start frame for SPRT(sequential probability ratio test) in speaker verification. The SPRT method is a effective algorithm that can reduce the test computational complexity. However, in making the decision procedure, SPRT can be executed on the assumption that the input samples are usually to be i.i.d. (Independent and Identically Distributed) samples from a probability density function (pdf), also it's not suitable method to apply for the short utterance. The proposed method can achieve SPRT regardless of the utterance length of the test data because it is method to generate the new test data through the sample shift of start frame. Also, the correlation property of data to be considered in the SPRT method can be effectively removed by employing the principal component analysis. Experimental results show that the proposed method increased the computational complexity of data for sample shift a little, but it has a good performance result more than a conventional method above the average 0.7% in EER (equal error rate).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.190-197
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• 2010

The ACR (Access Control Router) of WiBro processes location registration of the Correspondent Node and Home Agent as the Correspondent Node moves between ACRs. Therefore, the location update cost is low compared with MIPv6. However, all packets which are sent and received are sent through the ACR, so as the number of mobile nodes that are managed by the ACR increases, the cost of packet delivery also increases. Therefore, the communication state of the ACR domain remains smooth when the ACR which manages the mobile node in the ACR domain has good performance. However, network delays occur unless the ACR performs well, so the role of the ACR is important. In this paper, we analysis performance of the ACR for efficient realization of the WiBro standard. By using the Deny Probability and the Total Profit of ACR performance and apply it to the Random Walk Mobility model as the mobility model.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.109-118
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• 2010

Proxy mobile IPv6 (PMIPv6) protocol is a network-based mobility management protocol to support mobility for IPv6 nodes without host involvement. In PMIPv6, the Mobile Access Gateway (MAG) incurs a high signaling cost to update the location of a mobile node to the remote Local Mobility Anchor (LMA) if it moves frequently. This increases network overhead on the LMA, wastes network resources, and lengthens the delay time. Therefore, we propose a new mobility management scheme for minimizing signaling cost using the pointer forwarding. Our proposal can reduce signaling costs by registration with the neighbor MAG instead of the remote LMA using the pointer forwarding. The cost analysis using imbedded Markov chain presented in this paper shows that our proposal can achieve performance superior that of PMIPv6 scheme.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.1-13
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• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.741-750
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• 2011

In this paper, we derive an software mixed debugging model based on a Markov process, assuming that the length of time to perform the debugging is random and its distribution may depend on the fault type causing the failure. We assume that the debugging process starts as soon as a software failure occurs, and either a perfect debugging or an imperfect debugging is performed upon each fault type. One type is caused by a fault that is easily corrected and in this case, the perfect debugging process is performed. An Imperfect debugging process is performed to fix the failure caused by a fault that is difficult to correct. Distribution of the first passage time and working probability of the software system are obtained; in addition, an availability function of a software system which is the probability that the software is in working at a given time, is derived. Numerical examples are provided for illustrative purposes.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.165-177
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• 2008

An important issue in Multiagent reinforcement learning is how an agent should learn its optimal policy in a dynamic environment where there exist other agents able to influence its own performance. Most previous works for Multiagent reinforcement learning tend to apply single-agent reinforcement learning techniques without any extensions or require some unrealistic assumptions even though they use explicit models of other agents. In this paper, a Naive Bayesian based policy model of the opponent agent is introduced and then the Multiagent reinforcement learning method using this model is explained. Unlike previous works, the proposed Multiagent reinforcement learning method utilizes the Naive Bayesian based policy model, not the Q function model of the opponent agent. Moreover, this learning method can improve learning efficiency by using a simpler one than other richer but time-consuming policy models such as Finite State Machines(FSM) and Markov chains. In this paper, the Cat and Mouse game is introduced as an adversarial Multiagent environment. And then effectiveness of the proposed Naive Bayesian based policy model is analyzed through experiments using this game as test-bed.