• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.274-284
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• 2002

Word segmentation errors occurring in text preprocessing often insert incorrect words into recognition vocabulary and cause poor language models for Korean large vocabulary continuous speech recognition. We propose an automatic word segmentation algorithm using Markov chains and syllable-based n-gram language models in order to correct word segmentation error in teat corpora. We assume that a sentence is generated from a Markov chain. Spaces and non-space characters are generated on self-transitions and other transitions of the Markov chain, respectively Then word segmentation of the sentence is obtained by finding the maximum likelihood path using syllable n-gram scores. In experimental results, the algorithm showed 91.58% word accuracy and 96.69% syllable accuracy for word segmentation of 254 sentence newspaper columns without any spaces. The algorithm improved the word accuracy from 91.00% to 96.27% for word segmentation correction at line breaks and yielded the decomposition accuracy of 96.22% for compound-noun decomposition.

• Journal of KIISE
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• v.44 no.7
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• pp.719-725
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• 2017

To reduce management costs and improve performance, the European Telecommunication Standards Institute (ETSI) introduced the concept of network function virtualization (NFV), which can implement network functions (NFs) on cloud/datacenters. Within the NFV architecture, NFs can share physical resources by hosting NFs on physical nodes (commodity servers). For network service providers who support NFV architectures, an efficient resource allocation method finds utility in being able to reduce operating expenses (OPEX) and capital expenses (CAPEX). Thus, in this paper, we analyzed the network service chain embedding problem via an optimization formulation and found a close-optimal solution based on the Markov approximation framework. Our simulation results show that our approach could increases on average CPU utilization by up to 73% and link utilization up to 53%.

• Water for future
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• v.29 no.6
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• pp.179-188
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• 1996

The chronical sequences of daily precipitation are of great practical importance in the planning and operational processes of water resources system. A sequence of days with alternate dry day and wet day can be generated by two state Markov chain model that establish the subsequent daily state as wet or dry by previously calculated vconditional probabilities depending on the state of previous day. In this study, a synthetic generation model for obtaining the daily precipitation series is presented by classifying the precipitation amount in wet days into multi-states. To apply multi-state Markov chain model, the daily precipitation amounts for wet day are rearranged by grouping into thirty states with intervals for each state. Conditional probabilities as transition probability matrix are estimated from the computational scheme for stepping from the precipitation on one day to that on the following day. Statistical comparisons were made between the historical and synthesized chracteristics of daily precipitation series. From the results, it is shown that the proposed method is available to generate and simulate the daily precipitation series with fair accuracy and conserve the general statistical properties of historical precipitation series.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1990.06a
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• pp.633-637
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• 1990

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.231-231
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• 1993

• Communications of the Korean Mathematical Society
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• v.14 no.3
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• pp.629-629
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• 1999

• KCID journal
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• v.13 no.1
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• pp.110-117
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• 2006

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.121-125
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• 2004

In the most of recognizing systems of hand-written numbers. extraction of feature shape by using character elements shapes and a method of morphological analysis by using then extraction of feature shapes were usually used. In this paper, however, peculiar chain-code is used, and differential code which gets minimal value by differentiating the chain-code which is generated by the peculiar chain-code is made. We found this differential code is very successful in discriminating hand-written numbers according to the result of applying to most of the hand-written numbers. Testing recognition of hand-written numbers by HMM network. From the results, we can recognize of 96.1 percentage hand-written numbers but can not recognize extremely distorted hand-written numbers.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.825-838
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• 2014

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

• 한국해양학회지
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• v.22 no.2
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• pp.57-62
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• 1987

The Markov chain properties of the sea surface temperature (SST) anomalies, namely, the dependency of the monthly SST anomaly on that of the previous month, are studied based on the SST data for 28years(1957-1984) at 5 stations in the southeastern coast of Korea. Wi classified the monthly SST anomalies at each station into the low, the normal and the high state, and computed transition probabilities between SST anomalies of two successive months The standard deviation of SST anomalies at each station is used as a reference for the classification of SST anomalies into 3states. The transition probability of the normal state to remain in the same state is about 0.8. The transition probability of the high or the low states to remain in the same state is about one half. The SST anomalies have almost no probability to transit from the high (the low) state to the low (the high) state. Statistical tests show that the Markov chain properties of SST anomalies are stationary in tine and homogeneous in space. The multi-step Markov chain analysis shows that the 'memory' of the SST anomalies at the coastal stations remains about 3 months.