• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.107-120
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• 2010

The effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water was assessed using the SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. The fundamental input data (elevation, meteorological data, land use, soil, vegetation) was collected to calibrate and validate of the SLURP model for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang and Gosam) located in Anseongcheon watershed. Then, the CCCma CGCM2 data by SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (intergovernmental panel on climate change) was used to assess the future potential climate change. The future weather data for the year, m ms, m5ms and 2amms was downscaled by Change Factor method through bias-correction using 3m years (1977-2006) weather data of 3 meteorological stations of the watershed. In addition, the future land uses were predicted by modified CA (cellular automata)-Markov technique using the time series land use data fromFactosat images. Also the future vegetation cover information was predicted and considered by the linear regression between monthly NDVI (normalized difference vegetation index) from NOAA AVHRR images and monthly mean temperature using eight years (1998-2006) data.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.41 no.6
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• pp.466-470
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• 2008

Microsatellite DNA markers were used to investigate the genetic diversity and population structure of Pacific abalone Haliotis discus hannai collected from six locations (Uljin, Ulsan, Daechon, Taean, Wando, and Yosu) where hatchery-produced abalone have been released intensively. There was no distinguishable difference in the observed and expected heterozygosities between the six populations and a cultured population. However, there was a difference in the number of alleles per locus: 12.8 for the cultured population and 13.8 to 15.8 for the six populations. The proportion of stocked abalone ranged from 41.1 to 92.7% for wild-caught populations with a decreasing tendency of alleles per locus for an increasing proportion of stocked abalone. A departure from Hardy-Weinberg equilibrium (HWE) assessed using the Markov chain procedure (P<0.05) was observed in the six populations and cultured population at loci Hdh145 and Hdh5l2. The pairwise Fst test (P<0.05) showed a significant difference between the Uljin and Ulsan populations and four remaining populations (Wando, Daechon, Yosu, and the cultured population), among which the Wando population differed less than the other three populations (Daechon, Yosu, and the cultured population).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.4
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• pp.385-392
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• 2005

This paper describes an accuracy analysis of newly developed gravimetric geoid and an improvement of developed geoid using GPS/Levelling data. We developed the KGEOID05 model corrected with the correction term. The correction term is modelled using the difference between GPS/Levelling derived geoidal heights and gravimetric geoidal heights. The stochastic model used in the calculation of correction term is the least squares collocation technique based on second-order Markov covariance function. 373 GPS stations were used to model the correction term. The standard deviation of KGEOID05 is about 11 cm and it indicates that we can be determined accurate heights ($2{\sim}3\;cm$) when we made precise modelling using KGEOID05 and a few GPS measurements for the local area.

• Journal of the Korea Convergence Society
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• v.5 no.1
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• pp.9-15
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• 2014

Traffic accidents are caused by a variety of factors. Among the factors that cause traffic accidents are weather conditions at the time. There is a difference in the percentage of deaths according to traffic accidents, due to the weather conditions. In order to reduce the number of deaths due to traffic accidents, to predict the incidence of traffic accidents that occur in response to weather conditions is required. In this paper, it propose a model to predict the incidence of traffic accidents caused by weather conditions. Predictive modeling was applied to the theory of Markov processes. By applying the actual data for the proposed model, to predict the incidence of traffic accidents, it was compared with the number of occurrences in practice. In this paper, it is to support the development of traffic accident policy with the change of weather.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.56-62
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• 2001

In this paper, we adopt a missing data theory to speech recognition. It can be used in order to maintain high performance of speech recognizer when the missing data occurs. In general, hidden Markov model (HMM) is used as a stochastic classifier for speech recognition task. Acoustic events are represented by continuous probability density function in continuous density HMM(CDHMM). The missing data theory has an advantage that can be easily applicable to this CDHMM. A marginalization method is used for processing missing data because it has small complexity and is easy to apply to automatic speech recognition (ASR). Also, a spectral subtraction is used for detecting missing data. If the difference between the energy of speech and that of background noise is below given threshold value, we determine that missing has occurred. We propose a new method that examines the reliability of detected missing data using voicing probability. The voicing probability is used to find voiced frames. It is used to process the missing data in voiced region that has more redundant information than consonants. The experimental results showed that our method improves performance than baseline system that uses spectral subtraction method only. In 452 words isolated word recognition experiment, the proposed method using the voicing probability reduced the average word error rate by 12％ in a typical noise situation.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.20-26
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• 2000

In speaker verification system the HMM(hidden Markov model) parameter updating using small amount of data and the priori threshold decision are crucial factor for dealing with long-term variability in people voices. In the paper we present the speaker model updating technique which can be adaptable to the session-to-intra speaker variability and the priori threshold determining technique. The proposed technique decreases verification error rates which the session-to-session intra-speaker variability can bring by adapting new speech data to speaker model parameter through Baum Welch re-estimation. And in this study the proposed priori threshold determining technique is decided by a hybrid score measurement which combines the world model based technique and the cohen model based technique together. The results show that the proposed technique can lead a better performance and the difference of performance is small between the posteriori threshold decision based approach and the proposed priori threshold decision based approach.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.321-330
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• 2016

Run expectancy (RE) is the mean number of runs scored from a specific base runner/outs situation of an inning to the end of the inning. Win expectancy (WE) is the probability that a particular team will win the game at a specific game state such as half-inning, score difference, outs, and/or runners on base. In this paper, we derive RE and WE for the Korea Baseball Organization (KBO) League based on six-year data from 2007 to 2012 using a Markov chain model.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• Journal of Navigation and Port Research
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• v.43 no.4
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• pp.237-242
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• 2019

Behavioral errors of the seafarers are one of the major causes of collisions and are usually corrected through education and training. To correct this behavioral error, the structure in which the behavioral error occurs needs to be identified and analyzed. For this purpose, behavior observation data were obtained through ship maneuvering simulation for collision encounters. The 9-state behavior classification frame proposed by Reason was used for the behavior observation and 50 university students were involved in the experiment. Behavioral analysis used the behavioral model of collision avoidance success and failure, which was developed from the 9-state Left-to-Right Hidden Markov modeling technique. As a result of the experiment, the difference between behaviors of success and failure of collision avoidance was clearly identified, and the linkage between 9-state behaviors, required to prevent collision, was derived.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.329-347
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• 2017

Early research into category-based feature inference reported various phenomena in human thinking including typicality, diversity, similarity effects, etc. Later research discovered that participants' prior knowledge has an extensive influence on these sorts of reasoning. The current research tested the effects of causal knowledge on feature inference and conducted modeling on the results. Participants performed feature inference for categories consisted of four features where the features were connected either in common cause or common effect structure. The results showed typicality effects along with violations of causal Markov condition in common cause structure and causal discounting in common effect structure. To model the results, it was assumed that participants perform feature inference based on the difference between the probabilities of an exemplar with the target feature and an exemplar without the target feature (that is, $p(E_{F(X)}{\mid}Cat)-p(E_{F({\sim}X)}{\mid}Cat)$). Exemplar probabilities were computed based on causal model theory (Rehder, 2003) and applied to inference for target features. The results showed that the model predicts not only typicality effects but also violations of causal Markov condition and causal discounting observed in participants' data.