• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.137-137
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• 2010

Copper electroplating is a very popular and important technology for depositing high-quality conductor interconnections, especially in through silicon via (TSV). As this advanced packaging technique developing, a mass of copper and chemical solution are used, so attention to these chemical materials into the utilization and costs can not be ignored. An economical and practical real-time chemical solution monitoring has not been achieved yet. Either Red-green-blue (RGB) or optical emission spectroscopy (OES) color sensor can successfully monitor the color condition of solution during the process. The reaction rate, uniformity and quality can map onto the color changing. Hidden Semi Markov model (HSMM) can establish mapping from the color change to upper indicators, and artificial neural network (ANN) can be integrated to comprehensively determine its targets, whether the solution inside the container can continue to use.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.94-99
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• 1999

It is the hybrid structure of HMM and neural network(NN) that shows high recognition rate in speech recognition algorithms. And it is a method which has majorities of statistical model and neural network model respectively. In this study, we propose a new style of the hybrid structure of semi-continuous HMM(SCHMM) and radial basis function(RBF), which re-estimates weighting coefficients probability affecting observation probability after Baum-Welch estimation. The proposed method takes account of the similarity of basis Auction of RBF's hidden layer and SCHMM's probability density functions so as to discriminate speech signals sensibly through the learned and estimated weighting coefficients of RBF. As simulation results show that the recognition rates of the hybrid structure SCHMM/RBF are higher than those of SCHMM in unlearned speakers' recognition experiment, the proposed method has been proved to be one which has more sensible property in recognition than SCHMM.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.85-89
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• 1996

In this paper, transition constrained Hidden Markov Model(HMM) in which the transition between states occur only within prescribed time slot is proposed and the performance is evaluated in the noisy environment. The transition constrained HMM can explicitly limit the state durations and accurately de scribe the temporal structure of speech signal simply and efficiently. The transition constrained HMM is not only superior to the conventional HMM but also require much less computation time. In order to evaluate the performance of the transition constrained HMM, speaker independent isolated word recognition experiments were conducted using semi-continuous HMM with the noisy speech for 20, 10, 0 dB SNR. Experiment results show that the proposed method is robust to the environmental noise. The 81.08% and 75.36% word recognition rates for conventional HMM was increased by 7.31% and 10.35%, respectively, by using transition constrained HMM when two kinds of noises are added with 10dB SNR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1236-1251
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• 2013

Sharing geo-tagged photos has been a hot social activity in the daily life because these photos not only contain geo information but also indicate people's hobbies, intention and mobility patterns. However, the present raw geo-tagged photo routes cannot provide information as enough as complete GPS trajectories due to the defects hidden in them. This paper mainly aims at analyzing the large amounts of geo-tagged photos and proposing a novel travel route restoring method. In our approach we first propose an Interest Measure Ratio to rank the hot spots based on density-based spatial clustering arithmetic. Then we apply the Hidden Semi-Markov model and Mean Value method to demonstrate migration discipline in the hot spots and restore the significant region sequence into complete GPS trajectory. At the end of the paper, a novel experiment method is designed to demonstrate that the approach is feasible in restoring route, and there is a good performance.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.62.2-62.2
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• 2016

We present a newly developed algorithm based on a Bayesian method for 2D tilted-ring analysis of disk galaxies which operates on velocity fields. Compared to the conventional ones based on a chi-squared minimisation procedure, this new Bayesian-based algorithm less suffers from local minima of the model parameters even with high multi-modality of their posterior distributions. Moreover, the Bayesian analysis implemented via Markov Chain Monte Carlo (MCMC) sampling only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature is essential for performing kinematic analysis of an unprecedented number of resolved galaxies from the upcoming Square Kilometre Array (SKA) pathfinders' galaxy surveys. A standalone code, the so-called '2D Bayesian Automated Tilted-ring fitter' (2DBAT) that implements the Bayesian fits of 2D tilted-ring models is developed for deriving rotation curves of galaxies that are at least marginally resolved (> 3 beams across the semi-major axis) and moderately inclined (20 < i < 70 degree). The main layout of 2DBAT and its performance test are discussed using sample galaxies from Australia Telescope Compact Array (ATCA) observations as well as artificial data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1
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• pp.96-99
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• 1997

This paper proposes a very fast preprocessor for isolated word recognition. The proposed preprocessor has a small computational cost for extracting candidate words. In the preprocessor, we used a feature sorting algorithm instead of vector quantization to reduce the computational cost. In order to show the effectiveness of our preprocessor, we compared it to a speech recognition system based on semi-continuous hidden Markov Model and a VQ-based preprocessor by computing their recognition performances of a speaker independent isolated word recognition. For the experiments, we used the speech database consisting of 244 words which were uttered by 40 male speakers. The set of speech data uttered by 20 male speakers was used for training, and the other set for testing. As the results, the accuracy of the proposed preprocessor was 99.9% with 90% reduction rate for the speech database.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.103-112
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• 1999

In this paper, a speaker-independent isolated world recognition system for a car navigation system is implemented using a general digital signal processor. This paper presents a method combining SNR normalization with RAS as a noise processing method. The semi-continuous hidden markov model is adopted and TMS320C31 is used in implementing the real-time system. Recognition word set is composed of 69 command words for a car navigation system. Experimental results showed that the recognition performance has a maximum of 93.62% in case of a combination of SNR normalization and spectral subtraction, and the performance improvement rate of the system is 3.69%, Presented noise processing method showed good speech recognition performance in 5dB SNR in car environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.337-346
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• 2010

Climate change has a huge impact on various parts of the world. This study quantified and analyzed the effects on hydrological behavior caused by climate, vegetation canopy and land use change of Soyanggang dam watershed (2,694.4 $km^2$) using the semi-distributed model SWAT (Soil Water Assessment Tool). For the 1997-2006 daily dam inflow data, the model was calibrated with the Nash-Sutcliffe model efficiencies between the range of 0.45 and 0.91. For the future climate change projection, three GCMs of MIROC3.2hires, ECHAM5-OM, and HadCM3 were used. The A2, A1B and B1 emission scenarios of IPCC (Intergovernmental Panel on Climate Change) were adopted. The data was corrected for each bias and downscaled by Change Factor (CF) method using 30 years (1977-2006, baseline period) weather data and 20C3M (20th Century Climate Coupled Model). Three periods of data; 2010-2039 (2020s), 2040-2069 (2050s), 2070-2099 (2080s) were prepared for future evaluation. The future annual temperature and precipitation were predicted to change from +2.0 to $+6.3^{\circ}C$ and from -20.4 to 32.3% respectively. Seasonal temperature change increased in all scenarios except for winter period of HadCM3. The precipitation of winter and spring increased while it decreased for summer and fall for all GCMs. Future land use and vegetation canopy condition were predicted by CA-Markov technique and MODIS LAI versus temperature regression respectively. The future hydrological evaluation showed that the annual evapotranspiration increases up to 30.1%, and the groundwater recharge and soil moisture decreases up to 55.4% and 32.4% respectively compared to 2000 condition. Dam inflow was predicted to change from -38.6 to 29.5%. For all scenarios, the fall dam inflow, soil moisture and groundwater recharge were predicted to decrease. The seasonal vapotranspiration was predicted to increase up to 64.2% for all seasons except for HadCM3 winter.

• Journal of Korea Water Resources Association
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• v.42 no.1
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• pp.33-50
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• 2009

The impact on hydrologic components considering future potential climate, land use change and vegetation cover information was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated (1999 - 2000) and validated (2001 - 2002) for the upstream watershed ($260.4\;km^2$) of Gyeongancheon water level gauging station with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.77 to 0.60 and 0.79 to 0.60, respectively. Two GCMs (MIROC3.2hires, ECHAM5-OM) future weather data of high (A2), middle (A1B) and low (B1) emission scenarios of the IPCC (Intergovernmental Panel on Climate Change) were adopted and the data was corrected by 20C3M (20th Century Climate Coupled Model) and downscaled by Change Factor (CF) method using 30 years (1977 - 2006, baseline period) weather data. Three periods data of 2010 - 2039 (2020s), 2040 - 2069 (2050s), 2070 - 2099 (2080s) were prepared. To reduce the uncertainty of land surface conditions, future land use and vegetation canopy prediction were tried by CA-Markov technique and NOAA NDVI-Temperature relationship respectively. MIROC3.2 hires and ECHAM5-OM showed increase tendency in annual streamflow up to 21.4 % for 2080 A1B and 8.9 % for 2050 A1B scenario respectively. The portion of future predicted ET about precipitation increased up to 3 % in MIROC3.2 hires and 16 % in ECHAM5-OM respectively. The future soil moisture content slightly increased compared to 2002 soil moisture.