• KDI Journal of Economic Policy
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• v.40 no.4
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• pp.1-22
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• 2018

We estimate three continuous-time stochastic volatility models following the approach by Aït-Sahalia and Kimmel (2007) to compare the Korean and US stock markets. To do this, the Heston, GARCH, and CEV models are applied to the KOSPI 200 and S&P 500 Index. For the latent volatility variable, we generate and use the integrated volatility proxy using the implied volatility of short-dated at-the-money option prices. We conduct MLE in order to estimate the parameters of the stochastic volatility models. To do this we need the transition probability density function (TPDF), but the true TPDF is not available for any of the models in this paper. Therefore, the TPDFs are approximated using the irreducible method introduced in Aït-Sahalia (2008). Among three stochastic volatility models, the Heston model and the CEV model are found to be best for the Korean and US stock markets, respectively. There exist relatively strong leverage effects in both countries. Despite the fact that the long-run mean level of the integrated volatility proxy (IV) was not statistically significant in either market, the speeds of the mean reversion parameters are statistically significant and meaningful in both markets. The IV is found to return to its long-run mean value more rapidly in Korea than in the US. All parameters related to the volatility function of the IV are statistically significant. Although the volatility of the IV is more elastic in the US stock market, the volatility itself is greater in Korea than in the US over the range of the observed IV.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.63-63
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• 2021

Bed shear stress is important variable in river flow analysis. The bed shear stress has an effects on bed erosion, sediment transport, and mean flow characteristics. Quadratic formula to estimate bed shear stress is widely used, 𝜏=𝜌c_fu|u| in which friction coefficient, c_f, needs to be assigned to numerical models. The aim of this study is to estimate Chezy coefficient using bathymetry data measured by ADCP. Bed form geometry variables will be estimated form bed profile, then Chezy coefficient will be determined using estimated bed form geometry variables in order to set friction coefficient to numerical model. From the probability density function obtained from the bathymetry data, Chezy coefficient will be randomly generated since Chezy coefficient is not uniform over the space and it does not depend on spatial variables such as water depth and distance from river bank. Numerical test will be performed to find to demonstrate randomly extracted Chezy coefficient is appropriate. The result of this study is valuable in that the friction coefficient is estimated in consideration of the bed profile, and as a result, uncertainty of the friction coefficient can be reduced.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.203-214
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• 2018

The purpose of this study is to estimate the concentrated distribution area of nutria (Myocastor coypus) and potential suitable habitat and to provide useful data for the effective management direction setting. Based on the nationwide distribution data of nutria, the cross-validation value was applied to analyze the distribution density. As a result, the concentrated distribution areas thatrequired preferential elimination is found in 14 administrative areas including Busan Metropolitan City, Daegu Metropolitan City, 11 cities and counties in Gyeongsangnam-do and 1 county in Gyeongsangbuk-do. In the potential suitable habitat estimation using a MaxEnt (Maximum Entropy) model, the possibility of emergency was found in the Nakdong River middle and lower stream area and the Seomjin riverlower stream area and Gahwacheon River area. As for the contribution by variables of a model, it showed DEM, precipitation of driest month, min temperature of coldest month and distance from river had contribution from the highest order. In terms of the relation with the probability of appearance, the probability of emergence was higher than the threshold value in areas with less than 34m of altitude, with $-5.7^{\circ}C{\sim}-0.6^{\circ}C$ of min temperature of the coldest month, with 15-30mm of precipitation of the driest month and with less than 1,373m away from the river. Variables that Altitude, existence of water and wintertemperature affected settlement and expansion of nutria, considering the research results and the physiological and ecological characteristics of nutria. Therefore, it is necessary to reflect them as important variables in the future habitable area detection and expansion estimation modeling. It must be essential to distinguish the concentrated distribution area and the management area of invasive alien species such as nutria and to establish and apply a suitable management strategy to the management site for the permanent control. The results in this study can be used as useful data for a strategic management such as rapid management on the preferential management area and preemptive and preventive management on the possible spreading area.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.3-10
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• 1999

This paper describes the development and evaluation of a Korean address input system employing automatic speech recognition technique as user interface for input Korean address. Address consists of cities, provinces and counties. The system works on a window 95 environment of personal computer with built-in soundcard. In the speech recognition part, the Continuous density Hidden Markov Model(CHMM) for making phoneme like units(PLUs) and One Pass Dynamic Programming(OPDP) algorithm is used for recognition. For address recognition, Finite State Automata(FSA) suitable for Korean address structure is constructed. To achieve an acceptable performance against the variation of speakers, microphones, and environmental noises, Maximum a posteriori(MAP) estimation is implemented in adaptation. And to improve the recognition speed, fast search method using variable pruning threshold is newly proposed. In the evaluation tests conducted for the 100 connected words uttered by 3 males the system showed above average 96.0% of recognition accuracy for connected words after adaption and recognition speed within 2 seconds, showing the effectiveness of the system.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.694-702
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• 2014

Since Greenhouse Gas Inventory & Research Center (GIR) of Korea was founded in 2010, the annual greenhouse gas inventory reports, one of the collections of GIR's major affairs, have been published from 2012. In the reports many items related to greenhouse gas emission quantities are included, but among them uncertainty values are replaced to basic values which IPCC guideline suggests. Even though IPCC guideline suggests the equations of each Tier level in details, the guideline recommends developing nation's own methodology on uncertainty which is closely related to statistical problems such as the estimation of a probability density function or Monte carlo methods. In the road transportation sector the emissions have been calculated by Tier 1 but the uncertainties have not been reported. This study introduce a bootstrap technique and Monte carlo method to estimates annual emission quantity and uncertainty, given activity data and emission factors such as annual traveled distances, fuel efficiencies and emission coefficients.

• ETRI Journal
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• v.38 no.5
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.149-154
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• 2014

The cross-correntropy concept can be expressed with inner products of two different probability density functions constructed by Gaussian-kernel density estimation methods. Blind algorithms based on the maximization of the cross-correntropy (MCC) and a symbol set of randomly generated N samples yield superior learning performance, but have a huge computational complexity in the update process at the aim of weight adjustment based on the MCC. In this paper, a method of reducing the computational complexity of the MCC algorithm that calculates recursively the gradient of the cross-correntropy is proposed. The proposed method has only O(N) operations per iteration while the conventional MCC algorithms that calculate its gradients by a block processing method has $O(N^2)$. In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.12
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• pp.1346-1351
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• 2014

We propose a novel approach for stereophonic acoustic echo suppression using spectro-temporal filtering based on soft decision. Unlike the conventional approaches estimating the echo pathes directly, the proposed technique can estimate stereo echo spectra without any double-talk detector. In order to improve the estimation of echo spectra, the extended power spectrum density matrix and echo overestimation control matrix are applied on this method. In addition, this echo suppression technique is based on soft decision technique using speech absence probability in STFT domain. Experimental results show that the proposed method improves compared with the conventional approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.