• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.752-759
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• 2008

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.100.2-100.2
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• 2009

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.4
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• pp.19-29
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• 2007

This study compares various ground-truth designs of radar rainfall using rain-gauge data sets from Korea Meteorological Administration (KMA), AWS and Ministry of Construction and Transportation (MOCT). These Rain-gauge data sets and the Mt. Gwanak radar rainfall data for the same period were compared, and then the differences between two observed rainfall were evaluated with respect to the amount of bias. Additionally this study investigated possible differences in bias due to different storm characteristics. The application results showed no distinct differences between biases from three rain-gauge data sets, but some differences in their statistical characteristics. In overall, the design bias from MOCT was estimated to be the smallest among the three rain-gauge data sets. Among three storm events considered, the jangma with the highest spatial intermittency showed the smallest bias.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.1032-1041
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• 2007

The global navigation satellite system (GNSS), which is the core technique for the location based service, adopts the direct sequence/spread spectrum (DS/SS) as its modulation method. The success of a DS/SS system depends on the synchronization between the received and locally generated pseudo noise (PN) signals. As a step in the synchronization process, the tacking scheme performs fine adjustment to bring the phase difference between the two PN signals to zero. The most widely used tracking scheme is the delay locked loop with early minus late discriminator (EL-DLL). In the ideal case, the EL-DLL is the best estimator among various DLL. However, in the band-limited multipath environment, the EL-DLL has tracking bias. In this paper, the timing offset range of correlation function is divided into advanced offset range (AOR) and delayed offset range (DOR) centering around the correct synchronization time point. The tracking bias results from the following two reasons: symmetry distortion between correlation values in AOR and DOR, and mismatch between the time point corresponding to the maximum correlation value and the synchronization time point. The former and latter are named as the type I and type II tracking bias, respectively. In this paper, when the receiver has finite bandwidth in the presence of multipath signals, it is shown that the type II tracking bias becomes a more dominant error factor than the type I tracking bias, and the correlation values in AOR are not almost changed. Exploiting these characteristics, we propose a novel tracking bias mitigation scheme and demonstrate that the tracking accuracy of the proposed scheme is higher than that of the conventional scheme, both in the presence and absence of noise.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.17-28
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• 2011

This study applied the Kalman Filter for real-time forecasting the G/R (ground rain gauge rainfall/radar rainfall) ratio to correct the mean field bias of the very-short-range-forecast (VSRF) rainfall. The MAPLE-forecasted rainfall was used as the VSRF rainfall, also the methodology for deciding the G/R ratio was improved by evaluating the change of G/R ratio characteristics depending on the threshold and accumulation time. This analysis was done for the inland, mountain, and coastal regions, separately, for their comparison. As the results, more stable G/R ratio could be estimated by applying the threshold and accumulation time, whose forecasting accuracy could also be secured. The accuracy of the corrected rainfall forecasting by the forecasted G/R ratio was the best in the inland region but the worst in the coastal region.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.85-97
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• 2018

This paper investigates factors underlying a decrease in trading values in KOSPI 200 futures/options market on the basis of the current state of the markets. Among the factors that could affect trading values in KOSPI 200 derivatives market, we focus on the market activity of underlying assets as it has an impact on the trading of financial derivatives. Trading value and volatility are designated as market activity and the empirical results confirm that the market activity of the underlying assets is significant in explaining the decrease in trading values in KOSPI 200 futures/options market. To figure out fundamental reasons of the decrease in trading values in this market, we examine mitigation of home bias and decrease in leverage incentives as they are presumed to have influence on KOSPI 200 index market. As the global and local financial environment is time-varying, the degree of home bias and the leverage demand also changes. It implies that institutional change and/or policy effort to promote the trading of KOSPI 200 financial derivatives should be made taking into account the fact that considerable portion of the change in trading values in financial derivatives market depends on the state of the market.

• Environmental and Resource Economics Review
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• v.21 no.2
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• pp.271-299
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• 2012

The relationship between environment and economic growth has been controversial for a long time. The cores of controversy are endogeneity problem and omitted variable bias. This paper tests EKC (Environmental Kuznets Curves) hypothesis by considering econometric issues and estimates the effects of energy mix on $CO_2$ emissions empirically and tests with time series during 1981~2008. By the results of this analysis, we convince EKC Hypothesis which the relationship between $CO_2$ emissions and economic growth is the inverted U-shaped and the national energy mix contributes significantly to GHG mitigation. We also find that the nuclear energy has the greatest contribution for $CO_2$ mitigation and the renewable energy does not seem to contribute little to the $CO_2$ mitigation because the proportion of renewable energy in Korea is negligible. In terms of final energy consumption, $CO_2$ increases and transportation sector is statistically and significantly associated.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1177-1184
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• 2008

Non-line-of-sight (NLOS) propagation can severely weaken the accuracy of ranging and localization in wireless location systems. NLOS bias mitigation techniques have recently been proposed to relieve the NLOS effects, but positively rely on the capability to accurately distinguish between LOS and NLOS propagation scenarios. This paper proposes an energy-capture-based NLOS identification method for LDR-UWB systems, based on the analysis of the characteristics of the channel impulse response (CIR). With this proposed energy capture method, the probability of successfully identifying NLOS is much improved than the existing methods, such as the kurtosis method, the strongest path compare method, etc. This NLOS identification method can be employed in adaptive modulation scheme to decrease bit error ratio (BER) level for certain signal-to-noise ratio (SNR). The BER performance with the adaptive modulation can be significantly enhanced by selecting proper modulation method with the knowledge of channel information from the proposed NLOS identification method.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.509-512
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• 2024

자연어 처리 기술은 인간 언어의 이해와 처리에서 큰 진전을 이루었으나, 학습 데이터에 내재한 성별 편향이 모델의 예측 정확도와 신뢰성을 저하하는 주요한 문제로 남아 있다. 특히 성별 예측에서 이러한 편향은 더욱 두드러진다. 제로샷 분류 기법은 기존에 학습되지 않은 새로운 클래스를 효과적으로 예측할 수 있는 기술로, 학습 데이터의 제한적인 의존성을 극복하고 다양한 언어 및 데이터 제한 상황에서도 효율적으로 작동한다. 본 논문은 성별 클래스 확장과 데이터 구조 개선을 통해 성별 편향을 최소화한 새로운 데이터셋을 구축하고, 이를 제로샷 분류 기법을 통해 학습시켜 성별 편향성이 완화된 새로운 성별 예측 모델을 제안한다. 이 연구는 다양한 언어로 구성된 자연어 데이터를 추가 학습하여 성별 예측에 최적화된 모델을 개발하고, 제한된 데이터 환경에서도 모델의 유연성과 범용성을 입증한다.