지능형 운행체를 위한 비전 센서 기반 자이로 드리프트 감소

경민기(MinGi Kyung),당 코이 누엔(Dang Khoi Nguyen),강태삼(Taesam Kang),민덕기(Dugki Min),이정욱(Jeong-Oog Lee2) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.18 No.1

Accurate heading information is crucial for the navigation of intelligent vehicles. In outdoor environments, GPS is usually used for the navigation of vehicles. However, in GPS-denied environments such as dense building areas, tunnels, underground areas and indoor environments, non-GPS solutions are required. Yaw-rates from a single gyro sensor could be one of the solutions. In dealing with gyro sensors, the drift problem should be resolved. HDR (Heuristic Drift Reduction) can reduce the average heading error in straight line movement. However, it shows rather large errors in some moving environments, especially along curved lines. This paper presents a method called VDR (Vision-based Drift Reduction), a system which uses a low-cost vision sensor as compensation for HDR errors.

Subdaily precipitation downscaling of climate change scenarios

Taesam Lee,Changsam Jeong 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-

In the current study, a temporal downscaling model that combines a nonparametric stochastic simulation approach with a genetic algorithm is proposed. The proposed model was applied to Jinju station in South Korea for a historical time period to validate the model performance. The results revealed that the proposed model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. In addition, the occurrence and transition probabilities are well preserved in the downscaled hourly precipitation data. Furthermore, the RCP4.5 and RCP8.5 climate scenarios for the Jinju station were also analyzed, revealing that the mean and the wet-hour probability significantly increased and the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP8.5 than RCP4.5.

Independent decomposition analysis in stochastic simulation of streamflow

Taesam Lee 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-

We illustrate in the current study that fitting a univariate time series model to each extracted component might end up with the underestimation of the serial dependence that the observation data might contain. A alternative for parameter estimation is suggested to preserve the serial dependence of the observation variable using the relationship between the observation variable and the decomposed variable. The case study of the Upper Colorado River basin shows that some improvement is made through the suggested alternative.

Hourly Precipitation and Hydrological Implication from Downscaled Climate Scenarios over South Korea

Taesam Lee,Taewoong Park 한국기상학회 2015 한국기상학회 학술대회 논문집 Vol.2015 No.10

Introduction to the production procedure of representative annual maximum precipitation scenario for different durations based on climate change with statistical downscaling approaches

Lee Taesam 한국수자원학회 2018 한국수자원학회논문집 Vol.51 No.11

기후변화는 홍수의 가장 큰 원인이 되는 극치강우의 빈도와 크기에 매우 큰 영향을 미치고 있다. 특히, 우리나라에서 발생하는 대규모 재해는 강우에 의한 홍수피해가 대부분을 차지하고 있다. 이러한 홍수피해는 기후변화에 의한 극한강우의 발생 빈도가 높아짐에 따라 새로운 재해양상으로 전개되고 있다. 하지만, 미래 기후변화 시나리오 자료는 해상도의 한계로 인하여 중소규모 하천 및 도시유역에 요구되는 수준의 자료 수집이 불가능한 상태이다. 이러한 문제점을 개선하기 위하여 본 연구에서는 전지구모형에서 생산된 기후변화 시나리오에 대해서 여러 단계의 통계적 상세화 기법을 통하여 우리나라 전역에 대하여 미래 시나리오에 대한 빈도해석이 가능하도록 각 지점의 특성에 따라 시간적으로 상세화하기 위해 개발된 방 법 및 과정을 소개하였다. 이를 통해, 시간상세화 자료를 토대로 미래 강우에 대한 빈도해석과 기후변화에 따른 방재성능 목표강우량을 산정하는데 활용할 수 있도록 하였다. Climate change has been influenced on extreme precipitation events, which are major driving causes of flooding. Especially, most of extreme water-related disasters in Korea occur from floods induced by extreme precipitation events. However, future climate change scenarios simulated with Global Circulation Models (GCMs) or Reigonal Climate Models (RCMs) are limited to the application on medium and small size rivers and urban watersheds due to coarse spatial and temporal resolutions. Therefore, the current study introduces the state-of-the-art approaches and procedures of statistical downscaling techniques to resolve this limitation It is expected that the temporally downscaled data allows frequency analysis for the future precipitation and estimating the design precipitation for disaster prevention.

Nonparametric temporal downscaling with event-based population generating algorithm for RCM daily precipitation to hourly: Model development and performance evaluation

Lee, Taesam,Park, Taewoong Elsevier 2017 Journal of hydrology Vol.547 No.-

<P><B>Abstract</B></P> <P>It is critical to downscale temporally coarse GCM or RCM outputs (e.g., monthly or daily) to fine time scales, such as sub-daily or hourly. Recently, a temporal downscaling model employing a nonparametric framework (NTD) with k-nearest resampling and a genetic algorithm has been developed to preserve key statistics as well as the diurnal cycle. However, this model’s usage can be limited in estimating precipitation for design storms or floods because the key statistics of annual maximum precipitation (AMP), especially for longer hourly durations, present a systematic bias that cannot be preserved due to the discontinuity of multiday consecutive precipitation events in the downscaling procedure. In the current study, we develop an approach to downscale a consecutive daily precipitation at once focusing on the reproduction of AMP totals for different durations instead of day-by-day downscaling. The proposed model has been verified with the precipitation datasets for the 60 stations across South Korea over the period 1979–2005. Additionally, two validation studies were performed with the recent datasets of 2006–2014 and nearest neighbor stations. The verification and the two validation tests conclude that the population-based NTD (PNTD) model proposed in the current study is superior to the existing NTD model in preserving the key statistics of the observed AMP series and suitable for downscaling future climate scenarios.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel PNTD model was proposed to downscale RCM daily precipitation to hourly. </LI> <LI> The PNTD model was verified and validated with the datasets across South Korea. </LI> <LI> The results show that the proposed PNTD model is superior to the existing NTD model. </LI> </UL> </P>

Long-term forecasting cimate nonstationary oscillation processes using EMD

Taesam Lee,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-

We developed a stochastic model that captures long term nonstationary oscillations (NSOs) within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD. To evaluate the model performance, the model is tested with the synthetic data set from Rossler attractor and with global surface temperature anomalies (GSTA) data. The results of the attractor show that the proposed approach provides a good characterization of the NSOs. For GSTA data, the last 30 observations are truncated and compared to the generated data. Then the model is used to predict the evolution of GSTA data over the next 50 years. The results of the case study confirm the power of the EMD approach and the proposed NSO resampling (NSOR) method as well as their potential for the study of climate variables.

Conditional stochastic simulation model for spatial downscaling for assessing the effects of climate change on hydro-meteorological variables

Lee, Taesam,Ouarda, Taha B. M. J. Springer-Verlag 2018 Climatic change Vol.150 No.3

LASSO-based predictor section in downscaling GCM data

Taesam Lee,Dorra Hammami,TTaesam Lee,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-

The objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Quebec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters forcomparison.

Modeling of nonstationary oscillation hydroclimatic processes employing EMD

Taesam Lee,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-

Reproducing nonstationary oscillation (NSO) processes in a stochastic time series model is a difficult task because of the complexity of the nonstationary behaviors. In the current study, a novel stochastic simulation technique that reproduces the NSO processes embedded in hydroclimatic data series is presented. The proposed model reproduces NSO processes by utilizing empirical mode decomposition (EMD) and nonparametric simulation techniques (i.e., k-nearestneighbor resampling and block bootstrapping). The model was first tested with synthetic data sets from trigonometric functions and the Rossler system. The North Atlantic Oscillation (NAO) index was then examined as a real case study. This NAO index was then employed as an exogenous variable for the stochastic simulation of streamflows at the Romaine River in the province of Quebec, Canada. The results of the application to the synthetic data sets and the real-world case studies indicate that the proposed model preserves well the NSO processes along with the key statistical characteristics of the observations. It was concluded that the proposed model possesses a reasonable simulation capacity and a high potential as a stochastic model, especially for hydroclimatic data sets that embed NSO processes.