• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.11
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• pp.549-557
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• 2000

This study proposes a new numerical analysis method for assessing the outage cost of the composite power system with considering transmission system at load points. The proposed method comes from combination of the expected energy not served curve(EENSC) with the marginal outage cost function obtained at load points. Uncertainty of the outages of the generation and transmission systems was also included in this study. This study can be categorized into three processing parts as like as follows. Firstly, EENSC at load points was developed newly from the composite power system effective load duration curve which has been proposed by the authors. Secondly, this study proposes a new technical method for determining the coefficients of the marginal outage cost functions at load points in the composite power system(Generation and Transmission systems). It is a main key point that the mathematical expression for the marginal outage cost function at a load point is formulated and evaluated using relations between the GNP (or GDP) and the electrical energy demand at the load pint. Finally, the outage cost was calculated in this paper by combining the proposed EENSC with the marginal outage cost function evaluated at each load point. It is another important feature that the average costs for future at load points can be forescasted using the proposed approach. The effectiveness of the proposed new approach is demonstrated by the case studies with the IEEE-RTS.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.25-35
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• 2006

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home -like environment.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.