• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2335-2339
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• 2003

To prevent the critical situation due to the fault in the aircraft sensor system, the fault tolerant system with triple or quadruple redundancy can be made. However, if the faults are occurred in two or more than sensors simultaneously, the conventional fault detection process, such as cross-channel monitoring, may give the wrong fault alarm. For this case, we can detect the fault by estimating the state vector based on the system dynamics model, which is nonlinear for aircraft. In this paper, we propose the unscented Kalman filter to estimate the nonlinear state vector. This filter utilizes the so-called unscented transformation of sigma points featured the statistical characteristics of the random variable. For verification, we perform the simulations for F-16 aircraft with accelerometers, gyros, GPS and air data system.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1682-1692
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• 2021

Digital Twin, one of the core technologies of the Fourth Industrial Revolution, is attracting attention as a very suitable technology for disaster monitoring such as fires and earthquakes. In this paper, we implement a system equipped with UWB RTLS(Ultra-Wideband Real Time Location System), real-time remote control, and video streaming, which are element technologies for disaster monitoring digital twin. Since the proposed system structure is based on a cloud server, the actual location of the UWB indoor positioning-based client is transmitted to the user device in real time and stored on the cloud server for statistical and data analysis. In addition, we demonstrate through experiments that outliers occurs when the value of RSSI(Received Signal Strength Indicator) decreases due to communication collisions between UWB Tags, and propose an RSSI outlier correction algorithm to solve this problem.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.50-56
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• 2020

In this study, we investigated whether long short-term memory (LSTM) can be used in the future to predict F10.7 index data; the F10.7 index is a space environment factor affecting atomic oxygen erosion. Based on this, we compared the prediction performances of LSTM, the Autoregressive integrated moving average (ARIMA) model (which is a traditional statistical prediction model), and the similar pattern searching method used for long-term prediction. The LSTM model yielded superior results compared to the other techniques in the prediction period starting from the max/min points, but presented inferior results in the prediction period including the inflection points. It was found that efficient learning was not achieved, owing to the lack of currently available learning data in the prediction period including the maximum points. To overcome this, we proposed a method to increase the size of the learning samples using the sunspot data and to upgrade the LSTM model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.106-114
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• 2019

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as $MC_{pm}$, $MC^+_{pm}$ and $MC_{pl}$. These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.557-572
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• 2009

This article examines the financial soundness of the government employee pension system(GEPS). We use a model that simplifies the existing GEPS considering survival probability distribution of the life of employees. Two approaches were selected for the research: One is the expected net value of pension for an individual employee and the other is the default probability of the system from Monte-carlo simulation. The outcome reveals following three possibilities. First of all, the individual expected net value presents unfairness between the retiree's premium and the benefit he/she receives. Secondly, the Monte-carlo simulation suggests that the default is highly likely to happen in less than 30 years. Thirdly, the governmental reserve and subsidy for GEPS should be required to a certain degree in order to alleviate the probability of default less than 5 percent for the next 30 years.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.393-410
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• 2021

The Korean Long-Term Care Insurance (K-LTCI) provides financial support for long-term care service to people who need various types of assistance with daily activities. As the number of elderly people in Korea is expected to increase in the future, the demand for long-term care insurance would also increase over time. Projection of future expenditure on K-LTCI depends on the number of beneficiaries within the grading system of K-LTCI based on the test scores of applicants. This study investigated the suitability of mixture distributions to the model K-LTCI score distribution using recent empirical data on K-LTCI, provided by the National Health Insurance Service (NHIS). Based on the developed mixture models, the number of beneficiaries in each grade and its variability under the current grading system were estimated by simulation. It was observed that a mixture model is suitable for K-LTCI score distribution and may prove useful in devising a funding plan for K-LTCI benefit payment and investigating the effects of any possible revision in the K-LTCI grading system.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.13-24
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• 2017

The Sample Cohort DB supplied by the National Health Insurance Service is a valuable resource for statistical studies as well as for health and medical studies. It takes significant time and effort to extract data from this Cohort DB having a large size. As such, we introduce a database system, conveniently called the National Health Insurance Service Cohort DB Extract Tool (NICE Tool), which supports several useful operations for effectively and efficiently managing the Cohort DB. For example, researchers can extract variables and cases related with study by simply clicking a computer mouse without any prior knowledge regarding SAS DATA step or SQL. We expect that NICE Tool will facilitate the faster extraction of data and eventually lead to the active use of the Cohort DB for research purposes.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.203-208
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• 2015

This study was conducted to provide the agricultural climatological basic data for the reset of sowing period of the winter crop on the double cropping system with rice. During the past 30 years from 1981 to 2010, mean air temperature has risen by $0.45^{\circ}C$ per 10 years (with statistical significance), while precipitation has decreased by 6.74 mm per 10 years and the numbers of days for precipitation has reduced by 0.23 days per 10 years (with no statistical significance) in the sowing period ($1^{st}$ Oct. to $5^{th}$ Nov.) of winter crop. It was analyzed that double cropping system of rice and winter crops need to be reset in the way of delaying the sowing time of winter crops, because rising trend of temperature was clear while variability of precipitation was great and the trend was not clear in the sowing period of winter crops. We have also analyzed the meteorological features of the sowing period of winter crops in 2014, and found that mean air temperature in 2014 was higher than that in normal years (similar to recent temperature change feature) while precipitation in 2014 was much more frequent than that in normal years (unlike recent precipitation features). Such tendency in 2014 made the sowing of winter crops difficult because mechanical sowing could not be worked in flooded paddy fields. Heavy rain in October 2014 was also analyzed as a rare phenomenon.

• Journal of Korean Society on Water Environment
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• v.37 no.3
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• pp.204-216
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• 2021

It is important to understand the factors influencing the temporal and spatial variability of water quality in order to establish an effective customized management strategy for contaminated aquatic ecosystems. In this study, the spatial diversity of the 5-year (2015 - 2019) average total phosphorus (TP) concentration observed in 40 Total Maximum Daily Loads unit-basins in the Nakdong River watershed was analyzed using 50 predictive variables of watershed characteristics, climate characteristics, land use characteristics, and soil characteristics. Cross-correlation analysis, a two-stage exhaustive search approach, and Bayesian inference were applied to identify predictors that best matched the time-averaged TP. The predictors that were finally identified included watershed altitude, precipitation in fall, precipitation in winter, residential area, public facilities area, paddy field, soil available phosphate, soil magnesium, soil available silicic acid, and soil potassium. Among them, it was found that the most influential factors for the spatial difference of TP were watershed altitude in watershed characteristics, public facilities area in land use characteristics, and soil available silicic acid in soil characteristics. This means that artificial factors have a great influence on the spatial variability of TP. It is expected that the proposed statistical modeling approach can be applied to the identification of major factors affecting the spatial variability of the temporal average state of various water quality parameters.