• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1025-1034
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• 2015

Traditional Shewhart control charts assume that the observations are independent over time. Current progress in measurement and data collection technology lead to the presence of autocorrelated process data that may affect poor performance in statistical process control. One of the most popular charts for autocorrelated data is to model a correlative structure with an appropriate time series model and apply control chart to the sequence of residuals. Model parameters are estimated by an in-control Phase I reference sample since they are usually unknown in practice. This paper deals with the effects of parameter estimation on Phase II control limits to monitor autocorrelated data.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1191-1201
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• 2009

The cumulative sum(CUSUM) control charts are typically used for detecting small level shifts in process control. To control an auto-correlated process, the model-based control methods can be employed, in which the residuals from fitting a time series model are applied to the CUSUM chart. However, the persistent level shifts in the original process may lead to varying mean shifts in residuals, which may deteriorate detection performance significantly. Therefore, in this paper, focussing on ARMA(1,1), we propose a new CUSUM type control method which can detect the dynamic mean shifts in residuals especially with oscillating pattern effectively and, through the simulation study, evaluate its performance by comparing with other various CUSUM type control methods introduced so far.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.191-207
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• 2024

Many studies have been conducted to quickly detect out-of-control situations in autocorrelated processes. The most traditionally used method is a residual control chart, which uses residuals calculated from a fitted time series model. However, many procedures for monitoring autocorrelated processes using statistical learning methods have recently been proposed. In this paper, we propose a monitoring procedure using the latent vector of LSTM Autoencoder, a deep learning-based unsupervised learning method. We compare the performance of this procedure with the LSTM Autoencoder procedure based on the reconstruction error, the RNN classification procedure, and the residual charting procedure through simulation studies. Simulation results show that the performance of the proposed procedure and the RNN classification procedure are similar, but the proposed procedure has the advantage of being useful in processes where sufficient out-of-control data cannot be obtained, because it does not require out-of-control data for training.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.2 s.54
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• pp.115-121
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• 2009

The paper presents a signal-based damage detection algorithm of ARX model using dynamic acceleration data. An ARX model correlates acceleration data measured at two locations in a structure by considering those two sets of data as input and output signals. For detecting damage, the error between the measured data and the predicted response from the defined ARX model is computed in time and used for a statistical evaluation. A normal distribution function from the error in time is constructed and its statistical characteristic values are used for the evaluation of damage. By comparing the normal distribution functions before and after damage, three different types of damage indices are proposed. The efficiency and limitation of the proposed algorithm with the statistical evaluation of damage indices have been examined and discussed through laboratory experiments.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.126-126
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• 2016

남강은 낙동강 주요 지류인 동시에 낙동강 하류지역의 유지용수, 생활, 공업, 농업용수 공급 등에 중요 역할을 하고 있어 오염원 및 수질관리가 매우 중요하다고 볼 수 있다. 최근 남강댐 하류 및 남강합류 후 낙동강 본류인 창녕함안보 지점에서의 녹조 발생이 빈번해지고 있으며, 녹조현상에 대한 관심과 우려가 높아지고 있는 실정이다. 따라서 기존 호소의 녹조관리는 '조류경보제'에 의해서 관리되고 있지만 4대강 16개의 보 건설 이후 '수질예보제'와 같이 녹조관리를 위한 제도 및 정책이 시행되면서 조류관리의 중요성이 대두되고 있다. 본 연구에서는 기존의 많은 문헌들을 참고하여 조류의 영향인자를 파악하였으며, 남강유역의 물관리 기초자료를 수집하고 구축된 데이터 기반의 각 항목별 주요항목 영향인자 분석을 위한 상관성 분석을 실시하여 영향인자별 상관관계 우선순위를 선정하여 입력변수로 이용하였다. 그에 따른 데이터 마이닝을 통한 조류 발생특성을 고려하여 예측 모형인 다중회귀분석(Multiple Regression Analysis)을 구현하였다. 회귀분석 과정에서 다중공선성이 발생하는 변수에 대해서는 모형에서 제거하였으며, 잔차분석을 통해 이상치와 영향치를 검토하여 고려하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• Journal of Korean Society of Disaster and Security
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• v.15 no.2
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• pp.45-56
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• 2022

The countermeasure for the shortage of water during dry season and drought period has not been considered with return flowrate in detail. In this study, the outflow of STP was predicted through a data-based machine learning model, LSTM. As the first step, outflow, inflow, precipitation and water elevation were utilized as input data, and the distribution of variance was additionally considered to improve the accuracy of the prediction. When considering the variability of the outflow data, the residual between the observed value and the distribution was assumed to be in the form of a complex trigonometric function and presented in the form of the optimal distribution of the outflow along with the theoretical probability distribution. It was apparently found that the degree of error was reduced when compared to the case not considering where the variance distribution. Therefore, it is expected that the outflow prediction model constructed in this study can be used as basic data for establishing an efficient river management system as more accurate prediction is possible.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.421-421
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• 2018

효율적인 수자원 관리를 위하여 전국유역조사, 수자원 장기종합계획 등 다양한 사업이 수행되고 있으며, 이를 위하여 유출해석은 필수적인 항목이라 할 수 있다. 유출해석을 위하여 수문모형 또는 관측소의 유량자료가 활용되고 있으나, 이는 기존에 관측된 유량자료를 바탕으로 구축된 수위-유량관계 곡선식(Rating-curve)을 활용하여 재생산된 자료라 할 수 있다. 즉, 수위자료는 매시간 관측소에서 측정이 되지만, 유량자료의 경우 측정이 어려울 뿐만 아니라 변동성 및 불확실성이 크기 때문에 시계열 수위를 곡신식을 통해 유량으로 변환하여 활용하고 있다. 이와 같이 수위-유량관계 곡선식의 정확성이 수문자료 생산에 핵심 요소임에도 불구하고 이에 대한 연구는 제한적이며, 특히 홍수터 등의 영향을 고려하여 분할된 곡선의 천이점 접합시 곡선식의 정확도 향상을 위한 연구도 드문 편이다. 따라서 본 연구에서는 구간 분할된 곡선의 최적 천이점 선정을 위하여 Particle Swarm Optimization(PSO)기법을 활용하였으며, 총 5개 구간까지 구간별 목적함수로 RMSE, RSR, 결정계수 적용시 특성변화에 대한 연구를 수행하였다. 구간에 대하여 절대적인 오차를 산정하는 RMSE를 활용하는 경우 저수위 부분에 대한 오차가 증가하는 것을 확인할 수 있었으며, 상대적인 오차인 RSR, 결정계수를 활용하는 경우 전체 구간에 대한 오차를 보완할 수 있는 것으로 나타났다. PSO기법을 활용하여 도출된 곡선식에 대해서는 구간 및 전체구간에 대한 오차(RMSE, 결정계수, RSR, MAPE)를 활용하여 불확실성을 검토할 수 있도록 하였고, 잔차분석을 통한 이상치 및 회귀곡선에 대한 정규성 검토를 수행할 수 있는 툴을 개발하였다. 레이팅 커브를 작성하는데 있어 최적화 알고리즘을 활용하여 구간분할시 천이점 선정의 자동화로 천이점 선정에 소요되는 시간을 대폭 감축할 수 있을 뿐만 아니라, 구간별 오차를 종합적으로 고려하여 우수한 품질의 레이팅 커브를 도출할 수 있는 기반을 구축하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.47-56
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• 2014

Recently, the velocity of sea-level rising has increased due to the global warming and the natural disasters have been occurred many times. Therefore, there are various demands for the integration of vertical reference datums for the ocean and land areas in order to develop a coastal area and prevent a natural disaster. Currently, the vertical datum for the ocean area refers to Local Mean Sea Level(LMSL) and the vertical datum for the land area is based on Incheon Mean Sea Level(IMSL). This study uses 31 points of Tidal Gauge Bench Mark (TGBM) in order to compares and analyzes the geometric heights referring LMSL, IMSL, and the nationally determined geoid surface. 11 points of comparable data are biased more than 10 cm when the geometric heights are compared. It seems to be caused by the inflow of river, the relocation of Tidal Gauge Station, and the topographic change by harbor construction. Also, this study analyze the inclination of sea surface which is the difference between IMSL and LMSL, and it shows the inclination of sea surface increases from the western to southern, and eastern seas. In this study, it is shown that TGBM can be used to integrate vertical datums for the ocean and land areas. In order to integrate the vertical datums, there need more surveying data connecting the ocean to the land area, also cooperation between Korea Hydrographic and Oceanographic Administration and National Geographic Information Institute. It is expected that the integrated vertical datum can be applied to the development of coastal area and the preventative of natural disaster.