• 대한산업공학회지
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• 제39권2호
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• 한국석유지질학회지
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• 제6권1_2
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• pp.1-7
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• 1998

울릉분지 남서연변부의 다중채널 탄성파단면도를 이용하여 중기에서 말기 마이오세동안의 시퀀스 층서분석을 정밀 분석 하였다. 해퇴층서는 오랜 융기되고 변형된 하부층을 기저로 한 해침층서 위에 발달하였다. 뚜렷한 응축층이 해침과 해퇴층서사이를 구분 짓는다. 해침층서는 하부의 융기되고 변형된 지층위로 상향걸침한다. 해퇴층서는 구분 가능한 저해수준, 고해수준, 그리고 해침 체계역으로 구성된 여섯 개의 전진형 퇴적 시퀀스를 포함한다. 대부분의 퇴적 시퀀스는 분지선상지, 사면선상지, 그리고 전진복합체로 이루어진 저해수준 체계역이다. 잠재적인 석유 저류층은 분지선상지의 사암, 사면 선상지의 해저수로 충진 사암과 오버뱅크의 사암, 그리고 대륙붕지역의 침식곡 충진 사암들이다. 또한 해침퇴적층서내 천해사암층도 유망하다. 성공적인 탐사활동을 위해 연구 주변지역에 대한 정밀 시퀀스 층서분석, 탄성파 자료의 재처리, 그리고 3-D탄성파 탐사의 실시를 제안한다.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제13권1호
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• pp.31-48
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• 2009

Utilizing the path analysis method, the study explores the relationships among the influential factors in mathematics modeling academic achievement. The following conclusions are drawn: 1. Achievement motivation, creative inclination, cognitive style, the mathematical cognitive structure and mathematics modeling self-monitoring ability, those have significant correlation with mathematics modeling academic achievement; 2. Mathematical cognitive structure and mathematics modeling self-monitoring ability have significant and regressive effect on mathematics modeling academic achievement, and two factors can explain 55.8% variations of mathematics modeling academic achievement; 3. Achievement motivation, creative inclination, cognitive style, mathematical cognitive structure have significant and regressive effect on mathematics modeling self-monitoring ability, and four factors can explain 70.1% variations of mathematics modeling self-monitoring ability; 4. Achievement motivation, creative inclination, and cognitive style have significant and regressive effect on mathematical cognitive structure, and three factors can explain 40.9% variations of mathematical cognitive structure.

• 전력전자학회논문지
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• 제27권4호
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• Wind and Structures
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• 제16권3호
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• pp.279-300
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• 2013

This paper presents a new analysis framework for predicting the internal buffeting forces in bridge components under skew wind. A linear regressive model between the internal buffeting force and deformation under normal wind is derived based on mathematical statistical theory. Applying this regression model under normal wind and the time history of buffeting displacement under skew wind with different yaw angles in wind tunnel tests, internal buffeting forces in bridge components can be obtained directly, without using the complex theory of buffeting analysis under skew wind. A self-anchored suspension bridge with a main span of 260 m and a steel arch bridge with a main span of 450 m are selected as case studies to illustrate the application of this linear regressive framework. The results show that the regressive model between internal buffeting force and displacement may be of high significance and can also be applied in the skew wind case with proper regressands, and the most unfavorable internal buffeting forces often occur under yaw wind.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.93-100
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• 1988

The Auto Regressive Parameter Estimation and Pattern Classification of EKG Signal for Automatic Diagnosis. This paper presents the results from pattern discriminant analysis of an AR (auto regressive) model parameter group, which represents the HRV (heart rate variability) that is being considered as time series data. HRV data was extracted using the correct R-point of the EKG wave that was A/D converted from the I/O port both by hardware and software functions. Data number (N) and optimal (P), which were used for analysis, were determined by using Burg's maximum entropy method and Akaike's Information Criteria test. The representative values were extracted from the distribution of the results. In turn, these values were used as the index for determining the range o( pattern discriminant analysis. By carrying out pattern discriminant analysis, the performance of clustering was checked, creating the text pattern, where the clustering was optimum. The analysis results showed first that the HRV data were considered sufficient to ensure the stationarity of the data; next, that the patern discrimimant analysis was able to discriminate even though the optimal order of each syndrome was dissimilar.

• Structural Engineering and Mechanics
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• 제15권3호
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• pp.285-297
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• 2003

Complementing recent advances made in the field of structural health monitoring and damage detection, the concept of a wireless sensing network with distributed computational power is proposed. The fundamental building block of the proposed sensing network is a wireless sensing unit capable of acquiring measurement data, interrogating the data and transmitting the data in real time. The computational core of a prototype wireless sensing unit can potentially be utilized for execution of embedded engineering analyses such as damage detection and system identification. To illustrate the computational capabilities of the proposed wireless sensing unit, the fast Fourier transform and auto-regressive time-series modeling are locally executed by the unit. Fast Fourier transforms and auto-regressive models are two important techniques that have been previously used for the identification of damage in structural systems. Their embedment illustrates the computational capabilities of the prototype wireless sensing unit and suggests strong potential for unit installation in automated structural health monitoring systems.

• 한국정밀공학회지
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• 제17권4호
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• pp.240-246
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• 2000

An in-process estimation of the ground surface roughness is a bottle-neck and an essential field in conventional grinding operation. We defined the dimensionless average roughness factor (D.A.R.F) that exhibits a roughness characteristics of ground surface. The D.A.R.F was composed easily of the absolute average and the standard deviation values which were the analytic parameters of the acoustic emission (AE) signal generated during the machining process. The theoretical equation between the surface roughness and the D.A.R.F has been derived from the linear regressive analysis and verified its availability through the experimentation on the surface grinding machine.

• 한국산업융합학회 논문집
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• 제2권2호
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• pp.147-153
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• 1999

An in-process extraction method of the ground surface roughness is a bottle-neck and essential field in conventional machining process. We define the D.A.R.F(Dimensionless Average Roughness Factor) that has a roughness characteristic of ground surface. D.A.R.F include the absolute average and the standard deviation values which are the analytic parameters of the AE(Acoustic Emission) signal generated during the grinding operation. The theoretical equation between the surface roughness and the D.A.R.F has been derived from the linear regressive analysis and verified its availability through the experimentation on the surface grinding machine.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.115-119
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• 1996

An estimation of surface roughness value is a very important and difficult issue in grinding process. The definition of the D.A.R.F(Dimensionless Average Roughness Factor) has been made including the absolute average and tile standard deviation that are the parameters of the AE(Acoustic Emission) sign. The theoretical equation of the surface roughness applying the D.A.R.F has been derived from the regressive analysis and specified with respect to the availability through the experimental approach on the machine.