• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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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.

• Research in Mathematical Education
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• v.13 no.1
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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.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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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.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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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.

• The Korean Journal of Petroleum Geology
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• v.6 no.1_2 s.7
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• pp.1-7
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• 1998

A multichannel seismic profile from the southwestern margin of the Ulleung Basin, East Sea, was analysed in detail to interpret the middle to late Miocene sequence stratigraphic evolution of the area. A regressive package is overlying a transgressive package which, in turn, is underlain by older uplifted and deformed sedimentary layers. A prominent condensed section separates the regressive and transgressive packages. The transgressive package is characterized by onlapping onto the underlying uplifted and deformed strata. The regressive package contains six prograding sequences composed of seismically resolvable lowstand, highstand, and transgressive systems tracts. Most of the depositional sequences comprise lowstand systems tracts consisting of basin-floor fan, slope fan, and prograding complex. Potential reservoirs in the regressive package are turbidite sands in basin-floor fans, channel-fill sands and overbank sand sheets in slope fans, and incised valley-fill sands in the shelf. The shallow marine sands in transgressive packages are another type of reservoir. Detailed sequence stratigraphic analysis, seismic data reprocessing, and 3-D seismic survey are suggested for the successful hydrocarbon exploration in the study area.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.10
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• pp.840-845
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• 2012

An ARX(Auto-Regressive eXogenous) modeling technique for vortex dynamics in the BFS(Backward Facing Step) flow field is proposed in this paper. In order for the modeling of the dynamics, the spatial and temporal modes are extracted through POD(Proper Orthogonal Decomposition) analysis. Determining the orders of the inputs and outputs for an ARX structure is carried out by the spectrum analysis and temporal mode analysis, respectively. The order of input delay terms is also determined by the flow velocity. Finally the coefficients of the ARX model are designed by using an artificial neural network.

• Wind and Structures
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• v.16 no.3
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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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.1
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• pp.20-25
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• 2012

Gender differences have been studied by using spectral features such as median frequency (MDF) respectively. MDF is the most commonly used as a feature to describe muscle conduction velocity. The aim of this paper is to detect gender differences from surface EMG signals during isotonic contractions of the bicep Brachii. Eight volunteers participated in surface EMG recordings placed on the biceps brachii and each recording experiment continued until their exhaustion. We used feature values and regressive slopes and compared the feature changes from the onset to the endurance time to find gender differences. The result of experiments shows that the regressive slope of these features is valid to measure gender differences.

• Structural Engineering and Mechanics
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• v.15 no.3
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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.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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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.