• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.2
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• pp.451-457
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• 1998

This paper describes a modelling method for SCR(selective catalystic reduction) system in refuse incineration plant. We consider the SCR system as a single input and single output system. For modelling the SCR system, an auto regressive exogeneous(ARX) modelling method is used. In this case, we should design the white noise input for modelling and put it on the system as an input$(NH_3)$, and take an outlet NOx as an output. From these two relations, we design the ARX model with 45 second delay time and transform to a discrete system with sampling time of 0.5 second. Using the obtained SCR model, we verify that the outlet NOx is deeply related with stoker`s moving in boiler of refuse incineration plant.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.56-66
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• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.249-250
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• 2002

In this paper, the relationship among the 3-D surface topography parameters are studied. Several surface topography parameters that are important in tribology are calculated against various surface topography data. 3-D surface data with desired properties are generated by using the non-causal 2-D auto-regressive (AR) model. The non-causal 2-D AR model is a random 3-D surface topography model that can generate 3-D surface topography data with specified parameters.

• Structural Engineering and Mechanics
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• v.9 no.2
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• pp.141-152
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• 2000

Based on the discussion about some empirical coherency models resulted from earthquake-induced ground motion recordings at the SMART-1 array in Taiwan, and a heuristic model of the coherency function from elementary notions of stationary random process theory and a few simplifying assumptions regarding the propagation of seismic waves, a practical coherency model for spatially varying ground motions, which can be applied in aseismic analysis and design, is proposed, and the regressive coefficients are obtained using least-square fitting technique from the above recordings.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.561-570
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• 2009

Ultra-wideband communication systems based on impulse radio have merits that are possible for the high data rate transmission, high resolution ranging are positioning system. Conventionally, in order to accomplish these features, the high-speed ADC (Analog to Digital Convertor) is necessary to apply radio determination system operating in time domain. However, considering low rate - wireless personal area network (LR-WPAN) aims to low-cost hardware implementation, the expensive ADC converting GHz sampling per second is not appropriate. So, this paper introduces a low complex AR (Auto Regressive) model based non-coherent ranging scheme operating in frequency domain with using low-speed ADC utilizing analog Voltage Control Oscillator (VCO) mode for the frequency domain transformation. To verify the superiority of the proposed ranging and location algorithm working in frequency domain, the suggested IEEE 802.15.4a TG channel model is used to exploit affirmative features of the proposed algorithm with conducting the simulation results.

• Current Photovoltaic Research
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• v.11 no.1
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• pp.27-33
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• 2023

In this paper, a machine learning model by using a regression algorithm is proposed to estimate the power generation performance of the BIPV system. The physical model formula for estimating the generation performance and the proposed model were compared and analyzed. For the physical model formula, simple efficiency model, temperature correction model, and regressive physics model for changing an irradiance were used. As a result, when comparing the regressive physics model for changing an irradiance and the proposed model with the actual generation measured data, the respective RMSE values are 0.1497 kW, 0.0451 kW and the accuracy values are 86.44%, and 96.56%. Therefore, the proposed model implemented in this experiment can be useful in estimating power generation.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.84-86
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• 2001

A new navigation method is developed and implemented for mobile robot. The mobile robot navigation problem has traditionally been decomposed into the path planning and path following. Unlike tracking-based system, which minimize intercept time and moved mobile robot distance for optimal rendezvous point selection. To research of random moving object uses algorithm of Adaptive Control using Auto-regressive Model. A fine motion tracking object's trajectory is predicted of Auto-regressive Algorithm. Thus, the mobile robot can travel faster than the target wi thin the robot's workspace. The can select optimal rendezvous point of various intercept time.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.145-148
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• 2010

항만 구조물의 건전성 평가 기술의 개발을 위한 기초 연구로서, 강제진동해석을 통하여 케이슨 구조-지반 경계부의 손상에 대한 진동응답을 분석하고자 한다. 이를 위해 세 단계의 연구를 수행하였다. 첫째, 케이슨 구조물의 진동특성 분석을 위해 시간영역기반의 AR(auto-regressive)모델을 선정하였다. 둘째, 모형 케이슨 구조물을 대상으로 진동응답 계측실험을 수행하였으며, AR-모델을 통해 진동특징을 실험적으로 분석하였다. 셋째, 대상 케이슨 시스템의 유한요소모델을 구성하고, 구조-지반 경계부의 손상에 따른 동적응답 특성의 변화를 수치적으로 분석하였다. 이를 위해 강제진동을 모사 하였으며, 구조-지반 경계부의 강성변화에 따른 케이슨 구조물의 진동응답의 변화를 분석하였다.