• Title/Summary/Keyword: model based

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A Nonparametric Additive Risk Model Based On Splines

  • Park, Cheol-Yong
    • 한국데이터정보과학회:학술대회논문집
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    • 2006.11a
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    • pp.49-50
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    • 2006
  • We consider a nonparametric additive risk model that are based on splines. This model consists of both purely and smoothly nonparametric components. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

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Bootstrap Confidence Intervals for a One Parameter Model using Multinomial Sampling

  • Jeong, Hyeong-Chul;Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.2
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    • pp.465-472
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    • 1999
  • We considered a bootstrap method for constructing confidenc intervals for a one parameter model using multinomial sampling. The convergence rates or the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.

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Simulation Analysis of Active Roll Stabilizer for Automotives Based on AMESim

  • Liu, H.;Lee, J.C.;Yo, Y.C.
    • 유공압시스템학회:학술대회논문집
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    • 2010.06a
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    • pp.70-73
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    • 2010
  • In order to provide theoretical analysis for the active roll stabilizer (ARS), the simulation model based on AMESim is developed in the paper. The simplified vehicle rolling motion model is derived firstly, and then the entire ARS control system model is constructed. Furthermore, the simulation is implemented to confirm the roll control effect. The simulation results show that the derived model can be used as theoretical analysis for developing components of ARS control system.

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Sensitivity and Uncertainty Analysis of Two-Compartment Model for the Indoor Radon Pollution (실내 라돈오염 해석을 위한 2구역 모델의 민감도 및 불확실성 분석)

  • 유동한;이한수;김상준;양지원
    • Journal of Korean Society for Atmospheric Environment
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    • v.18 no.4
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    • pp.327-334
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    • 2002
  • The work presents sensitivity and uncertainty analysis of 2-compartment model for the evaluation of indoor radon pollution in a house. Effort on the development of such model is directed towards the prediction of the generation and transfer of radon in indoor air released from groundwater. The model is used to estimate a quantitative daily human exposure through inhalation of such radon based on exposure scenarios. However, prediction from the model has uncertainty propagated from uncertainties in model parameters. In order to assess how model predictions are affected by the uncertainties of model inputs, the study performs a quantitative uncertainty analysis in conjunction with the developed model. An importance analysis is performed to rank input parameters with respect to their contribution to model prediction based on the uncertainty analysis. The results obtained from this study would be used to the evaluation of human risk by inhalation associated with the indoor pollution by radon released from groundwater.

Comparison of global models for calculation of accurate and robust statistical moments in MD method based Kriging metamodel (크리깅 모델을 이용한 곱분해 기법에서 정확하고 강건한 통계적 모멘트 계산을 위한 전역모델의 비교 분석)

  • Kim, Tae-Kyun;Lee, Tae-Hee
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.678-683
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    • 2008
  • Moment-based reliability analysis is the method to calculate reliability using Pearson System with first-four raw moments obtained from simulation model. But it is too expensive to calculate first four moments from complicate simulation model. To overcome this drawback the MD(multiplicative decomposition) method which approximates simulation model to kriging metamodel and calculates first four raw moments explicitly with multiplicative decomposition techniques. In general, kriging metamodel is an interpolation model that is decomposed of global model and local model. The global model, in general, can be used as the constant global model, the 1st order global model, or the 2nd order global model. In this paper, the influences of global models on the accuracy and robustness of raw moments are examined and compared. Finally, we suggest the best global model which can provide exact and robust raw moments using MD method.

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Modified GOMS-Model for Mobile Computing (모바일 작업을 위한 수정된 GOMS-model에 대한 연구)

  • Lee, Suk-Jae;Myung, Ro-Hae
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.32 no.2
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    • pp.85-93
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    • 2009
  • GOMS model is a cognitive modeling method of human performance based on Goal, Operators, Methods, Selection rules. GOMS model was originally designed for desktop environment so that it is difficult for GOMS model to be implemented into the mobile environment. In addition, GOMS model would be inaccurate because the original GOMS model was based on serial processing, excluding one of most important human information processing characteristics, parallel processing. Therefore this study was designed to propose a modified GOMS model including mobile computing and parallel processing. In order to encompass mobile environment, an operator of 'look for' was divided into 'visual move to' and 'recognize' whereas 'point to' and 'click' were combined into 'tab.' The results showed that newly introduced operators were necessary to estimate more accurate mobile computing behaviors. In conclusion, modified-GOMS model could predict human performance more accurately than the original GOMS model in the mobile computing environment.

River Stage Forecasting Model Combining Wavelet Packet Transform and Artificial Neural Network (웨이블릿 패킷변환과 신경망을 결합한 하천수위 예측모델)

  • Seo, Youngmin
    • Journal of Environmental Science International
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    • v.24 no.8
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    • pp.1023-1036
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    • 2015
  • A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

Model-based and wavelet-based fault detection and diagnosis for biomedical and manufacturing applications: Leading Towards Better Quality of Life

  • Kao, Imin;Li, Xiaolin;Tsai, Chia-Hung Dylan
    • Smart Structures and Systems
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    • v.5 no.2
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    • pp.153-171
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    • 2009
  • In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

Modeling and Performance Analysis of Zone-Based Registration Considering Cell by Cell Expansion of Location Area (셀 단위로 증가하는 위치영역을 고려한 영역기준 위치등록의 모형화 및 성능 분석)

  • 김경희;백장현;정호연
    • Journal of the Korean Operations Research and Management Science Society
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    • v.28 no.3
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    • pp.67-79
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    • 2003
  • An efficient mobility management for mobile stations plays an important role in mobile communication networks. Even though many a mobility management schemes have been proposed, most of mobile communication networks adopt the mobility scheme based on zone-based registration. This paper studies the mobility management scheme that combines zone-based registration and 2-step selective paging. We assume cell by cell registration area, not ring by ring registration area of previous studies, and set up a new mobility model based on 2-dimensional random walk model considering the characteristic of zone-based registration to evaluate its performance exactly. We provide numerical results using proposed model to demonstrate the performance of zone-based registration and 2-step selective paging under various circumstances.

FEATURE-BASED SPATIAL DATA MODELING FOR SEAMLESS MAP, HISTORY MANAGEMENT AND REAL-TIME UPDATING

  • Kim, Hyeong-Soo;Kim, Sang-Yeob;Seo, Sung-Bo;Kim, Hi-Seok;Ryu, Keun-Ho
    • Proceedings of the KSRS Conference
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    • 2008.10a
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    • pp.433-436
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    • 2008
  • A demand on the spatial data management has been rapidly increased with the introduction and diffusion process of ITS, Telematics, and Wireless Sensor Network, and many different people use the digital map that offers various thematic spatial data. Spatial data for digital map can manage to tile-based and feature-based data. The existing tile-based digital map management systems have difficult problems of data construction, history management, and updating based on a spatial object. In order to solve these problems, this paper proposed the data model for the feature-based digital map management system that is designed for feature-based seamless map, history management, real-time updating of spatial data, and analyzed the validity and utility of the proposed model.

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