• Title/Summary/Keyword: Combination Model

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Development of Combination Runoff Model Applied by Genetic Algorithm (유전자 알고리즘을 적용한 혼합유출모형의 개발)

  • Shim, Seok-Ku;Koo, Bo-Young;Ahn, Tae-Jin
    • Journal of Korea Water Resources Association
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    • v.42 no.3
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    • pp.201-212
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    • 2009
  • The Tank model and the PRMS(Precipitation Runoff Modeling-modular System) model have been adopted to simulate runoff data from 1981 to 2001 year in the Seomgin-dam basin. However, the simulated runoff by each single model showed some deviations compared with the observed runoff, respectively. In this study a genetic algorithm combination runoff model has been proposed to minimize deviations between simulated runoff and observed runoff that should yield from single model such as Tank model or PRMS model. The proposed combination runoff model combining the simulated respective output of the Tank model and the PRMS model is to produce the optimum combination ratio of each single model applying to the genetic algorithm which may yield the minimum deviations between simulated runoff and observed one. The proposed combination runoff model has been applied to the Seomgin-dam basin. It has also been shown that the combination model by introducing optimal combination ratio should yield less deviations than single model such as the Tank model or the PRMS model.

Feature Compensation Method Based on Parallel Combined Mixture Model (병렬 결합된 혼합 모델 기반의 특징 보상 기술)

  • 김우일;이흥규;권오일;고한석
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.7
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    • pp.603-611
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    • 2003
  • This paper proposes an effective feature compensation scheme based on speech model for achieving robust speech recognition. Conventional model-based method requires off-line training with noisy speech database and is not suitable for online adaptation. In the proposed scheme, we can relax the off-line training with noisy speech database by employing the parallel model combination technique for estimation of correction factors. Applying the model combination process over to the mixture model alone as opposed to entire HMM makes the online model combination possible. Exploiting the availability of noise model from off-line sources, we accomplish the online adaptation via MAP (Maximum A Posteriori) estimation. In addition, the online channel estimation procedure is induced within the proposed framework. For more efficient implementation, we propose a selective model combination which leads to reduction or the computational complexities. The representative experimental results indicate that the suggested algorithm is effective in realizing robust speech recognition under the combined adverse conditions of additive background noise and channel distortion.

Leakage Analysis and Design Modification of the Combination-Type-Staggered-Labyrinth Seal (누설량 저감을 위한 래버린스 실의 설계개선 및 해석)

  • Ha, Tae-Woong
    • Tribology and Lubricants
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    • v.23 no.2
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    • pp.43-48
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    • 2007
  • Leakage reduction through annular type labyrinth seals of steam turbine is necessary for enhancing their efficiency. In this study, modified geometry of the original combination-type-staggered-labyrinth seal has been suggested and numerical analysis for leakage prediction has been carried out for the modified-combination-type-staggered-labyrinth seal both based on bulk-flow model and using the CFD code FLUENT. The theoretical analysis based on bulk-flow model yields leakage reduction of the modified combination type staggered labyrinth seal by about 11%. Comparing with the result of Bulk-flow model analysis, the leakage result of CFD analysis shows reasonable agreement within 9.8% error.

Analysis of The Lateral Motion of Tractor-Trailer Combination (I) Operator/Vehicle System Model for Forward Maneuver

  • Torisu, R.;Mugucia, S.W.;Takeda, J.
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 1993.10a
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    • pp.1137-1146
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    • 1993
  • In order to analyze lateral control in the forward manuever of a tractor- trailer combination , a human operator model and a kinematic vehicle model were utilized for the operator/vehicle system. By combining the vehicle and operator models, a mathematical model of the closed-loop operator/vehicle system was formulated. A computer program was developed so as to simulate the motion of the tractor-trailer combination . In order to verify the operator/vehicle system model, the results of the field trials were compared with the simulated results. There was found to be reasonably good agreement between the two.

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A Combination and Calibration of Multi-Model Ensemble of PyeongChang Area Using Ensemble Model Output Statistics (Ensemble Model Output Statistics를 이용한 평창지역 다중 모델 앙상블 결합 및 보정)

  • Hwang, Yuseon;Kim, Chansoo
    • Atmosphere
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    • v.28 no.3
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    • pp.247-261
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    • 2018
  • The objective of this paper is to compare probabilistic temperature forecasts from different regional and global ensemble prediction systems over PyeongChang area. A statistical post-processing method is used to take into account combination and calibration of forecasts from different numerical prediction systems, laying greater weight on ensemble model that exhibits the best performance. Observations for temperature were obtained from the 30 stations in PyeongChang and three different ensemble forecasts derived from the European Centre for Medium-Range Weather Forecasts, Ensemble Prediction System for Global and Limited Area Ensemble Prediction System that were obtained between 1 May 2014 and 18 March 2017. Prior to applying to the post-processing methods, reliability analysis was conducted to identify the statistical consistency of ensemble forecasts and corresponding observations. Then, ensemble model output statistics and bias-corrected methods were applied to each raw ensemble model and then proposed weighted combination of ensembles. The results showed that the proposed methods provide improved performances than raw ensemble mean. In particular, multi-model forecast based on ensemble model output statistics was superior to the bias-corrected forecast in terms of deterministic prediction.

Factors Affecting Selection & Combination of Earthwork Equipments (토공장비 선정 및 조합을 위한 영향요인 연구)

  • Choi, Jae-Hwi;Lee, Dong-Hoon;Kim, Sun-Hyung;Kim, Sun-Kuk
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2010.05a
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    • pp.201-205
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    • 2010
  • Earthwork is an essential initial work discipline in construction projects and open to significant impacts of several factors such as weather, site conditions, soil conditions, underground installations and available construction machinery, calling for careful planning by managers. However, selection and combination of construction machinery and equipment for earthwork still depends on experience or intuition of managers in construction sites, with much room left for proper management in terms of cost, schedule and environmental load control. This research aims to analyze the performance of earthwork equipment and establish relations among various factors affecting a model for optimizing selection and combination of earthwork equipment as a precursor to the development of such model. We expect the conclusions herein to contribute to optimizing selection and combination of earthwork equipment and provide basic inputs for the development of applicable model that can save costs, reduce schedule and mitigate environmental load.

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Forecasting Chinese Yuan/USD Via Combination Techniques During COVID-19

  • ASADULLAH, Muhammad;UDDIN, Imam;QAYYUM, Arsalan;AYUBI, Sharique;SABRI, Rabia
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.5
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    • pp.221-229
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    • 2021
  • This study aims to forecast the exchange rate of the Chinese Yuan against the US Dollar by a combination of different models as proposed by Poon and Granger (2003) during the Covid-19 pandemic. For this purpose, we include three uni-variate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Chinese Yuan against the US dollar by two combination criteria i.e. var-cor and equal weightage. After finding out the individual accuracy, the models are then assessed through equal weightage and var-cor methods. Our results suggest that Naïve outperforms all individual & combination of time series models. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models except the Naïve model, with the lowest MAPE value of 0764. The results suggesting that the Chinese Yuan exchange rate against the US Dollar is dependent upon the recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting which commensurate with the literature.

Combination Prediction for Nonlinear Time Series Data with Intervention (개입 분석 모형 예측력의 비교분석)

  • 김덕기;김인규;이성덕
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.293-303
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    • 2003
  • Under the case that we know the period and the reason of external events, we reviewed the method of model identification, parameter estimation and model diagnosis with the former papers that have been studied about the linear time series model with intervention, and compared with nonlinear time series model such as ARCH, GARCH model that it has been used widely in economic models, and also we compared with the combination prediction method that Tong(1990) introduced.

The study on the efficient Identification Model of Nonlinear dynamical system using Neural Networks (신경회로망을 이용한 비선형 동적인 시스템의 효과적인 인식모델에 관한 연구)

  • 강동우;이상배
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1995.10b
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    • pp.233-242
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    • 1995
  • In this paper, we introduce the identification model of dynamic system using the neural networks, We propose two identification models. The output of the parallel identification model is a linear combination of its past values as well as those of the input. The series-parallel model is a linear combination of the past values in the input and output of the plant. To generate stable adaptive laws, we prove that the series-parallel model is found to be proferable.

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An Optimal Combination of Illumination Intensity and Lens Aperture for Color Image Analysis

  • Chang, Y. C.
    • Agricultural and Biosystems Engineering
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    • v.3 no.1
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    • pp.35-43
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    • 2002
  • The spectral color resolution of an image is very important in color image analysis. Two factors influencing the spectral color resolution of an image are illumination intensity and lens aperture for a selected vision system. An optimal combination of illumination intensity and lens aperture for color image analysis was determined in the study. The method was based on a model of dynamic range defined as the absolute difference between digital values of selected foreground and background color in the image. The role of illumination intensity in machine vision was also described and a computer program for simulating the optimal combination of two factors was implemented for verifying the related algorithm. It was possible to estimate the non-saturating range of the illumination intensity (input voltage in the study) and the lens aperture by using a model of dynamic range. The method provided an optimal combination of the illumination intensity and the lens aperture, maximizing the color resolution between colors of interest in color analysis, and the estimated color resolution at the combination for a given vision system configuration.

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