• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2004년도 추계학술발표집
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• pp.221-232
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• 2004

Paper process is complex and multivariable system. Identification of a paper process model is imperative for the development of predictive control method. 13-level Pseudo-Random Sequence Signals were used to identify the plant model in which the neural network model was considered model as a real paper process. Results of simulations for identification using 13-level PRS signals and Prediction Error Method are compared with plant operation data. From the comparison, we can see that the dynamics of the model show good agreement with those of real plant.

• Fisheries and Aquatic Sciences
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• 제24권4호
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• pp.139-152
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• 2021

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• 제어로봇시스템학회논문지
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• 제8권6호
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• pp.524-532
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• 2002

This paper presents a new in-flight alignment method for an SDINS under large initial heading error. To handle large heading error, a new attitude error model is introduced. The attitude errors are divided into heading error and leveling errors using a newly defined horizontal frame. Some navigation error dynamic models are derived from the attitude error model for indirect feedback filtering of the in-flight alignment system. A Kalman filter with Position measurement is designed to estimate navigation errors as the indirect feedback filter Simulation results show that the proposed in-flight alignment method reduces the heading error very quickly from more than 40deg to about 5deg so as to apply a refined navigation filter. The total alignment process including leveling mode and navigation mode in addition to the proposed one allows large initial values not only in heading error but also in leveling errors.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.153-156
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• 1999

The technique is reported of identifying parameters in off-line process. The technique demands that closed-loop system consists of a reference and two-degree-of-freedom controllers (TDFC) in real process. A model process is the same as the real process except their parameters. Deviations are differences between the reference and the output of the plant or the model. The technique is based on minimizing identification error between the two deviations. The parameter differences between the plant and the model are characterized of mean value and of variance which are derived from the identification error. Consequently, the algorithm which identifies the unknown plant parameters is shown by minimizing the mean value and the variance, respectively, within double convergence loops. The technique is applied to course change of a ship. The plant deviation at the first trial is shown to occur in replacing the nominal parameters by the default parameters. The plant deviation at the second trial is shown to not occur in replacing the nominal parameters by the identified parameters. Hence, the identification technique is confirmed to be feasible in the real field.

• 한국기계가공학회지
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• 제15권5호
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• pp.66-72
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• 2016

Gearboxes are mechanical components that transmit power by adjusting input and output speed and torque. Their design requirements include small size, light weight, and long lifespan. We have investigated the effects of carrier pinhole position error on the load sharing and load distribution characteristics of a planetary gear set with four planet gears. The simulation model for a simple planetary gear set was developed and verified by comparing analytical results with a putative model. Then, we derived the load sharing and load distribution characteristics under various pinhole position error conditions using the prototypical simulation model. The results showed that the mesh load factor and face load factor increased with the pinhole position error, which then influenced the safety factor for tooth bending strength and surface durability.

• 디지털산업정보학회논문지
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• 제8권4호
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• pp.25-34
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• 2012

In this study, in the process of testing before the release of the software products designed, software testing manager in advance should be aware of the testing-information. Therefore, the effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and applied property of learning effect based on truncated time and delayed S-shaped software reliability. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model can be confirmed. This paper, a failure data analysis was performed, using time between failures, according to the small sample and large sample sizes. The parameter estimation was carried out using maximum likelihood estimation method. Model selection was performed using the mean square error and coefficient of determination, after the data efficiency from the data through trend analysis was performed.

• 한국측량학회지
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• 제17권3호
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• pp.213-223
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• 1999

부가가치가 큰 대규모 공공자료인 국가기본도 수치지도 자료기반의 고품질을 확보하고, 수치지도제작에서 발생할 수 있는 각종 오류들을 효율적으로 탐색할 수 있는 개선된 수치지도제작 모형을 구현하였다. 이를 위해 기존에 제작된 수치지도를 분석하고 수치지도 상에 포함될 수 있는 각종 오류를 대분류 자료층으로 구분하여 유형화하고, 유형화된 오류들을 탐색하기 위해 자동오류탐색 프로그램과 전산부호검사 프로그램을 개발하였으며, 순수육안탐색방법을 체계화하여 오류탐색방법에 따라 탐색 가능한 오류들을 범주화하였다. 수치지도제작 개선모형을 구현하기 위해 연구한 결과, 수치지도 제작과정에서 발생할 수 있는 각종 오류들이 체계적으로 탐색됨으로써, 수치지도 제작 공정별로 오류가 감소된 수치지도 자료기반의 구축이 가능하게 되었으며, 수치지도 자료기반의 오류가 최소화되어 품질이 향상됨을 확인할 수 있었다.

• 한국수자원학회논문집
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• 제39권8호
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• pp.677-690
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• 2006

본 연구에서는 LH-OAT (Latin Hypercube Ore factor At a Time) 민감도분석 방법과 SCE-UA (Shuffled Complex Evolution at University of Arizona) 최적화 기법을 적용하여 보청천 유역에서 SWAT모형에 대한 자동보정 방법을 제시하였다. LH-OAT 방법은 전역 민감도분석과 부분 민감도 분석의 장점을 조합하여 가용매개변수 공간에 대하여 효율적으로 매개변수의 민감도 분석이 가능하게 하였다. LH-OAT민감도 분석으로부터 결정된 매개변수의 민감도 등급은 SWAT 모형의 자동보정 과정에서 요구되는 보정대상 매개변수의 선택에 유용하게 적용될 수 있다. SCE-UA 방법을 적용한 SWAT모형의 자동보정 해석결과는 보정자료, 보정매개변수, 통계적 오차의 선택에 따라서 모형의 성능이 좌우되었다. 보정기간과 보정매개변수가 증가함에 따라 검증기간에 대한 RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), NMSE (Normalized Mean Square Error) 등의 모형오차는 감소하였지만, NAE (Normalized Average Error) 및 SDR(Standard Deviation Ratio)은 개선되지 않았다. SWAT모형의 보정에 적용되는 보정자료, 보정매개변수 및 모형평가를 위한 통계적 오차 선택이 해석결과에 미치는 복잡한 영향을 이해하기 위하여 다양한 대표유역을 대상으로 추가적인 연구가 필요하다.

• 대한인간공학회지
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• 제23권3호
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• pp.73-88
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• 2004

This study was contrived Hierarchical Information Search (HIS) model. HIS model is based on a “cognitive process” in which model, comprising basic human information processing mechanize and information interaction. Its process include 3 semantic cognitive processes: Schema-Association LTM, Form Domain, and Alternative Selection. Design methodology consists to elicitate memory, thinking and cognitive response variables. In case study, menu structure of mobile phone was applied. In result, a correlation between predictive error rate and real error rate was .892. and a correlation between selective and real reaction time was .697. This present to suggest a model of how the methodology could be applied to real system design effectively when this was used. HIS model could become one of the most important factors for success of product design. In the perspective, the systemic methodology would contribute to design a quantitative and predictive system.