• 한국농공학회:학술대회논문집
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• 한국농공학회 1998년도 학술발표회 발표논문집
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• pp.224-229
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• 1998

This study was conducted to develop the simple and convenient irrigation control model which can maintain the appropriate rates of irrigation and drainage of nutrient solution according to the environmental conditions and growth stages in soilless culture of cucumber. In order to obtain fundamental data for development of the model, investigation of the actual state of soilless culture practices was carried out. Most irrigation systems of soilless culture were controlled by the time clock. Evapotranspiration of cucumber in soilless culture was investigated and correlations with environmental conditions were analyzed, and its prediction model was developed. A irrigation control model based on the time clock control and there were considered seasons, weather conditions, and growth stages was developed. Applicability of the model was tested by simulation. Drainage rates of irrigation system controlled by conventional time clock, integrated solar radiation, and the developed model were 61%, 20%, and 32%, respectively in cucumber perlite culture.

• 품질경영학회지
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• 제25권3호
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• pp.96-107
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• 1997

The recent stream to reliability prediction is that it is totally inclusive in depth to consider even the operating and environmental condition at the level of finished goods as well as component itselves. In this study, firstly we present the reliability prediction methods by entire failure rate model which failure rate at the system level is added to the failure rate model at the component level. Secondly we build up the improved bases of reliability demonstration through a, pp.ication of Kaplan-Meier, Cumulative hazard, Johnson's methods as non-parametric and Maximum Likelihood Estimator under exponential & Weibull distribution as parametric. And also present the methods of curve fitting to piecewise failure rate under Weibull distribution, PRST (Probability Ratio Sequential Test), curve fitting to S-shaped reliability growth curve, computer programs of each methods. Lastly we show the practical for determination of optimal burn-in time as a method of reliability enhancement, and also verify the practical usefulness of the above study through the a, pp.ication of failure and test data during 1 year.

• 소성∙가공
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• 제14권3호
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• pp.215-223
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• 2005

A numerical analysis program is developed by FDM scheme for the prediction of microstructural transformation during heat treatment of steels. In this study, multi-phase model was used fur description of diffusional austenite transformations in low-alloy hypoeutectoid steels during cooling after austenitization. A fundamental property of the model consisting of coupled differential equations is that by taking into account the rate of austenite grain growth, it permits the prediction of the progress of ferrite, pearlite, and bainite transformations simultaneously during quenching and estimate the amount of martensite also by using K-M eq. In order to simulate the microstructural evolution during tempering process, another Avrami-type eq. was adopted and method for vickers hardness prediction was also proposed. To verify the developed program, the calculated results are compared with experimental ones of casting product. Based on these results, newly designed heat treatment process is proposed and it was proved to be effective for industry.

• 한국가금학회지
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• 제43권1호
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• pp.1-14
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• 2016

Prediction of growth patterns of commercial chicken strains is important. It can provide visual assessment of growth as function of time and prediction body weight (BW) at a specific age. The aim of current study is to compare the three nonlinear functions (i.e., Logistic, Gompertz, and von Betalanffy) for modeling the growth of twenty five commercial Korean native chicken (KNC) strains reared under a battery cage system until 32 weeks of age and to evaluate the three models with regard to their ability to describe the relationship between BW and age. A clear difference in growth pattern among 25 strains were observed and classified in to the groups according to their growth patterns. The highest and lowest estimated values for asymptotic body weight (C) for 3H and 5W were given by von Bertalanffy and Logistic model 4629.7 g for 2197.8 g respectively. The highest estimated parameter for maturating rate (b) was given by Logistic model 0.249 corresponds to the 2F and lowest in von Bertalanffy model 0.094 for 4Y. According to the coefficient of determination ($R^2$) and mean square of error (MSE), Gompertz and von Bertalanffy models were suitable to describe the growth of Korean native chicken. Moreover, von Bertalannfy model was well described the most of KNC growth with biologically meaningful parameter compared to Gompertz model.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2004년도 춘계 학술대회지
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• pp.104-105
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• 2004

[ $\bigcirc$ ] In the phenology model of ORYZA2000, the effect of photoperiod on the developmental rate was a little ignored because most crop parameters were measured with IRRI varieties which are insensitive to photoperiod, therefore it is very difficult to apply this phenology model directly to Korean varieties which are usually sensitive to photoperiod. $\bigcirc$ After introducing PPFAC and PPSE to improve the phenology model, the precision of heading date prediction was improved but not satisfied. $\bigcirc$ In the growth simulation using data from several regions, yield tended to be overestimated under high nitrogen applicated condition. $\bigcirc$ The precision of yield was much improved by introducing nitrogen use efficiency, but still different between regions because of different soil fertility or property of irrigation water between regions

• Structural Engineering and Mechanics
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• 제59권5호
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• pp.885-899
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• 2016

The energy-based approach to predict the fatigue crack growth behavior under constant and variable amplitude loading (VAL) of the aluminum alloy 2024 T351 has been investigated and detailed analyses discussed. Firstly, the plastic strain energy was determined per cycle for different block load tests. The relationship between the crack advance and hysteretic energy dissipated per block can be represented by a power law. Then, an analytical model to estimate the lifetime for each spectrum is proposed. The results obtained are compared with the experimentally measured results and the models proposed by Klingbeil's model and Tracey's model. The evolution of the hysteretic energy dissipated per block is shown similar with that observed under constant amplitude loading.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1045-1050
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• 2003

In this study, to investigate the effects of omitting low-amplitude cycles from a flight-simulation loading, crack growth tests are conducted on 2124-T851 aluminum alloy specimens. Three test spectra are generated by omitting small load ranges as counted by the rain-flow count method. The crack growth test results are compared with the data obtained from the flight-simulation loading. The experimental results show that omission of the load ranges below 5% of the maximum load does not significantly affect crack growth behavior, because these are below the initial stress intensity factor range. However, in the case of omitting the load ranges below 15% of the maximum load, crack growth rates decrease, and therefore crack growth curve deviates from the crack growth data under the flight-simulation loading. To optimize the load range that can be omitted, crack growth curves are simulated by the stochastic crack growth model. The prediction shows that the omission level can be extended to 8% of the maximum load and test time can be reduced by 59%.

• Genomics & Informatics
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• 제15권4호
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• pp.162-169
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• 2017

Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression's studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc.

• Structural Engineering and Mechanics
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• 제73권4호
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• pp.455-462
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• 2020

The hybrid method using the extended finite element method (XFEM) and the forward Euler approach is widely employed to predict the fatigue life of plate structures. Due to the accuracy of the forward Euler approach is determined by a small step size, the performance of fatigue life prediction of the hybrid method is not agreeable. Instead the forward Euler approach, a prediction method using midpoint method and support vector regression (SVR) is presented to evaluate the stress intensity factors (SIFs) and the fatigue life. Firstly, the XFEM is employed to calculate the SIFs with given crack sizes. Then use the history of SIFs as a function of either number of fatigue life cycles or crack sizes within the current cycle to build a prediction model. Finally, according to the prediction model predict the SIFs at different crack sizes or different cycles. Three numerical cases composed by a homogeneous plate with edge crack, a composite plate with edge crack and center crack are introduced to verify the performance of the proposed method. The results show that the proposed method enables large step sizes without sacrificing accuracy. The method is expected to predict the fatigue life of complex structures.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.