• International Journal of Fluid Machinery and Systems
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• v.3 no.4
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• pp.369-378
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• 2010

The creation of the hydraulic turbine flow factor map will undoubtedly benefit its design by decreasing both the design cycle time and product cost. In this paper, the geometry and flow variables, which effectively affect the flow factor, are proposed, analyzed and determined. These flow variables are further used to create the operating condition maps by using different model approaches categorized into Response Surface Method (RSM) and Artificial Neural Network (ANN). The accuracies of models created by different approaches are compared and the performances of model approaches are analyzed. The influences of chosen variables and the combination of Principle Component Analysis (PCA) and model approaches are also studied. The comparison results between predicted and actual flow factors suggest that two-hidden-layer Feed-forward Neural Network (FFNN), and one.hidden-layer FFNN with PCA has the best performance on forming this mapping, and are accurate sufficiently for hydraulic turbine design.

• Food Science and Biotechnology
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• v.15 no.5
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• pp.715-720
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• 2006

This study was performed to develop a predictive model for the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) using a response surface model with a combination of potassium lactate (PL), temperature, and pH. The growth parameters, specific growth rate (SGR), and lag time (LT) were obtained by fitting the data into the Gompertz equation and showed high fitness with a correlation coefficient of $R^2{\geq}0.9192$. The polynomial model was identified as an appropriate secondary model for SGR and LT based on the coefficient of determination for the developed model ($R^2\;=\;0.97$ for SGR and $R^2\;=\;0.86$ for LT). The induced values that were calculated using the developed secondary model indicated that the growth kinetics of L. monocytogenes were dependent on storage temperature, pH, and PL. Finally, the predicted model was validated using statistical indicators, such as coefficient of determination, mean square error, bias factor, and accuracy factor. Validation of the model demonstrates that the overall prediction agreed well with the observed data. However, the model developed for SGR showed better predictive ability than the model developed for LT, which can be seen from its statistical validation indices, with the exception of the bias factor ($B_f$ was 0.6 for SGR and 0.97 for LT).

• The Korean Journal of Ecology
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• v.4 no.1_2
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• pp.38-51
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• 1981

The present investigation was estimated the effect of temperature, precipitatiion, and time on the decomposition of litters with litter bags of Pinus densiffora and Quercus mongolica at Gure where elevation in 50m, and at Nogodan where elevation in 1300m on Mt. Jiri. As the above results, decomposition model was proposed to relation of the environmental conditions. And was investigated the production and decomposition of litters from the stands of various forest communities in Kwangneung, Mt. Jiri and Mt. Halla. The results are as follows; The models for the decay of organic carbon (C) was as follows: $C=Coe^{-Kt}$ (limiting factor;time) $C=Coe^{-K'te}$ (limiting factor;tempedrature) $C=Coe^{-KnP}$ (limiting factor:precipitation) As observed in litter bag method, the decomposition rate of litter in Pinus densiflora was slower than that of Quercus mongolica. The higher elevation, the slower decomposition rate. The decomposition of litters at Gure where elevation in 50m was equally influenced by temperature and precipitation. But at Nogodan where elevation in 1300m was much inflenced by precipitation. The decay constant of litters was larger in hardwood forest than in coniferous forest. In the same species, the more elevatiion, the less decomposition constant. The time required for the decay of 50%, 95^, 99% of the accumulated litters in the forest floor were faster in hardwood forest than in coniferous forest. In the same species, the higher elevatiion, the longer time required.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.287-297
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• 2018

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

• International Journal of Reliability and Applications
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• v.10 no.2
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• pp.101-107
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• 2009

In the accelerated tests, the importance of correct failure analysis must be strongly emphasized. Understanding the failure mechanisms is requisite for designing and conducting successful accelerated life test. Under this presumption, a rational method must be identified to relate the results of accelerated tests quantitatively to the reliability or failure rates in use conditions, using a scientific acceleration transform. Most widely used models for relating the results of accelerated tests quantitatively to the reliability or failure rates in use conditions are an accelerated failure time model and a proportional hazards model. The purpose of this research is to compare the usability of the accelerated failure time model and proportional hazards model in the accelerated life tests.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3490-3499
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• 1999

The hyper-geometric distribution software reliability growth model (HGDM) was recently developed and successfully applied to the problem of estimating the number of initial faults residual in a software at the beginning of the test-and-debug phase. Though the HGDM is a time-domain software reliability growth model(SRGM), it is not possible to compare the HGDM with other time-domain SRGMs. Furthermore the usual software reliability can not be computed. These drawbacks are derived from fact that the HGDM is not described in terms of the execution time. Thus we develop a continuous-time HGDM with binomial sensitivity factor in order to remove these drawbacks. Statistical characteristics of the suggested model are studied and its applicability is then examined by analyzing real test data sets. It is empirically shown that the continuous-time HGDM with binomial sensitivity factor can be used as an alternative to the current HGDM.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.160-161
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• 2016

In tall building construction, lift car for worker lifting is a critical factor for construction productivity. To reduce worker lifting time, existing researches have been conducted on lift car planning. While, research on lift car operation is insufficient. For the efficient reduction of worker lifting time, lift car operation control is needed with lift car planning. Therefore, this research suggests operation control model of lift car to reduce worker lifting time. According to the result of a model test, the operation control model contributes to reasonable reduction of worker lifting time.

• ETRI Journal
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• v.30 no.1
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• pp.81-88
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• 2008

The security threat posed by worms has steadily increased in recent years. This paper discusses the application of the optimal and sub-optimal Internet worm control via Pontryagin's maximum principle. To this end, a control variable representing the optimal treatment strategy for infectious hosts is introduced into the two-factor worm model. The numerical optimal control laws are implemented by the multiple shooting method and the sub-optimal solution is computed using genetic algorithms. Simulation results demonstrate the effectiveness of the proposed optimal and sub-optimal strategies. It also provides a theoretical interpretation of the practical experience that the maximum implementation of treatment in the early stage is critically important in controlling outbreaks of Internet worms. Furthermore, our results show that the proposed sub-optimal control can lead to performance close to the optimal control, but with much simpler strategies for long periods of time in practical use.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.51-58
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• 2013

An appropriate determination of time step is one of the important problems in atmospheric modeling. In this study, we investigate the sensitivity of numerical solutions to time step in a nonlinear atmospheric model. For this purpose, a simple nondimensional dynamical model is employed, and numerical experiments are performed with various time steps and nonlinearity factors. Results show that numerical solutions are not sensitive to time step when the nonlinearity factor is not influentially large and truncation error is negligible. On the other hand, when the nonlinearity factor is large (i.e., in a highly nonlinear regime), numerical solutions are found to be sensitive to time step. In this situation, smaller time step increases the intensity of the spatial filter, which makes small-scale phenomena weaken. This conflicts with the fact that smaller time step generally results in more accurate numerical solutions owing to reduced truncation error. This conflict is inevitable because the spatial filter is necessary to stabilize the numerical solutions of the nonlinear model.