• Journal of the Korean Ceramic Society
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• v.37 no.2
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• pp.134-139
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• 2000

The main objective of the present investigation is to show the feasibility of combinatorial chemistry by applying this method to phosphor syntehses. In this respect barium hexaaluminate phosphor was prepared by the split-pool combinatorial method, which enabled much more rapid search of optimum compositions of target phosphors than conventional synthetic methods. Barium hexaaluminate phosphors doped with Eu2+ exhibit blue emission while those co-doped with Mn2+ and Eu2+ exhibit green emission. Basically, the phosphor doped with 1.3 mole of Ba and 0.06~0.15 mole of Eu2+ exhibit the maximum value of emission intensity at 435${\mu}{\textrm}{m}$. Under the UV and VUV extitations, the barium hexaaluminate phosphor co-doped with Mn2+ and Eu2+ shows strong green emission.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.484-490
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• 2000

The abjective of the study is the development of the system for effective prediction of residual stresses using the backpropagation algorithm from the neural network. To achieve this goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, the neural network based on backpropagation algorithm might be controlled weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.8
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• pp.695-701
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• 2004

This study is to suggest a Probabilistic design determining configurations of slider air bearings with the dimensional manufacturing tolerances of the ABS. The probabilistic design problem is formulated to minimize the variation in flying height from a target value while satisfying the desired probabilities keeping the pitch and roll angles within a suitable range. The proposed approach first selves the deterministic optimization problem. Then, beginning with this solution, the RBDO is continued with the probabilistic constraints affected by the random variables with a fixed standard deviation in normal distribution. The RBDO results are directly compared with the values of the initial design and the results of the deterministic optimization, respectively. The reliability analyses are performed by the descriptive sampling (DS) to show the effectiveness and accuracy of the proposed approach. It is demonstrated that the Proposed RBDO approach can efficiently obtain an optimum solution satisfying all the desired probabilistic constraints.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.159-159
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• 2019

In this study, the simulation of sediment deposition at Sangju weir reservoir, South Korea, was carried out using artificial neural networks. The ANNs have typically been used in water resources engineering problems for their robustness and high degree of accuracy. Three basic variables namely turbid water inflow, outflow, and water stage have been used as input variables. It was found that ANNs were able to establish valid relationship between input variables and target variable of sedimentation. The R value was 0.9806, 0.9091, and 0.8758 for training, validation, and testing phase respectively. Comparative analysis was also performed to find optimum structure of ANN for sediment deposition prediction. 3-14-1 network architecture using BR algorithm outperformed all other combinations. It was concluded that ANN possess mapping capabilities for complex, non-linear phenomenon of reservoir sedimentation.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.61-73
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• 2009

Many software reliability growth models(SRGM) have been proposed since the software reliability issue was raised in 1972. The technology to estimate and grow the reliability of developing S/W to target value during testing phase were developed using them. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or Logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. I propose the methology to evaluate the SRGM using least square estimater and maximum likelihood estimater for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.11
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• pp.1195-1202
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• 2004

The international competition in refrigerator markets has continuously required the research for sound quality of a refrigerator to improve the quality of a life. In this paper, A new method for evaluation of the sound quality of a refrigerator is developed based on human sensibility engineering by using ANN(artificial neural network). In this paper, the loudness and the sharpness of the refrigerator's signals was used for the input value in ANN's training process because the loudness and the sharpness has a good correlation between the output of the ANN and the target of the individual evaluation In the training process. Two input factor was used repeatedly in the training process to get more optimum weighting value. And then finally we developed the sound quality index of a refrigerator. The developed sound quality index was confirmed by the 96.5 % of correlation between the output of the ANN and the real evaluation. It will be applied to evaluate the sound quality of a refrigerator in the industry.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.30-36
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• 2000

The hidden node plays a role of the functional units that classifies the features of input pattern in the given question. Therefore, a neural network that consists of the number of a suitable optimum hidden node has be on the rise as a factor that has an important effect upon a result. However there is a problem that decides the number of hidden nodes based on back-propagation learning algorithm. If the number of hidden nodes is designated very small perfect learning is not done because the input pattern given cannot be classified enough. On the other hand, if designated a lot, overfitting occurs due to the unnecessary execution of operation and extravagance of memory point. So, the recognition rate is been law and the generality is fallen. Therefore, this paper suggests a method that decides the number of neural network node with feature information consisted of the parameter of learning algorithm. It excludes a node in the Pruning target, that has a maximum value among the feature value obtained and compares the average of the rest of hidden node feature value with the feature value of each hidden node, and then would like to improve the learning speed of neural network deciding the optimum structure of the multi-layer neural network as pruning the hidden node that has the feature value smaller than the average.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.1
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• pp.117-123
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• 2008

In this study, PHABSIM which is a sample for 1D physical habitat and River2D, which is a sample for 2D physical habitat were applied to the main streams of Han River in order to calculate an optimum flow considering the habitats of fishes in determining the instream flow. Moreover, the Weighted Usable Area (WUA) of the two samples in each growth step (adults and spawning) of the target fish type was compared and reviewed. The optimal flow value was calculated by considering the conditions for inhabiting fishes. As a result of the correlation analysis for WUA from 1D and 2D samples was 0.87 to 0.99. The optimum flow considering the conditions of inhabiting fishes showed insignificant difference of $3m^3/s\;to\;5m^3/s$ with the exception of adults in Moon-Mak and spawning in Dal-Chun.

• Journal of Animal Science and Technology
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• v.53 no.3
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• pp.253-259
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• 2011

The aim of this work was to determine the method and predict the optimum conditions for egg quality stored for 7 days when combination treatments of irradiation and chitosan coating were applied using response surface methodology (RSM). A central composite design was chosen for the RSM in this study and the factors were irradiation dose (0~2 kGy) and concentration of chitosan coating material (0~2%). Performance of the irradiation and chitosan coating were evaluated by analyzing the egg quality and functional property factors. The predicted maximum level of Haugh units and foaming ability calculated by a developed model were 74.19 at 0 kGy of irradiation with coating by 0.96% chitosan solution and 50.83 mm at 2.0 kGy with 1.01%, respectively. The predicted minimum value of foam stability and 2-thiobarbituric acid reactive substances (TBARS) value were 2.97 mm at 0.39 kGy with 0.21% and 0.54 mg malonaldehyde/kg egg yolk at 0 kGy with 0.90% of chitosan solution, respectively. Results clearly showed that gamma irradiation negatively affected the Haugh unit and TBARS but positively affected the foaming capacity. The estimated value from the developed model by RSM was verified by no statistical difference with observed value. Therefore, RSM can be a good tool for optimization and prediction of egg quality when 2 or more treatments are combined. However, one should decide the target quality first to achieve a successful implementation of this technology.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.4
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• pp.352-359
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• 2013

To estimate the mesh selectivity of gillnet for ocellate spot skate (Okamejei kenojei), the field tests were carried out 12 times with six different mesh sizes (121.2mm, 137.7mm, 151.5mm, 168.3mm, 178.2mm, 189.4mm) in the coastal waters of Taean, Chungcheongnamdo of Korea, 2010~2011. In the field tests, the total number of species was 31, and that of catch was 1,410 and the total weight was 618,006g. The number and weight of ocellate spot skate which is main target in this study were 1,004 and 434,592g, respectively. The catch in number of ocellate spot skate occupied about 71.2% in total catch. The others of catch species were marbled sole (8.4%), sea raven (4.4%), japanese swimming crab (4.2%) and flatfish (4.1%) and so on. The range of body disk width (DW) of ocellate spot skates which were caught in this study was 15.2~35cm and the mode was 27~29cm. The estimation equation of mesh selectivity using the extended Kitahara's method was expressed as s $(R)=s(DW/m)={\exp}\{(-0.56R^3-1.80R^2+12.96R-9.99)-4.26\}$. The optimum value of DW/m for 1.0 of retention probability in this estimation equation was estimated 1.899 and DW/m was estimated to be 1.194, 1.314, 1.395, 1.461 and 1.520 when the retention probability were 0.1, 0.2, 0.3, 0.4 and 0.5, respectively. When applied to the retention probability of 0.5, the optimum mesh size was estimated to be 177.0mm on first maturity disk width 26.9cm of ocellate spot skate.