• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.173-178
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• 2007

This paper is to establish an appropriate model for predicting the temperature decreases in the batch transferred from the refining process to the caster in steel-making companies. Mathematical modeling of the temperature decreases between the processes is difficult, since the reaction mechanism by which the temperature changes in a molten steel batch is dynamic, uncertain and complex. Three soft computing techniques are examined using the same data, namely the multiple regression, fuzzy regression, and neural net (NN) models. To compare the accuracy of these three models, a limited number of input variables are selected from those variables significantly affecting the temperature decrease. The results show that the difference in accuracy between the three models is not statistically significant. Nonetheless, the NN model is recommended because of its adaptive ability and robustness. The method presented in this paper allows the temperature decrease to be predicted without requiring any precise metallurgical knowledge.

• 한국생물공학회:학술대회논문집
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• 2002.04a
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• pp.515-518
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• 2002

The method of foam fractionation can be applied to enrich proteins from a dilute protein solution if the proteins are hydrophobic and foam. If a protein, such as invertase, is hydrophilic, a dilute solution containing this protein may not foam. In that case, a batch foam fractionation process may not be appropriate for recovering a concentrated solution of that protein. In this paper, various concentrations of invertase were added to a dilute solution containing bromelain (a hydrophobic protein), in order to determine how the presence of a hydrophilic protein can affect the recovery of the desired hydrophobic protein. The effect of invertase on bromelain recovery was studied here at an initial bulk solution pH of 5 and an air superficial velocity of 4.6 cm/s.

• KSBB Journal
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• v.6 no.1
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• pp.15-25
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• 1991

The optimal glucose concentration for the high-density culture of recombinant yeasts was obtained using dynamic simulation. An adaptive and predictive algoritilm complimented by the rule base was proposed for the control of the fed-batch fermentation process. The measurement of process variables has relatively long sampling period and relatively long time delay characteristics. As one of the solution on these problems, prediction techniques and rule bases were added to a classical recursive identification and control algorithm. Rule bases were used in the determination of control input considering the difference between the predicted value and the measured value. A mathelnatical model was used in the estimation and interpretation of the changes of state variables and parameters. Better performances were obtained by employing the control algorithm proposed in the present study compared to the conventional adaptive control method.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2006.04a
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• pp.402-406
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• 2006

The physical and chemical characteristics of ALC were analyzed and showed 2.2 of specific gravity and 9.05 of pH. The results of leaching tests with standard method for soil and waste indicated heavy metals(Cu, Cd, Pb, $Cr^{6+}$) were under maximum concentration level. The anaerobic digestion sludge was attached in the surface of ALC within 90 hours. As the results of batch test, pH of the ALC and Bio-ALC were decreased from initial pH of ALC to 8.7 and 7.8 respectively Also, the concentration of heavy metals was rapidly eliminated in the solution with the batch test. The result of column experiment indicates that the removal efficiency of ALC was showed 66% of T-P, 60% of T-N, and 67% of CODcr. Also, removal efficiency of Bio-ALC was slightly higher than that of ALC in T-N (64%) and CODcr (74%).

• Journal of Korean Society on Water Environment
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• v.31 no.4
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• pp.399-406
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• 2015

A sequencing batch reactor was operated under different pH conditions to see the influence of free ammonia (FA) and free nitrous acid (FNA) to microbial community on ammonium partial nitrification. Long-term influences of FA and FNA were evaluated by polymerase chain reaction-denaturing gradient gel electrophoresis and fluorescence in situ hybridization. Nitrite accumulation was successfully achieved at pH 8.2 and 6.3. The shifts in the microbial community were observed when influent ammonia concentration increased to 1 g $NH_4$-N/L at pH 8.2, and then when pH was dropped to 6.3. Both Nitrosomonas and Nitrosospira were selected during the startup of the reactor, and eventually became dominant members as ammonia-oxidizing bacteria. The results of molecular microbiological analysis strongly suggested that the composition of microbial community was changed according to the method used to control nitrite-oxidizing bacteria.

• Nuclear Engineering and Technology
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• v.9 no.1
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• pp.15-31
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• 1977

New conception of batch and period is defined appropriate for the on-power refuelling scheme of a heavy water-moderated reactor, A computer code (“HWRCOST”) is developed using nuclear fuel cycle economic equations based on the continuous energy calculation method. The fuel cycle cost of the CANDU-PHW reactor is calculated and sensitivity analyses are performed with variation of uranium ore price, fabrication cost, spent fuel permanent disposal expenses, and capacity fctor.

• The Journal of Engineering Research
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• v.1 no.1
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• pp.31-40
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• 1997

We first describe an approximation method for fitting a k-state MMBP to the departure process of a D-BMAP/Geo/1/K queue. The fitting model is them used in a simple decomposition algorithm to analyze a tandem configuration of finite capacity queue with cell loss.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.65-70
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• 2020

CNN's technologies that represent emotional detection include primitive CNN algorithms, deployment normalization, and drop-off. We present the methods and data of the three experiments in this paper. The training database and the test database are set up differently. The first experiment is to extract emotions using Batch Normalization, which complemented the shortcomings of distribution. The second experiment is to extract emotions using Dropout, which is used for rapid computation. The third experiment uses CNN using convolution and maxpooling. All three results show a low detection rate, To supplement these problems, We will develop a deep learning algorithm using feature extraction method specialized in image processing field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.547-549
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• 2021

In Artificial Intelligence analysis, the process of creating a model and verifying it is a task that requires computational processing time because it is Batch Processing performed with already generated data. We need to model, validate, and predict real-time data, such as stocks and defense information, with data generated directly in front of us. As a solution to this, we solve it by applying techniques to segment the data required for artificial intelligence modeling tasks in order of time processing and distribute the data across multiple processes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.325-326
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• 2007