• Corrosion Science and Technology
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• v.13 no.2
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• pp.70-80
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• 2014

Duplex stainless steels with nearly equal fraction of the ferrite(${\alpha}$) phase and austenite(${\gamma}$) phase have been increasingly used for various applications such as power plants, desalination facilities due to their high resistance to corrosion, good weldability, and excellent mechanical properties. Hyper duplex stainless steel (HDSS) is defined as the future duplex stainless steel with a pitting resistance equivalent (PRE=wt.%Cr+3.3(wt.%Mo+0.5wt.%W)+30wt.%N) of above 50. However, when HDSS is welded with gas tungsten arc (GTA), incorporation of nitrogen in the Ar shielding gas are very important because the volume fraction of ${\alpha}$-phase and ${\gamma}$-phase is changed and harmful secondary phases can be formed in the welded zone. In other words, the balance of corrosion resistance between two phases and reduction of $Cr_2N$ are the key points of this study. The primary results of this study are as follows. The addition of $N_2$ to the Ar shielding gas provides phase balance under weld-cooling conditions and increases the transformation temperature of the ${\alpha}$-phase to ${\gamma}$-phase, increasing the fraction of ${\gamma}$-phase as well as decreasing the precipitation of $Cr_2N$. In the anodic polarization test, the addition of nitrogen gas in the Ar shielding gas improved values of the electrochemical parameters, compared to the Pure Ar. Also, in the erosion-corrosion test, the HDSS welded with shielding gas containing $N_2$ decreased the weight loss, compared to HDSS welded with the Ar pure gas. This result showed the resistance of erosion-corrosion was increased due to increasing the fraction of ${\gamma}$-phase and the stability of passive film according to the addition $N_2$ gas to the Ar shielding gas. As a result, the addition of nitrogen gas to the shielding gas improved the resistance of erosion-corrosion.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Journal of the Korean Institute of Telematics and Electronics
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• v.5 no.3
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• pp.9-24
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• 1968

This report is concerned with the optimum design of hyper-aprupt p-n junctiea silion diode and fabriction of this diode usable for electrical tuning application. Impurity profile in the junction was assumed to clean exponential function. With this assunntion, an optimum criterion for designing standard AM radio tuning capacitor was derived. In the diffusion process, after aluminum and antimony as impurties were deposited in vacuum on a P-type silicon wafer, the diffusion was followed by loading the wafer into the high temperature furnace.

• Microbiology and Biotechnology Letters
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• v.29 no.2
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• pp.96-103
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• 2001

Phaffia rhodozyma is the most promising microbial source of astaxanthin production, though wild-type strains are needed to increase the astaxanthin content for commercial production. To increase astaxanthin content for commercial production, a mutant strain of P. rhodozyma was selected and culture conditions of the mutant selected were optimized. P. rhodozyma was treated with mutagenic agent such as NTG, acriflavine, and UV in serial order and carotenoids hyper-producing mutant strain was selected based on the capabilities of cell growth on the agar plate containing chemical inhibitors and carotenoids production. Among the mutants tested, a mutant WS-2 was finally selected. Mutant WS-2 produced 1.26mg carotenoids/g-dry cell weight and this value was about- 4-folds higher than that of wild-type. The optimum culture conditions were $24^{\circ}C$ of temperature, 1.5vvm of aeration and 300rpm of agitation. In the optimized condition, cell and carotenoids concentrations were 7.62g/l and 14.9mg/l, respectively.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.705-720
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• 2012

In this paper, we introduce a hierarchical Bayesian model to simultaneously estimate the thresholds of each 6 cities. It was noted in the literature there was a dramatic increases in the number of deaths if the mean temperature passes a certain value (that we call a threshold). We estimate the difference of mortality before and after the threshold. For the hierarchical Bayesian analysis, some proper prior distribution of parameters and hyper-parameters are assumed. By combining the Gibbs and Metropolis-Hastings algorithm, we constructed a Markov chain Monte Carlo algorithm and the posterior inference was based on the posterior sample. The analysis shows that the estimates of the threshold are located at $25^{\circ}C{\sim}29^{\circ}C$ and the mortality around the threshold changes from -1% to 2~13%.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.1
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• pp.53-56
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• 2000

A thermostable esterase from the hyper thermophilic archaeon Sulfolobus solfataricus was partially purified 590-fold with $16.2\%$ recovery. The partially purified esterase had a specific activity of $29.5\;{\mu}mol\;min^{-1}mg^{-1}$ when the enzyme activity was determined using p-nitrophenyl butyrate as a substrate. The apparent molecular weight was about 100 kDa, while the optimum temperature and pH for esterase were $75^{\circ}C$ and 8.0, respectively. The enzyme showed high thermal stability and solvent tolerance in comparison to its mesophilic counterpart. The enzyme also showed chiral resolution activity for (S)-ibuprofen, indicating that S. solfataricus esterase can be used for the production of commercially important chiral drugs.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.4
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• pp.485-495
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• 2001

This paper presents a similarity solution for the directional casting of binary peritectic alloys in the presence of shrinkage-induced flow. The present model retains essential ingredients of alloy solidification, such as temperature-solute coupling, macrosegregation, solid-liquid property differences, and finite back diffusion in the primary phase. An algorithm for simultaneously determining the peritectic and liquidus positions is newly developed, which proves to be more efficient and stable than the existing scheme. Sample calculations are performed for both hypo- and hyper-peritectic compositions. The results show that the present analysis is capable of properly resolving the solidification characteristics of peritectic alloys so that it can be used for validating numerical models as a test solution.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.4
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• pp.45-50
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• 2003

In order to investigate the effect of surface treatment (Anodizing) and rolling on AI 5083-H131 alloy, under hyper velocity impact, a ballistic testing was conducted. Ballistic resistance of these materials was measured by a protection ballistic limit ($V_{50}$)' a statistical velocity with 50% probability of penetration. Perforation behavior and ballistic tolerance, described by penetration modes, were respectfully observed, by $V_{50}$ test and Projectile Through Plates (PTP) test at velocities greater than $V_{50}$. PTP tests were conducted with 0$^{\circ}$ obliquity at room temperature using 5.56mm ball projectiles. $V_{50}$ tests with 0$^{\circ}$ obliquity were also done with projectiles that were able to achieve near or complete penetration during PTP tests. Resistance to penetration, and penetration modes of Al 5052-H34 alloy were compared to those of Al 5083-H 131 alloy.