• Journal of the Korean Academy of Esthetic Dentistry
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• v.23 no.2
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• pp.86-94
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• 2014

In recent years, perhaps the biggest driver in new material development is the desire to improve crown and bridge esthetics compared to the traditional PFM or all-metal restorations. As such, zirconia, leucite-containing glass ceramic and lithium disilicate glass ceramic have become prominent in the dental practice. Each material type performs differently regarding strength, toughness, ease of machining and the final preparation of the material prior to placement. For example, glass ceramic are typically weaker materials which limits its use to single-unit restorations. On the other hand, zirconia has a high fracture toughness which enables multi-unit restorations. This material requires a long sintering procedure which excludes its use for fast chair side production. Developed hybrid material of CAD/CAM is contained nano ceramic elements. This new material, called a Resin Nano Ceramic is unique in durability and function. The material is not a resin or composite. It is also not a pure ceramic. The material is a mixture of both and consists of ceramic. Like a composite, the material is not brittle and is fracture resistant. Like a glass ceramic, the material has excellent polish retention for lasting esthetics. The material is easily machined chair side or in a dental lab, polishes quickly to an esthetic finish and if necessary, can be useful restoratives.

• Journal of Microbiology and Biotechnology
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• v.30 no.5
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• pp.770-776
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• 2020

Genistein is a type of isoflavonoid found predominantly in leguminous plants. Genistein has diverse biological activities, such as anthelmintic and antioxidant effects, as well as inhibitory effects on the growth of several cancers. In addition, genistein is well known as a phytoestrogen. In this study, we attempted to biologically synthesize genistein from either p-coumaric acid or naringenin using Escherichia coli as a biotransformation host. Four genes, Os4CL, PeCHS, RcIFS, and OsCPR, were used for genistein production. To functionally express RcIFS and OsCPR, two members of the cytochrome P450 family, in E. coli, the membrane-binding anchor domain of each gene was removed, and RcIFS and OsCPR were translationally fused to generate an RcIFS-OsCPR hybrid. Os4CL and PeCHS, or the RcIFS-OsCPR hybrid, were then transformed into E. coli BL21(DE3). Using these strains, we optimized our culture system at a laboratory scale in terms of the cell density, concentrations of substrate and isopropyl-β-D-thiogalactoside, temperature, and culture medium. Under the optimized culture conditions, genistein was produced at up to 35 mg/l and 18.6 mg/l using naringenin and p-coumaric acid, respectively.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.478-483
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• 2000

In this study to cope with the decreasing product's life-cycle a virtual simulator to realize the simulation environment similar to a real manufacturing line is developed. The developed simulator plays a role in reducing the product conversion time by alternating manufacturing components and work plans on the simulation as manufacturing lines change and actuating a virtual manufacturing lines change and actuating a virtual manufacturing line before a real production. The developed simulator realized a virtual manufacturing line on the simulation using various manipulators and work cells as manufacturing components. Also It can be shown that the simulator can cope with rapid change of a manufacturing line by developing a interface that a separate process is managed for each manufacturing module and a manipulator component and a work cell are changed for a user to become convenient to teach tasks of each work module. using Microsoft Visual C++ 6.0 and OpenGL of Silicon Graphics for libraries to realize 3-dimensional graphic and constructing a database system, a hybrid type of hierachical and relational model to develop a progra that has efficiency and standardization.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.668-672
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• 2005

Doosan DND, OLED manufacturing equipment maker, has developed the largest deposition system to produce $730{\times}920mm$ size AMOLED devices for the first time in the world. It is necessary for producing 40" AMOLED panels to develop the large-scaled vacuum technologies including ICP plasma, stretching glass chuck, organic deposition, metal deposition and hybrid encapsulation processes.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2989-2994
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• 2008

The shopping center in underground passage for efficient space utilization is increased in urban area. This study describes operation characteristics of HVAC systems with ventilation and individual heating and cooling unit for shopping center in underground passage. In order to compare energy saving, thermal environment and installation space, etc., an integrated simulator with heat production and distribution system was designed and constructed. Energy delivery efficiency is improved over 20%, and energy saving of the hybrid system is calculated as over 30% compared to conventional all air type in the case of heating. And also the results showed that humidity decreased about $5{\sim}6%$, also characteristics of thermal control is improved over 34%.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.1
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• pp.71-77
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• 2013

The world demand of renewable bioenergy as an alternative transportation fuel is greatly increasing. Research for bioethanol production is currently being progressed intensively throughout the world. Therefore it will be necessary to develop bioethanol production with cellulosic materials. In this study, the yield of ethanol production was evaluated by simultaneous saccharification and fermentation (SSF) using sodium hydroxide pretreated sorghum ${\times}$ sudangrass hybrids. Composition analysis of 11 varieties of sorghum ${\times}$ sudangrass hybrids was performed for selection of excellent variety to efficiently produce bioethanol. The content of cellulose, hemicellulose, lignin and ash of these varieties were 32~39%, 19~24%, 17~22% and 6~11%, respectively. Among these varieties, 4 varieties of sorghum ${\times}$ sudangrass hybrids were selected for the evaluation of ethanol yield and those were pretreated with 1 M NaOH solution at $150^{\circ}C$ for 30 min using high temperature explosion system. After pretreatment, samples were neutralized with tap water. It contained 52~57% of cellulose. Simultaneous saccharification and fermentation (SSF) was carried out for 48 h at $33^{\circ}C$ by Saccharomyces cerevisiae CHY1011 using Green star variety. The yield of ethanol was 92.4% and the amount of ethanol production was estimated at 6206 L/ha.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.260-267
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• 2017

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.7
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• pp.591-597
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• 2012

For the past few years, the concern for clean energy has been greatly increased. Ocean thermal Energy Conversion(OTEC) power plants are studied as a viable option for the supply of clean energy. In this study, we examined the thermodynamic performance of the OTEC power system for the production of electric power. Computer simulation programs were developed under the same condition and various working fluids for closed Rankine cycle, regenerative cycle, Kalina cycle, open cycle, and hybrid cycle. The results show that the regenerative cycle showed the best system efficiency. And then we examined the thermodynamic performance of regenerative cycle OTEC power system using the condenser effluent from Uljin nuclear power plant instead of the surface water. The highest system efficiency of the condition was 4.55% and the highest net power was 181 MW.