• Applied Chemistry for Engineering
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• v.27 no.6
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• pp.555-564
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• 2016

In recent years, methanol has attracted much attention since it can be cleanly manufactured by the combined use of atmospheric $CO_2$ recycling and water splitting via renewable energy. For the concept of "methanol economy", an active methanol synthesis catalyst should be prepared in a sophisticated manner rather than by empirical optimization approach. Even though Cu/ZnO-based catalysts prepared by coprecipitation are well known and have been extensively investigated even for a century, fundamental understanding on the precipitation chemistry and catalyst nanostructure has recently been achieved due to complexity of the necessary preparation steps such as precipitation, ageing, filtering, washing, drying, calcination and reduction. Herein we review the recent reports regarding the effects of various synthesis variables in each step on the physicochemical properties of materials in precursor, calcined and reduced states. The relationship between these characteristics and the catalytic performance will also be discussed because many variables in each step strongly influence the final catalytic activity, called "chemical memory". All discussion focuses on how to prepare a highly active Cu/ZnO-based catalyst for methanol synthesis. Furthermore, the preparation strategy we deliver here would be utilized for designing other coprecipitation-derived supported metal or metal oxide catalysts.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.640-654
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• 1996

This paper presents a synthesis method for multi-level Reed-Muller circuits using BDDs(Binary Decision Diagrams). The existing synthesis tool for Reed circuits, FACTOR, is not appropriate to the synthesis of large circuits because it uses matrix (map-type) to represent given logic functions, resulting in the exponential time and space in number of imput to the circuits. For solving this problems, a syntheisis method based on BDD is presented. Using BDDs, logic functions are represented compactly. Therefor storage spaces and computing time for synthesizing logic functions were greatly decreased, and this technique can be easily applied to large circuits. Using BDD representations, the proposed method extract best patterns to minimize multi-level Reed Muller circuits with good performance in area optimization and testability. Experimental results using the proposed method show better performance than those using previous methods〔2〕. For large circuits of considering the best input partition, synthesis results have been improved.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.237-250
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• 2003

Since an orthotropic steel deck bridge has large number of design variables and shows complex structural behavior, it would be very difficult and impractical to directly use a Conventional Single Level (CSL) optimization algorithm for its optimum design. Thus, in this paper, an Improved Multi Level Design Synthesis (IMLDS) optimization algorithm is proposed to improve the computational efficiency. In the proposed IMLDS algorithm, a coordination method is introduced to divide the bridge into main girders and orthotropic steel deck with preserving the characteristics of the structural behavior. For an efficient optimization of the bridge, the IMLDS algorithm incorporates the various crucial approximation techniques such as constraints deletion, Automatic Differentiation (AD), stress reanalysis, and etc. In the case of orthotropic steel deck system, optimum design problems are characterized by mixed continuous discrete variables and discontinuous design space. Thus, a modified Genetic Algorithm (GA) is also applied to optimize discrete member design for orthotropic steel deck. From the numerical example, the efficiency and convergency of the IMLDS algorithm proposed in this paper is investigated. It may be positively stated that the IMLDS algorithm will lead to more effective and practical design compared with previous algorithms.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.1-17
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• 2013

Metal oxides designed for application in conductometric gas sensors and approaches used for synthesis of metal oxides with improved gas sensing characteristics are discussed in present article.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.81-87
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• 1993

In this paper robust stability conditions are obtained for linear dynamical systems under structured nonlinear time-varying perturbations, using absolute stability theory and the concept of dissipative systems. The conditions are expressed in terms of solutions to linear matrix inequality(LMI). Based on this result, a synthesis methodology is developed for robust feedback controllers with worst-case H$_{2}$ perforrmance via convex optimization and LMI formulation.

• Proceedings of the Korean Ceranic Society Conference
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• 2003.10a
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• pp.212.1-212
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• 2003

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2293-2295
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• 2000

This paper describes the synthesis of robust decentralized controllers for uncertain large-scale discrete-time systems with time-delays in subsystem interconnections. Based on the Lyapunov method, a sufficient condition for robust stability, is derived in terms of a linear matrix inequality(LMI). The solutions of the LMI can be easily obtained using various efficient convex optimization techniques. A numerical example is given to illustrate the proposed method.

• Journal of Broadcast Engineering
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• v.13 no.4
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• pp.452-462
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• 2008

In this paper, we address issues associated with the real-time implementation of the MPEG-1/2 Layer-II (or MUSICAM) and MPEG-4 ER-BSAC decoders for Digital Multimedia Broadcasting (DMB) on TMS320C64x+ that is a fixed-point DSP processor with a clock speed of 330 MHz. To achieve the real-time requirement, they should be optimized in different steps as follows. First of all, a C-code level optimization is performed by sharing the memory, adjusting data types, and unrolling loops. Next, an algorithm level optimization is carried out such as the reconfiguration of bitstream reading, the modification of synthesis filtering, and the rearrangement of the window coefficients for synthesis filtering. In addition, the C-code of a synthesis filtering module of the MPEG-1/2 Layer-II decoder is rewritten by using the linear assembly programming technique. This is because the synthesis filtering module requires the most processing time among all processing modules of the decoder. In order to show how the real-time implementation works, we obtain the percentage of the processing time for decoding and calculate a RMS value between the decoded audio signals by the reference MPEG decoder and its DSP version implemented in this paper. As a result, it is shown that the percentages of the processing time for the MPEG-1/2 Layer-II and MPEG-4 ER-BSAC decoders occupy less than 3% and 11% of the DSP clock cycles, respectively, and the RMS values of the MPEG-1/2 Layer-II and MPEG-4 ER-BSAC decoders implemented in this paper all satisfy the criterion of -77.01 dB which is defined by the MPEG standards.

• Journal of Life Science
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• v.27 no.1
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• pp.38-43
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• 2017

Glycosyl aesculin, a potent anti-inflammatory agent, was synthesized by transglycosylation reaction, catalyzed by Thermotoga neapolitana ${\beta}-glucosidase$, with aesculin as an acceptor. The key reaction parameters were optimized using response-surface methodology (RSM) and $2{\mu}g$ of the enzyme. As shown by a statistical analysis, a second-order polynomial model fitted well to the data (p<0.05). The response surface curve for the interaction between aesculin and other parameters revealed that the aesculin concentration and reaction time were the primary factors that affected the yield of glycosyl aesculin. Among the tested factors, the optimum values for glycosyl aesculin production were as follows: aesculin concentration of 9.5 g/l, temperature of $84^{\circ}C$, reaction time of 81 min, and pH of 8.2. Under these conditions, 61.7% of glycosyl aesculin was obtained, with a predicted yield of 5.86 g/l. The maximum amount of glycosyl aesculin was 6.02 g/l. Good agreement between the predicted and experimental results confirmed the validity of the RSM. The optimization of reaction conditions by RSM resulted in a 1.6-fold increase in the production of glycosyl aesculin as compared to the yield before optimization. These results indicate that RSM can be effectively used for process optimization in the synthesis of a variety of biologically active glycosides using bacterial glycosidases.