• Journal of the Korean Crystal Growth and Crystal Technology
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• v.8 no.4
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• pp.599-604
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• 1998

We have investigated Pt and $RuO_2$ as a bottom electrode for ferroelectric capacitor applications. The bottom electrodes were prepared by using an RF magnetron sputtering method. Some of the investigated parameters were a substrate temperature, gas flow rate, RF power for the film growth, and post annealing effect. The substrate temperature strongly influenced the surface morphology and resistivity of the bottom electrodes as well as the film crystallographic structure. XRD results on Pt films showed a mixed phase of (111) and (200) peak for the substrate temperature ranged from RT to $200^{\circ}C$, and a preferred (111) orientation for $300^{\circ}C$. From the XRD and AFM results, we recommend the substrate temperature of $300^{\circ}C$ and RF power 80W for the Pt bottom electrode growth. With the variation of an oxygen partial pressure from 0 to 50%, we learned that only Ru metal was grown with 0~5% of $O_2$ gas, mixed phase of Ru and $RuO_2$ for $O_ 2$ partial pressure between 10~40%, and a pure $RuO_2$ phase with $O_2$ partial pressure of 50%. This result indicates that a double layer of $RuO_2/Ru$ can be grown in a process with the modulation of gas flow rate. Double layer structure is expected to reduce the fatigue problem while keeping a low electrical resistivity. As post anneal temperature was increased from RT to $700^{\circ}C$, the resistivity of Pt and $RuO_2$ was decreased linearly. This paper presents the optimized process conditions of the bottom electrodes for memory device applications.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Korean Chemical Engineering Research
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• v.47 no.6
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• pp.762-767
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• 2009

In this study, the feasibility of using vegetable oil extracted from tropical crop seed as a biodiesel feedstock was investigated by producing biodiesel and analysing the quality parameters as a transport fuel. In order to produce biodiesel efficiently, two step reaction process(pre-treatment and transesterificaion) was required because the tropical crop oil have a high content of free fatty acids. To determine the suitable acid catalyst for the pre-esterification, three kinds of acid catalysts were tested and sulfuric acid was identified as the best catalyst. After constructing the experimental matrix based on RSM and analysing the statistical data, the optimal pre-treatment conditions were determined to be 26.7% of methanol and 0.982% of sulfuric acid. Trans-esterification experiments of the pre-esterified oil based on RSM were carried out, then discovered 1.24% of KOH catalyst and 22.76% of methanol as the optimal trans-esterification conditions. However, the quantity of KOH was higher than the previously established KOH concentration of our team. So, we carried out supplemental experiment to determine the quantity of catalyst and methanol. As a result, the optimal transesterification conditions were determined to be 0.8% of KOH and 16.13% of methanol. After trans-esterification of tropical crop oil, the produced biodiesel could meet the major quality standard specifications; 100.8% of FAME, 0.45 mgKOH/g of acid value, 0.00% of water, 0.04% of total glycerol, $4.041mm^2/s$ of kinematic viscosity(at $40^{\circ}C$).

• Microbiology and Biotechnology Letters
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• v.26 no.5
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• pp.435-441
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• 1998

Optimal conditions for the production of natural color, betacyanin were investigated by varying light intensity, C/N ratio, concentrations of phosphate and kinds of elicitors. Batch cultivation was employed to characterize cell growth and betacyanin production of 32 days. The maximum specific growth rate, ${\mu}$$\sub$max/, was 0.3 (1/day) for batch cultivation. The maximum specific production rate, q$\^$max/$\sub$p/, was enhanced 0.11 (mg/g-cell/day) at 3 klux. A light intensity of 3 klux was shown to the best for both cell growth and betacyanin production. The maximum specific production rate was 0.125 (mg/g-cell/day) at 0.242 (1/day), the maximum specific growth rate. The dependence of specific growth rate on the light lintensity is fit to the photoinhibition model. The correlation between ${\mu}$ and q$\sub$p/ showed that the product formation parameters, ${\alpha}$ and ${\beta}$$\sub$p/ were 0.3756 (mg/cell) and 0.001 (mg/g-cell/day), respectively. The betacyanin production was partially cell growth related process, which is different from the production of a typical product in plant cell cultures. In C/N ratio experiment, high carbon concentration, 42.1 (w/w) improved cell growth rate while lower concentration, 31.6 (w/w) increased the betacyanin production rate. The ${\mu}$$\sub$max/ and q$\^$max/$\sub$p/ were 0.26 (1/day) and 0.075 (mg/g-cell/day), respectively. Beta vulgaris L. cells under 1.25 mM phosphate concentration produced 10.15 mg/L betacyanin with 13.46 (g-dry wt./L) of maximum cell density. The production of betacyanin was elongated by adding 0.1 ${\mu}$M of kinetin. This also increased the cell growth. Optimum culture conditions of light intensity, C/N, phosphate concentration were obtained as 5.5 klux, 27 (w/w), 1.25 mM, respectively by the response surface methodology. The maximum cell density, X$\sub$max/, and maximum production, P$\sub$max/, in optimized conditions were 16 (g-dry wt./L), 12.5 (mg/L) which were higher than 8 (g-dry wt./L), 4.48 (mg/L) in normal conditions. The ${\mu}$$\sub$max/ and q$\^$max/$\sub$p/ were 0.376 (1/day) and 0.134 (mg/g-cell/day) at the optimal condition. The overall results may be useful in scaling up hairy root cell culture system for commercial production of betacyanin.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.7
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• pp.667-678
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• 2002

In this paper, a new radiating structure with a multi-layered two-dimensional metallic disk array was proposed for shaping the flat-topped element pattern. It is an infinite periodic planar array structure with metallic disks finitely stacked above the radiating circular waveguide apertures. The theoretical analysis was in detail performed using rigid full-wave analysis, and was based on modal representations for the fields in the partial regions of the array structure and for the currents on the metallic disks. The final system of linear algebraic equations was derived using the orthogonal property of vector wave functions, mode-matching method, boundary conditions and Galerkin's method, and also their unknown modal coefficients needed for calculation of the array characteristics were determined by Gauss elimination method. The application of the algorithm was demonstrated in an array design for shaping the flat-topped element patterns of $\pm$20$^{\circ}$ beam width in Ka-band. The optimal design parameters normalized by a wavelength for general applications are presented, which are obtained through optimization process on the basis of simulation and design experience. A Ka-band experimental breadboard with symmetric nineteen elements was fabricated to compare simulation results with experimental results. The metallic disks array structure stacked above the radiating circular waveguide apertures was realized using ion-beam deposition method on thin polymer films. It was shown that the calculated and measured element patterns of the breadboard were in very close agreement within the beam scanning range. The result analysis for side lobe and grating lobe was done, and also a blindness phenomenon was discussed, which may cause by multi-layered metallic disk structure at the broadside. Input VSWR of the breadboard was less than 1.14, and its gains measured at 29.0 GHz. 29.5 GHz and 30 GHz were 10.2 dB, 10.0 dB and 10.7 dB, respectively. The experimental and simulation results showed that the proposed multi-layered metallic disk array structure could shape the efficient flat-topped element pattern.