• Applied Biological Chemistry
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• v.43 no.3
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• pp.218-224
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• 2000

The efforts were made to optimite ethanol extraction from persimmon leaf with the time of extraction$(1.5{\sim}2.5\;hrs)$, the temperature of extraction$(70{\sim}90^{\circ}C)$, and the concentration of ethanol$(0{\sim}40%)$ as three primary variables together with several functional characteristics of persimmon leaf as reaction variables. The conditions of extraction was best fitted by using response surface methodology through the center synthesis plan, and the optimal conditions of extraction were established. The contents of soluble solid and soluble tannin went up as the concentration of ethanol went up and the temperature of extraction went down, and the turbidity went down as the concentration of ethanol went down. Electron donation ability was hardly affected by the extraction temperature and had the tendency to go up as the concentration of ethanol went up. The inhibitory activity of xanthine oxidase(XOase) had the tendency to go up as both the concentration of ethanol and the temperature of extraction went up. The inhibitory activity of angiotensin converting enzyme(ACE), the significance of which still was not recognized, showed the maximum when the concentration of ethanol was 27%. In result, the optimal conditions of extraction was the extraction time of two hours, the extraction temperature of $75{\sim}81^{\circ}C$, and the ethanol concentration of $33{\sim}35%$.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.396-407
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• 2020

As fossil fuels are depleted worldwide, alternative resources is required to replace fossil fuels, and biofuels are in the spotlight as alternative resources. Biofuels are produced from biomass, which is a renewable resource to produce biofuels or bio-chemicals. Especially, in order to substitute fossil fuels, the research focusing the biofuel (biodiesel) production based on CO₂ and biomass achieves more attention recently. To produce biomass-based biodiesel, the development of a supply chain network is required considering the amounts of feedstocks (ex, CO₂ and water) required producing biodiesel, potential locations and capacities of bio-refineries, and transportations of biodiesel produced at biorefineries to demand cities. Although many studies of the biomass-based biodiesel supply chain network are performed, there are few types of research handled the uncertainty in CO₂ supply which influences the optimal strategies of microalgae-based biodiesel production. Because CO₂, which is used in the production of microalgae-based biodiesel as one of important resources, is captured from the off-gases emitted in power plants, the uncertainty in CO₂ supply from power plants has big impacts on the optimal configuration of the biodiesel supply chain network. Therefore, in this study, to handle those issues, we develop the two-stage stochastic model to determine the optimal strategies of the biodiesel supply chain network considering the uncertainty in CO₂ supply. The goal of the proposed model is to minimize the expected total cost of the biodiesel supply chain network considering the uncertain CO₂ supply as well as satisfy diesel demands at each city. This model conducted a case study satisfying 10% diesel demand in the Republic of Korea. The overall cost of the stochastic model (US$ 12.9/gallon·y) is slightly higher (23%) than that of the deterministic model (US$ 10.5/gallon·y). Fluctuations in CO₂ supply (stochastic model) had a significant impact on the optimal strategies of the biodiesel supply network.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Applied Biological Chemistry
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• v.54 no.2
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• pp.135-142
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• 2011

A feather-degrading bacterium Elizabethkingia meningoseptica CS2-1 was isolated from compost in a chicken farm. Cultured on a basal medium containing 2% chicken feather, the bacterium showed 729.7 ${\mu}mol/mL$ of amino acid. Optimal culture conditions for feather degradation by E. meningoseptica CS2-1 were $25^{\circ}C$, pH 7.5, and 180 rpm. The optimal pH and temperature for protease activity were 8.0 and $40^{\circ}C$, respectively. The composition of an optimal medium for amino acid production was 0.05% NH4Cl, 0.05% NaCl, 0.03% $K_2HPO_4$, 0.03% $KH_2PO_4$, 0.01% $MgCl_2{\cdot}6H_2O$, 0.1% urea, and 2% chicken feather. Characteristics of amino acids extracted from the optimal medium under the optimal culture conditions of E. meningoseptica CS2-1 were analyzed. The total amino acid content of strain CS2-1 was 1063 ${\mu}mol/mL$, which was 46% higher compared to the basal condition (729.7 ${\mu}mol/mL$). The essential amino acid content in the total amino acid was 315.9 ${\mu}mol/mL$, which was 44% higher than that of the basal condition. Major amino acids were proline (14%), aspartic acid (12%), glutamic acid (11%), serine (10%), alanine (10%), glycine (9%), and tyrosine (7%) by strain CS2-1. These results suggest that strain CS2-1 can be used as a potential microbial resource for the production of amino acid using chicken feathers.

• Korean Journal of Plant Resources
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• v.24 no.5
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• pp.642-649
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• 2011

Ginseng growth is largely affected by characteristics of soil in Ginseng field. In this study, we determined the critical ranges of physico-chemical properties of soil for optimization of ginseng growth by analyzing the soils from Anseong and Pocheon regions in Gyeonggi province. Fresh weight of ginseng was 2 to 5 fold higher in good growth field compared to poor growth field within Anseong region. In the case of Pocheon region, 1.5 to 2 fold differences of fresh weight of ginseng was observed between good and poor growth field. These results indicate the difference of ginseng growth even in the same region. Based on these results, critical ranges of physico-chemical properties of soils were determined by comparing the good and poor growth field of each regions, which are follows; more than 50% of soil porosity, 2.0~2.8 g/kg of total nitrogen, 500~900 mg/kg for Av. $P_2O_5$, 2.3~3.5 $cmol_c\;kg^{-1}$ for Exch. Ca in Anseong; less than 13% of liquid phase, 400~650 mg/kg for Av. $P_2O_5$, 4.0~4.7 $cmol_c\;kg^{-1}$ for Exch. Ca, less than 0.8 and 0.5 $cmol_c\;kg^{-1}$ for Exch. Mg and K, respectively, in Pocheon. Interestingly, we found that ginseng growth was affected by exchangeable base ratio (Ca:Mg:K) especially in Anseong region, which were 6:2:1 in good growth field while 4:2:1 in poor growth field. Critical ranges for nutrient contents of ginseng leaves were also characterized, which are less than 0.2% and 0.22% of each P and Mg, respectively, in Anseong, while less than 1.8% and 0.18% of each N and P, respecively, and 1.5~3.0% of K in Pocheon. In addition, we determined critical ranges for inorganic nutrient contents in the current study.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.9
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• pp.1081-1090
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• 2017

The purpose of this study was to develop a functional porridge prepared with mulberry leaf and mulberry fruit powder, which can ameliorate hypertension. The experiment was designed according to the central composite design. For optimization of the mixture ratio of mulberry leaf powder (MLP) and mulberry fruit powder (MFP), the independent variables were defined as MLP (X1) and MFP (X2) and the dependent variables were defined as K (Y1), Na (Y2), ${\gamma}$-aminobutyric acid (GABA) (Y3), cyanidin-3-glycoside (C3G) (Y4), rutin (Y5), and flavonoid (Y6). The optimal MLP to MFP mixture ratio according to the response surface method were 5.41 g of MLP and 2.65 g of MFP. The amounts of K, Na, GABA, C3G, rutin, and flavonoid in the optimal MLP and MFP mixture were 1,844.22 mg/100 g, 52.74 mg/100 g, 139.98 mg/100 g, 1,134.89 mg/100 g, 101.56 mg/100 g, and 201.28 mg/100 g, respectively. The amounts of Ca, K, Mg, and Na in the functional porridge at this optimal point were 27.66 mg/100 g, 131.32 mg/100 g, 19.57 mg/100 g, and 3.59 mg/100 g, respectively. Overall, this functional porridge can help reduce hypertension.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.6
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• pp.853-860
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• 2012

This study investigated the preference and recognition of cooked rice mixed with multigrains. The data for the analysis was collected from 464 people who were residing in Seoul, Gyeongsang and Jeolla area, and analyzed by the SPSS 18.0 program. The result showed that 77.8% of the respondents liked cooked rice mixed with multigrain, showing significant difference by age (p<0.001) and occupation (p<0.01). Of the respondents, 33.8% consumed cooked rice mixed with multigrains at least once a day, showing significant difference by gender (p<0.01), age (p<0.001) and occupation (p<0.001). The most popular type of grains to mix with rice were, in order, black rice (3.8)> brown rice (3.7)> barley (3.7)> proso millet (3.4)> foxtail millet (3.4)> SoRiTae (3.3)> sorghum (3.2)> adlay (3.2)> mung bean (3.1)> buckwheat (3.0)> BacTae (2.8). A total of 32.5% of the respondents answered that 21~30% was proper mixing ratio of multigrains-added cooked rice, showing age (p<0.001), occupation (p<0.001) and resident area (p<0.05). Three or four kinds of grains were preferred to mix cooked rice, showing significant difference by age and occupation (p<0.001). Of the respondents, 43.1% chose price reduction as the most desired improvement of multigrains in the market. Most of the subjects had affirmative view intake of cooked rice mixed with multigrains, but recognized that multigrains were expensive. From these results, this study will provide basic information for the increased availability of multigrains and optimization of the multigrain ratio mix.

• Food Science and Preservation
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• v.18 no.6
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• pp.980-985
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• 2011

This study was conducted to determine the acetic-acid fermentation properties of apple juice (initial alcohol content, apple juice concentration, acetic-acid content, and inoculum size) in flask scale. At the acetic-acid fermentation of apple juice with 3, 5, 7, and 9% initial alcohol content, the maximum acidity after 10-day fermentation was 5.88% when the initial alcohol content was 5%. The acetic-acid fermentation did not proceed normally when the initial alcohol content was 9%. When the initial Brix was $1^{\circ}$, the acidity gradually increased, and the acidity after 12-day acetic-acid fermentation was 4.48%. Above 4% acidity was attained faster when the apple juice concentration was 5 and 10 $^{\circ}Brix$ than when it was 1 and 14 $^{\circ}Brix$. When the initial acidity was 1% or above (0.3, 0.5, 1.0, and 2.0%), the acetic-acid fermentation proceeded normally. The acetic-acid fermentation also proceeded normally when the inoculum sizes were 10 and 15%, and the acidity after eight-day acetic-acid fermentation was 5.60 and 6.05%, respectively. Therefore, the following were considered the optimal acetic-acid fermentation conditions for apple cider vinegar: 5% initial alcohol content, 5 $^{\circ}Brix$ or above apple juice concentration, 1.0% or above initial acidity, and 10% or above inoculum size. Apple cider vinegar with above 5% acidity can be produced within 48 h under the following acetic-acid fermentation conditions: 7% initial alcohol content, about 1% initial acidity, and 10% inoculum volume at $30^{\circ}C$, 30 rpm, and 1.0 vvm, using 14 $^{\circ}Brix$ apple juice in a mini-jar fermentor as a pre-step for industrial-scale adaptation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.3-16
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• 2008

A Wireless Sensor Network (WSN for short) is a wireless network consisting of distributed small devices which are called sensor nodes or motes. Recently, there has been an extensive research on WSN and also on its security. For secure storage and secure transmission of the sensed information, sensor nodes should be equipped with cryptographic algorithms. Moreover, these algorithms should be efficiently implemented since sensor nodes are highly resource-constrained devices. There are already some existing algorithms applicable to sensor nodes, including public key ciphers such as TinyECC and standard block ciphers such as AES. Stream ciphers, however, are still to be analyzed, since they were only recently standardized in the eSTREAM project. In this paper, we implement over the MicaZ platform nine software-based stream ciphers out of the ten in the second and final phases of the eSTREAM project, and we evaluate their performance. Especially, we apply several optimization techniques to six ciphers including SOSEMANUK, Salsa20 and Rabbit, which have survived after the final phase of the eSTREAM project. We also present the implementation results of hardware-oriented stream ciphers and AES-CFB fur reference. According to our experiment, the encryption speeds of these software-based stream ciphers are in the range of 31-406Kbps, thus most of these ciphers are fairly acceptable fur sensor nodes. In particular, the survivors, SOSEMANUK, Salsa20 and Rabbit, show the throughputs of 406Kbps, 176Kbps and 121Kbps using 70KB, 14KB and 22KB of ROM and 2811B, 799B and 755B of RAM, respectively. From the viewpoint of encryption speed, the performances of these ciphers are much better than that of the software-based AES, which shows the speed of 106Kbps.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.