• KSBB Journal
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• v.22 no.5
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• pp.288-296
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• 2007

Monascus pilosus (KCCM 60160) in submerged culture was optimized based on culture medium and fermentation conditions. Monacolin-K (Iovastatin), one of the cholesterol lowing-agent which was produced by Monascus pilosus may maintain a healthy lipid level by inhibiting the biosynthesis of cholesterol. Plackett-Burman design and response surface method were employed to study the culture medium for the desirable monacolin-K production. As a result of experimental designs, optimized production medium components and concentrations (g/L) were determined on soluble starch 96, malt extract 44.5, beef extract 30.23, yeast extract 15, $(NH_4)_2SO_4$ 4.03, $Na_2HPO_4{\cdot}12H_2O$ 0.5, L-Histidine 3.0, $KHSO_4$ 1.0, respectively. Monacolin-K production was improved about 3 times in comparison with shake flask fermentation of the basic production medium. The effect of agitation speed (300, 350, 400 and 450 rpm) on the monacolin-K production were also observed in a batch fermenter. Maximum monacolin-K production with the basic production medium was 68 mg/L when agitation speed was 500 rpm. And it was found that all spherical pellets (average diameter of $1.0{\sim}1.5mm$) were dominant during fermentation. Based on the results, the maximum production of 185 mg/L of monacolin-K with the optimized production medium was obtained at pH (controlled) 6.5, agitation rate 400 rpm, aeration rate 1 vvm, and inoculum size 3%.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.13-17
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• 2010

Purpose: Partial volume effect (PVE) is the phenomenon to lower the accuracy of image due to low estimate, which is to occur from PET/CT 3D image acquisition. The more resolution is declined and the lesion is small, the more it causes a big error. So that it can influence the test result. Studied the optimum image reconstruction method by using variation of parameter, which can influence the PVE. Materials and Methods: It acquires the image in each size spheres which is injected $^{18}F$-FDG to hot site and background in the ratio 4:1 for 10 minutes by using NEMA 2001 IEC phantom in GE Discovey STE 16. The iterative reconstruction is used and gives variety to iteration 2-50 times, subset number 1-56. The analysis's fixed region of interest in detail part of image and compute % difference and signal to noise ratio (SNR) using $SUV_{max}$. Results: It's measured that $SUV_{max}$ of 10 mm spheres, which is changed subset number to 2, 5, 8, 20, 56 in fixed iteration to times, SNR is indicated 0.19, 0.30, 0.40, 0.48, 0.45. As well as each sphere's of total SNR is measured 2.73, 3.38, 3.64, 3.63, 3.38. Conclusion: In iteration 6th to 20th, it indicates similar value in % difference and SNR ($3.47{\pm}0.09$). Over 20th, it increases the phenomenon, which is placed low value on $SUV_{max}$ through the influence of noise. In addition, the identical iteration, it indicates that SNR is high value in 8th to 20th in variation of subset number. Therefore, to reduce partial volume effect of small lesion, it can be declined the partial volume effect in iteration 6 times, subset number 8~20 times, considering reconstruction time.

• Journal of the Korean Geographical Society
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• v.29 no.3
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• pp.233-252
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• 1994

One of major advantages of Lumped model is its ability to simulate extended flows. A further advantage is that it requires only conventional, readily available hydrological data (rainfall, evaporation and runoff). These two advantages commend the use of this type of model for the analysis of the hydrological effects of landuse change. Experimental Catchment(K11) of Kimakia site in Kenga experienced three phases of landuse change for sixteen and half years. The Institute of Hydrology offered the hydrological data from the catchment for this research. On basis of Blackie's(l972) 9-parameter model, a new model(R1131) was reorganized in consideration of the following aspects to reflect the hydrological characteristics of the catchment: 1) The evapotranspiration necessary for the landuse hydrology, 2) high permeable soils, 3) small catchment, 4) input option for initial soil moisture deficit, and 5) othel modules for water budget analysis. The new model is constructed as a 11-parameter, 3-storage, 1-input option model. Using a number of initial conditions, the model was optimized to the data of three landuse phases. The model efficiencies were 96.78%, 97.20%, 94.62% and the errors of total flow were -1.78%, -3.36%, -5.32%. The bias of the optimized models were tested by several techniques, The extended flows were simulated in the prediction mode using the optimized model and the data set of the whole series of experimental periods. They are used to analyse the change of daily high and low-flow caused by landuse change. The relative water use ratio of the clearing and seedling phase was 60.21%, but that of the next two phases were 81.23% and 83.78% respectively. The annual peak flows of second and third phase at a 1.5-year return period were decreased by 31.3% and 31.2% compared to that of the first phase. The annual peak flow at a 50-year return period in the second phase was an increase of only 4.8%, and that in the third phase was an increase of 12.9%. The annual minimum flow at a 1.5-year return period was decreased by 34.2% in the second phase, and 34.3% in the third phase. The changes in the annual minimum flows were decreased for the larger return periods; a 20.2% decrease in the second phase and 20.9% decrease in the third phase at a 50-year return period. From the results above, two aspects could be concluded. Firstly, the flow regime in Catchment K11 was changed due to the landuse conversion from the clearing and seedling phade to the intermediate stage of pine plantation. But, The flow regime was little affected after the pine trees reached a certain height. Secondly, the effects of the pine plantation on the daily high- and low-flow were reduced with the increase in flood size and the severity of drought.

• Korean Journal of Ecology and Environment
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• v.44 no.1
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• pp.31-41
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• 2011

To optimize the natural chemical agents against nuisance phytoplankton, we examined algal removal activity (ABA) of Plant-Mineral Composite (PMC), which already developed by our teams (Kim et al., 2010), on various conditions. The PMC are consisted of extracted-mixtures with indigenous plants (Camellia sinensis, Quercusacutissima and Castanea crenata) and minerals (Loess, Quartz porphyry, and natural zeolite), and characterized by coagulation and floating of low-density suspended solids. A simple extraction process was adopted, such as drying and grinding of raw material, water-extraction by high temperature-sonication and filtering. All tests were performed in 3 L plastic chambers varying conditions; six different concentrations ($0{\sim}1.0\;mL\;L^{-1}$), six light intensities ($8{\sim}1,400\;{\mu}mol\;m^{-2}s^{-1}$), three temperatures ($10{\sim}30^{\circ}C$), four pHs (7~10), five water depths (10~50 cm), and three different waters dominated by cyanobacteria, diatom, and green algae, respectively. Results indicate that the highest ABA of PMC was seen at $0.05\;mL\;L^{-1}$ in treatment concentrations, where showed a reduction of more than 80% of control phytoplankton biomass, while $1,400\;{\mu}mol\;m^{-2}s^{-1}$ in light intensity (>90%), $20{\sim}30^{\circ}C$ temperature (>60%), 7~9 in pH (>90%), below 50 cm in water depth (>90%), and cyanobacterial dominating waters (>80%), respectively. Over the test, ABA of PMC were more obvious on the algal biomass (chlorophyll-${\alpha}$) than suspended solids, suggesting a selectivity of PMC to particle size or natures. These results suggest that PMC agents can play an important role as natural agents to remove the nuisant algal aggregates or seston of eutrophic lake, where occur cyanobacterial bloom in a shallow shore of lake during warm season.

• Progress in Medical Physics
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• v.26 no.2
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• pp.79-86
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• 2015

The purpose of this study is to evaluate efficacy and feasibility of adaptive radiotherapy according to tumor volume change (TVC) in early stage non-small cell lung cancer (NSCLC) using stereotactic body radiotherapy (SBRT). Twenty-two lesions previously treated with SBRT were selected. SBRT was usually performed with a total dose of 48 Gy or 60 Gy in four fractions with an interval of three to four days between treatments. For evaluation of TVC, gross tumor volume (GTV) was contoured on each cone-beam computed tomography (CBCT) image used for image guidance. Intensity modulated radiotherapy (IMRT) planning was performed in the first CBCT (CBCT1) using a baseline plan. For ART planning (ART), re-optimization was performed at $2^{nd}$, $3^{rd}$, and $4^{th}$ CBCTs (CBCT2, CBCT3, and CBCT4) using the same angle and constraint used for the baseline plan. The ART plan was compared with the non-ART plan, which generated copying of the baseline plan to other CBCTs. Average GTV volume was 10.7 cc. Average TVC was -1.5%, 7.3%, and -25.1% in CBCT2, CBCT3, and CBCT4 and the TVC after CBCT3 was significant (p<0.05). However, the nine lesions were increased GTV in CBCT2. In the ART plan, $V_{20\;Gy}$, $D_{1500\;cc}$, and $D_{1000\;cc}$ of lung were significantly decreased (p<0.05), and $V_{30\;Gy}$ and $V_{32\;Gy}$ of the chest wall were also decreased (p<0.05). While D min of planning target volume (PTV) decreased by 8.3% in the non-ART plan of CBCT2 compared with the baseline plan in lesions with increased tumor size (p=0.021), PTV coverage was not compromised in the ART plan. Based on this result, use of the ART plan may improve target coverage and OAR saving. Thus ART using CBCT should be considered in early stage NSCLC with SBRT.

• Journal of Life Science
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• v.12 no.3
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• pp.242-249
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• 2002

The optimal culture conditions in 500$m\ell$ flask suture, 5$\ell$ jar fermenter and 2,000 $\ell$ large stale culture were investigated to maximize the production of antibiotic on Rhizoctonia solani AC4, the causal agent of vegetables damping-off, by the strain Bacillus ehimensis YJ-37. Starch (1.5%) as a carbon source, peptone (0.6%) as a nitrogen source and MgC1$_2$(0.15%) as a metal ion in the medium containing N $a_2$HP $O_4$(0.3%) showed the highest production of the antibiotic(s) in a rotary shake (200 rpm). Optimal initial pH of the culture medium, culture temperature and culture time for the antibiotic(s) production were pH 8.0, 32$^{\circ}C$ and 54hrs, respertively. Under the optimal renditions in flask culture, cell growth and antifungal activity (clear zone size) were 1.42 $\times$ 10$^{8}$ cfu/$m\ell$ (21g-DCW/ $\ell$) and 13.9 mm, respectively. In 5 $\ell$ jar fermenter (medium 3 $\ell$), optimal air flow, agitation speed and culture time for the antibiotic(s) production were 2 vvm, 200 rpm and 48hrs, respectively. Under the optimal conditions in 5 $\ell$ jar fermenter, tell growth and antifungal activity were 2.06 $\times$ 10$^{8}$ cfu/$m\ell$ (30g-DCW/ $\ell$) and 13.4 mm, respectively. Under the culture conditions of air flow (30 vvm) and agitation speed (200 rpm) at 32$^{\circ}C$ for 10 days in 2,000 $\ell$ large scale culture (medium 1,800 $\ell$, pH 8.0), cell growth and antifungal activity were 0.81$\times$10$^{8}$ cfu/$m\ell$ (12g-DCW/ $\ell$) and 8.6 mm, respectively.

• Clean Technology
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• v.27 no.4
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• pp.332-340
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• 2021

With the recent reinforcement of emission standards, it is necessary to make efforts to reduce NOx from air pollutant-emitting workplaces. The NOx reduction method mainly used in industrial facilities is selective catalytic reduction (SCR), and the most commercial SCR catalyst is the ceramic honeycomb catalyst. This study was carried out to reduce the NOx emitted from steel plants by applying De-NOx catalyst coated on metallic monolith. The De-NOx catalyst was synthesized through the optimized coating technique, and the coated catalyst was uniformly and strongly adhered onto the surface of the metallic monolith according to the air jet erosion and bending test. Due to the good thermal conductivity of metallic monolith, the De-NOx catalyst coated on metallic monolith showed good De-NOx efficiency at low temperatures (200 ~ 250 ℃). In addition, the optimal amount of catalyst coating on the metallic monolith surface was confirmed for the design of an economical catalyst. Based on these results, the De-NOx catalyst of commercial grade size was tested in a semi-pilot De-NOx performance facility under a simulated gas similar to the exhaust gas emitted from a steel plant. Even at a low temperature (200 ℃), it showed excellent performance satisfying the emission standard (less than 60 ppm). Therefore, the De-NOx catalyst coated metallic monolith has good physical and chemical properties and showed a good De-NOx efficiency even with the minimum amount of catalyst. Additionally, it was possible to compact and downsize the SCR reactor through the application of a high-density cell. Therefore, we suggest that the proposed De-NOx catalyst coated metallic monolith may be a good alternative De-NOx catalyst for industrial uses such as steel plants, thermal power plants, incineration plants ships, and construction machinery.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.