• Clean Technology
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• v.28 no.3
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• pp.218-226
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• 2022

As a result of the recent social transformation towards a hydrogen economy and carbon-neutrality, the demands for hydrogen energy have been increasing rapidly worldwide. As such, eco-friendly hydrogen production technologies that do not produce carbon dioxide (CO₂) emissions are being focused on. Among them, ammonia (NH₃) is an economical hydrogen carrier that can easily produce hydrogen (H₂). In this study, Ru/Al₂O₃ catalyst coated onmetallic monolith for hydrogen production from ammonia was prepared by a dip-coating method using a catalyst slurry mixture composed of Ru/Al₂O₃ catalyst, inorganic binder (alumina sol) and organic binder (methyl cellulose). At the optimized 1:1:0.1 weight ratio of catalyst/inorganic binder/organic binder, the amount of catalyst coated on the metallic monolith after one cycle coating was about 61.6 g L^-1. The uniform thickness (about 42 ㎛) and crystal structure of the catalyst coated on the metallic monolith surface were confirmed through scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis. Also, a numerical optimization regression equation for NH3 conversion according to the independent variables of reaction temperature (400-600 ℃) and gas hourly space velocity (1,000-5,000 h^-1) was calculated by response surface methodology (RSM). This model indicated a determination coefficient (R²) of 0.991 and had statistically significant predictors. This regression model could contribute to the commercial process design of hydrogen production by ammonia decomposition.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.117-137
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• 2009

Recently, intelligent traffic information systems have enabled people to forecast traffic conditions before hitting the road. These convenient systems operate on the basis of data reflecting current road and traffic conditions as well as distance-based data between locations. Thanks to the rapid development of ubiquitous computing, tremendous context data have become readily available making vehicle route planning easier than ever. Previous research in relation to optimization of vehicle route planning merely focused on finding the optimal distance between locations. Contexts reflecting the road and traffic conditions were then not seriously treated as a way to resolve the optimal routing problems based on distance-based route planning, because this kind of information does not have much significant impact on traffic routing until a a complex traffic situation arises. Further, it was also not easy to take into full account the traffic contexts for resolving optimal routing problems because predicting the dynamic traffic situations was regarded a daunting task. However, with rapid increase in traffic complexity the importance of developing contexts reflecting data related to moving costs has emerged. Hence, this research proposes a framework designed to resolve an optimal route planning problem by taking full account of additional moving cost such as road traffic cost and weather cost, among others. Recent technological development particularly in the ubiquitous computing environment has facilitated the collection of such data. This framework is based on the contexts of time, traffic, and environment, which addresses the following issues. First, we clarify and classify the diverse contexts that affect a vehicle's velocity and estimates the optimization of moving cost based on dynamic programming that accounts for the context cost according to the variance of contexts. Second, the velocity reduction rate is applied to find the optimal route (shortest path) using the context data on the current traffic condition. The velocity reduction rate infers to the degree of possible velocity including moving vehicles' considerable road and traffic contexts, indicating the statistical or experimental data. Knowledge generated in this papercan be referenced by several organizations which deal with road and traffic data. Third, in experimentation, we evaluate the effectiveness of the proposed context-based optimal route (shortest path) between locations by comparing it to the previously used distance-based shortest path. A vehicles' optimal route might change due to its diverse velocity caused by unexpected but potential dynamic situations depending on the road condition. This study includes such context variables as 'road congestion', 'work', 'accident', and 'weather' which can alter the traffic condition. The contexts can affect moving vehicle's velocity on the road. Since these context variables except for 'weather' are related to road conditions, relevant data were provided by the Korea Expressway Corporation. The 'weather'-related data were attained from the Korea Meteorological Administration. The aware contexts are classified contexts causing reduction of vehicles' velocity which determines the velocity reduction rate. To find the optimal route (shortest path), we introduced the velocity reduction rate in the context for calculating a vehicle's velocity reflecting composite contexts when one event synchronizes with another. We then proposed a context-based optimal route (shortest path) algorithm based on the dynamic programming. The algorithm is composed of three steps. In the first initialization step, departure and destination locations are given, and the path step is initialized as 0. In the second step, moving costs including composite contexts into account between locations on path are estimated using the velocity reduction rate by context as increasing path steps. In the third step, the optimal route (shortest path) is retrieved through back-tracking. In the provided research model, we designed a framework to account for context awareness, moving cost estimation (taking both composite and single contexts into account), and optimal route (shortest path) algorithm (based on dynamic programming). Through illustrative experimentation using the Wilcoxon signed rank test, we proved that context-based route planning is much more effective than distance-based route planning., In addition, we found that the optimal solution (shortest paths) through the distance-based route planning might not be optimized in real situation because road condition is very dynamic and unpredictable while affecting most vehicles' moving costs. For further study, while more information is needed for a more accurate estimation of moving vehicles' costs, this study still stands viable in the applications to reduce moving costs by effective route planning. For instance, it could be applied to deliverers' decision making to enhance their decision satisfaction when they meet unpredictable dynamic situations in moving vehicles on the road. Overall, we conclude that taking into account the contexts as a part of costs is a meaningful and sensible approach to in resolving the optimal route problem.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.37-50
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• 2020

Information security has become an important issue in the world. Various information and communication technologies, such as the Internet of Things, big data, cloud, and artificial intelligence, are developing, and the need for information security is increasing. Although the necessity of information security is expanding according to the development of information and communication technology, interest in information security investment is insufficient. In general, measuring the effect of information security investment is difficult, so appropriate investment is not being practice, and organizations are decreasing their information security investment. In addition, since the types and specification of information security measures are diverse, it is difficult to compare and evaluate the information security countermeasures objectively, and there is a lack of decision-making methods about information security investment. To develop the organization, policies and decisions related to information security are essential, and measuring the effect of information security investment is necessary. Therefore, this study proposes a method of constructing an investment portfolio for information security measures using game theory and derives an optimal defence probability. Using the two-person game model, the information security manager and the attacker are assumed to be the game players, and the information security countermeasures and information security threats are assumed as the strategy of the players, respectively. A zero-sum game that the sum of the players' payoffs is zero is assumed, and we derive a solution of a mixed strategy game in which a strategy is selected according to probability distribution among strategies. In the real world, there are various types of information security threats exist, so multiple information security measures should be considered to maintain the appropriate information security level of information systems. We assume that the defence ratio of the information security countermeasures is known, and we derive the optimal solution of the mixed strategy game using linear programming. The contributions of this study are as follows. First, we conduct analysis using real performance data of information security measures. Information security managers of organizations can use the methodology suggested in this study to make practical decisions when establishing investment portfolio for information security countermeasures. Second, the investment weight of information security countermeasures is derived. Since we derive the weight of each information security measure, not just whether or not information security measures have been invested, it is easy to construct an information security investment portfolio in a situation where investment decisions need to be made in consideration of a number of information security countermeasures. Finally, it is possible to find the optimal defence probability after constructing an investment portfolio of information security countermeasures. The information security managers of organizations can measure the specific investment effect by drawing out information security countermeasures that fit the organization's information security investment budget. Also, numerical examples are presented and computational results are analyzed. Based on the performance of various information security countermeasures: Firewall, IPS, and Antivirus, data related to information security measures are collected to construct a portfolio of information security countermeasures. The defence ratio of the information security countermeasures is created using a uniform distribution, and a coverage of performance is derived based on the report of each information security countermeasure. According to numerical examples that considered Firewall, IPS, and Antivirus as information security countermeasures, the investment weights of Firewall, IPS, and Antivirus are optimized to 60.74%, 39.26%, and 0%, respectively. The result shows that the defence probability of the organization is maximized to 83.87%. When the methodology and examples of this study are used in practice, information security managers can consider various types of information security measures, and the appropriate investment level of each measure can be reflected in the organization's budget.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.434-449
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• 2019

In this study, we develop a new cross-shore sediment module which takes the effect of infra-gravity waves of bound mode, and boundary layer streaming on the sediment transport into account besides the well-known asymmetry and under-tow. In doing so, the effect of individual random waves occurring during the unit observation period of 1 hr on sediment transport is also fully taken into account. To demonstrate how the individual random waves would affect the sediment transport, we numerically simulate the non-linear shoaling process of random wavers over the beach of uniform slope. Numerical results show that with the consistent frequency Boussinesq Eq. the application of which is lately extended to surf zone, we could simulate the saw-tooth profile observed without exception over the surf zone, infra-gravity waves of bound mode, and boundary-layer streaming accurately enough. It is also shown that when yearly highest random waves are modeled by the equivalent nonlinear uniform waves, the maximum cross-shore transport rate well exceeds the one where the randomness is fully taken into account as much as three times. Besides, in order to optimize the free parameter K involved in the long-shore sediment module, we carry out the numerical simulation to trace the yearly shoreline change of Mang-Bang beach from 2017.4.26 to 2018.4.20 as well, and proceeds to optimize the K by comparing the traced shoreline change with the measured one. Numerical results show that the optimized K for Mang-Bang beach would be 0.17. With K = 0.17, via yearly grand circulation process comprising severe erosion by consecutively occurring yearly highest waves at the end of October, and gradual recovery over the winter and spring by swell, the advance of shore-line at the northern and southern ends of Mang-Bang beach by 18 m, and the retreat of shore-line by 2.4 m at the middle of Mang-Bang beach can be successfully duplicated in the numerical simulation.

• Explosives and Blasting
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• v.30 no.2
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• pp.29-42
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• 2012

Variables influencing the free face movement due to rock blasting include the physical and mechanical properties, in particular the discontinuity characteristics, explosive type, charge weight, burden, blast-hole spacing, delay time between blast-holes or rows, stemming conditions. These variables also affects the blast vibration, air blast and size of fragmentation. For the design of surface blasting, the priority is given to the safety of nearby buildings. Therefore, blast vibration has to be controlled by analyzing the free face movement at the surface blasting sites and also blasting operation needs to be optimized to improve the fragmentation size. High-speed digital image analysis enables the analyses of the initial movement of free face of rock, stemming optimality, fragment trajectory, face movement direction and velocity as well as the optimal detonator initiation system. Even though The high-speed image analysis technique has been widely used in foreign countries, its applications can hardly be found in Korea. This thesis aims at carrying out a fundamental study for optimizing the blast design and evaluation using the high-speed digital image analysis. A series of experimentation were performed at two large surface blasting sites with the rock type of shale and granite, respectively. Emulsion and ANFO were the explosives used for the study. Based on the digital images analysis, displacement and velocity of the free face were scrutinized along with the analysis fragment size distribution. In addition, AUTODYN, 2-D FEM model, was applied to simulate detonation pressure, detonation velocity, response time for the initiation of the free face movement and face movement shape. The result show that regardless of the rock type, due to the displacement and the movement velocity have the maximum near the center of charged section the free face becomes curved like a bow. Compared with ANFO, the cases with Emulsion result in larger detonation pressure and velocity and faster reaction for the displacement initiation.

• Journal of Life Science
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• v.28 no.5
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• pp.577-586
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• 2018

We recently reported the potential probiotic properties of Lactobacillus brevis SBB07 isolated from blueberries. The present study investigates the effect of culture conditions such as temperature, initial pH, culture time, and medium constituent for industrial application. The ingredients of the medium to improve cell growth were selected by Plackett-Burman design (PBD) and central composite design (CCD) within a desirable range. The PBD was applied with 19 factors: seven carbon sources, six nitrogen sources, and six microelements. Protease peptone, corn steep powder (CSP), and yeast extract were found to be significant factors for the growth of SBB07. The CCD was then applied with three variables found from the PBD at five levels, and the optimum values were decided for the three variables: protease peptone, CSP, and yeast extract. In the case of the growth of SBB07, the proposed optimal media contained 2.0% protease peptone, 2.5% CSP, and 2.0% yeast extract, and the maximum dried-cell weight was predicted to be 2.93963 g/l. By the model verification, it was confirmed that the predicted and actual results are similar. Finally, the study investigated the effects of incubation temperature and initial pH at the optimized medium. It was confirmed that the dried-cell weight increased from $2.2933{\pm}0.0601g/l$ to $3.85{\pm}0.0265g/l$ when compared to the basal medium at $37^{\circ}C$ and initial pH 8.0. Establishing the optimal culture condition for SBB07 provides good potential for applications in probiotics and can serve as the foundation for the industrialization of materials.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• Toxicological Research
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• v.33 no.2
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• pp.107-118
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• 2017

Although alternative test methods based on the 3Rs (Replacement, Reduction, Refinement) are being developed to replace animal testing in reproductive and developmental toxicology, they are still in an early stage. Consequently, we aimed to develop alternative test methods in male animals using mouse spermatogonial stem cells (mSSCs). Here, we modified the OECD TG 489 and optimized the in vitro comet assay in our previous study. This study aimed to verify the validity of in vitro tests involving mSSCs by comparing their results with those of in vivo tests using C57BL/6 mice by gavage. We selected hydroxyurea (HU), which is known to chemically induce male reproductive toxicity. The 50% inhibitory concentration ($IC_{50}$) value of HU was 0.9 mM, as determined by the MTT assay. In the in vitro comet assay, % tail DNA and Olive tail moment (OTM) after HU administration increased significantly, compared to the control. Annexin V, PI staining and TUNEL assays showed that HU caused apoptosis in mSSCs. In order to compare in vitro tests with in vivo tests, the same substances were administered to male C57BL/6 mice. Reproductive toxicity was observed at 25, 50, 100, and 200 mg/kg/day as measured by clinical measures of reduction in sperm motility and testicular weight. The comet assay, DCFH-DA assay, H&E staining, and TUNEL assay were also performed. The results of the test with C57BL/6 mice were similar to those with mSSCs for HU treatment. Finally, linear regression analysis showed a strong positive correlation between results of in vitro tests and those of in vivo. In conclusion, the present study is the first to demonstrate the effect of HU-induced DNA damage, ROS formation, and apoptosis in mSSCs. Further, the results of the current study suggest that mSSCs could be a useful model to predict male reproductive toxicity.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.731-745
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• 2021

In this study, the monthly temperature of the Han River basin was predicted by statistical multiple regression models that use global climate indices and weather data of the target region as predictors. The optimal predictors were selected through teleconnection analysis between the monthly temperature and the preceding patterns of each climate index, and forecast models capable of predicting up to 12 months in advance were constructed by combining the selected predictors and cross-validating the past period. Fore each target month, 1000 optimized models were derived and forecast ranges were presented. As a result of analyzing the predictability of monthly temperature from January 1992 to December 2020, PBIAS was -1.4 to -0.7%, RSR was 0.15 to 0.16, NSE was 0.98, and r was 0.99, indicating a high goodness-of-fit. The probability of each monthly observation being included in the forecast range was about 64.4% on average, and by month, the predictability was relatively high in September, December, February, and January, and low in April, August, and March. The predicted range and median were in good agreement with the observations, except for some periods when temperature was dramatically lower or higher than in normal years. The quantitative temperature forecast information derived from this study will be useful not only for forecasting changes in temperature in the future period (1 to 12 months in advance), but also in predicting changes in the hydro-ecological environment, including evapotranspiration highly correlated with temperature.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.11
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• pp.1401-1407
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• 2008

1-Deoxynojirimycin (DNJ) is a strong $\alpha$-glucosidase inhibitor which inhibits hyperglycemia in animals. To select the Bacillus strains highly producing DNJ, 4,000 strains were isolated from soil and grain samples. By the inhibitory activity against $\alpha$-glucosidase, nine Bacillus strains were selected and then identified by 16S rDNA sequencing. B. subtilis S10 was finally selected as the best strain for the production of DNJ. Various carbon sources and nitrogen sources in culture medium were evaluated for the highest production of DNJ. As the results, the optimized concentration of carbon source and nitrogen source was 1.0% galactose and 1.6% polypeptone and the concentration of DNJ produced was 0.75 g/L. The effect of culture supernatant of B. subtilis S10 on lowering blood glucose level was investigated in streptozotocin (STZ)-induced diabetic mice model. Mice were randomly assigned to control group (saline) and three test groups such as acarbose group, silkworm powder group and B. subtilis S10 group. After eight-week oral feeding, blood glucose levels of the B. subtilis S10 and silkworm powder groups were respectively $209.1{\pm}19.6\;mg/dL$ (59.1%) and $208.6{\pm}39.8\;mg/dL$ (59.0%) lower than $510{\pm}10\;mg/dL$ of the control group. These results indicated that the culture supernatant of B. subtilis S10 was able to reduce the blood glucose level in STZ-induced diabetic mice.