• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.1
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• pp.171-178
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• 2019

Fiber-reinforced polymer (FRP) composites have proved to be reliable as strengthening materials. Most of existing studies used single types of FRP composites. Therefore, in this experimental study, carbon FRP sheet, aramid FRP sheet, and hybrid FRP plate including glass fibers were fabricated, and the effect of pre-stressed FRP composites on flexural strengthening of reinforced concrete (RC) beams was investigated. In total, eight RC beam specimens were fabricated, including one control beam (specimen N) without FRP composites and seven FRP-strengthened beams. The main parameters were type of FRP composite, the number of anchors used for pre-stressing, and thickness of FRP plates. As a result, the beam strengthened with pre-stressed FRP plate showed superior performance to the non-strengthened one in terms of initial strength, strength and stiffness at yielding, and ultimate strength. As the number of anchors and thickness of FRP plate (i.e., amount of FRP plates) increased, the strengthening effect increased as well. When hybrid FRP plates were pre-stressed, the strengthening effect was higher in comparison with pre-stressed single type FRP plate.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.9
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• pp.1373-1380
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• 2019

Objective: This study ascertained effects of cactus pear in association with different roughage in the diet of F1 Holstein/Zebu cows on intake, nutrient digestibility, nitrogen balance, ingestive behavior and performance. Methods: Eight cows with $72{\pm}11$ days of lactation were used. The experimental design was simultaneous in two $4{\times}4$ Latin squares. Four experimental diets were used: Diet 1, sorghum silage as exclusive roughage; Diet 2, sorghum silage associated with cactus pear in a proportion of 50% of the roughage (dry matter basis); Diet 3, elephant grass (Pennisetum purpureum cv. Roxo) as exclusive roughage; Diet 4, elephant grass associated with cactus pear in a proportion of 50% of the roughage. The roughage:concentrate ratio was 75:25. Results: Dry matter intake (p = 0.01) was higher with sorghum silage. There were differences in dry matter intake (p = 0.01), crude protein (p<0.01), ether extract (p = 0.01), non-fibrous carbohydrates (p<0.01) and total digestible nutrients (p = 0.01) among the diets. Cactus pear in the diet reduced water intake by 44.52% (p<0.01). The nitrogen balance was 59.71% and 27.49% lower in animals treated with exclusive sorghum silage and sorghum silage associated with cactus pear in relation to diets with elephant grass and elephant grass associated with cactus pear, respectively (p<0.01). The diets did not influence the milk production (p = 0.70), 3.5% fat corrected milk production (p = 0.72) or feed efficiency (p = 0.61). Conclusion: The association of cactus pear with sorghum or elephant grass silage does not alter milk production, reduces the intake of dry matter and water and improves the digestibility of nutrients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.639-647
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• 2019

Recently, as the semiconductor integration technology due to miniaturization and high density of electronic equipment have developed, it is importantly recognized the application of thermal control system in order to release inner heat generated from chips, modules, In this study, we considered the heat transfer and pressure drop characteristics in a horizontal channel with four blocks using k-${\omega}$ SST turbulence model During CFD (Computational Fluid Dynamics) analysis, the parameters applied block width, block height, heat source and turbulence generator placement etc. As the boundary conditions of analysis, the channel inlet temperature and flow velocity were respectively 300 K and 3.84 m/s, the heat flux was $358W/m^2$. As a result, the heat transfer performance was decreased as the block width ratio (w/h) was increased, while it was increased as the block height ratio (h/w) was increased. In addition, as the arrangement of heat source size was increased to high heat flux from low heat flux, it was influenced by heat source size and the heat transfer coefficient showed a tendency to increase, When the turbulence generator was installed in the upper part of block No. 1 position the closely to the channel entrance, the heat transfer characteristics was greatly influenced on the whole of four heating blocks. and in oder to consider the pressure drop characteristics, we are able to select the most appropriate turbulence generator's position.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.514-520
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• 2019

The reduction in performance due to inbreeding (i.e. inbreeding depression) has long been documented in plant and animal populations. The population of Korean native pigs are small and this breed is valuable in Korea.. This study was aimed to investigate effects of inbreeding depression on total number of piglets born (TNB) and number of piglets born alive (NBA) in Korean native pigs. We used 2,806 pedigree and 303 sows's data with 483 phenotypic records. After estimating genetic parameters for each traits, inbreeding depression was estimated using a mixed model in which the inbreeding coefficient was included as a covariate. Korean native pigs had high heritability for each traits. Inbreeding coefficient constantly increased from 1998 to 2017~2018 but there is no reduction for each traits in 2017~2018 in compared with those in 1998. Significant inbreeding depression was detected for TNB (p=0.03) but not for NBA (p=0.41). In addition there are significantly positive interactions between inbreeding coefficient and breeding value for both traits (p<0.05). These results suggest that Korean native pigs are still having genetic variation for TNB and NBA, which could overcome reproductive risks from inbreeding coefficient increase.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.135-141
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• 2011

Recent advances in mobile devices and development of various mobile applications dealing with private information of users made user authentication in mobile devices a very important issue. This paper presents a new user authentication method based on touch screen interfaces. This method uses for authentication the PIN digits as well as the exact locations the user touches to input these digits. Our method is fully compatible with the regular PIN entry method which uses numeric keypads, and it provides better usability than the behavioral biometric schemes because its PIN registration process is much simpler. According to our experiments, our method guarantees EERs of 12.8%, 8.3%, and 9.3% for 4-digit PINs, 6-digit PINs, and 11-digit cell phone numbers, respectively, under the extremely conservative assumption that all users have the same PIN digits and cell phone numbers. Thus we can guarantee much higher performance in identification functionality by applying this result to a more practical situation where every user uses distinct PIN and sell phone number. Finally, our method is far more secure than the regular PIN entry method, which is verified by our experiments where attackers are required to recover a PIN after observing the PIN entry processes of the regular PIN and our method under the same level of security parameters.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.34-41
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• 2021

The purpose of this research is to improve analysis methods of determining the contents of fatty acids in infant formulas and follow-up formulas. A gas chromatography (GC) method was performed on a GC system coupled to flame ionization detector, with a fused silica capillary column (SP2560, 100 m×0.25 mm, 0.20 ㎛). The method was validated using standard reference material (SRM, NIST 1849a). Performance parameters for method validation such as specificity, linearity, limits of detection (LOD) and quantification (LOQ), accuracy and precision were examined. The linearity of standard solution with correlation coefficient was higher than 0.999 in the range of 0.1-5 mg/mL. The LOD and LOQ were 0.01-0.06 mg/mL and 0.03-0.2 mg/mL, respectively. The recovery using standard reference material was confirmed and the precision was found to be between 0.8% and 2.9% relative standard deviation (RSD). Optimized methods were applied in sample analysis to verify the reliability. All the tested products had acceptable contents of fatty acids compared with component specification for nutrition labeling. The result of this research will provide efficient experimental information and strengthen the management of nutrients in infant formula and follow-up formula.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.105-119
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• 2021

An increase in the frequency and intensity of both floods and droughts has been recently observed due to an increase in climate variability. Especially, land-use change associated with industrial structure and urbanization has led to an imbalance between water supply and demand, acting as a constraint in water resource management. Accurate rainfall-runoff analysis plays a critical role in evaluating water availability in the water budget analysis. This study aimed to explore various continuous rainfall-runoff models over the Soyanggang dam watershed. Moreover, the ensemble modeling framework combining multiple models was introduced to present scenarios on streamflow considering uncertainties. In the ensemble modeling framework, rainfall-runoff models with fewer parameters are generally preferred for effective regionalization. In this study, more than 40 continuous rainfall-runoff models were applied to the Soyanggang dam watershed, and nine rainfall-runoff models were primarily selected using different goodness-of-fit measures. This study confirmed that the ensemble model showed better performance than the individual model over different flow regimes.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.43-50
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• 2021

A Leg Mating Unit (LMU) is a device utilized during the float-over installation of offshore structures that include hyperelastic pads and mating part. The hyperelastic pads absorb the loads, whereas the mating part works as guidance between topside and supporting structures during the mating sequence of float-over installation. In this study, the design optimization of an LMU for the float-over installation of floating-type offshore structures is conducted to enhance the performance and to satisfy the requirements defined by classification society regulations. The initial dimensions of the LMU are referred to the dimensions of those used in fixed-type float-over installation because only the location and the number of LMUs are known. The two-parameter Mooney-Rivlin model is adopted to describe the hyperelastic pads under given material parameters. Geometric variables, such as the thickness, height, and width of members, as well as configuration variables, such as the angle and number of members, are defined as design variables and are parameterized. A sampling-based design sensitivity analysis based on latin hypercube sampling method is performed to filter the important design variables. The design optimization problem is formulated to minimize the total mass of the LMU under maximum von Mises stress and reaction force constraints.