• Tunnel and Underground Space
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• v.29 no.3
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• pp.172-183
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• 2019

The generalized Hoek-Brown (GHB) failure criterion developed by Hoek et al. (2002) is a nonlinear function which defines a stress condition at failure of rock mass. The relevant strength parameter values are systematically determined using the GSI value. Since GSI index is a value quantifying the condition of in-situ rock mass, the GHB criterion is a practical failure condition which can take into the consideration of in-situ rock mass quality. Considering that most rock mechanics engineers are familiar with the linear Mohr-Coulomb criterion and that many rock engineering softwares incorporate Mohr-Coulomb criterion, the equations for the equivalent friction angle and cohesion were also proposed along with the release of the GHB criterion. The proposed equations, however, fix the lower limit of the minor principal stress range, where the linear best-fitting is performed, with the tensile strength of the rock mass. Therefore, if the tensile stress is not expected in the domain of analysis, the calculated equivalent friction angle and cohesion based on the equations in Hoek et al. (2002) could be less accurate. In order to overcome this disadvantage of the existing equations for equivalent friction angle and cohesion, this study proposes the analytical formula which can calculate optimal equivalent friction angle and cohesion in any minor principal stress interval, and verified the accuracy of the derived formula.

• Korean Journal of Optics and Photonics
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• v.33 no.6
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• pp.295-302
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• 2022

In this paper, a wedge prism application method was studied to design a full-high-definition (FHD)-class high-resolution flexible endoscope. In the case of the conventional flexible endoscope optical system, the F number is made large or a liquid lens is applied to obtain the same imaging performance in a wide depth of field. However, there is a problem in that the diameter of the optical system increases because an additional light guide and equipment are required. To solve this problem, two wedge prisms were applied to the flexible endoscope optical system to adjust the image distance for each object distance. First, two wedge prisms were symmetrically placed on the designed endoscopic optical system. An image distance satisfying the target imaging performance according to each objective distance was derived. Next, the wedge prism decenter value for controlling the image distance was derived. By combining these two data, a wedge prism decenter value that satisfied the target imaging performance at each object distance was applied in multi configurations. As a result of the optimal design applied with the wedge prism, a target imaging performance of more than 20% of the modulation transfer function for a resolution of 178 cycles/mm was satisfied in the entire depth of field of 100 mm-7 mm.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.315-323
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• 2023

Marine accidents due to loss of stability of small ships have continued to increase over the past decade. In particular, since sudden winds have been pointed out as main causes of most small ship accidents, safety measures have been established to prevent them. In this regard, to prevent accidents caused by sudden winds, a systematic analysis technique is required. The aim of the present study was to develop a probabilistic approach to estimate extreme value and evaluate effects of wind on motion characteristics of ships. The present study included studies of motion analysis, extraction of extreme values, and motion characteristics. A series analysis was conducted for three conditions: wave only, wave with uniform wind speed, and wave with the NPD wind model. Hysteresis filtering and Peak-Valley filtering techniques were applied to time-domain motion analysis results for extreme value extraction. Using extracted extreme values, the goodness of fit test was performed on four distribution functions to select the optimal distribution-function that best expressed extreme values. Motion characteristics of a fishing boat were evaluated for three periodic motion conditions (Heave, Roll, and Pitch) and results were compared. Numerical analysis was performed using a commercial solver, ANSYS-AQWA.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Culinary science and hospitality research
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• v.18 no.2
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• pp.182-196
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• 2012

This study investigates the optimal application of perilla leaves to fresh noodles in terms of a variety of beneficial effects and functions such as the anti-microbial function of the leaves. First, we measured the water contents of the fresh noodles and found that the ones added with 7% lyophilized perilla leaf extract showed the maximum water contents whereas the control noodles without perilla leaf extract the minimum. Increasing amount of raw perilla leaf extract or lysophilzed perilla leaves in the fresh noodles elevated the L value and significantly reduced the A value. The mass and volume of the fresh noodles were gradually decreased with raw perilla leaves added. Addition of raw perilla leaf extract and lyophilized perilla leaves decreased the turbidity of the fresh noodles. Hardness of the fresh noodles was increased by the addition of lyophilized Perilla leaves. Next, we tested the effect of the addition of sesame leaves on microbial growth. No microbes were found in the fresh noodles in the absence or presence of sesame leaves at day 0. When the noodles were stored for 3 days, the greatest number of bacteria was detected in the noodles without perilla leaves while addition of perilla leaves lowered the amount of bacteria in the noodles. We then performed the sensory test. For the raw perilla leaf extract addition, the noodles with 9% of extract exhibited the highest in appearance, flavor, color (6.47), texture (6.60), and overall acceptability (7.67). Texture was the highest in the ones with 3% (6.87) and 5% (6.20) of extracts added. Overall acceptability (7.07) was the best when 3% perilla leaves were added. Overall, 9% addition of raw perilla leaf extract or 3% addition of lysophilized perilla leaves showed optimal tastes.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.117-130
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• 2005

In both deteministic user Optimal Traffic Assignment Model (UOTAM) and stochastic UOTAM, travel time, which is a major ccriterion for traffic loading over transportation network, is defined by the sum of link travel time and turn delay at intersections. In this assignment method, drivers actual route perception processes and choice behaviors, which can become main explanatory factors, are not sufficiently considered: therefore may result in biased traffic loading. Even though there have been some efforts in Stochastic UOTAM for reflecting drivers' route perception cost by assuming cumulative distribution function of link travel time, it has not been fundamental fruitions, but some trials based on the unreasonable assumptions of Probit model of truncated travel time distribution function and Logit model of independency of inter-link congestion. The critical reason why deterministic UOTAM have not been able to reflect route perception cost is that the route perception cost has each different value according to each origin, destination, and path connection the origin and destination. Therefore in order to find the optimum route between OD pair, route enumeration problem that all routes connecting an OD pair must be compared is encountered, and it is the critical reason causing computational failure because uncountable number of path may be enumerated as the scale of transportation network become bigger. The purpose of this study is to propose a method to enable UOTAM to reflect route perception cost without route enumeration between an O-D pair. For this purpose, this study defines a link as a least definition of path. Thus since each link can be treated as a path, in two links searching process of the link label based optimum path algorithm, the route enumeration between OD pair can be reduced the scale of finding optimum path to all links. The computational burden of this method is no more than link label based optimum path algorithm. Each different perception cost is embedded as a quantitative value generated by comparing the sub-path from the origin to the searching link and the searched link.

• Journal of Energy Engineering
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• v.25 no.1
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• pp.113-121
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• 2016

Polymer electrolyte membrane fuel cell(PEMFC) requires cooling system to maintain the proper operating temperature(about $65^{\circ}C{\sim}75^{\circ}C$) because the efficiency and power are affected by operating temperature. In order to retain the operating temperature of PEMFC, cooling system and coolant control logic are needed. Hardware-in-the-loop simulation(HILS) is one of effective methods to study and evaluate control algorithm. In this paper, the HILS system was designed to study the coolant control algorithm. The models of HILS system consisted of PEMFC, heat exchanger, and external environment associated with temperature. The hardwares in HILS system are 3-way valves, pumps, and a heat exchanger. The priority control and the control target temperature were investigated to improve the control performance using HILS. The 3-way valve in $1^{st}$ cooling circuit was selected as priority control target. The under limit value of $2^{nd}$ 3-way valve set as a function of PEMFC power and $2^{nd}$ circuit coolant temperature to correct temperature control performance. As a result, the temperature of PEMFC is stably controlled.

• Applied Biological Chemistry
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• v.47 no.1
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• pp.96-106
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• 2004

Response surface methodology (RSM) was applied in roasting processes of perilla leaves to develop a high quality perilla leaf tea. The Hunter color parameters and electron donating ability were monitored to optimize organoleptic properties of perilla leaf tea. The roasting processes were based on the central composite design with primary variables-roasting temperature $(140{\sim}220^{\circ}C)$, time $(5{\sim}25)$, and reaction variables-sensory test, electron donating ability. From the variables, the roasting condition was optimized using statistical analysis system (SAS) program as developing the functional tea using perilla leaf. Hunter color L and b values of the powdered samples increased with the roasting processes, but Hunter color a value decreased. Electron donating ability was influenced by roasting temperature (p<0.01) and time (p<0.01), and optimum condition selected was at $220^{\circ}C$ for 15 min with coefficient of determinations $(R^2)$ above 0.98. After preference test of perilla leaf tea using parameter of taste, color, and flavor, we can estimate that the optimal roasting condition of preilla leaf for function tea manufacturing are $210{\sim}220^{\circ}C$ for $10{\sim}20$ min by response surface methodology (RSM). Tyrosinase, xanthine oxidase and electron donating ability were 10.14, 14.37 and 59.19% of perilla leaf tea.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.368-368
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• 2017

Rice is the world's three major crops that is the staple food of Asian. Japonica type rice is consumed in Korea. Rice cultivation area decreases steadily. That is affected by decrease of the farm income with the inventory of 1.7 million tons, which is 2 to 3 times more than the optimal amount due to the decrease in the consumption of rice per person and cultivate high yield varieties. In recent years, as income level has improved, interest in health has been high and consumption for health food has been steadily increasing. For health food, rice is added by adding grains such as millet, sorghum, oats, beans and colored rice rather than white rice. In 1997, RDA(Rural Development Administration) developed black rice 'Heuknambyeo', and then 20 varieties were bred until 2017. In CBARES(Chungbuk Agricultural Research and Extension Services), we have developed new rice varieties 'Cheongpungheukchal' in 2010, 'Cheongpungheukhyangchal' in 2014, 'Cheongpungheukchal' is high in farming preference because of high yield. Black rice has high content of GABA and water-soluble phenol, DPPH radical scavenging activity, and consumers are interested in the function in the body. Therefore, functional and antioxidant activities(anthocyanin, total polyphenol, ${\alpha}-glucosidase$ inhibition) and antioxidant activities(ABTs, DPPH) were analyzed by comparing white and black rice. Testing varieties are 'Chucheongbyeo', best quality cultivars 'Daebo' and 'Samgwang' as white rice which are cultivated much in Chungbuk area, and black rice are 10 varieties including 'Cheongpungheukchal', 'Cheongpungheukhyangchal', 'Josaengheukchal' and so on. It has transplanted on 25th May, at CBARES research paddy by 100% fertilizer recommendation rate. Harvesting time was 50 days after heading by varieties, and has researched growth properties, yield and yield components, functional and antioxidant activities. Anthocyanin content was not measured because there was no pigmented in white rice, the highest value of anthocyanin content was 'Shintoheukmi' and in the range of 125.6~249.6mg/100g by black rice varieties. Total polyphenol content was high 'Cheongpungheukchal' and 'Shintoheukmi' and in the range of 96.68~244.34 mg/100g in black rice, white rice lower than blackish rice at 19.84~22.51mg/100g. ${\alpha}-glucosidase$ inhibition was high 'Cheongpungheukchal', 'Cheongpungheukhyangchal' and 'Shintoheukmi' in the rage of 75.87~98.85% by black rice varieties, especially 'Samgwang' was 80.75% and the other white rice was higher than 58~68%.

• Korean Journal of Veterinary Research
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• v.29 no.4
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• pp.589-599
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• 1989

A total of 13 synchronized dairy cattle(Holstein) were used to determine pregnancy rates in relation to plasma progesterone, estradiol-$17{\beta}$ levels and serum chemical values on the day of last $PGF_{2{\alpha}}$ injection and day of frozen/thawed bovine embryo transfer. The pregnancy rate of recipients with 1.0~4.0ng/ml of progesterone levels at the day of last $PGF_{2{\alpha}}$ injection was higher than that of recipients with below 1.0ng/ml or above 4.0ng/ml of progesterone levels. On the day of transfer, optimal progesterone levels were between 1.0ng/ml and 4.0ng/ml coinciding with a pregnancy rate of 88.9%. Pregnancy rate decreased when progesterone levels were below 1.0ng/ml(33.3%) or above 4.0ng/ml(0%). Corpus luteum grade did not affect pregnancy rate and this result revealed that manual palpation of corpus luteum was not valid criterion of corpus luteum function. Progesterone levels as well as pregnancy rate did not significantly differ whether the corpus luteum was on the right($1.62{\pm}1.33ng/ml$; 63.5%) or left ovary($1.99{\pm}0.61ng/ml$; 85.0%). Estradiol-$17{\beta}$ levels were not significantly different between pregnant and nonpregnant recipients, but estradiol-$17{\beta}$ levels($82.2{\pm}13.5$ VS. $72.3{\pm}10.1pg/ml$) were higher at below 1.0ng/ml of progesterone, and pregnancy rates(33.3 VS. 80%) tended to be lower than above 1.0ng/ml of progesterone. Total cholesterol levels on the day of last $PGF_{2{\alpha}}$ injection and day of transfer did not affect pregnancy rate. Calcium and inorganic phoshorus levels belonged to normal range in most of the recipients. These range did not affect pregnancy rate. In reviewing above results, plasma progesterone levels(1.0~4.0ng/ml) at the time of transfer are diagnostic value for screening recipients prior to transfer of frozen/thawed bovine embryos.