• Journal of Practical Agriculture & Fisheries Research
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• v.19 no.1
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• pp.139-151
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• 2017

This study was conducted to confirm the rumen fermentation characteristics of large-scale CoenzymeQ10(CoQ10) producing bacteria R. spharoides in rumen. We conducted in vitro continuous culture test to investigate the characteristics of rumen fermentation with 5% R. spharoides as a direct fed microorganism. A rumen microbial fermentation characteristic has stability at after 12 days for 15 day of experimental period. pH value, NH₃-N, microbial protein synthesis, ADF digestibility and NDF digestibility were not shown significantly differences between control and treatment. However, UDP was significantly higher in treatment than control (p<0.05). CoQ10 concentration was 336.0mg/l with 5% R. spharoides. On the other hands, CoQ10 was not detected without R. spharoides. Our study was shown that R. spharoides can produce CoQ10 in rumen environment without harmful effects on rumen fermentation parameter. CoQ10 in rumen may transfer into cow milk through cow metabolism. This strategy might be helpful for producing functional dairy cow milk.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.704-711
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• 2023

This paper discusses the control method of BLDC(Brushless Direct Current) motor that has similar electrical characteristics with DC motor but has improved its lifespan and reliability. The BLDC motor can improve durability and speed stability by using rotor position information to eliminate commutators that require mechanical contact with DC motors. In this study, a controller for a DC motor was designed based on the fact that the current in the windings of a BLDC motor is a square-wave current like the current flowing in the armature of a DC motor. Next, the designed controller was applied to a 3-phase BLDC motor to confirm the effectiveness of the controller. In detail, a single-phase DC motor with electrical parameter values of a three-phase BLDC motor was modeled and a PI controller for motor speed control was designed by applying the root locus method to the derived system. The speed control simulation of the DC motor was performed to confirm the validity of the controller, and the same controller was applied to the speed control of the 3-phase BLDC motor implemented in MATLAB. From the simulation, similar results of the DC motor were obtained in the 3 phase BLDC motor and confirmed the usefulness of the proposed control scheme.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Journal of Life Science
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• v.25 no.5
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• pp.487-495
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• 2015

We carried out pH stability, chemical inhibition, chemical modification, and pH-dependent kinetic parameter assessments to further characterize protocatechuate 3,4-dioxygenase from Pseudomonas pseudoalcaligenes KF707. Protocatechuate 3,4-dioxygenase was stable in the pH range of 4.5~10.5. L-ascorbate and glutathione were competitive inhibitors with $K_{is}$ values of 0.17 mM and 0.86 mM, respectively. DL-dithiothreitol was a noncompetitive inhibitor with a $K_{is}$ value of 1.57 mM and a $K_{ii}$ value of 8.08 mM. Potassium cyanide, p-hydroxybenzoate, and sodium azide showed a noncompetitive inhibition pattern with $K_{is}$ values of 55.7 mM, 0.22 mM, and 15.64 mM, and $K_{ii}$ values of 94.1 mM, 8.08 mM, and 662.64 mM, respectively. $FeCl_{2}$ was the best competitive inhibitor with a $K_{is}$ value of $29{\mu}M$. $FeCl_{3}$, $MnCl_{2}$, $CoCl_{2}$, and $AlCl_{3}$ were also competitive inhibitors with $K_{is}$ values of 1.21 mM, 0.85 mM, 3.98 mM, and 0.21 mM, respectively. Other metal ions showed noncompetitive inhibition patterns. The pH-dependent kinetic parameter data showed that there may be at least two catalytic groups with pK values of 6.2 and 9.4 and two binding groups with pK values of 5.5 and 9.0. Lysine, cysteine, tyrosine, carboxyl, and histidine were modified by their own specific chemical modifiers, indicating that they are involved in substrate binding and catalysis.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Korean Journal of Food Science and Technology
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• v.37 no.3
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• pp.352-359
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• 2005

Uncertainty involved during determination of vitamin C content in infant formula was quantified by indophenol titration method. Uncertainty sources in measurand, such as purity, weight, final volume of standard, volume of standard solution used for titration, sample weight, final volume of sample, extraction solution used for titration, titration of extraction solution and standard solution by indophenol solution were identified and used as parameters for combined standard uncertainty based on Guide to the expression of uncertainty in measurement (GUM) and Draft EURACHEM/CITAC Guide. Uncertainty parameters of each source in measurand were identified as resolution, reproducibility and stability of chemical balance, standard material purity, repeatability, reproducibility, end point of titration, 1 mL pipet, 5 mL autopipet, and 100 mL mass flask. Each uncertainty component was evaluated by types A and B and included to calculate combined uncertainty. Analytical test result for traceability under laboratorial conditions using Certified Reference Material (CRM) test was certified as $108.4{\pm}1.7mg/100g$, which was within CRM certification range of $114.6{\pm}6.6mg/100g$. Uncertainty test result of vitamin C content of 5 g sampling was $56.7{\pm}2.44mg/100g$. Uncertainty could be reduced by identification of uncertainty sources and components related with vitamin C determination by indophenol titration method and by decreasing uncertainty sources and components.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.496-503
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• 2016

To analyze an infinite slope that is reinforced with stabilizing piles, the forces on the stabilizing pile were estimated by the theory of plastic deformation and the theory of plastic flow and the effects of diverse factors on the factor of safety of an infinite slope were investigated. According to the results of the analyses, the factor of the safety of the slope reinforced with stabilized piles were increased tremendously and the factor of safety decreased as the center to center distance of the stabilizing pile increased. The effect of the existence of seepage of the infinite slope with stabilizing piles on the factor of safety appears to be insignificant. Considering the formulated factor of safety of an infinite slope with stabilizing piles, the width and length of the element of the infinite slope and force on the stabilizing pile influence the factor of safety of the infinite slope with a stabilizing pile including the soil strength parameter, inclination of the slope and depth of the slope, which are important for calculating the factor of safety of a non-reinforced infinite slope. The factor of safety of an infinite slope with stabilizing piles derived from the theory of plastic deformation were increased significantly with the internal friction angle of the soil, and the minimum and the maximum factor of safety under the conditions considered in this study were 13.7 and 65.6, respectively. As the diameter of the stabilizing pile increased, the forces on the stabilizing pile also increased but the factor of safety of the infinite slope with stabilizing piles decreased due to the effects of the width and the length of the element of the infinite slope. The factor of safety of the infinite slope with stabilizing piles derived from plastic flow were much larger than that of the non-reinforced infinite slope and the factor safety of the infinite slope with a stabilizing pile increased with increasing product of the flow velocity and plastic viscosity ( ) and the factor of safety of the infinite slope with stabilizing piles decreased with increasing center to center distance of the pile.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of Animal Science and Technology
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• v.48 no.5
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• pp.699-708
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• 2006

Ground rice, barley and corn were fed separately to the ruminally cannulated Hanwoo (Korean native cattle) for comparing their in situ and in vitro degradabilities, microbial growth, pH and gas production. It has been found that nearly all the dry matter (DM) and organic matter (OM) in barley and rice disappeared during 24 hr suspension in the rumen, but those in corn were only reduced by around 67%. Water soluble DM and OM fractions(‘a’), ranked from highest to lowest was corn, then rice and finally barley, but the order was reversed for content ‘b’, degradable fraction during time ‘t’. Judging by the degradation parameter of ‘b’ fraction, degradation rates per hour of DM and OM for barley were 38.3% and 37.2% respectively, significantly higher than those for rice (7.7% and 5.6%) and corn (4.1% and 1.3%). In general, results obtained from in vitro degradability of DM and OM were lower than those from in situ trials, but the ranking order of degradability was in agreement between both trials. In particular, ground rice has relatively lower in vitro microbial growth than corn or barley, but exhibited higher gas production. In addition, in vitro microbial growth of ground rice increased with up to 12 hr of incubation period, thereafter experienced a decrease with extended incubation time. pH of in vitro solution of rice decreased following 9 hr of incubation but gas production increased rapidly during the same period. From the results of DM and OM degradabilities and pH changes of in vitro solution with incubation time, it is concluded that rice represents a good source of energy for stability of rumen fermentation.