• 한국연소학회:학술대회논문집
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• 2012.04a
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• pp.201-202
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• 2012

The isothermal flow structure and mixing characteristics of a hybrid/dual swirl jet combustor for micro-gas turbine were numerically investigated. Location of pilot nozzle, angle and direction of swirl vane were varied as main parameters with constant fuel flow rates for each nozzle. As a result, the variation in location of pilot nozzle resulted in significant change in turbulent flow field near burner exit, in particular, center toroidal recirculation zone (CTRZ) as well as turbulent intensity, and thus flame stability and emission characteristics might be significantly changed. The swirl angle of $45^{\circ}$ provided similar recirculating flow patterns in a wide range of equivalence ratio (0.5~1.0). Compared to the co-swirl flow, the counter-swirl flow leaded to the reduction in CTRZ and fuel-air mixing near the burner exit and a weak interaction between the pilot partially premixed flame and the lean premixed flame. With the comparison of experimental results, it was confirmed that the case of co-swirl flow and swirl $angle=45^{\circ}$ would provided an optimized combustor performance in terms of flame stability and pollutant emissions.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.12
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• pp.1042-1047
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• 2007

A micro cyclone combustor was developed to be used as a heat source of thermoelectric power generator (TPG). The cyclone combustor was designed so that fuel and air were supplied to the combustion chamber separately. The mixing and flow characteristics in the combustor were investigated numerically. The global equivalence ratio ($\Phi$), defined using the fuel and air flow rates, was introduced to examine the flow features of the combustor. The mixing of fuel and air inside the combustor could be well understood using the fuel concentration distribution. It was found that the weak recirculating zone was formed upper the fuel-supplying tube in case of ${\Phi}$<1.0. In addition, it was found that small regions that have a negative axial velocity exist near the fuel injection ports. It is assumed that these negative axial velocity regions can stabilize a flame inside the micro cyclone combustor.

• Clean Technology
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• v.26 no.3
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• pp.211-220
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• 2020

This experimental study investigates the design parameters to achieve ultra low NOx combustion of coal using a 80 kW capacity single-burner furnace. The influence of key design parameters such as SN, overall and burner-zone equivalence ratios, primary/secondary air ratio, overfire air (OFA) ratio were tested for a total of 81 cases. The results showed that weak swirl intensity of the burner leads to higher NOx emission whereas strong swirl intensity accompanies increased CO concentration desipte lower NOx emission. Therefore, finding an appropirate swirl intensity is essential for the burner design. Larger flow rate of secondary air increased NOx emission, whereas smaller flow rate stretches the flame and increased CO emission. The lowest NOx emission of 82 ppm (6% O₂) was achieved at the optimal condition of the present burner deisgn. It is expected to furrther lower the NOx emission by introducing splitting the burner secondary air into three or four streams.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.157-164
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• 2015

An efficient retrieval of useful information is a prerequisite of an optimal decision making system. Hence, A research of data mining techniques finding useful patterns from the various forms of data has been progressed with the increase of the application of Big Data for convergence and integration with other industries. Each technique is more likely to have its drawback so that the generalization of retrieving useful information is weak. Another integrated technique is essential for retrieving useful information. In this paper, a uncertainty measure of information is calculated such that algebraic probability is measured by Bayesian theory and then information entropy of the probability is measured. The proposed measure generates the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Hence, the optimal decision rules are induced. Through simulation deciding contact lenses, the proposed approach is compared with the equivalence and value-reduct theories. As the result, the proposed is more general than the previous theories in useful decision-making.

• Journal of the Korean Mathematical Society
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• v.50 no.3
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• pp.557-578
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• 2013

For an ideal $\mathcal{I}$ of a preadditive category $\mathcal{A}$, we study when the canonical functor $\mathcal{C}:\mathcal{A}{\rightarrow}\mathcal{A}/\mathcal{I}$ is local. We prove that there exists a largest full subcategory $\mathcal{C}$ of $\mathcal{A}$, for which the canonical functor $\mathcal{C}:\mathcal{C}{\rightarrow}\mathcal{C}/\mathcal{I}$ is local. Under this condition, the functor $\mathcal{C}$, turns out to be a weak equivalence between $\mathcal{C}$, and $\mathcal{C}/\mathcal{I}$. If $\mathcal{A}$ is additive (with splitting idempotents), then $\mathcal{C}$ is additive (with splitting idempotents). The category $\mathcal{C}$ is ample in several cases, such as the case when $\mathcal{A}$=Mod-R and $\mathcal{I}$ is the ideal ${\Delta}$ of all morphisms with essential kernel. In this case, the category $\mathcal{C}$ contains, for instance, the full subcategory $\mathcal{F}$ of Mod-R whose objects are all the continuous modules. The advantage in passing from the category $\mathcal{F}$ to the category $\mathcal{F}/\mathcal{I}$ lies in the fact that, although the two categories $\mathcal{F}$ and $\mathcal{F}/\mathcal{I}$ are weakly equivalent, every endomorphism has a kernel and a cokernel in $\mathcal{F}/{\Delta}$, which is not true in $\mathcal{F}$. In the final section, we extend our theory from the case of one ideal$\mathcal{I}$ to the case of $n$ ideals $\mathcal{I}_$, ${\ldots}$, $\mathca{l}_n$.

• Food Science and Biotechnology
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• v.18 no.1
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• pp.113-117
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• 2009

A method was developed using enhanced liquid chromatography coupled with electrospray ionization mass spectrometry for the analysis and quantitation of 2 main potato glycoalkaloids, $\alpha$-chaconine, and $\alpha$-solanine, without any pre-concentration or derivatisation steps. Calibration curves generated by this technique exhibited a linear dynamic range from 0.025 to $50{\mu}g/mL$ and from 0.05 to $50{\mu}g/mL$ for $\alpha$-chaconine and $\alpha$-solanine, respectively. Matrix effects were evaluated by comparing calibration curves measured in matrix-matched and solvent-based systems. Ion suppression due to matrix effects was weak and extraction recoveries of 88 to 114% were obtained in different sample matrices spiked with analyte concentrations ranging from 15 to $35{\mu}g/mL$. Potatoes that had been genetically modified to tolerate glufosinate contained the same glycoalkaloid levels as their non-transgenic counterpart. We suggest complementing compositional comparison assessment strategy by validating quantitative analytical methods for the toxic glycoalkaloids in potato plants.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.