• Journal of Digital Convergence
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• v.11 no.6
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• pp.199-204
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• 2013

Speech recognition system is shall be composed model of learning from the inaccurate input speech. Similar phoneme models to recognize, because it leads to the recognition rate decreases. Therefore, in this paper, we propose a method of speech recognition optimal learning model configuration using the Bhattacharyya algorithm. Based on feature of the phonemes, HMM feature extraction method was used for the phonemes in the training data. Similar learning model was recognized as a model of exact learning using the Bhattacharyya algorithm. Optimal learning model configuration using the Bhattacharyya algorithm. Recognition performance was evaluated. In this paper, the result of applying the proposed system showed a recognition rate of 98.7% in the speech recognition.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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• pp.335-342
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• 2015

The increasing interests on patents have led many individuals and companies to apply for many patents in various areas. Applied patents are stored in the forms of electronic documents. The search and categorization for these documents are issues of major fields in data mining. Especially, the keyword extraction by which we retrieve the representative keywords is important. Most of techniques for it is based on vector space model. But this model is simply based on frequency of terms in documents, gives them weights based on their frequency and selects the keywords according to the order of weights. However, this model has the limit that it cannot reflect the relations between keywords. This paper proposes the advanced way to extract the more representative keywords by overcoming this limit. In this way, the proposed model firstly prepares the candidate set using the vector model, then makes the graph which represents the relation in the pair of candidate keywords in the set and selects the keywords based on this relationship graph.

• Applied Chemistry for Engineering
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• v.30 no.3
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• pp.337-344
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• 2019

In this study, an antioxidant was extracted from Lonicera japonica and Sophora japonica L, which was optimized by using the central composite design (CDD) model of response surface methodology (RSM). The response value of CDC model establishes the extraction yield and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity. The extraction time, volume ratio of ethanol/ultrapure water, and extraction temperature were selected as quantitative factors. According to the result of CDC, optimal extraction conditions of Lonicera japonica were as follows; the extraction time of 2.08 h, volume ratio of ethanol/ultrapure water of 41.53 vol.%, and extraction temperature of $55.08^{\circ}C$. At these conditions the expected results indicated that the yield and DPPH radical scavenging activity were estimated as 37.88 wt% and 40.37%, respectively. On the other hand, optimal extraction conditions of Sophora japonica L. could be found as the extraction time of 2.13 h, volume ratio of ethanol/ultrapure water of 62.89 vol.%, and temperature of $50.42^{\circ}C$. Under the conditions, the (possible) maximum values of yield and DPPH radical scavenging activity were found as 35.43 wt% and 55.7%, respectively.

• Journal of Environmental Science International
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• v.9 no.2
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• pp.159-164
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• 2000

For the purpose of development of a liquid membrane permeator which separates metal ions from aqueous solutions continuously and effectively, a continuous membrane permeator with the membrane solution trapped between extraction and stripping phases by two micro-porous hydrophilic films was manufactured. Experimental researches on the separation of zinc ion from aqueous solutions were performed in the liquid membrane permeator with 30 vol % D2EHPA solution in kerosine as liquid membrane. As results, the liquid membrane permeator separates zinc ion from aqueous solutions continuously and effectively in the wide range of operating conditions. A simple mass transfer rate model using equilibrium constant of the extraction reaction for the system used were proposed, and the model was compared with experimental results of separation of zinc ion in the permeator. And the effects of operating factors, such as space time, pH of extraction solution, extraction temperature, on the separation rate of zinc ion in the permeator were experimentally examined.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.763-766
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• 2003

A direct parameter extraction method using several two-port parameter equations derived in cutoff and active bias modes has been studied to obtain an accurate Gummel-Poon BJT model. First, dc model parameters were extracted from slopes and y-axis intercepts of I-V curve and Gummel plot. The pad capacitances and junction capacitance parameters were determined by using measured S-parameter sets in the cutoff bias. The resistance and transit time parameters were extracted by using measured S-parameter sets in the active bias.

• Applied Chemistry for Engineering
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• v.29 no.5
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• pp.565-570
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• 2018

In this study, the antioxidant was extracted from Broussonetia kazinokii using ultrasound-assisted extraction (UAE) and optimized by using a response surface methodology. The response value of the central composite design model establishes the extraction yield and the DPPH radical scavenging activity. The extraction time and temperature and volume ratio of ethanol/ultrapure water were selected as quantitative factors. When considering both the main and interaction effects, the factor having the greatest influence on the extraction yield and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity was the volume ratio of ethanol/ultrapure water. The results of optimal extraction conditions were the extraction time (19.92 min), volume ratio of ethanol/ultrapure water (54.23%), and ultrasonic irradiation power (557.65 W). We could also obtained expected results of the yield = 38.93 wt% and DPPH radical scavenging activity = 55.33% under these conditions.

• Journal of Navigation and Port Research
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• v.36 no.9
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• pp.741-751
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• 2012

This article analyzes the interdependency between nonrenewable marine sand resources and renewable fishery resources by the developed dynamic bioeconomic model. The developed bioeconomic model is applied to a case study of efficient sustainable management for marine sand mining, which adversely affects a valuable blue crab fishery and its habitat in Korea. The socially-efficient extraction plan for marine sand and the time-variant environmental external costs to society in terms of diminished harvest rate of blue crab are determined. To take into account long-term effects from destroyed fishery habitat, a Beverton-Holt age structure model is integrated into the bioeconomic model. The illustrative results reveal that the efficient sand extraction plan is dynamically constrained by the stock size of the blue crab fishery over time. Thus, the dynamic environmental external cost is more realistic resource policy option than the classical fixed external cost for determining socially optimal extraction plans. Additionally, the economic value of bottom habitat, which supports the on- and off-site commercial blue crab fishery is estimated. The empirical results are interpreted with emphasis on guidelines for management policy for marine sand mining.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.818-833
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• 2021

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model's F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3229-3235
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• 2021

The key process used in nuclear industries for the management of radiotoxicity associated with spent fuel in a closed fuel cycle is solvent extraction. An understanding of hydrodynamics and mass transfer is of primary importance for the design of mass transfer equipment used in solvent extraction processes. Understanding the interfacial phenomenon and the associated hydrodynamics of the liquid drops is essential for model-based design of mass transfer devices. In this work, the phenomenon of drop formation at the tip of a nozzle submerged in quiescent immiscible liquid phase is revisited. Previously reported force balance based models and empirical correlations are analyzed. Experiments are carried out to capture the process of drop formation using high-speed imaging technique. The images are digitally processed to measure the average drop diameter. A correlation based on the force balance model is proposed to estimate drop diameter and jet length. The average drop diameter obtained from the proposed model is in good agreement with experimental data with an average error of 6.3%. The developed model is applicable in both the necking as well as jetting regime and is validated for liquid-liquid systems having low, moderate and high interfacial tension.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.147-149
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• 2004

There are no authentic solutions in a face region extraction problem though it is an important part of pattern recognition and has diverse application fields. It is not easy to develop the facial region extraction algorithm because the facial image is very sensitive according to age, sex, and illumination. In this paper, to solve these difficulties, a fuzzy color filer based on the facial region extraction algorithm is proposed. The fuzzy color filter makes the robust facial region extraction enable by modeling the skin color. Especially, it is robust in facial region extraction with various illuminations. In addition, to identify the fuzzy color filter, a linear matrix inequality(LMI) optimization method is used. Finally, the simulation result is given to confirm the superiority of the proposed algorithm.