• Journal of Microbiology and Biotechnology
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• v.28 no.7
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• pp.1052-1060
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• 2018

Numerous studies suggest that the effects of lactic acid bacteria (LAB) on oxidative stress in vivo are correlated with their antioxidative activities in vitro; however, the relationship is still unclear and contradictory. The antioxidative activities of 27 Lactobacillus plantarum strains isolated from fermented foods were determined in terms of 2,2-diphenyl-1-picrylhydrazyl, hydroxyl radical, and superoxide radical scavenging abilities, reducing activity, resistance to hydrogen peroxide, and ferrous chelating ability in vitro. Two fuzzy synthetic evaluation models, one with an analytic hierarchy process and one using entropy weight, were then used to evaluate the overall antioxidative abilities of these L. plantarum strains. Although there was some difference between the two models, the highest scoring strain (CCFM10), the middle scoring strain (CCFM242), and the lowest scoring strain (RS15-3) were obtained with both models. Examination of the antioxidative abilities of these three strains in $\text\tiny{D}$-galactose-induced oxidative stress mice demonstrated that their overall antioxidative abilities in vitro could reveal the abilities to alleviate oxidative stress in vivo. The current study suggests that assessment of overall antioxidative abilities with fuzzy synthetic models can guide the evaluation of probiotic antioxidants. It might be a more quick and effective method to evaluate the overall antioxidative abilities of LAB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4142-4159
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• 2019

We have developed composite measures of supercomputer technology, reflecting various factors of supercomputers using Martino's scoring model. CPUs, accelerators, memory, interconnection networks, and power consumption are chosen as factors of the model. The weight values of the factors are derived based on a survey of 129 domestic and international experts. The measured values are then standardized to integrate measurement units of the factors in the model. This model has been applied to 50 supercomputers, and rank correlation analysis was performed using representative measures. As a consequence, the ranking drastically changes except for the 1st and 2nd supercomputers on the TOP500. In addition, the characteristics of memory and interconnection networks influence the ranking, and the results demonstrate that the proposed model has low correlations with HPL and HPCG but a high correlation with Green500. This indicates that power consumption is an important factor that has a significant effect on the measures of supercomputer technology. In addition, it is determined that the differences between the HPL ranking and the proposed model ranking are influenced by power consumption, CPU theoretical peak performance, and main memory bandwidth in order of significance. In conclusion, the composite measures proposed in this study are more suitable for comprehensively describing supercomputer technology than existing performance measures. The findings of this study are expected to support decision making related to management and policy in the procurement and operation of supercomputers.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.205-209
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• 1999

일반화된 지수생존모형(generalized exponential survival model)을 고려하여 이 모형의 모수를 추정하는 수정된 FS(modified Fisher scoring)방법을 제안한다. 이를 위해 우도방정식(likelihood equation)을 유도하고 초기추정치 (initial estimate)를 포함한 추정알고리즘(estimating algorithm)을 개발한다.

• Physical Therapy Korea
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• v.12 no.4
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• pp.20-25
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• 2005

The Gross Motor Function Measure (GMFM) is an internationally widely used outcome measure. The aim of this study was to evaluate the structural properties of the Korean version of GMFM using the Rasch Model, with regard to scoring within rehabilitation centers in Korea. GMFM data for 206 children with cerebral palsy were collected from 11 outpatient rehabilitation facilities by 29 pediatric therapists. The Winsteps software was used to refine the rating scale. This study suggests that the scoring categories of the Korean version of the GMFM should be collapsed from 0 (subject does not initiate task), 1 (subject initiates task), 2 (subject partially completes task), 3 (subject completes task) to 0 (subject does not initiate task), 1 (subject initiates or partially completes task), 2 (subject completes task) for better accuracy in estimating the gross motor function of children with cerebral palsy.

• Environmental Mutagens and Carcinogens
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• v.16 no.1
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• pp.30-34
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• 1996

This study was performed to examine the availability of using medaka (Oryzias latipes) in teratological test. Medaka embryos were collected within 2 hours post-fertilization and cultured in petri dishes containing buffered saline until hatching. The embryos were treated with 0.56 mg/l chlorpyrifos-methyl and 10 mM methyl methanesulfonate at 20 stages (about 35 hours post-fertilization). Eleven developmental features were selected and observed from 33 stages (about 9 days post-fertilization). Scoring system was developed and applicated for the measurement of potential teratological effects by the test compound. Chlorpyrifos-methyl did not induce teratological effect in medaka embryos. However, we found teratological test using medaka embryo reduced the cost, labors, period and space of experiment significantly compared with teratological study using rodents. Above findings strongly suggest that medaka embryo can be used as a lab animal model for teratogenicity test instead of rodents.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.229-238
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• 2004

Cross-sell models are used to predict the probability or value of a current customer buying a different product or service from the same company. Selling to current customers is one of the easiest way to increase profits and allows companies to carefully manage offers to avoid over-soliciting and possibly alienating their customers. In this study, by using the real database of an insurance company in Korea, we try to explain the steps of actual data mining process. Especially, this study aims to develop cross-sell models to predict the probability which a current customer of automobile insurance buys long-term insurance product.

• MALSORI
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• no.49
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• pp.95-105
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• 2004

An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. For this purpose we develope a speech recognizer which automatically classifies pronunciation errors when Koreans speak a foreign language. In order to develope the methods for automatic assessment of pronunciation quality, we propose a language model based score as a machine score in the speech recognizer. Experimental results show that the language model based score had higher correlation with human scores than that obtained using the conventional log-likelihood based score.

• IE interfaces
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• v.1 no.2
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• pp.1-12
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• 1988

A justification methodology which evaluates the tactical aspects of an FMS project is proposed. For evaluation of quantifiable tactical costs/savings a method of internal rate of return on incremental investment is developed while for the tactical ones which are difficult to quantify a weighted factor scoring model is proposed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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• pp.167-177
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• 2000

The problem of system reliability is very important issue in the nuclear power plant, because the failure of its system brings about extravagant economic loss, environment destruction, and quality loss. This paper therefore proposes a normalized scoring model by the qualitative factors order to evaluate the robust reliability of nuclear power plants under uncertainty. Especially, the qualitative factors including risk, functional, human error, and quality function factors for the robust justification has been also introduced. Finally, the analytical reliability and safety assessment model developed in this paper can be used in the real nuclear power plant.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.125-132
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• 2022

Sentence compression is a natural language processing task that generates concise sentences that preserves the important meaning of the original sentence. For grammatically appropriate sentence compression, early studies utilized human-defined linguistic rules. Furthermore, while the sequence-to-sequence models perform well on various natural language processing tasks, such as machine translation, there have been studies that utilize it for sentence compression. However, for the linguistic rule-based studies, all rules have to be defined by human, and for the sequence-to-sequence model based studies require a large amount of parallel data for model training. In order to address these challenges, Deleter, a sentence compression model that leverages a pre-trained language model BERT, is proposed. Because the Deleter utilizes perplexity based score computed over BERT to compress sentences, any linguistic rules and parallel dataset is not required for sentence compression. However, because Deleter compresses sentences only considering perplexity, it does not compress sentences by reflecting the linguistic information of the words in the sentences. Furthermore, since the dataset used for pre-learning BERT are far from compressed sentences, there is a problem that this can lad to incorrect sentence compression. In order to address these problems, this paper proposes a method to quantify the importance of linguistic information and reflect it in perplexity-based sentence scoring. Furthermore, by fine-tuning BERT with a corpus of news articles that often contain proper nouns and often omit the unnecessary modifiers, we allow BERT to measure the perplexity appropriate for sentence compression. The evaluations on the English and Korean dataset confirm that the sentence compression performance of sentence-scoring based models can be improved by utilizing the proposed method.