• 한국식품위생안전성학회지
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• 제32권3호
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• pp.206-210
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• 2017

본 연구에서는 축산식품인 편육을 대상으로 황색포도상구균의 성장예측모델을 개발하였다. 편육에서 황색포도상구균의 성장패턴은 4, 10, 20, $37^{\circ}C$의 보관온도에서 측정되었으며, 황색포도상구균은 각각의 저장 온도에서 모두 증가하는 것으로 나타났다. 편육에 오염된 황색포도상구균의 생육결과를 토대로 Baranyi model을 이용하여 유도기(LPD)와 최대성장률(${\mu}_{max}$)을 산출한 결과, 유도기는 4, 10, 20, $37^{\circ}C$에서 212.81, 79.67, 3.12, 2.21 h으로 온도에 반비례한 것으로 나타났고 최대성장률은 같은 보관온도에서 0.004, 0.009, 0.130, 0.568 log CFU/g/h으로 온도에 비례한 것으로 조사되었다. 2차 모델은 ${\mu}_{max}$의 경우, square root model, LPD는 polynomial equation을 사용하여 산출하였고, 개발한 모델의 적합성을 평가하기 위해 통계적 지표인 RMSE 값을 계산한 결과, 비교적 0에 가까운 0.42로 도출되어 모델이 적합한 것으로 확인되었다. 따라서 개발된 모델이 편육에 대한 황색포도상구균의 성장 예측모델로 사용 가능하다고 판단되어지며, 편육에서의 식중독을 예방하고 미생물학적 위생관리기준을 설정하는데 기초자료로 활용될 수 있을 것으로 사료된다.

• Asia pacific journal of information systems
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• 제25권1호
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• pp.83-97
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• 2015

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.

• Journal of applied mathematics & informatics
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• 제8권1호
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• pp.243-252
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• 2001

The points of failure of a decomposition process are defined to be the union of the points of failure from two component point processes for software reliability systems. Because sampling from the likelihood function of the decomposition model is difficulty, Gibbs Sampler can be applied in a straightforward manner. A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For model determination, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. A numerical example with a simulated data set is given.

• 한국식품저장유통학회지
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• 제19권5호
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• pp.633-638
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• 2012

시판 샐러드제품의 구입부터 가정까지의 이동 및 소비직전까지 적정하지 않은 온도관리를 예상하여 단기간의 온도 abuse 상황을 설정하고 미생물적 유통기한을 도출하였다. 보다 효율적인 유통기한 설정을 위해 예측미생물학의 3단계 모델인 PMP 7.0을 활용하여 그 활용가능성을 조사하였다. 부적절한 온도에서의 abuse 시간이 증가할수록 미생물은 빠르게 증식하여 샐러드 제품의 유통기한 시점으로 판단되는 log 7 CFU/mL에 도달하는 시간이 짧아졌다. 온도가 증가할수록 0.020에서 1.083까지 grow rate도 증가했으며, 이 모델의 적합도를 나타내는 $r^2$의 값은 전 실험구에서 0.9 이상을 나타내었다. PMP 7.0으로 예측된 미생물의 증식양상은 온도에 따라 0.017~0.235 CFU/mL/hr로 나타났으며, 모든 구에서 0.994~1.000까지 높은 수준의 $r^2$을 나타내었다. 또 PMP를 활용하여 도출한 유통기한의 경우도 온도가 증가함에 따라 감소하였다. 실측된 값을 바탕으로 한 샐러드 제품의 유통기한은 유통매장에 도착하기까지 48시간 소요될 것으로 예상할 경우 유통기한은 109.1~63.0시간까지로 추정되며 PMP로 도출된 유통기한(24.1~626.5시간)에 비해 짧게 나타났다. 이는 온도 abuse에 의한 영향 및 fail safe에 해당하는 결과로 안전성 측면에서는 유리하나 관리적 측면에서 과도한 기준의 설정 등을 통해 관리비용의 증가 등의 단점이 발생할 수 있는 것으로 판단된다. 즉, 예측미생물학을 활용하여, 유통기한 설정 및 품질관리를 위한 초기 미생물 기준 설정시 특정 식품에 적용하는 것은 효율적인 시도가 될 것이나, 이를 전반적인 기준으로 설정하는 것은, 통계적, 실제적 오류 발생이 가능할 것으로 오히려 관련 효율을 저해할 수 있을 것이다.

• Molecular & Cellular Toxicology
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• 제3권3호
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• pp.159-164
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• 2007

Stimulation of epidermal growth factor receptor (EGFR) is essential in signaling pathway of tumor cells. Thus, EGFR has intensely studied as an anticancer target. We developed hologram quantitative structure activity relationship (HQSAR) models for data set which consists of tricyclic azepine derivatives showing inhibitory activities for EGFR. The optimal HQSAR model was generated with fragment size of 6 to 7 while differentiating fragments having different atom and connectivity. The model showed cross-validated $q^2$ value of 0.61 and non-cross-validated $r^2$ value of 0.93. When the model was validated with an external set excluding one outlier, it gave predictive $r^2$ value of 0.43. The contribution maps generated from this model were used to interpret the atomic contribution of each atom to the overall inhibition activity. This can be used to find more efficient EGFR inhibitors.

• 한국축산식품학회지
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• 제31권6호
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• pp.899-906
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• 2011

The objectives of this study were to apply the Baranyi model to predict the growth of natural microflora in liver sausage with added kimchi powder. Kimchi powder was added to the meat products at 0, 1, 2, and 3% levels. To determine and quantify the natural microflora in the meat products, total plate counts and counts of anaerobic bacteria and lactic acid bacteria were examined throughout the 28 d of storage. The obtained data were applied to the Baranyi growth model. The indices used for comparing predicted and observed data were $B_f$, $A_f$, root mean square error (RMSE), and $R^2$. Twelve predictive models were characterized by a high $R^2$ and small RMSE. The Baranyi model was useful in predicting natural microflora levels in these meat products with added kimchi powder during storage.

• Food Science and Biotechnology
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• 제16권2호
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• pp.238-242
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• 2007

Square root models were developed for predicting the kinetics of growth of Listeria monocytogenes in sesame leaves as a function of temperature (4, 10, or $25^{\circ}C$). At these storage temperatures, the primary growth curves fit well ($R^2=0.898$ to 0.980) to a Gompertz equation to obtain lag time (LT) and specific growth rate (SGR). The square root models for natural logarithm transformations of the LT and SGR as a function of temperature were obtained by SAS's regression analysis. As storage temperature ($4-25^{\circ}C$) decreased, LT increased and SGR decreased, respectively. Square root models were identified as appropriate secondary models for LT and SGR on the basis of most statistical indices such as coefficient determination ($R^2=0.961$ for LT, 0.988 for SGR), mean square error (MSE=0.l97 for LT, 0.005 for SGR), and accuracy factor ($A_f=1.356$ for LT, 1.251 for SGR) although the model for LT was partially not appropriate as a secondary model due to the high value of bias factor ($B_f=1.572$). In general, our secondary model supported predictions of the effects of temperature on both LT and SGR for L. monocytogenes in sesame leaves.

• 한국소프트웨어감정평가학회 논문지
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• 제17권1호
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• pp.59-66
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• 2021

Recently, conceptual information model is changing fast, and these changes are coming about as a result of individual tendency, social cultural, new circumstances and societal shifts within big data environment. Despite the data is growing more and more, now is the time to commit ourselves to the development of renewable, invaluable information of social/live commerce. Because we have problems with various insoluble data, we propose about deep learning prediction model-based object classification in social commerce of big data environment. Accordingly, it is an increased need of social commerce platform capable of handling high volumes of multiple items by users. Consequently, responding to rapid changes in users is a very significant by deep learning. Namely, promptly meet the needs of the times, and a widespread growth in big data environment with the goal of realizing in this paper.

• Journal of Microbiology and Biotechnology
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• 제17권9호
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• pp.1437-1444
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• 2007

A response surface model was developed for predicting the growth rates of Staphylococcus aureus in tryptic soy broth (TSB) medium as a function of combined effects of temperature, pH, and NaCl. The TSB containing six different concentrations of NaCl (0, 2, 4, 6, 8, and 10%) was adjusted to an initial of six different pH levels (pH 4, 5, 6, 7, 8, 9, and 10) and incubated at 10, 20, 30, and $40^{\circ}C$. In all experimental variables, the primary growth curves were well ($r^2=0.9000$ to 0.9975) fitted to a Gompertz equation to obtain growth rates. The secondary response surface model for natural logarithm transformations of growth rates as a function of combined effects of temperature, pH, and NaCl was obtained by SAS's general linear analysis. The predicted growth rates of the S. aureus were generally decreased by basic (pH 9-10) or acidic (pH 5-6) conditions and higher NaCl concentrations. The response surface model was identified as an appropriate secondary model for growth rates on the basis of correlation coefficient (r=0.9703), determination coefficient ($r^2=0.9415$), mean square error (MSE=0.0185), bias factor ($B_f=1.0216$), and accuracy factor ($A_f=1.2583$). Therefore, the developed secondary model proved reliable for predictions of the combined effect of temperature, NaCl, and pH on growth rates for S. aureus in TSB medium.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.37-43
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• 2019

The research aims to study the relationship between export performance and stock return of Vietnamese fishery companies. To conduct this study, quarterly data was collected for period from 2010-2018 of 13 fishery companies listing in Ho Chi Minh Stock Exchange (HOSE) and Ha Noi Stock Exchange (HNX). The export performance was measured by export intensity, export growth and export market coverage. In addition, interest rate, exchange rate, GDP, firm size, profitability, and financial leverage were considered as the control variables in the research model. Panel data analysis with Generalized Least Squares model was employed to estimate the predictive regression. The findings indicated that export intensity and export growth have a significant and positive relationship with stock returns. However, export market coverage has not a significant relationship with stock return at the 0.05 level. Profitability, financial leverage, and exchange rate have a positive relationship, while interest rate and GDP have no relation to stock return at the 0.05 significance level. The findings imply that investors should consider the export intensity instead of export growth and export market coverage as selecting stock of fishery exports firms to invest; managers should increase export intensity to increase company's stock price or firm market value.