• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.244-244
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• 2017

Recently, A study is actively underway to apply to various industries, which are one of the major changes in the key drivers of the industry 4.0.. The data generated by the ship include various indicators such as the fuel volume, engine power, ground speed, speed, speed, main engine rpm, DFOC, SFOC, and FOC. This paper analyzes the sensitivity of the Gathering data and analyzes the impact energy efficiency of the vessel operation by analyzing the influence among each parameter, using the mathematical models, you create an surrogate model using the math model, comparative analysis of actual measurement data and predictive results were analyzed. Through the use of big data analysis technology, it is possible to identify the sensitivity between the energy efficiency related variables of the ship, The possibility of utilization of fuel efficiency indicators using of the surrogate model is identified.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.305-311
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• 2013

The size of the market as the economy continues to evolve, in order to make the right decisions to accurately predict the economic problems the market has emerged as an important issues. To express the modern economic system, the largest of the various economic indicators, pillars stock indicators analysis and decision-making with a proper understanding of the problem for the application of the model is suitable for time-series data concealment HMM. Based on this time series model and the calculation of the time and cost savings dimension decrease techniques for the estimation and prediction of the model was applied to the problem was to verify the validity. As a result, the model predictions in both the short term rather than long-term predictions of the model estimates the optimal predictive value similar pattern very similar to both the actual data and was able to confirm that.

• Information Systems Review
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• v.20 no.4
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• pp.25-41
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• 2018

Applying predictive analytics to enterprise processes is an effective way to reduce operation costs and enhance productivity. Accordingly, the ability to predict business processes and performance indicators are regarded as a core capability. Recently, several works have predicted processes using deep learning in the form of recurrent neural networks (RNN). In particular, the approach of predicting the next step of activity using static or dynamic RNN has excellent results. However, few studies have given attention to applying deep learning in the form of dynamic RNN to predictions of process performance indicators. To fill this knowledge gap, the study developed an approach to using process mining and dynamic RNN. By utilizing actual data from a large domestic company, it has applied the suggested approach in estimating timely stocking in purchasing process, which is an important indicator of the process. The analytic methods and results of this study were presented and some implications and limitations are also discussed.

• Korean Journal of Food Science and Technology
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• v.37 no.6
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• pp.1012-1017
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• 2005

Predictive growth model of putrefactive bacteria of surimi-based imitation crab in the modified surimi-based imitation crab (MIC) broth was investigated. The growth curves of putrefactive bacteria were obtained by measuring cell number in MIC broth under different conditions (Initial cell number, $1.0{\times}10^2,\;1.0{\times}10^3$ and $1.0{\times}10^4$ colony forming unit (CFU)/mL; temperature, $15^{\circ}C,\;20^{\circ}C\;and\;25^{\circ}C$) and applied them to Gompertz model. The microbial growth indicators, maximum specific growth rate constant (k), lag time (LT) and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of putrefactive bacteria was become fast with rising temperature and fastest at $25^{\circ}C$. LT and GT were become short with rising temperature and shortest at $25^{\circ}C$. There were not significant differences in k, LT and GT by initial cell number (p>0.05). Polynomial model, $k=-0.2160+0.0241T-0.0199A_0$, and square root model, $\sqrt{k}=0.02669$ (T-3.5689), were developed to express the combination effects of temperature and initial cell number, The relative coefficient of experimental k and predicted k of polynomial model was 0.87 from response surface model. The relative coefficient of experimental k and predicted k of square root model was 0.88. From above results, we found that the growth of putrefactive bacteria was mainly affected by temperature and the square root model was more credible than the polynomial model for the prediction of the growth of putrefactive bacteria.

• Korean Journal of Food Science and Technology
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• v.36 no.2
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• pp.349-354
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• 2004

Predictive growth model of Vibrio parahaemolyticus in modified surimi-based imitation crab broth was investigated. Growth curves of V. parahaemolyticus were obtained by measuring cell concentration in culture broth under different conditions ($Initial\;cell\;level,\;1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}\;colony\;forming\;unit\;(CFU)/mL$; temperature, 15, 25 37, and $40^{\circ}C$; pH 6, 7, and 8) and applying them to Gompertz model. Microbial growth indicators, maximum specific growth rate (k), lag time (LT), and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of V. parahaemolyticus increased with increasing temperature, reaching maximum rate at $37^{\circ}C$. LT and GT were also the shortest at $37^{\circ}C$. pH and initial cell number did not influence k, LT, and GT values significantly (p>0.05). Polynomial model, $k=a{\cdot}\exp(-0.5{\cdot}((T-T_{max}/b)^{2}+((pH-pH_{max)/c^{2}))$, and square root model, ${\sqrt{k}\;0.06(T-9.55)[1-\exp(0.07(T-49.98))]$, were developed to express combination effects of temperature and pH under each initial cell number using Gauss-Newton Algorism of Sigma plot 7.0 (SPSS Inc.). Relative coefficients between experimental k and k Predicted by polynomial model were 0.966, 0.979, and 0.965, respectively, at initial cell numbers of $1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}CFU/mL$, while that between experimental k and k Predicted by square root model was 0.977. Results revealed growth of V. parahaemolyticus was mainly affected by temperature, and square root model showing effect of temperature was more credible than polynomial model for prediction of V. parahaemolyticus growth.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.73-86
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• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1163-1171
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• 2021

Objective: To analyze the correlations between intraoperative ultrasound and MRI metrics of the spinal cord in degenerative cervical myelopathy and identify novel potential predictive ultrasonic indicators of neurological recovery for degenerative cervical myelopathy. Materials and Methods: Twenty-two patients who underwent French-door laminoplasty for multilevel degenerative cervical myelopathy were followed up for 12 months. The Japanese Orthopedic Association (JOA) scores were assessed preoperatively and 12 months postoperatively. Maximum spinal cord compression and compression rates were measured and calculated using both intraoperative ultrasound imaging and preoperative T2-weight (T2W) MRI. Signal change rates of the spinal cord on preoperative T2W MRI and gray value ratios of dorsal and ventral spinal cord hyperechogenicity on intraoperative ultrasound imaging were measured and calculated. Correlations between intraoperative ultrasound metrics, MRI metrics, and the recovery rate JOA scores were analyzed using Spearman correlation analysis. Results: The postoperative JOA scores improved significantly, with a mean recovery rate of 65.0 ± 20.3% (p < 0.001). No significant correlations were found between the operative ultrasound metrics and MRI metrics. The gray value ratios of the spinal cord hyperechogenicity was negatively correlated with the recovery rate of JOA scores (ρ = -0.638, p = 0.001), while the ventral and dorsal gray value ratios of spinal cord hyperechogenicity were negatively correlated with the recovery rate of JOA-motor scores (ρ = -0.582, p = 0.004) and JOA-sensory scores (ρ = -0.452, p = 0.035), respectively. The dorsal gray value ratio was significantly higher than the ventral gray value ratio (p < 0.001), while the recovery rate of JOA-motor scores was better than that of JOA-sensory scores at 12 months post-surgery (p = 0.028). Conclusion: For degenerative cervical myelopathy, the correlations between intraoperative ultrasound and preoperative T2W MRI metrics were not significant. Gray value ratios of the spinal cord hyperechogenicity and dorsal and ventral spinal cord hyperechogenicity were significantly correlated with neurological recovery at 12 months postoperatively.

• Management & Information Systems Review
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• v.32 no.1
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• pp.63-85
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• 2013

This study tries to investigate the fundamental implications inherent in inventory asset information(specifically, unexpected inventory investment) by analyzing how the relationship between unexpected inventory investment and future operating performance. And we study how is the response of the stock market participants to the fundamental implications inherent in inventory asset information. Prior papers often assume the efficient market and they view the significant relation between stock prices and financial indicators as evidence of the contribution of such indicators to future earnings. Leading indicators are attracting the market's attention for equity valuation. We study whether one leading indicator (unexpected Inventories) forecasts future earnings, and whether market participants fully reflect the predictive ability when they sets share prices(Mishkin test, 1983). Our empirical results of the study are summarized as follows. Current unexpected inventory investment is negatively associated with future operating performance. Also, our evidence is that the stock market participants overprice the contribution of unexpected inventory investment when predicting future earnings. Furthermore, a hedge strategy that uses the overpricing gives significant future abnormal returns. The overall results help the users of financial reports, researchers of accounting, and the accounting principle setting body.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.63-70
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• 2022

The essence of the definition of "tolerance" is analyzed. Motivational, knowledge and behavioral criteria for tolerance of future teachers are highlighted. Indicators of the motivational criterion are the formation of value orientations, motivational orientation, and the development of empathy. Originality and productivity of thoughts and judgments, tact of dialogue, pedagogical ethics and tact are confirmed as indicators of the knowledge criterion. The behavioral criterion includes social activity as a life position, emotional and volitional endurance, and self-control of one's own position. The formation of tolerance is influenced by a number of factors: the social environment, the information society, existing stereotypes and ideas in society, the system of education and relationships between people, and the system of values. The main factors that contribute to the education of tolerance in future teachers are highlighted. Analyzing the structure of tolerance, it is necessary to distinguish the following functions of tolerance: - motivational (determines the composition and strength of motivation for social activity and behavior, promotes the development of life experience, because it allows the individual to accept other points of view and vision of the solution; - informational (understanding the situation, the personality of another person); - regulatory (tolerance has a close connection with the strong - willed qualities of a person: endurance, selfcontrol, self-regulation, which were formed in the process of Education); - adaptive (allows the individual to develop in the process of joint activity a positive, emotional, stable attitude to the activity itself, which the individual carries out, to the object and subject of joint relations). The implementation of pedagogical functions in the information society: educational, organizational, predictive, informational, communicative, controlling, etc. provides grounds to consider pedagogical tolerance as an integrative personal quality of a representative of any profession in the field of "person-person". The positions that should become conditions for the formation of tolerance of the future teacher in the information society are listed.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.151-154
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• 2008

Information on the area and spatial distribution of crop fields is needed for biomass production, arrangement of water resources, trace gas emission estimates, and food security. The present study aims to monitor crops status during the growing season by estimating its aboveground biomass and leaf area index (LAI) from field reflectance taken with a hand-held radiometer. Field reflectance values were collected over specific spectral bandwidths using a handheld radiometer(LI-1800). A methodology is described to use spectral reflectance as indicators of the vegetative status in crop cultures. Two vegetation indices were derived from these spectral measurements. In this paper, first we analyze each spectral reflectance characteristics of vegetation in the order of growth stage. Vegetation indices (NDVI, GNDVI) were calculated from crop reflectance. And assess the nature of relationships between LAI and VI, as measured by the in situ NDVI and GNDVI. Among the two VI, NDVI showed predictive ability across a wider range of LAI than did GNDVI. Specific objectives were to determine the relative accuracy of these two vegetation indices for predicting LAI. The results of this study indicated that the NDVI and GNDVI could potentially be applied to monitor crop agriculture on a timely and frequent basis.