• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.157-162
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• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.457-469
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• 2022

Hydraulic concrete buildings in the northwest of China are often subject to the combined effects of low-temperature frost damage, during drying and wetting cycles, and salt erosion, so the study of concrete deterioration prediction is of major importance. The prediction model of the relative dynamic elastic modulus (RDEM) of four different kinds of modified concrete under the special environment in the northwest of China was established using Grey residual Markov theory. Based on the available test data, modified values of the dynamic elastic modulus were obtained based on the Grey GM(1,1) model and the residual GM(1,1) model, combined with the Markov sign correction, and the dynamic elastic modulus of concrete was predicted. The computational analysis showed that the maximum relative error of the corrected dynamic elastic modulus was significantly reduced, from 1.599% to 0.270% for the BS2 group. The analysis error showed that the model was more adjusted to the concrete mixed with fly ash and mineral powder, and its calculation error was significantly lower than that of the rest of the groups. The analysis of the data for each group proved that the model could predict the loss of dynamic elastic modulus of the deterioration of the concrete effectively, as well as the number of cycles when the concrete reached the damaged state.

• Computers and Concrete
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• v.25 no.6
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• pp.517-523
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• 2020

An experimental system on three point bending notched beams was established to study the fracture process of concrete. In this system, the acoustic emission (AE) was used to build the cumulative generation order (AGO) and dynamically track the process of microcrack evolution in concrete. A grey-cusp catastrophe model was built based on AE parameters. The results show that the concrete beams have significant catastrophe characteristic. The developed grey-cusp catastrophe model, based on AGO, can well describe the catastrophe characteristic of concrete fracture process. This study also provides a theoretical and technical support for the application of AE in concrete fracture prediction.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.3
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• pp.133-141
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• 2012

Service and manufacturing companies' efforts are increasingly focused on utilizing services to satisfy customers' needs and survive in today's competitive market environment. The value of services depends mainly on service reliability that is identified by satisfaction derived from the relationship between customer and service provider. In this paper, we extend concepts from the failure modes and effects analysis of tangible systems to services. We use an event-based process model to facilitate service design and represent the relationships between functions and failures in a service. The objective of this research is to propose a method for evaluating service reliability based on service processes using fuzzy failure mode effects analysis (FMEA) and grey theory. We define the failure mode of service as interaction ways that can be failed in a service delivery process. The fuzzy set theory is used to characterize service reliability based on linguistic terms during FMEA. Grey theory is employed to determine the degree of relation and ranking among risk factors that are represented as potential failure causes. To demonstrate implementation of the proposed method, we use a case study involving a typical automotive service operation.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1061-1072
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• 2005

A hydrological grey model is developed to forecast short-term river runoff from the Naju watershed located at upstream of the Youngsan estuary dam in Korea. The runoff of the Naju watershed is measured in real time at the Naju streamflow gauge station, which is a key station for forecasting the upstream inflow and operating the gates of the estuary dam in flood period. The model's governing equation is formulated on the basis of the grey system theory. The model parameters are reparameterized in combination with the grey system parameters and estimated with the annealing-simplex method In conjunction with an objective function, HMLE. To forecast accurately runoff, the fifth order differential equation was adopted as the governing equation of the model in consideration of the statistic values between the observed and forecast runoff. In calibration, RMSE values between the observed and simulated runoff of two and six Hours ahead using the model range from 3.1 to 290.5 $m^{3}/s,\;R^2$ values range from 0.909 to 0.999. In verification, RMSE values range from 26.4 to 147.4 $m^{3}/s,\;R^2$ values range from 0.940 to 0.998, compared to the observed data. In forecasting runoff in real time, the relative error values with lead-time and river stage range from -23.4 to $14.3\%$ and increase as the lead time increases. The results in this study demonstrate that the proposed model can reasonably and efficiently forecast runoff for one to six Hours ahead.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.53-62
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• 2020

The COVID 19 pandemic has led to a new global recession and is still causing a lot of issues because of the delays in the employment of people. This scenario has severe consequences for many countries' labor markets in the world. This problem's complexity and importance requires an integrated method of subjective and objective evaluation rather than intuitive decisions. This research aims to investigate sustainable indexes for assessing the unemployment problem by using a Multi-Criteria Decision-Making Model (MCDM). Grey theory and Decision Making Trial and Evaluation Laboratory (GDEMATEL) are deployed to transform the experts' opinions into quantitative data. The analysis based on 20 crucial criteria is employed to determine the weights of sustainability of unemployment problems. The results revealed that the top ten of determinants are Economic growth, Industrialization, Foreign direct investment, Real GDP per capita, Education level, Trade Openness, Capacity Utilization Rate, Urbanization, Employability skills, Education system expansion, which have the most significant effects on the unemployment rate under COVID 19 impacts. Furthermore, GDEMATEL could effectively assess the sustainable indicators for unemployment problems in "deep and wide" aspects. The study proposes the Grey MCDM model, contributes to the literature, provides future research directions, and helps policymakers and researchers achieve the best solutions to the unemployment problems under "economic shocks."

• Journal of Korea Water Resources Association
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• v.42 no.1
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• pp.61-73
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• 2009

To efficiently carry out the flood management of a multipurpose dam, two flood forecasting models are developed, each of which has the capabilities of forecasting upstream inflows and flood discharges downstream of a dam, respectively. The models are calibrated, validated, and evaluated by comparison of the observed and the runoff forecasts upstream and downstream of Namgang Dam. The upstream inflow forecasting model is based on the Grey system theory and employs the sixth order differential equation. By comparing the inflows forecasted by the models calibrated using different data sets with the observed in validation, the most appropriate model is determined. To forecast flood discharges downstream of a dam, a Grey model is integrated with a modified Muskingum flow routing model. A comparison of the observed and the forecasted values in validation reveals that the model can provide good forecasts for the dam's flood management. The applications of the two models to forecasting floods in real situations show that they provide reasonable results. In addition, it is revealed that to enhance the prediction accuracy, the models are necessary to be calibrated and applied considering runoff stages; the rising, peak, and falling stages.

• Journal of The Korean Astronomical Society
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• v.49 no.1
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• pp.1-8
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• 2016

The aim of this study is to describe the physical processes taking place in the solar photosphere. Based on 3D hydrodynamic simulations including a detailed radiation transfer scheme, we investigate thermodynamic structures and radiation fields in solar surface convection. As a starting model, the initial stratification in the outer envelope calculated using the solar calibrations in the context of the standard stellar theory. When the numerical fluid becomes thermally relaxed, the thermodynamic structure of the steady-state turbulent flow was explicitly collected. Particularly, a non-grey radiative transfer incorporating the opacity distribution function was considered in our calculations. In addition, we evaluate the classical approximations that are usually adopted in the onedimensional stellar structure models. We numerically reconfirm that radiation fields are well represented by the asymptotic characteristics of the Eddington approximation (the diffusion limit and the streaming limit). However, this classical approximation underestimates radiation energy in the shallow layers near the surface, which implies that a reliable treatment of the non-grey line opacities is crucial for the accurate description of the photospheric convection phenomenon.

• Computers and Concrete
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• v.26 no.2
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• pp.175-183
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• 2020

Fly ash has become an important component of concrete as supplementary cementitious material with the development of concrete technology. To make use of fly ash efficiently, four types of fly ash with particle size distributions that are in conformity with four functions, namely, S.Tsivilis, Andersen, Normal and F distribution, respectively, were prepared. The four particle size distributions as functions of the strength and pore structure of concrete were thereafter constructed and investigated. The results showed that the compressive and flexural strength of concrete with the fly ash that conforming to S.Tsivilis, Normal, F distribution increased by 5-10 MPa and 1-2 MPa, respectively, compared to the reference sample at 28 d. The pore structure of the concrete was improved, in which the total porosity of concrete decreased by 2-5% at 28 d. With regarding to the fly ash with Andersen distribution, it was however not conducive to the strength development of concrete. Regression model based on the grey multiple linear regression theory was proved to be efficient to predict the strength of concrete, according to the characteristic parameters of particle size and pore structure of the fly ash.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.