• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• 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 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.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.

• Advances in concrete construction
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• v.13 no.1
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• pp.11-23
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• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1321-1335
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• 2022

This study was conducted to investigate the effect of absorption coefficient models on the P1 radiation model for a premixed hydrogen flame containing the water vapor. A CFD combustion simulation analysis was performed using XiFoam, one of the open-source CFD solvers in OpenFOAM. The solver using the flamelet combustion model has been modified to implement radiative heat transfer. The absorption coefficient models used in this study the grey-mean model and constant model, and for comparison, case without radiation was added. This CFD simulation study consisted of benchmarking the THAI HD-15 and HD-22 experiments. The difference between the two tests is the inclusion of water vapor in the condition before ignition. In the case of the HD-22 experiment containing water vapor in the initial condition, the simulation results show that the grey-mean absorption coefficient model has a strong influence on the temperature decrease of the flame and on the change in pressure inside the vessel.

• Journal of Korea Foundry Society
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• v.14 no.6
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• pp.548-557
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• 1994

Grey iron containing a small amount of Cu and Mo to improve the effect of heat treatment and microstructures were poured in to the mold and them austenized at $900^{\circ}C$. After austenitizing the specimens of castings were austempered $210^{\circ}C$, $250^{\circ}C$, $300^{\circ}C$ and $350^{\circ}C$. The effects of matrix structures, mechanical properties and wear characteristics were investigated by austempering temperatures. Tensile strength and hardness of austempered grey iron are increased and the amount of retained austenite is decreased as austempering temperature is lower even though the amount of retained autenite in it only 4%. The amount of rolling wear loss are increased as rolling revolution is increased and wear loss of austempered grey iron under dry rolling condition is characterized by three models; initial wear, stationary wear and abnormal wear. It has been found that the amount of wear loss was increased with increasing maxium compressive stress and rolling revolution.

• Journal of Distribution Science
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• v.19 no.8
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• pp.47-56
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• 2021

Purpose: Maritime sector is fundamental to international trade; there is no doubt that seafarers have played an essential role in maritime shipping and distribution science industry. Thus, this study uses Grey models to predict the number of seafarers in Vietnam expecting to provide a range of future seafarers. Research design, data and methodology: Statistics data are adopted for numbers of seafarers by Vietnam Maritime Administration categorizing into three types: Officers at Management level, Officers at Operational level and Navigation - Engine officer cadet. Results: The results have showed that a lack of qualified seafarers in the distribution industry, which has become a global issue and Vietnam is facing challenges of providing enough supply of seafarers in the next few years. Since there has been a concern of the unbalance between demand and supply of seafarers, researches in maritime sector needs a high accuracy in forecasting the number of available qualified seafarers in Vietnam. Conclusion: This method can be applied to predict numbers of other human resources in transportation, distribution and/or logistics industries when the information is poor and insufficient. The next few years are predicted to witness a downtrend in sailors - oilers which leads to the fact that the total number of available seafarers is decreased.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.335-336
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• 2009

In this paper, I propose an automatic approach to annotating images dynamically based on MBRM(Multiple Bernoulli Relevance Models) using GLCM(Grey Level Co-occurrence Matrix). MBRM is more appropriate to annotate images compare with multinomial distribution. The model is used in limited test set, MSRC-v2 (Microsoft Research Cambridge Image Database). The results show that this model is significantly outperforms previously reported results on the task of image annotation and retrieval.