• International Journal of Contents
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• v.7 no.3
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• pp.6-12
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• 2011

In this paper, we show how such enhancement of Farwell-Donchin BCI enables a fresh, inexperienced user to achieve quickly an accurate BCI control with a high information transfer rate. This paper presents the results of a BCI experiment where the participant, who had no previous BCI experience, obtained, in about 20 min, a highly reliable and fast control over the BCI spelling device based on the Farwell-Donchin paradigm. Offline analysis showed that the high performance of the BCI was, to a high extent, due to the use of the ERP component N1, in addition to component P300, which has been considered the only ERP component important for the prediction of user's choice in the Farwell-Donchin paradigm in many publications.

• Bulletin of the Korean Chemical Society
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• v.32 no.12
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• pp.4195-4198
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• 2011

Intramolecular ${\pi}-{\pi}$ and ${\sigma}-{\pi}$ interactions are omnipresent for numerous energetic and structural phenomena in nature, and the exact description of these nonbonding interactions plays an important role in the accurate prediction of the three-dimensional structures for numerous interesting molecular systems such as protein folding and polymer shaping. We have selected two prototype molecular systems for benchmarking calculations of intramolecular ${\pi}-{\pi}$ and ${\sigma}-{\pi}$ interactions. Accurately describing conformational energy of such systems requires highly elaborate but very expensive ab initio methods such as coupled cluster singles, doubles, and (triples) (CCSD(T)). Our calculations reveal a double hybrid density functional incorporating dispersion correction (B2PLYP-D) that agrees excellently with the CCSD(T) results, indicating that B2PLYP-D can serve as a practical method of choice.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.9
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• pp.174-180
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• 2000

A reliable analytic model that determines the cross sectional shape of a workpiece(material) in round-oval(or oval-round) pass sequence has been developed. the cross sectional shape of an outgoing workpiece is predicted by using the linear interpolation of the radius of curvature of an incoming workpiece and that of roll groove to the roll axis direction. The requirements we placed on the choice of the weighting function were to ensure boundary conditions specified. The validity of the analytic model has been examined by hot rod rolling experiment with the roll gap and specimen size changed. The cross sectional shape and area of a workpiece predicted by the proposed analytic model were good agreement with those obtained experimentally. It was found that the analytic model has not only simplicity and accuracy for practical usage but also save a large amount of computational time compared with finite element method.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.449-456
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• 1998

A theoretical critical heat flux (CLE) model. based on lateral bubble coalescence on the heated wall, is proposed to predict the subcooled flow boiling CHF in a uniformly heated vertical tube. The model is based on the concept that a single layer of bubbles contacted to the heated wall events a bulk liquid from reaching the wall at near CHF condition. Comparisons between the model predictions and experimental data result in satisfactory agreement within less than 9.73 % root-mean-square error by the appropriate choice of the critical void fraction in the bubbly layer. The present model shows comparable performance with the CHF look-up table of Groeneveld et al.

• Journal of the Korean Chemical Society
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• v.44 no.6
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• pp.501-506
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• 2000

Some previous linear equations to predict hydrocarbon heats of formation are generalized. The basic molecular descriptors used for the QSPR analysis are atoms and chemcal bonds. This particular choice makes the method extremely simple and quite inexpensive. The predictions for a set of 19 alkenes yield deviations which are similar to experimental uncertainties. Some possible extensions of the method are pointed out.

• Computers and Concrete
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• v.3 no.2_3
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• pp.177-195
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• 2006

Slipforming is a construction method in which the forms move continuously during the placement of concrete. This paper presents the development of a computer aided program designated as "CADSLIPFORM" for slipforming operations. The program incorporates maturity methods for the prediction of initial setting times of slipform concrete layers using laboratory data (time-temperature histories and setting times of concrete mixtures at different temperatures) and generates slipform mock-up times. The performance of CADSLIPFORM is validated by comparing simulated mock-up times with those estimated in the field through conventional hard front by rod (R) method. Moreover, the program versatility is demonstrated by illustrating mock-up simulations for different cases with variable slipform parameters such as: number and thickness of concrete layers, concrete temperature (simulating variable setting times) and slipform speed. The program also incorporates the choice of Freiesleben Hansen & Pederson (FHP) and Carino & Tank (CT) maturity functions. CADSLIPFORM can assist user to develop reliable schedule of slipforming operation suitable for a specific project by optimizing various slipform parameters.

• Steel and Composite Structures
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• v.45 no.6
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• pp.877-894
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• 2022

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO₂ emissions in the construction industry. The compressive strength (f_c) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f_c of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• Health Policy and Management
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• v.2 no.1
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• pp.115-147
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• 1992

The purposes of this paper are twofold : to identify what pathway insured patients are seeking medical care services through, and then, to provide the basis for the prediction and evaluation of the effects of a new policy intervention. To change the patient flow across different types of medical care facilities, this intervention has been enforced since July 1, 1989. It is mainly aimed at discouraging the use of the tertiary hospitals by imposing some restrictions on the patient's choice. The data for analysis were obtained from the claims to the insurance for govermment and school employees. The sample was drawn from the claims for about 1% of the enrollees using medical care facilities during 2 years since January 1, 1985. The sample included 91, 483 for 1985 and 81,914 for 1986, among them the number of patients to initiate the use of medical care service were 66,757 and 59,498 respectively. This paper analysed what types of and how many medical care facilities the patient with same disease had used.