• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.417-426
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• 2018

An alternative developmental toxicity test using mouse embryonic stem cell derived embryoid bodies has been developed. This alternative method is not to administer chemicals to animals, but to treat chemicals with cells. This study suggests the use of Discriminant Analysis, Support Vector Machine, Artificial Neural Network and k-Nearest Neighbor. Algorithm performance was compared with accuracy and a weighted Cohen's kappa coefficient. In application, various classification techniques were applied to cytotoxicity data to classify drug toxicity and compare the results.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.419-424
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• 2016

Named entity recognition is required to improve the retrieval accuracy of patent documents or similar patents in the claims and patent descriptions. In this paper, we proposed an automatic named entity recognition for patents by using a conditional random field that is one of the best methods in machine learning research. Named entity recognition system has been constructed from the training set of tagged corpus with 660,000 words and 70,000 words are used as a test set for evaluation. The experiment shows that the accuracy is 93.6% and the Kappa coefficient is 0.67 between manual tagging and automatic tagging system. This figure is better than the Kappa coefficient 0.6 for manually tagged results and it shows that automatic named entity tagging system can be used as a practical tagging for patent documents in replacement of a manual tagging.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.193-202
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• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Imaging Science in Dentistry
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• v.48 no.2
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• pp.121-125
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• 2018

Purpose: Detecting laryngeal cartilages (triticeous and thyroid cartilages) on panoramic radiographs is important because they may be confused with carotid artery calcifications in the bifurcation region, which are a risk factor for stroke. This study assessed the efficiency of panoramic radiography in the diagnosis of calcified laryngeal cartilages using cone-beam computed tomography (CBCT) as the reference standard. Materials and Methods: A total of 312 regions(142 bilateral, 10 left, 18 right) in 170 patients(140 males, 30 females) were examined. Panoramic radiographs were examined by an oral and maxillofacial radiologist with 11 years of experience. CBCT scans were reviewed by 2 other oral and maxillofacial radiologists. The kappa coefficient(${\kappa}$) was calculated to determine the level of intra-observer agreement and to determine the level of agreement between the 2 methods. Diagnostic indicators(sensitivity, specificity, accuracy, and false positive and false negative rates) were also calculated. P values <.05 were considered to indicate statistical significance. Results: Eighty-two images were re-examined to determine the intra-observer agreement level, and the kappa coefficient was calculated as 0.709 (P<.05). Statistically significant and acceptable agreement was found between the panoramic and CBCT images (${\kappa}=0.684$ and P<.05). The sensitivity, specificity, diagnostic accuracy rate, the false positive rate, and the false negative rate of the panoramic radiographs were 85.4%, 83.5%, 84.6%, 16.5%, and 14.6%, respectively. Conclusion: In most cases, calcified laryngeal cartilages could be diagnosed on panoramic radiographs. However, due to variation in the calcifications, diagnosis may be difficult.

• Wind and Structures
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• v.16 no.1
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• pp.47-60
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• 2013

A dominant opening in a windward wall, which generates large internal pressures in a building, is a critical structural design criterion. The internal pressure fluctuations are a function of the dominant opening area size, internal volume size and external pressure at the opening. In addition, many buildings have background leakage, which can attenuate internal pressure fluctuations. This study examines internal pressure in buildings for a range of dominant opening areas, internal volume sizes and background porosities. The effects of background porosity are incorporated into the governing equation. The ratio of the background leakage area $A_L$ to dominant opening area $A_W$ is presented in a non-dimensional format through a parameter, ${\phi}_6-A_L/A_W$. Background porosity was found to attenuate the internal pressure fluctuations when ${\phi}_6$ is larger than 0.2. The dominant opening discharge coefficient, ${\kappa}$ was estimated to lie between 0.05 to 0.40 and the effective background porosity discharge coefficient ${\kappa}^{\prime}_L$, was estimated to be between 0.05 to 0.50.

• 한국전산유체공학회:학술대회논문집
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• 2002.05a
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• pp.79-87
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• 2002

The present work analyzed the effect of mixing vane shape on the flow structure and heat transfer downstream of mixing vane in a subchannel of fuel assembly, by obtaining velocity and pressure fields, turbulent intensity, flow-mixing factors, heat transfer coefficient and friction factor using three-dimensional RANS analysis. NJl5, NJ25, NJ35, NJ45, which were designed by the authors, were tested to evaluate the performances in enhancing the heat transfer. Standard $\kappa-\epsilon$ model is used as a turbulence closure model, and, periodic and symmetry conditions are set as boundary conditions. The flow blockage ratio is kept constant, but the twist angle of mixing vane is changed. The results with three turbulence models( $\kappa-\epsilon$, $\kappa-\omega$, RSM) were compared with experimental data.

• Journal of Ocean Engineering and Technology
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• v.15 no.3
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• pp.1-8
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• 2001

With uniform heat generation within the wall of the cylinder placed in a cross flow, heat flows by conduction in the circumferential direction due to the asymmetric nature of the fluid flow around the perimeter of the cylinder. The circumferential heat flow affects the wall temperature distribution to such an extent that in some cases significantly different results may be obtained for geometrically similar surfaces. In the present investigation, the effects of circumferential wall heat conduction on local convective heat transfer is investigated for the case of forced convection around horizontal cylinder in cross flow of air. Two-dimensional temperature distribution $T_w$/(${\gamma}$,${\theta}$) is presented through the numerical analysis. The one-dimensional and two-dimensional solutions are in good agreement with experimental results of local heat transfer coefficients.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2018.06a
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• pp.53-53
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• 2018

Thermoelectric materials can convert directly waste heat to electricity and vice versa. The improvement of the thermoelectric efficiency strongly depends on the dimensionless figure of merit, $ZT=S^2{\sigma}T/{\kappa}$, where S is the Seebeck coefficient, ${\sigma}$ is the electrical conductivity, T is the absolute temperature, and ${\kappa}$ is the thermal conductivity. The thermal conductivity consists of the electronic contribution (${\kappa}_e$) and phonon contribution (${\kappa}_{ph}$). It is very challenge to increase the power factor, $S^2{\sigma}$ and to reduce the thermal conductivity simultaneously because the power factor and electronic thermal conductivity are coupled. One strategy is to decrease the phonon thermal conductivity. The phonon thermal conductivity can be decreased by controlling the grain size and structural defects such as dislocations and twinning. In order to achieve enhancements in thermoelectric efficiency, microstructures that can form numerous interfaces have been investigated intensively for controlling the transport of charge carriers and heat carrying phonons. In this presentation, we report the heterogeneous microstructure of $Mg_2Si_{0.6}Sn_{0.4}$ thermoelectric materials and investigation of its influence on thermoelectric properties.

• Journal of computational fluids engineering
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• v.21 no.2
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• pp.12-24
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• 2016

The standard ${\kappa}-{\varepsilon}$ and realizable ${\kappa}-{\varepsilon}$ models are adopted to improve the prediction performance on the recirculating flow. In this paper, the backward facing step flows are used to assess the prediction performance of the recirculation zone. The model constants of turbulence model are obtained by the experimental results and they have a different value according to the flow. In the case of an isotropic flow situation, decaying of turbulent kinetic energy should follow a power law behavior. In accordance with the power law, the coefficients for the dissipation rate of turbulent kinetic energy are not universal. Also, the other coefficients as well as the dissipation coefficient are not constant. As a result, a suitable coefficients can be varied according to each of the flow. The changes of flow over the backward facing step in accordance with model constants of the ${\kappa}-{\varepsilon}$ models show that the reattachment length is dependent on the growth rate(${\lambda}$) and the ${\kappa}-{\varepsilon}$ models can be improved the prediction performance by changing the model constants about the recirculating flow. In addition, it was investigated for the curvature correction effect of the ${\kappa}-{\varepsilon}$ models in the recirculating flow. Overall, the curvature corrected ${\kappa}-{\varepsilon}$ models showed an excellent prediction performance.

• The Journal of the Korean dental association
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• v.54 no.11
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• pp.880-896
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• 2016

The comparison of the repeated measurements is often needed to see whether they agree sufficiently, when a measurement is repeated under identical conditions by different raters. Such investigations are often analyzed inappropriately, by using correlation coefficient. The purpose of this study is to introduce statistical methods for accessing the agreement of the repeated measurements, which include Bland-Altman plot, intra class correlation, Passing-Bablok regression and Cohen's kappa coefficient, and to show how to execute them using examples.