• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.409-416
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• 2013

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.2
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• pp.55-60
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• 2009

By applying NDVI(Normalized Difference Vegetation Index) and TCT(Tasseled-Cap Transformation) image algorithm on the basis of KOMSAP-2 MSC(Multi Spectral Camera) image(Jun. 12, 2007) for Naenam-myeon, Gyeongju city in this study, DN distribution map was drawn up. Discrimination analysis of image algorithm for the accuracy improvement of forest type classification was conducted through the comparative analysis between the distribution maps of NDVI and TCT DN, and forest field surveying data, and finally, the accuracy of the forest type classification was verified through the overlay analysis with the forest field surveying data. Through this study, it is thought that low cost and high efficiency will be able to be expected in the process of the examination for the automation practicality of the forest type classification and of the production of the accurate forest type classification map by using KOMPSAT-2 MSC image.

• Advances in environmental research
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• v.3 no.2
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• pp.131-149
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• 2014

Two different model selection indices, the Akaike information criterion (AIC) and the coefficient of determination ($R^2$), are used to discriminate competing isotherm equations for aqueous pollutant removal systems. The former takes into account model accuracy and complexity while the latter considers model accuracy only. The five types of isotherm shape in the Brunauer-Deming-Deming-Teller (BDDT) classification are considered. Sorption equilibrium data taken from the literature were correlated using isotherm equations with fitting parameters ranging from two to five. For the isotherm shapes of types I (favorable) and III (unfavorable), the AIC favors two-parameter equations which can easily track these simple isotherm shapes with high accuracy. The $R^2$ indicator by contrast recommends isotherm equations with more than two parameters which can provide marginally better fits than two-parameter equations. To correlate the more intricate shapes of types II (multilayer), IV (two-plateau) and V (S-shaped) isotherms, both indices favor isotherm equations with more than two parameters.

• Journal of the Korean Burn Society
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• v.24 no.1
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• pp.1-6
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• 2021

Purpose: The purpose of this study was to evaluate the accuracy of four prediction models in adult burn patients. Methods: This retrospective study was conducted on 696 adult burn patients who were treated at burn intensive care unit (BICU) of Hallym University Hangang Sacred Heart Hospital from January 2017 to December 2019. The models are ABSI, APACHE IV, rBaux and Hangang score. Results: The discrimination of each prediction model was analyzed as AUC of ROC curve. AUC value was the highest with Hangang score of 0.931 (0.908~0.954), followed by rBaux 0.896 (0.867~0.924), ABSI 0.883 (0.853~0.913) and APACHE IV 0.851 (0.818~0.884). Conclusion: The results of evaluating the accuracy of the four models, Hangang score showed the highest prediction. But it is necessary to apply the appropriate prediction model according to characteristics of the burn center.

• Korean Journal of Agricultural Science
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• v.43 no.4
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• pp.581-588
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• 2016

The objective of this study was to investigate the accuracy of visual discrimination of soft fat pork carcasses when subjecting carcasses to quality grade evaluations. In addition, the quality of the longissimus dorsi muscle from soft fat carcasses was investigated. Iodine values of back fat from soft fat carcasses evaluated by visual discrimination were significantly higher than those from firm fat carcass (p < 0.05). However, those values were lower than the standard for soft fat (iodine value = 70). There were no significant differences in linoleic acid content, b-values, and L-values (p < 0.05) of back fat between firm and soft fat carcasses evaluated by visual discrimination. Color of longissimus dorsi muscle from soft fat carcasses (iodine value higher than 70) was not different from that of firm fat carcass (iodine value lower than 70). Except for linoleic acid, there were no significant differences in any fatty acid contents between longissimus dorsi muscles from firm fat and soft fat carcasses. Monounsaturated fatty acid content of longissimus dorsi muscles from soft fat carcasses was significantly lower than those of firm fat carcass (p < 0.05). However polyunsaturated fatty acid content was significantly higher (p < 0.05) in longissimus dorsi muscles from soft fat carcasses. In conclusion, visual discrimination results for soft fat pork carcass were inaccurate. Therefore, other indicators should be required to evaluate soft fat pork carcasses. In contrast, the quality of longissimus dorsi muscle from soft fat carcasses was superior in terms of fatty acid composition compared with that of firm fat carcasses.

• The Korea Journal of Herbology
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• v.39 no.3
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• pp.23-35
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• 2024

Objective : In this study, we performed quantitative comparison on the content of 10 marker compounds in cinnamon barks from different species and found chemical discrimination between genuine Cinnamomum cassia and other Cinnamomum species (Non C. cassia). Methods : Cinnamon bark samples were extracted using the ultrasonication in 100% methanol for 30 minutes. The samples were analysed using high-performance liquid chromatography with statistical analysis. Results : The analytical method developed in this study met all validation criteria and was applied to the quantification of the 10 marker compounds in cinnamon bark samples. The major chemical discrimination of C. cassia were identified as low content of epicatechin and eugenol, and high contents of benzaldehyde, cinnamaldehyde and cinnamic acid compared to other Non C. cassia samples. Especially, among other compounds, the content of cinnamaldehyde was the highest in the C. cassia and Non C. cassia samples. The result of principal component analysis showed that the samples of C. cassia and Non C. cassia were clearly differentiated via benzaldehyde, cinnamaldehyde, cinnamic acid, eugenol, and epicatechin, which influenced on clustering C. cassia and Non C. cassia samples. Conclusion : C. cassia and Non C. cassia samples were chemically discriminated using the quantitative HPLC analysis. Based on this, it is possible to control the quality of herbal medicines containing Cinnamomi Cortex. It is necessary to further improve the accuracy of discrimination between C. cassia and Non C. cassia species to evaluate cinnamon bark quality.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.259-264
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• 2020

The aim of this study was to investigate the feasibility of discrimination 12 different cultivar of sorghum × sudangrass hybrid (Sorghum genus) seed through near infrared spectroscopy (NIRS). The amount of samples for develop to the best discriminant equation was 360. Whole samples were applied different three spectra range (visible, NIR and full range) within 680-2500 nm wavelength and the spectrastar 2500 Near near infrared was used to measure spectra. The calibration equation for discriminant analysis was developed partial least square (PLS) regression and discrimination equation (DE) analysis. The PLS discriminant analysis model for three spectra range developed with mathematic pretreatment 1,8,8,1 successfully discriminated 12 different sorghum genus. External validation indicated that all samples were discriminated correctly. The whole discriminant accuracy shown 82 ~ 100 % in NIR full range spectra. The results demonstrated the usefulness of NIRS combined with chemometrics as a rapid method for discrimination of sorghum × sudangrass hybrid cultivar through seed.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.551-553
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• 1999

This paper explores the utility of a new classifier fusion approach to discrimination. Multiple classifier fusion, a popular approach in the field of pattern recognition, uses estimates of each individual classifier's local accuracy on training data sets. In this paper we investigate the effectiveness of fusion methods compared to individual algorithms, including the artificial neural network and k-nearest neighbor techniques. Moreover, we propose an efficient meta-classifier architecture based on an approximation of the posterior Bayes probabilities for learning the oracle.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.61-64
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• 2003

In this paper, we implement PAMD(Positive Answering Machine Detection) for discrimination human and ARS. We are used Grunt detection, Glitch Noise detection and Tone detection for PAMD. It distinguishes voice signals from ring-back tone and glitch noise respectively. And as a second step, it judges whether human responses or ARS responses after integrating pattern changes like initial response period, the number of voice data, each time of voice data period and glitch noise. The accuracy is about 9375 in ASR and about 98％ in Mobile phone.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.62-67
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• 2005

A new picking algorithm has been developed on real-time basis for finding the onset of P wave as well as discriminating the micro seismic signal from artificial noise. Unlike the previous methods which have used the STA/LTA ratio for discriminating the P arrivals, we have adopted the slope discrimination methods for identifying the P onset. As result, this algorithm has been turned out to be efficient in both accuracy and computation in on-line system.