• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.93-107
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• 2009

This study investigated consumer preferences for Internet medical information service in China using the multi-attribute utility theory. The multi-attribute utility theory is a compositional approach for modeling consumer preferences wherein researchers calculate the overall service utility by summing up the evaluation results for each attribute. We found that Chinese Internet medical information users consider the availability of information and quick response to be the most important attributes. Further, they think that the comment feature is less important as compared to other attributes such as costs and updates. In addition, we found that the Internet users having more Internet experience consider these attributes to be more important as compared to the people who are just beginning to surf the Internet. For any successful Internet business, Internet marketers should assess individual-level preference and accordingly organize a fresh campaign. As of now, Internet marketers need estimation methods to predict the market performance of new services in many different business environments. We believe that the multi-attribute utility theory is a useful approach in this regard.

• Progress in Medical Physics
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• v.30 no.4
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• pp.128-138
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• 2019

Purpose: Segmental analysis of volumetric modulated arc therapy (VMAT) is not clinically used for compositional error source evaluation. Instead, dose verification is routinely used for plan-specific quality assurance (QA). While this approach identifies the resultant error, it does not specify which machine parameter was responsible for the error. In this research study, we adopted an approach for the segmental analysis of VMAT as a part of machine QA of linear accelerator (LINAC). Methods: Two portal dose QA plans were generated for VMAT QA: a) for full arc and b) for the arc, which was segmented in 12 subsegments. We investigated the multileaf collimator (MLC) position and dosimetric accuracy in the full and segmented arc delivery schemes. A MATLAB program was used to calculate the MLC position error from the data in the dynalog file. The Gamma passing rate (GPR) and the measured to planned dose difference (DD) in each pixel of the electronic portal imaging device was the measurement for dosimetric accuracy. The eclipse treatment planning system and a MATLAB program were used to calculate the dosimetric accuracy. Results: The maximum root-mean-square error of the MLC positions were <1 mm. The GPR was within the range of 98%-99.7% and was similar in both types of VMAT delivery. In general, the DD was <5 calibration units in both full arcs. A similar DD distribution was found for continuous arc and segmented arcs sums. Exceedingly high DD were not observed in any of the arc segment delivery schemes. The LINAC performance was acceptable regarding the execution of the VMAT QA plan. Conclusions: The segmental analysis proposed in this study is expected to be useful for the prediction of the delivery of the VMAT in relation to the gantry angle. We thus recommend the use of segmental analysis of VMAT as part of the regular QA.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1042-1042
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.537-549
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• 2023

The distribution characteristics of tidal flat sediment components are used as an essential data for coastal environment analysis and environmental impact assessment. Therefore, a reliable classification map of surface sedimentary facies is essential. This study evaluated the applicability of regression kriging to generate a classification map of the sedimentary facies of tidal flats. For this aim, various factors such as the number of field survey data and remote sensing-based auxiliary data, the effect of regression models on regression kriging, and the comparison with other prediction methods (univariate kriging and regression analysis) on surface sedimentary facies classification were investigated. To evaluate the applicability of regression kriging, a case study using unmanned aerial vehicle (UAV) data was conducted on the Hwang-do tidal flat located at Anmyeon-do, Taean-gun, Korea. As a result of the case study, it was most important to secure an appropriate amount of field survey data and to use topographic elevation and channel density as auxiliary data to produce a reliable tidal flat surface sediment facies classification map. In addition, regression kriging, which can consider detailed characteristics of the sediment distributions using ultra-high resolution UAV data, had the best prediction performance compared to other prediction methods. It is expected that this result can be used as a guideline to produce the tidal flat surface sedimentary facies classification map.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1248-1248
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• 2001

Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and considerable compositional changes in milk, reducing milk quality. The potential of near infrared (NIR) spectroscopy in the region from 1100 to 2500nm and chemometric method for classification to detect milk from mastitic cows was investigated. A total of 189 milk samples from 7 Holstein cows were collected for 27 days, consecutively, and analyzed for somatic cells (SCC). Three of the cows were healthy, and the rest had mastitis periods during the experiment. NIR transflectance milk spectra were obtained by the InfraAlyzer 500 spectrophotometer in the spectral range from 1100 to 2500nm. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. The classification of the samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two concentration of SCC - 200 000 cells/ml and 300 000 cells/ml, respectively, were used as thresholds fer separation of healthy and mastitis cows. The best detection accuracy was found for models, obtained using 200 000 cells/ml as threshold and smoothed absorbance data - 98.41% from samples in the calibration set and 87.30% from the samples in the independent test set were correctly classified. SIMCA results for classes, based on 300 000 cells/ml threshold, showed a little lower accuracy of classification. The analysis of changes in the loading of first PC factor for group of healthy milk and group of mastitic milk showed, that separation between classes was indirect and based on influence of mastitis on the milk components. The accuracy of mastitis detection by SIMCA method, based on NIR spectra of milk would allow health screening of cows and differentiation between healthy and mastitic milk samples. Having SIMCA models, mastitis detection would be possible by using only DIR spectra of milk, without any other analyses.

• Journal of Powder Materials
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• v.23 no.2
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• pp.143-148
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• 2016

A microfluidic reactor with computer-controlled programmable isocratic pumps and online detectors is employed as a combinatorial synthesis system to synthesize and analyze materials for fabricating CdSe quantum dots for various applications. Four reaction condition parameters, namely, the reaction temperature, reaction time, Cd/Se compositional ratio, and precursor concentration, are combined in synthesis condition sets, and the size of the synthesized CdSe quantum dots is determined for each condition. The average time corresponding to each reaction condition for obtaining the ultraviolet-visible absorbance and photoluminescence spectra is approximately 10 min. Using the data from the combinatorial synthesis system, the effects of the reaction conditions on the synthesized CdSe quantum dots are determined. Further, the data is used to determine the relationships between the reaction conditions and the CdSe particle size. This method should aid in determining and selecting the optimal conditions for synthesizing nanoparticles for diverse applications.

• Journal of Electrochemical Science and Technology
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• v.2 no.3
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• pp.146-151
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• 2011

The formation of Li-Si-O phases, $Li_4SiO_4$ and $Li_2SiO_3$ from the starting materials SiO and $Li_2O$ are analyzed using Vienna Ab-initio Simulation (VASP) package and the total energies of Li-Si-O compounds are evaluated using Projector Augmented Wave (PAW) method and correlated the structural characteristics of the binary system SiO-$Li_2O$ with experimental data from electrochemical method. Despite $Li_2SiO_3$ becomes stable phase by virtue of lowest formation energy calculated through VASP, the experimental method shows presence of $Li_4SiO_4$ as the only product formed when SiO and $Li_2O$ reacts during slow heating to reach $550^{\circ}C$ and found no evidence for the formation of $Li_2SiO_3$. Also, higher density of $Li_4SiO_4$(2.42 g $ml^{-1}$) compared to the compositional mixture $1SiO_2-2Li_2O$ (2.226 g $ml^{-1}$) and better cycle capacity observed through experiment proves that $Li_4SiO_4$ as the most stable anode supported by better cycleabilityfor lithium ion battery remains as paradox from the point of view of VASP calculations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1231-1237
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• 2022

It is necessary to dynamic recognition system with real time loading height and pressure of the loading waste, the drying of wood, batteries, and plastic wastes, which are representative compositional wastes, and the carbonization changes on the surface. The dynamic context awareness service constituted a platform based on Universal Middleware system using BCN convergence communication service as a Ambient SDK model. A context awareness system should be constructed to determine the cause of the fire based on the analysis data of fermentation heat point with natural ignition from the load waste. Furthermore, a real-time dynamic service platform that could be apply to the configuration of scenarios for each type from early warning fire should be built using Universal Middleware. Thus, this issue for Internet of Things realize recognition platform for analyzing low temperature fired fire possibility data should be dynamically configured and presented.

• Journal of Korean Society of Forest Science
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• v.79 no.2
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• pp.216-221
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• 1990

Vegetational data from Mt. Gyeryong and Deokyu in central Korea were analysed in relation to 15 environmental variables. Two multivariate methods were applied : two-way indicator species analysis (TWINSPAN) for classification and detrended canonical correspondence analysis(DCCA), a recent technique which extracts ordination axes that can be related to environmental factors. The relationship between the distribution of dominant species of forest vegetation and soil condition in Mt. Gyeryong and Deokyu was investigated by analyzing elevation and soil nutrition gradient. Quercus mongolica forest was distributed in the high elevation and good nutrition area, Carpinzrs laxiflora and Fraxinus rhynclzophylla forest in the medium elevation and good nutrition area, Piszus densiflora-Quercus mongolica and Quercus variabilis forest in the medium elevation and medium nutrition area, Styrax jabozaica forest in the low elevation and medium nutrition area, and Pinus densiflora forest in the low elevation and poor nutrition area. The dominant compositional gradient related to elevation.