• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.15-34
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• 2006

This paper introduces an empirical modeling technique. This technique uses a set of sample results which are collected from a few small scale simulations. Empirical models are developed by applying a couple of statistical estimation techniques to these samples. We built two types of models for cache miss rates in Symmetric Multiprocessor systems. One is for the changes of input data set size while the specification of target system is fixed. The other is for the changes of the number of processors in target system while the input data set size is fixed. To develop accurate models, we built individual model for every kind of cache misses for each shared data structure in a program. The final model is then obtained by integrating them. Besides, combined use of Least Mean Squares and Robust Estimations enhances the quality of models by minimizing the distortion due to outliers. Empirical modeling technique produces extremely accurate models without analysis on sample data. In addition, since only snail scale simulations are necessary, once a set of samples can be collected, empirical method can be adopted in any research areas. In 17 cases among 24 trials, empirical models present extremely low prediction errors below $1\%$. In the remaining cases, the accuracy is excellent, as well. The models sustain high quality even when the behavioral characteristics of programs are irregular and the number of samples are barely enough.

• Archives of Pharmacal Research
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• v.28 no.5
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• pp.604-611
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• 2005

Polyethylene glycol (PEG) 6000-based solid dispersions (SDs), by incorporating various pharmaceutical excipients or microemulsion systems, were prepared using a fusion method, t o compare the dissolution rates and bioavailabilities in rats. The amorphous structure of the drug in SDs was also characterized by powder X-ray diffractometry (XRD) and differential scanning calorimetry (DSC). The ketoconazole (KT), as an antifungal agent, was selected as a model drug. The dissolution rate of KT increased when solubilizing excipients were incorporated into the PEG-based SDs. When hydrophilic and lipophilic excipients were combined and incorporated into PEG-based SDs, a remarkable enhancement of the dissolution rate was observed. The PEG-based SDs, incorporating a self microemulsifying drug delivery system (SMEDDS) or microemulsion (ME), were also useful at improving the dissolution rate by forming a microemulsion or dispersible particles within the aqueous medium. However, due to the limited solubilization capacity, these PEG-based SDs showed dissolution rates, below 50% in this study, under sink conditions. The PEG-based SD, with no pharmaceutical excipients incorporated, increased the maximum plasma concentration (C$_{max}$) and area under the plasma concentration curve (AUC$_{0-6h}$) two-fold compared to the drug only. The bioavailability was more pronounced in the cases of solubilizing and microemulsifying PEG-based SDs. The thermograms of the PEG-based SDs showed the characteristic peak of the carrier matrix around 60$^{\circ}C$, without a drug peak, indicating that the drug had changed into an amorphous structure. The diffraction pattern of the pure drug showed the drug to be highly crystalline in nature, as indicated by numerous distinctive peaks. The lack of the numerous distinctive peaks of the drug in the PEG-based SDs demonstrated that a high concentration of the drug molecules was dissolved in the solid-state carrier matrix of the amorphous structure. The utilization of oils, fatty acid and surfactant, or their mixtures, in PEG-based SD could be a useful tool to enhance the dissolution and bioavailability of poorly water-soluble drugs by forming solubilizing and microemulsifying systems when exposed to gastrointestinal fluid.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.270-275
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• 2006

The cultivated pearls collected for the study were pretreated through the removal of contaminants and the surface bleaching for easy dyeing. Coloring of pearls are necessary after selecting dyes adsorbable to the Conchiolin layer, a kind of hard protein formed in the seawater, covering the surface of the pretreated pearls. Dyes adsorbable to the Conchiolin layers are mostly basic dyes such as Rhodamine 6G(R6G), Rhodamine B(RB), Methylene Blue(MB) etc. and the binary and ternary competitive adsorption were performed by mixing two or three dyes together. The multi-dye adsorption data were compared with the predictions from the ideal adsorbed solution theory(IASI) combined with the single-dye adsorption model, the Langmuir or the Redlich-Peterson(RP) model. The quality of prediction was compared by using determination coefficient($R^2$) and standard deviation(SSE) values. Predictions from the IAST were found to be in good agreement with the data for the R6G/RB binary adsorption to the pearl layers not fractionated with their size, except for the adsorption data for RB at high concentrations. Among the three binary adsorption systems, R6G/RB, R6G/MB, and MB/RB, only the RB sorption data in the R6G/RB binary system was in poor agreement with the IAST prediction. Competitive adsorption data in ternay systems were in good agreement with the predictions from the IAST except for the RB data.

• Proceedings of the Korea Association of Information Systems Conference
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• 2005.12a
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• pp.281-295
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• 2005

The study of purpose is to analyze the effects of quality factors of ERP system on organizational performance. Where as many preceding studies have been focused on the factors of general service quality, this study analyzed interrelation between quality factors and organizational performance of corporation implemented ERP system using questionnaire. As compared with preceding studies which mainly concerned with system quality factors and information quality factors, this study specially added service quality to analyzing interrelation with organizational performance. Questionnaire consists of survey for the interrelation between ERP system implemented corporation and organizational performance. The results are as follows. First, quality factors of ERP system effected on organizational performance by t-test. Second, quality factors of ERP system showed significance statistically on the effects of organizational performance by t-test. The results of this study have a great expectation for the role of offering useful insight and information in the field of business and education. First, make sure that quality factors have significant effects on organizational performance when implemented ERP system in the business, investment and research on quality factors should be combined with. Second, in a sense that this study was added to service quality in research of quality factors by the detailed approaches which different from preceding studies, it is of highly practical use. Third, this study presents scientific foundation on quality improvement for quality factors of ERP system.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.173-193
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• 2010

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component-based software development to promote application interaction and integration within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web services repositories not only be well-structured but also provide efficient tools for an environment supporting reusable software components for both service providers and consumers. As the potential of Web services for service-oriented computing is becoming widely recognized, the demand for an integrated framework that facilitates service discovery and publishing is concomitantly growing. In our research, we propose a framework that facilitates Web service discovery and publishing by combining clustering techniques and leveraging the semantics of the XML-based service specification in WSDL files. We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the Web service domain. We have developed a Web service discovery tool based on the proposed approach using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web services repositories. We believe that both service providers and consumers in a service-oriented computing environment can benefit from our Web service discovery approach.

• Korean Chemical Engineering Research
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• v.53 no.3
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• pp.302-308
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• 2015

Isothermal vapor liquid equilibria for the binary system of 3-methylpentane with ethylene glycol monopropyl ether ($C_3E_1$) and ethylene glycol isopropyl ether ($iC_3E_1$) were measured at 303.15, 318.15, and 333.15K. In our previous work, phase equilibria for the binary system of $C_3E_1$ mixtures were investigated according to the chain length of alkane, alcohol or those isomer. But in this study, we discussed the different effect of $C_3E_1$ and its isomer, $iC_3E_1$, on the phase equilibria. The measured systems were correlated with a Peng-Robinson equation of state (PR EOS) combined with Wong-Sandler mixing rule for the vapor phase, and NRTL, UNIQUAC, and Wilson activity coefficient models for the liquid phase. All the measured systems showed good agreement with the correlation results. And it was found that the phase equilibria showed very little difference between the $iC_3E_1$ mixture system and the $C_3E_1$ mixture system.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.837-842
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• 2017

In this paper, as a basic study for combination of turbo equalizer and 4D-8PSK-TCM(4 Dimensional 8 Phase Shift Keying Trellis Coded Modulation) system, which is recommended for X-band band satellite communication in CCSDS, a system combining 8PSK-TCM system and turbo equalizer has designed and performance evaluation of the system has performed. BER performance of 8PSK and 8PSK-TCM systems has evaluated and analyzed in AWGN, Proakis B and Proakis C channels in detail. As results of simulation, BER performance of 8PSK-TCM system is better than 8PSK system in AWGN environment. Furthermore, it can be confirmed that the system combining the 8PSK-TCM system with the turbo equalizer can further improve the BER performance through iterative equalization in the ISI channel environment. The 8PSK-TCM system combined with the turbo equalizer requires SNR of 5.9 dB for BER performance of $10^{-3}$ with 5 iterative equalization in Proakis B and SNR of 9.1 dB for BER of $10^{-2}$ with 5 iterative equalization in Proakis C.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.33-54
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• 2010

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of public Web service repositories have been proposed, but the Web service taxonomy generation has not been satisfactorily addressed. Unfortunately, most existing Web service taxonomies are either too rudimentary to be useful or too hard to be maintained. In this paper, we propose a Web service taxonomy generation framework that combines an artificial neural network based clustering techniques with descriptive label generating and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service repositories. We report on some preliminary results demonstrating the efficacy of the proposed approach.