• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.45-48
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• 2009

In this paper, we use the optimization design theory based on the finite element method and implement the optimal design of electromagnetic devices using COMSOL interface. COMSOL is one of the commercial EM software. Shape information for the design optimization is extracted by CAD in EM software. To calculate the shape of optimal design, sensitive analysis is applied to the design processing in MATLAB. To achieve the design objective in this paper, objective function is defined. According to the sensitive analysis based on the finite element method, we change the design variable after the sensitivity of the objective function is computed. To verify the proposed method, the results are compared with the initial design.

• Journal of Distribution Science
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• v.20 no.9
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• pp.47-59
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• 2022

Purpose: This study aims to analyze a triangulation model: 1) the effect of entrepreneurship education (EE) on entrepreneurship knowledge (EK) and entrepreneurship mindset (EM) and 2) the effect of EK on EM. Entrepreneurship education is a medium and pedagogical tool to cultivate EK and EM with the purpose enhancing of students who will be interested in entrepreneurial activities. Knowledge of adequate entrepreneurship is a stimulus strategic tool to develop the entrepreneurial mindset of students. Research design, data, and methodology: There were 278 respondents from Business and Non-Business both Indonesian and Malaysian students. The research design was quantitative and evaluated three hypotheses by PLS-SEM using WarpPLS v.7 software. Statistic descriptive for respondent used SPSS IBM v.26. Results: The results showed that the three hypotheses had supported with a significant level of p-value < 0.001. It's meant EE enhanced both EK and EM. Furthermore, increasing EM was not only by EE, but also EM could be increased through EK. Conclusions: The novelty of this research contributes to filling the knowledge gap in the development of pedagogy in the pursuit of entrepreneurship using a triangulation model of the relationship among EE, EK, and EM.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.912-919
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• 2005

When we apply binary classification to multi-class classification for text categorization, we use the One-Against-All method generally, However, this One-Against-All method has a problem. That is, documents of a negative set are not labeled by human. Thus, they can include many noisy documents in the training data. In this paper, we propose that the Sliding Window technique and the EM algorithm are applied to binary text classification for solving this problem. We here improve binary text classification through extracting noise documents from the training data by the Sliding Window technique and re-assigning categories of these documents using the EM algorithm.

• 한국전자현미경학회:학술대회논문집
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• 2005.11a
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• pp.201-202
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• 2005

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.467-478
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• 2002

With the rapid development of wired or wireless Internet, various kinds of resource constrained mobile devices, such as cellular phone, PDA, homepad, smart phone, handhold PC, and so on, have been emerging into personal or commercial usages. Most software to be embedded into those devices has been forced to have the characteristic of flexibility rather than the fixedness which was an inherent property of embedded system. It means that recent technologies require the flexible embedding into the variety of resource constrained mobile devices. A document processor for XML which has been positioned as a standard mark-up language for information representation on the Web, is one of the essential software to be embedded into those devices for browsing the information. In this paper, a framework for configurable XML processor called EmXJ is designed and implemented for flexible embedding into various types of resource constrained mobile devices, and its advantages are compared to conventional XML processors.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.4
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• pp.444-449
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• 2010

The aim of this study was to measure the hair melanins of various colors and to find the relationship between the quantity of melanins and hair color phenotypes in alpacas. According to the Munsell color system, 3 healthy alpacas were selected for each of the 22 different hair color phenotypes (66 alpacas altogether). Alpaca hair was taken from the lateral thoracic region and then dissolved with different solutions to obtain melanins. The values of alkali-soluble melanins (ASM), eumelanin (EM) and pheomelanin (PM) were measured by spectrophotometric assay, and labeled as Sp.ASM, Sp.EM and Sp.PM, respectively. Data were analyzed using SPSS11.5 software. Results showed that average Sp.ASM and Sp.PM were increased as the color deepened from white to black, ranging from 0.500 to 4.543 for Sp.ASM and from 0.268 to 1.457 for Sp.EM. However, average Sp.PM had no such apparent relationship with color. Based on the value of Sp.ASM and EM, 7 hues were produced and gray was a single hue. Most of the data were in a normal distribution (p>0.10). ANOVA analysis showed that mean values of Sp.ASM, Sp.EM and Sp.PM were significantly different (p<0.05). The results also showed that Sp.ASM was positively correlated with Sp.EM but the correlation between Sp.ASM and Sp.PM was not significantly different from 0. It is concluded that EM is the major constituent of alpaca hair melanin; there is a significant correlation among ASM, EM and alpaca hair colors, and EM is the most reliable parameter for distinguishing these groups.

• Journal of Astronomy and Space Sciences
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• v.19 no.2
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• pp.97-106
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• 2002

The design feature of KOMPSAT-2 simulator based on object oriented design methodology in terms of unified modeling language (UML) has been discussed in this paper. Also, we present how to embed flight software into the simulator. Flight software em-bedding for KOMPSAT-2 simulator is compared to that of the KOMPSAT-1 simulator.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.579-590
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• 2003

When we analyze incomplete data, i.e., data with missing values, we need treatment for the missing values. A common way to deal with this problem is to delete the cases with missing values. Various other methods have been developed. Among them are EM algorithm and regression algorithm which can estimate missing values and impute the missing elements with the estimated values. In this paper, we introduce multiple imputation software SOLAS which generates multiple data sets and imputes with them.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.57-64
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• 2014

Most previous studies of software fault prediction model which determines the fault-proneness of input modules have focused on supervised learning model using training data set. However, Unsupervised learning model is needed in case supervised learning model cannot be applied: either past training data set is not present or even though there exists data set, current project type is changed. Building an unsupervised learning model is extremely difficult that is why only a few studies exist. In this paper, we build unsupervised models using representative clustering algorithms, EM and DBSCAN, that have not been used in prior studies and compare these models with the previous model using K-means algorithm. The results of our study show that the EM model performs slightly better than the K-means model in terms of error rate and these two models significantly outperform the DBSCAN model.