• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.195-204
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• 2014

In this paper, we propose an automatic modulation classification method for ten digitally modulated baseband signals, such as 2-FSK, 4-FSK, 8-FSK, MSK, BPSK, QPSK, 8-PSK, 16-QAM, 32-QAM, and 64-QAM based on higher order statistics of cyclostationary process. The first order cyclic moments and higher order cyclic cumulants of the signal are used as features of the modulation signals. The proposed method consists of two stages. At the first stage, we classify modulation signals as M-FSK and non-FSK using peaks of the first order cyclic moment. At the next step, we apply the Gaussian mixture model-based classifier to classify non-FSK. Simulation results are demonstrated to evaluate the proposed scheme. The results show high probability of classification even in the presence of frequency and phase offsets.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1161-1167
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• 2018

In order to cope with dramatically increasing malware variant, malware classification research is getting diversified. Recent research tend to grasp individual limits of existing malware analysis technology (static/dynamic), and to change each method into "hybrid analysis", which is to mix different methods into one. Futhermore, it is applying machine learning to identify malware variant more accurately, which are difficult to classify. However, accuracy and scalability of trade-off problems that occur when using all kinds of methods are not yet to be solved, and it is still an important issue in the field of malware research. Therefore, to supplement and to solve the problems of the original malware classification research, we are focusing on developing a new malware classification system in this research.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.661-674
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• 2018

This paper studies how the audience perceives the types of classical performances in their performance choice, analyzes its effects on performance invigoration, and investigates how such an audience perception affects the marketing application of performance on the supply side. For this, based on data of performance programs delivered in five representative concert halls for last three years as well as the classification of previous literature, we classified performance types into five categories such as Authentic classic, Omnibus, Lecture, Crossover and Mixture type. And then, we surveyed the audience of classical music. From statistical analyses, we found first that the audience perceives the types of classical performances and utilizes their perception as an element in choosing performances. Second, the perception of classical performance types has significant effects on audience development and progress in circumstances of high audience acceptance and satisfaction. Finally, this result confirms that suppliers can employ this audience perception as a marketing strategy by setting target audience based on such perception.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.7-13
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• 2023

This study investigated the criteria for evaluating the impact of artificial intelligence services. The study classified AI evaluation targets into two areas: AI service and AI technology, and identified influence, sustainability, efficiency, effectiveness, and appropriateness as potential evaluation criteria. The time aspect of AI service evaluation was divided into pre-evaluation and post-evaluation, with pre-evaluation focused on reviewing items during development and design. The AI service area was classified into public, private, and mixed forms, and the impact assessment was classified as vertical or horizontal. The application of AI services was divided into normative and regulatory aspects, and the purpose of the evaluation could be impact or process evaluation. The subject and field of the AI service could also be used for classification purposes. The results of this study can be used to support the creation of AI service impact policies and countermeasures. However, further research is needed to develop specific indicators based on the criteria identified in this study to evaluate the impact of AI services.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.4
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• pp.274-286
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• 2005

Fracture behaviors and acoustic emission (AE) characteristics of single-edge-notched monolithic aluminum plates and glass fiber/aluminum hybrid laminate plates have been investigated under tensile loads. AE signals from monolithic aluminum could be classified into two different types: signals with low frequency band and high frequency band. High frequency signals were detected in the post stage of loading beyond displacement of 0.45mm. For glass fiber/aluminum laminates, AE signals with high amplitude and long duration were additionally confirmed on FFT frequency analysis, which corresponded to macro-crack propagation and/or delamination between A1 and fiber layers. On the basis of the above AE analysis and fracture observation with optical microscopy and ultrasonic T scan, characteristic features of AE associated with fracture processes of single-edge-notched glass fiber/aluminum laminates were elucidated according to different fiber ply orientations.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.269-278
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• 2011

There are many researches that have considered the distribution functions and appropriate covariates corresponding to the scores in order to improve the accuracy of a diagnostic test, including the ROC curve that is represented with the relations of the sensitivity and the specificity. The ROC analysis was used by the regression model including some covariates under the assumptions that its distribution function is known or estimable. In this work, we consider a general situation that both the distribution function and the elects of covariates are unknown. For the ROC analysis, the mixtures of normal distributions are used to estimate the distribution function fitted to the credit evaluation data that is consisted of the score random variable and two sub-populations of parameters. The AUC measure is explored to compare with the nonparametric and empirical ROC curve. We conclude that the method using normal mixtures is fitted to the classical one better than other methods.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.4
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• pp.332-337
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• 2013

National investment was performed in the research and development of renewable energy because of climate change by air pollution, exhaustion of energy sources, energy security, and so on. Biodiesel fuel of the renewable energy is highlighted as friendly environment energy, it is possible to operate in regular diesel engines when it is blended with invariable ratios without making any changes. Emission factors have been estimated for commercial ship from various research institutes; however, it is difficult to develop emission factors for military vessels. In this work, biodiesel blended fuel emission factors for sulfur dioxide and carbon dioxide were quantitatively estimated from propulsive diesel engine installed on naval vessel using fuel property analysis. In addition, exhaust emissions were quantitatively calculated on the basis of fuel consumption rate with biodiesel content by percentage.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.781-788
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• 2005

LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

• Journal of the Mineralogical Society of Korea
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• v.24 no.3
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• pp.225-234
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• 2011

The mineralogy and mixing state were investigated by the high resolution scanning electron microscopy combined with energy-dispersive X-ray analysis on particles of the total suspended solid (TSP) samples collected during the Asian dust event, spring, 2009. Relatively large particles were dominated by quartz, plagioclase, K-feldspar, amphibole, biotite, muscovite, chlorite, and calcite. Clay minerals usually occur as thin coatings on the coarse minerals or as aggregates. Calcite nanofibers are often admixed with clay platelets in the clay coatings and aggregates. Dust particles were classified on the basis of their main minerals. The single-particle mineralogy and mixing state of the TSP sample are consistent with those of $PM_{10}$ samples in previous studies.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.411-419
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• 2003

An area corresponding to the spatial resolution of optical remote sensor imagery often includes more than one pure surface material. In such case, a pixel value represents a mixture of spectral reflectance of several materials within it. This study attempts to apply the spectral mixture analysis on forest and to evaluate the information content of endmember fractions resulted from the spectral unmixing. Landsat-7 ETM+ image obtained over the study area in the Kwangneung Experimental Forest was initially geo-referenced and radiometrically corrected to reduce the atmospheric and topographic attenuations. Linear mixture model was applied to separate each pixel by the fraction of six endmember: deciduous, coniferous, soil, built-up, shadow, and rice/grass. The fractional values of six endmember could be used to separate forest cover in more detailed spatial scale. In addition, the soil fraction can be further used to extract the information related to the canopy closure. We also found that the shadow effect is more distinctive at coniferous stands.