• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.5
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• pp.611-619
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• 2014

Combining similar data provides larger data sets through conducting test for homogeneity of several samples under various production processes or samples from different LOTs. The test for homogeneity has been applied to either variable or attribute data, and for variable data set physical homogeneity has been tested without consideration of the specification to the set. This paper proposes a method for test of homogeneity based on quality level through using both variable data and the specification. Quality-based test for homogeneity as a way of combining data is implemented by test for coefficient of variation in the proposed method. The method was verified through the application to the data set in open literature. And possibility to combine performance data for various types of thermal battery was discussed in order to estimate operation reliability.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.3
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• pp.26-31
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• 2009

A new LFSR based test compression scheme is proposed by reducing the maximum number of specified bits in the test cube set, smax, virtually. The performance of a conventional LFSR reseeding scheme highly depends on smax. In this paper, by using different clock frequencies between an LFSR and scan chains, and grouping the scan cells, we could reduce smax virtually. H the clock frequency which is slower than the clock frequency for the scan chain by n times is used for LFSR, successive n scan cells are filled with the same data; such that the number of specified bits can be reduced with an efficient grouping of scan cells. Since the efficiency of the proposed scheme depends on the grouping mechanism, a new graph-based scan cell grouping heuristic has been proposed. The simulation results on the largest ISCAS 89 benchmark circuit show that the proposed scheme requires less memory storage with significantly smaller area overhead compared to the previous test compression schemes.

• Journal of Science Education
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• v.41 no.2
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• pp.195-212
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• 2017

Small group learning is an educational approach to allow students to solve the problems and to achieve a common goal. Especially, small group learning in science education is one of the most important educational approaches and effective to ensure understanding of a topic. Small group learning consisting of three students in science education maximize student understanding and learning efficiency. However, It is reported that the effects of small group learning on achievement show different results, corresponding to different grouping methods(homogeneous/heterogeneous). This study investigated the effects of grouping method on difference of ecological niche of photosynthesis concepts. To achieve this, 1107 7th students were composed of homogeneous and heterogeneous groups classified into top, middle, and bottom levels. The photosynthesis units were divided into four categories: the photosynthesizing place, the substances of photosynthesis, required materials for the photosynthesizing, and environmental factors affecting photosynthesis. A questionnaire was composed by selecting concepts having a frequency of 4% or more based on prior studies on the change of the ecological status of photosynthesis. The questionnaire was scored in terms of relativity and understanding on each of the proposed concepts in the four categories. The result of this study is as set forth below. 1) There was an enhancement of learning the concept of science in small group classes consisting of 3 students. 2) To enhance the average upon composing of a group, it is proposed that the group should be formed homogeneously, and to reduce the deviation between the members, it is proposed that the group should be formed heterogeneously. Through this study, it is expected that specific studies verifying the difference or effect on the duplicity of results are conducted based on the composition of groups.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1164-1171
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• 2012

Recently, machine olfactory systems as an artificial substitute of the human olfactory system are being studied actively because they can scent dangerous gases and identify the type of gases in contamination areas instead of the human. In this paper, we present an effective design method for the gas identification system. Even though dimensionality reduction is the very important part, in pattern analysis, We handled effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, we constructed the hierarchical rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.387-394
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• 2013

Data mining is the process of discovering hidden, non-trivial patterns in large amounts of data records in order to be used very effectively for analysis and forecasting. Because hundreds of variables give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. Hence cluster analysis is a main task of data mining and is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. In this paper system implementation is of great significance, which defines a new definition based on information-theoretic entropy and analyse the analogue behaviors of objects at hand so as to address the measurement of uncertainties in the classification of categorical data. The sources were taken from a survey aimed to identify of job guidance from students in high school pyeongtaek. we show how variable precision information-entropy based rough set can be used to group student in each section. It is proved that the proposed method has the more exact classification than the conventional in attributes more than 10 and that is more effective in job guidance for students.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.501-514
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• 2013

A grouping process through the similarity comparison is required to effectively classify and respond a malicious code. When we have a use of the past similarity criteria to be used in the comparison method or properties it happens a increased problem of false negatives and false positives. Therefore, in this paper we apply to choose variety of properties to complement the problem of behavior analysis on the heuristic-based of 2nd step in malicious code auto analysis system, and we suggest a similarity comparison method applying AHP (analytic hierarchy process) for properties weights that reflect the decision-making technique. Through the similarity comparison of malicious code, configured threshold is set to the optimum point between detection rates and false positives rates. As a grouping experiment about unknown malicious it distinguishes each group made by malicious code generator. We expect to apply it as the malicious group information which includes a tracing of hacking types and the origin of malicious codes in the future.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.277-285
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• 2004

We present a new hybrid stereo matching technique in terms of the co-operation of area-based stereo and feature-based stereo. The core of our technique is that feature matching is carried out by the reference of the disparity evaluated by area-based stereo. Since the reference of the disparity can significantly reduce the number of feature matching combinations, feature matching error can be drastically minimized. One requirement of the disparity to be referenced is that it should be reliable to be used in feature matching. To measure the reliability of the disparity, in this paper, we employ the self-consistency of the disunity Our suggested technique is applied to the detection of 3D line segments by 2D line matching using our hybrid stereo matching, which can be efficiently utilized in the generation of the rooftop model from urban imagery. We carry out the experiments on our hybrid stereo matching scheme. We generate synthetic images by photo-realistic simulation on Avenches data set of Ascona aerial images. Experimental results indicate that the extracted 3D line segments have an average error of 0.5m and verify our proposed scheme. In order to apply our method to the generation of 3D model in urban imagery, we carry out Preliminary experiments for rooftop generation. Since occlusions are occurred around the outlines of buildings, we experimentally suggested multi-image hybrid stereo system, based on the fusion of 3D line segments. In terms of the simple domain-specific 3D grouping scheme, we notice that an accurate 3D rooftop model can be generated. In this context, we expect that an extended 3D grouping scheme using our hybrid technique can be efficiently applied to the construction of 3D models with more general types of building rooftops.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1235-1242
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• 2019

While several machine learning technique has been implemented for Android malware categorization, there is still difficulty in analyzing due to overfitting problem and including of un-executable code, etc. In this paper, we introduce our implemented tool to address these problems. Tool is consists of approximately 1,500 lines of Java code, and perform Flow analysis on set of APIs, or on control flow graph. Our tool groups all the API by its relationship and only perform analysis on actually executing code. Using our tool, we grouped 39032 APIs into 4972 groups, and 12123 groups with result of including class names. We collected 7,000 APKs from 7 families and evaluated our feature reduction technique, and we also reduced features again with selecting APIs that have frequency more than 20%. We finally reduced features to 263-numbers of feature for our collected APKs.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.5 no.3
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• pp.76-85
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• 2024

The purpose of this study is to propose a new method of analysis focusing on interconnections between species rather than traditional biodiversity analysis, which represents ecosystems in terms of species and individual counts such as species diversity and species richness. This new approach aims to enhance our understanding of ecosystem networks. Utilizing data from the 4th National Natural Environment Survey (2014-2018), the following eight taxonomic groups were targeted for our study: herbaceous plants, woody plants, butterflies, Passeriformes birds, mammals, reptiles & amphibians, freshwater fishes, and benthonic macroinvertebrates. A co-occurrence frequency analysis was conducted using nationwide data collected over five years. As a result, in all eight taxonomic groups, the degree value represented by a linear regression trend line showed a slope of 0.8 and the weighted degree value showed an exponential nonlinear curve trend line with a coefficient of determination (R²) exceeding 0.95. The average value of the clustering coefficient was also around 0.8, reminiscent of well-known social phenomena. Creating a combination set from the species list grouped by temporal information such as survey date and spatial information such as coordinates or grids is an easy approach to discern species distributed regionally and locally. Particularly, grouping by species or taxonomic groups to produce data such as co-occurrence frequency between survey points could allow us to discover spatial similarities based on species present. This analysis could overcome limitations of species data. Since there are no restrictions on time or space, data collected over a short period in a small area and long-term national-scale data can be analyzed through appropriate grouping. The co-occurrence frequency analysis enables us to measure how many species are associated with a single species and the frequency of associations among each species, which will greatly help us understand ecosystems that seem too complex to comprehend. Such connectivity data and graphs generated by the co-occurrence frequency analysis of species are expected to provide a wealth of information and insights not only to researchers, but also to those who observe, manage, and live within ecosystems.