• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.761-766
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• 2011

A hedge is a linguistic device to express uncertainties. Hedges are used in a sentence when the writer is uncertain or has doubt about the contents of the sentence. Due to this uncertainty, sentences with hedges are considered to be non-factual. There are many applications which need to determine whether a sentence is factual or not. Detecting hedges has the advantage in information retrieval, and information extraction, and QnA systems, which make use of non-hedge sentences as target to get more accurate results. In this paper, we constructed Korean hedge corpus, and extracted generalized hedge cue-word patterns from the corpus, and then used them in detecting hedges. In our experiments, we achieved 78.6% in F1-measure.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.479-486
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• 2005

The ontology was proposed to construct the logical basis of semantic web. Ontology represents domain knowledge in the formal form and it enables that machine understand domain knowledge and provide appropriate intelligent service for user request. However, the construction and the maintenance of ontology requires large amount of cost and human efforts. This paper proposes an automatic ontology construction method for defining relation between concepts in the documents. The Proposed method works as following steps. First we find concept pairs which compose association rule based on the concepts in domain specific documents. Next, we find pattern that describes the relation between concepts by clustering the context between two concepts composing association rule. Last, find generalized pattern name by clustering the clustered patterns. To verify the proposed method, we extract relation between concepts and evaluate the result using documents set provide by TREC(Text Retrieval Conference). The result shows that proposed method cant provide useful information that describes relation between concepts.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.667-680
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• 2007

The bio-data processing is used for a suitable purpose with bio-signals, which are obtained from human individuals. Recently, there is increasing demand that the bio-data has been widely applied to various applications. However, it is often that the number of data within each class is limited and the number of classes is large due to the property of problem domain. Therefore, the conventional pattern recognition systems and classification methods are suffering form low generalization performance because the system using the lack of data is influenced by noises of that. To solve this problem, we propose a modified additive factor model for bio-data generation, with two factors; the class factor which affects properties of each individuals and the environment factor such as noises which affects all classes. We then develop a classification system through defining a new similarity function using the proposed model. The proposed method maximizes to use an information of the class classification. So, we can expect to obtain good generalization performances with robust noises from small number of datas for bio-data. Experimental results show that proposed method outperforms significantly conventional method with real bio-data.

• The Mathematical Education
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• v.50 no.2
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• pp.213-231
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• 2011

The purpose of this study is to analyze the 6th graders' understanding of the concepts of variable on various aspects of school algebra. For this purpose, the test of concepts of variable targeting a sixth-grade class was conducted and then two students were selected for in-depth interview. The level of mathematics achievement of the two students was not significantly different but there were differences between them in terms of understanding about the concepts of variable. The results obtained in this study are as follows: First, the students had little basic understanding of the variables and they had many cognitive difficulties with respect to the variables. Second, the students were familiar with only the symbol '${\Box}$' not the other letters nor symbols. Third, students comprehended the variable as generalizers imperfectly. Fourth, the students' skill of operations between letters was below expectations and there was the student who omitted the mathematical sign in letter expressions including the mathematical sign such as x+3. Fifth, the students lacked the ability to reason the patterns inductively and symbolize them using variables. Sixth, in connection with the variables in functional relationships, the students were more familiar with the potential and discrete variation than practical and continuous variation. On the basis of the results, this study gives several implications related to the early algebra education, especially the teaching methods of variables.

• Journal of Advanced Navigation Technology
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• v.22 no.4
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• pp.295-301
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• 2018

Recently, GPS(Global Positioning System) array antenna technology is generally used and widely adopted as a national infrastructure structure and aero-vehicles for protection the GPS signal reception. Until now, the 2-dimensional planar array is universally used for its applications in the array antenna signal processing, however relatively higher altitude air vehicles such as UAV experiences additional null zones induced by low altitude GPS interferences which is located in a symmetry zone of antenna horizontal plane and this could make the receiving antenna pattern coverage reduction. In this paper, we improved 20% of the beam pattern receiving performance and 13 dB correlation value improvement by eliminating the interference mirror images.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.35-40
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• 2019

Traditional Malware Detection is susceptible for detecting malware which is modified by polymorphism or obfuscation technology. By learning patterns that are embedded in malware code, machine learning algorithms can detect similar behaviors and replace the current detection methods. Data must collected continuously in order to learn malicious code patterns that change over time. However, the process of storing and processing a large amount of malware files is accompanied by high space and time complexity. In this paper, an HDFS-based distributed processing system is designed to reduce space complexity and accelerate feature extraction time. Using a distributed processing system, we extract two API features based on filtering basis, 2-gram feature and APICFG feature and the generalization performance of ensemble learning models is compared. In experiments, the time complexity of the feature extraction was improved about 3.75 times faster than the processing time of a single computer, and the space complexity was about 5 times more efficient. The 2-gram feature was the best when comparing the classification performance by feature, but the learning time was long due to high dimensionality.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Education of Primary School Mathematics
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• v.11 no.2
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• pp.105-119
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• 2008

Functional thinking, which focuses on the relationship between two or more varying quantities, is one of the key strands of algebraic thinking. This article is a case study that aimed to investigate how 3rd grade elementary students might make their functional thinking. The results showed that students not only understood the functional situation well but also created a record of the corresponding values of quantities, typically using descriptive writings and pictures. But when they tried to find a pattern and make a generalization, the students showed various difficulties. This paper concludes with implications on how to promote students' functional thinking from early grades in the elementary school.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.83-90
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• 1994

In this paper, we propose a new learning algorithm, ASOFM(Adaptive Self Organizing Feature Map), to solve the defects of Kohonen's Self Organiaing Feature Map. Kohonen's algorithm is sometimes stranded on local minima for the initial weights. The proposed algorithm uses an object function which can evaluate the state of network in learning and adjusts the learning rate adaptively according to the evaluation of the object function. As a result, it is always guaranteed that the state of network is converged to the global minimum value and it has a capacity of generalized learning by adaptively. It is reduce that the learning time of our algorithm is about $30\%$ of Kohonen's.

• Korean Journal of Cognitive Science
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• v.2 no.1
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• pp.33-50
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• 1990

Recently, neural networks have been proposed as computaional models for hard prlblems that the brain appears to solve easily. This paper proposes a hierarchical network which practically recognizes printed Hangul characters based on the various psychological stueies. This system is composed of a type classification netwotk and six recognition networks. The former clessifier input character images into one of the six thper by their overall sturcture, and the latter further classify them into character code. Extperiments with most frequently used 990 printed hangul characters conform the superiority of the propsed system. After all, neural nework approach turns out to be very reasonable through a comparison with statistical classifier and an analysis of mis-classification and generalization capability.