• Sleep Medicine and Psychophysiology
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• v.8 no.1
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• pp.67-71
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• 2001

Independent Component Analysis (ICA) is a blind source separation method using unsupervised learning and mutual information theory created in the late eighties and developed in the nineties. It has already succeeded in separating eye movement artifacts from human scalp EEG recording. Several characteristic sleep waves such as sleep spindle, K-complex, and positive occipital sharp transient of sleep (POSTS) can be recorded during sleep EEG recording. They are used as stage determining factors of sleep staging and might be reflections of unknown neural sources during sleep. We applied the ICA method to sleep EEG for sleep waves separation. Eighteen channel scalp longitudinal bipolar montage was used for the EEG recording. With the sampling rate of 256Hz, digital EEG data were converted into 18 by n matrix which was used as a original data matrix X. Independent source matrix U (18 by n) was obtained by independent component analysis method ($U=W{\timex}X$, where W is an 18 by 18 matrix obtained by ICA procedures). ICA was applied to the original EEG containing sleep spindle, K-complex, and POSTS. Among the 18 independent components, those containing characteristic shape of sleep waves could be identified. Each independent component was reconstructed into original montage by the product of inverse matrix of W (inv(W)) and U. The reconstructed EEG might be a separation of sleep waves without other components of original EEG matrix X. This result (might) demonstrates that characteristic sleep waves may be separated from original EEG of unknown mixed neural origins by the Independent Component Analysis (ICA) method.

• Communications of Mathematical Education
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• v.24 no.3
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• pp.709-730
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• 2010

The inverse function of a one-to-one correspondence is explained with a graph, a numerical formula or other useful expressions. The purpose of this paper is to know how low achieving students understand the learning contents needed reversible thinking about irrational functions. Low achieving students in this study took paper-pencil test and their written answers were collected. They made various mistakes in solving problems. Their error types were grouped into several classes and identified in this analysis. Most students did not connected concepts that they learned in the lower achieving students to think in reverse order in case of and to visualize concepts of functions. This paper implies that it is very important to take into account students' accommodation and reversible thinking activity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.173-179
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• 1998

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neural net, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Menas) arithmatics, to decide whether a requested call that is not trained in learning phase to be connected or not. The system generates the estimated traffic pattern of the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmatics. The input to the NN is the vector consisted of traffic parameters which is the means and variances of the number of cells arriving inthe interval. After training(using error back propagation algorithm), when the NN is used for decision making, the decision as to whether to accept or reject a new call depends on whether the output is greater or less then decision threshold(+0.5). This method is a new technique for call admi sion control using the membership values as traffic parameter which declared to CAC at the call set up stage, and is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simmulation. it is founded the performance of the suggested method outforms compared to the conventional NN method.

• Journal of Elementary Mathematics Education in Korea
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• v.17 no.1
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• pp.105-128
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• 2013

One of the biggest changes in 2007 Curriculum Revision is introduction of letter, equation, direct proportion and inverse proportion in fifth and sixth grade of mathematics. The purpose of this study is to provide some implications about teaching-learning method for introduction of letters, teaching and learning activities of equation between the 6th Curriculum and 2007 Curriculum Revision. The below conclusions were drawn from findings obtained in this study. First, the letter and expression were learned in fifth and sixth grade until 6th Curriculum and were learned in seventh grade in middle school of 7th Curriculum. But letter, equation are introduced in 2007 Curriculum Revision again. The overall contents of letter and expression were learned on the 'Relationship' domain in the 6th Curriculum, it were learned on the 'Letter and expression' domain in the 7th Curriculum and is learned on the 'Regularity and problem-solving' domain in the 2007 Curriculum Revision. Second, teaching method of these contents was to promise some definitions at first and then to solve exercises in the 6th Curriculum. But leaning was forced to improve student's problem-solving in the 7th Curriculum. To reduce student's pressure offers at a minimum mathematics terms and to provide problem situations to students who contact daily, it is emphasized on learner's communication in the 2007 Curriculum Revision. We want to be easily connected elementary mathematics and higher mathematics through this study about letter, equation. We recognized how we teach the letter and expression to reduce misconceptions and draw a transition from arithmetic thinking to algebraic thinking and want to be continue of another studies.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.205-247
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• 2024

The purpose of this study is to investigate how information use behavior and satisfaction appear by personality type for liberal arts learning among college students, and to propose a customized information service plan that can help college students study in university libraries. To this end, a survey was conducted on 169 university students enrolled in C University. The analysis consisted of demographic characteristics, MBTI personality type, information use behavior, satisfaction, and university library service perception survey. Frequency analysis, cross-analysis, multinomial logistic regression, one-way ANOVA, and hierarchical regression analysis were performed on the collected data using the SPSS 29 statistical program. As a result of the study, first, significant results were found in 'preferred information sources', 'information source consideration factors', and 'information collection patterns' according to personality type. Second, there were statistically significant differences in satisfaction according to personality type in 'system utilization ability', 'data selection ability', and 'the degree of recognition of the usefulness of learning activities'. Third, in the relationship between preferred information sources and satisfaction based on personality types and information use behaviors, there appears to be an inverse relationship when the content includes various topics with a lack of academic depth or expertise. However, the preference for 'social media' is positively correlated with 'satisfaction with search results,' as it provides diverse perspectives and viewpoints in liberal education

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.511-522
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• 2014

During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency - Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.

• Journal of Elementary Mathematics Education in Korea
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• v.20 no.2
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• pp.215-238
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• 2016

The 2015 national revised curriculum was notified officially the last year. The intent and direction of the revision caused more or less change for mathematical contents to be taught and is expected to cause a considerable change in math class. In the level of elementary school mathematics, it turned that several contents were deleted or moved to the upper grades because the revision focused especially both on reducing students' burden of learning and on fostering the mathematical key competences. This study aims to examine the relevance of the change through investigation of the national curriculums for elementary school mathematics since 1946. The mathematical contents to be analyzed in this study were mixed calculation of natural numbers, mixed calculation of fractions and decimal fractions, position and direction of objects, are/hectare and ton, the range of numbers and estimating, surface and volume of cylinders, pattern and correspondence, and direct/inverse proportionality, which were changed in any aspect relative to 2009 national revised curriculum. Based on the results of these analyses, the discussion will provide some suggestions for setting the direction of elementary mathematics curriculum.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.107-118
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.

• Journal of the Korean Society for information Management
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• v.18 no.3
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• pp.159-178
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• 2001

This paper presents an automatic text summarization model which selects representative sentences from sentence clusters to create a summary. Summary generation experiments were performed on two sets of test documents after learning the optimum environment from a training set. Centroid clustering method turned out to be the most effective in clustering sentences, and sentence weight was found more effective than the similarity value between sentence and cluster centroid vectors in selecting a representative sentence from each cluster. The result of experiments also proves that inverse sentence weight as well as title word weight for terms and location weight for sentences are effective in improving the performance of summarization.