• Journal of KIISE:Databases
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• v.37 no.4
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• pp.177-184
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• 2010

In these day, many data including sensor, delivery, credit and stock data are generated continuously in massive quantity. It is difficult to learn from these data because they are large in volume and changing fast in their concepts. To handle these problems, learning methods based in sliding window methods over time have been used. But these approaches have a problem of rebuilding models every time new data arrive, which requires a lot of time and cost. Therefore we need very simple incremental learning methods. Bayesian method is an example of these methods but it has a disadvantage which it requries the prior knowledge(probabiltiy) of data. In this study, we propose a learning method based on attribute values. In the proposed method, even though we don't know the prior knowledge(probability) of data, we can apply our new method to data. The main concept of this method is that each attribute value is regarded as an expert learner, summing up the expert learners lead to better results. Experimental results show our learning method learns from data very fast and performs well when compared to current learning methods(decision tree and bayesian).

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.421-440
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• 2010

Our country has excessive amount of learning per hour compared with Japan and Singapore. And as there is no consistence for definition of time between grades, it deteriorates understanding of students. Our country teaches students focusing on time algorism whereas Japan and Singapore teaches their students focusing on flow of time. In composing of mathematics textbook in Korea, Japan and Singapore, textbook of our country contains far more of learning compared with the amount designated in textbooks. Textbooks of Japan contains less teaching elements, but instead it contains much activities to expedite time sense As time is distributed in activities of students, it is more important to construct textbooks with experience of students rather than algorism approaches. In addition, textbooks of Singapore contains various examples and clarified concepts compared with those of our country. Like above, time teaching deployment methods of Japan and Singapore gives us good lessons for teaching time in our country, and it is expected be good reference for future development of our textbooks.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.194-198
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• 2017

In this paper, we propose a revised Deep Convolutional Neural Network (DCNN) model to extract Protein-Protein Interaction (PPIs) from the scientific literature. The proposed method has the merit of improving performance by applying various global features in addition to the simple lexical features used in conventional relation extraction approaches. In the experiments using AIMed, which is the most famous collection used for PPI extraction, the proposed model shows state-of-the art scores (78.0 F-score) revealing the best performance so far in this domain. Also, the paper shows that, without conducting feature engineering using complicated language processing, convolutional neural networks with embedding can achieve superior PPIE performance.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.58-66
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• 2020

The blur effect, which is generated by various external factors such as out-of-focus and object movement, degrades high-frequency components in the original sharp image. Based on this observation, we propose a novel method for blur detection using textural features. Specifically, the proposed method simultaneously adopts learning-based and watershed-based textural features, which effectively detect the blur on various situations. Moreover, we employ the region-based refinement to improve the processing time while also increasing detection accuracy. Experimental results demonstrate that the proposed method provides the competitive performance compared to previous approaches in literature.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.5
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• pp.305-310
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• 2019

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.

• Journal of The Korean Astronomical Society
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• v.53 no.6
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• Journal of The Korean Association For Science Education
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• v.27 no.6
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• pp.497-509
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• 2007

Among many remedial approaches against the increasing unfavorableness toward school science, one of the prevalent findings studied by affective experts is that students' achievement in science and their attitude toward it has a relatively weak relationship. In contrast, cognitive experts assert that the conceptual change involves more than cognitive aspects, and may be influenced by affective factors such as beliefs, motivation, learning attitudes, and sociocultural contexts. The latter regards continuous conceptual change as leading to better student understanding of science with variables of students' attitude toward science. As an initial step toward illuminating the affective-cognitive learning aspects of science, the purpose of this study is to develop an instrument for analyzing the relationship between students' conceptual understanding and affective factors. Cognitive questionnaires from the database of distribution in students' misconceptions of physics (DMP project), and affective questionnaires from the Relevance of Science Education (ROSE project) are integrated into our instrument. The respondents are high school students in Okayama prefecture, Japan. Through the pilot test, the authors integrated attitude toward science (AS) and interest inventory (II) from ROSE into cognitive understanding (CD) from DMP. Statistical methodologies such as factor analysis and item total correlation theoretically discerned the effective sixty-three items from the two original item pools. Having discussed two validity issues, the authors suggest ongoing research associated with our affective-cognitive research perspective.

• Fisheries and Aquatic Sciences
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• v.25 no.3
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• pp.151-157
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• 2022

Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.

• Journal of The Korean Association For Science Education
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• v.32 no.8
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• pp.1295-1317
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• 2012

There has been disagreement on the importance of definitions in science education. Yager (1983) believes that one crisis in science education was due to the considerable emphasis upon the learning of definitions. Hobson (2004) disagrees with physics textbooks that do not provide general definition on energy. Some textbooks explain that "there is no completely satisfactory definition of energy" or they can only "struggle to define it." In general, imprecise definitions in textbooks (Bauman, 1992) and inaccuracies in definition provided by teachers (Galili & Lehavi, 2006) may cause alternative conceptions. Besides, there are at least four challenges in defining physical concepts: precision, circularity, context and completeness in knowledge. These definitional problems that have been discussed in The Feynman Lectures, may impede the learning of physical concepts. A meta-study approach is employed to examine about five hundreds journal papers that may discuss definitions in physics, problems in defining physical concepts and how they may result in alternative conceptions. These journal papers are mainly selected from journals such as American Journal of Physics, International Journal of Science Education, Journal of Research in Science Teaching, Physics Education, The Physics Teachers, and so on. There are also comparisons of definitions with definitions from textbooks, Dictionaries of Physics, and English Dictionaries. To understand the nature of alternative conception, Lee et al. (2010) have suggested a theoretical framework to describe the learning issues by synthesizing cognitive psychology and science education approaches. Taking it a step further, this study incorporates the challenges in semantics and epistemology, proposes that there are at least four variants of alternative conceptions. We may coin the term, 'alternative definitions', to refer to the commonly available definitions, which have these four problems in defining physics concepts. Based on this study, alternative definitions may result in at least four variants of alternative conceptions. Note that these four definitional problems or challenges in definitions cannot be easily resolved. Educators should be cognizant of the four variants of alternative conceptions which can arise from alternative definitions. The concepts of alternative definitions can be useful and possibly generalized to science education and beyond.

• Journal of the Korean Institute of Educational Facilities
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• v.24 no.6
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• pp.11-18
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• 2017

Education facilities have much affect to make a good condition for the learning environment. Therefore, various approaches have been conducted to improve the physical, social and educational achievement. Especially, the physical aspect is very important to get rid of the building defect and improve the student their learning environment. For these, it needs to explain the performance and function of components and materials, which is linked with the deterioration degree. The deterioration degree is a imperative factor to make a decision whether the component would be repaired or not and to provide the repair scope of its component. In this paper, it aimed at making the deterioration degree model of roof proof under the hypothesis of which deterioration degree would be equal the repair cost at this time. Results of the study are shown that first, the $3^{rd}$ function is most proper to explain the deterioration degree model among 11 functions in view of resulted statistics. Second, the inflection of deterioration is shown at 15yr of the elementary school and 13yr of the middle and high school. This study has a limit of disclassification of the component or materials and it is, therefore, favorable to include the classification of waterproof material and work. These results would make a change from the breakdown maintenance to preventive maintenance and give a decent the learning environment for student.