• Communications of Mathematical Education
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• v.9
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• pp.1-13
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• 1999

수학 교육에서 수학 지식의 추상적 특성으로 인하여 수학 학습에 중요한 발생적 측면으로서 “concrete“ 에 대한 학습론적인 연구가 부족하였다. 또한 구체적 감각 조작 단계에서 형식적 추상적 조작 단계로의 아동의 인지 발달을 강조하다보니 ”concrete“와 ”abstract“의 통상적인 의미가 이분화 됨으로서 수학 학습에서 모든 연령과 수준에 무관한 상보적이고 상호 작용하는 가치를 수학 교육 연구에서 잊고 있었다. 본 논문은 발생적인 그리고 구성주의적 수학 학습에서 ”concrete”가 가져야 할 새로운 의미를 제안하였다. 새로운 의미의 “concrete“는 다양한 경험과 사물 그리고 지식과의 관계 맺음을 의미하는 ”connected“와 같은 맥락을 갖는다고 보고 몇 가지 수학교육에 관련된 의의와 중요성을 제시하였다.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.6
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• pp.163-172
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• 2021

To specify a formal semantics for a programming language is to do a significant part for design, standardization and translation of it. The Python is popular and powerful, it is necessary to do research for a formal semantics to specify a static and dynamic semantics for Python clearly in order to design a similar language and do an efficient translation. This paper presents the Action Equation 2.0 that specifies a formal semantics for Python to change and update Action Equation. To measure the execution time for Python programs, we implemented the semantic structure specified in Action Equation 2.0 in Java, and prove through simulation that Action Equation 2.0 is a real semantic structure that can be implemented. The specified Action Equation 2.0 is compared to other descriptions, in terms of readability, modularity, extensibility, and flexibility and then we verified that Action Equation 2.0 is superior to other formal semantics.

• Journal of Educational Research in Mathematics
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• v.13 no.3
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• pp.227-246
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• 2003

In this paper, some terms of high school mathematics which read Chinese characters phonetically in Hangul are studied semantically. Nowadays, most terms of high school mathematics are terms of Chinese characters given the reading of them in Hangul alphabet. In such terms of Chinese characters, there are many loan-words from daily life and newly coined terms. Some such terms are examined in respect of meaningfulness and rule-ness. The degree of meaningfulness and rule-ness of loan-words from daily life are relative. Students seems familiar to loan-words usually, but it is difficult to know whether students seems to be familiar to loan-words or not. Degree of familiarity to a certain loan-word must be relative. In loan-words, there are such terms whose mathematical meaning is different from daily life meaning. Such terms are strong in rule-ness. Newly coined terms are strong in rule-ness. Students are not familiar to newly coined terms which are not used in daily life and have only mathematical meaning. In coining new terms using Chinese character, unit characters are related directly or indirectly to concept which unit characters want to express. So, It is possible to guess something unit characters want to express by investigating them. According to Vinner(1991), images can be evoked. But in case of reading Chinese characters phonetically in Hangul, it can not be guaranteed for Hangul mathematical terms to evoke images which the original mathematical terms evoked. To solve such problems semantic investigation of mathematical terms has been suggested. Through this process, transplanting images which the original mathematical terms evoked into Hangul terms are planned.

• Korean Journal of Cognitive Science
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• v.23 no.2
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• pp.133-164
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• 2012

Paul Churchland(1989) suggests the theory of representation from the results of cognitive biology and connectionist AI studies. According to the theory, our representations of the diverse phenomena in the world can be represented as the positions of phase state spaces with the actions of the neurons or of the assembly of neurons. He insists connectionist AI neural networks can have the semantical category systems to recognize the world. But Fodor and Lepore(1996) don't look the perspective bright. From their points of view, the Churchland's theory of representation stands on the base of Quine's holism, and the network semantics cannot explain how the criteria of semantical content similarity could be possible, and so cannot the theory. This thesis aims to excavate which one is the better between the perspective of the theory and the one of Fodor and Lepore's. From my understandings of state space theory of representation, artificial nets can coordinates the criteria of contents similarity by the learning algorithm. On the basis of these, I can see that Fodor and Lepore's points cannot penetrate the Churchlands' theory. From the view point of the theory, we can see how the future's artificial systems can have the conceptual systems recognizing the world. Therefore we can have the perspectives what cognitive scientists have to focus on.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.199-215
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• 2020

A seagrass that is marine vascular plants plays an important role in the marine ecosystem, so periodic monitoring ofseagrass habitatsis being performed. Recently, the use of dronesthat can easily acquire very high-resolution imagery is increasing to efficiently monitor seagrass habitats. And deep learning based on a convolutional neural network has shown excellent performance in semantic segmentation. So, studies applied to deep learning models have been actively conducted in remote sensing. However, the segmentation accuracy was different due to the hyperparameter, various deep learning models and imagery. And the normalization of the image and the tile and batch size are also not standardized. So,seagrass habitats were segmented from drone-borne imagery using a deep learning that shows excellent performance in this study. And it compared and analyzed the results focused on normalization and tile size. For comparison of the results according to the normalization, tile and batch size, a grayscale image and grayscale imagery converted to Z-score and Min-Max normalization methods were used. And the tile size isincreased at a specific interval while the batch size is allowed the memory size to be used as much as possible. As a result, IoU was 0.26 ~ 0.4 higher than that of Z-score normalized imagery than other imagery. Also, it wasfound that the difference to 0.09 depending on the tile and batch size. The results were different according to the normalization, tile and batch. Therefore, this experiment found that these factors should have a suitable decision process.

• Korean Journal of Logic
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• v.22 no.1
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• pp.43-86
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• 2019

If 1st order ZFC is consistent(has a model($M_1$)) it has a transitive denumerable model($M_2$). This leads to a paradoxical situation called 'Skolem paradox'. This can be easily resolved by Skolem's typical resolution. but In the process, we must accept the model theoretic relativity for the concept of set. This relativity can generate a situation where the meaning of the set concept, for example, is given differently depending on the two models. The problem is next. because the sentence '¬denu(PN)' which indicate that PN is not denumerable is equally true in two models, A indistinguishability problem that the concept <¬denu> is not formally indistinguishable in ZFC arise. First, I will give a detail analysis of what the nature of this problem is. And I will provide three ways of responding to this problem from the standpoint of supporting ZFC. First, I will argue that <¬denu> concept, which can be relative to the different models, can be 'almost' distinguished in ZFC by using the formalization of model theory in ZFC. Second, I will show that <¬denu> can change its meaning intrinsically or naturally, by its contextual dependency from the semantic considerations about quantifier that plays a key role in the relativity of <¬denu>. Thus, I will show the model-relative meaning change of <¬denu> concept is a natural phenomenon external to the language, not a matter of responsible for ZFC.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.45-51
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• 2021

With the development of deep learning, semantic segmentation methods are being studied in various fields. There is a problem that segmenation accuracy drops in fields that require accuracy such as medical image analysis. In this paper, we improved PSPNet, which is a deep learning based segmentation method to minimized the loss of features during semantic segmentation. Conventional deep learning based segmentation methods result in lower resolution and loss of object features during feature extraction and compression. Due to these losses, the edge and the internal information of the object are lost, and there is a problem that the accuracy at the time of object segmentation is lowered. To solve these problems, we improved PSPNet, which is a semantic segmentation model. The multi-scale attention proposed to the conventional PSPNet was added to prevent feature loss of objects. The feature purification process was performed by applying the attention method to the conventional PPM module. By suppressing unnecessary feature information, eadg and texture information was improved. The proposed method trained on the Cityscapes dataset and use the segmentation index MIoU for quantitative evaluation. As a result of the experiment, the segmentation accuracy was improved by about 1.5% compared to the conventional PSPNet.

• Journal for History of Mathematics
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• v.3 no.1
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• pp.7-20
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• 1986

• 프린팅코리아
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• s.49
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• pp.158-161
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• 2006

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.113-125
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• 2011

As information resources have become more various and the number of the resources has increased, knowledge customization on the social web has been becoming more difficult. To reduce the burden, we offer a framework for context-based similarity calculation for knowledge customization using ontology on the CBR. Thereby, we newly developed context- and intensity-based similarity calculation methods which are applied to extraction of the most similar case considered semantic similarity and syntactic, and effective creation of the user-tailored knowledge using the selected case. The process is comprised of conversion of unstructured web information into cases, extraction of an appropriate case according to the user requirements, and customization of the knowledge using the selected case. In the experimental section, the effectiveness of the developed similarity methods are compared with other edge-counting similarity methods using two classes which are compared with each other. It shows that our framework leads higher similarity values for conceptually close classes compared with other methods.