• Research in Mathematical Education
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• v.27 no.1
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• pp.1-24
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• 2024

In this paper, the author analyzed characteristics of deep mathematics learning in problem solving and problem-posing classroom teaching. Based on a simple wrong plane geometry problem, the author describes the classroom experience how one expert Chinese mathematics teacher guides students to modify geometry problems from solution to investigation, and guides the students to learn how to pose mathematics problems in inquiry-based deep learning classroom. This also demonstrates how expert mathematics teacher can effectively guide students to teach deep learning in regular classroom.

• Research in Mathematical Education
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• v.27 no.2
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• pp.195-210
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• 2024

Researchers and curricula continue to call for proof to serve a central role in learning of mathematics throughout kindergarten to grade 12 and beyond. Despite its prominence and recognition gained during past decades, proof is still a stumbling block for both teachers and students. Research efforts have been made to address issues related to teaching and learning of proof. An area in which such research efforts have been made is analysis of curriculum material (i.e. textbook analysis) with a focus on proof. This study is another research effort in this area of research through investigating the guidance offered in curriculum materials with the following research question: What is the nature (e.g., kinds of content knowledge, pedagogical content knowledge) of guidance is offered for teachers to implement proof tasks in grade 8 geometry textbooks? Results indicate that the guidance offered for proof tasks are concerned more with content knowledge about the content-specific instructional goals than with pedagogical content knowledge which supports teachers in preparing in-class interactions with students to teach proof.

• The Pure and Applied Mathematics
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• v.8 no.1
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• pp.33-46
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• 2001

The purpose of this paper is to study totally umbilical coisotropic sub-manifold(M. g, SM) of a semi-Riemannian manifold(M,g)

• The Pure and Applied Mathematics
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• v.15 no.3
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• pp.209-215
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• 2008

The purpose of this paper is to study the geometry of null Bertrand curves in a Lorentz manifold.

• Journal of the Korean Chemical Society
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• v.66 no.1
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• pp.61-66
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• 2022

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.404-407
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• 2008

Several ITO-less PDP cell structures are presented to improve luminous efficacy. The ITO-less PDP have been applied recently at actual panel manufacture. The influence of ITO-less PDP cell structure on the discharge characteristics has been investigated by using three-dimensional fluid simulation. The variations of electrode geometry parameters such as gap distance, cross bar length, and hump length are investigated for the optimization of cell design.

• Transactions of the Korean hydrogen and new energy society
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• v.15 no.2
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• pp.159-165
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• 2004

Fuel cell makes electricity through chemical reaction. Bipolar-plate distribute hydrogen, oxidation using channel geometry condensation of water vapor inside channels of bipolar-plates lowers efficiency of fuel cell. Usually high pressured gas supply is used to solve the water condensation problem with serpentine type channel geometry. In this study, a new channel geometry shows feasible to minimize lowering efficiency due to water condensation through numerical and experimental analysis.

• East Asian mathematical journal
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• v.28 no.4
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• pp.453-474
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• 2012

The conic sections is an important topic in the current high school geometry. It has been recognized by many researchers that high school students often have difficulty or misconception in the learning of the conic sections because they are taught the conic sections only with algebraic perspective or analytic geometry perspective. In this research, we suggest a way of teaching the conic sections using a dynamic geometry software based on some mathematics teaching and learning theories such as Freudenthal's and Dienes'. Students have various experience of constructing and manipulating the conic sections for themselves and the experience of deriving the equations of the quadratic curves under the teacher's careful guidance. We identified this approach was a feasible way to improve the teaching and learning methods of the conic sections.

• Bulletin of the Korean Chemical Society
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• v.25 no.9
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• pp.1392-1396
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• 2004

The micellization of dodecylpyridinum chloride (DPC) assembled on aqueous gold nanoparticles has been studied as a function of concentration using Surface-Enhanced Raman Scattering (SERS). At the low concentration, the strong SERS band of the benzene ring moiety was observed at 1025 $cm^{-1}$, and assigned to “trigonal ring breathing”. According to high concentration of DPC, a new strong band was also appeared at 1012 $cm^{-1}$, which was assigned to “totally symmetry ring breathing”. The difference of two spectra seems to ascribe to the geometry of polar head group, i.e., pyridinium cation. These geometry exist flat-down at low concentration, whereas standing-up or tilted geometry at high concentration. The critical micelle concentration (CMC) was first obtained from the ratio of intensities of the two bands related to the benzene ring moiety by vibrational spectroscopy, and was about 28 mM. After the CMC, the benzene ring moiety in the micelle state was more restricted than in monomer state because there is no more change of intensities at 1012 $cm^{-1}$. In addition, the size of gold-assembled micelle was estimated using light scattering and it was about 328.3 nm.

• Journal of Information Science Theory and Practice
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• v.7 no.2
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• pp.32-39
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• 2019

A convolutional neural network (CNN) has been widely used in facial expression recognition (FER) because it can automatically learn discriminative appearance features from an expression image. To make full use of its discriminating capability, this paper suggests a simple but effective method for CNN based FER. Specifically, instead of an original expression image that contains facial appearance only, the expression image with facial geometry visualization is used as input to CNN. In this way, geometric and appearance features could be simultaneously learned, making CNN more discriminative for FER. A simple CNN extension is also presented in this paper, aiming to utilize geometric expression change derived from an expression image sequence. Experimental results on two public datasets (CK+ and MMI) show that CNN using facial geometry visualization clearly outperforms the conventional CNN using facial appearance only.