Earticle

본 연구는 마음챙김 수준에 따른 시간타이밍의 암묵적 학습 효과 차이를 분석함으로써 암묵적 학습의 제한적 효과 를 검증하는데 목적을 두었다. 실험참여자는 1-2학년 학부 남학생 30명을 대상으로 마음챙김 수준에 따라 높은 마음챙김 집단과 낮은 마음챙김 집단에 각각 15명씩 배정하였다. 실험과제는 시간 타이밍 과제로써 집단 간 상대적 타이밍 오차와 절대적 타이밍 오차를 측정하여 분석하였다. 주요결과를 요약하면 다음과 같다. 습득단계에서 상대 및 절대타이밍 오차는 분단 간에 유의한 차이를 보였지만 두 집단 간에는 차이는 없었다. 하지만 높은 마음챙김 집단이 낮은 마음챙김 집단보다 상대 및 절대 타이밍 오차 수준이 다소 높았다. 파지와 전이단계에서는 높은 마음챙김 집단이 낮은 마음챙김 집단보다 상대 및 절대 타이밍 오차 수준이 다소 낮았다. 결론적으로 학습자의 기질적인 마음챙김 수준에 따른 제한적 암묵적 학습 효과차이의 발생은 암묵적 학습과정에서 학습자 특성에 따라 다를 수 있음을 시사하였다. 기질적으로 높은 마음챙김의 학습자는 운동과제에 대한 암묵학습적 속성과 환경에 빠른 적응을 위해 유연하게 주의력을 강화하고 다양한 감각 및 지각 력을 촉진시켜 과제수행력을 높이는 역량이 발휘될 수도 있다고 판단된다.

The purpose of this study was to verify the limited effect of implicit learning by analyzing the difference of implicit learning effect of time timing according to the level of mindfulness. The participants were assigned 15 students each to a high-mindful group and a low-mindful group of 30 students depending on their level of mindfulness. The experimental task was a time timing task. The measuring data was analyzing by relative and absolute timing errors between groups. The main results are summarized as follows. In the acquisition phase, relative and absolute timing errors showed significant differences between trial blocks, but there were significant no differences between the two groups. However the level of relative and absolute timing error of high-mindful group was somewhat higher than that of low-mindful group. In the retention and transition phase, there were significant no differences between the two groups. the relative and absolute timing error of high-mindful group were somewhat lower than the lower mindful group. In conclusion, the possibility of a limited implicit learning effect difference depending on the learner's temperate level of mindfulness suggested that the characteristics of the learner and the exercise task may be important in the implict learning process. However, it is believed that learners with high-mindful disposition may be able to demonstrate their ability to improve their task performance by flexibly strengthening their attention and promoting diverse senses and perceptual abilities to quickly adapt to the environment and implicit learning of exercise tasks.

수학교과서는 교사가 수업을 하거나 평가를 위해 중요하게 생각하는 교수⋅학습 자료로서, 수학교과서의 서술 내용은 학생이 수학적 개념 을 논리적으로 이해하는 것에 영향을 미친다. 이에 본 연구에서는 음함수 또는 매개변수로 나타낸 함수의 미분에 관한 8종의 미적분 교과서의 서술을 분석하였다. 연구 결과는 다음과 같다. 첫째, 8종의 교과서는 음함수를 서술하는 과정에서 정의역과 공역을 적절히 제한하여 함수로 만들 수 있다고 하였다. 둘째, 1종의 교과서만이 매개변수로 나타낸 함수를 서술하는 과정에서 y가 x의 함수 관계라고 표현하였으며, 나머지 7종의 교과서는 두 변수 x와 y가 관계를 갖는다고 표현하였다. 셋째, 7종의 교과서는 매개변수로 나타낸 함수의 미분에 관한 과제로 x의 함수가 아닌 y를 제시하고 있다. 수학교과서의 서술 내용은 함수가 아닌 방정식도 미분할 수 있다는 오개념의 원인이 될 수 있으며, 함수와 미분 개념에 대한 논리적인 문제를 야기할 수 있다.

Mathematics textbooks are considered essential teaching and learning materials that teachers rely on for conducting lessons and assessments. The content of these textbooks can significantly influence students' logical understanding of mathematical concepts. This study analyzes the descriptions related to the differentiation of functions represented by implicit functions or parameters in high school calculus textbooks. The findings are as follows: First, all eight textbooks indicated that it is possible to appropriately restrict the domain and codomain when describing implicit functions to define them as functions. Second, only one textbook expressed the relationship of y as a function of x when describing functions represented by parameters, while the other seven textbooks described the relationship between the two variables x and y. Third, seven textbooks presented y not a function of x as the differentiation tasks concerning functions represented by parameters. The descriptions in mathematics textbooks can be a source of misconceptions and may lead to logical issues regarding concepts.

This paper presents fuzzy classification strategy for the hole-filling that can classify the incomplete mass point clouds and improve the precision. The irregular model is complex object that some part is smooth and some parts are irregular including sharp features. Therefore, we put kNN and curvature of mass point clouds to fuzzy inference system to divide the type of the hole of mass point clouds and the output of FIS can determine which part of point clouds belong to. For different kind holes, corresponding algorithm is given. Point clouds in the smooth area are reconstructed by implicit directly and ones in other regions of thin or sharp area are reconstructed by attach points. This method is simpler than those complex methods used on the whole point clouds directly. The experiment results show that classification can save much time and surface reconstruction is very fine.

This paper presents a fuzzy normal estimate for mass point clouds of irregular models in reconstruction. The irregular model is complex object that some part is smooth and some parts are irregular including sharp features. Therefore, we put kNN and curvature of mass point clouds to fuzzy inference system to divide the kind of point clouds and the output of FIS can determine which part of tooth point clouds belong to. For different kinds point clouds, corresponding algorithm is given. Point clouds in the smooth area are estimated normal by PCA directly and ones in other regions of thin or sharp area are estimated by checker and attach points. This method is simpler than those complex methods used on the whole point clouds directly. The experiment results show that much time is saved and surface reconstruction is very fine than PCA and WLOP.

In order to remove redundant data and resolve conflicts in point clouds, we proposed fuzzy clustering reduction strategy in this paper. Original point clouds are decreased before computing other pretreatments. The proposed method involves three processes: reduction of the original data using fuzzy clustering while the point clouds are divided into sub-domains using octree structure, generation of the sub-surface that is fitted the sub-surface by implicit function in each sub-domain, the normal alignment that are computed normal of sub-surface and inference the global normal of surface using iteratively propagate algorithm. The method is suitable to reduce mass point clouds to reconstruct surface that can keep the property of surface. The experimental results show that the model with less sharp feature is more effective than complex model to reduce point clouds by fuzzy clustering.

This paper presents a self-organized fuzzy neural network (SOFNN) surface reconstruction algorithm suitable for point clouds without normal. It overcomes the defect of traditional Delaunay triangulation which is difficult to reconstruct point clouds with noises and implicit function which is limited to the number of point clouds and point clouds are required very strict. The SOFNN is based on the fuzzy clustering method optimizing training data before learning fuzzy rules, in order to remove noise data and resolve conflicts in data. The approach not only reduce computational burden of neural network, but also make it easy to fit the surface for point clouds without normal and suitable for mass point clouds. The feature of the SOFNN has dynamic self-organized structure, fast learning speed and flexibility in learning. The experiment results show that is very fine.

3D face reconstruction and 3D face recognition belonged to different fields previously. In order to solve these two issues in a unified way, this paper designed a kind of regionalized implicit function feature (RIFF) algorithm frame. Firstly according to the biology characteristic, the face was segmented to five regions with different resolutions; secondly the control matrix which expressed the human face was solved for 3d face reconstruction; then dimensionality reduction was did for this control matrix; finally the low dimensional character description was computed completely for 3d face classification. The specific experiments for the RIFF method are performed and the results show that this method can acquire significant effect in the performance of reconstruction and recognition.