• Journal of Elementary Mathematics Education in Korea
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• v.11 no.2
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• pp.177-197
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• 2007

This study discussed the climbing learning method which studied and practiced by Professor Saito Noboru. This is the learning method which is devised to know not only the relationship of the learning factors but the systemic or structural connection of whole studying contents- affects children's math learning ability through practical class to both the lower and the higher grades. To achieve the purpose of this study, these following issues were set; A. Develop the teaching and learning course of mathematics by applying the climbing learning method. B. Execute the mathematics lesson according to the climbing learning method and analyze the learning achievement. C. Analyze the difference between application of the climbing learning method and that of the learning method by student's level in mathematics. D. Analyze what the climbing learning method gives a shift of the recognition of learning mathematics. In order to accomplish these study issues, we analyzed the text book of math not only for children but also for teachers and developed the teaching and learning course applied the climbing learning method with advice of experts. It was chosen two different homogeneous groups each, third year for lower grade group and fifth year for higher grade group. It was done the experimental group lesson applying the climbing learning method and general lesson for the control group. After then, t-test against independent samples was done depending on the result of the student's assessment(T1, T2). These two groups' students were divided into smaller groups based on result of achievement level regardless of gender. These subgroups were confirmed the difference of learning ability between upper and lower level group. As regarding the result making out grades of faith and attitude for math, t-test was used on independent sample. At the same time, experimental groups were tested using learning attitude with the learning structure chart. Through this study the following results are obtained and the conclusion was drawn. Firstly, although applying the climbing learning method to the lesson does not have significant effect to the lower grade of elementary school student's achievement it has significant influence on the higher grade student's achievement. Second, as a result of analyzing the difference between the climbing learning method and the learning method by student's level in mathematics, it is of no beneficial effect to the lower grade both upper level and lower level. However, it has appreciable effect to the higher grade classes both upper level and low level. Especially, upper level students have higher effect than low level students. Third, climbing learning method does not affect to the faith and attitude of the lower grade students positively, but it has affirmative effect to the higher grade students'. As a result of the survey of the experimental groups which were applied to the climbing loaming method, the lesson by using the learning structure chart proved to be helpful to the both the lower and higher grade. The best advantage of using the learning structure chart, children say, is easily understood whole contents of studying and is useful for review. Furthermore, using the learning structure chart is more efficient compared with previous learning method and is given the successful result to self-directed learning. In conclusion, keeping up with the current of the thought of education, we suggest a scheme as a new teaching method from the constructive learning method which emphasize the self-directed learning.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.1
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• pp.23-41
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• 2010

This research applies the climbing learning method that, a Japanese professor, Saito Noboru established and practiced, to fourth and sixth graders in an elementary school in order to analyze its effect on mathematical creativity, attitude toward mathematical creativity, so called CAS(Creative Attitude Scale) and academic achievement of the subject. The goal is to explore methods that can enhance students' mathematical creativity. To address these tasks, the research developed a teaching-learning scheme and learning structure chart that applies the climbing learning method. Next, the research organized two homogeneous groups among 124 students in fourth and sixth grades in S elementary school, located in the city of Busan. The experiment group went through classes that applied climbing learning method, while the control group received regular teaching. The following describes the research findings. After the experiment, the research conducted t-test for the independent sample based on the test result in terms of mathematical creativity, CAS and academic achievement of the subject. For mathematical creativity, all four constructing factor showed statistically significant differences at significance level of 5%. For CAS, statistically significant difference was revealed at significance level of 0.1%. However, in regard to a test of academic achievement for fourth and sixth graders, statistically significant difference was not detected at significance level of 5% even though the average score of the students in the experiment group was higher by 6 points. The research drew the following conclusion. Firstly, classes that apply climbing learning method can be more effective than regular classes in enhancing mathematical creativity of elementary school students. Secondly, the climbing learning method has positive impact on inclination for mathematical creativity of elementary school students. The research suggests that the climbing learning method can be an effective teaching-learning tool to improve students' mathematical creativity and inclination for mathematical creativity.

• Journal of Korea Game Society
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• v.17 no.5
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• pp.133-142
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• 2017

Recently, screen climbing contents such as sports climbing learning program and screen climbing games. Especially, there are many researches on screen climbing games. In this paper, we propose the skeleton correction method based on the body area of a climber to improve the posture recognition accuracy. The correction method consists of the modified skeletal frame normalization with abnormal skeleton joint filtering, the classification of body area into joint parts, and the final skeleton joint correction. The skeletal information obtained by the proposed method can be used to compare the climber's posture and the ideal climbing posture.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.243-250
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• 2016

Recently, public interest in virtual reality (VR) and augmented reality (AR) has increased. Therefore, computer graphics-related research has been actively conducted. This has included research on virtual space related to human posture implementation. However, such research has focused on general posture in humans. This paper presents a system with reference to the basic posture in sports climbing and the inverse kinematics method for generating the positions and behavior of virtual characteristics in a three-dimensional virtual space. The simulation based on the inverse kinematics method, produced with an inverse kinematics solver and initial pose animation from motion capture, provides realistic and natural movement. We designed a simulation system to generate correct posture and motions similar to those in sports climbing by applying the basic procedure of sports climbing. The simulation system provides help for producing content about sports climbing, such as learning programs for novice climbers and sports climbing games.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.449-456
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• 2019

This paper is a study on the construction of a wall-climbing mobile robot using vacuum suction and wheel-type movement, and a comparison of the performance of an automatic wall crack detection algorithm based on machine learning that is suitable for such an embedded environment. In the embedded system environment, we compared performance by applying recently developed learning methods such as YOLO for object learning, and compared performance with existing edge detection algorithms. Finally, in this study, we selected the optimal machine learning method suitable for the embedded environment and good for extracting the crack features, and compared performance with the existing methods and presented its superiority. In addition, intelligent problem - solving function that transmits the image and location information of the detected crack to the manager device is constructed.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.