• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2414-2433
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• 2019

The aim of this paper is to design a 3D laser radar prototype based on laser triangulation. The mathematical model of distance sensitivity is deduced; a pixel-distance conversion formula is discussed and used to complete 3D scanning. The center position extraction algorithm of the spot is proposed, and the error of the linear laser, camera distortion and installation are corrected by using the proposed weighted average algorithm. Finally, the three-dimensional analytic computational algorithm is given to transform the measured distance into point cloud data. The experimental results show that this 3D laser radar can accomplish the 3D object scanning and the environment 3D reconstruction task. In addition, the experiment result proves that the product of the camera focal length and the baseline length is the key factor to influence measurement accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.427-427
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• 2021

During the rainy season, heavy downpours are always a source of concern for the world. Flooding and heavy rains can devastate communities, disrupt agriculture, and contribute to traffic accidents.. Weir and flow hall effect sensors are the conventional analytical methods for measuring flow rate; in this paper, we analyzed manhole flowrate statistics. The measurement of the flow rate of a notch/weir is a time-consuming task that necessitates continuous mathematical analysis. . We created three types of IoT sensors in this study: (HC-SR04 ultrasonic, YF-S201 magnetic, and HB100 radar), which take the sensor's real-time input signal and estimate the flow using a notch equation and a previously calibrated optimized coefficient of discharge. The proposed systems are cost-effective, but in terms of accuracy, we found that the HC-SR04 ultrasonic sensor is the best of the three systems

• Steel and Composite Structures
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• v.49 no.1
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• pp.31-45
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• 2023

The network theory studies interconnection between discrete objects to find about the behavior of a collection of objects. Also, nanomaterials are a collection of discrete atoms interconnected together to perform a specific task of mechanical or/and electrical type. Therefore, it is reasonable to use the network theory in the study of behavior of super-molecule in sport nano-scale. In the current study, we aim to examine vibrational behavior of spherical nanostructured composite with different geometrical and materials properties. In this regard, a specific shear deformation displacement theory, classical elasticity theory and analytical solution to find the natural frequency of the spherical nano-composite sport structure equipment. The analytical results are validated by comparison to finite element (FE). Further, a detail comprehensive results of frequency variations are presented in terms of different parameters. It is revealed that the current methodology provides accurate results in comparison to FE results. On the other hand, different geometrical and weight fraction have influential role in determining frequency of the structure.

• Communications of Mathematical Education
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• v.27 no.4
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• pp.449-472
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• 2013

Instructional materials including problem situations or problems or tasks on real-life situations are considered as an important and significant factor to lead a successful math instruction. MiC Textbook is a representative one showing good examples and tasks including fluent realistic situations on the basis of the background of the Freudenthal's theory. This study explores concretely and in detail the type of level of mathematical tasks, by the subject of MiC Textbook. To accomplish this, this study reconstructs and establishes an elaborated analysis framework using 'the cognitive demand level' suggested by Stein, et, al. The cognitive demand level is comprized of four elements such as Memorization Tasks, Procedures Without Connections Tasks, Procedures With Connections Tasks, and Doing Mathematics Tasks. Memorization Tasks and Procedures Without Connections Tasks are considered as low level tasks, and Procedures With Connections Tasks and Doing Mathematics Tasks are as high level tasks. MiC Textbook is comprized of the four areas of 'number', 'algebra', 'geometry and measurement', and 'data analysis and statistics'. This study deals with the tasks relevant to Function content dealt with in MiC 3 level Textbook, and explore the level of cognitive demands on each task.

• Education of Primary School Mathematics
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• v.20 no.2
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• pp.163-175
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• 2017

The purpose of the study is to identify the effect of length and right angle indication on the understanding of the concept of the figure when presenting the task of classifying the plane figures. we recorded thirty three 4th grade students' performance with eye-tracking technologies and analyzed the correct answer rate and gaze duration. The findings from the study were as follows. First, correctness rate increased and Gaze duration decreased by marking length in isosceles triangle and equilateral triangle. Second, correctness rate increased and Gaze duration decreased by marking right angle in acute angle triangle and obtuse triangle. Based on these results, it is necessary to focus on measuring the understanding of the concept of the figure rather than measuring the students' ability to measure by expressing the length and angle when presenting the task of classifying the plane figures.

• Education of Primary School Mathematics
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• v.15 no.2
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• pp.91-106
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• 2012

This study reports pre-service teachers' problem solving process on the problem-based learning(PBL) employed in an elementary mathematics method course. The subjects were 6 pre-service teachers(students). The data were collected from classroom observation. The research results were described by problem solving stages. In understanding the problem stage, students identified what problem stand for and made a problem solving planned sheet. In curriculum investigation stage, students went through investigation and re-investigation process for solving the task. In problem solving stage, students selected the best strategy for solving the task and presented and shared about problem solving results.

• Journal of Elementary Mathematics Education in Korea
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• v.15 no.1
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• pp.77-93
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• 2011

Jisuguimundo(地數龜文圖) is a magic hexagon created by Suk-Jung Choi in his book about three hundreds years ago in Korea. Recently attention is focused on jisuguimundo, and it is known that jisuguimundos exist when magic number is from 77 to 108, however a general method making jisuguimundos is not known so far. Up to now, methods of making jisuguimundos using computers are known. In this study, a method making jisuguimundos is suggested using pairs of two numbers with sum p and q ($p{\neq}q$) alternately when magic number is from 88 to 92, and from 94 to 98, without using computer in elementary math class as a task for problem solving. Mathematical theory is introduced for this method, and jisuguimundos are presented which are found out through this method. Elementary students are expected to make their own jisuguimundo using this method.

• Education of Primary School Mathematics
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• v.21 no.4
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• pp.397-413
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• 2018

The purpose of this study is to analyze the level of statistical literacy among fifth and sixth graders in the data presentation task based on the speculative data generation process. For the research, the data presentation tasks based on the speculative data generation process was designed and statistical literacy standards for evaluating the student's level was presented based on prior studies. It is meaningful that the stepwise presentation of the students' statistical literacy and analysis of their developmental patterns can help them to find their current position and reach a higher level of performance. In this study, the standard of statistical literacy level was clarified based on the previous research, and a new perspective was presented about the data presentation instruction in the statistical education by analyzing the students' responses by each level.

• Korean Medical Education Review
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• v.15 no.1
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• pp.46-53
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• 2013

The purpose of this study was to analyze the relationship among medical students' learning motivation, characteristics of multiple intelligence, and academic achievement. The participants were 144 medical students. The data were collected by administering learning motivation tests (self-confidence, self-efficacy, level of task, emotion of learning, learning behavior, failure tolerance, task difficulty, and academic self-efficacy), a multiple intelligence test (linguistic intelligence, logical-mathematical intelligence, musical intelligence, bodily-kinesthetic intelligence, spatial intelligence, interpersonal intelligence, intrapersonal intelligence, and naturalistic intelligence), and two semesters of grades. There is a correlation between multiple intelligences and learning motivation. Among academic self-efficacy of academic motivation, the self-control efficacy (0.28) and behavior (0.18) subscales are significantly positively correlated with academic achievement. However, the emotion subscale (-0.18) was significantly negatively correlated. Learning motivation was correlated with two of the eight multiple intelligence profiles: the intrapersonal intelligence (0.18) and bodily-kinesthetic intelligence (-0.19). The structural equation modeling analysis showed that the behavior and self-control efficacy subscales of intrapersonal intelligence had an impact on academic achievement. An analysis according to the academic achievement group showed significant differences in self-control efficacy and emotion subscales with intrapersonal intelligence. A positive relationship can be observed between learning motivation and some characteristics of multiple intelligence of medical school students. In light of the findings, it is worth examining whether we can control medical students' learning motivation through educational programs targeting self-control efficacy and intrapersonal intelligence.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.