• Journal of Digital Convergence
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• v.18 no.7
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• pp.229-236
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• 2020

This study aimed to analyze the effects of nursing students' select task value and academic self-efficacy on learner's learning engagement. The subjects of this study consisted of 186 nursing students who completed the major course with Smart Learning of a university in G city. Data were collected from September 1 to November 30. 2018. This study was designed as a research study and multiple regression analysis was conducted to analyze the effects of task value and academic self-efficacy on learning engagement. The results showed that the degree of influence on learning engagement was in order of academic self-efficacy(β=.515) and task value(β=.244). It was found that both task value (r=.52, p<.001)and academic self-efficacy(r=.64, p<.001) had a significant positive effect on learning engagement. Based on the results of this study, we proposed teaching and learning strategies and suggestions for strengthening learner's learning engagement in smart learning which has recently been applied to increase the effectiveness of education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.15-23
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• 2022

Among the learning methods for Continuous Learning environments, "Learning without Forgetting" has fixed regularization strengths, which can lead to poor performance in environments where various data are received. We suggest a way to set weights variable by identifying the features of the data we want to learn. We applied weights adaptively using correlation and complexity. Scenarios with various data are used for evaluation and experiments showed accuracy increases by up to 5% in the new task and up to 11% in the previous task. In addition, it was found that the adaptive weight value obtained by the algorithm proposed in this paper, approached the optimal weight value calculated manually by repeated experiments for each experimental scenario. The correlation coefficient value is 0.739, and overall average task accuracy increased. It can be seen that the method of this paper sets an appropriate lambda value every time a new task is learned, and derives the optimal result value in various scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1109-1114
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• 2003

In this paper, task control architecture is proposed for a mobile robot with behaviors based on cognition theory to endow the robot intelligence. In the task control architecture, task manager is introduced especially for the management of computational resource. The management is based on classical RMS (Rate Monotonic Strategy), but with online rate modulation strategy. The rate modulation is performed using the value variances of behavior execution for the task. Because the values are based on natively uncertain sensor information, they are modeled using PDF (probability Density Function). As a rate modulation process, the range of the rate modulation is defined firstly by real-time constraints of RMS and discrete control stability of behaviors. With the allowable range, rate modulations are performed considering harmonic bases to maintain utilization bound without decrease. To evaluate the efficiency of the proposed rate modulation strategy, a simulation test is performed to compare the efficiency between the control architecture with the proposed strategy and previous one. A performance index with the formalization of propensity of resource allocation is proposed and utilized for the simulation test. To evaluate the appropriateness of the performance index, the performance index is compared with practical one through a practical simulation test.

• The Mathematical Education
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• v.57 no.4
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• pp.353-369
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• 2018

This study started with the following questions. Suppose that students do not accept various forms of geometric series tasks as the same task. Also, let's say that the approach was different for each task. Then, when they realize that they are the same task, how will students connect the different approaches? This study is a process of pro-actively confirming whether or not such a question can be made. For this purpose, three students in the second grade of high school participated in the teaching experiment. The results of this study are as follows. It also confirmed how the students think about the various types of tasks in the geometric series. For example, students have stated that the value is 1 in a series type of task. However, in the case of the 0.999... type of task, the value is expressed as less than 1. At this time, we examined only mathematical expressions of students approaching each task. The problem of reachability was not encountered because the task represented by the series symbol approaches the problem solved by procedural calculation. However, in the 0.999... type of task, a variety of expressions were observed that revealed problems with reachability. The analysis of students' expressions related to geometric series can provide important information for infinite concepts and limit conceptual research. The problems of this study may be discussed through related studies. Perhaps more advanced research may be based on the results of this study. Through these discussions, I expect that the contents of infinity in the school field will not be forced unilaterally because there is no mathematical error, but it will be an opportunity for students to think about the learning method in a natural way.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.38-40
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• 2021

Medical domain has a massive number of data records without the response value. Self-supervised learning is a suitable method for medical data since it learns pretext-task and supervision, which the model can understand the semantic representation of data without response values. However, since self-supervised learning performance depends on the expression learned by the pretext-task, it is necessary to define an appropriate Pretext-task with data feature consideration. In this paper, to actively exploit the unlabeled medical data into artificial intelligence research, experimentally find pretext-tasks that suitable for the medical data and analyze the result. We use the x-ray image dataset which is effectively utilizable for the medical domain.

• The Journal of Korean Association of Computer Education
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• v.16 no.1
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• pp.81-95
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• 2013

Academic procrastination has been a concern for educators. The purpose of this study is to investigate the relationship among task value, academic self-efficacy, autonomous motivation, self-regulated learning, and academic procrastination in e-learning courses. Based on the literature review, the path model was proposed and tested with 212 university students who registered in e-learning courses. The results showed that task value and academic self-efficacy positively influenced autonomous motivation and self-regulated learning, and autonomous motivation and self-regulated learning reduced the level of academic procrastination directly or indirectly. Implications and strategies to reduce procrastination were discussed based on the findings.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.44.4-44
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• 2001

A new approach to design and control mobile manipulators is presented in this paper, associating genetic algorithm to multicriteria optimization to generate and value the robots according to the constraints and aims of the task. Then the first step of this approach is detailed, as topologies and configurations of manipulators that can assume position, trajectory, speed or force task are studied.

• Design & Manufacturing
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• v.16 no.3
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• Journal of the Ergonomics Society of Korea
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• v.25 no.3
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• pp.17-24
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• 2006

In this paper, we propose a stress analysis on foot by lifting task attitudes. Maximum force and peak pressure were measured on 8 body regions by Pedar system in order to analysis the stress which is affected by task style and angle on foot when Manual Materials Handling task. As for the peak pressure of the whole foot as to the task height during the lifting task, the height from Knuckle to Shoulder was the least in the peak pressure. Also, as for the maximum force and the peak pressure of the whole foot as to the task angle during the lifting task, it could be seen that the more an angle increases, the stress influencing on a foot jumps. As for the maximum force and the peak pressure by foot region as to the task height in case of the lifting task, the height from Knuckle to Shoulder is indicated the smallest value in the maximum force and the peak pressure, thus there is necessary to attain the work design that considered this. Also, as for the maximum force by foot region as to the task angle in case of the lifting task, 0° tasking is indicated to be least, thus there is necessity to be attained the tasking design in a bid to prevent the existence of an angle. The results of this paper are thought to be helpful to the suitable work design, to the prevention of musculoskeletal disorders related to the lower limbs, and to the design of ergonomic safety shoes.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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• pp.415-423
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• 1997

The purposes of this study were to investigate the intercorrelations among various motivational patterns and learning strategies and to examine the differences in motivation and strategy usage in terms of students' science achievement level, gender, and grade. A questionnaire on achievement goal, self-efficacy, self-concept of ability, expectancy, value, causal attributions, and learning strategies was administered to 360 junior high/high school students (178 males, 182 females). Students who adopted performance-oriented goal tended not to be task oriented. Task-oriented students had high levels of self-efficacy, high self-concept of ability, and expectancies for future performance in science. They also valued science and attributed thier failures to the lack of effort. However, performance-oriented students evaluated their ability negatively, did not value science, and attributed thier failures to uncontrollable causes. With respect to learning strategy, task-oriented students tended to use deep-level strategy, whereas performance-oriented students tended to use surface-level strategy and not to use deep-level strategy. High-achieving students, boys, and junior high school students were more task-oriented, evaluated their ability more positively, and valued science more than low-achieving students, girls, and high school students, respectively. High-achieving students and boys also used deep-level strategy more than each of their counterparts. However, no significant difference in learning strategy was found between junior high school students and high school students. Educational implications of these findings are discussed.