• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.3
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• pp.308-315
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• 1991

This paper deals with the global dynamic behavior of multiple autonomous mobile robots with suggested navigation strategies within unbounded and bounded spatial domain. We derive some navigation strategies of robots wirh complete detectability with finite range to reach their goal states without collision which is motivated by Coulomb's law regarding repulsive and attractive forces between electrical charges. An analysis of the dynamic behavior of the interacting robots with the suggested navigation strategies under the assumption that communication is not permissible between robots is made and some examples are illustrated by computer simulation. The convergence of robot motions to their goal states under certain conditions is established by considering their global dynamic behavior even when some objects are close to their goal points.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.24-29
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• 2013

In this paper, we propose a fuzzy inference model for a navigation algorithm for a mobile robot that intelligently searches goal location in unknown dynamic environments. Our model uses sensor fusion based on situational commands using an ultrasonic sensor. Instead of using the "physical sensor fusion" method, which generates the trajectory of a robot based upon the environment model and sensory data, a "command fusion" method is used to govern the robot motions. The navigation strategy is based on a combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance based on a hierarchical behavior-based control architecture. To identify the environments, a command fusion technique is introduced where the sensory data of the ultrasonic sensors and a vision sensor are fused into the identification process. The result of experiment has shown that highlights interesting aspects of the goal seeking, obstacle avoiding, decision making process that arise from navigation interaction.

• Genomics & Informatics
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• v.15 no.3
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• pp.108-111
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• 2017

Recently developed computational methods allow the imputation of human leukocyte antigen (HLA) genes using intergenic single nucleotide polymorphism markers. To improve the imputation accuracy in HLA imputation, it is essential to increase the sample size and the diversity of alleles in the reference panel. Our software, MergeReference, helps achieve this goal by providing a streamlined pipeline for combining multiple reference panels into one.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.829-834
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• 2013

This study intended to find out an efficient teaching-learning method by identifying the effect of achievement goal directivity and self-regulated learning strategy on the level of learning achievement in class. This study analyzed the relationship between the achievement goal directivity and the average and standard deviation of self-regulated learning strategy as well as the relationship between the achievement goal directivity and the learning achievement of self-regulated learning strategy and also analyzed the effect of achievement goal directivity and self-regulated learning strategy on the level of learning achievement, based on the result of survey targeting 133 university students attending the Department of Social Welfare. The findings show that the higher the tendency of achievement goal directivity and self-regulated learning ability, the higher the academic performance, which means that it is necessary to resolve any anxiety through motivation control training and mentoring learning for academic achievement of students and inducing their proactive participation in class.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.349-355
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• 2012

Today, the computing environments is very complex, so researches that endow a system with the self-healing's ability that recognizes problem arising in a target system are being an important issues. However, the existing methodology, the goal for the new requirements for self-healing system developers to model and analyze the constraints that must be greater efforts. Therefore, in this paper are aware of problems detected by the system to solve the problem is the analysis of goal-based scenarios. In addition, there is a pre and post applying a strategy to be dynamically reconfigured to show you how to self-healing. These proposed new requirements for methodology, self-healing reduces the load on the developer's analysis.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.465-471
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• 2016

This study intends to figure out the relationship between autonomy and achievement motivation of the university players. For this purpose, Survey was performed to Seoul and Metropolitan area's 25 sports 396 Players., And using SPSS 18 and AMOS program descriptive statistics, exploratory factor analysis, confirmatory factor analysis, reliability analysis were conducted. Based on purified data through the process, the relationship between autonomy and achievement motivation was verified with correlation analysis and regression analysis. As a result, autonomy was recognized by male player higher than female player, and in goal-orientation of the access tendency of achievement goal orientation, male player showed higher scores. And in mastery-avoidance of avoidance, female player showed higher scores and in performance-avoidance, male player showed higher scores. According to regression analysis, autonomy affects mastery-approach and achievement goal orientation of the performance-approach.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• Journal of Engineering Education Research
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• v.21 no.4
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• pp.20-27
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• 2018

In this paper, we conducted an analysis of existing materials of material mechanics, surveying educational demands, and selecting art fields for mechanical engineering in collaboration with experts in the fields of aesthetics and renaissance in order to develop educational materials conversed with humanities and arts. In addition, as a core education tool for convergence education, it was developed with an education kits that can be disassembled, assembled and carried by students. As a results of research, education materials were introduced various arts, linked to the engineering thinking. And measurement experiments of material characteristics were carried in a general classroom using developed education kits. From a paper results, the artistic sensitivity of the students will be enhanced and the students' creative problem solving ability will be improved as the ultimate goal of convergence education.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.13-24
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• 2021

This was a one-group pretest-protest pre-experimental study designed to find out the effects of writing programs on college students using flipped learning for training convergence talents with complex problem solving skills. Data were collected from 27 C university students in G-do, from September 1st to October 30th, 2020. Analysis was done using IBM SPSS 25.0 for frequency (percentage), average, standard deviation, and paired t-test. The study showed that self leadership was enhanced by an average of 0.45 points (p<.001), metacognition by 0.87 points (p<.001), goal orientation by 0,77 points (p<.001), and creativity by 0.51 points (p<.001), so was statistically significant. The study demonstrated that writing programs using filpped learning had the effect of improving complex problem solving skills. so the results are expected to be used as basic data for the development of educational programs for traning convergence talents with problem solving skills at university-level. Also, for current college students that can freely access the online environment, development of various e-learning classes that apply flipped learning is needed.