• Journal of Digital Convergence
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• v.13 no.4
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• pp.147-161
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• 2015

The purpose of study was to test the effects of CEO' transformational leadership and learning organization on innovative behavior in the Knowledge-based Service Organization showing the characteristics of convergence service and the moderating role of learning organization between transformational leadership and innovative behavior. For this study, the data were collected from 348 Knowledge-based Service industrial employees in metropolitan area by using structured questionnaires. Collected data were analyzed by hierarchical regression technique. The results showed that both of CEO' transformational leadership and seven learning organization building factors had a positive effect on employees' innovative behavior. And also found out the only four out of the seven learning organization building factors, namely 'Create continuous learning opportunities', 'Promote inquiry and dialogue', 'Encourage collaboration and team learning', 'Provide strategic leadership for learning' had the moderate roles between CEO's transformational leadership and employees' innovative behavior. The theoretical and practical implications of the findings were discussed and the directions for future research were presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1832-1837
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• 2022

In the SW education of non-majors who encounter liberal arts education experience difficulties in the SW development environment and understanding they encounter for the first time, relevance to their major, and convergence thinking ability. In order to compensate for the difficulties of non-major learners in liberal arts education, a relatively easily accessible software was used to utilize a demonstration-oriented model that can be applied to beginners in SW education. In order to understand the logical flow of applications and problem solving used in real life, we proposed a convergence SW teaching method that combines repeated implementation through demonstration by the instructor and imitation of the learner, and learning indicators to increase the learning satisfaction and achievement of the learner. In the experiment applying the teaching and learning method proposed in this paper, meaningful results were shown when evaluating the learning effect, academic achievement, learning satisfaction, and teaching and learning method aspects of SW education.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.85-94
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• 2002

In this paper, we show that the 2nd-order iterative learning control algorithm with CITE is more effective and has better convergence performance than the algorithm without CITE in the case of the existence of initialization errors, for the trajectory-tracking control of dynamic systems with unidentified parameters. In contrast to other known methods, the proposed learning control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a CITE term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances and initialization errors. And the convergence proof of the proposed algorithm in the case of the existence of initialization error is given in detail, and the effectiveness of the proposed algorithm is shown by simulation results.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.257-268
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• 2024

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. This paper utilizes various supervised classification machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes. Moreover, based on our finding the KNN and Decision models achieved the highest accuracy rates at 70%, surpassing other models. The Random Forest model followed closely with an accuracy score of 62%. Among the evaluated models, the Naive Bayes model demonstrated the lowest accuracy at 56%. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.15-20
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• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.142-146
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• 2023

We present a method for generating 3D structures and rendering objects by combining VAE (Variational Autoencoder) and GAN (Generative Adversarial Network). This approach focuses on generating and rendering 3D models with improved quality using residual learning as the learning method for the encoder. We deep stack the encoder layers to accurately reflect the features of the image and apply residual blocks to solve the problems of deep layers to improve the encoder performance. This solves the problems of gradient vanishing and exploding, which are problems when constructing a deep neural network, and creates a 3D model of improved quality. To accurately extract image features, we construct deep layers of the encoder model and apply the residual function to learning to model with more detailed information. The generated model has more detailed voxels for more accurate representation, is rendered by adding materials and lighting, and is finally converted into a mesh model. 3D models have excellent visual quality and accuracy, making them useful in various fields such as virtual reality, game development, and metaverse.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.51-58
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• 2015

The utilization of Big data in the field of education has spread around the developed countries. However, in Korea, there are only experimental approaches related to Bigdata, yet for the related researches and services to appear. Therefore, it is the situation that needs to understand the reason for poor use of big data in the e-Learning industry, study and seek out alternatives to solve these problems. The result of this study shows that it was investigated that the high level of understanding of Bigdata has recognized large impact on e-Learning of Big Data and the more large-scale sales companies have recognized large impact on e-Learning of Big Data in the e-Learning industry. In conclusion, this study makes a proposal to expand the training and utilization policies of Bigdata relating to different sales scales.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.300-305
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• 2023

Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.247-254
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• 2021

The purpose of this study is to investigate the effects of action learning on the satisfaction of majors by cultivating task-solving ability through self-reflection in the form of team learning for a certain period of time in the form of team learning by using action learning for students taking convergence curriculum in universities. The subjects of the study were 40 students from the Department of Sports Rehabilitation, a convergence of the Department of Sports and Health Management and the Department of Physical Therapy located in Jeollanam-do. This was conducted to confirm the difference in the effect of satisfaction. Comparison of changes in groups of experimental groups with action learning teaching methods showed significant differences in self-directed learning skills, problem-solving skills, and major satisfaction(p<.001)(p<.05). A significant difference in self-directed learning ability, problem-solving ability, and major satisfaction was also shown in the comparison of changes in control groups that applied traditional teaching methods(p<.05). Comparison of changes between groups showed significant differences in self-directed learning skills, problem-solving skills and major satisfaction(p<.05). Applying the action learning teaching method to the level of students in the convergence course will improve self-directed learning skills, problem-solving skills, and major satisfaction, and further research will be needed to expand the target and add variables to combine qualitative research.