• Journal of Korea Multimedia Society
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• 제20권10호
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• pp.1671-1677
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• 2017

In this paper, we propose a machine learning method for random noise removal in image deblurring. The proposed method uses a logistic regression to select reliable data to use them, and, at the same time, to exclude data, which seem to be corrupted by random noise, in the deblurring process. The proposed method uses commonly available images as training data. Simulation results show an improved performance of the proposed method, as compared with the median filtering based reliable data selection method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 한국항해항만학회 2015년도 추계학술대회
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• pp.227-229
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• 2015

Training students to use a Marine VHF presents challenges because materials are either out of date or cost prohibitive. It is also difficult to teach in a "real world" setting. This presentation gives a few ideas of how to give practical training and a fair testing method.

• Journal of The Korean Society of Integrative Medicine
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• 제5권1호
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• pp.1-9
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• 2017

Purpose : This study is to examine the effects of different task-related circuit training by types of tasks on the depression and quality of life in stroke patients. Method : Forty-four chronic stroke patients were divided into a dual motor circuit task training group, a dual cognitive circuit task training group and a simple task training group. Over the course of eight weeks, before training, all the patients were identically encouraged to receive conservative physical therapy for 30 minutes, five times a week for a total of eight weeks with individual additional tasks. The dual motor circuit tasks training consisted of continuous circuit training motor tasks and additional motor tasks and the dual cognitive circuit task training consisted of tasks combining the same circuit training motor tasks and additional cognitive tasks. The simple task training consisted of natural walks on a flat terrain to the front, rear and lateral sides of the terrain. Result : As for the Stroke-Specific Quality of Life(SS-QOL) that showed significant diffe rences in comparison between the groups over the training period, the dual motor circuit task training group showed statistically significant differences in both different types of tasks at 8 weeks(p<.05). The score of Hospital Anxiety and Depression Scale(HADS) decreased in three groups, in the HADS showed significant changes over the training time in the three training groups(p<.05). Conclusion : It could be seen that the practical and continuous dual circuit task training was more effective than simple task training on quality of life. In comparison between the types of dual tasks, the dual motor circuit task training group showed more effects than the dual cognitive circuit task training group. This researcher hopes that the results of this study will be actively applied as rehabilitation methods for chronic stroke patients.

• Journal of Korea Entertainment Industry Association
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• 제15권3호
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• pp.203-210
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• 2021

Yuri Vasilyev - actor, director and drama teacher. Russian meritorious artist, winner of the stage "Medal of Friendship" awarded by Russian President Vladimir Putin; academician of the Petrovsky Academy of Sciences and Arts in Russia, professor of the Russian National Academy of Performing Arts, and professor of the Bavarian Academy of Drama in Munich, Germany. The physiological sense stimulation method based on the improvement of voice, language and motor function of drama actors. On the basis of a systematic understanding of performing arts, Yuri Vasiliev created a unique training method of speech expression and skills. From the complicated art training, we find out the most critical skills for focused training, which we call basic skills training. Throughout the whole training process, Professor Yuri made a clear request for the actor's lines: "action! This is the basis of actors' creation. So action is the key! Action and voice are closely linked. Actor's voice is human voice, human life, human feeling, human experience and disaster. It is also the foundation of creation that actors acquire their own voice. What we are engaged in is pronunciation, breathing, tone and intonation, speed and rhythm, expressiveness, sincerity, stage voice and movement, gesture, all of which are used to train the voice of actors according to the standard of drama. In short, Professor Yuri's training course is not only the training of stage performance and skills, but also contains a rich view of drama and performance. I think, in addition to learning from the means and methods of training, it is more important for us to understand the starting point and training objectives of Professor Yuri's use of these exercises.

• Journal of the Society of Naval Architects of Korea
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• 제56권5호
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• pp.457-463
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• 2019

Damage control is a very important preliminary and primary activity to improve the survivability of naval ships by preventing spread of damage, and various types of onboard damage control training are conducted regularly on naval ships. The scenarios for these trainings should be well organized to improve the training efficiency. However, at present, it takes much time and effort to generate the training scenarios and there is a problem that the procedures and contents of the scenarios vary widely depending on the persons who generate, without the established methods and standards. In this paper, an efficient generation method of damage control onboard training scenarios has been established, especially for flood and fire o n naval ships. Also a computer program has been developed based on the established method. The results showed that this method and computer program reduce the time and effort to generate these scenarios, and it is hoped that the method be used as a ROK Navy Standard.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.575-588
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• 2012

Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.

• Journal of Internet of Things and Convergence
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• 제7권1호
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• pp.21-25
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• 2021

Augmented reality systems are widely used in the fields of entertainment, shopping, education, and training, and the augmented reality technology is gradually increasing in importance. When augmented reality technology is used for education or training, it must be possible to represent different virtual objects depending on the work stage even for the same marker. Also, since the training content varies depending on the situation, it is necessary to describe it using a training scenario. In order to solve this problem, we propose a scenario markup language for an augmented reality system that can create training content based on a scenario and connect it with an augmented reality system. The scenario markup language for augmented reality provides functions such as a method for connecting a scene, a marker and a virtual object, a method for grasping the state of equipment or sensor value, and a method for moving a scene according to conditions. The augmented reality scenario markup language can flexibly increase the usefulness and expandability of the augmented reality system usage method and content usage.

• Journal of KIISE:Software and Applications
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• 제37권7호
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• pp.491-502
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• 2010

When human beings acquire and develop knowledge through education, their prior knowledge influences the next learning process. As this is a fact that should be considered in machine learning, we need to examine the effects of controlling the order of training sequence on machine learning. In this research, the role of the supervisor is extended to control the order of training samples, in addition to just instructing the target values for classification problems. The supervisor sequences the training examples categorized by SOM to the learning model which in this case is MLP. The proposed method is distinguished in that it selects the most instructive example from categories formed by SOM to assist the learning progress, while others use SOM only as a preprocessing method for training samples. The result shows that the method is effective in terms of the number of samples used and time taken in training.

• Journal of Educational Research in Mathematics
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• 제24권4호
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• pp.537-554
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• 2014

In this study, we investigated a good way for teacher professional development. Based on this way, we designed teacher training program. We carried out teacher training program for 50 secondary school mathematics teachers in July 2014. In this teacher training courses, teachers conducted recording mathematics teaching-learning processes by dialogue between teacher and student according to Socratic method. We also shared the practices of teacher educators, teachers and colleagues. In this teacher training, we tried to cultivate teachers' abilities needed to a good mathematics instruction. And we aimed to equip the attitude that guided reflection on their mathematics class. Through the teacher training, teachers recognized the need to study on the thinking of students and take into account students' expected reaction on the part of learners. Also they developed an attitude as reflective practitioners and recognized the need of teacher learning communities for their professional development.

• Journal of KIISE:Software and Applications
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• 제37권10호
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• pp.723-735
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• 2010

Evaluating the level of achievement and providing the knowledge which is appropriate at the evaluated level have great influence in studying of the human beings. This shows the importance of the order of training and the training order should be considered in machine learning. In this research, to assess the influence of the order of training, we propose a method of controlling the order of training samples utilizing the experience of supervisor in the training of MLP. The supervisor finds out the current state of MLP using teaching experience and student evaluation, and then selects the most instructive sample for MLP in that state. We use CRF to represent and utilize the experience of supervisor. While the proposed method is similar to active learning in selecting samples, it is basically different in that selection is not to reduce the number of samples to be used but to assist the learning progress. The result from classification problem shows that the method is usually effective in terms of time taken in training in contrast to random selection.