• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.246-254
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• 2020

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.367-375
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• 2021

This study developed and applied an artificial intelligence education program as an educational method for increasing computational thinking of elementary school students and verified its effectiveness. The educational program was designed based on the results of a demand analysis conducted using Google survey of 100 elementary school teachers in advance according to the ADDIE(Analysis-Design-Development-Implementation-Evaluation) model. Among Machine Learning for Kids, we use scratch for block-based programming and develop and apply textbooks to improve computational thinking in the programming process of learning the principles of artificial intelligence and solving problems directly by utilizing numerical data. The degree of change in computational thinking was analyzed through pre- and post-test results using beaver challenge, and the analysis showed that this study had a positive impact on improving computational thinking of elementary school students.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.203-209
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• 2011

This paper proposed new approach for the information gifted discrimination test usign EPL. We tried to distinguish high level thinking in the information gifted through test of this study. We designed discrimination test by the features of EPL and developed testing item tool like single-sprit and multi-sprite. These items are divided into adding block, changing the block order, modifying value io block, and changing a block. We designed this testing tool that the elementary students can solve items without an experience on learning programming language. We expect the testing tool io proposed this study will help to discriminate the information gifted effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.288-301
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• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• The Journal of Sustainable Design and Educational Environment Research
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• v.9 no.3
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• pp.20-33
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• 2010

The purpose of this research is to investigate and analyze the recognition of students and teachers towards an influence of a subject classroom system to teacher's learning activities and student's learning attitude. The study was subjected to students and teachers at a school that has innovatively operated subject classroom system from before 2009. The results of the research are as follows. First, the result of investigation shows that the quality of class has been improved. The formats of managing class and class materials have become various. Second, there is an affirmative exchange in student's learning attitude, such as student's active participation, concentration, preparation and interest toward a class. Third, the fifty percent of teachers answered it that a block time system and intensive study system is required to manage an efficient subject classroom system. Lastly, the investigation shows that teachers and students are generally satisfied with running the subject classroom system. However, the satisfaction ratio of students is lower than the one of teachers.

• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.583-593
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• 2017

In order to overcome the difficulties of programming education for beginners, various research strategies such as UMC(Use-Modify-Create), design based learning, discovery learning and play learning are applied. In this study, we developed a HVC(History-VR Coding-Collaboration) learning strategy model for the improvement of learner's computational thinking. The HVC model is composed of a combination module of block type. We developed a 12th session storytelling - based virtual reality programming curriculum. As a result, HVC model and SW education program showed significant difference in improvement of learner's computational thinking.

• Research in Community and Public Health Nursing
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• v.18 no.1
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• pp.156-164
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• 2007

Purpose: This study was to investigate adequate strategies of PBL in community health nursing for learning in the real community situation. Method: Data were collected in a PBL class of 14 third-year students who solved problems and assessed PBL in community health nursing related to visiting nursing and chronic diseases. Results: The students guessed situations diversely, chose learning issues widely and mapped the learning concepts specifically. In the assessment of the presentation, the peers of the same group gave the highest score $29.00{\pm}3.36$, the tutor lowest score $22.83{\pm}5.15$. In 5-point Likerts scale, the group dynamic was highest ($4.18{\pm}.61$) and the presentation was lowest ($3.59{\pm}.84$). Conclusion: The group needs to include students who have experiences in the practice at the health centers. The PBL class should be managed by the block system along with the conventional learning. Students needs to practice the self-directed learning and the presentation in a first semester and then PBL. The introduction of community health nursing begins with the conventional lecture and the programs on life circle and health centers through PBL in the comprehensive curriculums.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.675-681
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, It is necessary to analyze the effects of the CMAC control parameters an the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with prespecified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.195-201
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• 2022

For a efficient distance education environment, the need for correct problem recommendation guides considering the learner's exact learning pattern is increasing. In this paper, we study block chain based smart contract technology to suggest a method for presenting the optimal problem recommendation path for individual learners based on the data given by situational weights to the problem patterns of learners collected in the distance education environment. For the performance evaluation of this study, the learning satisfaction with the existing similar learning environment, the usefulness of the problem recommendation guide, and the learner data processing speed were analyzed. Through this study, it was confirmed that the learning satisfaction improved by more than 15% and the learning data processing speed was improved by more than 20% compared to the existing learning environment.