• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.791-802
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• 1996

In this paper, we discuss a distributed shared memory management scheme based on multi-threaded programming model for a large-scale loosely coupled multiprocessor system. The scheme covers three major issues in the distribued shared memory;the address translation table management, the block coherence maintenance, and the block placement policy. The scheme efficiently resolves the general problems occurred in the distributed shared memory such as a false sharing, an unnecessary replication, a block bouncing, and an address aliasing phenomenon. It also provides the application transparency, good scalability, easy implementation, and multithreaded programming model to users.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.04a
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• pp.243-250
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• 1991

This paper concerns the development of NC programming system TIG (Turning with Interactive Graphics) with interactive graphics for turning operation. The system cosists of the processor, the post-processor and the system-user interface. Different from previous segment contour based NC graphic programming systems, the frliability and efficiencyof programming is realized by using Boolean operation with block unit based ICONs for the geometry definition. The tool motion can be also displayed on the screen together with the part contour. The system calculate automatically the number of passes based on the user specified cutting conditions.

• Journal of Practical Engineering Education
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• v.9 no.2
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• pp.107-117
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• 2017

here are many cases that take a software education as a required course for non-major students in university curriculums. However, non-major students are experiencing various difficulties in the process of learning programming languages, and there is also the opposite opinion in terms of their effectiveness. In this study, we developed a design based software education model (DBSEM) and curriculum to solve these problems and applied it to undergraduate non-undergraduate students for the last 8 years. In the proposed method, we provide a specialized educational tool such as 'block-based programming tool', but developed 'core module' and 'concept learning module' for computational thinking and applied 'prototype design module' and coding strategy based on it. As a result, non-major undergraduates could easily learn block-based scripting tools and acquire core concepts of computational thinking.

• The Journal of Korean Association of Computer Education
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• v.19 no.2
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• pp.1-10
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• 2016

This study is for developing a programming education course to improve pre-service teachers' pedagogical content knowledge(PCK) of programming education. A 40-hour training course was designed with App Inventor, a block-based mobile programming environment, and with problem-based learning method and project-based learning method. After the curriculum was adopted to 12 undergraduate students, the effect of education was tested with a programming PCK questionnaire. As a result, after a 20-hour problem-based learning class, overall score and teaching method score were enhanced significantly. After another 20-hour project-based learning class, content knowledge, teaching method, and curriculum score were improved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.20-25
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• 2002

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy for evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• Journal of The Korean Association of Information Education
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• v.26 no.3
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• pp.197-207
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• 2022

In this paper, we develop a machine learning based block code generation and recommendation model for the purpose of reducing cognitive load of learners during coding education that learns the learner's block that has been made in the block programming environment using natural processing model and fine-tuning and then generates and recommends the selectable blocks for the next step. To develop the model, the training dataset was produced by pre-processing 50 block codes that were on the popular block programming language web site 'Entry'. Also, after dividing the pre-processed blocks into training dataset, verification dataset and test dataset, we developed a model that generates block codes based on LSTM, Seq2Seq, and GPT-2 model. In the results of the performance evaluation of the developed model, GPT-2 showed a higher performance than the LSTM and Seq2Seq model in the BLEU and ROUGE scores which measure sentence similarity. The data results generated through the GPT-2 model, show that the performance was relatively similar in the BLEU and ROUGE scores except for the case where the number of blocks was 1 or 17.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.1015-1024
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• 2021

There are various educational programming environments in which students can train artificial intelligence (AI) using block-based programming languages, such as Entry, Machine Learning for Kids, and Teachable Machine. However, these programming environments are designed so that students can train AI through a separate menu, and then use the trained model in the code editor. These approaches have the advantage that students can check the training process more intuitively, but there is also the disadvantage that both the training menu and the code editor must be used. In this paper, we present a novel artificial intelligence block that can perform both AI training and programming in the code editor. While this AI block is presented as a Scratch block, the training process is performed through a Python server. We describe the blocks in detail through the process of training a model to classify a blue pen and a red pen, and a model to classify a dental mask and a KF94 mask. Also, we experimentally show that our approach is not significantly different from Teachable Machine in terms of performance.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.616-626
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• 2014

In this paper, we introduce a real-time stereo-to-multiview conversion system using FPGA and GPU. The system is based on two different devices so that it consists of two major blocks. The first block is a disparity estimation block that is implemented on FPGA. In this block, each disparity map of stereoscopic video is estimated by DP(dynamic programming)-based stereo matching. And then the estimated disparity maps are refined by post-processing. The refined disparity map is transferred to the GPU device through USB 3.0 and PCI-express interfaces. Stereoscopic video is also transferred to the GPU device. These data are used to render arbitrary number of virtual views in next block. In the second block, disparity-based view interpolation is performed to generate virtual multi-view video. As a final step, all generated views have to be re-arranged into a single image at full resolution for presenting on the target autostereoscopic 3D display. All these steps of the second block are performed in parallel on the GPU device.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.43-50
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• 2021

Recently, researches and projects of companies based on artificial intelligence have been actively carried out. Various services and systems are being grafted with artificial intelligence technology. They become more intelligent. Accordingly, interest in deep learning, one of the techniques of artificial intelligence, and people who want to learn it have increased. In order to learn deep learning, deep learning theory with a lot of knowledge such as computer programming and mathematics is required. That is a high barrier to entry to beginners. Therefore, in this study, we designed and implemented a web-based deep learning platform called DeepBlock, which enables beginners to implement basic models of deep learning such as DNN and CNN without considering programming and mathematics. The proposed DeepBlock can be used for the education of students or beginners interested in deep learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.635-637
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• 2016

The purpose of this study was to apply other programming tools such as robots parish. The robot was utilized for Lego Mindstorms NXT. Programming tools were used to block generic programming tools were used in the NXT-G, was used as a simulation programming tools MSRDS, mobile App Inventor is a programming tool (App Inventor). It can lead to interesting effects of learning and learning based on three programming tool above.