• Title/Summary/Keyword: block learning

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Learning-based Super-resolution for Text Images (글자 영상을 위한 학습기반 초고해상도 기법)

  • Heo, Bo-Young;Song, Byung Cheol
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.4
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    • pp.175-183
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    • 2015
  • The proposed algorithm consists of two stages: the learning and synthesis stages. At the learning stage, we first collect various high-resolution (HR)-low-resolution (LR) text image pairs, and quantize the LR images, and extract HR-LR block pairs. Based on quantized LR blocks, the LR-HR block pairs are clustered into a pre-determined number of classes. For each class, an optimal 2D-FIR filter is computed, and it is stored into a dictionary with the corresponding LR block for indexing. At the synthesis stage, each quantized LR block in an input LR image is compared with every LR block in the dictionary, and the FIR filter of the best-matched LR block is selected. Finally, a HR block is synthesized with the chosen filter, and a final HR image is produced. Also, in order to cope with noisy environment, we generate multiple dictionaries according to noise level at the learning stage. So, the dictionary corresponding to the noise level of the input image is chosen, and a final HR image is produced using the selected dictionary. Experimental results show that the proposed algorithm outperforms the previous works for noisy images as well as noise-free images.

Design for Deep Learning Configuration Management System using Block Chain (딥러닝 형상관리를 위한 블록체인 시스템 설계)

  • Bae, Su-Hwan;Shin, Yong-Tae
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.14 no.3
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    • pp.201-207
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    • 2021
  • Deep learning, a type of machine learning, performs learning while changing the weights as it progresses through each learning process. Tensor Flow and Keras provide the results of the end of the learning in graph form. Thus, If an error occurs, the result must be discarded. Consequently, existing technologies provide a function to roll back learning results, but the rollback function is limited to results up to five times. Moreover, they applied the concept of MLOps to track the deep learning process, but no rollback capability is provided. In this paper, we construct a system that manages the intermediate value of the learning process by blockchain to record the intermediate learning process and can rollback in the event of an error. To perform the functions of blockchain, the deep learning process and the rollback of learning results are designed to work by writing Smart Contracts. Performance evaluation shows that, when evaluating the rollback function of the existing deep learning method, the proposed method has a 100% recovery rate, compared to the existing technique, which reduces the recovery rate after 6 times, down to 10% when 50 times. In addition, when using Smart Contract in Ethereum blockchain, it is confirmed that 1.57 million won is continuously consumed per block creation.

Analysis of student perception of learning block-type educational programming tools in online learning environment (온라인 학습 환경에서의 블록형 교육용 프로그래밍 도구 학습에 대한 학생 인식 분석)

  • Lee, SangHyeon;Ann, SungHun
    • Journal of The Korean Association of Information Education
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    • v.24 no.6
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    • pp.519-528
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    • 2020
  • Due to COVID-19, most schools are conducting online learning. In this study, a total of 12 block-type educational programming tool classes were conducted in the form of online learning for 6th grade elementary school students, and then quantitatively and qualitatively analyzed students' perceptions of their learning experience on entry learning. As a result of the analysis, it was found that the learner easily recognized the use of the entry program and the difficulty of learning contents, and the learning satisfaction was high. When students face difficulties, it was found that they received the most help from the hint function provided by the site itself, and they were found to be less aware of the necessity of teachers when learning entry. As a result of the qualitative analysis, it was found that the learner felt a lot of novelty and fun through easy and simple operation. On the other hand, it was found that the learning contents and hints were not understood, which made them difficult and felt that the hint contents were insufficient. It was found that students felt a sense of accomplishment by creating and manipulating programs as they wish.

Cost-optimal Preventive Maintenance based on Remaining Useful Life Prediction and Minimum-repair Block Replacement Models (잔여 유효 수명 예측 모형과 최소 수리 블록 교체 모형에 기반한 비용 최적 예방 정비 방법)

  • Choo, Young-Suk;Shin, Seung-Jun
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.45 no.3
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    • pp.18-30
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    • 2022
  • Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

Sub-Pixel Rendering Algorithm Using Adaptive 2D FIR Filters (적응적 2차원 FIR 필터를 이용한 부화소 렌더링 기법)

  • Nam, Yeon Oh;Choi, Ik Hyun;Song, Byung Cheol
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.3
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    • pp.113-121
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    • 2013
  • In this paper, we propose a sub-pixel rendering algorithm using learning-based 2D FIR filters. The proposed algorithm consists of two stages: the learning and synthesis stages. At the learning stage, we produce the low-resolution synthesis information derived from a sufficient number of high/low resolution block pairs, and store the synthesis information into a so-called dictionary. At the synthesis stage, the best candidate block corresponding to each input high-resolution block is found in the dictionary. Next, we can finally obtain the low-resolution image by synthesizing the low-resolution block using the selected 2D FIR filter on a sub-pixel basis. On the other hand, we additionally enhance the sharpness of the output image by using pre-emphasis considering RGB stripe pattern of display. The simulation results show that the proposed algorithm can provide significantly sharper results than conventional down-sampling methods, without blur effects and aliasing.

Machine Learning Based Prediction of Bitcoin Mining Difficulty (기계학습 기반 비트코인 채굴 난이도 예측 연구)

  • Lee, Joon-won;Kwon, Taekyoung
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.29 no.1
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    • pp.225-234
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    • 2019
  • Bitcoin is a cryptocurrency with characteristics such as de-centralization and distributed ledger, and these features are maintained through a mining system called "proof of work". In the mining system, mining difficulty is adjusted to keep the block generation time constant. However, Bitcoin's current method to update mining difficulty does not reflect the future hash power, so the block generation time can not be kept constant and the error occurs between designed time and real time. This increases the inconsistency between block generation and real world and causes problems such as not meeting deadlines of transaction and exposing the vulnerability to coin-hopping attack. Previous studies to keep the block generation time constant still have the error. In this paper, we propose a machine-learning based method to reduce the error. By training with the previous hash power, we predict the future hash power and adjust the mining difficulty. Our experimental result shows that the error rate can be reduced by about 36% compared with the current method.

Deep Learning-based Super Resolution Method Using Combination of Channel Attention and Spatial Attention (채널 강조와 공간 강조의 결합을 이용한 딥 러닝 기반의 초해상도 방법)

  • Lee, Dong-Woo;Lee, Sang-Hun;Han, Hyun Ho
    • Journal of the Korea Convergence Society
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    • v.11 no.12
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    • pp.15-22
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    • 2020
  • In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

A Study on the Change of the Perception of Students' Computational Thinking and Scientific Attitudes in Earth Science Classes Using a Block-based Coding (블록형 코딩프로그램을 활용한 지구과학 수업에서 학생들의 컴퓨팅 사고력에 대한 인식 및 과학적 태도 변화 연구)

  • Han, Shin;Kim, Hyoungbum
    • Journal of the Korean Society of Earth Science Education
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    • v.12 no.2
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    • pp.131-140
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    • 2019
  • In this study, a block-base coding that could develop computing thinking was applied to Earth science teaching and learning to identify how the perception of computational thinking and scientific attitude was changed as part of creativity education. Based on the results of the study, the conclusions are as follows: First, an Earth science education program was developed using a block-based coding for elementary school students. The 12-hour program was designed for inquiry activities to encourage students to engage in various thinking by providing them with activity-oriented problems. Second, the Earth science education program using a block-based coding showed significant results in confidence in the use of a computer program, integrated learning with a computer, computational thinking, and problem-solving factors with computational thinking. Third, the Earth science education program using block-based coding showed significant differences in the categories of curiosity, criticism, cooperation, persistence, and creativity. It could be judged that it was effective for students in the process of questioning and trying to solve the problem themselves.

Predictive Analysis of Ethereum Uncle Block using Ensemble Machine Learning Technique and Blockchain Information (앙상블 머신러닝 기법과 블록체인 정보를 활용한 이더리움 엉클 블록 예측 분석)

  • Kim, Han-Min
    • Journal of Digital Convergence
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    • v.18 no.11
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    • pp.129-136
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    • 2020
  • The advantages of Blockchain present the necessity of Blockchain in various fields. However, there are several disadvantages to Blockchain. Among them, the uncle block problem is one of the problems that can greatly hinder the value and utilization of Blockchain. Although the value of Blockchain may be degraded by the uncle block problem, previous studies did not pay much attention to research on uncle block. Therefore, the purpose of this study attempts to predict the occurrence of uncle block in order to predict and prepare for the uncle block problem of Blockchain. This study verifies the validity of introducing new attributes and ensemble analysis techniques for accurate prediction of uncle block occurrence. As a research method, voting, bagging, and stacking ensemble analysis techniques were employed for Ethereum's uncle block where the uncle block problem actually occurs. We used Blockchain information of Ethereum and Bitcoin as analysis data. As a result of the study, we found that the best prediction result was presented when voting and stacking ensemble techniques were applied using only Ethereum Blockchain information. The result of this study contributes to more accurately predict the occurrence of uncle block and prepare for the uncle block problem of Blockchain.

Design of Machine Learning Education Program for Elementary School Students Based on Sound Data (소리 데이터를 활용한 블록 기반의 초등 머신러닝 교육 프로그램 설계)

  • Ko, Seunghwan;Lee, Junho;Moon, Woojong;Kim, Jonghoon
    • 한국정보교육학회:학술대회논문집
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    • 2021.08a
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    • pp.7-11
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    • 2021
  • This study designs block-based machine learning education program using sound data that can be easily applied in elementary schools. The education program designed its goals and directions based on the results of a demand analysis conducted on 70 elementary school teachers in advance according to the ADDIE model. Scratch in Machine Learning for Kids was used for block-based programming, and the education program was designed to discover regularity of data values using sound data, learn the principles of artificial intelligence, and improve computational thinking in the programming process. In a later study, the education program needs to verify what changes there are in attitudes and computational thinking about artificial intelligence.

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