• Title/Summary/Keyword: AI-enhanced education

Search Result 15, Processing Time 0.017 seconds

Analysis of the Effects of Reading Education Using S-PUMA Teaching Method on Elementary Students' Literary Imagination and Computational Thinking (S-PUMA 교수법을 활용한 글 읽기 교육이 초등학생의 문학적 상상력과 컴퓨팅사고력에 미치는 영향 분석)

  • Eol Sohn;Youngsik Jeong
    • Journal of The Korean Association of Information Education
    • /
    • v.26 no.6
    • /
    • pp.567-577
    • /
    • 2022
  • Interest in AI and SW education is growing as digital literacy is emphasized in the revised elementary school curriculum for 2022. There are numerous restrictions on how pupils can enhance their digital literacy because there are only 34 class hours available for information education in elementary schools. Therefore, other subjects and information education must be blended in order to ensure class hours for AI and SW instruction. In this study, we investigated the impact of S-PUMA reading instruction on the literary imagination and computational thinking of elementary school pupils. To conduct this study, two classes of sixth graders in an elementary school were chosen and split into an experimental group and a control group. Over the course of five sessions, only the experimental group received reading instruction using the S-PUMA teaching approach. It was discovered that reading instruction with the S-PUMA teaching methodology enhanced literary imagination and computational thinking. Further study is required to identify whether the improvement in creative imagination, a component of literary imagination, is a result of the S-PUMA teaching approach or a natural result of the subject matter of the lesson.

On dynamic flight response of golf ball containing nanoparticles for improving quality

  • Yuwei Du;Guowen Ai;M. Kaffash
    • Advances in nano research
    • /
    • v.15 no.6
    • /
    • pp.579-585
    • /
    • 2023
  • This research delves into the intricate dynamics of the flight response exhibited by a golf ball that incorporates nanoparticles with the goal of enhancing its overall quality. The golf ball is meticulously modeled utilizing beam elements, and the impact of nanoparticles is intricately captured through the application of the Halpin-Tsai theory. Employing a numerical solution, the study thoroughly explores the flight response of the golf ball, taking into account the nuanced effects of the embedded nanoparticles. By scrutinizing the aerodynamic characteristics through advanced simulations, this investigation aims to provide valuable insights that could potentially revolutionize the design and performance of golf equipment, offering a pathway towards superior quality and enhanced functionality in the realm of golf ball technology. Results show that increase in the volume percent of nanoparticles, improves the flight response of the golf ball.

An Enhancement Method of Document Restoration Capability using Encryption and DnCNN (암호화와 DnCNN을 활용한 문서 복원능력 향상에 관한 연구)

  • Jang, Hyun-Hee;Ha, Sung-Jae;Cho, Gi-Hwan
    • Journal of Internet of Things and Convergence
    • /
    • v.8 no.2
    • /
    • pp.79-84
    • /
    • 2022
  • This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

A Study of Reinforcement Learning-based Cyber Attack Prediction using Network Attack Simulator (NASim) (네트워크 공격 시뮬레이터를 이용한 강화학습 기반 사이버 공격 예측 연구)

  • Bum-Sok Kim;Jung-Hyun Kim;Min-Suk Kim
    • Journal of the Semiconductor & Display Technology
    • /
    • v.22 no.3
    • /
    • pp.112-118
    • /
    • 2023
  • As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

  • PDF

A Case Study on the Introduction and Use of Artificial Intelligence in the Financial Sector (금융권 인공지능 도입 및 활용 사례 연구)

  • Byung-Jun Kim;Sou-Bin Yun;Mi-Ok Kim;Sam-Hyun Chun
    • Industry Promotion Research
    • /
    • v.8 no.2
    • /
    • pp.21-27
    • /
    • 2023
  • This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.