• 한국수학교육학회지시리즈C:초등수학교육
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• 제27권3호
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• pp.199-226
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• 2024

인공지능(AI)과 디지털 기술의 도입 등과 같은 디지털 기반 변화의 시대를 맞아, 2025년에는 수학, 영어, 정보 교과에 AI 디지털교과서를 단계적으로 도입하는 교육혁신이 추진되고 있다. 본 연구는 2023년 11월 전국 132명의 초등학교 교사를 대상으로 실시한 설문조사를 통해 교사들의 수학 AI 디지털교과서에 대한 이해도, 핵심 기술의 필요성, 수업 활용에 대한 인식, 그리고 AI 디지털교과서의 학교 현장에의 안착을 위한 요구사항을 조사하였다. 분석 결과, 대다수 교사들은 수학 AI 디지털교과서의 도입과 필요성에 대해 낮은 인식을 보였지만, 일부 교사들은 개인별 맞춤형 학습 및 효과적인 교수·학습 지원 가능성을 인식하고 있었다. 또한, 교사들은 AI 디지털교과서의 학습 진단과 교사 재구성 기능의 필요성을 높게 평가했으며, 수업에서의 유용성을 긍정적으로 평가했지만, AI 디지털교과서의 도입으로 인해 교실에서의 상호작용성은 저하시킬 것이라고 우려했다. 이는 AI 디지털교과서의 성공적 도입 및 활용을 위해 교사연수 및 정보 제공을 통한 인식 변화의 필요성을 시사하며, 구체적이고 실용적인 활용 방안 제공, 디지털 과잉 사용 및 의존에 대한 대안 모색, 핵심 기술의 지속적 개발 등, 이와 관련한 연구의 지속적인 필요성을 제언한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권1호
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• 공학교육연구
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• 제23권2호
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• pp.14-23
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• 2020

Based on the constructivist learning environments model and the learner-centered psychological principles, STEAM education program with the theme of eliminating smog was developed. Through the program, senior elementary school students will learn and apply the convergence knowledge of science, technology, engineering, arts and mathematics such as the human body's respiratory system (S), immune system (S), big data (M, T), computer programming(M), and aduino sensor utilization (E) directly to solve the problem. After expert validity testing, we found that developed program meet the standards of STEAM education program development and can develop creative thinking skills to find and solve problems in students' daily lives. In addition, this study is meaningful in providing a reference example for the development of STEAM education programs that enhance convergence knowledge in the future.

• Advances in concrete construction
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• 제11권1호
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• Structural Engineering and Mechanics
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• 제89권2호
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• pp.213-223
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• 2024

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.

• Nuclear Engineering and Technology
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• 제56권1호
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• pp.64-69
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• 2024

According to the market research report, the nuclear decommissioning services market is currently experiencing considerable growth, with a projected Compound Annual Growth Rate (CAGR) of nearly 13% during the 2020-2024 forecast period. This expansion is primarily fueled by the advancement of Industry 4.0, in conjunction with the emergence of cutting-edge technologies such as the Internet of Things, big data, artificial intelligence, and 5G. Even though the fact that robots have already been utilized in the nuclear industry, their adoption has been hindered by conservative regulations. However, the nuclear decommissioning market presents an opportunity for the advancement of robotics technology. The British have already invested heavily in encouraging the use of intelligent robots for nuclear decommissioning, and other countries, such as Taiwan, should follow suit. Taiwan's flourishing robotics development industry in manufacturing, logistics, and other domains can be leveraged to introduce advanced robotics in the decommissioning of its nuclear power plants. By doing so, Taiwan can establish itself as a competitive player in the nuclear decommissioning services market for the next two decades.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4531-4544
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• 2021

Because the traditional network information security vulnerability risk assessment method does not set the weight, it is easy for security personnel to fail to evaluate the value of information security vulnerability risk according to the calculation value of network centrality, resulting in poor evaluation effect. Therefore, based on the network security data element feature system, this study designed a quantitative assessment method of network information security vulnerability detection risk under single transmission state. In the case of single transmission state, the multi-dimensional analysis of network information security vulnerability is carried out by using the analysis model. On this basis, the weight is set, and the intrinsic attribute value of information security vulnerability is quantified by using the qualitative method. In order to comprehensively evaluate information security vulnerability, the efficacy coefficient method is used to transform information security vulnerability associated risk, and the information security vulnerability risk value is obtained, so as to realize the quantitative evaluation of network information security vulnerability detection under single transmission state. The calculated values of network centrality of the traditional method and the proposed method are tested respectively, and the evaluation of the two methods is evaluated according to the calculated results. The experimental results show that the proposed method can be used to calculate the network centrality value in the complex information security vulnerability space network, and the output evaluation result has a high signal-to-noise ratio, and the evaluation effect is obviously better than the traditional method.

• 전기전자학회논문지
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• 제27권3호
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• pp.213-219
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• 2023

경찰청 통계자료에 따르면 코로나19 이후 문자 또는 메신저를 이용한 스미싱(Smishing) 범죄가 급증하였다. 또한 정부 기관에 접수된 공공기관 사칭 건수의 대부분이 백신접종 및 보상 관련하여 가짜 URL(Uniform Resource Locator)을 클릭하도록 유도하는 수법이 다수 사용되었다. 주로 URL의 정보를 숨긴 단축 URL을 사용하며 탐지할 때 URL 기반 탐지방법은 URL의 정보를 숨기면 제대로 탐지할 수 없고, 콘텐츠 기반 탐지 방법은 탐지 속도가 느리고 많은 자원을 사용한다. 이에 본 논문에서는 GRU(Gated Recurrent Units)를 이용한 단축 URL을 판별하는 과정을 통해 일반 URL일 때 transformer를 통한 URL 기반 탐지, 단축 URL일때 XGBoost를 이용한 콘텐츠 기반 탐지하는 시스템을 제안한다. 제안한 탐지 시스템의 F1-Score는 94.86이었고, 처리시간은 평균 5.4초가 소요되었다.

• Korean Journal of Radiology
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• 제22권4호
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• 한국수학교육학회지시리즈A:수학교육
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• 제62권3호
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• pp.303-326
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• 2023

수학은 계통성이 강한 학문으로 이전 단계에서의 학습 결손이 다음 학습에 큰 영향을 주기 때문에 학생들의 학습이 잘 이루어졌는지 수시로 확인하고, 즉각적으로 피드백을 제공해 주는 것이 필요하며, 이를 위해 수학교육에서 인공지능 교육시스템(ITS)을 활용할 수 있다. 이에 본 연구에서는 개인 맞춤형 수학 학습을 실행하기 위해 적용될 수 있는 인공지능 교육시스템의 기능이 무엇인지 살펴보고, 이를 실제로 적용해 본 결과를 분석하여 인공지능 교육시스템을 활용한 개인 맞춤형 수학 학습의 효과성을 구체적으로 살펴보는 것을 목적으로 하였다. 이를 위해 개인 맞춤형 학습과 수학교육에서 인공지능이 활용된 선행연구 내용을 분석하여 개인 맞춤형 수학 학습을 위한 인공지능 교육시스템의 기능을 추출하고, 이것을 반영한 학습 및 수업을 설계하여 초등학교 5학년 학생들에게 약 3개월 간 적용해 본 결과를 분석하였다. 그 결과, 개인 맞춤형 수학 학습을 위해 활용될 수 있는 인공지능 교육시스템의 기능은 크게 진단 및 평가, 분석 및 예측, 피드백 및 콘텐츠 제공으로 나눌 수 있었다. 또한 이러한 기능을 반영한 학습 설계를 초등학생들에게 적용한 결과, 개인 맞춤형 수학 학습에 인공지능 교육시스템이 어떻게 효과적으로 활용될 수 있는지에 대한 시사점을 얻었다. 그리고 앞으로 인공지능 교육시스템을 활용한 개인 맞춤형 수학 학습이 더욱 효과적으로 이루어질 수 있기 위해 더 정교한 기술과 자료 개발이 필요하다는 점을 제언하였다.