• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2004년도 하계학술논문발표회 논문집
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• pp.163-169
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• 2004

• 한국지구과학회지
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• 제39권4호
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• pp.388-400
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• 2018

The purpose of this study was to explore the nine components of computational thinking (CT) practices and their operational definitions from the view of science education and to develop a CT practice framework that is going to be used as a planning and assessing tool for CT practice, as it is required for students to equip with in order to become creative problem solvers in $21^{st}$ century. We employed this framework into the earlier developed STEAM programs to see how it was valid and reliable. We first reviewed theoretical articles about CT from computer science and technology education field. We then proposed 9 components of CT as defined in technology education but modified operational definitions in each component from the perspective of science education. This preliminary CTPF (computational thinking practice framework) from the viewpoint of science education consisting of 9 components including data collection, data analysis, data representation, decomposing, abstraction, algorithm and procedures, automation, simulation, and parallelization. We discussed each component with operational definition to check if those components were useful in and applicable for science programs. We employed this CTPF into two different topics of STEAM programs to see if those components were observable with operational definitions. The profile of CT components within the selected STEAM programs for this study showed one sequential spectrum covering from data collection to simulation as the grade level went higher. The first three data related CT components were dominating at elementary level, all components of CT except parallelization were found at middle school level, and finally more frequencies in every component of CT except parallelization were also found at high school level than middle school level. On the basis of the result of CT usage in STEAM programs, we included 'generalization' in CTPF of science education instead of 'parallelization' which was not found. The implication about teacher education was made based on the CTPF in terms of science education.

• Molecular & Cellular Toxicology
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• 제3권3호
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• pp.159-164
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• 2007

Stimulation of epidermal growth factor receptor (EGFR) is essential in signaling pathway of tumor cells. Thus, EGFR has intensely studied as an anticancer target. We developed hologram quantitative structure activity relationship (HQSAR) models for data set which consists of tricyclic azepine derivatives showing inhibitory activities for EGFR. The optimal HQSAR model was generated with fragment size of 6 to 7 while differentiating fragments having different atom and connectivity. The model showed cross-validated $q^2$ value of 0.61 and non-cross-validated $r^2$ value of 0.93. When the model was validated with an external set excluding one outlier, it gave predictive $r^2$ value of 0.43. The contribution maps generated from this model were used to interpret the atomic contribution of each atom to the overall inhibition activity. This can be used to find more efficient EGFR inhibitors.

• 대한금속재료학회지
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• 제47권1호
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• pp.32-37
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• 2009

Molecular dynamic simulations on the structural evolution of the Si(001) vicinal surfaces, which are tilted with respect to [100] and [110] directions were performed by using the empirical Tersoff potential. Tersoff potential was implemented at LAMMPS code and confirmed to describe the properties of Si. When the steps are generated along [100] direction, symmetric dimer rows formed with respect to the step edges. On the other hand, when the steps are generated along [110] direction, alternating dimer rows form with respect to the step edges. The configurational differences between the two vicinal surfaces were discussed in terms of the surface diffusion and the possibility of preventing step bunching for the (001) vicinal surface tilted along [100] direction was suggested.

• 정보교육학회논문지
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• 제16권4호
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• pp.463-474
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• 2012

정보과학기술의 발달로 급변하는 사회에서 정보교육의 필요성에 대한 목소리가 높아지고 있다. 하지만 초등학교의 대부분의 교사들은 정보교육의 필요성을 체감하지 못하고 핵심원리인 정보과학적 사고에 대하여 인지하지 못하고 있는 실정이다. 본 연구에서는 예비 초등교사들이 정보교육의 필요성을 체감하고 정보과학적 사고를 올바로 인지할 수 있도록 Logo를 이용한 정보과학적 사고 기반의 알고리즘 학습 프로그램을 설계하고 시행하였다. 사전, 사후검사를 분석한 결과 예비교사의 논리적 사고력 중 상관논리, 조합논리와 논리적 사고력 합계의 평균이 유의미하게 상승하였다. 또한 정보교육과 정보과학적 사고에 대한 예비교사들의 인식이 긍정적으로 변화한 것으로 나타났다.

• 정보교육학회논문지
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• 제23권6호
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• pp.639-653
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• 2019

본 연구에서는 Computational Thinking기반의 인공지능교육을 위한 프레임워크와 인지적 학습환경 구성의 절차를 구현하고자 하였으며, 추후 인공지능교육을 위한 교육과정 설계의 이론적 근거를 제시하고자 하였다. 연구의 결과를 토대로 데이터수집 및 발견의 단계에서 추상화 과정을 통해 알고리즘과 문제해결의 모형을 선택하는 학습모형을 제시하였고 이를 자동화하여 평가하는 단계를 기반으로 문제해결 및 예측하는 과정을 수행함으로써 인공지능을 활용한 문제해결력을 기를 수 있는 Computational Thinking 기반 AI의 교수학습모형을 제시하였다. 인공지능교육에 대한 인지적 학습환경과 관련된 연구를 분석하여 Computational Thinking의 핵심 사고과정 중 하나인 추상화의 단계를 중심으로 절차를 구성하였으며, Agency(학습보조)에서 Modeling(인지적 구조화)으로의 전이를 토대로 학습구성의 단계를 제시하였다. 본 연구에서 제시한 인공지능교육의 프레임워크와 인지적 학습환경 구성의 절차는 Computational Thinking을 기반으로 제시되었다는 점에서 특징을 갖고 있으며 추후 인공지능기반 교수학습연구의 근간이 될 것으로 기대한다.

• 공학교육연구
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• 제22권6호
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• pp.21-27
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• 2019

In the last few years, interest in computer science education has increased significantly. The curriculum is being revised to introduce computer science. Although interest has focused on coding as the main subject, in fact the computer science includes much more than coding. It engages people in being creative with technology as well as understanding the fundamental principles of computer science. Therefore, it is important to consider the curriculum to provide a foundation by teaching and learning computer science. The curriculum is required the development of courses to teach computer science for non-majors in general education. To think like a computer scientist on the knowledge of computer science is computational thinking. In order to maximize the effectiveness of teaching and learning for computational thinking, various teaching methods and supplementary learning materials, and activities should be developed and provided.

• BMB Reports
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• 제56권1호
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• pp.43-48
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• 2023

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.

• Journal of information and communication convergence engineering
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• 제21권4호
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• pp.261-267
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• 2023

Public research, which requires large computational resources, utilizes the supercomputers of the National Supercomputing Center in the Republic of Korea. The average utilization rate of resources over the past three years reached 80%. Therefore, to ensure the operational stability of this national infrastructure, specialized centers have been established to distribute the computational demand concentrated in the national centers. It is necessary to predict the computational demand accurately to build an appropriate resource scale. Therefore, it is important to estimate the inflow and outflow of computational demand between the national and specialized centers to size the resources required to construct specialized centers. We conducted a logit model analysis using the probabilistic utility theory to derive the preferences of individual users for future supercomputer resources. This analysis shows that the computational demand share of specialized centers is 59.5%, which exceeds the resource utilization plan of existing specialized centers.