• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.203-206
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• 2013

본 논문에서는 컴퓨팅 집약적인 현대와 미래 사회를 대비하기 위한 컴퓨터 교육의 목표를 계산적 사고 및 이를 바탕으로 한 협력적 문제 해결 능력의 향상으로 보고 이를 실현하기 위한 컴퓨터 교육의 전망과 과제를 제시하였다. 계산적 사고 향상을 위한 컴퓨터 교육은 컴퓨터과학을 전공하는 학습자 뿐 아니라 모든 학습자를 대상으로 이루어져야 하며 이를 위해 초 중등학교 컴퓨터 교육과정의 체계적인 개선 및 계산적 사고를 바탕으로 한 간학문적 융합 학습 활동의 설계 및 평가 전략의 개발이 요구된다. 또한 여학생, 특수교육대상 학생과 같은 정보 소외 계층 학습자들의 컴퓨터과학에 대한 관심과 참여를 조장하고 협력적 문제 해결 활동을 지원하기 위한 학습 환경을 제시하여야 한다. 따라서 이러한 과제를 해결하기 위한 새로운 접근으로 예술과 컴퓨터과학의 융합 교육 활동인 E-Textiles 프로젝트의 특성과 연구 동향, 국내 적용 방안을 제시하였다.

• Nuclear Engineering and Technology
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• v.37 no.2
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• pp.127-138
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• 2005

Approaches to several recent issues in the operation of nuclear power plants using computational intelligence are discussed. These issues include 1) noise analysis techniques, 2) on-line monitoring and sensor validation, 3) regularization of ill-posed surveillance and diagnostic measurements, 4) transient identification, 5) artificial intelligence-based core monitoring and diagnostic system, 6) continuous efficiency improvement of nuclear power plants, and 7) autonomous anticipatory control and intelligent-agents. Several changes to the focus of Computational Intelligence in Nuclear Engineering have occurred in the past few years. With earlier activities focusing on the development of condition monitoring and diagnostic techniques for current nuclear power plants, recent activities have focused on the implementation of those methods and the development of methods for next generation plants and space reactors. These advanced techniques are expected to become increasingly important as current generation nuclear power plants have their licenses extended to 60 years and next generation reactors are being designed to operate for extended fuel cycles (up to 25 years), with less operator oversight, and especially for nuclear plants operating in severe environments such as space or ice-bound locations.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.421-429
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• 2019

This paper presents an analytical model for the estimation of initial lateral stiffness of steel moment resisting frames with masonry infills. However, rather than focusing on the single bay-single storey substructure, the developed model attempts to estimate the global stiffness of multi-storey and multi-bay frames, using an assembly of equivalent springs and taking into account the shape of the lateral loading pattern. The contribution from each infilled frame panel is included as an individual spring, whose properties are determined on the basis of established diagonal strut macro-modeling approaches from the literature. The proposed model is evaluated parametrically against numerical results from frame analyses, with varying number of frame stories, infill openings, masonry thickness and modulus of elasticity. The performance of the model is evaluated and found quite satisfactory.

• Prospectives of Industrial Chemistry
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• v.23 no.1
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• pp.42-54
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• 2020

계속적인 충·방전이 가능하여 반영구적으로 사용이 가능한 2차 전지는 친환경 소재로 주목받고 있으며, 노트북 컴퓨터와 휴대전화, 캠코더 등 소형 전자기기뿐만 아니라 전기자동차의 핵심소재이다. 전기자동차 시장의 성장과 더불어 중대형 에너지 저장용 2차 전지 시장의 규모는 더욱 확대되고 있어 관련된 소재의 개발 경쟁과 관심이 날이 갈수록 뜨거워지고 있다. 따라서 소재개발 측면에서 2차 전지 핵심 소재의 물성 발현의 원리 등을 이해하고 최적 소재 설계를 위해서는 원자 레벨에서의 소재 설계 접근법이 필요하다. 따라서 실험적인 연구가 어려운 부분과 원자단위에서의 물질 현상에 대한 이해 그리고 연구 개발의 효율성 증진을 위해서 전산재료과학(computational materials science) 기술이 광범위하게 활용될 수 있다. 본 기고문에서는 리튬이온전지에서의 전극 소재에 대한 전산재료모사의 활용과 연구동향에 대하여 소개하고자 한다.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.142-148
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• 2023

In this paper, we identify performance issues in executing compute kernels from PolyBench, which includes compute kernels that are the core computational units of various data-intensive workloads, such as deep learning and data-intensive applications, on Processing-in-Memory (PIM) devices. Therefore, using our in-house simulator, we measured and compared the various performance metrics of workloads based on traditional out-of-order and in-order processors with Processing-in-Memory-based systems. As a result, the PIM-based system improves performance compared to other computing models due to the short-term data reuse characteristic of computational kernels from PolyBench. However, some kernels perform poorly in PIM-based systems without a multi-layer cache hierarchy due to some kernel's long-term data reuse characteristics. Hence, our evaluation and analysis results suggest that further research should consider dynamic and workload pattern adaptive approaches to overcome performance degradation from computational kernels with long-term data reuse characteristics and hidden data locality.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.283-290
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• 2006

Cable-stayed bridge is a bridge that consists of one or more pylons, with cables supporting the deck. Cable-stayed bridges have come into wide use recently because of their economy, stability, and excellent appearance. It is possible to achieve a uniform moment distribution in the stiffening girders mainly by prestressing the cables, which leads to a more economical design in material and weight than other types of bridges. However, to achieve a more uniform moment distribution is vague objective, so it cannot be easily defined as the optimization problem. In other words, the minimization of cost or weight as the objective is not directly related to the optimization of cable prestressing. Therefore, it has been considered as one of the most important, difficult and also interesting topics among many researchers and bridge engineers to determine the optimal tensioning strategy how to apply prestressing forces of the cables of cable-stayed bridge. A number of approaches (Wang et al. 1993, $Negr\~{a}o\;and\;Sim\~{o}es$ 1997, Agrawal 1997, Janjic et al. 2003) to determine the optimal cable tensions have been proposed in the literature. Among these approaches the unit load method (Janjic et al. 2003) is considered in this paper because it can take into account the actual construction process while other approaches are based on the configuration of the final structure only. In this paper, '2-step approach' based on the unit load method is proposed to find the optimal tensioning strategy especially for the atypical asymmetric bridge under construction, which has continuous deck supported by one pylon and stay cables. Some numerical results will be given to show the validity of the new approach suggested in this paper.

• Bulletin of the Korean Chemical Society
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• v.33 no.3
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• pp.933-940
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• 2012

Recently, it has been shown that quantum coherence appears in energy transfers of various photosynthetic lightharvesting complexes at from cryogenic to even room temperatures. Because the photosynthetic systems are inherently complex, these findings have subsequently interested many researchers in the field of both experiment and theory. From the theoretical part, simplified dynamics or semiclassical approaches have been widely used. In these approaches, the quantum-classical Liouville equation (QCLE) is the fundamental starting point. Toward the semiclassical scheme, approximations are needed to simplify the equations of motion of various degrees of freedom. Here, we have adopted the Poisson bracket mapping equation (PBME) as an approximate form of QCLE and applied it to find the time evolution of the excitation in a photosynthetic complex from marine algae. The benefit of using PBME is its similarity to conventional Hamiltonian dynamics. Through this, we confirmed the coherent population transfer behaviors in short time domain as previously reported with a more accurate but more time-consuming iterative linearized density matrix approach. However, we find that the site populations do not behave according to the Boltzmann law in the long time limit. We also test the effect of adding spurious high frequency vibrations to the spectral density of the bath, and find that their existence does not alter the dynamics to any significant extent as long as the associated reorganization energy is changed not too drastically. This suggests that adopting classical trajectory based ensembles in semiclassical simulations should not influence the coherence dynamics in any practical manner, even though the classical trajectories often yield spurious high frequency vibrational features in the spectral density.

• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.113-124
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• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.5
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• pp.894-899
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• 2011

Traditional Korean Medicine (TKM) has been actively researched through various approaches, including computational methods. This paper aims at providing an overview of domestic studies using the computational techniques in TKM field. A literature search was conducted in Korean publications using OASIS system, and major studies of data mining in TKM were identified. A review was presented in six diagnosis fields, including sasang constitution diagnosis, eight constitution diagnosis, tongue diagnosis, pattern diagnosis for stroke, diagnosis based on ontology, diagnosis for cause of disease. They collect clinical data themselves for experiments and primarily applied a algorithm of decision tree, SVM, neural network, case-based reasoning, ontology reasoning, discriminant analysis. In the future, there needs to identify which algorithm is suitable to diagnosis or other fields of TKM.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.17-20
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• 2000

Structural genomics aims to provide a good experimental structure or computational model of every tractable protein in a complete genome. Underlying this goal is the immense value of protein structure, especially in permitting recognition of distant evolutionary relationships for proteins whose sequence analysis has failed to find any significant homolog. A considerable fraction of the genes in all sequenced genomes have no known function, and structure determination provides a direct means of revealing homology that may be used to infer their putative molecular function. The solved structures will be similarly useful for elucidating the biochemical or biophysical role of proteins that have been previously ascribed only phenotypic functions. More generally, knowledge of an increasingly complete repertoire of protein structures will aid structure prediction methods, improve understanding of protein structure, and ultimately lend insight into molecular interactions and pathways. We use computational methods to select families whose structures cannot be predicted and which are likely to be amenable to experimental characterization. Methods to be employed included modern sequence analysis and clustering algorithms. A critical component is consultation of the presage database for structural genomics, which records the community's experimental work underway and computational predictions. The protein families are ranked according to several criteria including taxonomic diversity and known functional information. Individual proteins, often homologs from hyperthermophiles, are selected from these families as targets for structure determination. The solved structures are examined for structural similarity to other proteins of known structure. Homologous proteins in sequence databases are computationally modeled, to provide a resource of protein structure models complementing the experimentally solved protein structures.