• 성인간호학회지
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• 제20권5호
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• pp.731-742
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• 2008

Purpose: The purpose of this study was to analyze how a nursing student recognizes PBL with simulation education and its relationship to academic achievement. Methods: The study objects were the students in C college who learn through PBL using simulator for 15 weeks(September 2007 to December 2007). Learning element recognition was developed by Cho(2002) and three key evaluations(performance, self-evaluation, and colleague evaluation) were designed by professors. Results: Learning element recognition ranged from 2.37 to 4.83 with the average at 3.94. For Learning element recognition, students who preferred discussion score 4.15. This was statistically more significant than those who do not. Students who preferred presentations show significantly higher score in colleague evaluation. For Learning element recognition and academic achievement, self-evaluation and colleague evaluation showed relationship to PBL learning element. Conclusion: There was definitely a relationship with PBL learning element and academic achievement after learning the PBL with simulation education.

• Smart Structures and Systems
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• 제29권3호
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• Structural Engineering and Mechanics
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• 제90권4호
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• pp.403-415
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• 2024

We looked at how the damping qualities of epoxy composites changed when different amounts of graphite nanoplatelets (GNP) were added, from 0% to 6% by weight. A mix of free and forced vibration tests helped us find the key GNP content that makes the damper ability better the most. We also created a Representative Volume Element (RVE) model to guess how the alloys would behave mechanically and checked these models against testing data. An Artificial Neural Network (ANN) was also used to guess how these compounds would react to motion. With proper hyperparameter tweaking, the ANN model showed good correlation (R²=0.98) with actual data, indicating its ability to predict complex material behavior. Combining these methods shows how GNPs impact epoxy composite mechanical properties and how machine learning might improve material design. We show how adding GNPs to epoxy composites may considerably reduce vibration. These materials may be used in industries that value vibration damping.

• 대한공업교육학회지
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• 제42권1호
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• pp.106-124
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• 2017

이 연구는 초 중등 발명 지식재산교육의 목표 및 교육과정에 대한 적정 편성 방안을 도출하기 위하여 전문가 대상으로 2차 델파이 조사를 실시하였다. 이를 통한 연구의 결론은 다음과 같다. 첫째, 이 연구에서 제안한 학교급별 발명 지식재산교육의 핵심 목적은 타당한 것으로 나타났다. 핵심 목적은 초등학교가 '발명 인식과 태도 함양'(M=4.5), 중학교가 '발명 과정과 기법 이해'(M=4.2), 일반계고가 '발명 기법 적용 및 평가'(M=4.1), 특성화고가 '직무 발명 이해와 적용'(M=4.6)으로 나타났다. 학교급별 교육목적 및 목표도 타당한 것으로 분석되었다. 둘째, 이 연구에서 제안한 초 중등 발명 지식재산교육의 핵심 학습요소에 대한 적정 편성 방안은 선행문헌의 편성 실태와 대동소이하게 나타났으나, 학교급별 발명 지식재산교육의 목적 및 목표에 따른 각 학습요소의 편성 비중은 전반적인 개선이 필요한 것으로 나타났다. 초등학교와 중학교의 경우 발명의 기본적인 학습요소(발명의 이해(A), 창의성 이해와 활동(B), 문제 인식과 활동(C), 문제해결과 활동(D), 발명 융합 지식(E), 발명 기법과 실제(F))에 집중하여 편성하고(73.2%, 65.1%), 고등학교는 초 중학교에서 집중했던 기본 학습요소의 비중을 낮추며(51.0%) 발명과 진로(H), 특허 출원(K) 등 발명과 연계되어 확장된 학습요소들의 비중을 높여 편성하는 것이 적정한 것으로 분석되었다. 셋째, 초 중등 발명 지식재산교육 체계는 학교급별 발명 지식재산교육의 목적 및 목표를 지향점으로 설정하고, 이를 달성하기 위해 핵심 학습요소는 계속성, 계열성, 통합성 등 타일러(Tyler)의 학습조직 원리에 근거하여 학교급별로 적정 편성 방안을 구축해야 한다. 특히 특성화고는 초 중학교뿐만 아니라 일반계고와의 차별화가 필요하며 직무 발명 이해와 적용을 위한 중등 단계 직업교육에서의 발명 지식재산교육체계 구축이 필요하다.

• 한국빅데이터학회지
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• 제3권2호
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• pp.11-18
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• 2018

본 연구는 빅데이터 품질 진단의 핵심 요소인 도메인 기반 품질 진단을 위한 도메인 자동 판별에 관한 연구다. 빅데이터의 가치와 활용도의 증가와 4차 산업혁명의 대두로, 법률, 의료, 금융 등 IT와 융합된 다양한 분야에서 빅데이터를 활용하여 새로운 가치를 창출하려는 노력을 진행중이다. 하지만, 신뢰도가 낮은 데이터에 기반한 분석은 과정과 결과 모두에서 치명적인 문제를 발생하며, 분석 결과에 따른 판단 또한 신뢰하기 어려워 진다. 이처럼 신뢰도가 높은 데이터의 필요성 또한 증가하였지만, 데이터의 품질 확보에 대한 연구와 그에 대한 결과는 미비하다. 본 연구는 데이터 품질 향상을 위한 진단 평가의 핵심적 요소인 도메인 기반 품질 진단에서, 수작업으로 진행되었던 도메인 판별 작업을 머신러닝을 이용하여 자동화 함으로써, 작업시간을 단축하는 것을 목표로 한다. 데이터 베이스에 저장된, 도메인이 판별되어 있는 데이터의 특성에 관한 정보들을 추출하여 변수화하고, 이를 머신러닝을 이용하여 도메인 판별을 자동화 한다. 이를 빅데이터 품질 진단에 활용하고, 품질 향상에 기여하도록 한다.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.967-985
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• 2019

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• 한국도서관정보학회지
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• 제48권4호
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• pp.49-76
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• 2017

자유학기제는 학생 참여 중심의 수업방법 개선과 진로교육 강화로 교육혁신을 추구하고 있으며, 이를 통하여 핵심역량의 함양을 목적으로 하고 있다. 변화하는 교육환경에서 학교도서관의 이용, 공공도서관의 지원 활동 현황과 개선 방안, 그리고 대학도서관과 문헌정보학과의 역할을 고찰하였다. 학교도서관은 핵심역량 함양에 효과적인 교육 인프라임에도 불구하고 활용도는 매우 낮으며, 공공도서관의 학교 지원 프로그램은 외부 특강과 탐방에 치우쳐 도서관의 자료와 서비스를 경험하는 기회를 제공하기에 미흡하다. 교육계가 공공도서관의 지원 활동에 큰 관심을 갖고 있으므로 학교도서관의 입지를 강화할 수 있는 협력 프로그램을 개발할 필요가 있다. 그리고 지역의 대학도서관과 문헌정보학과의 교수, 학생을 연계하여 직업체험, 진로교육, 그리고 역량 함양을 아우르는 입체적 교육 프로그램 개발에 공공도서관의 주도적 역할이 기대된다.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2001년도 The Seoul International Simulation Conference
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• pp.37-43
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• 2001

New product development has been a key element fur organizational evolution. The bulk of research about new product strategy has focused solely on new product development function itself. This paper investigates cross-functional elements in new product development. More specifically, we suggest that there must exist a fit between new product strategy and value chain strategy. It means that, in order to support new product development activity, there must exist a relevant value chain strategy. We consider three types of integration - internal integration, customer integration, and supplier integration - as strategic elements of value chain strategy. For the case of new product strategy, we consider market newness and product technology unfamiliarity as strategic elements. We also consider two types of learning characteristic, i.e., \\\"fast-adaptive learning\\\" and \\\"slow-adaptive leaning\\\" as control factor. Learning characteristic represents firms organizational capability related with organizational learning. For example, fur fast-adaptive learning case, the effect of integration appears early in time. System dynamics simulation is employed to verify our research framework. The results exhibit that there must exist cross-functional relationships between value chain strategy and new product strategy in order to shorten total development time.al development time.

• 시스템엔지니어링학술지
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• 제16권2호
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• pp.16-26
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• 2020

Improving operator performance in complex and time-critical situations is critical to maintain plant safety and operability. These situations require quick detection, diagnosis, and mitigation actions to recover from the root cause of failure. One of the key challenges for operators in nuclear power plants is information management and following the control procedures and instructions. Nowadays Digital Twin technology can be used for analyzing and fast detection of failures and transient situations with the recommender system to provide the operator or maintenance engineer with recommended action to be carried out. Systems engineering approach (SE) is used in developing a digital twin for the CEDM system to support operator actions when there is a misalignment in the control element assembly group. Systems engineering is introduced for identifying the requirements, operational concept, and associated verification and validation steps required in the development process. The system developed by using a machine learning algorithm with a text mining technique to extract the required actions from limiting conditions for operations (LCO) or procedures that represent certain tasks.