• Korean Journal of Adult Nursing
• /
• v.20 no.5
• /
• pp.731-742
• /
• 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
• /
• v.29 no.3
• /
• pp.475-484
• /
• 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
• /
• v.90 no.4
• /
• pp.403-415
• /
• 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
• /
• v.42 no.1
• /
• pp.106-124
• /
• 2017

This study used the secondary Delphi method for experts, in order to propse a proper formation plan for the goal and curriculum of elementary and secondary invention/intellection property education. Its results are as following; First, the key objective of invention/intellectual property education for each school level is evaluated as appropriate. With regard to the key objective, elementary schools are aiming at 'fostering awareness and attitude for invention'(M=4.5), middle schools, 'understanding of invention process and method'(M=4.2), general high schools, 'application and evaluation of invention method'(M=4.1), and specialized high schools, 'understanding and application of Employee Invention'(M=4.6). The objective and goal of education for each school level are also evaluated as appropriate. Second, although the proper formation plans for a key learning element of elementary and secondary invention/intellectual property education were almost identical to an actual formation of preceding literature, overall change is required for the formation balance of each learning element, according to the objective and goal of school-leveled invention/intellectual property education. An appropriate formation shall be focusing on basic learning elements (A, B, C, D, E, and F) for elementary and middle schools(73.2%, 65.1%), lowering somewhat the former elements and increasing expanded learning elements for high schools(51.0%), which are connected to the invention, course(H), and patent application(K). Third, elementary and secondary invention/intellectual property education system should be oriented to its objective and goal. In order to reach this, an appropriate formation plan should be made for each school level, based on the principle of Tyler's learning organization, such as continuity, sequence and integration, which are key learning element. Specialized high schools, in particular, need to be differentiated from general ones, as well as elementary and middle schools. Additionally, for understanding and applying an employee invention, invention/intellectual property education system needs to be established in the phase of secondary occupational education.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.11-18
• /
• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Journal of Information Processing Systems
• /
• v.15 no.4
• /
• pp.967-985
• /
• 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
• /
• v.11 no.4
• /
• pp.68-80
• /
• 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.

• Journal of Korean Library and Information Science Society
• /
• v.48 no.4
• /
• pp.49-76
• /
• 2017

Free learning semester aims to foster key competencies in junior high students via reinforcing student participation and career education. This paper investigates the programs of school and public libraries, as well as the roles that university libraries and LIS could play in the changing educational environment. School libraries, though an essential element in the educational infrastructure, are seriously underused. In addition, the support programs of public libraries tend to focus on guest lectures or field trips, rather than reinforcing the students to experience the foundational library services. Public libraries are in a good position to play an active role in strengthening school libraries. They should take a proactive role in forming a partnership with university libraries, LIS faculty and students, and in developing diverse programs ranging from vocational/career education to enhancing key competencies.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.10a
• /
• pp.37-43
• /
• 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.

• Journal of the Korean Society of Systems Engineering
• /
• v.16 no.2
• /
• pp.16-26
• /
• 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.