• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.5
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• pp.638-644
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• 2022

The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.149-156
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• 2016

This paper is concerned with education courses operating practices for basic computer literacy training. In this study, we propose an example for students to effectively measure and evaluate the achievement of defined ability in the performance measurement and analysis, learning objectives and learning outcomes set in operation throughout the course. Through research and development use case presents the tools for teaching method and effective and objective measurement of the related subjects. And based on the results, we propose the possibility of utilizing NCS-based course operation and education certification. In this study, the measurement process is based on the association with the objective of the development and operation, and measurement tools, measuring tools for measuring learning outcomes associated with the curriculum design methods for the measurement and evaluation of the case of the operation of the course units of learning outcomes and the method proposed.

• Smart Structures and Systems
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• v.30 no.3
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• Journal of Korean Academy of Nursing Administration
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• v.9 no.1
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• pp.75-88
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• 2003

Purpose : The Purposes of this study was to develop a learning organization measurement for nurses, and to test the validity and reliability of the measurement. Method : This study was conducted through 3 phases -theoretical framework choice, measurement items selection, and the testing of validity and reliability. In order to test reliability and validity of the measurement, data were collected from the 261 nurses, working in the 1 hospital with more 800 beds. The data obtained were analyzed by SPSS for Window program using percentages, Factor Analysis, Cronbach's alpha coefficients. Result : As a result of the study, 2 scales -Learning Organization Building Scale, and Knowledge Management Process Scale- were developed. Learning Organization Building Scale was consisted of 23 items, 5 factors. 5 factors explained 60.26% of the total variance, and the Cronbach's alpha of this scale was .8807. Knowledge Management Process Scale was consisted of 17 items, 4 factors. 4 factors explained 66.14% of the total variance, and the Cronbach's alpha of this scale was .9147. Conclusion : The Study supports the validity and reliability of the scales. Therefore, these scales can be effectively utilized for many researches about Learning organization of Nurse, and Nursing organization in the Hospital Setting.

• Research in Mathematical Education
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• v.25 no.3
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• pp.201-225
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• 2022

This paper details case study vignettes that focus on enhancing the teaching and learning of geometry and measurement in the elementary grades with attention to pedagogical practices for teaching through problem solving with rigor and centering equitable teaching practices. Rigor is a matter of equity and opportunity (Dana Center, 2019). Rigor matters for each and every student and yet research indicates historically disadvantaged and underserved groups have more of an opportunity gap when it comes to rigorous mathematics instruction (NCTM, 2020). Along with providing a conceptual framework that focuses on the importance of equitable instruction, our study unpacks ways teachers can leverage their deep understanding of geometry and measurement learning trajectories to amplify the mathematics through rigorous problems using multiple approaches including learning by doing, challenged-based and mathematical modeling instruction. Through these vignettes, we provide examples of tasks taught through rigorous problem solving approaches that support conceptual teaching and learning of geometry and measurement. Specifically, each of the three vignettes presented includes a task that was implemented in an elementary classroom and a vertically articulated task that engaged teachers in a professional learning workshop. By beginning with elementary tasks to more sophisticated concepts in higher grades, we demonstrate how vertically articulating a deeper understanding of the learning trajectory in geometric thinking can add to the rigor of the mathematics.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.864-875
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• 2018

In this paper, we propose a measurement method for height of white light scanning interferometer using deep learning. In order to measure the fine surface shape, a three-dimensional surface shape measurement technique is required. A typical example is a white light scanning interferometer. In order to calculate the surface shape from the measurement image of the white light scanning interferometer, the height of each pixel must be calculated. In this paper, we propose a neural network for height calculation and use virtual data generation method to train this neural network. The accuracy was measured by inputting 57 actual data to the neural network which had completed the learning. We propose two new functions for accuracy measurement. We have analyzed the cases where there are many errors among the accuracy calculation values, and it is confirmed that there are many errors when there is no interference fringe or outside the learned range. We confirmed that the proposed neural network works correctly in most cases. We expect better results if we improve the way we generate learning data.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.108-113
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• 2022

The deep-learning-based measurement method with the through-focus scanning optical microscopy (TSOM) estimated the size of the object using the classification. However, the measurement performance of the method depends on the number of subdivided classes, and it is practically difficult to prepare data at regular intervals for training each class. We propose an approach to measure the size of an object in the TSOM image using the deep-learning regression model instead of using classification. We attempted our proposed method to estimate the top critical dimension (TCD) of through silicon via (TSV) holes with 2461 TSOM images and the results were compared with the existing method. As a result of our experiment, the average measurement error of our method was within 30 nm (1σ) which is 1/13.5 of the sampling distance of the applied microscope. Measurement errors decreased by 31% compared to the classification result. This result proves that the proposed method is more effective and practical than the classification method.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.129-141
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• 2024

In recent times, there has been a shift towards student-centered higher education policies, leading to a growing interest among universities to improve students' learning outcomes. To aid in this endeavor, this study aims to provide guidance for University K to enhance their learning outcome management by comparing and analyzing their learning outcome indicators with those of other domestic and foreign universities. The study examined detailed measurement questions from major learning outcome measurement tools such as AHELO, NSSE, and CLA+. Upon comparison and analysis of University K's major learning outcome indicators with those of other universities, it was found that most of the indicators overlapped. However, some indicators such as student support/facilities for learning, instructor quality, and communication were absent from University K. Therefore, it is crucial to decide whether to add these indicators to the existing learning outcomes or to confirm them through other surveys. Moreover, even for the same indicator, some indicators with different measurement need to consider changing the measurement.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.1
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• pp.66-73
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• 2012

Purpose: This study was done to identify the effectiveness of a web based learning program on self efficacy, knowledge, and competence in measurement of blood pressure in college nursing students. Method: This study was an experimental research study. Data were collected from April 20 to June 1. 2011. The participants were 68 first year nursing students (experimental group 37, control group 31). The collected data were analyzed with the PASW 18.0 program, using ${\chi}^2$-test, t-test, and Cronbach's ${\alpha}$. Results: The mean score for self efficacy in blood pressure measurement in the experimental group was 61.9 and in the control group 60.7. This result was statistically significant (t=3.301, p=.002). The mean score for knowledge of blood pressure measurement in the experimental group was 11.5 and in the control group 10.8. This result was statistically significant (t=2.910, p=.005). But effectiveness of competence in blood pressure measurement was not significant. Conclusion: The study results show that the web based learning program was effective for self efficacy and knowledge in blood pressure measurement but not for competence indicating.-a need to develop strategies to improve competence in blood presessure measurement for these students.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.1-11
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• 2017

The purpose of the study is to develop an online measurement scale for diagnosing self-directed learning ability for students in Korea. In order to achieve the goal, literature review, case analyses, and experts interview were carried out and finalized the blended-learning based SMMIS model. A total of 1,626 elementary, middle and high school students participated in the scale survey to validate the credibility and validity. Based on these results, an online measurement tool for self-directed learning was developed. The tool can be used in blended-learning environment to maximize its effectiveness. In conclusion, we discussed about implications and strategies for the blended-learning based on self-directed learning program model for young students, and suggested future vision and further research.