• Journal of The Korean Association For Science Education
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• v.32 no.7
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• pp.1139-1156
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• 2012

The purpose of this study was to investigate students' and mentors' perceptions of Research and Education (R&E) programs in science high schools and science academies. The sample included 1,466 science high school/science academy students and 310 mentors. They filled out the survey, which consisted of the perceptions of R&E performance procedures (Selecting a topic, Learning topic-related knowledge, Designing and performing the research study, and Evaluating and presenting results), and R&E environment (Research period, meeting opportunities with mentor/subject, learning/experimental environment). The results showed that differences existed in the perceptions of R&E performance procedures and R&E environment, especially on selecting topics and learning topic-related knowledge stages. At the end of the paper, suggestions were included for improving R&E.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• Corrosion Science and Technology
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• v.18 no.6
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• pp.253-257
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• 2019

A deep learning based real-time painting surface inspection algorithm is proposed herein, designed for developing an autonomous inspection drone. The painting surface inspection is usually conducted manually. However, the manual inspection has a limitation in obtaining accurate data for correct judgement on the surface because of human error and deviation of individual inspection experiences. The best method to replace manual surface inspection is the vision-based inspection method with a camera, using various image processing algorithms. Nevertheless, the visual inspection is difficult to apply to surface inspection due to diverse appearances of material, hue, and lightning effects. To overcome technical limitations, a deep learning-based pattern recognition algorithm is proposed, which is specialized for painting surface inspections. The proposed algorithm functions in real time on the embedded board mounted on an autonomous inspection drone. The inspection results data are stored in the database and used for training the deep learning algorithm to improve performance. The various experiments for pre-inspection of painting processes are performed to verify real-time performance of the proposed deep learning algorithm.

• The Journal of Korean Association of Computer Education
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• v.10 no.3
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• pp.31-38
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• 2007

The purpose of the study was to examine the correlations between team cognition and team process and team performance in e-learning based team learning environment. 55 graduate students consisting of 11 teams participated in the study voluntarily during the spring semester. In the result, it was found that team cognition had no relationship with team process and performance although sub-variables of team cognition and team process as well as team performance had a significant relation with each other. Some further research issues were addressed in terms of team leadership and potential variables affecting each variables.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.1
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• pp.37-45
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• 2022

It is well known that the Kano model measures customer satisfaction and classifies quality attributes into must-be, attractive as well as one-dimensional. The main purpose of this study is to investigate the dynamics of e-learning quality attributes by applying the proposed method using Kano's satisfaction index in the rapidly changing online learning environment. For this, the current study examined 27 e-learning quality attributes and conducted a comparative study using Kano's results obtained in 2013 and 2020. The result shows that the dynamics of quality attributes suggested by Kano(2001) is confirmed in the case of e-learning. The proposed approach shows better results in terms of Kano's direct classification method, and has potential application areas such as IPA(Importance-Performance Analysis) in the area of risk assemement. Some suggestions for better understanding of the proposed SI-DI diagram are also included in this study.

• Child Health Nursing Research
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• v.23 no.1
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• pp.70-80
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• 2017

Purpose: In this study a caregiver's role improvement program was developed and tested to identify the effect on uncertainty, stress, and role performance of caregivers with hospitalized children. Methods: The design of this study was a quasi-experimental study with a nonequivalent control group and a non-synchronized design. Thirty-three caregivers were assigned to the experimental group and 33 to the control group. Data were collected from March 5 2016 to April 10 2016. For the experimental treatment, each individual was given on-site education with situated learning (given 30 minutes each, for 2 sessions), and self-repetition learning activities were performed from the e-book. Data were analyzed using t-test, ${\chi}^2-test$, Fisher's exact test, paired t-test, and independent t-test. Results: The level of uncertainty and stress decreased, and role performance level improved for these caregivers with hospitalized children. Conclusion: The findings of this study show that using on-site education through situated learning and self-repetition learning with an e-book as in the caregiver's role improvement program is an effective intervention. Therefore, utilizing the caregiver role improvement program developed in this study is recommended as an effective intervention for caregivers of hospitalized children.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1508-1513
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• 2004

Examining the market orientation and learning orientaiton and these relationship with business performance has received considerable interest in the last decade. Despite these interest, our understanding of the structure of both market orientation and learning orientaion and the mechanism of those effect on business performance is apparently limited in figuring out the railroad industry in Korea. The purpose of this research was to conceptualize and measure the organizational culture dimension from the integrative framework of market orientation and learning orientation, and to analyze its relationship with business performance in the railroad industry. The findings from this research are such as follows; market orientation is a set of three interrelated components, i.e., customer orientation, competitor orientation, and inter-functional coordination. This finding is similar to that of Narver and Slater(1990; 1994) who conceptualized market-oriented culture as a combined set of those 3 components. Learning-oriented culture also has a significant positive effect on business performance. The research findings suggest that both market orientation and learning orientation do exist as different organizational culture dimensions to acquire the sustainable competitive advantage in the railroad industry.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.627-637
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• 2023

As the population and income levels rise, meat consumption steadily increases annually. However, the number of farms and farmers producing meat decrease during the same period, reducing meat sufficiency. Information and Communications Technology (ICT) has begun to be applied to reduce labor and production costs of livestock farms and improve productivity. This technology can be used for rapid pregnancy diagnosis of sows; the location and size of the gestation sacs of sows are directly related to the productivity of the farm. In this study, a system proposes to determine the number of gestation sacs of sows from ultrasound images. The system used the YOLOv7-E6E model, changing the activation function from sigmoid-weighted linear unit (SiLU) to a multi-activation function (SiLU + Mish). Also, the upsampling method was modified from nearest to bicubic to improve performance. The model trained with the original model using the original data achieved mean average precision of 86.3%. When the proposed multi-activation function, upsampling, and AutoAugment were applied, the performance improved by 0.3%, 0.9%, and 0.9%, respectively. When all three proposed methods were simultaneously applied, a significant performance improvement of 3.5% to 89.8% was achieved.

• The Journal of Korean Institute for Practical Engineering Education
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• v.5 no.1
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• pp.28-33
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• 2013

In this paper, we proposed a smart learning model that reflects the environment of information and communication that rapidly changing with the advent of smart devices. There are varieties of screen size and performance of smart devices, but they can be classified smart phones, smart pads, personal computer(PC) and Smart TV. In this paper, we look for appropriate services model with method of interaction and educational content as per each device type, then present the development direction of smart learning that reflects the information communication environment and future smart learning service model according to the characteristics of each device.