• Journal of Digital Policy
• /
• v.3 no.1
• /
• pp.1-10
• /
• 2024

The purpose of this study was to identify the predictors of self-regulated learning strategies among nursing university students. Data were collected from 212 nursing university students in G metropolitan city and K city. The SPSS WIN 23.0 version program was used for data analysis. The data were analyzed using Pearson's correlation coefficient and multiple regression. There were significant correlations between media multitasking and self-regulated learning strategies(r=.45, p<.001), learning flow and self-regulated learning strategies(r=.59, p<.001), and media multitasking and learning flow(r=.32, p<.001). Friendship satisfaction, media multitasking and learning flow explained 45% of the variance for self-regulated learning strategies. To increase the self-regulated learning strategies among nursing university students, it is necessary to develop multiple interventions that enhance friendship satisfaction, media multitasking and learning flow.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.10
• /
• pp.5095-5111
• /
• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• Korean Journal of Adult Nursing
• /
• v.19 no.5
• /
• pp.70-79
• /
• 2007

Purpose: This study was to evaluate clinical competence in relation to self-directed learning, critical thinking disposition, and participating in PBL(Problem-Based Learning) group activities of nursing students. Methods: Data were collected from 108 nursing students in Oct. 2006. Results: Clinical competence had a significant positive correlation with self-directed learning, critical thinking disposition, and participation in PBL group activities. There was a significant difference in clinical competence according to interpersonal relationship. Participation in PBL group activities was the most influential factor of clinical competence($R^2$=.34). Also, the influence of clinical competence increased with the addition of self-directed learning($R^2$=.42). Conclusion: It is essential to encourage the self-directed learning and participation in PBL group activities for the improvement of clinical competence. It is relatively important for clinical competence to consider the educational environment systematically.

• Journal of Korean Clinical Health Science
• /
• v.4 no.2
• /
• pp.571-581
• /
• 2016

Purpose. This study was to identify the relationship between self-directed learning and nursing student's satisfaction of clinical practice. Methods. This study was designed to measure the level of satisfaction according to the contents, guidance, environment, time, and the evaluation of clinical practice. Two hundred thirty-one nursing student from baccalaureate program in H-gun was sampled. A scale consisting of 75 questions, developed by the researcher, was used to gather data from September 14 through 27, 2012. The data was analyzed by descriptive statistics, paired t-test, pearson correlation, and multiple linear regression using the SPSS WIN 18.0 program. Results. The results were summarized : Self-directed learning didn't show a significant different by grade(t=0.83, p=.934). Nursing student's satisfaction of clinical practice didn't show a significant different by grade(t=0.26, p=.798). The relationship between self-directed learning correlated with the degree of nursing student's satisfaction in clinical practice(r=.44, p<.001). The factors of self-directed learning described nursing student's satisfaction of clinical practice as $R^2$=.215(F=9.858, p<.001). Conclusions. It was found that a higher degree of satisfaction in clinical practice depends on a higher degree of self-directed learning. Therefore, nursing faculty should plan intervention to improve satisfaction level of clinical practice by fostering self-directed learning.

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.139-149
• /
• 2019

In this paper, we present auto-annotation tool and synthetic dataset using 3D CAD model for deep learning based object detection. To be used as training data for deep learning methods, class, segmentation, bounding-box, contour, and pose annotations of the object are needed. We propose an automated annotation tool and synthetic image generation. Our resulting synthetic dataset reflects occlusion between objects and applicable for both underwater and in-air environments. To verify our synthetic dataset, we use MASK R-CNN as a state-of-the-art method among object detection model using deep learning. For experiment, we make the experimental environment reflecting the actual underwater environment. We show that object detection model trained via our dataset show significantly accurate results and robustness for the underwater environment. Lastly, we verify that our synthetic dataset is suitable for deep learning model for the underwater environments.

• Journal of Korea Multimedia Society
• /
• v.23 no.11
• /
• pp.1396-1405
• /
• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.20 no.43
• /
• pp.175-195
• /
• 1997

The learning curve has an important effect the growth of corporation. But, in Korea, the study and inference on the learning rate of each industry are unprepared, and so, Korean industires have difficult in productivity and cost. At this point, this study infers the learning rate of the oil industries and investigates the productivity and growth of them. In conclusion, this study presents the direction of the oil industries' development. With the intention of this objects, this study seizes the status which is concerned the total quantity, the operating rate, the plant capacity, the indicators concerning productivity, the investment of R & D and the scales, and then, infers and verifies the relevancy in connection with the learning rate. In the oil industry, the average rate of learning is 65.96% from 1982 to 1994 which the total quantity and the average operation time are used to infer the rate. To observe the low rate within a same period of time, this study takes the consequences that the learning rate is almost indentical with them each year. This steady state is caused by a difference between the employee and the decision maker about the acquirement and assimiliated of technology. When the high-quality technologies posses the environment to applicate in the scene of labor with them, this technology applies to the productivities. As the learning rate increases, the productivity has more effectiveness. The result of analysis about the effectiveness of the learning rate follows that the R & D unfoldes to exist and does not contribute to the growth of the oil industry. To analyze the variables of the growth, such as the learning rate, the investement of R & D, the operating rate and the gross value added to property, plant and equipment, the model is established and examined. The business strategy in the oil industry must be developed to achive the internal growth as well as the external.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.271-279
• /
• 2023

The purpose of this study was to investigate the effect of learning flow, problem solving ability, professor-student interaction of academic achievement in nursing students. Data were collected from 274 nursing students in B city and analyzed by t-test, ANOVA, Pearson correlation coefficient, and hierarchial multiple regression using SPSS/WIN 22.0. The degree of academic achievement in nursing students was 3.70±0.70. There were significant differences in academic achievement with grade(F=4.755, p=.003), campus life satisfaction(F=5.643, p=.004), major satisfaction(t=5.794, p=.003), adapting to COVID-19(F=7.961, p<.001), satisfaction to non-face-to-face environment class(F=18.353, p<.001). There was positive correlation between academic achievement and learning flow(r=.649, p<.001), problem solving ability(r=.333, p<.001), professor-student interaction(r=.479, p<.001). The factors affecting academic achievement of the study subjects were learning flow(β=.563, p<.001), professor-student interaction(β=.280, p<.001), with an explanatory power of 52.0%. Therefore, strategies increase the academic achievement of nursing students in untact lecture, and environment improvement to increase learning flow and professor-student interaction are needed.

• Journal of the Semiconductor & Display Technology
• /
• v.19 no.3
• /
• pp.12-18
• /
• 2020

In semiconductor manufacturing, defect detection is critical to maintain high yield. Currently, computer vision systems used in semiconductor photo lithography still have adopt to digital image processing algorithm, which often occur inspection faults due to sensitivity to external environment. Thus, we intend to handle this problem by means of using Mask R-CNN instead of digital image processing algorithm. Additionally, Mask R-CNN can be trained with image dataset pre-processed by means of the specific designed digital image filter to extract the enhanced feature map of Convolutional Neural Network (CNN). Our approach converged advantage of digital image processing and instance segmentation with deep learning yields more efficient semiconductor photo lithography inspection system than conventional system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.469-481
• /
• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.