• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.39-46
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• 2015

Knowledge space theory is a theory that provides a guidelines for human learners' possible education decisions and has been used in various educational environment. However, traditional methodologies using the knowledge space theory have always depended on handwork system and it is necessary to learn programming language such as Visual Basic and R, causing time consuming situations. In order to overcome those issues on the environment of education we propose a new Knowledge Structure Analysis System that not just analyzes learners' knowledge structures automatically but to provide critical learning path for the learners based on knowledge space theory. Proposed system is implemented by using rApache generating critical learning path computing Chi-square value. This provides an automatic way of analyzing knowledge structure in learners' knowledge space and shows systematic reviews for the knowledge space.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.182-189
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• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• Journal of dental hygiene science
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• v.23 no.4
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• pp.271-281
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• 2023

Background: This study aims to improve dental hygiene education by investigating the relationship between metacognition, learning flow, and problem-solving abilities in dental hygiene majors. Methods: A survey was conducted on 2nd to 4th-year students from dental hygiene programs, with 132 responses analyzed. Data analysis involved t-tests and ANOVA to examine the differences in metacognition, learning flow, and problem-solving abilities based on the general characteristics. Multiple regression analysis was employed to investigate the factors influencing the dependent variable, which is problem-solving abilities. The collected data were analyzed using SPSS. Results: First, when comparing metacognition, learning flow, and problem-solving abilities based on the general characteristics of the study participants, statistically significant differences were observed in common factors such as major satisfaction, subjective academic performance, GPA (grade point average), and reason for major choice (p<0.05). Second, it was found that there is a significant positive correlation between metacognition, learning flow, and problem-solving abilities in dental hygiene students (r≥0.79, p<0.05). In other words, higher levels of metacognition and learning flow were associated with better problem-solving abilities. Third, factors influencing problem-solving abilities were identified, with both metacognition and learning flow having a statistically significant positive impact. It was also noted that metacognition had a greater influence on problem-solving abilities compared to learning flow (adjusted R²=0.815, p<0.05). Conclusion: To enhance the core competency of problem-solving abilities, it is essential to improve metacognition and learning flow. To enhance metacognition and promote learning flow, strategies such as goal setting, utilizing effective learning methods, boosting self-efficacy, managing the learning environment, choosing activities that foster immersion, stress management, self-assessment and feedback integration, improving focus, and utilization a variety of learning experiences will be necessary.

• Asia Marketing Journal
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• v.15 no.3
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• pp.79-95
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• 2013

New product innovation is a process of embodying new knowledge in a product and technology licensing is getting popular as a means to innovations and introduction of new product to the market in today's competitive global market environment. Incumbents often rely on technology licensing to access new product opportunities created by other firms. Prior research has examined various aspects of technology licensing agreements such as specific contract terms of licensing agreements, e.g., distribution of control rights, exclusivity of licensing agreements, cross-licensing, and the scope of licensing agreements. This study aims to provide answers to an important, but under-researched question: why do some incumbents initiate more licensing agreement for exploratory learning while others do it for exploitative learning along the innovation process? We attempt to extend our knowledge of licensing agreements from an organizational learning perspective. Technology licensing as a specific form of interfirm linkages can be initiated with different learning objectives along the process of new product innovation. The exploratory stages of the innovation process such as discovery or research stages involve extensive searches to create new knowledge or capabilities, whereas the exploitative stages of the innovation process such as application or test stages near the commercialization are more focused on developing specific applications or improving their efficiency or reliability. Thus, different stages of the innovation process generate different types of learning and the resulting technological resources. We examine when incumbents as licensees initiate more licensing agreements for exploratory learning objectives and when more for exploitative learning objectives, focusing on two factors that may influence a firm's formation of exploratory and exploitative licensing agreements: 1) its past radical and incremental innovation experience and 2) its internal investments in R&D and marketing. We develop and test our hypotheses regarding the relationship between a firm's radical and incremental new product experience, R&D investment intensity and marketing investment intensity, and the likelihood of engaging in exploratory and exploitive licensing agreements. Using data collected from various secondary sources (Recap database, Compustat database, and FDA website), we analyzed technology licensing agreements initiated in the biotechnology and pharmaceutical industries from 1988 to 2011. The results of this study show that incumbents initiate exploratory rather than exploitative licensing agreements when they have more radical innovation experience and when they invest in R&D activities more intensively; in contrast, they initiate exploitative rather than exploratory licensing agreements when they have more incremental innovation experience and when they invest in marketing activities more intensively. The findings of this study contribute to the licensing and interfirm cooperation studies. First, this study lays a foundation to understand the organizational learning aspect of technology licensing agreements. Second, this study sheds lights on how a firm's internal investments in R&D and marketing are linked to its tendency to initiate licensing agreements along the innovation process. Finally, the findings of this study provide important insight to managers regarding which technologies to gain via licensing agreements. This study suggests that firms need to consider their internal investments in R&D and marketing as well as their past innovation experiences when they initiate licensing agreements along the process of new product innovation.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.63-73
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• 2023

The purpose of this study is to identify the level of learning flow, learning satisfaction, academic self-efficacy, academic achievement, and academic stress of nursing students who experienced non-face-to-face online lectures, and to investigate the correlation between variables and the factors affecting academic stress. The data of this study was collected from 143 students at a nursing college in Seoul, through a Google online questionnaire from September 1, 2023 to September 25, 2023, and descriptive statistics, Student's t-test, analysis of variance, Pearson's Correlation, and linear multiple regression were conducted using SPSS Statistics 25.0. Following an analysis of the difference according to general characteristics, academic stress showed significant difference according to Motivation for applying to department(F=4.465, p=.005) and Major satisfaction(F=36.499, p=.000) of the subjects. The result of analyzing the correlation academic stress was negatively correlated with learning flow (r=-.464, p<.010), academic self-efficacy (r=-.522, p<.010), and academic achievement (r=-.379, p<.010), but learning satisfaction was not correlated with academic stress. Variables affecting academic stress were major satisfaction (𝛽=.367, p<.01), learning flow (𝛽=-.186, p<.05), and academic self-efficacy (𝛽=-.241, p<.05), and the explanatory power for academic stress was 40%. The results of this study can be used as basic data for intervention programs for relieving academic stress of nursing students.

• The Journal of Korea Robotics Society
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• v.16 no.1
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• pp.49-55
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• 2021

Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.57-67
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• 2018

The $4^{th}$ industrial revolution has brought innovation in the educational environment. The purpose of this study is to verify the educational effectiveness of smart learning environment especially with the computational thinking. A big data analysis was performed to confirm that computational thinking is the one to prepare the 4th industrial revolution. To teach computational thinking at university, educational design should be careful. This study verified the relationship between improvement of computational thinking ability and major of students with coding education. There was difference in effectiveness of the coding education depending on the major of students, it means students must be guaranteed to be educated by the differentiated coding education for different major. This study extracted factors of computational thinking through literature review. Thirteen research hypotheses were applied for the statistical analysis in R language. It was proved that expectation of class and improvement of abstraction ability and algorithmic thinking ability had mediation effect to the relationship between knowledge acquisition and problem-solving abilities. Based on this study, effectiveness of education can be improved, and it will lead to produce a lot of distinguished students who are ready for the 4th industrial revolution.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Journal of IKEEE
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• v.26 no.3
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• pp.488-493
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• 2022

In manufacturing sites, bearing fault in eletrically driven motors cause the entire system to shut down. Stopping the operation of this environment causes huge losses in time and money. The reason of this bearing defects can be various factors such as wear due to continuous contact of rotating elements, excessive load addition, and operating environment. In this paper, a motor driving environment is created which is similar to the domestic manufacturing sites. In addition, based on the established environment, we propose a dataset for bearing fault detection by collecting changes in vibration characteristics that vary depending on normal and defective conditions. The sensor used to collect the vibration characteristics is Microphone G.R.A.S. 40PH-10. We used various machine learning models to build a prototype bearing fault detection system trained on the proposed dataset. As the result, based on the deep neural network model, it shows high accuracy performance of 92.3% in the time domain and 98.3% in the frequency domain.