• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.372-376
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• 2008

Nowadays, active teachers expend much time and effort in preparing for classes. Especially, the basic formative calsses in art education have much effect when they are done with a lot of material(plaster casts, etc.) and lighting effect, but not in real. Proposed web-based learning instrument makes possible basic formative classes with simple operation by loading several materials and lighting operator on the web. In this paper, we propose web-based learning instrument to improve basic formative classes. Students will easily be able to express shadow and shade by lights with this web-based learning instrument in basic formative classes.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1075-1080
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• 2022

Recently, the effort is continuously applied in machine learning and deep learning algorithm which is represented as artificial intelligence algorithm in the varies field such as prediction, classification and clustering. In this paper, we propose detection algorithm for un-peeling status of PCB protection film by using Dectron2. We use 42 images of data as training and 19 images of data as testing based on 61 images which was taken under the condition of a critical reflection angel of 42.8°. As a result, we get 16 images that was detected and 3 images that was not detected among 19 images of testing data.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• Asia pacific journal of information systems
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• v.29 no.1
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• pp.35-49
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• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.89-103
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• 2012

This paper analyzed the effect of learning motivation and conation(endurance, effort) on the consultant' entrepreneurship(innovativeness, proactiveness, risk- taking) and competencies(ability, knowledge, attitude), and the mediating effect of the entrepreneurship on the consultant's competencies. The result shows that the learning motivation and conation have a positive impact on the partial factors of the consultant's entrepreneurship(innovativeness, proactiveness) and competencies(ability, knowledge), but not impact on the risk- taking and the attitude. Innovativeness and proactiveness have an positive impact on the consultant's competencies, but not the risk-taking. Innovation and proactiveness fully mediated the effect of learning motivation on the ability, and partially mediated on the knowledge. The effect of learning conation on the ability and knowledge was partially mediated by the innovation, not by the proactiveness. This study shows that the management of the learning motivation and conation, the education of entrepreneurship(innovativeness, proactiveness) are very important for the cultivating the consultant' competencies.

• Korean Journal of Cognitive Science
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• v.7 no.3
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• pp.37-60
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• 1996

From the biopsychological point of view,learning could be defined as the processes to transfer the information that we obtain from environment to the neural circuits in the brain.In the studies to determine the biological substrates of learning and memory,there was a remarkable effort to identify neural circuits related with a specific type of learning and to describe the mechanixm of neural plasticity of learning and memory,under the assumption that the memory orinformation may be stored as a modificationof neural synapes in the central nerviys system.On the other hand,there was a different kind of tendency to analyze the mechanism of interactions between neural substrates involved in learning and memory,under the assumption that a specific information may be represented in the patterns of comples neural network of the central nervous system.The present review,in the former position.focused on the research methods and the chracteristics and finding of the investigations employing animal model systems to indentify the essential site of engram for learning and memory.Specifically,the review presents major advances in ourunderstanding of the memory trace circuit for a specific type of learning,with the use of animal model system,the detemination of the critical lodi of neuaral plastic chabges In learing abd memory,and the neurophysiological an biocemical mechanixms of the neural modifia by learint.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.65-71
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• 2017

With advances in technology, many researchers have made an effort to find out educational methods with customized instruction. The purpose of the research is to investigate i) if flipped learning is beneficial for the students taking intermediate-level English grammar and writing class compared with the traditional class, ii) if the flipped learning class is advantageous for all the score level students in terms of student achievement and iii) if the students feel motivated with the flipped learning class. T-test was utilized to determine any differences between pretest and posttest in student achievement. The result in terms of the academic achievement revealed that the flipped classroom approach for the low score group was found to be the least effective among others. In the case of flipped learning teaching method, the instructor should develop contents according to the level of learners. The development of customized contents tailored to the level of learners will enhance learners' learning achievement.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.383-392
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• 2018

This study investigates convergent factors to TOEIC learning flow among some health college students in TOEIC class. The questionnaire was performed on 255 health college students in TOEIC class from college located in J area from May 1st, 2018 to May. 25th, 2018 by using an unregistered self-administered questionnaire. The hierarchical multiple regression analysis shows the following results. The TOEIC learning flow of respondents turned out to be significantly higher in following groups: a group in which self efficacy is higher, a group in which academic control is higher, a group in which school resilience is higher. The results show explanatory power of 43.4%. As the results of the study, it is necessary to make an effort to increase self efficacy, academic control and school resilience to improve the TOEIC learning flow among health college students. These results can be used in development and operation of TOEIC learning program to higher TOEIC learning flow in health college students. Further studies need the analysis of structural equation model effecting TOEIC learning flow of health college students.

• Korean journal of medical education
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• v.30 no.4
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• pp.295-308
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• 2018

Purpose: Although medical curricula are now better structured for integration of biomedical sciences and clinical training, most teaching and learning activities still follow the older teacher-centric discipline-specific formats. A newer pedagogical approach, known as Collaborative Learning Cases (CLCs), was adopted in the medical school to facilitate integration and collaborative learning. Before incorporating CLCs into the curriculum of year 1 students, two pilot runs using the action research method was carried out to improve the design of CLCs. Methods: We employed the four-phase Kemmis and McTaggart's action research spiral in two cycles to improve the design of CLCs. A class of 300 first-year medical students (for both cycles), 11 tutors (first cycle), and 16 tutors (second cycle) were involved in this research. Data was collected using the 5-points Likert scale survey, open-ended questionnaire, and observation. Results: From the data collected, we learned that more effort was required to train the tutors to understand the principles of CLCs and their role in the CLCs sessions. Although action research enables the faculty to improve the design of CLCs, finding the right technology tools to support collaboration and enhance learning during the CLCs remains a challenge. Conclusion: The two cycles of action research was effective in helping us design a better learning environment during the CLCs by clarifying tutors' roles, improving group and time management, and meaningful use of technology.