• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.329-335
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• 2023

Avocado, a superfood selected by Time magazine and one of the late ripening fruits, is one of the foods with a big difference between local prices and domestic distribution prices. If this sorting process of avocados is automated, it will be possible to lower prices by reducing labor costs in various fields. In this paper, we aim to create an optimal classification model by creating an avocado dataset through crawling and using a number of deep learning-based transfer learning models. Experiments were conducted by directly substituting a deep learning-based transfer learning model from a dataset separated from the produced dataset and fine-tuning the hyperparameters of the model. When an avocado image is input, the model classifies the ripeness of the avocado with an accuracy of over 99%, and proposes a dataset and algorithm that can reduce manpower and increase accuracy in avocado production and distribution households.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.980-993
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• 2020

Deep-learning based style transfer has recently attracted great attention, because it provides high quality transfer results by appropriately reflecting the high level structural characteristics of images. This paper deals with the problem of providing more stable and more diverse style transfer results of such deep-learning based style transfer method. Based on the investigation of the experimental results from the wide range of hyper-parameter settings, this paper defines the problem of the stability and the diversity of the style transfer, and proposes a partial loss normalization method to solve the problem. The style transfer using the proposed normalization method not only gives the stability on the control of the degree of style reflection, regardless of the input image characteristics, but also presents the diversity of style transfer results, unlike the existing method, at controlling the weight of the partial style loss, and provides the stability on the difference in resolution of the input image.

• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.581-592
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• 2020

This research aims to explore the effect of participation in 'peer tutoring(learning tutoring)' program designed for low achiever students, and to provide an explanation for the improvement of related extracurricular activity. For this, firstly, the study analyzed differences between goal attainment group and non-attainment group in self-directed learning readiness, learning presence and learning transfer. Secondly, the relationships between three variables were analyzed. Based on an online survey of 154 low achievers participating in learning tutoring, two research questions were examined using t-test, correlation and hierarchical multiple regression analyses. Our findings show that firstly, the academic achievement after participating in tutoring improved more than before. Secondly, there were differences in three variables by gender and grades. Also, there were differences in three variables between two groups. Finally, there was a high positive correlation between three variables, and 71% of learning transfer was explained by self-directed learning readiness and learning presence. Based on these findings, the practical implications are discussed regarding the improvement of tutoring program.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.111-134
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• 2017

This study aims to identify factors to the effectiveness of librarians' continuing education, which is regarded as learning transfer, and to examine the relationship between the effectiveness and the factors identified. Learning transfer is defined as an intention to apply the lessons learned in the education to the learner's practice. For this purpose, a questionnaire survey was conducted to investigate continuing education experiences of university librarians. Findings indicate that characteristics of individual librarians, educational program, and organizational environment have to be taken into consideration to manage librarians' continuing education effectively. Therefore, it is suggested that the university library as an organization should control pre- and post-activities of continuing librarians' education at the library in order to maximize the learning transfer as the effectiveness of the education. Suggestions are developed with factors to the learning transfer to improve the effectiveness.

• Management & Information Systems Review
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• v.32 no.3
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• pp.43-59
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• 2013

The most important thing in training of organization is that how effectively it can be made most of the performance among the staff. It will be useless if the knowledge which gaining after training can not be applied. Therefore the transfer of learning is studied since it is important for decision of training. We studied the factors of transfer of learning and carried out a survey targeting the public officials of Gangwon province with the factors we made a study. We define the factor of both promoted and interrupted in training and suggest the way of improving it. The first, the modeling of competency can stimulate the desire of achievement and complete a course of training among staff of organizations. The second, the construction of training program and organizational culture just for Gangwon province can increase the satisfaction of training among the learners. The third, the establishment of management system after training can reinforce the capability making use of train. The sharing of each information with boss at the office can help to stimulate the function of feedback after training as well.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.339-348
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• 2021

In recent software defect prediction research, defect prediction between cross projects and cross-version projects are actively studied. Cross-version defect prediction studies assume WP(Within-Project) so far. However, in the CV(Cross-Version) environment, the previous work does not consider the distribution difference between project versions is important. In this study, we propose an automated Bayesian optimization framework that considers distribution differences between different versions. Through this, it automatically selects whether to perform transfer learning according to the difference in distribution. This framework is a technique that optimizes the distribution difference between versions, transfer learning, and hyper-parameters of the classifier. We confirmed that the method of automatically selecting whether to perform transfer learning based on the distribution difference is effective through experiments. Moreover, we can see that using our optimization framework is effective in improving performance and, as a result, can reduce software inspection effort. This is expected to support practical quality assurance activities for new version projects in a cross-version project environment.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.33-43
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• 2021

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

• The Journal of Korean Institute for Practical Engineering Education
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• v.1 no.1
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• pp.7-12
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• 2009

The worldwide spread of the internet has made it an important topic in our everyday lives. It has not only changed ordinary lives, but also the whole spectrum of modern society. Thus, it is necessary to understand the characteristic changes in learners as well as social demand in this drastic transformation period and to modify the goal and methods of learning to nurture future intellects. To achieve this, there have been recent attempts to invent new learning methods involving Web 2.0, which focuses on the user. Out of the various aspects of Web 2.0, the blog is expected to invoke learner-oriented education and active discussions between learners, in that the individual manages the blog autonomously and there is much interaction between users. This study will construct a learning system using the blog, apply it on real learners, and analyze its effect on the learners' scholastic achievements, interests, and learning transfer.

• Smart Media Journal
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• v.10 no.1
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• pp.55-62
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• 2021

We applied machine learning of semi-supervised learning, transfer learning, and federated learning as examples of AI use cases that can be applied to the three major industries(Automobile industry, Energy industry, and AI/Healthcare industry) of Gwangju Metro-city, and established an ML strategy for AI services for the major industries. Based on the ML strategy of AI service, practical approaches are suggested, the semi-supervised learning approach is used for automobile image recognition technology, and the transfer learning approach is used for diabetic retinopathy detection in the healthcare field. Finally, the case of the federated learning approach is to be used to predict electricity demand. These approaches were tested based on hardware such as single board computer Raspberry Pi, Jaetson Nano, and Intel i-7, and the validity of practical approaches was verified.