• Journal of Engineering Education Research
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• v.27 no.1
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• pp.32-40
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• 2024

This study attempted to develop an efficient management plan that allows both workers and organizations to coexist by analyzing the factors that influence the level of organizational immersion of engineering students. Analysis methods included frequency analysis, t-test, pearson correlation analysis, and hierarchical analysis. Firstly, self-esteem and transfer of learning were influential factors on organizational commitment. Second, self-esteem and transfer of learning were influencing factors of self-efficacy. Third, self-efficacy was an influential factor in organizational commitment. Fourth, self-efficacy appeared as a mediating effect on self-esteem and organizational immersion in learning transfer. Therefore, it is necessary to look for various factors that can increase self-efficacy, and to find opportunities for students to be highly immersed in the organization while studying at the same time.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1685-1695
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• 2021

To generate a deep learning model with high performance, a large training dataset should be required. However, it requires a lot of time and cost to generate a large training dataset in remote sensing. Therefore, the importance of transfer learning of deep learning model using a small dataset have been increased. In this paper, we performed transfer learning of trained model based on open datasets by using orthoimages and digital maps to detect changes of building objects in multitemporal orthoimages. For this, an initial training was performed on open dataset for change detection through the HRNet-v2 model, and transfer learning was performed on dataset by orthoimages and digital maps. To analyze the effect of transfer learning, change detection results of various deep learning models including deep learning model by transfer learning were evaluated at two test sites. In the experiments, results by transfer learning represented best accuracy, compared to those by other deep learning models. Therefore, it was confirmed that the problem of insufficient training dataset could be solved by using transfer learning, and the change detection algorithm could be effectively applied to various remote sensed imagery.

• Journal of Agricultural Extension & Community Development
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• v.23 no.3
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• pp.233-242
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• 2016

This study aimed to analyze educational satisfaction and the relationship between learning transfer and learning persistence shown after actual education targeting students who participated in the agricultural education and training. Conclusions based on the study results can be suggested as follows. First, of the factors related to learning persistence, satisfaction of educational contents turned out to be a statistically significant factor with a positive effect in the agricultural education and training. Students participating in the agricultural education and training have a conspicuous object to learn for improving ability which is necessary for and applicable to agriculture. Second, of the three factors related to learning transfer in the agricultural education and training, satisfaction of educational contents, educational facilities and satisfaction of environment turned out to have a positive effect. Third, results show that satisfaction of instructors does not affect both learning persistence and learning transfer. Lastly, in case of education and training for field practice, this study is suggesting the necessity of research by accessing in a concrete and detailed manner such as learning contents, instructors, educational facilities and satisfaction of environment from the comprehensive concept of educational satisfaction in the directivity of study related to satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.845-850
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• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• Journal of Engineering Education Research
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• v.12 no.3
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• pp.3-12
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• 2009

The recent development of engineering design education has brought enormous influence in many engineering educations. But, most studies in this area have focused only on the system or curriculum development rather than on the effect of the program to the real situation, the transfer. The purpose of this study is to identify the effects of learning and learning transfer in engineering design program at the university level. Transfer is defined as the use of trained knowledge and skill back on the job. The results of the study are as follows. First, learner characteristics and curriculum design had a significant influence on learning effectiveness. Second, learner characteristics had a significant influence an learning transfer. Also, the learning had a significant influence an learning transfer.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.2
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• pp.351-358
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• 2018

Reinforcement learning is a machine learning method which aims to determine a policy to get optimal actions in dynamic and stochastic environments. But reinforcement learning has high computational complexity and needs a lot of time to get solution, so it is not easily applicable to uncertain and continuous environments. To tackle the complexity problem, AC (actor-critic) method is used and it separates an action-value function into a value function and an action decision policy. Also, in transfer learning method, the knowledge constructed in one environment is adapted to another environment, so it reduces the time to learn in a reinforcement learning method. In this paper, we present AC method and transfer learning method to solve the problem of a reinforcement learning method. Finally, we analyze the case study which a transfer learning method is used to solve BS(base station) switching problem in wireless access networks.

• Educational Technology International
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• v.16 no.2
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• pp.183-200
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• 2015

This study investigated the effect of learning achievements and cognitive load according to different types of presenting learning materials and epistemological beliefs (EB). Learning achievements in this study were composed by retention and transfer of ill-structured problem. A total of 80 college students participated in the study. Prior to the learning, students were guided to fill out a questionnaire regarding epistemological beliefs and a prior knowledge test. The students of each group studied with a different type of reading material: full text (FT), full text including key questions (KeyFT) and full text including a concept map (CmFT). After a session of study was finished, they were asked to complete the posttest: retention and transfer. The results showed that there was a significant difference in transfer achievements. CmFT outperformed higher scores than the other types. There was no significant difference in retention among the groups. It is strongly believed that the types of presenting learning materials may have affected the understanding of ill-structured problem solving skills. Students with sophisticated EB showed higher achievements on retention and transfer than naive-EB and mixed-EB. Even though the data showed decrease of the cognitive load on the type of materials and EB, there were no significant differences on the cognitive load. We should consider a positive effect of types of presenting learning materials and EB enhancing capabilities of solving ill-structured problems in real life.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• Management & Information Systems Review
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• v.37 no.3
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• pp.1-25
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• 2018

This research performed the empirical test for the effects of learning transfer on perceived usefulness, perceived ease of use, self-efficacy and work environment using 390 employees who have experienced e-learning in domestic and foreign companies. Analyzed the mediating effects of self-efficacy and work environment in addition to direct effect of each factor on learning transfer. The results showed that perceived usefulness and perceived ease-of-use of e-learning learner had a positive(＋) effect on self-efficacy and a positive influence on supervisor and peer support and organizational climate. Self-efficacy showed a positive effect on learning transfer, and supervisor support, peer support and organizational climate had a positive influence on learning transfer as well. Perceived usefulness also had a positive effect on learning transfer. However, perceived ease-of-use had no significant effect on learning transfer. As a result of the mediating effect analysis, self-efficacy and work environment were analyzed to have mediating effects between perceived usefulness, perceived ease of use, and learning transfer. The implications of this study are as follows. First, this study designed a new research model that reflects factors influencing the effect of learning transfer on acceptance of e-learning that is common in corporate education. It has derived a research model of perceived usefulness and perceived ease-of-use, which were used as mediating variables for external characteristics factors, as independent variables, using self-efficacy and work environment as mediating variables, which were studied as external factors. Second, most of the studies on technology acceptance model and learning transfer are conducted in a single country. The reliability was enhanced by testing the study models using different samples from 26 countries. Third, perceived usefulness and ease-of-use in existing studies have been considered as key determinants of acceptance intention and learning transfer. This study explored the mediating effects of learner and environmental factors on the accepted information technology and strengthened and supplemented the path of learning transfer of perceived usefulness and ease-of-use. In addition, based on the sample analysis of various countries used in this study, it is expected that future international comparative studies will be possible.