• Journal of Biomedical Engineering Research
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• v.41 no.1
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• The Journal of Information Systems
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• v.17 no.4
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• pp.233-260
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• 2008

Universities have recognized e-Learning Systems as the critical IT resources which contribute to improving the competitiveness of the universities as well as the quality of the traditional class-based lectures. Instructors deliver the main contents in the class. Other supplementary activities like online discussions, sharing of teaching-learning materials, submission of homeworks, communication among the learners and between the instructors and the learners, and so on can be efficiently facilitated using e-Learning Systems. In other words, e-Learning Systems enable a blended learning combined class-based lectures and e-learning in a variety of ways. Nonetheless, compared to the level of implementation of e-Learning Systems, the usage of both the instructors and the learners is not high. Accordingly, this study examines the determinants to affect on the usage of e-Learning Systems from the learners perspective. To draw the key determinants, we review the IS literatures related to adoption or use of the IS like Media Richness Theory (MRT), Technology Acceptance Model (TAM), Social Influence Model (SIM), and Self-efficacy Model. The variables are drawn out to be expected on the usage of e-Learning like Media Richness, Ease of Use from MRT, TAM and Instructor's Influence, Co-learner's Influence from SIM, and Self-efficacy. To test our model and hypotheses, we have collected data in the class-based lectures using e-Learning System complementary. The results of the test with 192 data are as follows: Firstly, it shows that the Instructor's Influence and the Media Richness are the influential determinants to affect on the Perception of Usefulness of e-Learning Systems. Additionally, the Co-learner's Influence and Ease of Use in order is significant to the Perception of Usefulness. Secondly, as to the degree of use of the e-Learning Systems, the Co-leaner's Influence, the Media Richness, and the Ease of Use are, in that order, the significant determinants. The Perception of Usefulness, also, founded a key factor on increasing the use of e-Learning Systems. On the other hand, the Instructor's Influence is not significant to the use of e-Learning Systems. Finally, it has been found that Self-efficacy is significant to the Perception of Media Richness, Ease of Use, but not significant to the Perception of Usefulness.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.195-203
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• 2011

In-person lectures have structural issues that active communications in the classroom are limited because of the environments where the instructor usually delivers learning contents in a unilateral manner. Therefore, microcontents activities using real-time microblogging were suggested as complementary measures for the lecture in this study. Fourteen students in K University participated in the learning activity for eight weeks using a microblog during instructions. As a result, it was found that participants' positive learning activities increased by producing and collaborating ideas through real-time microblogging. Based on the results, suggestions were made as follows: strategies for the attention to the class, quality management of microcontents, and the development of blended learning design should be more studied further.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.3
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• pp.27-38
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• 2000

This paper proposes how the recurrent neural network controller for a Khepera mobile robot with an obstacle avoiding ability can be determined by co-adaptation of the evolution and learning, The proposed co-adaptation scheme consists of two folds： a population of NN controllers are evolved by the genetic algorithm so that the degree of obstacle avoidance might be reduced through the global searching and each NN controller is trained by CRBP learning so that the running behavior is adapted to its outer environment through the local searching. Experimental results shows that the NN controller coadapted by evolution and learning outperforms its non-learning equivalent evolved by only genetic algorithm in both the ability of obstacle avoidance and the convergence speed reaching to the required running behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of the Korean earth science society
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• v.23 no.8
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• pp.666-675
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• 2002

The purpose of this study was to develop the level-defferentiated Web Based Instruction(WBI) program, to examine its effects on the science achievement self-directed learning characteristics, and the students’ perceptions on the WBI learning. For this purpose, the advanced and complementary WBI program of level-differentiated curriculum was developed to adapt to class fields and examine instruction facilitating efficiency. Designed and developed the WBI program make it possible to teach students according to the level-differentiated learning for the chapter, ‘weather and climate’ in high school science curriculum. The results of this study are as follows: First, level-differentiated WBI was effective to encourage self-concept, learning eagerness, future-oriented self-apprehension, creativity, self-assessment of the student’s self-directed teaming characteristics. There was no interaction effect of treatment and students’ learning ability at the self-directed learning characteristics. Second, the scores of science achievement of WBI group were significantly higher than those of conventional lecture group. There was interaction effect of treatment and students’ learning ability. However level-differentiated WBI has no effect on openness, initiative, responsibility of the student’s self-directed learning characteristics. There was interaction effect of treatment and students’ learning ability at the science achievement, Third, in the perception questionnaire of WBI teaming, many students showed the WBI teaming was good in terms of causing interaction between learners and web based learning materials including various images and animations. However there are several students who showed learning difficulties. For example they wonder which part is more important and what order is proper to study in hypertext environment.

• Journal of Educational Research in Mathematics
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• v.9 no.2
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• pp.383-404
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• 1999

This study began with an epistemological question about the nature of mathematical cognition in relation to the learner's activity. Therefore, by examining Piaget's 'reflective abstraction' theory which can be an answer to the question, we tried to get suggestions which can be given to the mathematical education in practice. 'Reflective abstraction' is formed through the coordination of the epistmmic subject's action while 'empirical abstraction' is formed by the characters of observable concrete object. The reason Piaget distinguished these two kinds of abstraction is that the foundation for the peculiar objectivity and inevitability can be taken from the coordination of the action which is shared by all the epistemic subjects. Moreover, because the mechanism of reflective abstraction, unlike empirical abstraction, does not construct a new operation by simply changing the result of the previous construction, but is forming re-construction which includes the structure previously constructed as a special case, the system which is developed by this mechanism is able to have reasonability constantly. The mechanism of the re-construction of the intellectual system through the reflective abstraction can be explained as continuous spiral alternance between the two complementary processes, 'reflechissement' and 'reflexion'; reflechissement is that the action moves to the higher level through the process of 'int riorisation' and 'thematisation'; reflexion is a process of 'equilibration'between the assimilation and the accomodation of the unbalance caused by the movement of the level. The operational learning principle of the theorists like Aebli who intended to embody Piaget's operational constructivism, attempts to explain the construction of the operation through 'internalization' of the action, but does not sufficiently emphasize the integration of the structure through the 'coordination' of the action and the ensuing discontinuous evolvement of learning level. Thus, based on the examination on the essential characteristic of the reflective abstraction and the mechanism, this study presents the principles of teaching and learning as following; $\circled1$ the principle of the operational interpretation of knowledge, $\circled2$ the principle of the structural interpretation of the operation, $\circled3$ the principle of int riorisation, $\circled4$ the principle of th matisation, $\circled5$ the principle of coordination, reflexion, and integration, $\circled6$ the principle of the discontinuous evolvement of learning level.

• Informatization Policy
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• v.22 no.4
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• pp.91-104
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• 2015

This study tests the effects of differential association, definitions, differential reinforcement and imitation from social learning theory in the explanation of university students' crimes in social network services. In addition, this study tests the interaction effects between social learning factors and other factors such as low self-control, subcultural environment, and crime opportunity for the integrated approach. Using data from 486 university students in Seoul, results show that both definition and imitation have significant influences on crimes, even though differential association and differential reinforcement factors have no significant influences on crimes in social network services. Results also reveal that there are significant interaction effects between definition and subcultural environment, which meana that definition has a strong effect on crimes in high subcultural environment. In addition, it is found that reinforcement has also a strong effect on crimes in high crime opportunity and that interaction effect between imitation and low self-control is significant, which means that imitation has a strong effect on crimes in low self-control students.

• Journal of Technology Innovation
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• v.29 no.2
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• pp.1-30
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• 2021

Recent years have witnessed various attempts of firms to acquire new knowledge. Purchasing intellectual property or merger and acquisition (M&A) can be such attempts, but joint venture can also be an effective way internalizing new complementary assets from external partners. However, due to difficulties in the formation and implementation of learning strategies, many joint ventures have failed to acquire necessary knowledge. In this respect, based on contingency theory and dynamic capability, the current research aims to investigate the failure case of knowledge transfer in an international joint venture - KEFICO established by Hyundai motors and BOSCH. Case firm optimized for hardware technology but did not establish a differentiated learning strategy and organizational structure to acquire software skills, which are intellectuals of different natures. Due to this inconsistency, it was not able for KEFICO to absorb new type of knowledge (skills related to engine control system). This study suggests the theoretical framework illustrating the case and provides some important implications for organizational learning.