• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.527-540
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• 2001

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5427-5445
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• 2019

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.545-550
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• 2022

In Deep Learning method, it is well known that it requires large amount of data to train the deep neural network. And it also requires the labeling of each data to fully train the neural network, which means that experts should spend lots of time to provide the labeling. To alleviate the problem of time-consuming labeling process, some methods have been suggested such as weak-supervised method, one-shot learning, self-supervised, suggestive learning, and so on. In this manuscript, those methods are analyzed and its possible future direction of the research is suggested.

• The Journal of Information Systems
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• v.11 no.1
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• pp.51-74
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• 2002

The purpose of this study is to empirically investigate critical success factors for effective implementation of web-based distance learning system. First of all, four critical success factors are theoretically derived from reviewing previous research. They are: (1) learner-related factor including the variables such as teaming ability, learning attitude, and attending motivation, (2) environmental factor including the variables of physical and mental support for learners, (3) instructional design factor represented by one variable, the degree of appropriateness of learning contents, and (4) the factor concerning the level of self-directed learning readiness embracing the variables such as curiosity for learning, openness towards challenge of learning and affection for learning. Subsequently, the relationships between these four critical success factors and the degree of learning satisfaction are empirically investigated. The data for empirical analysis of the research are collected from 1,020 respondents who have already passed the web-based distance learning courses which have been implemented in Information and Communication Officials Training Institute. Out of 1,020 responded questionnaires, 875 data were available for statistical analyses. The main results of this study are as follows. Firstly, the most important factor for successful implementation of the web-based distance learning system is shown to be the instructional design factor, and in the next place, the self-directed learning readiness factor, the environmental factor and the learner-related one in sequence. Secondly, additional analysis of the variables included in the instructional design factor shows that availability of practical information and knowledge is the most influencing variable, and next, interesting composition of contents, reasonable learning amount, optimal level of instruction, and understandable explanation are significantly important in the descending order. Lastly, among learning motivators, strong intention of acquiring business knowledges and skills is found to be the most important satisfier in the web-based distance learning. The theoretical contribution of this study is to derive a comprehensive model of critical success factors for implementing the web-based distance learning system. And, the practical implication of this study is to propose efficient and effective guidelines for developing and operating the web-based distance learning system in the various kinds of organizations.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of The Korean Association For Science Education
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• v.32 no.6
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• pp.1063-1071
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• 2012

The purpose of this study is to investigate the relation between the classification ability quotient and cortisol-hormonal change of middle school students. Thirty-three students, second graders in middle school, performed the classification task that can be an indicator of students' classification ability. And then amount of the secreted hormone was analyzed during task performance. The study results were as follows: First, the classification methods of students mostly utilized visual, qualitative. Their classification patterns for each subject were static, partial, and non-comparative. Second, the amount of stress-hormone was secreted from students during the experiment decreased in overall after the free classification. It seemed that student-centered activity relieved stress. Third, the classification ability quotient turned out to be significantly correlated to the stress hormone, which means that there was a close relationship between classification ability and stress level. It was also considered that stress had a positive effect on the improvement of classification ability. This study provided physiologically more accurate information on the stress increased in the learning process than other conventional studies based on reports or interviews. Finally, researchers could recognize the effect of stress in the cognitive activity and the need to find an appropriate level of stress in learning processes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.41-44
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• 2021

In this paper, we propose a monitoring system that can monitor gas leakage concentrations in real time and forecast the amount of gas leaked after one minute. When gas leaks happen, they typically lead to accidents such as poisoning, explosion, and fire, so a monitoring system is needed to reduce such occurrences. Previous research has mainly been focused on analyzing explosion characteristics based on gas types, or on warning systems that sound an alarm when a gas leak occurs in industrial areas. However, there are no studies on creating systems that utilize specific gas explosion characteristic analysis or empirical urban gas data. This research establishes a deep learning model that predicts the gas explosion risk level over time, based on the gas data collected in real time. In order to determine the relative risk level of a gas leak, the gas risk level was divided into five levels based on the lower explosion limit. The monitoring platform displays the current risk level, the predicted risk level, and the amount of gas leaked. It is expected that the development of this system will become a starting point for a monitoring system that can be deployed in urban areas.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.1-10
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• 2021

Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.