• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.67-74
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• 2018

The purpose of this study was to analyze the structural relationship among emotional labor and work performance of information security professionals. To this end, we conducted a questionnaire survey on 176 security workers and analyzed the collected data using structural equation modeling (SEM). It was found that the frequency of emotional display was positively related to deep acting and surface acting. Also, the intensity and variety of emotional display was positively related to deep acting and surface acting. In addition, deep acting had a positive relationship with work performance and surface acting had an significantly positive relationship with work performance. The results of this study are meaningful to understand the influence of the emotional aspect of security workers on work performance. Therefore, the overall findings suggest that the training programs and education for the improvement of emotional labor capacity of deep acting are continuously required.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.573-580
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• 2017

Deep Neural Network (DNN) is a large layered neural network which is consisted of a number of layers of non-linear units. Deep Learning which represented as DNN has been applied very successfully in various applications. However, many issues in DNN have been identified through past researches. Among these issues, generalization is the most well-known problem. A Recent study, Dropout, successfully addressed this problem. Also, Dropout plays a role as noise, and so it helps to learn robust feature during learning in DNN such as Denoising AutoEncoder. However, because of a large computations required in Dropout, training takes a lot of time. Since Dropout keeps changing an inter-layer representation during the training session, the learning rates should be small, which makes training time longer. In this paper, using mutation operation, we reduce computation and improve generalization performance compared with Dropout. Also, we experimented proposed method to compare with Dropout method and showed that our method is superior to the Dropout one.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.411-418
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• 2018

With the proliferation of artificial intelligence technology, it is becoming important to obtain, store, and utilize scientific data in research and science sectors. A number of methods for extracting meaningful objects such as graphs and tables from research articles have been proposed to eventually obtain scientific data. Existing extraction methods using heuristic approaches are hardly applicable to electronic documents having heterogeneous manuscript formats because they are designed to work properly for some targeted manuscripts. This paper proposes a prototype of an object extraction system which exploits a recent deep-learning technology so as to overcome the inflexibility of the heuristic approaches. We implemented our trained model, based on the Faster R-CNN algorithm, using the Google TensorFlow Object Detection API and also composed an annotated data set from 100 research articles for training and evaluation. Finally, a performance evaluation shows that the proposed system outperforms a comparator adopting heuristic approaches by 5.2%.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.25-37
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• 2019

Inspired by the rapid development of deep learning and online biometrics-based authentication, we propose a handwritten one-time password authentication system which employs deep learning-based handwriting recognition and writer verification techniques. We design a convolutional neural network to recognize handwritten digits and a Siamese network to compute the similarity between the input handwriting and the genuine user's handwriting. We propose the first application of the second edition of NIST Special Database 19 for a writer verification task. Our system achieves 98.58% accuracy in the handwriting recognition task, and about 93% accuracy in the writer verification task based on four input images. We believe the proposed handwriting-based biometric technique has potential for use in a variety of online authentication services under the FIDO framework.

• Tunnel and Underground Space
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• v.33 no.2
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• pp.71-82
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• 2023

This study presents a direct measurement method for grain bulk modulus, which is important hydraulic-mechanical properties of rock, and conducts the experiment to investigate the grain bulk modulus of sandstone. In addition, the factors affecting the grain bulk modulus were investigated, comparing volumetric characteristics of rocks with different properties. As a result of the experiment, it was confirmed that the theoretically estimated bulk modulus is overestimated than the direct measured one. The possibility of the difference was analyzed, discussing the existence of non-connected pore space due to particle structure of the rock. Finally, the experimental results showed that the direct measurement suggested in this study can reliably predict the grain bulk modulus of sandstone.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.99-104
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• 2007

The deep soil mixing, on ground modification technique, has been used for many diverse applications including building and bridge foundations, port and harbor foundations, retaining structures, liquefaction mitigation, temporary support of excavation and water control. This method has the basic objective of finding the most efficient and economical method for mixing cement with soil to secure settlements through improvement of stability on soft ground. In this research, the experiments were conducted on a laboratory scale with the various test conditions of mixing method; the angle of mixing wing, mixing speed. Strength and shapes of improved soil of these test conditions of deep mixing method were analysed. From the study, it was found that the mixing conditions affect remarkably to the strength and shapes of improved soils.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• Journal of Aerospace System Engineering
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• v.18 no.1
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• pp.21-28
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• 2024

In this paper, two prediction models based on deep neural network that could predict cross-sectional stiffness of a rotor blade were proposed. Herein, we employed structural and material information of cross-section. In the case of a prediction model that used material properties as the input of the network, it was designed to predict the cross-sectional stiffness by considering elastic modulus of each cross-sectional member. In the case of the prediction model that used structural information as a network input, it was designed to predict the cross-sectional stiffness by considering the location and thickness of cross-sectional members as network input. Both prediction models based on a deep neural network were realized using data obtained by cross-sectional analysis with KSAC2D (Konkuk section analysis code - two-dimensional).

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.262-271
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• 2024

In this paper, by applying deep learning, one of the A.I. techniques, through angle information, which is optical observation data generated when observing satellites at observatories, distance information from observatories is learned to predict range data, thereby increasing the precision of satellite's orbit determination. To this end, we generated observational data from GMAT, reduced the learning data error of deep learning through preprocessing of the generated observational data, and conducted deep learning through MATLAB. Based on the predicted distance information from learning, trajectory determination was performed using an extended Kalman filter, one of the filtering techniques for trajectory determination, through GMAT. The reliability of the model was verified by comparing and analyzing the orbital determination with angular information without distance information and the orbital determination result with predicted distance information from the model.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.471-485
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• 2024

This study explores the effective utilization of generative AI for conducting in-depth interviews within the lean startup environment. With recent technological advancements, the application of generative AI in enhancing operational productivity has been on the rise across various organizations, and this trend extends to the lean startup milieu. The research develops specific guidelines and a guidebook aimed at assisting practitioners in lean startups to conduct in-depth interviews using AI, even amidst the constraints of limited time and capital. The proposed guidebook facilitates practitioners to swiftly design and conduct interviews, thereby promoting an agile and flexible working environment within lean startups. Moreover, this study investigates practical methods for applying text-based generative AI services like ChatGPT 4 and Luyten in the fields of design and interviewing, thereby contributing to the academic discussion and practical implementation in these areas. The significance of this research lies in its potential to broaden the horizon of scholarly debate and practical application of generative AI in lean startups.