• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.213-220
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• 2023

This paper presents a model simulating the folding process and collision dynamics of "ddakji", a traditional Korean game played using paper tiles (which are also referred to as ddakji). The model uses two A4 sheets as the base materials for ddakji. The folding process involves a series of boundary conditions that transform the wing part of the paper structure into a twisted configuration. A rigid plate boundary condition is also adopted for squeezing, establishing the shape and stress state of the game-ready ddakji through dynamic relaxation analysis. The gaming process analysis involves a forced displacement of the striking ddakji to a predetermined collision position. Collision analysis then follows at a given speed, with the objective of overturning the struck ddakji--a winning condition. A genetic algorithm-based optimization analysis identifies the optimal collision conditions that result in the overturning of the struck ddakji. For efficiency, the collision analysis is divided into two stages, with the second stage carried out only if the first stage predicts a possible overturn. The fitness function for the genetic algorithm during the first stage is the direction cosine of the struck ddakji, whereas in the second stage, it is the inverse of the speed, thus targeting the lowest overall collision speed. Consequently, this analysis provides optimal collision conditions for various compression thicknesses.

• Korean Journal of Culture and Social Issue
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• v.23 no.4
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• pp.527-556
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• 2017

The purpose of this study was to examine the effect of group success on organizational commitment in organizational situations. It also aimed to investigate the dynamics in the relevance of motivational attitudes to the individual and group level variables(collective efficacy, group cohesiveness). This study used a multi-level model and tested a series of hypotheses through meso-mediation procedure. The results from 613 naval officials in 36 groups provided evidence that; (1) collective efficacy mediated the relationship between group success and group cohesiveness, (2) the cross-level main effects of group success and collective efficacy were shown on vocationa[l self-efficacy, (3) group cohesiveness exerted positive influence on organizational commitment and (4) the meso-mediation effects among the variables at the multi-level were revealed. It was found that the degree of work motivation and motivational attitudes depended on the group's contextual factors, and that each group's shared perceptions on group performance outcomes could be an important motivational source and cornerstone leading to group cohesiveness. The implications and limitations of these study as well as the direction for future study were discussed.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.

• Journal of Technology Innovation
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• v.31 no.2
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• pp.59-82
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• 2023

The purpose of this research is to conduct the empirical analysis of the short- and long-term causal relationship between public R&D investment, corporate R&D investment, and university R&D investment on economic growth in Korea. To this end, based on the time series data from 1976 to 2020, a causality test was conducted through the unit root test, cointegration test, and vector error correction model (VECM). As a result, it was found that there is a long-run equilibrium relationship between economic growth in Korea, public R&D investment, corporate R&D investment, and university R&D investment, in which a causal relationship exists in the long run. Also, while public R&D investment has a short-term effect on economic growth, corporate and university R&D investment does not have a short-term effect on economic growth. In addition, the results shows that there is a bidirectional causal relationship between economic growth and public R&D investment, corporate R&D investment and public R&D investment, and university R&D investment and public R&D investment in the short term. Through this research, it was empirically found that a highly mutual relationship exists between public R&D investment, corporate R&D investment, university R&D investment and economic growth. In order to increase the ripple effect of R&D investment on economic growth in the future, R&D investment between universities and corporations should be mutually promoted, and R&D investment by corporations should have a positive effect on public R&D investment so that public R&D investment can contribute to future economic growth.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.99-113
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• 2023

In a customer-based industry, customer retention is the competitiveness of a company, and improving customer retention improves the competitiveness of the company. Therefore, accurate prediction and management of potential dormant customers is paramount to increasing the competitiveness of the enterprise. In particular, there are numerous competitors in the domestic card industry, and the government is introducing an automatic closing system for dormant card management. As a result of these social changes, the card industry must focus on better predicting and managing potential dormant cards, and better predicting dormant customers is emerging as an important challenge. In this study, the Recurrent Neural Network (RNN) methodology was used to predict potential dormant customers in the card industry, and in particular, Long-Short Term Memory (LSTM) was used to efficiently learn data for a long time. In addition, to redefine the variables needed to predict dormant customers in the card industry, Unified Theory of Technology (UTAUT), an integrated technology acceptance theory, was applied to redefine and group the variables used in the model. As a result, stable model accuracy and F-1 score were obtained, and Hit-Ratio proved that models using LSTM can produce stable results compared to other algorithms. It was also found that there was no moderating effect of demographic information that could occur in UTAUT, which was pointed out in previous studies. Therefore, among variable selection models using UTAUT, dormant customer prediction models using LSTM are proven to have non-biased stable results. This study revealed that there may be academic contributions to the prediction of dormant customers using LSTM algorithms that can learn well from previously untried time series data. In addition, it is a good example to show that it is possible to respond to customers who are preemptively dormant in terms of customer management because it is predicted at a time difference with the actual dormant capture, and it is expected to contribute greatly to the industry.

• Journal of the Korean Geotechnical Society
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• v.26 no.11
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• pp.89-98
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• 2010

A method to design a critical height of embankments is presented so as to mobilize fully soil arching in pile-supported embankments. The behavior of the load transfer of embankment weights on pile cap beams was investigated by a series of model tests performed on pile-supported embankments with relatively wide space between cap beams. The model tests explained that the behavior of the load transfer depended very much on the height of embankments, because soil arching could be mobilized in pile-supported embankments only under enough high embankments. The measured vertical loads on cap beams coincided with the predicted ones estimated by the theoretical equations, which have been presented in the previous studies on the basis of load transfer mechanisms according to either the punching shear failure mode during low filling stage or the soil arching failure mode during high filling stage. The mechanism of the load transfer was shifted beyond a critical height of embankment from the punching shear mechanism to the soil arching mechanism. Therefore, in order to mobilize soil arching in pile-supported embankments, the embankments should be designed at least higher than the critical height. A theoretical equation to estimate the critical height could be derived by equalizing the vertical loads estimated by the load transfer mechanisms on the basis of both the punching shear and the soil arching. The derived theoretical equation could predict very well the experimental critical height of embankment.

• Journal of the Korean Geotechnical Society
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• v.23 no.6
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• pp.37-51
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• 2007

In this study, pile load tests have been carried out to develop new P-y curves and then, to investigate the effects of pile rigidities on laterally loaded offshore drilled shafts in Incheon marine clay. This paper consists mainly of two parts: the first part, performance of a series of lateral load tests on small- and full-scale piles under one- and two-way loadings and the second part, comparison between the measured and predicted results by using O'Neill's and Matlock's clay models. Based on the results obtained, it is shown that relatively good agreements in bending moments and lateral displacements were obtained between the measured results using calculated P-y curves and predicted ones by O'Neill's and Matlock's clay models. The cases were considered with varying rigidity factors based on pile diameter, length and subgrade soil reaction. Through comparisons, it is found that soil P-y curve influences highly the behavior of flexible pile rather than that of rigid pile.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.321-335
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• 2023

Monitoring sediment loads in natural rivers is the key process in river engineering, but it is costly and dangerous. In practice, suspended loads are directly measured, and total loads, which is a summation of suspended loads and bed loads, are estimated. This study proposes a real-time sediment discharge monitoring system using the horizontal acoustic Doppler current profiler (H-ADCP) and support vector regression (SVR). The proposed system is comprised of the SVR model for suspended sediment concentration (SVR-SSC) and for total loads (SVR-Q_TL), respectively. SVR-SSC estimates SSC and SVR-Q_TL mimics the modified Einstein procedure. The grid search with K-fold cross validation (Grid-CV) and the recursive feature elimination (RFE) were employed to determine SVR's hyperparameters and input variables. The two SVR models showed reasonable cross-validation scores (R²) with 0.885 (SVR-SSC) and 0.860 (SVR-Q_TL). During the time-series sediment load monitoring period, we successfully detected various sediment transport phenomena in natural streams, such as hysteresis loops and sensitive sediment fluctuations. The newly proposed sediment monitoring system depends only on the gauged features by H-ADCP without additional assumptions in hydraulic variables (e.g., friction slope and suspended sediment size distribution). This method can be applied to any ADCP-installed discharge monitoring station economically and is expected to enhance temporal resolution in sediment monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.140-142
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• 2021

Mobile communication technology after 5th generation requires high speed, hyper-connection, and low latency communication. In order to meet technical requirements for secure hyper-connectivity, low-spec IoT devices that are considered the end of IoT services must also be able to provide the same level of security as high-spec servers. For the purpose of performing these security functions, it is required for cryptographic keys to have the necessary degree of stability in cryptographic algorithms. Cryptographic keys are usually generated from cryptographic random number generators. At this time, good noise sources are needed to generate random numbers, and hardware random number generators such as TRNG are used because it is difficult for the low-spec device environment to obtain sufficient noise sources. In this paper we used the chip which is based on quantum characteristics where the decay of radioactive isotopes is unpredictable, and we presented a variety of methods (TRNG) obtaining an entropy source in the form of binary-bit series. In addition, we conducted the NIST SP 800-90B test for the entropy of output values generated by each TRNG to compare the amount of entropy with each method.