• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.150-160
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• 2020

The purpose of this study was to test the effect of socially-perfectionism on depression by mediating ambivalence over emotional expressiveness and experiential avoidance. For this purpose, 201 participants across the country conducted the survey, a measure of MPS, AEQ-K, AAQ-II, CES-D. The descriptive statistics, Pearson correlation, path coefficient were conducted using SPSS 23.0. The indirect effect was examined using bootstrap in PROCESS Macro. The results of the study are as follows. First, all the variables had meaningful positive correlations. Secondly, When socially-perfectionism affects depression, ambivalence over emotional expressiveness, and experiential avoidance were mediated. As a result, the implications and limitations of the study were discussed.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.89-97
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• 2008

Chaotic behavior in motor systems is undesired dynamics in real-time implementation since the speed is oscillated in a wide range and the torque is changed by a random manner. We present an adaptive control approach for time-varying permanent-magnet synchronous motors (PMSM) with chaotic phenomenon. We consider that its parameters are changed randomly within certain bounds. First, a nonlinear system model of a PMSM is transformed to derive a nominal linear control strategy. Then, an auxiliary control for compensating real-time control error occurred by system perturbation due to parameter change is designed by using Lyapunov stability theory. Numerical simulation is accomplished for evaluating its efficiency and reliability comparing with the traditional control method. Additionally, we test our control method in real-time motor experiment including a PSoC based drive system to demonstrate its practical applicability.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.2
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• pp.77-82
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• 2016

The purpose of this paper is to develop the algorithm of human arousal inducing interface using steady-state visual evoked potential(SSVEP) and its verification through experiments. In order to develop the model, computer-based SSVEP program simulation is preliminary performed. From the results of the simulation, stimulus pattern is decided to checkerboard and SSVEP frequency range is set into beta wave (13~30Hz). After the experiment on proving the effect of SSVEP flashing stimulation while driving by installing it at the location of people mostly falling asleep in the highway, the result confirms that both during the night and the day, after SSVEP flashing stimulation, a wave Beta immediately increases and the subjects keep high stimulation for the 5 minute maintaining stage.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.82-89
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• 2019

The reason why NCS education is conducted in college is because students are expected to improve their skills through practical education. In addition, NCS education is expected to have a positive impact on job competence and competitiveness. The purpose of this study is to explain the necessity of NCS education, and to analyze whether the education transition and practical performance are manifested. For this purpose, the research model was composed of "NCS Education ${\Rightarrow}$ Educational Transition ${\Rightarrow}$ Practical Results" and surveyed about 300 college students in the metropolitan area. If NCS education is necessary in practice, colleges and other educational institutions should plan to make NCS education operate efficiently. As a analysis result, it was confirmed that the internal consistency was secured and both the intensive validity & discriminant validity were confirmed in the validity. As a result of using the structural equation model, this research model was found to be suitable. As a result of the hypothesis test, it is analyzed that the standardization coefficient of the research hypothesis is above the appropriate level and the correlation is highly formed.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.125-131
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• 2019

This study tries to grasp the factor that affects the acceptance of beacon as an O2O marketing tool. This study examined whether there is a difference between beacon accepter as a means of marketing communication and non-accepter in terms of related variables. As a result, there were significant differences between beacon perceiver and non-perceiver in 'smartphone usage' and 'brand consciousness'. In order to understand the predictive variables influencing beacon acceptance as a means of marketing communication, this study used 'perceiving beacon as a marketing communication media' as a dependent variable to perform logistic regression analysis. As a result of the analysis, it was found that 'smartphone usage' and 'brand consciousness' were the predictive variables affecting beacon perceiving. This study tried to analysis the results in the viewpoint of perceived usefulness and ease-of-use which were insisted by TAM.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.223-231
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• 2021

The study analyzes the relationship between job stress, organizational trust, and the job change intentions of beauty industry workers. Three-hundred sixty-nine workers in the beauty industry with longer than three months of working experience were chosen as this study. The study used to perform frequency analysis, exploratory factor and reliability, correlation, confirmation, and structural equation model verification. The following results are as follows. First, job stress had a positive influence. Second, stress related to fairness had a negative effect on organizational trust. Third, organizational trust had a negative effects. In conclusion, organizational trust and stress related to fairness are the important variables. Furthermore, this study suggests that each organization needs to make an effort to increase organizational trust and to relieve stress related to fairness in order to reduce the job change intentions.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.7-13
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• 2021

In recent years, the importance of environmental regulations is increasing in the shipping industry, and the demands of the industry for this are rapidly increasing. Accordingly, the demand of ship owners is increasing as the shaft generator is a technology that responds to environmental regulations that can be applied to ships the fastest. The shaft generator is a device that can increase the fuel consumption rate of the main propulsion engine by installing an electric motor in the main propulsion engine and using it variably according to the load environment. It operates by the power of the motor at low speeds, and when a sudden load is required, the main propulsion engine and motor operate together, enabling efficient operation. In this paper, the diesel engine and shaft generator of a tug boat are modeled using MATLAB Simulink, and the fuel consumption rate is verified through simulation.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.603-611
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• 2022

The purpose of this study was to test the reliability and validity of the SART(Situation Awareness Rating Technique) tool to measure the situational awareness of patients with acute coronary syndrome. The translated SART was tested on 118 hospitalized patients with acute coronary syndrome to verify the reliability and validity of the tool. The analysis was conducted with Cronbach's alpha, exploratory & confirmatory factor analysis. Study findings found that the tool's reliability and model fit index improved when the two problematic items, namely, Situation Awareness 1 (Instability of Situation) and Situation Awareness 7 (Spare Mental Capacity) were removed. According to the results, the revised K-SART is deemed to be an appropriate method for assessing the degree of situational awareness of patients with acute coronary syndrome in decision making about treatment-seeking.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.237-251
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• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.