• Asia Marketing Journal
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• v.13 no.1
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• pp.1-26
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• 2011

Measuring the quality of city life (QCL) is important for city marketing given that QCL influences the city brand image and resident city relationship. Despite its importance, most previous measures of community well being were developed in the context of small towns, and limited attention has been given to a subjective measure of QCL in the context of a large city. This study develops and tests a subjective measure of quality of city life (QCL) in the context of a large metropolitan city. Quality of city life (QCL) refers to the degree of need satisfaction and feelings of happiness one experiences during the course of city life. The results from a survey of 507 residents from 25 major districts in Seoul indicate that the QCL measure has convergent and discriminate validity. The results also indicate that QCL has predictive validity in relation to satisfaction with city services, trust in the city government, word of mouth communication, and a sense of citizen pride. The managerial and policy implications of this study are discussed.

• Korean Journal of Culture and Social Issue
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• v.22 no.2
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• pp.263-283
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• 2016

The present study aimed at examining the factor structure, reliability and validity of the Korean version of the Continuing Bonds Scale (K-CBS). In study 1, exploratory factor analysis was administered to 293 bereaved adults who had experienced the death of a loved one, and it revealed a single factor structure with 10 items that explained 52.59% of the total variance. The K-CBS showed good internal consistency with Cronbach's alpha of .92. In study 2, confirmatory factor analysis in a different sample of 200 bereaved adults indicated satisfactory standardized regression weights of all items. However, one item had a squared multiple correlation less than .40, hence, this item was discarded, and 9 items remained for the final scale. The single factor model with 9 items displayed a good fit. The K-CBS had strong positive correlation with grief symptoms, and weak positive correlation with depression. After controlling for grief, however, the K-CBS was predictive of a decrease in depression. The K-CBS was positively associated with posttraumatic growth. In addition, significant differences in scores of the K-CBS were shown among groups based on the deceased's relation to the bereaved and expectedness of loss. These results suggest that the K-CBS is a reliable and valid instrument to measure continuing bonds. Finally, implications, limitations, and directions for future research were discussed.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Atmosphere
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• v.34 no.1
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• pp.55-67
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• 2024

This paper aims to provide a detailed introduction to the concept of the Ratio of Predictable Component (RPC) and the Signal-to-Noise Paradox. Then, we derive insights from them by exploring the paradoxical features by conducting a seasonal and regional analysis through ensemble expansion in KMA's climate prediction system (GloSea). We also provide an explanation of the ensemble generation method, with a specific focus on stochastic physics. Through this study, we can provide the predictability limits of our forecasting system, and find way to enhance it. On a global scale, RPC reaches a value of 1 when the ensemble is expanded to a maximum of 56 members, underlining the significance of ensemble expansion in the climate prediction system. The feature indicating RPC paradoxically exceeding 1 becomes particularly evident in the winter North Atlantic and the summer North Pacific. In the Siberian Continent, predictability is notably low, persisting even as the ensemble size increases. This region, characterized by a low RPC, is considered challenging for making reliable predictions, highlighting the need for further improvement in the model and initialization processes related to land processes. In contrast, the tropical ocean demonstrates robust predictability while maintaining an RPC of 1. Through this study, we have brought to attention the limitations of potential predictability within the climate prediction system, emphasizing the necessity of leveraging predictable signals with high RPC values. We also underscore the importance of continuous efforts aimed at improving models and initializations to overcome these limitations.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.319-348
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• 2008

Self-efficacy, achievement goals, task value, and attribution are some of the representative motivation constructs that explain adolescents' cognition, affect, and behavioral patterns in achievement settings. These constructs have won researchers' recognition by demonstrating explanatory and predictive utility that transcends various social and cultural milieus learners are exposed to. Korean adolescents' motivation is generally in line with this universal trend and can be described adequately with these constructs. Nonetheless, there also exist a host of indigenous factors that shape these motivation constructs to be uniquely Korean. The purpose of the present article was to explore some of the socio-cultural factors that appear to wield particularly determining effects on Korean adolescents' academic motivation. Review of the relevant literature identified interdependent self-construal, traditional morals of filial piety, familism, educational fervor, academic elitism, and the college entrance system as important cultural, social, and policy-related such factors. Also discussed in this article were the roles of these factors in creating more immediate psychological learning environments for Korean adolescents, such as parent-child relationships, teacher-student relationships, and classroom goal structures.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.

• Resources Recycling
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• v.32 no.6
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• pp.25-33
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• 2023

With the growth of the battery industry, a rapid increase in the production and usage of lithium-ion batteries is expected, and in line with this, much interest and effort is being paid to recycle waste batteries, including production scrap. Although much effort has been made to recycle cathode material, much attention has begun to recycle anode material to secure the supply chain of critical minerals and improve recycling rates. The proximate analysis that measures the content of coal can be used to analyze graphite in anode material, but it cannot accurately analyze due to the interaction between the components of the black mass. Therefore, in this study, thermogravimetric analysis of each component of black mass was measured as the temperature increased up to 950℃ in an oxygen atmosphere. As a result, in the case of cathode material, no change in mass was measured other than a mass reduction of about 5% due to oxidation of the binder and conductive material. In the case of anode material, except for a mass reduction of about 2% due to the binder, all mass reduction were due to the graphite(fixed carbon). In addition, metal conductors (Al, Cu) were oxidized and their mass increased as the temperature increased. Thermal analysis results of mixed samples of cathode/anode show similar results to the predictive values that can be calculated through each cathode and anode analysis results.

• Korean journal of applied entomology
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• v.62 no.3
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• pp.215-220
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• 2023

A recent discovery of drywood termites (Cryptotermes domesticus) in a residential facility in Seoul has raised significant concern. This exotic insect species, which can damage timber and wooden buildings, necessitates an immediate investigation of potential infestation. In this study, we assessed the climatic suitability for this termite species using a species distribution modeling approach. Global distribution data and bioclimatic variables were compiled from published sources, and predictive models for climatic suitability were developed using four modeling algorithms. An ensemble prediction was made based on the mean occurrence probability derived from the individual models. The final model suggested that this species could potentially establish itself in tropical coastal regions. While the climatic suitability in South Korea was generally found to be low, a careful investigation is still warranted due to the potential risk of colonization and establishment of this species.

• Journal of the Korean Applied Science and Technology
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• v.39 no.6
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• pp.783-791
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• 2022

This Research is a descriptive study conducted to identify the academic stress, interpersonal relationships, and degree of adaptation to college life of nursing students who experienced COVID-19, and to identify factors influencing college life adaptation. The subjects of the research were sophomore students enrolled in 3 university nursing departments in G city. For data analysis, descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis were analyzed. The research result showed a significant negative correlation (r=-.584, p<.001) for academic stress and college life adaptation, and a significant positive correlation (r=.505, p<.001) for interpersonal relationships and college life adaptation. The regression model to confirm the influencing factors on college life adaptation was shown to be significant (F=64.462 p<.001). Academic stress (β=-.542, p<.001), interpersonal relationships (β=.339, p<.001), and housing type (β=.199, p<.001) were found to be significant predictive factors. The explanatory power of these variables was 54.6%. Through the results of this research, it will be possible to provide basic data for developing educational programs to reduce academic stress, improve positive and smooth interpersonal relationships, and improve emotional support for college life adaptation.