• Architectural research
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• v.12 no.2
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• pp.53-62
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• 2010

The purpose of this study is to examine design preferences for the sales spaces of duty-free shops (DFSs) by conducting image evaluations. The results will help improve quality by influencing designs for the construction, extension or remodeling of these shops. An image measurement method, the semantic differential method, was used to measure cognitive structure using photos of shops. Photos were collected of the DFS at Jeju Island, as well as photos of brand stores designed by architects. Two sets of 16 photos (32 different photos in all) were selected according to photo classification standards and design concepts, both decided by reviewing previous studies and related materials. The evaluation and survey were done by two sets of subjects: sales employees, who have experience and special knowledge of the evaluation of sales space; and students majoring in architecture. To strengthen the evaluation results, I conducted a preliminary survey and a main survey, verifying and complementing findings. 116 surveys were conducted, of which 14 were of poor quality and rejected, leaving and 102 to be analyzed. The collected surveys were statistically analyzed, using SPSS 12.0 for Windows. Reliability, image profile, factor and multi-dimensional scaling analyses were conducted. As a result, image evaluation structure and characteristics were obtained for sales spaces of DFSs, confirming the difference between them and other spaces.

• Korean Journal of Childcare and Education
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• v.12 no.6
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• pp.79-102
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• 2016

The purpose of this study was to identify school adjustment groups by applying a Latent Profile Analysis(LPA) and to investigate the effects of children's emotional problems and study habits on determining the membership of these groups. LPA and multiple logistic regression were conducted using the data of 2,200 third-graders from the Korean Children and Youth Panel Study. The results are listed as follows. First, four school adjustment groups were identified: adjustment, approach to adjustment, maladjustment risk, and maladjustment group. Second, accomplishment value and mastery goal orientation were relatively strong predictors of membership of the school adjustment groups. Time management was also a significant variable that predicted the membership of maladjustment or the maladjustment-risk group. Third, attention problems and depression were the most consistent predictors of membership of maladjustment or the maladjustment-risk group. Physical symptoms and social withdrawal were also significant. Based on the results, implications for intervention to promote early school adjustment were discussed.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.97-104
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• 2003

The appearance of various portal web sites that have individual customers, customizing information operate importantly upon a content. But most current portal sites that has a goal for international electronic commerce use information for customer to a simply individual profile and don't create more and new information that customizing. In this paper, we propose a system that generates a new customizing information with classification and analysis in detail and provides automatically to individual customers. The goal of our research is the development of personalizing auto generation agent that composed form of e-mail from preference of each individual user using open rate and mouse event Information for e-mail.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.673-679
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• 2005

Urban conditions such as underground facilities and ambient noises due to cultural activity restrict the application of conventional geophysical techniques in general. We used the linear array microtremor technique which uses these noises as strong energy source. The result parameter of the survey is shear wave velocity profile which had been applied as an fundamental information for the determination of the rock support type in tunnel design. This study was the first case in Korea which utilized a surface geophysical technique yielding successful result in urban area especially under the existing rail ways. The quantitative relation between the shear wave velocity from this method and the rock mass rating(RMR) determined from the inspection of the cores recovered from the same boreholes showed high statistical relationship. These correlations were then used to relate the shear-wave velocity to RMR along the entire profile.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Journal of Korean Society for Atmospheric Environment
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• v.6 no.1
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• pp.11-21
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• 1990

To quantitatively estimate the effect of yellow sand(loess) fromt he Northern China, various soil sources having similar chemical compositions to yellow sands should be separated and identified. After that, mass contribution for yellow sand can be calculated. The study showed that it was impossible to solve this problem by the traditional bulk analyses. However, particle-by-particle analysis by a CCSEM (computer controlled scanning electron microscope) gave enormous potentials to solve it. To perform this study, seven soil source data analyzed by CCSEM were obtained from Texas, U.S.A. Initially, each soil date was classified into two groups, coarse and fine particle groups since the particle number distribution showed a minimum occurring at 5.2$\mu$m of aerodynamic diameter. Particles in each group were then classified into one of the 283 homogeneous particle classes by the universal classification rule which had been built by an expert system in the early study. Further, mass fractions and their uncertainties for each class in each source were calculated by the Jackknife method, and then source profile matrix for the 7 soil sources was created. To use the profile matrix in the study of source contribution, it is necessary to test the degree of collinearity among sources. The profiles were tested by the singular value decomposition method. As a result, each soil source characterized by artificially created variables was totally independent each other and is ready to use in source contribution studies as a receptor model.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.54-65
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• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• Human Ecology Research
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• v.59 no.2
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• pp.215-231
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• 2021

The study set out to identify the number of types of parental attitude profiles and their characteristics, as perceived by adolescents. The study also examined whether predictor variables (parent life satisfaction and self-esteem) influence parenting attitude profiles and whether these profiles, in turn, influence adolescent academic engagement and academic helplessness. The sample consisted of data on 2,590 adolescents and their parents from the 2018 Korean Children and Youths Panel Survey (KCYPS). The adolescents were in the first grade of middle school. Using Mplus, the author applied latent profile analysis to identify the parenting attitude profiles and predictor and outcome variables associated with these profiles. Three profiles were identified based on major features: (1) coercion-inconsistency (8.3%), (2) general (47.3%), and (3) warmth-autonomy support (44.4%). Parent life satisfaction and self-esteem predicted the classification of the profiles. Also, the parental profiles identified the level of adolescent academic engagement and academic helplessness. The findings have important implications for family policies and practices given the significance of parent psychological status, particularly life satisfaction and self-esteem, on their adolescent children's academic engagement and academic helplessness.

• Korean Journal of Culture and Social Issue
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• v.30 no.3
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• pp.349-378
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• 2024

This study aimed to identify latent profiles of anxiety and anger symptoms experienced by Korean adults during the COVID-19 pandemic and factors that influence group classification. To this end, a latent profile analysis(LPA) was conducted on 1,434 Korean adults from February to March 2021 after the pandemic was announced. First, as a result of confirming the suitability of the latent model, a 5-class-model was found to have the best model fit with 'low anxiety/high anger group(Group 1)', 'low anxiety/low anger group(Group 2)', 'middle anxiety/middle anger group(Group 3)', 'middle anxiety/high anger group(Group 4)', 'high anxiety/high anger group(Group 5)'. Second, gender, economic status, emotion-focused coping, and dysfunctional coping were significant predictors of group classification. Third, there were differences between latent groups in PTSD symptom and suicidal ideation. Finally, discussions and implications of the study were discussed.