• Journal of Industrial Convergence
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• v.15 no.2
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• pp.27-36
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• 2017

In this study, we analyzed service quality factors of O2O delivery app based on Kano model and survey, and classified service quality into several dimensions. As a result of the analysis, the one dimensional quality factors were accurate information transmission, variety of restaurants, diversity of payment methods, diversity of menu selection, discomfort resolution, kindness of service, taste and quality of food, hygiene and cleanliness, Attractive quality factors such as updated information, reliable reviews, various ordering methods, fast delivery, brand image, discount point payment and accumulation. Although the must-be quality factor did not appear, it turned out that the discomfort resolution was close to the must-be quality factor. The indifferent quality factors were informational services, events and promotions. The O2O delivery app market is continuing to grow and competition is getting more and more intense. The results of this study will help O2O delivery app vendors to establish strategies to focus on certain quality of service factors.

• Nutrition Research and Practice
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• v.15 no.2
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• pp.266-277
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• 2021

BACKGROUND/OBJECTIVES: This study analyzed the quality of lunches provided in senior leisure service (SLS) facilities and compared institutional foodservice (IF) and non-institutional foodservice (non-IF). SUBJECTS/METHODS: Data of 390 adults aged 65 years or older who ate lunches in SLS facilities were analyzed using the information from the 2013-2017 Korea National Health and Nutrition Examination Survey. The participants were classified into IF (n = 129) and non-IF (n = 261) groups according to meal type provided. The intake of major food groups, energy and nutrients, and nutrient adequacy ratio (NAR) and mean adequacy ratio (MAR) were analyzed. The diversity of meals was evaluated by food group patterns, dietary diversity score (DDS) and dietary variety score (DVS). Energy intake was adjusted in model 1, while energy and sex were adjusted in model 2. All confounding variables were adjusted in model 3. RESULTS: The intake of seafoods (P < 0.001 in models 1, 2, and 3), seaweeds (P < 0.01 in models 1 and 2), and dairy products (P < 0.05 in models 1, 2, and 3) was significantly higher in the IF group. No significant difference existed in energy intake; however, the intake of all nutrients except carbohydrate and vitamin C was significantly higher in the IF group. NAR of all nutrients, excluding vitamin C, was higher in the IF group, and MAR was also higher in the IF group (P < 0.001 in models 1, 2, and 3). The IF group had significantly higher DDS and DVS than the non-IF group (P < 0.001). CONCLUSIONS: The lunches provided in SLS facilities were better in terms of quantity and quality when provided through IF than through non-IF. More systematic foodservice programs should be implemented in SLS facilities, especially in facilities wherein users prepare their own meals.

• Journal of Industrial Convergence
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• v.22 no.2
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• pp.13-22
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• 2024

In the current epoch of educational technology (EdTech), the realization of a personalized learning system has become increasingly important. This is due to the growing diversity of today's learners in terms of backgrounds, learning styles, and abilities. Traditional educational methods that deliver the same content to all learners often fail to take this diversity into account. This paper identifies models that comprehensively analyze learners' characteristics, interests, and learning histories to meet the growing demand for learner-centered education. Based on these models, we have designed a personalized learning system. This system is structured to support autonomous learning tailored to the learner's current level and goals by identifying strengths and weaknesses based on the learner's learning history. In addition, the system is designed to extend necessary learning elements without changing its architecture. Through this research, we can identify the essential foundations for constructing a user-tailored learning system and effectively develop a system architecture to support personalized learning.

• Journal of Hospice and Palliative Care
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• v.21 no.2
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• pp.41-50
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• 2018

Spirituality is an essential part of human beings. Spiritual care, designed to meet the spiritual needs of terminally ill patients and their families, is one of the most important aspects of hospice and palliative care (HPC). This study reviewed and analyzed literature utilizing the most commonly used Korean and international healthcare databases to identify care models that adequately address the spiritual needs of terminally ill patients and their families in practice. The results of this study show that spirituality is an intrinsic part of humans, meaning that people are holistic beings. The literature has provided ten evidence-based theories that can be used as models in HPC. Three of the models focus on how the spiritual care outcomes of viewing spiritual health, quality of life, and coping, are important outcomes. The remaining seven models focus on implementation of spiritual care. The "whole-person care model" addresses the multidisciplinary collaboration within HPC. The "existential functioning model" emphasizes the existential needs of human beings. The "open pluralism view" considers the cultural diversity and other types of diversity of care recipients. The "spiritual-relational view" and "framework of systemic organization" models focus on the relationship between hospital palliative care teams and terminally ill patients. The "principal components model" and "actioning spirituality and spiritual care in education and training model" explain the overall dynamics of the spiritual care process. Based on these models, continuous clinical research efforts are needed to establish an optimal spiritual care model for HPC.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• The Journal of the Korea Contents Association
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• v.14 no.8
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• pp.505-516
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• 2014

This study was aimed at identifying the influence of Creative Thinking Filtering Mode program in international gifted program: how much it influences to improve the cognitive domains of creativity (fluency, flexibility, originality) and affective domains of creativity (independence, curiosity, diversity, sensitivity, sense of humor, individuality. To analyze data, ANCOVA(Analysis of Covariance)test was conducted, and the results are as belows. Firstly, the group applied in CTFM program was higher than controlled group on the domains of cognitive and affective. Specifically, in the factors of fluency, flexibility and originality among three cognitive domains and factors of individuality.In affective domains of creativity, independence, curiosity, diversity, a sense of humor among the five factors except of sensitivity were higher. Secondly, the result of analyzing the difference between before and after applying CTFM program was that three elements in cognitive domains : fluency, flexibility and originality improved, especially, the fluency was the most improved. Thirdly, the result of analyzing the difference of affective factor between before and after applying CTFM program was that the originality, diversity, a sense of humor and individuality among the 6 elements of affective domain improved, especially the individuality was the most improved.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.123-130
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• 2023

As part of the research on cultural factors that determine international tourism demand, this study was conducted based on regional interest and the need for understanding religion. The purpose of this study is to empirically test how religious factors affect tourism demand in Korea to find out that religious factors are important considerations in establishing tourism policies and strategies. To achieve the purpose of this study, the research target areas were selected as Thailand, Indonesia, and the Philippines, which have relatively many tourists visiting Korea among Southeast Asian countries and are well known for their religious characteristics. GDP and nominal exchange rate, which are economic factors, were selected as explanatory variables. And religious diversity was selected as a characteristic factor variable of the tourism demand model based on the characteristic theory. An empirical analysis was conducted through a gravity model. As a result of the estimation, it was found that GDP has a positive effect on tourism demand in Korea. Nominal exchange rate variables and religious diversity variables were found to have a negative effect on tourism demand in Korea. We have confirmed that religion is an important factor in choosing tourist destinations for Filipino, Thai, and Malaysian tourists visiting Korea, and they choose religiously similar destinations.

• KIEAE Journal
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• v.6 no.4
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• pp.41-50
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• 2006

A greater number of persons with disabilities including an increasing number of aging population has brought concerns on diversity and equality in use of products and environments. Universal design concepts have been introduced rapidly and widely in the built environments in Korea. The purpose of this study was to analyze a model house built in 2006, in the U.S. as a universal design home, systematically examine the universal design model house features, and explore possibility of application of the universal design features in the U.S. into a Korean setting. For this study, site analysis and contents analysis methods were used. Although housing norm, behavioral patterns and environmental contexts would be different, majority of universal design features appeared in the model house were expected to apply. Overall universal design features in the model house sought for attractive, comfort, convenient and safe environment with a little cost. Universal design features in the model house appeared in unobtrusive ways. Universal design considerations on floor materials in living room and bath and furniture arrangement need to modify or replace according to Korean lifestyle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.632-657
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• 2022

Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.