• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.39-46
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• 2019

This paper investigates the importance of various environmental factors that have a strong influence on strawberry yields grown in greenhouse using the pattern recognition methods. The environmental factors influencing the production of strawberries were six factors such as average inside temperature, average inside humidity, average $CO_2$ level, average soil temperature, cumulative solar radiation, and average illumination. The results of analyzing the observed data using Dynamic Time Warping (DTW) showed that the most significant factor influencing the strawberry production was average soil temperature, average inside humidity, and cumulative solar radiation. Second, the results of analyzing the observed data using Multidimensional Scaling (MDS) showed that the most influential factors on the strawberry yields, such as average $CO_2$ level, average inside humidity, and average illumination were differently given for each farms. However, these results are based on the distance in 3D space and can be deduced from the fact that there is not a large difference between these distances. Therefore, in order to increase the harvest of strawberries cultivated in the farms, it is necessary to manage the environmental factors such as thoroughly controlling the humidity and maintaining the concentration of $CO_2$ constantly by ventilation of the greenhouse.

• The Journal of Economics, Marketing and Management
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• v.10 no.2
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• pp.1-13
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• 2022

Purpose: The findings are expected to be used as basic data for policy establishment for systematic support and upbringing of small and medium-sized suppliers through the current status and characteristics of the industrial structure of the MRO consumable materials industry as a whole and the market trend. Research design, data, and methodology: This survey is conducted in 2019 mainly for companies that operate consumable materials delivery business, and the survey size is about 25,000 in advance (selected) and about 2,000 in the main survey. Using cluster analysis and multidimensional scaling, we derive the visualization of the homogeneous grouping of cases and the relationship structure between them. Results: Based on the attributes of reason for not having an online trading system, it is classified into three and four clusters for industry and company size, respectively, and the feature and pattern of each individual can be are relatively evaluated and visualized. Conclusions: Small and medium-sized consumable material suppliers specialize in products rather than fierce pricing strategies or external expansion strategies and it is more effective to establish a plan to promote the growth of both large and small enterprises through cooperation with large corporations.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.181-198
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• 2024

Fashion stress is a pertinent aspect of modern consumer culture that has been underexplored in academic research. This study developed a conceptual framework of fashion stress and a multidimensional scale to measure consumers' fashion stress. The qualitative study included literature reviews on consumption stress, shopping stress, and consumer behavior, as well as focus group interviews to gain insight into various dimensions of fashion stress. NVivo 12.0 was used to analyze the qualitative data and identify core categories following the grounded theory methodology. The quantitative study involved a preliminary and a primary surveys to verify the validity and reliability of the fashion stress scale. A total of 220 questionnaires were used for data analysis. The results show that fashion stress consists of eight factors: care, shopping, fit, brand, financial, closet, style, and disposal. Choice difficulty plays a significant role in all factors of fashion stress. Moreover, shopping stress had a negative impact on impulse buying, while other factors such as fit, brand, closet, and disposal stress had a positive impact on impulse buying. Thus, fashion stress is a potential antecedent of impulsive consumer behavior. The results also confirm the validity and reliability of the scale. The fashion stress scale developed in this study offers researchers a valuable tool for assessing and understanding consumer experiences.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.394-405
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• 2019

The purpose of this study is to clarify the concept of the frail elderly and to obtain theoretical evidence. The research method was conducted using the basic principles for conceptual analysis of Walker and Avant(2005). As a Result of a review of the literature about how to utilize the concept of a frail elderly, frail elderly might be in the intermediate state of health and disease. They can be defined as physically vulnerable in the sarcopenia, inflammation, insulin resistance, and preceding advanced disease, lead to hospitalization, falls, disability, and death. The attributes were physiological, psychological, and socio-environmental and economic factors, so they had multidimensional factors. They were required the assist daily living of another person. Also, their attributes had decreased the amount of recovery time and degree, and exhaustion. The attributes of frail elderly consisted of these facts: dynamic process, multidimensional factors, dependency, vulnerability. The frail elderly was a dynamic process that involves the possibility of change to health and disease, and include physical, mental, cognitive, and social environmental factors. In addition, the frail elderly was difficulty in daily life, physical vulnerability and difficulty in adaption. In conclusion, frail elderly as defined by the results of this study will contribute to the foundation of health care systems, including community visiting nursing to understand the level of frail elderly and systemic management to do not go into long term care.

• Educational Technology International
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• v.8 no.2
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• pp.73-91
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• 2007

The purpose of this study is to explore tools for systemic and integrated teaching analysis in recognition of problems derived from the existing teaching analysis which have been held fragmentarily and temporarily. In order to do so, a teaching analysis tools is identified by examining the current services of video-taping and analysis, which are the most representative teaching analysis methods among the Centers for Teaching and Learning (CTLs) in Korea, and by redefining teaching analysis through literature review. A teaching analysis is to be done to challenge teachers to change and grow by providing a motive to reflect on the act of teaching and carry out improvements, and it has to be held covering the general act of teaching and examined through diverse methods in systemic and multidimensional perspectives over a full period of teaching. In this study, an act of teaching is examined in four areas: planning, teaching skill, evaluation and reflection, and teaching analysis frameworks according to an act of teaching are suggested by periods of before, during, and after a term. Teaching analysis methods are also suggested by the frameworks.

• Journal of the Korean association of regional geographers
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• v.21 no.3
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• pp.569-582
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• 2015

This study deals with six spa touristy places to analyze the similarity in image and selection factor recognition through multidimensional scaling method. The result is as following. First, as a result of analysis in the similarity in Image of the 6 touristy Spa places, each "Asan and Onyang" and "Suanbo and Ducksan" form different similar image groups. However, Yoosung does not share the similarity in Image that other Spa places own. Second, as a result of analysis of selection factors in the six touristy spa places, it is found out that there is no big difference in selection factors such as 'spa facility', 'a fee to use', and 'quality of service' in the six spa places. Yet, Onyang, Yoosung, Ducksan, and Suanbo spa reflect high selection factor as 'a recognized spa place' different from Asan and Dogo where the reflection of selection factor is low. Onyang, Yoosung, and Dogo regions reflect high selection factor as a 'Touristy destination' while Asan reflects low selection factor.

• Ocean and Polar Research
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• v.32 no.1
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• pp.1-13
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• 2010

To assess short-term variation of summer phytoplankton community structure in different water masses, phytoplankton and environmental factors were monitored from 31 stations on and off the southern coasts of Korea, from June 18 to June 20 2009. According to multidimensional scaling (MDS) and cluster analysis based on phytoplankton community data from each station, the southern sea was divided into two groups. The first group included stations in the south-eastern region of Jeju Island, which is strongly influenced by the Kuroshio warm current. The second group located along the coastal region of the southern sea, which was mainly comprised of Bacillariophyceae and Crytophyceae. Of these stations, St. 13 and 28 formed a temperature front caused by different hydrological conditions. In particular, nutrients and Chl.a concentrations in these two stations were significantly higher compared to those in the other stations. This indicates that phytoplankton population and subsequent microalgal growth under high nutrient concentrations vary in different water masses. Our results support the theory that phytoplankton community structure in the southern sea of Korea can be influenced on a short-term scale by different water masses and currents.

• Journal of the Korean Society of Physical Medicine
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• v.15 no.2
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• pp.109-120
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• 2020

PURPOSE: This study was conducted to verify the effects of a functional correction of the pain of patients with chronic low back pain(CLBP), and to examine the effect of dysfunctional factors on health-related quality of life. METHODS: A preliminary survey was first conducted on 90 patients with CLBP after functional orthodontic treatment. Some revised questionnaires were also prepared. The survey was distributed for approximately eight weeks, and 215 copies were used as the final analysis data, except for questionnaires that were inadequate, error or non-response. RESULTS: Path analysis using the structural equation model of CLBP patients showed a positive correlation between all the path coefficients and the potential factors. The multidimensional relationship between pain and dysfunction after orthognathic treatment was confirmed using three subdivisions of the pain variables as independent variables and the dysfunctional variables as the dependent variables. Multiple regression analysis was performed to examine the effects of pain on the dysfunction. To identify the multidimensional relationship between dysfunction and the health-related quality of life, eight sub-factors of dysfunctional variables were set as the independent variables, and multiple regression was analyses were performed with the dependent variables of the health-related quality of life. CONCLUSION: This study examined the structural and influence relationships of the functional correction with pain, dysfunction, and health-related quality of life. The results, suggest that a functional orthodontic treatment can be used as a positive program for the health-related quality of life. In addition, this study is meaningful in that it provieds useful information for intervention such as psychosocial change of patients.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.4 s.44
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• pp.379-391
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• 1992

This study was intended to identify the constructing factors and the evaluative dimensions of clothing images. A questionnaire consisted of 110 words expressing clothing images was developed, and eight clothing photographs were selected as stimuli. 298 female subjects aged between 22 to 37 responsed to the 110 words for two photographs during September in 1991. After survey, 110 words were reduced to 62 words based on their independence, then factor analysis was conducted. As a result of factor analysis,6 factors-grace, modernity, unattractive- ness, activeness, dressiness, and youthfulness were found out as constructing factors of clothing images. One additional interest was the effect of design line to the formation of clothing images. ANOVA identified that curved line designs were perceived to be more graceful, modern, dressy, and youthful, and straight line designs were perceived to be more unattractive and active. The other interest was the effect of image factors to the total evaluation. So, regression was used. Consequently, the most influential factor to the total evaluation was found out as grace, followed by unattractiveness, modernity, youthfulness and activeness in a descending order. To identify the evaluative dimensions of clothing images, nine words of unattractiveness image factor were eliminated, and multidimensional scaling analysis was employed. Here, three dimensions were judged to be appropriate to explain the result. The first dimension in the multidimensional space was the evaluation in 'mannish image versus feminine image'. The second was the evaluation in 'simple image versus decorative image'. The third was the evaluation in 'pastoral image versus urbane image'.