• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.149-158
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• 2019

Due to the rapid development of IT (information technology) and internet, products become smart and able to collect, process and produce information and can think of themselves to provide better service to consumers. However, research on the characteristics of smart product is still sparse. In this paper, we report the systemic development of a scale to measure the perceived product smartness associated with smart product. To develop product smartness scale, this study follows systemic scale development processes of item generation, item reduction, scale validation, reliability and validity test consequently. And, after acquiring a large amount of qualitative interview data asking the definition of smart product, we add a unique process to reduce the initial items using both a text mining method using 'r' s/w and traditional reliability and validity tests including factor analysis. Based on an initial qualitative inquiry and subsequent quantitative survey, an eight-factor scale of product smartness is developed. The eight factors are multi-functionality, human-like touch, ability to cooperate, autonomy, situatedness, network connectivity, integrity, and learning capability consequently. Results from Korean samples support the proposed measures of product smartness in terms of reliability, validity, and dimensionality. Implications and directions for further study are discussed. The developed scale offers important theoretical and pragmatic implications for researchers and practitioners.

• Archives of Plastic Surgery
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• v.48 no.3
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• pp.269-277
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• 2021

Background The Sunnybrook facial grading scale is a comprehensive scale for the evaluation of facial paralysis patients. Its results greatly depend on subjective input. This study aimed to develop and validate an automated Sunnybrook facial grading scale (SBface) to more objectively assess disfigurement due to facial paralysis. Methods An application compatible with iOS version 11.0 and up was developed. The software automatically detected facial features in standardized photographs and generated scores following the Sunnybrook facial grading scale. Photographic data from 30 unilateral facial paralysis patients were randomly sampled for validation. Intrarater reliability was tested by conducting two identical tests at a 2-week interval. Interrater reliability was tested between the software and three facial nerve clinicians. Results A beta version of the SBface application was tested. Intrarater reliability showed excellent congruence between the two tests. Moderate to strong positive correlations were found between the software and an otolaryngologist, including the total scores of the three individual software domains and composite scores. However, 74.4% (29/39) of the subdomain items showed low to zero correlation with the human raters (κ<0.2). The correlations between the human raters showed good congruence for most of the total and composite scores, with 10.3% (4/39) of the subdomain items failing to correspond (κ<0.2). Conclusions The SBface application is efficient and accurate for evaluating the degree of facial paralysis based on the Sunnybrook facial grading scale. However, correlations of the software-derived results with those of human raters are limited by the software algorithm and the raters' inconsistency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1494-1507
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• 2018

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

• Korean Journal of Human Ecology
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• v.14 no.1
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• pp.1-13
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• 2005

This study has a purpose of developing a scale to evaluate maternal beliefs on preschool children's education. The subjects were 307 mothers of preschool children in Gwang-ju. The methods for data analyses included a factor analysis for construct validity, Pearson correlations between beliefs and learning-related activities for construct validity, and Cronbach's a for reliability. 4 factors were found, through literature review, in parental beliefs: passive learning, active learning, instruction, and expectation for academic-related skills acquisition. Factor analysis revealed that the 4-factor solution is the best fit. Correlations between beliefs and learning-related activities were statistically significant. Cronbach's a ranged from .65 to .87 for 4 sub-scales. It was concluded that the maternal beliefs scale is acceptable for use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1543-1561
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• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• Biomedical Science Letters
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• v.19 no.1
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• pp.41-47
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• 2013

Pluripotent stem cells (PSCs) have lots of potential in biomedical sciences owing to its potential to differentiate into any kind of cells in the body. However, it is still a challenge to culture PSCs on a large scale for application to regenerative medicine. Herein, we introduce a synthetic polymer that enables large-scale suspension culture of human PSCs. By employing suspension culture, it became unnecessary to use conventional substrata such as mouse embryonic fibroblast (MEF) or Matrigel$^{TM}$, which are believed to be main causative sources of xenogeneic contamination in cultured human PSCs in vitro. Human PSCs were cultured in the medium in which elastin-like polypeptide (ELP) dissolved. The ELP in the medium became harden as temperature increases by transforming the medium into a semi-solid gel that supported growth of human PSCs in suspension. Gel-sol transition temperature of ELP can be adjusted by modifying the peptide sequence in which 5 amino acids, Val-Pro-Gly-Xaa-Gly, repeated sequentially. We constructed 3D suspension media having transition temperature around $33{\sim}35^{\circ}C$ using an ELP consisted of 40, 60, or 80 repeats of a monomer, which was Val-Pro-Gly-Val-Gly. Among the ELPs, ELP80 was chosen as the best ELP to support growth of human PSCs in suspension culture. This result suggests that the ELP80 can be a medium component for culturing human PSCs in large-scale.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.6
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• pp.977-993
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• 2017

In this study, we develop a scale to measure brand image attributes related to the symbolic use of fashion brands, and then, test the validity and reliability of the scale. In Study 1, a comprehensive literature review was conducted to generate the initial set of measurement items. Nominal Group Technique was subsequently conducted to refine the measurement items in a qualitative way. In Study 2, an expert survey was performed to further refine the measurement items in a quantitative way. In Study 3, a consumer survey was performed to determine the final set of measurement items and validate it. The scale of brand attribute symbolism consists of 21 items with six factors (i.e., Strength, Intellect, Cheerfulness, Traditional Femininity, Nature, and Affordability). The six-factor, 21-item scale is found valid and reliable. Implications, limitations of this study, and suggestions for future research are also discussed.

• Korean Journal of Human Ecology
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• v.1 no.1
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• pp.44-65
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• 1998

The purpose of this study are to measure the overall level of consumer consciousness and behavior on environmental problems of high school girl students and to analyze the most influential factors related to the socio-demography and the education on environment. This study also aims at inspiring the students with sound view of environments and furnishing basic data for the development of consumer education program. The researcher used questionnaires on consumer consciousness scale and consumer behavior scale for the purpose of this study. 640 students were surveyed randomly in Kwangju and Chonnam, and 608 questionnaires were selected as final analysis data. Major Findings were as follows: 1. The average score of consumer consciousness and behavior on environmental problems of the high school girls were 86.0 on scale of 110 (72.7 on scale of 100) and 63.3 on scale of 115(43.8 on scale of 100). 2. The consumer consciousness and behavior on environmental problems of the high school girls showed partial differences according to the socio-demography variable. 3. The consumer consciousness and behavior on environmental problems of the high school girls showed partial differences according to the education to environment variables. 4. The consumer behavior on environmental problems had a positive relationship with the consumer consciousness. 5. The most influential variables on the consumer consciousness were necessity of the education on environment, the type of school, region, and monthly allowance money in sequence. And the most influential variables on the consumer behavior were the campaign on environment in home, participation in environment preservation, cosciousness of environment, and education experience on environment in sequence. (Korean J Human Ecology 1(1) : 44∼65, 1998)

• Annual Conference on Human and Language Technology
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• 1996.10a
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• pp.177-188
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• 1996