• Fashion & Textile Research Journal
• /
• v.23 no.5
• /
• pp.559-575
• /
• 2021

The popularity of K-pop, the development of diverse media and communications, and rapid globalization are increasing consumers' attachment to celebrities. These celebrities, called "Human brand", have a growing impact on consumers. However, research on idol celebrities has been conducted mainly on teenagers. Furthermore, there are limits to the studies on consumers' attachment to idol celebrities who do not advertise specific products and intention to purchase products which are not advertised but related to the idol human brand. Therefore, this study has been conducted on 301 out of 400 adult women in their twenties to forties in Korea to examine the relationship between the characteristics of the idol human brand, attachment to the idol human brand, desire to imitate the idol human brand, desire to identify with the idol human brand and its fandom community, and the effect on purchase intention. For the statistical analysis, SPSS and AMOS were used. The study found that the characteristics of the idol human brand which are trust, professionality, social attractiveness, and physical attractiveness positively influenced attachment to the idol human brand. The attachment to the idol human brand positively affected the imitation desire toward the idol human brand, and the identification desire with the idol human brand, and to identify with its fandom community. Additionally, the desire to imitate and to identify with the idol human brand and its fandom community positively affected the intention. Furthermore, the relationships between all variables were found to have a significant mediating effect.

• Nutrition Research and Practice
• /
• v.13 no.3
• /
• pp.247-255
• /
• 2019

BACKGROUND/OBJECTIVES: Accurate, early identification of acutely malnourished children has the potential to reduce related child morbidity and mortality. The current World Health Organisation (WHO) guidelines classify non-oedematous acute malnutrition among children under five using Mid-Upper Arm Circumference (MUAC) or Weight-for-Height Z-score (WHZ). However, there is ongoing debate regarding the use of current MUAC cut-offs. This study investigates the diagnostic performance of MUAC to identify children aged 6-24 months with global (GAM) or severe acute malnutrition (SAM). SUBJECTS/METHODS: Cross-sectional, secondary data from a community sample of children aged 6-24 months in Niger were used for this study. Children with complete weight, height and MUAC data and without clinical oedema were included. Using WHO guidelines for GAM (WHZ < -2, MUAC < 12.5 cm) and SAM (WHZ < -3, MUAC < 11.5 cm), the sensitivity (Se), specificity (Sp), predictive values, Youden Index and Receiver Operating Characteristic (ROC) curves were calculated for MUAC when compared with the WHZ reference criterion. RESULTS: Of 1161 children, 23.3% were diagnosed with GAM using WHZ, and 4.4% with SAM. Using current WHO cut-offs, the Se of MUAC to identify GAM was greater than for SAM (79 vs. 57%), yet the Sp was lower (84 vs. 97%). From inspection of the ROC curve and Youden Index, Se and Sp were maximised for MUAC < 12.5 cm to identify GAM (Se 79%, Sp 84%), and MUAC < 12.0 cm to identify SAM (Se 88%, Sp 81%). CONCLUSIONS: The current MUAC cut-off to identify GAM should continue to be used, but when screening for SAM, a higher cut-off could improve case identification. Community screening for SAM could use MUAC < 12.0 cm followed by appropriate treatment based on either MUAC < 11.5 cm or WHZ < -3, as in current practice. While the practicalities of implementation must be considered, the higher SAM MUAC cut-off would maximise early case-finding of high-risk acutely malnourished children.

• ETRI Journal
• /
• v.46 no.2
• /
• pp.290-306
• /
• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• ETRI Journal
• /
• v.25 no.2
• /
• pp.121-132
• /
• 2003

To enhance the productivity of software development and accelerate time to market, software developers have recently paid more attention to a component-based development (CBD) approach due to the benefits of component reuse. Among CBD processes, the identification of reusable components is a key but difficult process. Currently, component identification depends mainly on the intuition and experience of domain experts. In addition, there are few systematic methods or tools for component identification that enable domain experts to identify reusable components. This paper presents a systematic method and its tool called a component identifier that identifies software components by using object-oriented domain information, namely, use case models, domain object models, and sequence diagrams. To illustrate our method, we use the component identifier to identify candidates of reusable components from the object-oriented domain models of a banking system. The component identifier enables domain experts to easily identify reusable components by assisting and automating identification processes in an earlier development phase.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.1165-1168
• /
• 2005

The video device is used to gain lots of informations in indoor environment. The one of informations is the information to identify the moving object. The methods to identify the moving object are to recognize the face, the gait and to analyze the hue histogram of the clothes. The hue data is effective at the environment of multi-video. In this paper, we describe the existing research about to identify the moving object in the environment of multi-video and find its problems. finally, we present the enhanced methods to solve its problems. In the future, the method will be use for recognizing the location of object in ubiquitous home.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.915-926
• /
• 2020

Outlier detection methods help one to identify unusual instances in data that may correspond to erroneous, exceptional, or surprising events or behaviors. This work studies conditional outlier detection, a special instance of the outlier detection problem, in the context of incorrect data label identification. Unlike conventional (unconditional) outlier detection methods that seek abnormalities across all data attributes, conditional outlier detection assumes data are given in pairs of input (condition) and output (response or label). Accordingly, the goal of conditional outlier detection is to identify incorrect or unusual output assignments considering their input as condition. As a solution to conditional outlier detection, this paper proposes the ratio-based outlier scoring (ROS) approach and its variant. The propose solutions work by adopting conventional outlier scores and are able to apply them to identify conditional outliers in data. Experiments on synthetic and real-world image datasets are conducted to demonstrate the benefits and advantages of the proposed approaches.

• Journal of Power System Engineering
• /
• v.21 no.3
• /
• pp.66-73
• /
• 2017

In order to identify the design parameters of a hydraulic engine mount with a nonlinear characteristics, an experimental method has been used generally. The method takes a considerable time and expense because of preparing an experimental apparatus, conducting a test, and analyzing results. Therefore, this paper presents a simple method to identify the design parameters of a cylindrical hydraulic engine mount, and optimize the design parameters. The physical model and mathematical equations of the mount were derived, and values of the design parameters of the mount were identified by optimization method with minimizing difference between the analytical results with the equations and the experimental results. This method is more simpler than the conventional experiment method and identify successfully the design parameters. In addition, the technique can optimize the design parameters of the mount to improves the isolation performance of the mount.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.93-95
• /
• 2003

Vector-borne diseases have been the most important worldwide health problem for many years and still represent a constant and serious risk to a large part of the world’s population. GIS and RS is used to evaluate and model the relationships between environmental factors/indicators and the incidences of viral diseases. The aim of the study is to identify the risk factors in Dengue Haemorrhagic Fever DHF) from the highest prevalence area and lowest prevalence area in Sukhothai province, Thailand using statistical, spatial and GIS Modeling. Results obtained in the study of the Dengue show that it is now possible to identify and localize precisely environmental indicators and factors of viral diseases.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.4
• /
• pp.13-26
• /
• 1999

When we try to design a production planning system for a manufacturing company, it is a time consuming task to analyze various planning activities and identify inter-relationship among a lot of decisions made for the production planning. Most of the research efforts have been concentrated to well-organized independent decision-making problems that may usually be identified only after analyzing the characteristics of the decision-making process as a whole. In this paper, a methodology is suggested to characterize the whole process of the production planning for a manufacturing company and reduce the complexity of decision-making problems. The methodology is based on an experience of developing a production planning software for an automobile component manufacturer in korea. First, it is explained how to identify and represent the dependency among various decision-making variables. And a methodology is proposed to analyze the identified dependency among decision variables and identify decision-making process. Lastly, a practical example is provided to illustrate the analysis procedure in this paper.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.1
• /
• pp.55-64
• /
• 2016

The development of high-speed rail (HSR) has had a major impact on the power supply grid. Based on the monitored waveforms of HSR, a pattern recognition approach is proposed for the first time in this paper to identify the operating conditions. To reduce the data dimensions for monitored waveforms, the principal component analysis (PCA) algorithm was used to extract the characteristics and their waveforms from the monitored waveforms data. The dynamic time wrapping (DTW) algorithm was then used to identify the operating conditions of the HSR. Cases studies show that the proposed approach is effective and feasible, and that it is possible to identify the real-time operating conditions based on the monitored waveforms.