• Knowledge Management Research
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• v.24 no.2
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• pp.137-159
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• 2023

This study focuses on consumers' perceptions of virtual influencers, which many companies recently used for marketing. This study uses the Q methodology to derive what kind of perception consumers have about virtual influencers who work with various appearances, background stories, and worldviews as components. In addition, we want to see how the expected value of virtual influencers differs by age group. To this end, 34 statements were produced through preliminary interviews and literature reviews. This study showed that some consumers preferred appearances similar to humans, despite recognizing that virtual influencers are fictional characters. Some other consumers preferred to feel like a fictional character by maintaining virtuality, confirming that there are both opposite consumers. In addition, consumers expect virtual influencers to have consistency and expertise in the content field covered, and some consumers do not prefer to show an overly commercial appearance. This study will likely provide implications for companies that want to utilize virtual influencers in considering which ones to use for target customers in marketing activities.

• The Journal of Internal Korean Medicine
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• v.44 no.3
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• pp.313-353
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• 2023

Objectives: Heart failure is a chronic disease with increasing prevalence rates despite advancements in medical technology. Korean medicine utilizes herbal prescriptions to treat heart failure, but little is known about the specific herbal medicines comprising the network of herbal prescriptions for heart failure. This study proposes a novel methodology that can efficiently develop prescriptions and facilitate experimental research on heart failure by utilizing existing databases. Methods: Herbal medicine prescriptions for heart failure were identified through a PubMed search and compiled into a Google Sheet database. NetMiner 4 was used for network analysis, and the individual networks were classified according to the herbal medicine classification system to identify trends. K-HERB NETWORK was utilized to derive related prescriptions. Results: Network analysis of heart failure prescriptions and herbal medicines using NetMiner 4 produced 16 individual networks. Uhwangcheongsim-won (牛黃淸心元), Gamiondam-tang (加味溫膽湯), Bangpungtongseong-san (防風通聖散), and Bunsimgi-eum (分心氣飮) were identified as prescriptions with high similarity in the entire network. A total of 16 individual networks utilized K-HERB NETWORK to present prescriptions that were most similar to existing prescriptions. The results provide 1) an indication of existing prescriptions with potential for use to treat heart failure and 2) a basis for developing new prescriptions for heart failure treatment. Conclusion: The proposed methodology presents an efficient approach to developing new heart failure prescriptions and facilitating experimental research. This study highlights the potential of network pharmacology methodology and its possible applications in other diseases. Further studies on network pharmacology methodology are recommended.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.105-122
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• 2022

According to the statistics about the fatal crashes that have occurred on the expressways for the last 5 years, those who died on the shoulders of the road has been as 3 times high as the others who died on the expressways. It suggests that the crashes on the shoulders of the road should be fatal, and that it would be important to prevent the traffic crashes by cracking down on the vehicles intruding the shoulders of the road. Therefore, this study proposed a method to detect a vehicle that violates the shoulder lane by using the Faster R-CNN. The vehicle was detected based on the Faster R-CNN, and an additional reading module was configured to determine whether there was a shoulder violation. For experiments and evaluations, GTAV, a simulation game that can reproduce situations similar to the real world, was used. 1,800 images of training data and 800 evaluation data were processed and generated, and the performance according to the change of the threshold value was measured in ZFNet and VGG16. As a result, the detection rate of ZFNet was 99.2% based on Threshold 0.8 and VGG16 93.9% based on Threshold 0.7, and the average detection speed for each model was 0.0468 seconds for ZFNet and 0.16 seconds for VGG16, so the detection rate of ZFNet was about 7% higher. The speed was also confirmed to be about 3.4 times faster. These results show that even in a relatively uncomplicated network, it is possible to detect a vehicle that violates the shoulder lane at a high speed without pre-processing the input image. It suggests that this algorithm can be used to detect violations of designated lanes if sufficient training datasets based on actual video data are obtained.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• 한국노년학
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• v.30 no.3
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• pp.779-791
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• 2010

This study was to examine the differential impacts of social experiences and conditions on health among men and women aged 65 years or older, using data of the "2004 Survey on living Status of the Korean Elderly". The outcome variables were any disability, self-rated health, multiple morbidity, and self-rated quality of life. Multiple Classification Analysis was used to test the differential exposure to social factors contributes to gender difference in health. Gender differences in vulnerability of each individual socioeconomic, psycho-social, and behavioral factors for health were assessed by comparing logit coefficients in men and women. I found that gender difference in exposure to social factors contribute to inequalities in health between older men and women, however, gender inequalities remained after controlling for differential exposure except in case of quality of life. In addition, gender differences in health were further explained by differential vulnerabilities to social factors between men and women. Findings of this study may affirm the importance of further and deeper investigation of gender differences in health in later life. Gender sensitive approach in health planning and polices for the elderly is also suggested.

• Asia Marketing Journal
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• v.13 no.4
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• pp.139-158
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• 2012

This study was intended to classify tie-in promotion tools by the criteria of benefit-fit between consumer and tie-in promotions. Tie-in promotion tools include tie-in price reductions, tie-in coupons, tie-in memberships, tie-in contests, tie-in sweepstakes, tangible and intangible tie-in premiums, tie-in payment terms, tie-in samples, tie-in events(culture event, charity event, experience event) and tie-in fund·rebates. The fit between consumer pursuit benefit and tie-in promotion supplying benefit was used as a classification criteria on the basis of Lee et al.'s study in 2011. For the experiment, one stimuli and 12 scenarioes were developed. 100 pieces of data were obtained for each scenario. As a result, benefit fit was subsequently divided into two factors: hedonic-benefit fit and utilitarian-benefit fit. Tie-in promotion tools were then classified into 4 types: high hedonic benefit-added, high utilitarian benefit-added, low hedonic benefit-added, and low utilitarian benefit-added. In previous research, tie-in promotion type was mainly divided by the evaluative criteria on company's viewpoint such as horizontal/vertical or intra-company/ inter-company, which reflects mutual exclusiveness between two criteria. Whereas, in this study, tie-in promotion type was divided by evaluative criteria on consumer's viewpoint such as hedonic- benefit fit/utilitarian-benefit fit. The classifications in this study practically reflect benefit-added of tie-in promotion type superadded one benefit coexisting two benefits.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.107-115
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• 2023

High turbidity in source water can have adverse effects on water treatment plant operations and aquatic ecosystems, necessitating turbidity management. Consequently, research aimed at predicting river turbidity continues. This study developed a multi-class classification model for prediction of turbidity using LightGBM (Light Gradient Boosting Machine), a representative ensemble machine learning algorithm. The model utilized data that was classified into four classes ranging from 1 to 4 based on turbidity, from low to high. The number of input data points used for analysis varied among classes, with 945, 763, 95, and 25 data points for classes 1 to 4, respectively. The developed model exhibited precisions of 0.85, 0.71, 0.26, and 0.30, as well as recalls of 0.82, 0.76, 0.19, and 0.60 for classes 1 to 4, respectively. The model tended to perform less effectively in the minority classes due to the limited data available for these classes. To address data imbalance, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm was applied, resulting in improved model performance. For classes 1 to 4, the Precision and Recall of the improved model were 0.88, 0.71, 0.26, 0.25 and 0.79, 0.76, 0.38, 0.60, respectively. This demonstrated that alleviating data imbalance led to a significant enhancement in Recall of the model. Furthermore, to analyze the impact of differences in input data composition addressing the input data imbalance, input data was constructed with various ratios for each class, and the model performances were compared. The results indicate that an appropriate composition ratio for model input data improves the performance of the machine learning model.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.207-215
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• 2023

This study was compared the soil environment and growth and yield of onion (Allium cepa L.) treated with non-mulching (NM) and mulching polyethylene film (PEF) and two biodegradable films (BFI and BFII) commonly used in farmhouses. Visual observation confirmed the degradation of BFI and BFII films after 150 days after tansplanting (DAT). BFII increased light penetration into the films and reduced the weight maintenace after 180 DAT, with a high decompostion at 30 days after soil tilling. Soil moisture contents much fluctuated between -14 kP and - 0 kPa in NM plots, increasing the minimum soil temperature of BFI plots. Mulching treatments decreased soil organic matter contents but did not subtantially increase soil mineral nutrients, soil bulk density, and number of bacteria compared to those of NM plots. Onion root growth was increased by PEF and BFI treatments at an early growth stage, 60 DAT, with the most remarkable stem extension observed for PEF and BFI treatments after 150 DAT. PEF and BFI treatments increased the bulb's diameter, length, weight, and lodging at 180 DAT. BFI treatments exhibited a high portion of the "very large" category producing with 55.3 tons ha^-1 based on the classification into bulb size, followed by PE (49.3 tons), NM (9.4 tons), and BFII treatments (2.7 tons) at 230 DAT.

• Industry Promotion Research
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• v.8 no.4
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• pp.187-200
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• 2023

The purpose of this paper is to correspond to four-elements in astrology theory, an intellectual from ancient times, that show personality temperament among MBTI, a representative personality type test in modern times, furthermore, by examining 16 personality type cards in tarot, a play culture and fortune telling culture in which the four-element theory is integrated in symbols, it is a comparative consideration that connects the characteristics of the character types contained in them to the 16 personality types of MBTI. The four preferred types of MBTI are Extravesion(E) and Introversion(I), Sensing(S) and Intuition(N), Thinking(T) and Feeling(F), Judgment(J) and Perception(P). Among them, Western four-elements were able to respond to Fire, Water, Air, and Earth in the order of NF(iNtuitive Feeling Type), SF(Sensory Feeling Type), NT(iNtuitive Thinking Type), and ST(Sensory Thinking Type). This is a result that can be derived by comparing individual personality theory and MBTI temperament theory among the symbols contained in ancient astrological theories. And the classification of boys, knights, queens, and kings in the four classes of person cards could be divided according to the MBTI attitude index. The boy showed an adaptive introvert using I and P, the knight showed an adaptive extrovert using E and P, the queen showed a decisive introvert using I and J, and the king showed an adaptive extrovert using E and J.