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Ngoc Dan Thanh NGUYEN(Ngoc Dan Thanh NGUYEN ),Trong Phuc NGO(Trong Phuc NGO ),Ngoc Van MAI(Ngoc Van MAI ),Kim Ngan TRA(Kim Ngan TRA ),Tran Huy Hoang LE(Tran Huy Hoang LE ) 한국유통과학회 2023 유통과학연구 Vol.21 No.4
Purpose: This study aims to analyze the impact of Brand Anthropomorphism and Intimacy on Brand Engagement, and at the same time analyze the regulatory effect of Brand Reputation on the relationship between Brand Anthropomorphism and Intimacy and the relationship between Intimacy and Brand Engagement in terms of distribution brand. Results: The findings show that Brand Anthropomorphism, Intimacy, and Brand Reputation are important value factors in customers’ minds toward their behavior, and from there, they will contribute to creating positive emotions and interactions between consumers and brands. Research design, data, and methodology: This article used the quantitative technique utilizing PLS-SEM software to test the hypothesis with 1,060 samples. Collected data shows that consumers in Ho Chi Minh City have positive emotions and interactive and social behaviors toward smartphone brands. Conclusion: The study has demonstrated the conclusions and proposed solutions to help smartphone brands build Brand Anthropomorphism while enhancing Brand Reputation thereby achieving Intimacy, which leads to consumer Brand Engagement. In addition, this study complements the concept of Brand Anthropomorphism which is lacking in theoretical background and is the first study in Vietnam to explore the prefixes and suffixes of the concept of Brand Anthropomorphism and the regulatory role of Brand Reputation.
Le-Hai Cao,Hoang Van-Phuc,Doan Van Sang,Le Dai Phong 한국전자파학회 2022 Journal of Electromagnetic Engineering and Science Vol.22 No.3
Hand gesture recognition is an efficient and practical solution for the non-contact human–machine interaction in smart devices. To date, vision-based methods are widely used in this research area, but they are susceptible to light conditions. To address this issue, radar-based gesture recognition using micro-Doppler signatures can be applied as an alternative. Accordingly, the use of a novel densely convolutional neural network model, Dop-DenseNet, is proposed in this paper for improving hand gesture recognition in terms of classification accuracy and latency. The model was designed with cross or skip connections in a dense architecture so that the former features, which can be lost in the forward-propagation process, can be reused. We evaluated our model with different numbers of filter channels and experimented with it using the Dop-Net dataset, with different time lengths of input data. As a result, it was found that the model with 64 3 × 3 filters and 200 time bins of micro-Doppler spectrogram data could achieve the best performance trade-off, with 99.87% classification accuracy and 3.1 ms latency. In comparison, our model remarkably outperformed the selected state-of-the-art neural networks (GoogLeNet, Res- Net-50, NasNet-Mobile, and MobileNet-V2) using the same Dop-Net dataset.