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Xiaoru Gao,Seung-Chan Choi,Sunghoon Kim,Kihyun Kim,Madhavi Chakrabarty 글로벌지식마케팅경영학회 2023 Global Marketing Conference Vol.2023 No.07
In order to communicate brand concepts and values to the young generations, many brands are highly active on social media platforms such as Twitter, Facebook, and Instagram. Brand-Generated Content has already become the most common marketing strategy for fashion brands and plays a significant role in influencing consumers’ purchase intention. With increased competition on social media platforms, companies need to understand which posting features can bring in more consumer engagements such as number of likes and comments on social media platforms. In this paper, we develop (1) a model to predict the number of likes and (2) a methodology to detect anomaly of posts that have unusually high percentage of negative user comments based on Instagram design variables, including semantic text meanings, facial expressions, color scheme and background of photos, and post timing, among others. We collected a data set of brand-generated Instagram posts from ten fashion brands. The data covers the image, text, and user comments posted between 2019 and 2020. Image features were extracted using Convolutional Neural Network, and text topics were generated through Latent Dirichlet Allocation. Our results will help managers design Instagram posts to increase consumer engagement and to reduce negative consumer reactions.
The Research on AUV Attitude Control based on least Squares Support Vector Machines
Song Xiaoru,Luo Dezhu,Wang Huihua 보안공학연구지원센터(IJUNESST) 2015 International Journal of u- and e- Service, Scienc Vol.8 No.1
The Attitude control model for AUV is highly nonlinear and strong coupling six degrees of freedom model. With it, and complex disturbance of the marine environment a new control strategy chaos least squares support vector machines is proposed. The control model is infinitely approximated by least squares support vector machine. The parameters are optimized Chaos theory. It is formed the Chaos-LSSVM model. The purpose is to obtain excellent attitude control of quality. The Chaos-LSSVM predictive controller for AUV attitude is designed combined with a simplified longitudinal hydrodynamic equations and the Chaos-LSSVM model. Meanwhile the stability of the attitude control system is ensured by the Lyapunov stability theory. Finally, examples of simulation results show that the control model can obtain a good control effect, improve the underwater vehicle dynamic positioning attitude control effectiveness and robustness.
曲曉茹(Qu Xiaoru) 중국어문학연구회 2010 중국어문학논집 Vol.0 No.65
This paper analyses the semantic features of “看?子”, “看上去”, “看起?” and “看?”, which are common difficulties in the TCFL(Teaching Chinese as a Foreign Language). “看?子” is related to the semantic features like "emphasis on appearances" and "informing", while “看上去” has semanticfeatures like. "the sensual perceptionof surface", "distance" and instantaneity". Besides, “看起?”'s semantic features are "information points" and "comment", contrast to “看?”'s "logical inference" and "subjective". These differences are caused by the semantic differences of “?子”, “上去”, “起?” and “?” which come after “看”.
曲曉茹(Qu Xiaoru) 중국어문학연구회 2009 중국어문학논집 Vol.0 No.55
본 논문은 외국인을 대상으로 중국어 교육을 할 때 늘 부딪히는 “看?”“看到”의 차이를 밝히는 것을 목적으로 삼아 양자 화용에 있어서의 특징을 분석하였다. “看?”은 구체적인 사물이 눈에 들어왔음을 의미하는 반면 “看到”는 추상적인 사물에 대한 생각, 느낌을 밝히는 데에 있다. 그리고 “看?”는 “瞬?性”,“偶然性”, “非可控性”이라는 의미 특징을 갖고 있으며 “看到”는 “思考性”,“?点性”, “目的性”,“持?性”이라는 의미 특징을 갖고 있다. “看?”“看到”의 이런 차이는 “看” 뒤의 “?”과“到”의 의미상 차이에서 비롯되었다는 것으로 밝혔다.