Search

Your search keyword '"Wu, Jinfeng"' showing total 684 results
Start Over Author "Wu, Jinfeng" Publication Year Range Last 10 years
684 results on '"Wu, Jinfeng"'

21. Multimodal biometric fusion sentiment analysis of teachers and students based on classroom surveillance video streaming

Author

Zhang Tianxing, Dahlan Hadi Affendy Bin, Xie Zengsheng, Wu Jinfeng, Chen Yingping, Pan Qianying, and Huang Ying

Subjects
biometric identification, pca, fusion algorithm, expression recognition, sentiment analysis, 97b20, Mathematics, QA1-939
Abstract

In the education system, teachers and students as the main body of the classroom; their emotional state in the classroom school is an important indicator of the effectiveness of the classroom. This study first explores biometric recognition, based on the needs of the classroom curriculum and the classroom monitoring as a sensor, to propose a multimodal biometric fusion detection method based on the fusion of face and gait recognition. The PCA algorithm is used to optimize the face recognition as well as the occlusion situation in the classroom to improve gait recognition, and then the face and gait are fused based on the decision layer to achieve the detection and recognition of the identity situation of teachers and students. On this basis, an expression recognition model is established using the attention mechanism, and an emotion analysis system is designed for the classroom curriculum. According to the empirical evidence of multimodal biometric fusion sentiment analysis, the mAP accuracy of this paper’s fusion method is 100% in Euclidean distance, and the accuracy is higher than 99% in cosine distance, which is obviously better than other methods, and the accuracy of this paper’s fusion recognition is above 95% under any condition limitations. At the same time, the correct rate of recognition of emotions such as listening, appreciation, resistance, doubt, and inattention are all higher than 85%, and the five indexes of average absolute error, Pearson correlation coefficient, Accuarcy5, Accuarcy2, and F12 score of this paper’s sentiment analysis have achieved the best results comparing with other sentiment analysis models, which proves the generalization and validity of this paper’s sentiment analysis.

Published
2024
Full Text
View/download PDF

27. Agent-based simulations of China inbound tourism network

Author

Wu, Jinfeng, Wang, Xingang, and Pan, Bing

Subjects
Physics - Physics and Society
Abstract

Based on the results of a large-scale survey, we construct an agent-based network model for the independent inbound tourism of China and, by the approach of numerical simulation, investigate the dynamical responses of the tourist flows to external perturbations in different scenarios, including the closure of a tourist city, the opening of a new port in western China, and the increase of the tourism attractiveness of a specific city. Numerical results show that: (1) the closure of a single city in general will affect the tourist visitations of many other cities in the network and, comparing to the non-port cities, the overall visitation volume of the system is more influenced by closing a port city; (2) the opening of a new port city in western China will attract more tourists to the western cities, but has a negligible impact on either the overall visitation volume or the imbalanced tourist distribution; and (3) the increase of the tourism attractiveness of a non-port (port) city normally increases (decreases) the overall visitation volume, yet there are exceptions due to the spillover effect. Furthermore, by increasing the tourism attractiveness of a few cities simultaneously, we investigate also the strategy of multiple-city-upgrade in tourism development. Numerical results show that the overall tourist volume is better improved by upgrading important non-port cities that are geographically distant from each other. The study reveals the rich dynamics inherent in complex tourism network, and the findings could be helpful to the development and management of China inbound tourism., Comment: 25 pages, 8 figures, 3 tables

Published
2018

41. Machine learning models for daily net radiation prediction across different climatic zones of China.

Author

Yu, Haiying, Jiang, Shouzheng, Chen, Minzhi, Wang, Mingjun, Shi, Rui, Li, Songyu, Wu, Jinfeng, Kui, Xiu, Zou, Haoting, and Zhan, Cun

Subjects
CLIMATIC zones, ARTIFICIAL neural networks, STANDARD deviations, SURFACE of the earth, STATISTICAL correlation
Abstract

Net radiation (Rn), a critical component in land surface energy cycling, is calculated as the difference between net shortwave radiation and longwave radiation at the Earth's surface and holds significant importance in crop models for precision agriculture management. In this study, we examined the performance of four machine learning models, including extreme learning machine (ELM), hybrid artificial neural networks with genetic algorithm models (GANN), generalized regression neural networks (GRNN), and random forests (RF), in estimating daily Rn at four representative sites across different climatic zones of China. The input variables included common meteorological factors such as minimum and maximum temperature, relative humidity, sunshine duration, and shortwave solar radiation. Model performance was assessed and compared using statistical parameters such as the correlation coefficient (R2), root mean square errors (RMSE), mean absolute errors (MAE), and Nash–Sutcliffe coefficient (NS). The results indicated that all models slightly underestimated actual Rn, with linear regression slopes ranging from 0.810 to 0.870 across different zones. The estimated Rn was found to be comparable to observed values in terms of data distribution characteristics. Among the models, the ELM and GANN demonstrated higher consistency with observed values, exhibiting R2 values ranging from 0.838 to 0.963 and 0.836 to 0.963, respectively, across varying climatic zones. These values surpassed those of the RF (0.809–0.959) and GRNN (0.812–0.949) models. Additionally, the ELM and GANN models showed smaller simulation errors in terms of RMSE, MAE, and NS across the four climatic zones compared to the RF and GRNN models. Overall, the ELM and GANN models outperformed the RF and GRNN models. Notably, the ELM model's faster computational speed makes it a strong recommendation for Rn estimates across different climatic zones of China. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

49. FRISCO: Managing fire-induced risks of water quality contamination

Author

Parente, Joana Raquel Mendes Cação, primary, Nunes, João Pedro, additional, Dias, Luis, additional, Benali, Akli, additional, Marques, Marta, additional, Pelayo, Oscar González, additional, Sá, Ana Cristina Lopes de, additional, Jahanianfard, Dina, additional, Nitzsche, Niels, additional, Faria, Beatriz, additional, Aparício, Bruno, additional, Calheiros, Tomás, additional, Wu, Jinfeng, additional, Baartman, Jantiene, additional, Föllmi, Dante, additional, Brito, Carlos, additional, and Pastor, Amandine Valérie, additional

Published
2022
Full Text
View/download PDF

50. Online Prediction of Industrial Process Index: An Edge-Cloud Collaborated Approach

Author

Lu, Shaowen, Zhang, Yajun, and Wu, Jinfeng

Abstract

In this article, an edge-cloud collaborative prediction algorithm is proposed for the problem of industrial process index forecasting. In the proposed algorithm, the cloud-based models are updated periodically, while the edge-side models are updated multiple times using the latest data during each cloud update cycle. We use a linear model for the edge-side and a nonlinear long short term memory model for the cloud-based model and design a novel synchronization mechanism to update the models of both sides alternatively. In order to mitigate potential oscillations caused by frequent updates to the edge-side models, two additional coefficients are introduced. We provide a formal analysis of the convergence of prediction errors. The proposed algorithm is validated using both simulation and real industrial data. The results show that the forecast accuracy and stability are remarkably improved compared to other methods.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results