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Your search keyword '"Huang, Xiaofu"' showing total 27 results
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27 results on '"Huang, Xiaofu"'

2. Numerical simulation analysis of the effect of Drainage Board length difference on vacuum pre-compression considering time factor

Author

Peng, Jianfeng, Huang, Xiaofu, Chen, Youxiang, Pan, Ziyu, Wu, Jiangnan, Zheng, Zheng, Editor-in-Chief, Xi, Zhiyu, Associate Editor, Gong, Siqian, Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Baochang, Series Editor, Zhang, Wei, Series Editor, Zhu, Quanxin, Series Editor, Zheng, Wei, Series Editor, Zende, Abhijit Mohanrao, editor, Tian, Yongding, editor, Chen, Lingkun, editor, and Jahromi, Saeed Ghaffarpour, editor

Published
2024
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3. The effect of vacuum combined surcharge preloading soft foundation treatment on adjacent pile foundation

Author

Pang, Jianfeng, Huang, Xiaofu, Chen, Yixiang, Pan, Ziyu, Wu, Jiangnan, Zheng, Zheng, Editor-in-Chief, Xi, Zhiyu, Associate Editor, Gong, Siqian, Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Baochang, Series Editor, Zhang, Wei, Series Editor, Zhu, Quanxin, Series Editor, Zheng, Wei, Series Editor, Zende, Abhijit Mohanrao, editor, Tian, Yongding, editor, Chen, Lingkun, editor, and Jahromi, Saeed Ghaffarpour, editor

Published
2024
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4. Numerical simulation of combined vacuum loading soft foundation treatment considering time effect and well resistance effect

Author

Zhang, Xionghui, Peng, Jianfeng, Huang, Xiaofu, Lin, Guanghui, Liu, Fagui, Zheng, Zheng, Editor-in-Chief, Xi, Zhiyu, Associate Editor, Gong, Siqian, Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Baochang, Series Editor, Zhang, Wei, Series Editor, Zhu, Quanxin, Series Editor, Zheng, Wei, Series Editor, Zende, Abhijit Mohanrao, editor, Tian, Yongding, editor, Chen, Lingkun, editor, and Jahromi, Saeed Ghaffarpour, editor

Published
2024
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10. KESAR: A High-Accuracy Prediction Algorithm and Solar-Aware Routing Strategy for Outdoor WSN

Author

Ma, Dongchao, Huang, Xiaofu, Du, Xinlu, Ma, Li, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Zhe, editor, Wu, Fan, editor, and Das, Sajal K., editor

Published
2021
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11. SWAF: A Distributed Solar WSN Adaptive Framework

Author

Hu, Yuekun, Ma, Dongchao, Huang, Xiaofu, Du, Xinlu, Xiao, Ailing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Qiu, Meikang, editor

Published
2020
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14. KEFSAR: A Solar-Aware Routing Strategy For Rechargeable IoT Based On High-Accuracy Prediction.

Author

Ma, Dongchao, Wang, Dongmei, Huang, Xiaofu, Hu, Yuekun, and Ma, Li

Abstract

The high energy density of solar energy gives wireless sensor networks advantages in outdoor monitoring applications. However, long-term stable monitoring is challenging due to frequent weather changes, shading by buildings and trees, etc. The existing research usually uses two technologies to solve the above problems: (1) the energy prediction algorithm, and (2) the energy-aware routing strategy. However, in an actual deployment, frequent weather changes can significantly reduce the accuracy of the existing prediction algorithms. When using the algorithms as the support for energy-aware routing, the network lifetime is less than ideal. The existing routing strategies are in need of further improvement. Because of its lack of environmental adaptability, nodes consume energy quickly and have a high mortality rate. Therefore, aiming at the long-term stability of solar wireless sensor networks, this paper proposes a prediction algorithm based on classification and recurrent neural networks, and integrates the shadow judgement method from our previous research to correct the predicted values. Furthermore, we propose a routing optimization model that can flexibly adjust the target according to the solar intensity. The experimental results show that the prediction and routing scheduling algorithm can significantly improve the energy prediction accuracy (30–50%) and prolong the network lifetime (10–42%) in outdoor small sensor scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
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17. The key drivers and barriers of manure application: A farm survey in Quzhou County

Author

HUANG Xiaofu, ZHANG Tao, and HOU Yong

Subjects
Environmental sciences, farmer attitudes, barriers, Agriculture (General), policy incentives, GE1-350, theory of planned behavior, drivers, manure application, S1-972
Abstract

Replacing chemical fertilizer with animal manure can effectively reduce the costs of crop production and environmental protection and promote agricultural green development. However, there are many social and economic factors in agricultural production hindering the application of manure. It is of great importance to understand the key drivers and barriers of manure application and improve manure recycling and fertilizer reduction. The theory of planned behavior was applied to study the intentions of individual farmers to apply manure by using a detailed questionnaire in Quzhou County, Hebei Province. Our results showed that only 25% cereal farmers used manure, while the share of manure users in vegetable and fruit systems were up to 95% and 87%, respectively. The key barriers of manure application are high costs and negative agronomic and environmental effects. Cereal farmers perceived more barriers than vegetable and fruit farmers. The major drivers included increment of crop yields, improvement of soil fertility, and increment of agricultural income. Social participants had a positive effect on manure application. To promote the application of manure by farmers, government need to participate together to provide policy incentives, strengthen guidance, and improve farmers′ awareness of manure.

Published
2022

22. Selecting Publishing Points for the Optimal Sharing of Predictive Monitoring Information of a Service Process

Author

Cao Jian, Qi Qing, and Huang Xiaofu

Subjects
Service (systems architecture), Service quality, Point (typography), Artificial neural network, business.industry, Computer science, 02 engineering and technology, Machine learning, computer.software_genre, Order (business), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Reinforcement learning, 020201 artificial intelligence & image processing, Artificial intelligence, Web service, business, Publication, computer
Abstract

In order to facilitate collaboration and improve service quality, progress information together with predicted information (Predictive Monitoring Information, PMI) should be published to partners or customers in a timely manner. Currently, the publishing points of PMI are assigned to certain positions empirically before the execution of a service process. However, because of the dynamic nature of service processes, a decision to publish PMI should be made based on the actual progress and predictions of the future. Therefore, an adaptive approach to select the publishing points of PMI for service processes is proposed. We design a method to quantify the benefits of notifying the delay risk at a point in the service process using long short-term memory (LSTM) neural networks. Furthermore, the deep reinforcement learning model is applied to select the publishing points for PMI dynamically. Experiments on a real-world dataset prove that our approach can select the optimal publishing points.

Published
2019

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