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366 results on '"Sun, Duo"'

26. Information filtering based on transferring similarity

Author

Sun, Duo, Zhou, Tao, Liu, Jian-Guo, Liu, Run-Ran, Jia, Chun-Xiao, and Wang, Bing-Hong

Subjects
Physics - Data Analysis, Statistics and Probability, Physics - Physics and Society
Abstract

In this Brief Report, we propose a new index of user similarity, namely the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E {\bf 73} 026120 (2006)], and is relevant to the missing link prediction problem., Comment: 4 pages, 4 figures

Published
2008
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27. Personal Recommendation via Modified Collaborative Filtering

Author

Liu, Runran, Jia, Chunxiao, Zhou, Tao, Sun, Duo, and Wang, Binghong

Subjects
Physics - Data Analysis, Statistics and Probability, Physics - Physics and Society
Abstract

In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard Person correlation, we take into account the influence of node's degree. Substituting this new definition of similarity for the standard Person correlation, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF., Comment: 7 pages, 8 figures and 1 table

Published
2008
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28. Flame stability and burner condition monitoring through optical sensing and digital imaging

Author

Sun, Duo

Subjects
669.8, TA Engineering (General). Civil engineering (General)
Abstract

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for flame stability and burner condition monitoring on fossil-fuel-fired furnaces. A review of methodologies and technologies for the monitoring of flame stability and burner condition is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating optical sensing, digital imaging, digital signal/image processing and soft computing techniques, is proposed. Based on this strategy, a prototype flame imaging system is developed. The system consists of a rigid optical probe, an optical-bearn-splitting unit, an embedded photodetector and signal-processing board, a digital camera, and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and radiation signals. Power spectral density, oscillation frequency, and a proposed universal flame stability index are used for the assessment of flame stability. Kernel-based soft computing techniques are employed for burner condition monitoring. Specifically, kernel principal components analysis is used for the detection of abnormal conditions in a combustion process, whilst support vector machines are used for the prediction of NO x emission and the identification of flame state. Extensive experimental work was conducted on a 9MW th heavy-oil-fired combustion test facility to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 660MWth heavy-oil-fired boiler to investigate the cause of the boiler vibration from a flame stability point of view. Results Obtained from the tests are presented and discussed.

Published
2012
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34. A Network Traffic Intrusion Detection Method for Industrial Control Systems Based on Deep Learning.

Author

Jin, Kai, Zhang, Lei, Zhang, Yujie, Sun, Duo, and Zheng, Xiaoyuan

Subjects
INTRUSION detection systems (Computer security), DEEP learning, INDUSTRIAL controls manufacturing, COMPUTER network traffic, CONVOLUTIONAL neural networks, DUNG beetles, WATER storage
Abstract

The current mainstream intrusion detection models often have a high false negative rate, significantly affecting intrusion detection systems' (IDSs) practicability. To address this issue, we propose an intrusion detection model based on a multi-scale one-dimensional convolutional neural network module (MS1DCNN), an efficient channel attention module (ECA), and two bidirectional long short-term memory modules (BiLSTMs). The proposed hybrid MS1DCNN-ECA-BiLSTM model uses the MS1DCNN module to extract features with a different granularity from the input data and uses the ECA module to enhance the weight of important features. Finally, the model carries out sequence learning through two BiLSTM layers. We use the dung beetle optimizer (DBO) to optimize the hyperparameters in the model to obtain better classification results. Additionally, we use the synthetic minority oversampling technique (SMOTE) to fill several samples to reduce the local false negative rate. In this paper, we train and test the model using accurate network data from a water storage industrial control system. In the multi-classification experiment, the model's accuracy was 97.04%, the precision was 97.17%, and the false negative rate was 2.95%; in the binary classification experiment, the accuracy and false negative rate were 99.30% and 0.7%. Compared with other mainstream methods, our model has a higher score. This study provides a new algorithm for the intrusion detection of industrial control systems. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Intrinsically Bioactive Manganese–Eumelanin Nanocomposites Mediated Antioxidation and Anti‐Neuroinflammation for Targeted Theranostics of Traumatic Brain Injury (Adv. Healthcare Mater. 16/2022)

Author

Sun, Duo, primary, Liu, Kaijun, additional, Li, Yang, additional, Xie, Tian, additional, Zhang, Mi, additional, Liu, Yu, additional, Tong, Haipeng, additional, Guo, Yu, additional, Zhang, Qianhui, additional, Liu, Heng, additional, Fang, Jingqin, additional, and Chen, Xiao, additional

Published
2022
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43. Retinoschisin Deficiency Induces Persistent Aberrant Waves of Activity Affecting Neuroglial Signaling in the Retina

Author

Eleftheriou, Cyril G., primary, Corona, Carlo, additional, Khattak, Shireen, additional, Alam, Nazia M., additional, Ivanova, Elena, additional, Bianchimano, Paola, additional, Liu, Yang, additional, Sun, Duo, additional, Singh, Rupesh, additional, Batoki, Julia C., additional, Prusky, Glen T., additional, McAnany, J. Jason, additional, Peachey, Neal S., additional, Romano, Carmelo, additional, and Sagdullaev, Botir T., additional

Published
2022
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45. An Effective Local Routing Strategy on the Communication Network

Author

Li, Yu-jian, Wang, Bing-hong, Xi, Zheng-dong, Yin, Chuan-yang, Yang, Han-xin, Sun, Duo, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, and Zhou, Jie, editor

Published
2009
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46. Hollow ZnWO4/In2O3 Nanotubes for Ultrasensitive and Rapid Trace Detection of Triethylamine.

Author

Sun, Duo, Wang, QiLin, Wang, Wei, Chen, Yu, and Ruan, Shengping

Abstract

The directional modulation of metal oxide semiconductors (MOSs) can be effective in developing high-performance gas-sensing materials. Herein, we propose an efficient strategy by modifying ZnWO4 on In2O3 to achieve controllable modulation of oxygen vacancies and energy band structures in ZnWO4/In2O3 heterostructures. The sensor based on optimized ZnWO4/In2O3 exhibits significantly enhanced performance to detect triethylamine, including a lower operating temperature (100 °C), a higher responsivity (Ra/Rg = 221.2 for 100 ppm), and a lower detection limit (500 ppb). In addition, the fabricated sensor exhibits faster response and good selectivity. The improved gas-sensing performance could be attributed to the acid–base interaction between triethylamine gas and ZnWO4, the formation of heterojunctions, and the increase of oxygen vacancies to achieve a synergistic modulation of the chemical and energy band structures. It is anticipated that our current investigation could offer insights and a useful strategy for triethylamine detection. [ABSTRACT FROM AUTHOR]

Published
2023
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