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155 results on '"Kong, Lijun"'

2. A Game-Theoretic Learning Framework for Multi-Agent Intelligent Wireless Networks

Author

Wang, Ximing, Wang, Jinlong, Chen, Jin, Yang, Yijun, Kong, Lijun, Liu, Xin, Jia, Luliang, and Xu, Yuhua

Subjects
Computer Science - Computer Science and Game Theory
Abstract

In this article, we introduce a game-theoretic learning framework for the multi-agent wireless network. By combining learning in artificial intelligence (AI) with game theory, several promising properties emerge such as obtaining high payoff in the unknown and dynamic environment, coordinating the actions of agents and making the adversarial decisions with the existence of malicious users. Unfortunately, there is no free lunch. To begin with, we discuss the connections between learning in AI and game theory mainly in three levels, i.e., pattern recognition, prediction and decision making. Then, we discuss the challenges and requirements of the combination for the intelligent wireless network, such as constrained capabilities of agents, incomplete information obtained from the environment and the distributed, dynamically scalable and heterogeneous characteristics of wireless network. To cope with these, we propose a game-theoretic learning framework for the wireless network, including the internal coordination (resource optimization) and external adversarial decision-making (anti-jamming). Based on the framework, we introduce several attractive game-theoretic learning methods combining with the typical applications that we have proposed. What's more, we developed a real-life testbed for the multi-agent anti-jamming problem based on the game-theoretic learning framework. The experiment results verify the effectiveness of the proposed game-theoretic learning method., Comment: Because the authors have a conflict of interest, the manuscript is withdrawal

Published
2018

4. Hydroelectric Unit Vibration Signal Feature Extraction Based on IMF Energy Moment and SDAE.

Author

Liu, Dong, Kong, Lijun, Yao, Bing, Huang, Tangming, Deng, Xiaoqin, and Xiao, Zhihuai

Subjects
FEATURE extraction, FOURIER analysis, MOMENTS method (Statistics), FOURIER transforms, COUPLINGS (Gearing), HILBERT-Huang transform
Abstract

Aiming at the problem that it is difficult to effectively characterize the operation status of hydropower units with a single vibration signal feature under the influence of multiple factors such as water–machine–electricity coupling, a multidimensional fusion feature extraction method for hydroelectric units based on time–frequency analysis and unsupervised learning models is proposed. Firstly, the typical time–domain and frequency–domain characteristics of vibration signals are calculated through amplitude domain analysis and Fourier transform. Secondly, the time–frequency characteristics of vibration signals are obtained by combining the complementary ensemble empirical mode decomposition and energy moment calculation methods to supplement the traditional time–domain and frequency–domain characteristics, which have difficulty in comprehensively reflecting the correlation between nonlinear non–stationary signals and the state of the unit. Finally, in order to overcome the limitations of shallow feature extraction relying on artificial experience, a Stacked Denoising Autoencoder is used to adaptively mine the deep features of vibration signals, and the extracted features are fused to construct a multidimensional feature vector of vibration signals. The proposed multidimensional information fusion feature extraction method is verified to realize the multidimensional complementarity of feature attributes, which helps to accurately distinguish equipment state types and provides the foundation for subsequent state identification and trend prediction. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Development of SSRs Based on the Whole Genome and Screening of Bolting-Resistant SSR Marker in Brassica oleracea L.

Author

Zhao, Tong, Miao, Liming, Zou, Minghua, Hussain, Iqbal, Yu, Hongrui, Li, Jia, Sun, Nairan, Kong, Lijun, Wang, Shenyun, Li, Jianbin, and Yu, Xiaolin

Subjects
COLE crops, GENETIC markers in plants, MICROSATELLITE repeats, GENOMES, WHOLE genome sequencing, CHROMOSOMES, POLYMERASE chain reaction
Abstract

Simple sequence repeats (SSRs), also known as microsatellites, stand out as the most crucial molecular markers in both animals and plants owing to their high polymorphism, extensive information content, ease of detection through polymerase chain reaction (PCR) assays, and widespread distribution across the genome. In this study, a total of 125,443 SSR loci were identified from the whole-genome sequence of B. oleracea, and 82,948 primer pairs for SSR have been designed. Furthermore, each primer pair is designated with a unique identifier (ranging from BolSSR00001 to BolSSR82984). Our findings indicated that certain markers within them could be transferred to other cruciferous crops. In addition, a total of 336 pairs of SSR primers have been used to screen the polymorphism between the bolting-resistant and bolting-easy gene pools. After the test of verification with F2 generation individual plants, we obtained an SSR dominant marker, BolSSR040196, linked with bolting-resistant locus in cabbage, and the genetic distance between this SSR marker and the bolting-resistant locus was 10.69 cM. Moreover, BolSSR040196 is located on the C05 chromosome with a CT motif, characterized by a repeat of 9 in bolting-easy plants and 11 in bolting-resistant plants. Haplotype analysis showed that the correct prediction rate reached 82.35%. The BolSSR040196 marker can be used in marker-assisted selection (MAS) breeding, offering a straightforward and efficient approach for bolting-resistant cabbage breeding in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A Multilayer Perceptron Neural Network-Based Spectrum Prediction Approach with Gray Decision

Author

Ge, Jincheng, Xu, Yuhua, Liu, Dianxiong, Kong, Lijun, Chen, Xueqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mu, Jiasong, editor, Jia, Min, editor, Wang, Wei, editor, Feng, Xuhong, editor, and Zhang, Baoju, editor

Published
2019
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29. AICAR, an AMP-Activated Protein Kinase Activator, Ameliorates Acute Pancreatitis-Associated Liver Injury Partially Through Nrf2-Mediated Antioxidant Effects and Inhibition of NLRP3 Inflammasome Activation

Author

Kong, Lijun, primary, Zhang, Hewei, additional, Lu, Chaosheng, additional, Shi, Keqing, additional, Huang, Hongjian, additional, Zheng, Yushu, additional, Wang, Yongqiang, additional, Wang, Dan, additional, Wang, Hongwei, additional, and Huang, Wei, additional

Published
2021
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38. A Multilayer Perceptron Neural Network-Based Spectrum Prediction Approach with Gray Decision

Author

Dianxiong Liu, Kong Lijun, Yuhua Xu, Jincheng Ge, and Xueqiang Chen

Subjects
Artificial neural network, Computer science, business.industry, Computer Science::Neural and Evolutionary Computation, 05 social sciences, 050801 communication & media studies, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Spectrum (topology), 0508 media and communications, Cognitive radio, Computer Science::Computer Vision and Pattern Recognition, Multilayer perceptron, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, business, Gray (horse), Statistic, Multilayer perceptron neural network
Abstract

In cognitive radio networks (CRNs), spectrum prediction for inferring spectrum availability can help unlicensed users to discover spectrum holes earlier and to improve spectrum utilization more efficiently. Multilayer perceptron (MLP) neural network-based spectrum prediction model can identify the traffic characteristics of the spectrum only using the history data of the spectrum status. We investigate the statistic characteristics of the MLP neural network’s outputs, propose the gray decision to improve the performance of the MLP-base spectrum predictor. We prove that the performance of MLP-base predictor with gray decision will be improved significantly when the spectrum status change frequently.

Published
2018

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