Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm

Niu, Guoqiang; Li, Xiaoyong; Wan, Xin; He, Xinzhong; Zhao, Yinzhong; Yi, Xiaohui; Chen, Chen; Liang, Xujun; Ying, Guang‐Guo; Huang, Mingzhi

doi:10.1016/j.jclepro.2022.131140

Cited by 39 publications

(11 citation statements)

References 36 publications

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“…These threats are characterized by strong concealment, long incubation period, and strong sustainability. In the face of these new challenges [8,9], the limitations of the existing security management platforms without effective solution.…”

Section: Big Data Storage For Intrusion Detection Based On Hadoopmentioning

confidence: 99%

Intrusion Detection Method Based on Deep Learning

Tian

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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In today’s information age, the scale of the Internet is growing, the information capacity is also expanding explosively, and network security is becoming more and more important. Intrusion detection is regarded as a traditional security protection technology and is a key means to ensure the security of the network environment. Among them, the deep belief network performs well, and it can automatically learn abstract features for classification. In order to further improve the detection rate and reduce the false positive rate, it is necessary to improve the detection rate of small sample data. This paper builds an intelligent deep learning model and analysis model for intrusion detection data based on TensorFlow. By learning to identify network intrusion characteristic data, the characteristic data and model are stored in the big data storage system built by Hadoop. This algorithm has achieved good experiment result. Build a model knowledge base and an intrusion feature behavior library, use the decision tree model to automatically match the security control strategy, realize a highly intelligent security control model with self-learning ability, and solve the rapid identification of unknown intrusion behaviors. Experiments show that the algorithm can effectively improve the detection rate.

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Section: Big Data Storage For Intrusion Detection Based On Hadoopmentioning

confidence: 99%

Intrusion Detection Method Based on Deep Learning

Tian

Zhang

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…These threats are characterized by strong concealment, long incubation period and strong sustainability. In the face of these new challenges [8,9], the limitations of the existing security management platforms without effective solution.…”

Section: Big Data Storage For Intrusion Detection Based On Hadoopmentioning

confidence: 99%

Research on Intrusion Detection Method Based on Deep Learning

Tian

Zhang

et al. 2023

Preprint

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In today's information age, the scale of the Internet is growing, the information capacity is also expanding explosively, and network security is becoming more and more important. Intrusion detection is regarded as a traditional security protection technology and is a key means to ensure the security of the network environment. Among them, the deep belief network performs well, and it can automatically learn abstract features for classification. In order to further improve the detection rate and reduce the false positive rate, it is necessary to improve the detection rate of small sample data. This paper builds an intelligent deep learning model and analysis model for intrusion detection data based on TensorFlow. By learning to identify network intrusion characteristic data, the characteristic data and model are stored in the big data storage system built by Hadoop. This algorithm has achieved good experiments. result. Build a model knowledge base and an intrusion feature behavior library, use the decision tree model to automatically match the security control strategy, realize a highly intelligent security control model with self-learning ability, and solve the rapid identification of unknown intrusion behaviors. Experiments show that the algorithm can effectively improve the detection rate.

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“…A deep belief network [21][22][23][24] is a deep learning algorithm model. Compared with traditional machine learning, which requires manual feature extraction, expert prior knowledge and other defects, it can use big data to automatically extract features nonlinearly.…”

Section: Theorymentioning

confidence: 99%

A method for improving the accuracy of wide-range current transformers based on deep belief networks

Ouyang

Feng

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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With the proposal of the national dual carbon strategic goal, the permeability of new energy power generation in the power grid continues growing. The inherent intermittency and uncertainty lead to fluctuations in line power, which widens the working current range of the metering current transformer. Therefore, it is difficult for traditional methods to effectively meet the measurement error requirements. To this end, this paper proposes an adaptive error compensation method for wide-range current transformers based on deep belief networks. With its unique advantages in feature extraction and pattern recognition, deep belief network has become an effective method for transformer error feature extraction in the context of big data. In this method, the original data signal measured by the transformer is sent to the deep belief network algorithm for training, and the error feature is automatically extracted. An adaptive calibration system for wide-range current transformers is developed, and an error compensation experiment is carried out. The experimental results show that the method proposed in this paper can simply and efficiently identify the error characteristics of the wide-range current transformers and self-adaptively identify them, which improves the accuracy of the measurement results of the wide-range current transformers.

show abstract

Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm

Cited by 39 publications

References 36 publications

Intrusion Detection Method Based on Deep Learning

Intrusion Detection Method Based on Deep Learning

Research on Intrusion Detection Method Based on Deep Learning

A method for improving the accuracy of wide-range current transformers based on deep belief networks

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