Discussion on the treatment of various dependencies in a time-dependent risk monitoring model of redundant systems

Chen, Sijuan; Zhang, Zhijian; Zhang, Min; Zhang, Huazhi; Wang, He; Ma, Yingfei; Xu, Aidong; Zheng, Gangyang; Sun, Dabin

doi:10.1016/j.anucene.2020.108114

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Once the data flow security requirements are determined, to control the data flow risk within a reasonable range, security strategies need to be formulated and implemented. The security policy can be formulated according to relevant standards 18 or designed by ourselves. The formulation of the security strategy should consider the following three points: …”

Section: Methodsmentioning

confidence: 99%

The data flow risk monitoring system of the expressway networking system based on deep learning

Zhong

Ding

Zhang

et al. 2022

Int J of Intelligent Sys

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To ensure the reliability and safety of expressway networking systems, this paper designs a data flow risk monitoring system for expressway networking systems based on deep learning. The monitoring system is composed of data flow risk analysis, formulation of safety strategy, real-time monitoring, and disaster recovery. Data flow risk analysis is the basis for the operation of each part of the system. Meanwhile, indexes such as network management, data assets, and network resources are selected to build a data flow risk monitoring index system. The deep convolution neural network model is constructed, and the data flow risk monitoring index data are input into the deep convolution neural network to extract the index data features through the convolution and pooling process.Based on this, feature mapping is realized with a multilayer perceptron, and the index data risk classification results of data flow risk monitoring are output by the SoftMax classifier. The experimental resultsshow that the monitoring system can obtain accurate data flow risk analysis results which effectively reduces the data loss rate, alleviate the impact of different types of malicious attacks, and ensure the stability and security of the experimental object.

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Section: Methodsmentioning

confidence: 99%

The data flow risk monitoring system of the expressway networking system based on deep learning

Zhong

Ding

Zhang

et al. 2022

Int J of Intelligent Sys

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Research and application of safety monitoring technology of distribution automation based on SOM neural network

Han,

Li,

et al. 2024

International Journal of Low-Carbon Technologies

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For automatic situation monitoring in power distribution safety monitoring, data features are mainly extracted by single hidden layer neural network, which makes the standard error of monitoring results larger. Therefore, the research and application of power distribution automation safety monitoring technology based on SOM neural network are proposed. Preprocess the power grid, collect distribution operation data, set multi-dimensional monitoring nodes according to the collected data, build a distribution operation status monitoring model and analyze the data fusion technology of distribution automation safety monitoring according to the model. On this basis, the distribution automation safety monitoring system is defined, the output node correction weight and the reverse output node correction weight are calculated, the SOM neural network identification model is constructed and the research and application of the distribution automation safety monitoring technology are completed under the action of the gravitational function between individuals within the target time. The experimental results show that the change curve of the number of vulnerabilities and the actual number of false positives is consistent, the number of vulnerabilities is small and the monitoring results are more accurate; The state of the safety monitoring equipment of distribution automation is normal. After applying the method in this paper, the change curve is consistent with the actual value, and the delay time is less than 15 ms.

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Review on the Application of Living PSA in Nuclear Power

Zhang,

Xu,

Zhang

et al. 2024

Energies

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With the increasing standards of safety management in nuclear power plants, Living Probabilistic Safety Assessment (Living PSA) technology has begin to play an increasingly important role in their operation. This paper aims to provide an overview of the application and development of Living Probabilistic Safety Assessment (Living PSA) technology in nuclear power plant safety monitoring and risk assessment, examining the key technologies and future challenges. Initially, we summarize the current safety needs in regard to nuclear power, examine the policy on configuration risk management technology for nuclear power plants, and outline its importance and development process in nuclear power plant safety management. Subsequently, we discuss the basic principle of Living PSAs and the working method of risk monitoring based on Living PSAs, including information monitoring data collection, online identification, real-time model updating, and risk calculation. Within the Living PSA framework, model development is not merely about creating a theoretical or static representation; it is a dynamic and ongoing process that involves a deep understanding and precise simulation of the behavior of nuclear power plant systems and components. This represents the main research efforts in Living PSAs at present. Additionally, this paper identifies the key technologies of Living PSAs in an in-depth manner, such as the reliability-model-updating technology and model building in dynamic reliability analyses, including the fault tree model, multi-layer flow model, GO-FLOW model etc. The paper lists the work of some scholars in this area in recent years, which helps readers and researchers to clearly understand the current progress of Living PSA technologies in terms of model establishment and updating. Finally, the paper summarizes the challenges and future development of Living PSA and emphasizes the possible problems in data quality, human factor engineering, and the development of Living PSA technologies in the future. In the future, Living PSAs will provide more solid support for the realization of safer and more economical methods of operating nuclear power plants.

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Discussion on the treatment of various dependencies in a time-dependent risk monitoring model of redundant systems

Cited by 3 publications

References 12 publications

The data flow risk monitoring system of the expressway networking system based on deep learning

The data flow risk monitoring system of the expressway networking system based on deep learning

Research and application of safety monitoring technology of distribution automation based on SOM neural network

Review on the Application of Living PSA in Nuclear Power

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