[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Yang, Chen; Zonghao, Li; Xue-yan, Guo; Wei, Yu; Jin, Jing; Zhu, Liang

doi:10.1155/2019/2946158

Cited by 27 publications

(24 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In connection with the above shortcomings, experts are actively looking for a technique that would give a high-quality outcome that can adapt to the constant changes of the threat landscape, exclude the inadequate and irrelevant expert assessments, and allow reuse of previous evaluations. The most promising method in this area is the artificial neural network (ANN) approach, which addresses the challenges of existing methods, particularly with regards to flexibility and adaptability, although it requires a lot of time and intellectual resources [39][40][41][42]. In addition, the ANN has intelligent features such as self-learn, and thus it is possible to find the best way to solve the problem, accumulating information about external and internal processes.…”

Section: Shortcomings Of Existing Methods and Possible Solutionsmentioning

confidence: 99%

Information Security Risk Assessment

et al. 2021

View full text Add to dashboard Cite

Information security risk assessment is an important part of enterprises’ management practices that helps to identify, quantify, and prioritize risks against criteria for risk acceptance and objectives relevant to the organization. Risk management refers to a process that consists of identification, management, and elimination or reduction of the likelihood of events that can negatively affect the resources of the information system to reduce security risks that potentially have the ability to affect the information system, subject to an acceptable cost of protection means that contain a risk analysis, analysis of the “cost-effectiveness” parameter, and selection, construction, and testing of the security subsystem, as well as the study of all aspects of security.

show abstract

Section: Shortcomings Of Existing Methods and Possible Solutionsmentioning

confidence: 99%

Information Security Risk Assessment

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Dong et al studied the international transport network and indicated that the massive costs, the environment, and technology were the main restraining factors for constructing transport corridors [25]. Yang et al and Jin et al concluded that economic risk (risk brought about the social economic situation of the target region), population risk (average quality of people's life of the target region), traffic risk (the current national overall traffic level of the target region), and political risk (the current domestic policies and regulations of the target region) were the main risks that need to be considered for railway construction [26,27]. Liu et al established an evaluation index system for investment risk of highway construction, in which political risk, economic risk, environment risk, management risk, and technological risk were listed as five risks [28].…”

Section: Risk-related Factors In Constructing Transport Corridorsmentioning

confidence: 99%

“…(3) Policy-related risk. Policy incentives are important for the construction of transport infrastructure [26][27][28]. Preferential policies often come along with supporting measures such as tax deductions and exemptions, which can bring in capital, projects, and other factors, and they can significantly promote regional economic development and the construction of transport infrastructure.…”

Section: Risk-related Factors In Constructing Transport Corridorsmentioning

confidence: 99%

Construction of the Primorsky No. 1 and No. 2 International Transport Corridors: Risk Evaluation and Mitigation Policies

et al. 2021

View full text Add to dashboard Cite

The construction of the Primorsky No. 1 and No. 2 international transport corridors is of great strategic significance to China and Russia. These corridors will solve the problem of the absence of an estuary in Northeast China by sailing from Russian ports and concurrently greatly improve Russia’s position in the world’s transport system. However, existing research studies mainly focus on the strategic significance of these corridors, while research studies on the identification, evaluation, and mitigation policies of their construction risks are rare, which inhibits the construction progress. To promote the construction of these corridors, we firstly identify the main risks in the construction of these corridors, which are economic, transport infrastructure-related, policy-related, eco-environmental, and disaster-related risks. Then, we establish a comprehensive evaluation system, and by applying the entropy method, we quantitatively evaluate the degrees of risks in regions along the transport corridors, reveal their spatial distribution patterns, and identify the key prevention and control regions for these risks. Afterwards, we propose targeted mitigation policies such as establishing innovative cooperation modes and a special foundation to mitigate economic risk and formulate further regional development policies to mitigate policy-related risk. This research will provide scientific support for risk prevention and control for the construction of these corridors.

show abstract

“…Among these models, the BP neural network is currently the most popular neural network model in application. It has the universal advantages of all neural networks, self-learning and self-adaptive ability, nonlinear mapping ability, and high fault-tolerance rate (Yang et al, 2019). The BP algorithm's main idea is to divide the learning process into two stages; the first stage is the forward propagation process.…”

Section: Introductionmentioning

confidence: 99%

Application of Rough Set and Neural Network in Water Energy Utilization

et al. 2021

View full text Add to dashboard Cite

In water energy utilization, the damage of fault occurring in the power unit operational process to equipment directly affects the safety of the unit and efficiency of water power conversion and utilization, so fault diagnosis of water power unit equipment is especially important. This work combines a rough set and artificial neural network and uses it in fault diagnosis of hydraulic turbine conversion, puts forward rough set theory based on the tolerance relation and defines similarity relation between samples for the decision-making system whose attribute values are consecutive real numbers, and provides an attribute-reducing algorithm by making use of the condition that approximation classified quality will not change. The diagnostic rate of artificial neural networks based on a rough set is higher than that of the general three-layer back-propagation(BP) neural network, and the training time is also shortened. But, the network topology of an adaptive neural-fuzzy inference system is simpler than that of a neural network based on the rough set, the diagnostic accuracy is also higher, and the training time required under the same error condition is shorter. This algorithm processes consecutive failure data of the hydraulic turbine set, which has avoided data discretization, and this indicates that the algorithm is effective and reliable.

show abstract

[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Cited by 27 publications

References 22 publications

Information Security Risk Assessment

Information Security Risk Assessment

Construction of the Primorsky No. 1 and No. 2 International Transport Corridors: Risk Evaluation and Mitigation Policies

Application of Rough Set and Neural Network in Water Energy Utilization

Contact Info

Product

Resources

About