Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network ...Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimi- zation of mine safety assessment and getting reasonable accurate safety assessment result.展开更多
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is propo...Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network(BPNN) is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.展开更多
Introduced the theory of three types of hazardous sources, and it recognized and analysed such three types of hazardous sources as the factor of inherent hazardous source, factor of inducing hazardous source and facto...Introduced the theory of three types of hazardous sources, and it recognized and analysed such three types of hazardous sources as the factor of inherent hazardous source, factor of inducing hazardous source and factor of men, which affect the safety and reliability of coal-dust explosion risk system and then builds up the risk factor indices of coal-dust explosion according to analysis of conditions inducing the coal-dust explosion. It fixes the risk degree of coal-dust explosion risk system by analyzing loss probability and loss scope of risk system and by means of the probabilistic hazard evaluation method and risk matrix method, etc.. According to the feature of strong capability of nonlinear approximation of BP neural network, the paper designed the structure of BP neural network for the risk evaluation of the mine coal-dust explosion with BP neural network. And the weight of the network was finally determined by training the given samples so that the risk degree of samples to be measured could be exactly evaluated and the risk of mine coal-dust explosion could be alarmed in good time.展开更多
Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many paramete...Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts for ward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.展开更多
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method...Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.展开更多
To improve the quality of multimedia services and stimulate secure sensing in Internet of Things applications, such as healthcare and traffic monitoring, mobile crowdsensing(MCS) systems must address security threats ...To improve the quality of multimedia services and stimulate secure sensing in Internet of Things applications, such as healthcare and traffic monitoring, mobile crowdsensing(MCS) systems must address security threats such as jamming, spoofing and faked sensing attacks during both sensing and information exchange processes in large-scale dynamic and heterogeneous networks. In this article, we investigate secure mobile crowdsensing and present ways to use deep learning(DL) methods, such as stacked autoencoder, deep neural networks, convolutional neural networks, and deep reinforcement learning, to improve approaches to MCS security, including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DLbased approaches compared to traditional security schemes and identify the challenges that must be addressed to implement these approaches in practical MCS systems.展开更多
基金Supported by the National Natural Science Foundation of China(50274060) and State Administration of Work Safety(03-103)
文摘Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimi- zation of mine safety assessment and getting reasonable accurate safety assessment result.
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
基金National Natural Science Foundation of China(60363087 ,90104005 and 60473023)
文摘Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network(BPNN) is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.
文摘Introduced the theory of three types of hazardous sources, and it recognized and analysed such three types of hazardous sources as the factor of inherent hazardous source, factor of inducing hazardous source and factor of men, which affect the safety and reliability of coal-dust explosion risk system and then builds up the risk factor indices of coal-dust explosion according to analysis of conditions inducing the coal-dust explosion. It fixes the risk degree of coal-dust explosion risk system by analyzing loss probability and loss scope of risk system and by means of the probabilistic hazard evaluation method and risk matrix method, etc.. According to the feature of strong capability of nonlinear approximation of BP neural network, the paper designed the structure of BP neural network for the risk evaluation of the mine coal-dust explosion with BP neural network. And the weight of the network was finally determined by training the given samples so that the risk degree of samples to be measured could be exactly evaluated and the risk of mine coal-dust explosion could be alarmed in good time.
文摘Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts for ward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.
文摘Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.
基金supported in part by the National Natural Science Foundation of China under Grant 61671396 and 91638204in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University(No.2018D08)in part by Science and Technology Innovation Project of Foshan City,China(Grant No.2015IT100095)
文摘To improve the quality of multimedia services and stimulate secure sensing in Internet of Things applications, such as healthcare and traffic monitoring, mobile crowdsensing(MCS) systems must address security threats such as jamming, spoofing and faked sensing attacks during both sensing and information exchange processes in large-scale dynamic and heterogeneous networks. In this article, we investigate secure mobile crowdsensing and present ways to use deep learning(DL) methods, such as stacked autoencoder, deep neural networks, convolutional neural networks, and deep reinforcement learning, to improve approaches to MCS security, including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DLbased approaches compared to traditional security schemes and identify the challenges that must be addressed to implement these approaches in practical MCS systems.
