The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual incom...The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also,as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.展开更多
In this paper, a mechanism of bi-directional proxy is proposed, which supports authentication based on identity, and endue different users with different network access permissions. This technology is purposed with a ...In this paper, a mechanism of bi-directional proxy is proposed, which supports authentication based on identity, and endue different users with different network access permissions. This technology is purposed with a new idea towards the implementation of network security, which has a promising future in applications. Key words network security - firewall - bi-directional proxy server - identity authentication CLC number TP 368.5 Foundation item: Supported by the National Natural Science Foundation of China (60173051), The National Research Foundation for the Doctoral Program of Higher Education of China (20030145029). Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institution of the Ministry of Education; National 863 High-tech Program (2003AA414210)Biography: GAO Fu-xiang (1961-), male, Professor, Master, research direction: computer network security.展开更多
In bi-directional three-node cooperation, one regenerative strategy with network coding and power optimization is proposed for system sum-rate under a total energy constraint. In this paper, the network coding and pow...In bi-directional three-node cooperation, one regenerative strategy with network coding and power optimization is proposed for system sum-rate under a total energy constraint. In this paper, the network coding and power optimization are applied to improve system sum-rate. But max-rain optimization problem in power allocation is a NP-hard problem. In high Signal-to-Noise Ratio regime, this NP-hard problem is transformed into constrained polynomial optimization problem, which can be computed in polynomial time. Although it is a suboptimal solution, numerical simulations show that this strategy enhances the system sum-rate up to 45% as compared to a traditional four-phase strategy, and up to 13% as compared to the three-phase strategy without power optimization.展开更多
Nowadays,web systems and servers are constantly at great risk from cyberattacks.This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory(LSTM)network in c...Nowadays,web systems and servers are constantly at great risk from cyberattacks.This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory(LSTM)network in combination with the ensemble learning technique.First,the binary classification module was used to detect the current abnormal flow.Then,the abnormal flows were fed into the multilayer classification module to identify the specific type of flow.In this research,a deep learning bidirectional LSTM model,in combination with the convolutional neural network and attention technique,was deployed to identify a specific attack.To solve the real-time intrusion-detecting problem,a stacking ensemble-learning model was deployed to detect abnormal intrusion before being transferred to the attack classification module.The class-weight technique was applied to overcome the data imbalance between the attack layers.The results showed that our approach gained good performance and the F1 accuracy on the CICIDS2017 data set reached 99.97%,which is higher than the results obtained in other research.展开更多
Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on w...Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.展开更多
The request/transmit based upstream bandwidth resource allocation policy of DOCSIS introduces a trouble to the quality of the data service provided in the I-IFC networks. In this paper, the mechanism of the upstream d...The request/transmit based upstream bandwidth resource allocation policy of DOCSIS introduces a trouble to the quality of the data service provided in the I-IFC networks. In this paper, the mechanism of the upstream data transrmitting and the process of data service transmitting in the HFC networks are described in detail, and the perfor- mance of the data service in HFC networks is analyzed. An advanced upstream bandwidth resource allocation policy is proposed to improve the quality of the data service in the HFC networks.展开更多
基金supported by the National Natural Science Foundation of China(61503407,61806219,61703426,61876189,61703412)the China Postdoctoral Science Foundation(2016 M602996)。
文摘The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also,as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.
文摘In this paper, a mechanism of bi-directional proxy is proposed, which supports authentication based on identity, and endue different users with different network access permissions. This technology is purposed with a new idea towards the implementation of network security, which has a promising future in applications. Key words network security - firewall - bi-directional proxy server - identity authentication CLC number TP 368.5 Foundation item: Supported by the National Natural Science Foundation of China (60173051), The National Research Foundation for the Doctoral Program of Higher Education of China (20030145029). Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institution of the Ministry of Education; National 863 High-tech Program (2003AA414210)Biography: GAO Fu-xiang (1961-), male, Professor, Master, research direction: computer network security.
基金Supported by the High Technology Research and Development Program of China (No. 2006AA01Z282 2007CB310608)
文摘In bi-directional three-node cooperation, one regenerative strategy with network coding and power optimization is proposed for system sum-rate under a total energy constraint. In this paper, the network coding and power optimization are applied to improve system sum-rate. But max-rain optimization problem in power allocation is a NP-hard problem. In high Signal-to-Noise Ratio regime, this NP-hard problem is transformed into constrained polynomial optimization problem, which can be computed in polynomial time. Although it is a suboptimal solution, numerical simulations show that this strategy enhances the system sum-rate up to 45% as compared to a traditional four-phase strategy, and up to 13% as compared to the three-phase strategy without power optimization.
文摘Nowadays,web systems and servers are constantly at great risk from cyberattacks.This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory(LSTM)network in combination with the ensemble learning technique.First,the binary classification module was used to detect the current abnormal flow.Then,the abnormal flows were fed into the multilayer classification module to identify the specific type of flow.In this research,a deep learning bidirectional LSTM model,in combination with the convolutional neural network and attention technique,was deployed to identify a specific attack.To solve the real-time intrusion-detecting problem,a stacking ensemble-learning model was deployed to detect abnormal intrusion before being transferred to the attack classification module.The class-weight technique was applied to overcome the data imbalance between the attack layers.The results showed that our approach gained good performance and the F1 accuracy on the CICIDS2017 data set reached 99.97%,which is higher than the results obtained in other research.
基金support of national natural science foundation of China(No.52067021)natural science foundation of Xinjiang(2022D01C35)+1 种基金excellent youth scientific and technological talents plan of Xinjiang(No.2019Q012)major science&technology special project of Xinjiang Uygur Autonomous Region(2022A01002-2)。
文摘Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.
文摘The request/transmit based upstream bandwidth resource allocation policy of DOCSIS introduces a trouble to the quality of the data service provided in the I-IFC networks. In this paper, the mechanism of the upstream data transrmitting and the process of data service transmitting in the HFC networks are described in detail, and the perfor- mance of the data service in HFC networks is analyzed. An advanced upstream bandwidth resource allocation policy is proposed to improve the quality of the data service in the HFC networks.