In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
With the great commercial success of several IPTV (internet protocal television) applications, PPLive has received more and more attention from both industry and academia. At present, PPLive system is one of the most ...With the great commercial success of several IPTV (internet protocal television) applications, PPLive has received more and more attention from both industry and academia. At present, PPLive system is one of the most popular instances of IPTV applications which attract a large number of users across the globe; however, the dramatic rise in popularity makes it more likely to become a vulnerable target. The main contribution of this work is twofold. Firstly, a dedicated distributed crawler system was proposed and its crawling performance was analyzed, which was used to evaluate the impact of pollution attack in P2P live streaming system. The measurement results reveal that the crawler system with distributed architecture could capture PPLive overlay snapshots with more efficient way than previous crawlers. To the best of our knowledge, our study work is the first to employ distributed architecture idea to design crawler system and discuss the crawling performance of capturing accurate overlay snapshots for P2P live streaming system. Secondly, a feasible and effective pollution architecture was proposed to deploy content pollution attack in a real-world P2P live streaming system called PPLive, and deeply evaluate the impact of pollution attack from following five aspects:dynamic evolution of participating users, user lifetime characteristics, user connectivity-performance, dynamic evolution of uploading polluted chunks and dynamic evolution of pollution ratio. Specifically, the experiment results show that a single polluter is capable of compromising all the system and its destructiveness is severe.展开更多
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金Project(2007CB311106) supported by National Basic Research Program of ChinaProject(242-2009A82) supported by National Information Security Special Plan Program of China
文摘With the great commercial success of several IPTV (internet protocal television) applications, PPLive has received more and more attention from both industry and academia. At present, PPLive system is one of the most popular instances of IPTV applications which attract a large number of users across the globe; however, the dramatic rise in popularity makes it more likely to become a vulnerable target. The main contribution of this work is twofold. Firstly, a dedicated distributed crawler system was proposed and its crawling performance was analyzed, which was used to evaluate the impact of pollution attack in P2P live streaming system. The measurement results reveal that the crawler system with distributed architecture could capture PPLive overlay snapshots with more efficient way than previous crawlers. To the best of our knowledge, our study work is the first to employ distributed architecture idea to design crawler system and discuss the crawling performance of capturing accurate overlay snapshots for P2P live streaming system. Secondly, a feasible and effective pollution architecture was proposed to deploy content pollution attack in a real-world P2P live streaming system called PPLive, and deeply evaluate the impact of pollution attack from following five aspects:dynamic evolution of participating users, user lifetime characteristics, user connectivity-performance, dynamic evolution of uploading polluted chunks and dynamic evolution of pollution ratio. Specifically, the experiment results show that a single polluter is capable of compromising all the system and its destructiveness is severe.
