eMule是建立在点对点(peer to peer)技术上,以eDonkey协议为基础的文件共享软件。eMule的集中程度是混合性的,并非是完全无中心服务器。文章从协议的角度论述了eMule是如何实现文件共享,分析了客户端到服务器端、客户端到客户端之间的TC...eMule是建立在点对点(peer to peer)技术上,以eDonkey协议为基础的文件共享软件。eMule的集中程度是混合性的,并非是完全无中心服务器。文章从协议的角度论述了eMule是如何实现文件共享,分析了客户端到服务器端、客户端到客户端之间的TCP通讯、UDP通讯的原理及相关的信息序列图。展开更多
The steered response power-phase transform (SRP-PHAT) sound source localization algorithm is robust in a real environment. However, the large computation complexity limits the practical application of SRP-PHAT. For a ...The steered response power-phase transform (SRP-PHAT) sound source localization algorithm is robust in a real environment. However, the large computation complexity limits the practical application of SRP-PHAT. For a microphone array, each location corresponds to a set of time differences of arrival (TDOAs), and this paper collects them into a TDOA vector. Since the TDOA vectors in the adjacent regions are similar, we present a fast algorithm based on clustering search to reduce the computation complexity of SRP-PHAT. In the training stage, the K-means or Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering algorithm is used to find the centroid in each cluster with similar TDOA vectors. In the procedure of sound localization, the optimal cluster is found by comparing the steered response powers (SRPs) of all centroids. The SRPs of all candidate locations in the optimal cluster are compared to localize the sound source. Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computational load of the proposed method with those of the conventional SRP-PHAT algorithm. The results show that the proposed method is able to reduce the computational load drastically and maintains almost the same localization accuracy and robustness as those of the conventional SRP-PHAT algorithm. The difference in localization performance brought by different clustering algorithms used in the training stage is trivial.展开更多
Source search is an important problem in our society,relating to finding fire sources,gas sources,or signal sources.Particularly,in an unexplored and potentially dangerous environment,an autonomous source search algor...Source search is an important problem in our society,relating to finding fire sources,gas sources,or signal sources.Particularly,in an unexplored and potentially dangerous environment,an autonomous source search algorithm that employs robotic searchers is usually applied to address the problem.Such environments could be completely unknown and highly complex.Therefore,novel search algorithms have been designed,combining heuristic methods and intelligent optimization,to tackle search problems in large and complex search spaces.However,these intelligent search algorithms were not designed to address completeness and optimality,and therefore commonly suffer from the problems such as local optimums or endless loops.Recent studies have used crowd-powered systems to address the complex problems that cannot be solved by machines on their own.While leveraging human intelligence in an AI system has been shown to be effective in making the system more reliable,whether using the power of the crowd can improve autonomous source search algorithms remains unanswered.To this end,we propose a crowd-powered source search approach enabling human-AI collaboration,which uses human intelligence as external supports to improve existing search algorithms and meanwhile reduces human efforts using AI predictions.Furthermore,we designed a crowd-powered prototype system and carried out an experiment with both experts and non-experts,to complete 200 source search scenarios(704 crowdsourcing tasks).Quantitative and qualitative analysis showed that the sourcing search algorithm enhanced by crowd could achieve both high effectiveness and efficiency.Our work provides valuable insights in human-AI collaborative system design.展开更多
文摘eMule是建立在点对点(peer to peer)技术上,以eDonkey协议为基础的文件共享软件。eMule的集中程度是混合性的,并非是完全无中心服务器。文章从协议的角度论述了eMule是如何实现文件共享,分析了客户端到服务器端、客户端到客户端之间的TCP通讯、UDP通讯的原理及相关的信息序列图。
基金supported by the National Natural Science Foundation of China(Grant Nos. 60971098 and 61201345)the Beijing Key Laboratory of Advanced Information Science and Network Technology(Grant No.XDXX1308)
文摘The steered response power-phase transform (SRP-PHAT) sound source localization algorithm is robust in a real environment. However, the large computation complexity limits the practical application of SRP-PHAT. For a microphone array, each location corresponds to a set of time differences of arrival (TDOAs), and this paper collects them into a TDOA vector. Since the TDOA vectors in the adjacent regions are similar, we present a fast algorithm based on clustering search to reduce the computation complexity of SRP-PHAT. In the training stage, the K-means or Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering algorithm is used to find the centroid in each cluster with similar TDOA vectors. In the procedure of sound localization, the optimal cluster is found by comparing the steered response powers (SRPs) of all centroids. The SRPs of all candidate locations in the optimal cluster are compared to localize the sound source. Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computational load of the proposed method with those of the conventional SRP-PHAT algorithm. The results show that the proposed method is able to reduce the computational load drastically and maintains almost the same localization accuracy and robustness as those of the conventional SRP-PHAT algorithm. The difference in localization performance brought by different clustering algorithms used in the training stage is trivial.
基金supported by the National Natural Science Foundation of China(No.62202477)Postgraduate Scientific Research Innovation Project of Hunan Province(No.QL20210012).
文摘Source search is an important problem in our society,relating to finding fire sources,gas sources,or signal sources.Particularly,in an unexplored and potentially dangerous environment,an autonomous source search algorithm that employs robotic searchers is usually applied to address the problem.Such environments could be completely unknown and highly complex.Therefore,novel search algorithms have been designed,combining heuristic methods and intelligent optimization,to tackle search problems in large and complex search spaces.However,these intelligent search algorithms were not designed to address completeness and optimality,and therefore commonly suffer from the problems such as local optimums or endless loops.Recent studies have used crowd-powered systems to address the complex problems that cannot be solved by machines on their own.While leveraging human intelligence in an AI system has been shown to be effective in making the system more reliable,whether using the power of the crowd can improve autonomous source search algorithms remains unanswered.To this end,we propose a crowd-powered source search approach enabling human-AI collaboration,which uses human intelligence as external supports to improve existing search algorithms and meanwhile reduces human efforts using AI predictions.Furthermore,we designed a crowd-powered prototype system and carried out an experiment with both experts and non-experts,to complete 200 source search scenarios(704 crowdsourcing tasks).Quantitative and qualitative analysis showed that the sourcing search algorithm enhanced by crowd could achieve both high effectiveness and efficiency.Our work provides valuable insights in human-AI collaborative system design.
