ID 原文 译文
46326 提出一种基于拍卖模型的移动社交网络数据转发激励机制—AMIM。 A data forwarding incentive mechanism based on auction model in mobile social network was proposed.
46327 通过对一级密封价格拍卖模型进行扩展，并采用虚拟支付的交易方式，将节点间的数据转发过程抽象为拍卖交易模型。 In this incentive mechanism, the first-price sealed auction mode was extended, the transaction mode of virtual currency payment was adopted, and the procedure of data forwarding between nodes was abstracted into the auction transaction model.
46328 基于节点的资源状态、虚拟货币量和数据属性，给出了关于数据转发交易的买卖双方估价函数，节点依据相应的估价函数和博弈策略给出相应的报价。 Based on the node's resource state, the virtual currency and the data property, the evaluation function of data forwarding transaction was given, and then the node gives the corresponding price according to the evaluation function and game strategy.
46329 通过博弈分析给出了 AMIM 机制的纳什均衡解，数据转发请求节点将选择出价最低且低于其估价的竞拍节点为本次数据转发的服务节点， Through the game analysis, the Nash equilibrium solution of AMIM was found, and the lowest bidder, of which the bid price was lower than the evaluation of data forwarding request node would been selected as the service provider for this data forwarding.
46330 激励理性的用户节点为使其自身利益最大化而自愿参与数据转发交易。 In this incentive mechanism, the rational mobile nodes were enforced to voluntarily participate in data forwarding cooperation to maximize their own interests.
46331 仿真实验结果表明，采用 AMIM 机制后，网络系统的能量消耗有所下降，数据转发成功率与效率均有明显提高。 The simulation experiment shows that AMIM mechanism can effectively reduce the energy consumption and improve the success rate and efficiency of data forwarding in the whole network system.
46332 在动态手势特征提取和识别方面，利用运动学模式解决动态手势识别问题，在光流场基础上计算出散度模式，旋度模式，对称模式，反对称模式，梯度张量第二、第三主不变模式，应变张量第二、第三主不变模式以及自旋转张量第三主不变模式； Compared to static gestures, dynamic gestures had some new characteristics. The problems of dynamic gestures recognition was spewed by using kinematics mode, such as divergence modes, curl modes, symmetric andant-symmetric modes, the second and third principal invariant modes of the gradient tensor, the second and third principal invariant modes of the strain tensor and the third principal invariant modes of spin tensor;
46333 进一步提出一种基于多实例学习的方法，将每一个动态手势的所有运动主模式构成一个动态手势词袋，将未知类型动态手势的运动主模式与词袋空间中对应运动主模式进行相似度计算， Further, a framework based on multi-instance learning was proposed, organize all these principle modes for each gesture were organized to a dynamic gestures bag-of-words, and the similarity between the mode of unknown type dynamic gestures and the all bag-of-words were calculated.
46334 利用最近邻方法对手势进行识别。 Then, the nearest neighbor method was used to recognize the dynamic gestures.
46335 实验结果表明：基于多实例运动学主模式学习的动态手势识别方法取得了较高的识别率。 The experimental results show that the dynamic gestures recognition based on multi-instance kinematics features principal mode learning methods can obtain a higher recognition rate.