ID 原文 译文
1743 且成本低，无需电池。 It is compatible with existing commercial Wi-Fi devices and has low cost and no battery.
1744 针对无线局域网(Wireless Local Area Networks，WLAN)中密集部署无线接入点(Access Point，AP)导致的能耗和同频干扰问题，提出了一种基于贝叶斯博弈的节能机制 (BaYesian Game based Energy Saving scheme，BYG-ES)。 Aiming at the problem of energy consumption and co-channel interference caused by dense deployment ofaccess points (APs)in wireless local area networks (WLANs), we propose a Bayesian game-based energy saving scheme(BYG-ES).
1745 首先，对通用 AP 设备的能耗进行测量与分析，构建 AP 发射功率-负载-能耗的关系模型; Firstly, we measure energy consumption on the different real AP devices, and model the relationship among AP transmission power, forwarding load and energy consumption.
1746 然后，基于该关系模型及软件定义网络控制器实时收集的网络状态信息，设计基于贝叶斯博弈的能耗优化模型; Then, based on the mathematical relationship model and net-work status information collected by controller of software-defined network in real time, a Bayesian game based energy opti-mization design is proposed.
1747 最后，利用社会选择函数求解能耗优化模型，获得干扰限制下最优的休眠 AP 集合和发射功率配置规则，完成用户流量卸载和 AP 发射功率的调整，同时保证 AP 参与博弈的诚实性。 Finally, the social selection function is used to solve the energy optimization model, and then the sets of sleeping APs and transmit power configuration rules under the interference constraints are obtained to complete user traffic off loading and AP transmit power configuration. Meanwhile, the BYG-ES can guarantee honesty of all participantsduring the Bayesian game.
1748 实验结果表明，BYG-ES 节能机制能在减小系统能耗的同时提高网络性能。 Simulation results demonstrate that the BYG-ES can minimize system energy consumption andimprove network performances simultaneously.
1749 本文提出了两种基于 N-gram 特征的恶意代码可视化方法。 We proposed two new methods for visualization analysis based on N-gram features of malware.
1750 方法一以空间填充曲线的形式表示，解决了灰度图方法不能定位字符信息进行交互分析的问题; Method 1uses space filling curves to solve the problem that the existing gray scale method cannot locate character information for inter-active analysis.
1751 方法二可视化恶意代码的 2-gram 特征，解决了重置代码段或增加冗余信息来改变全局图像特征的问题。 Method 2 visualizes the bi-gram features of malware to solve the problem that the attackers may relocate code sections or add redundant data to change the global image features of the visualized results.
1752 经深度融合网络验证所提方法的识别与分类性能，取得了较优的结果。 We designed the deep fu-sion networks to validate the detection and classification performances of the proposed methods, and the experimental resultsare very promising.