ID 原文 译文
18025 随着Android应用的广泛使用，Android恶意软件数量迅速增长，对用户的财产、隐私等造成的安全威胁越来越严重。 With the prosperous of Android applications, Android malware has been scattered everywhere, whichraises the serious security risk to users.
18026 近年来基于深度学习的Android恶意软件检测成为了当前安全领域的研究热点。 On the other hand, the rapid developing of deep learning fires thecombat between the two sides of malware detection.
18027 该文分别从数据采集、应用特征、网络结构、效果检测4个方面，对该研究方向已有的学术成果进行了分析与总结，讨论了它们的局限性与所面临的挑战，并就该方向未来的研究重点进行了展望。 Inducing deep learning technologies into Android malwaredetection becomes the hottest topic of society. This paper summarizes the existing achievements of malwaredetection from four aspects: Data collection, feature construction, network structure and detection performance.Finally, the current limitations and facing challenges followed by the future researches are discussed.
18028 在一定环境条件下，当系统的量测方程没有进行验证或校准时，使用该量测方程往往会产生未知的系统误差，从而导致较大的滤波误差。 Under certain environmental conditions, the unknown system errors often occur and yield to largerfiltering errors when the unverified or uncalibrated measurement equation is used.
18029 增量方程的引入可以有效解决欠观测系统的状态估计问题。 Incremental equation can be introduced, which can effectively solve the problem of state estimation for the systems under poor observation condition.
18030 该文考虑带未知噪声统计的线性离散增量系统，首先提出一种基于新息的噪声统计估计算法。 In this paper, the linear discrete incremental system with unknown noise statistics is considered.Firstly, a noise statistics estimation algorithm is proposed based on innovation.
18031 可以得到系统噪声统计的无偏估计。 The unbiased estimation ofsystem noise statistics can be obtained.
18032 进而，提出一种新的增量系统自适应Kalman滤波算法。 Furthermore, a new incremental system adaptive Kalman filteringalgorithm is proposed.
18033 相比已有的自适应增量滤波算法，该文所提算法得到的状态估计精度更高。 Compared with the existing adaptive incremental filtering algorithm, the stateestimation accuracy of the proposed algorithm is higher.
18034 两个仿真实例证明了其有效性和可行性。 Two simulation examples prove its effectiveness andfeasibility.