ID	原文	译文
2413	本文在分析差分法误差来源的基础上，基于 Tikhonov 正则化给出了一种新的群时延计算方法。	Based on the analysis of the er-ror source of the difference method, this paper presents a novel method of group delay measurement based on the Tikhonov regularization.
2414	比较分析得出该方法能够在存在测量误差的情况下，精确得到具有较高频率分辨率的群时延。	The comparative analysis shows that the method can obtain the group delay value more precisely with higher frequency resolution when the measurement error is included.
2415	在实际给出的测量验证中，通过与矢量网络分析仪得到的群时延数据对比，验证了该方法的有效性。	In the actual data calculation and verification, the validity of the method verified by comparison to the group delay data obtained by the vector network analyzer.
2416	将物理不可克隆函数(Physical Unclonable Function，PUF)与椭圆曲线上的无证书公钥密码体制相结合，提出一种面向物联网的安全通信方案，在节点设备不存储任何秘密参数的情况下，实现设备间消息的安全传递。	By combining the Physical Unclonable Function (PUF)with the certificateless public key cryptosystem onthe elliptic curve, a secure communication scheme for IoT is proposed. The secure transmission of messages is realized on thecondition of node devices not storing any secret parameters.
2417	方案无需使用高计算复杂度的双线性对运算，并提供了消息认证机制。	The proposed scheme eliminates the need for bilinear pairing whose computing complexity is high and provides a message authentication mechanism.
2418	安全性分析表明，该方案不仅能够抵抗窃听、篡改、重放等传统攻击，而且可以有效防范节点设备可能遭到的复制攻击。	Security analysis demonstrates that the scheme can not only resist the traditional attacks such as eavesdropping, tampering and replay, but also protect the nodedevice from replication attacks.
2419	对比结果显示，相较于同类方案，该方案明显降低了设备的资源开销。	Compared with related schemes, the proposed scheme obviously decreases the resource over-head of devices.
2420	为解决复杂网络环境网络入侵事件特征复杂多变、新型入侵检测度低、检测时间长、难以实现实时检测的问题，本文提出一种基于核极限学习机(Kernel Extreme Learning Machine，KELM)选择性集成的网络入侵检测方法(SEoKELM-NID)。	To solve the problem of the low detection accuracy of new intrusions with long detection time due to the complex and changeable nature of network intrusions, this paper proposes a network intrusion detection method based on the selective learning of Kernel Extreme Learning Machines (KELMs).
2421	该方法采用 Bagging 策略独立快速训练出多个 KELM 子学习器;	First, based on the high efficiency learning characteris-tics of the single KELM learner, multiple KELMs are trained independently by the Bagging strategy.
2422	然后基于边缘距离最小化(MarginDistance Minimization，MDM)准则对 KELM 子学习器的集成增益进行度量，通过选择增益度高的部分 KELM 子学习器进行选择性集成，获得泛化能力强、效率高的选择性集成学习器;	Then, based on the mar-gin distance minimization (MDM)guidelines, KELM learners are integrated by selecting a part of them with high gains based on the MDM-based gain measures.