ID 原文 译文
19735 根据原子范数的信号模型，该算法首先选择一个具有特殊结构的发射滤波器组以及一组正交信号，将MIMO雷达发射波形设计问题转化为原子范数最小化问题， According to the signal model of atomic norm, firstly a multi-rank transmit beamformer and a set of orthogonal signals are selected. Then the transmit beampattern matching design problem is formulated into an atomicnorm minimization problem.
19736 然后采用半正定规划(SDP)对原子范数进行求解，得到(半)正定Toeplitz矩阵，并对其进行范德蒙分解实现发射滤波器组的估计，最后综合发射滤波器组与正交信号获得MIMO雷达的发射波形。 The multi-rank transmit beamformer is achieved by Vander monde decomposition method of positive semidefinite Toeplitz matrix, which is attained by the solution of the atomic norm minimization problem with Semi-Definite Programming (SDP). Finally, the transmit waveforms can be acquired from the resulting multi-rank transmit beamformer and existing orthogonal waveforms.
19737 理论分析与仿真结果表明，该算法满足等能量发射准则以及小的峰均功率比(PAPR)。 The theoretical analysis and simulation results verify that the proposed method satisfies the uniform element power constraint and low Peak to Average Power Ratio (PAPR).
19738 同时，该算法相比于现有算法有较低的运算量以及良好的匹配性能。 Simultaneously, compared with current methods,the proposed method has lower computational burden and comparable matching performance.
19739 针对短时交通流数据的非线性和随机性特点，为提高它的预测精度和收敛速度，该文从模型构建和算法两方面提出一种整合移动平均自回归(ARIMA)模型和遗传粒子群算法优化小波神经网络(GPSOWNN)相结合的预测模型和算法。 In view of the nonlinear and stochastic characteristics of short-term traffic flow data, this article proposes a prediction model and algorithm based on hybrid Auto-Regressive Integrated Moving Average(ARIMA) and Genetic Particle Swarm Optimization Wavelet Neural Network (GPSOWNN) in order to improve its prediction accuracy and rate of convergence.
19740 在模型构建方面，将ARIMA模型预测值和灰色关联系数大于0.6的相关性强的前3个时刻的历史数据作为小波神经网络(WNN)的输入，在兼顾历史数据的平稳和非平稳的情况下，进行了模型结构简化。 In terms of model construction, the ARIMA model prediction value and the historical data of the first three moments with strong correlation with gray correlation coefficient greater than 0.6 are used as input of the Wavelet Neural Network(WNN), and the structure of the model is simplified considering both the stationary and non-stationary historical data.
19741 在算法方面，通过遗传粒子群算法对小波神经网络的参数初始值进行最优选取，可使其结果在不易陷入局部最优的条件下加快网络训练收敛速度。 In terms of algorithm, by using the genetic particle swarm optimization algorithm to select optimally the initial values of the wavelet neural network, the results can speed up the convergence of network training under the condition that it is not easy to fall into local optimum.
19742 实验结果表明，在预测精度方面，该方法的模型明显优于整合移动平均自回归模型和遗传粒子群算法优化小波神经网络，在收敛速度方面，用遗传粒子群算法优化模型明显优于仅用遗传算法优化模型。 The experimental results show that the proposed model is superior to hybrid ARIMA and GPSOWNN in terms of prediction accuracy, the genetic particle swarm optimization algorithm is superior to the genetic algorithm optimization model in terms of convergence speed.
19743 该文对轻量级分组密码算法Simeck在积分攻击下的安全性进行了研究。 The security of lightweight block cipher Simeck against integral attack is evaluated in this paper.
19744 通过向前解密扩展已有的积分区分器，构造了16轮Simeck48和20轮Simeck64算法的高阶积分区分器，并在新区分器的基础上，利用等价子密钥技术和部分和技术，结合中间相遇策略和密钥扩展算法的性质，实现了24轮Simeck48和29轮Simeck64算法的积分攻击。 First, a 16-round and a 20-round high-order integral distinguisher of Simeck48 and Simeck64 are constructed by decrypting the existed integral distinguisher forward. Then, combined with the meet-in-the-middle strategy andsubkey relationship, the integral attacks on 24-round Simeck48 and 29-round Simeck64 are first proposedutilizing the equivalent-subkey and partial-sum technologies based on the new integral distinguishers.