ID 原文 译文
5074 所提理性委托计算协议在满足传统安全性的同时，又考虑了参与者的行为偏好，更符合大数据环境下的委托计算模式。 The proposed rational delegation computing protocol not only satisfies the tradi-tional security, but also considers the behavioral preference of participants, which is more in line with the delegation-computing mode under the big data environment.
5075 提出了一种在互耦条件下基于酉张量分解的多输入多输出（MIMO）雷达非圆目标稳健的角度估计算法。 A robust angle estimation method for noncircular targets based on unitary tensor decomposition with mutualcoupling in multiple-input multiple-output (MIMO) radar was proposed.
5076 所提算法首先在张量域利用互耦系数矩阵的带状对称 Toeplitz 结构来消除未知互耦的影响， Firstly, utilizing the banded symmetric Toeplitzstructure of the mutual coupling matrix to eliminate the influence of unknown mutual coupling in tensor field.
5077 然后通过构造一个特殊的增广张量捕获非圆信号的非圆特性与其固有的多维结构特性， Then aspecial augmented tensor was constructed to capture the no circularity and its inherent tensor multidimensional structureof noncircular signals.
5078 并利用增广张量的 centro-Hermitian 特性通过酉变换转化为实值张量， And taking advantage of the centro-Hermitian characteristic of the augmented tensor to transformthe sub-tensor into real-values tensor by the unitary transformation.
5079 最后利用高阶奇异值分解（HOSVD）获得信号子空间，结合实值子空间技术获得目标的离开方向（DoD）和到达方向（DoA）估计。 Finally, the signal subspace estimation based on ten-sor was obtained by taking advantage of the higher-order singular value decomposition (HOSVD) technology, and then the direction-of-departure (DoD) and direction-of-arrival (DoA) estimation was obtained by utilizing the real-values sub-space technology.
5080 由于同时利用信号的非圆结构与多维结构特性，所提算法具有比现有的子空间算法更准确的角度估计性能， Due to the consideration of both the noncircularity and multidimensional structure, the proposed algo-rithm has better recognition performance than the existing angle estimation methods.
5081 同时所提算法只需要实值运算，具有较低的运算复杂度。 At the same time, the proposed al-gorithm only requires real-valued operations and has lower computational complexity.
5082 仿真结果表明，所提算法具有有效性与优越性。 Simulation experiments verify the effectiveness and superiority of the proposed algorithm.
5083 针对目前利用启发式算法学习贝叶斯网络结构易陷入局部最优、寻优效率低的问题，提出一种基于混合樽海鞘−差分进化算法的贝叶斯网络结构学习算法。 Aiming at the disadvantages of Bayesian network structure learned by heuristic algorithms, which were trap-ping in local minimums and having low search efficiency, a method of learning Bayesian network structure based on hy-brid binary slap swarm-differential evolution algorithm was proposed.