ID	原文	译文
2503	实验结果表明，与现有技术相比，该方法对墙体子空间的确定更加精准有效，提高了穿墙雷达墙体杂波干扰抑制能力，改善了墙后目标的成像质量。	Compared with the traditional subspace technique, this method has a better per-formance in wall subspace extraction. Meanwhile, the capability of suppressing wall clutter is enhanced, and the quality of thetarget imaging is improved.
2504	针对现有树突状细胞算法(dendritic cell algorithm，DCA)在不同类型设备的故障检测中严重依赖人工经验定义输入信号，缺乏适应性和完备性，提出了一种基于数值微分的树突状细胞故障检测模型———NDDC-FD。	Currently, the DCA (dendritic cell algorithm)relies heavily on artificial experience to define the input sig-nals in fault detection of different types of equipment, which is lack of adaptability and completeness. To address this prob-lem, we propose a dendritic cell fault detection model based on numerical differentiation———NDDC-FD.
2505	首先，引入变化是系统危险发生的征兆和外在表现的思想，提出了一种基于变化危险感知的信号自适应提取方法，采用数值微分描述数据的变化，再从变化中提取输入信号。	In first place, ac-cording to change is the symptom and outward expression of system which is in danger, an adaptive signal extraction method based on danger perception of system status change is proposed, which uses numerical differentiation to calculate the changeto extract the input signals.
2506	其次，原 DC 模型中异常抗原的评价方式对突变性故障能有效检测，却无法及时发现渐变性故障，提出了采用 T 细胞浓度作为故障评价指标。	Next, the anomaly antigen evaluation method of original DC model can effectively detect abruptfault, but it can't detect incipient fault in time. Therefore, the fault evaluation indicator based on concentration of T cells is proposed.
2507	最后，在 DAMADICS 和 TE 两个基准平台上，将本文方法与原 DCA 算法和传统主元分析法(principal component analysis，PCA)进行比较测试。	Finally, our method is tested on DAMADICS and TE benchmark, and compared with DCA and PCA (principalcomponent analysis).
2508	实验结果表明NDDC-FD 方法不仅提高了原 DCA 算法的适应性，且和 DCA、PCA 相比具有较高检测率的同时，更能较早地检测到渐变性故障。	The results show that NDDC-FD method not only improves the adaptability of DCA, but also has high-er detection rate than DCA and PCA, and has lower detection delay time in incipient fault detection.
2509	因此，本文提出的故障检测方法 NDDC-FD 具有一般性，且性能良好。	Overall, our method isgenerality and has well performance in the fault detection of industrial equipment.
2510	针对鲸鱼优化算法容易陷入局部极值和收敛速度慢的问题，提出了一种结合自适应权重和模拟退火的鲸鱼优化算法。	Aiming at the problem that whale optimization algorithm is easy to fall into local extreme value and slow convergence speed, this paper proposes a whale optimization algorithm based on adaptive weight and simulated annealing.
2511	通过改进的自适应权重策略来调整算法的收敛速度，通过模拟退火增强鲸鱼优化算法的全局寻优能力。	The improved convergence weight strategy is used to adjust the convergence speed of the algorithm, and the global optimiza-tion ability of the whale optimization algorithm is enhanced by simulated annealing.
2512	仿真实验中计算了 18 个测试函数，对比了粒子群算法、海豚回声定位算法和标准鲸鱼算法并进行统计分析，	In the simulation experiment, 18 test functions were calculated and the genetic algorithm, the particle swarm optimization algorithm and the standard whale algo-rithm were compared and statistically analyzed.