Stability analysis for linear time-varying systems using bicoprime factorization

This paper presents a bicoprime factorization (BCF) approach to deal with various stability problems for linear discrete time-varying (LTV) systems within the framework of nest algebra. Based on the bicoprime factorization, we give necessary and sufficient conditions for (strongly) simultaneous stabilization of three systems, which generalize similar results for systems by coprime factorization (CF). Finally, when the bicoprime factorization for the plant and controller has additive perturbations, we derive sufficient conditions using the norm inequality for robust stability of the system.     

Digital twin-assisted fault diagnosis system for robot joints with insufficient data

The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps. However, deep learning-based fault diagnosis needs a multitude of measured fault data, which is difficult to obtain for various reasons. To solve the problem of insufficient data, a digital twin-assisted fault diagnosis system for robot joints is proposed. First, a simplified dynamics model of the robot joint is developed to generate the virtual entity data which can be used as the X-domain data for the digital twin model. Second, a CycleGAN-based digital twin model is proposed to map the virtual entity (X-domain) data to the physical entity (Y-domain) utilizing only a small amount of measured data. In the end, a test-rig for the robot joint is built to simulate the robot's working conditions, and the CNN-ResNet classifier is utilized to verify the effectiveness of the simulated data generated by the digital twin model. The results show that the fault diagnosis accuracy can be increased from 32.5% to 98.86% utilizing only 400 sets of measured data.     

基于 Lamb 波能量和飞行时间的碳纤维复合材料 疲劳损伤成像

碳纤维复合材料被广泛应用于航空航天等高新技术领域,其在服役过程中会产生疲劳损伤,埋下安全隐患,因此需要对其健康状况进行监测,利用损伤概率成像算法能够得到直观反映结构健康状况的图像,但传统的损伤概率成像算法在无损伤区域的损伤概率高,难以准确定位损伤,针对以上问题,提出基于Lamb波能量和飞行时间的损伤概率成像算法。将待测区域均匀划分成N个像素点,计算每条通道的Lamb波能量与飞行时间损伤因子,确定各通道损伤因子影响区域的概率值并叠加,得到每个像素点的损伤概率并成像。实验结果表明,与目前常用的基于能量损伤因子和互相关损伤因子的损伤概率成像算法进行对比,提出的方法能够直观地反映碳纤维复合材料缺陷情况,并且识别效果更优,成像误差显著减小,误差error分别降低了4.420、2.117、2.055和4.732、2.380、2.647,能够更准确地识别缺陷,有效地保障碳纤维复合材料结构的安全应用。     

结合数据增强和改进 YOLOv4 的水下目标检测算法

针对水下低质量成像、水下目标形态大小各异、以及水下目标重叠或遮挡导致水下目标检测精度低的问题,提出一种结合数据增强和改进YOLOv4(you look only once)的水下目标检测算法,在YOLOv4的主干特征提取网络CSPDarknet53中添加卷积块注意力机制(convolutional block attention module, CBAM),以提高网络模型特征提取能力;在路径聚合网络(path aggregation network, PANet)中添加同层跳接和跨层跳接结构,以增强网络模型多尺度特征融合能力;通过数据增强方法PredMix(prediction-mix)模拟水下生物重叠、遮挡等显示不完全的情形,以增强网络模型鲁棒性。实验结果表明,结合数据增强和改进YOLOv4的水下目标检测算法在URPC2018(underwater robot picking control 2018)数据集上的检测精度提升到了78.39%,比YOLOv4高出7.03%,充分证明所提算法的有效性。     

融合 CEEMD 和 TCN 的受热面积灰预测研究

对锅炉受热面积灰程度的有效预测,可为锅炉提升生产效率和故障预警提供重要依据。 采用清洁因子来评估受热面的 灰污沉积状况,针对其序列非线性、非平稳性的特点,提出一种基于互补集合经验模态分解与时间卷积网络的受热面积灰预测 方法。 首先,通过互补集合经验模态分解将经过小波阈值去噪处理后的原始序列分解为一组子序列分量;然后,针对不同子序 列分别构建基于时间卷积网络的时序预测模型,并优化网络超参数提升预测准确性;最后,将各 IMF 分量的预测结果叠加得出 清洁因子的预测数值。 由实验结果可得,相较于其他两种模型,预测精度分别提高 62. 1%和 57. 1%,CEEMD-TCN 模型对受热 面积灰状况预测精度最高,验证了该模型的准确性和可靠性。     

氢燃料电池船舶直流综合电力系统研究及应用

研发氢燃料电池船舶是实现航运业零碳排放的可行途径,也是推动造船业转型升级的重要发展方向,但传统的电力系统集成模式逐渐成为制约氢燃料电池船舶产业化应用的关键因素,构建以新能源为主体的船舶新型直流综合电力系统成为解决该问题的突破点。首先,系统梳理了氢燃料电池船舶和船舶综合电力系统的发展概况;其次,以中国第一艘氢燃料电池游艇"蠡湖"号为例,详细介绍了氢燃料电池船舶直流综合电力系统的关键技术及其在实船上的应用;然后,针对不同的航行工况重点分析了氢燃料电池船舶的5种电力驱动模式;最后,结合相关技术和配套设备的研发进程,分析并探讨船用氢燃料电池的局限性、推广可行性及未来的发展方向,期望能为燃料电池船舶的技术创新和工程应用提供参考。     

基于期望方向图匹配的MIMO雷达恒模发射波形设计算法研究

为提升MIMO雷达发射功率利用率,改善对目标的探测性能,研究了期望方向图匹配下的发射波形设计方法。建立了发射波形恒模约束下的方向图匹配加权优化模型;基于循环优化和majorization-minimization(MM)算法对原始非凸优化问题进行求解,通过构造合适的上界函数,可将原始问题松弛为等式约束下的序列线性规划问题,并能保证算法的收敛性;由于每一次迭代都能给出子问题的闭式解,因而所提方法的计算复杂度较低;仿真实验表明了所提方法的有效性。     

Artificial intelligence in cyber security: research advances,challenges, and opportunities

Artificial Intelligence Review - In recent times, there have been attempts to leverage artificial intelligence (AI) techniques in a broad range of cyber security applications. Therefore, this paper...