隔水套管波流联合作用下非线性动力响应

考虑流及波流联合作用,研究了深水套管的涡激非线性振动．将套管简化为梁模型,计及Morison非线性流体动力和涡激荷载,建立套管的涡激振动方程．采用Korolov函数求解套管的固有频率和模态,提出了计算涡激非线性动力响应的Galerkin方法,计算了160 m水深中170 m长套管的固有频率和模态,研究了流引起的主共振和波流联合引起的组合共振．计算结果表明波流联合作用下套管的动力响应明显增大,结果也揭示了波流联合激励下套管复杂的动力响应特性．     

Numerical study of chaotic oscillations in the electron beam with virtual cathode in the external non-uniform magnetic fields

In this Letter the results of theoretical investigations of the chaotic microwave oscillator based on the electron beam with a virtual cathode are presented. Nonlinear non-stationary processes in these electron systems are studied by means of numerical analysis of 2.5D model. It was discovered that the non-uniform external magnetic field value controls the dynamical regime of oscillations in the virtual cathode oscillator. The processes of the chaotization of output microwave radiation are described and interpreted from the point of view of the formation and interaction of electron structures (bunches) in the electron beams. The numerical results have shown that the investigated electron system with virtual cathode could be considered as a promising controlled source of wideband chaotic oscillations in the microwave range.     

深蚀刻二元光学元件

提出一种新颖的，蚀刻位相深度超过２π的深蚀刻二元光学技术，并从理论上分析深蚀刻二元光这元件与衍射效率的关系，然后用计算机进行模拟与分析，得到深蚀刻二元光学元件特性后一些初步研究结果。     

An Electrocardiogram Signal Classification Algorithm Based on Improved Deep Residual Shrinkage Networks北大核心CSCD

心电信号分类是医疗保健领域的重要研究内容.针对大多数方法不能很好地降低样本数量少的类别漏诊率,以及降低预处理操作的复杂性问题,提出了一种基于改进深度残差收缩网络(IDRSN)的心电信号分类算法(即DRSL算法).首先,使用合成少数类过采样技术(SMOTE)扩充数量少的类别样本,从而解决了类不平衡问题;其次,利用改进深度残差收缩网络提取空间特征,其残差模块可以避免网络层加深造成的过拟合,压缩激励和软阈值化子网络可以提取重要局部特征并自动去除噪声;然后,通过长短期记忆网络(LSTM)提取时间特征;最后,利用全连接网络输出分类结果.在MIT-BIH心律失常数据集上的实验结果表明,该算法的分类性能优于IDRSN、DRSN、GAN+2DCNN、CNN+LSTM_ATTENTION、SE-CNN-LSTM分类算法.     

焊缝超声相控阵检测数据深度学习降噪方法

超声相控阵检测技术在焊缝检测中具有广泛的应用.超声相控阵检测技术检测信号中常混入噪声导致检测成像时难以分辨真实的缺陷特征.这些噪声主要为无关的反射信号和局部相关的结构噪声,传统的超声图像降噪方法难以有效滤除这些噪声,且存在计算效率低、参数优化复杂等问题.该文提出了一种基于深度学习的焊缝超声相控阵检测技术检测S扫图像的降...     

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).     

基于多域特征提取和深度学习的声源被动测距*

由于实际海洋环境中存在大量的非高斯噪声,一些基于高斯假设的传统去噪方法在实际海洋环境中性能下降甚至失效。针对非高斯噪声,如α稳定分布噪声、非平稳行船噪声下的脉冲信号的去噪与重构,该文提出一种基于深度学习的方法。去噪模型首先通过学习带噪信号短时傅里叶变换谱与残差谱之间的映射关系以去除环境噪声,之后对去噪信号的时频谱进行逆变换重构脉冲信号。仿真实验结果表明,深度学习模型在非高斯噪声环境下脉冲信号的去噪与重构任务中有着良好的表现,在实测样本上也表现出良好的泛化性,体现了一定的工程应用价值。     

不规则H形量子势垒增强AlGaN基深紫外发光二极管性能

针对AlGaN基多量子阱中有效的平衡载流子注入问题,研究了有源区势垒层中Al组分调制形成的非规则H形量子势垒对AlGaN基深紫外发光二极管(LED)器件性能的影响及载流子的输运行为。研究发现,与多量子阱中常用的单Al组分势垒相比,加入Al组分较高的双尖峰势垒可以有效地提高内量子效率和光输出功率。进一步研究表明,电子在有源区因凸起的尖峰势垒而得到了有效的阻挡,减少了电子的泄露,而空穴获得更多的动能从而穿过较高的势垒进入有源区。因此,采用非对称H形量子势垒的深紫外LED器件中载流子输运实现了较好的平衡,量子阱中的载流子复合速率远高于普通的深紫外发光二极管。     

对史密斯-帕塞尔自由电子激光光栅的研究

为了研究史密斯-帕塞尔自由电子激光的输出频率和光栅槽深、光栅槽长、光栅槽宽的关系,对于基于矩形光栅的史密斯-帕塞尔自由电子激光利用粒子模拟软件进行模拟和理论分析。首先,利用粒子模拟软件模拟对于基于矩形光栅的史密斯-帕塞尔自由电子激光进行了研究,发现史密斯-帕塞尔自由电子激光的输出频率随光栅槽深、光栅槽长、光栅槽宽的增大而减少。接着,对史密斯-帕塞尔自由电子激光的光栅槽进行了理论分析,发现每个光栅槽都可以等效为一个LC谐振电路,并发现在史密斯-帕塞尔自由电子激光中存在两种辐射,一种是史密斯-帕塞尔辐射,另一种是LC振荡辐射。最后,对光栅槽的LC振荡辐射进行了估算,发现史密斯-帕塞尔自由电子激光输出频率的模拟值与光栅槽的LC振荡辐射估算值的数量级均为102 GHz,且变化规律上一致。据此推测决定史密斯-帕塞尔自由电子激光输出频率的应该是光栅槽,而不是谐振腔。     

Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics. The conventional prediction methods based on numerical simulation often demand huge computational resources, which are difficult to balance between accuracy and efficiency. Here, we present a data-driven deep neural network (DNN) method to realize fast aerodynamic noise prediction while maintaining accuracy. The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions. Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory, a dataset composed of 1216 samples is established. With reference to the deep learning method, a DNN framework is proposed to map the relationship between spatial coordinates, inlet velocity and overall sound pressure level. The root-mean-square-errors of prediction are below 0.82 dB in the test dataset, and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation. This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.