Control of unpolarized emission in closely stacked InAs quantum dot structure

In this work, we studied the effect of some growth parameters on the polarization behavior of InAs/GaAs closely stacked quantum dot (CSQDs). In particular, we focused on the surface reconstruction time of GaAs spacer, its thickness and the number of QD layers. We found that the most effective parameter to enhance the TM/TE intensity ratio is the surface reconstruction time of the GaAs spacer before the subsequent QD deposition. By varying this parameter between 20 s and 120 s, a TM/TE ratio as high as 0.86 has been achieved. A further fine tuning of GaAs spacer thickness and QD layer number increased this ratio up to a value of 0.92 in structures containing only 3 QD layers.     

层状耦合约瑟夫森双结中的孤子解

基于Sakai-Bodin-Petersen理论，我们求解了对称的双约瑟夫森结的耦合非线性方程组．结果表明，线性化方程组的同相和反相解所对应的两种磁通运动模式也存在于原始的非线性方程组的解中，同时非线性方程组独有的位相锁定的扭折（kinks）形式的磁通运动在低能情况下会演化为孤子解．     

一种用于堆叠结构有机发光二极管的新的电荷生成层

本文报道了一种用于堆叠结构有机电致发光器件的新的电荷生成层: LiF/Al/V₂O₅,采用这种电荷生成层的堆叠器件的两个发光单元互相独立,不受影响.说明在外加电场下,这种电荷生成层具有向邻近的发光单元注入电子空穴的能力.而堆叠了两个相同发光单元的器件的电流效率在相同的电流密度下约为普通单层结构的1.7倍.同时这种电荷生成层避免了溅射indium tin oxide(ITO)和金属、有机物共掺,只需要热蒸发,生长工艺简单. 关键词： 堆叠结构 有机电致发光器件 电荷生成层     

光导开关级联Blumlein型脉冲网络设计

为了驱动重复频率运行高阻抗X光管,设计了基于光导开关的级联Blumlein型脉冲形成网络。采用数值模拟方法优化网络参数,从网络充电电压一致性、输出脉冲波形畸变、电压叠加效率、负载预脉冲幅值出发确定了充电电感和支撑电感设计原则。实验表明:光导开关工作场强23.2 kV/cm,激光触发能量3.5 mJ时,阻抗约15.6 的Blumlein型脉冲形成网络电压转换效率为0.71,2级级联网络电压叠加效率0.96。     

Stacked partial least squares regression analysis for spectral calibration and prediction

Two novel algorithms which employ the idea of stacked generalization or stacked regression, stacked partial least squares (SPLS) and stacked moving‐window partial least squares (SMWPLS) are reported in the present paper. The new algorithms establish parallel, conventional PLS models based on all intervals of a set of spectra to take advantage of the information from the whole spectrum by incorporating parallel models in a way to emphasize intervals highly related to the target property. It is theoretically and experimentally illustrated that the predictive ability of these two stacked methods combining all subsets or intervals of the whole spectrum is never poorer than that of a PLS model based only on the best interval. These two stacking algorithms generate more parsimonious regression models with better predictive power than conventional PLS, and perform best when the spectral information is neither isolated to a single, small region, nor spread uniformly over the response. A simulation data set is employed in this work not only to demonstrate this improvement, but also to demonstrate that stacked regressions have the potential capability of predicting property information from an outlier spectrum in the prediction set. Moisture, oil, protein and starch in Cargill corn samples have been successfully predicted by these new algorithms, as well as hydroxyl number for different instruments of terpolymer samples including and excluding an outlier spectrum. Copyright © 2009 John Wiley & Sons, Ltd.     

A composite (stacked) picture generation technique for spectral profile representation of dye laser

In this paper, a new technique for data representation format from video recording of spectral profile of tunable dye laser has been formulated. Only a part of the acquired images acquired sequentially in programmed time interval, was used to make the composite picture to measure variation of the parameters. A CCD camera based imaging system was used to confine the spectral pattern. No additional hardware other than image capturing device was used. The technique developed reduces the storage space required in the computer and also enhances the measurement speed significantly. The software algorithm developed has been used effectively to measure various parameters of tunable dye lasers and other optical sources.     

小宽带堆积啁啾脉冲传输放大特性

 采用啁啾脉冲堆积的方法可获得ns量级宽带整形激光脉冲,对堆积啁啾脉冲时间波形的影响因素及传输放大后脉冲波形与光谱形状的特性进行了研究。结果表明：在传输过程中脉冲的演化规律与窄带脉冲的演化规律基本一致,而且传输放大过程也不会改变脉冲的调制结构,但光谱形状在传输放大过程中发生了变化,初步认为是由非线性效应造成的;小宽带堆积啁啾脉冲具有与窄带脉冲基本一致的的增益水平。     

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.     

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder

As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.     

Coils for ingestible capsules: Near-field magnetic induction link

Monolayer- and compact-multilayer-stacked ingestible TX coils are investigated for ingestible capsule systems. The inductive link through the human body is modeled. The efficiency of the near-field magnetic induction link budget is evaluated in the air, in the homogeneous human body and in the three-layer human body. The variations of the position and the orientation of the TX capsule coil are taken into account to evaluate the coupling response between the TX ingestible and RX on-body coils.