3D Laser Micro‐ and Nanoprinting: Challenges for Chemistry

3D printing is a powerful emerging technology for the tailored fabrication of advanced functional materials. This Review summarizes the state‐of‐the art with regard to 3D laser micro‐ and nanoprinting and explores the chemical challenges limiting its full exploitation: from the development of advanced functional materials for applications in cell biology and electronics to the chemical barriers that need to be overcome to enable fast writing velocities with resolution below the diffraction limit. We further explore chemical means to enable direct laser writing of multiple materials in one resist by highly wavelength selective (λ‐orthogonal) photochemical processes. Finally, chemical processes to construct adaptive 3D written structures that are able to respond to external stimuli, such as light, heat, pH value, or specific molecules, are highlighted, and advanced concepts for degradable scaffolds are explored.     

Reduced‐order modelling of an adaptive mesh ocean model

A novel proper orthogonal decomposition (POD) model has been developed for use with an advanced unstructured mesh finite‐element ocean model, the Imperial College Ocean Model (ICOM, described in detail below), which includes many recent developments in ocean modelling and numerical analysis. The advantages of the POD model developed here over existing POD approaches are the ability:

• 1. To increase accuracy when representing geostrophic balance (the balance between the Coriolis terms and the pressure gradient). This is achieved through the use of two sets of geostrophic basis functions where each one is calculated by basis functions for velocities u and v.
• 2. To speed up the POD simulation. To achieve this a new numerical technique is introduced, whereby a time‐dependent matrix in the discretized equation is rapidly constructed from a series of time‐independent matrices. This development imparts considerable efficiency gains over the often‐used alternative of calculating each finite element over the computational domain at each time level.
• 3. To use dynamically adaptive meshes in the above POD model.

Copyright © 2008 John Wiley & Sons, Ltd.     

无刷直流力矩电机系统的自适应扰动力矩补偿研究

陀螺漂移测试转台无刷直流力矩电机系统中存在波动力矩和负载力矩振动，这严重地影响了转台速率平稳度。为提高转台速率状态位置跟踪精度，设计了一种自适应补偿方法。该方法包含一个参自适应律和等效PID控制律，它利用前馈补偿原理，来估计电机中未知参数以及波动力矩和负载力矩参数并给与补偿。该自适应补偿方法保证了闭环系统全局稳定性和对期望位置信号的渐进跟踪。仿真结果证明：该方法有效地提高了转台速率状态跟踪精度。     

An Unsupervised Video Stabilization Algorithm Based on Key Point Detection

In recent years, video stabilization has improved significantly in simple scenes, but is not as effective as it could be in complex scenes. In this study, we built an unsupervised video stabilization model. In order to improve the accurate distribution of key points in the full frame, a DNN-based key-point detector was introduced to generate rich key points and optimize the key points and the optical flow in the largest area of the untextured region. Furthermore, for complex scenes with moving foreground targets, we used a foreground and background separation-based approach to obtain unstable motion trajectories, which were then smoothed. For the generated frames, adaptive cropping was conducted to completely remove the black edges while maintaining the maximum detail of the original frame. The results of public benchmark tests showed that this method resulted in less visual distortion than current state-of-the-art video stabilization methods, while retaining greater detail in the original stable frames and completely removing black edges. It also outperformed current stabilization models in terms of both quantitative and operational speed.     

DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection

To ensure the normal operation of the system, the enterprise’s operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long–short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.     

Community Detection in Semantic Networks: A Multi-View Approach

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.     

激光烧蚀瑞利－泰勒不稳定性模拟

给出了激光烧蚀流体不稳定性计算程序EUL2D的物理方程,介绍了计算中使用的活动网格和一些技术问题处理。EUL2D程序的计算结果与Takabe公式、FAST2D程序和LASNEX程序的结果较好符合。数值计算日本大阪大学激光烧蚀瑞利－泰勒不稳定性实验,再现了实验结果。发现了横向电子热传导烧蚀在长波长扰动的非线性瑞利－泰勒不稳定性演变中起重要作用。     

静电排斥型微机电系统变形镜驱动器

为了克服常规静电微机械驱动器所存在的静电吸合现象,基于非均匀分布的静电场可以产生排斥力的原理,设计了一种新型的微机电系统变形镜驱动器。驱动器由5组电极构成,最大的一组由中心质量块和位于正下方的下电极组成,其它4组分别位于各条边上。中心质量块由4根L形弹簧支撑,每根弹簧分别固定于驱动器的4个锚点。利用有限元软件对驱动器的频率响应和暂态响应特性进行了仿真,结果表明,谐振频率高达4 kHz,暂态响应时间小于0.05 s。利用表面硅工艺加工出了驱动器,并利用白光扫描干涉仪对驱动器的静态位移电压特性进行了测试,测得驱动器在70 V激励电压下的形变量为1.4 μm。     

微加工薄膜变形镜本征模分析

介绍了变形镜本征模的构造过程及其特点,分析了从荷兰OKO公司购买的37单元微加工薄膜变形镜的本征模,并用前65阶Zernike多项式分析微加工薄膜变形镜的37阶本征模的特性。搭建了以微加工薄膜变形镜作为波前校正器、变形镜本征模为波前复原控制算法以及高分辨率Shack-Hartmann波前传感器为波前探测器的自适应光学实验系统。实验结果表明：变形镜本征模可以实现模式筛选,也可作为自适应光学系统的波前复原算法。     

基于贝叶斯参数优化的无模型自适应硅单晶直径控制

直拉硅单晶的生长过程涉及多场多相耦合与复杂的物理化学变化,其中工艺参数的波动是导致晶体直径不均匀的重要原因,如何实现工艺参数的控制以获得理想的、均匀的晶体直径具有重要的研究意义。本文分析现有控制方法存在不稳定以及控制效果不佳的问题后,提出基于贝叶斯参数优化的无模型自适应控制模型来控制硅单晶生长过程中的晶体直径。首先以坩埚上升速度与加热器的功率作为控制输入参数,晶体直径作为输出,搭建无模型自适应控制模型,并分析算法的稳定性。其次将控制模型进行仿真实验,发现硅单晶直径控制模型中不同的超参数设定会影响控制过程的迭代次数以及控制效果。最后,利用贝叶斯优化超参数的取值范围,并进行最终的仿真实验,结果表明,经贝叶斯参数优化后的控制模型计算快、迭代次数少,输出的晶体直径稳定,同时将生长工艺参数控制在实际生产要求范围内。因此,基于贝叶斯参数优化的无模型自适应控制实现了硅单晶直径均匀稳定的有效控制,具有结合工程背景的实际应用前景。