Interface and defects engineering for multilayer laser coatings

High-reflective multilayer laser coatings are widely used in advanced optical systems from high power laser facilities to high precision metrology systems. However, the real interface quality and defects will significantly affect absorption/scattering losses and laser induced damage thresholds of multilayer coatings. With the recent advances in the control of coating design and deposition processes, these coating properties can be significantly improved when properly engineered the interface and defects. This paper reviews the recent progress in the physics of laser damage, optical losses and environmental stability involved in multilayer reflective coatings for high power nanosecond near-infrared lasers. We first provide an overview of the layer growth mechanisms, ways to control the microstructures and reduce layer roughness, as well as the nature of defects which are critical to the optical loss and laser induced damage. Then an overview of interface engineering based on the design of coating structure and the regulation of deposition materials reveals their ability to improve the laser induced damage threshold, reduce the backscattering, and realize the desirable properties of environmental stability and exceptional multifunctionality. Moreover, we describe the recent progress in the laser damage and scattering mechanism of nodule defects and give the approaches to suppress the defect-induced damage and scattering of the multilayer laser coatings. Finally, the present challenges and limitations of high-performance multilayer laser coatings are highlighted, along with the comments on likely trends in future.     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

Molecular and colloidal self-assembly at the oil–water interface

Self-assembly is a versatile bottom-up approach for fabricating novel supramolecular materials with well-defined nano- or micro-structures associated with functionalities. The oil-water interface provides an ideal venue for molecular and colloidal self-assembly. This paper gives an overview of various self-assembled materials, including nanoparticles, polymers, proteins, and lipids, at the oil-water interface. Focus has been given to fundamental principles and strategies for engineering the self-assembly process, such as control of pH, ionic strength and use of external fields, to achieve complex soft materials with desired functionalities, such as nanoparticle surfactants, structured liquids, and proteinosomes. It has been shown that self-assembly at the oil-water interface holds great promise for developing well-structured complex materials useful for many research and industrial applications.     

固体间界面的物理模型和界面对声波的反射

简要描述了模拟两固体间界面特性的弹簧模型，该模型最早是根据静力学方法提出的，后来用固体间界面薄层的声波反射方法加以改进，从界面弹簧模型可以方便地得到界面外近似边界条件，其中包含界面“弹簧”振子的劲度常数和质量，文章还给出了两相间固体中界面声反射系数的表达式，介绍了测量界面劲度常数的超声反射谱方法。最后讨论了仍关声波与界面相互作用研究领域中最近的一些研究进展。     

Monte Carlo study of interfacial silicon suboxide layers and oxidation kinetics

A simple simulation scheme that simultaneously describes the growth kinetics of SiO₂ films at the nanometer scale and the SiO_x/Si interface dynamics (its extent, and spatial/temporal evolution) is presented. The simulation successfully applies to experimental data in the region above and below 10 nm, reproduces the Deal and Grove linear-parabolic law and the oxide growth rate enhancement in the very thin film regime (the so-called anomalous region). According to the simulation, the oxidation is governed mainly by two processes: (a) the formation of a transition suboxide layer and (b) its subsequent drift towards the silicon bulk. We found that it is the superposition of these two processes that produces the crossover from the anomalous oxidation region behavior to the linear-parabolic law.     

空气/水界面水分子的取向和运动

对四种不同的实验构型下空气／水界面自由O-H键在3700cm~(-1)的和频振动光谱的分析表明,水分子在空气／水界面的取向运动只可能是在平衡位置附近有限角度之内的受限转动。界面水分子的自由O-H键取向距界面法线约33°,而取向分布或运动的宽度不超过15°。这一图像显著地不同于Wei等人(Phys. Rev. Lett.86,4799(2001))只通过单一的SFG实验构型所得出结论,即：空气／水界面的水分子在超快的振动弛豫时间内在很大的角度范围内运动。     

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.     

模糊对向传播网络数据融合目标识别方法

重点研究了模糊对向传播网络 (FCPN)模型。针对数据融合和目标识别的特点 ,提出了基于模糊对向传播网络的融合目标识别方法和改进的模糊对向传播网络 (MFCPN)融合结构。利用仿真数据对网络的训练算法和融合结构进行了实验研究。结果表明 ,模糊对向传播网络较误差后向传播网络 (BPN)能够有效地实现融合识别 ;改进的模糊对向传播网络融合结构是可行的。同时还对FCPN和MFCPN应用于前视红外 (FLIR)和可见光摄像机目标跟踪系统进行了应用研究。     

Time Series Prediction Based on Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series.