Graphene-Based Heterostructure Composite Sensing Materials for Detection of Nitrogen-Containing Harmful Gases

Graphene-based heterostructure composite is a new type of advanced sensing material that includes composites of graphene with noble metals/metal oxides/metal sulfides/polymers and organic ligands. Exerting the synergistic effect of graphene and noble metals/metal oxides/metal sulfides/polymers and organic ligands is a new way to design advanced gas sensors for nitrogen-containing gas species including NH₃ and NO₂ to solve the problems such as poor stability, high working temperature, poor recovery, and poor selectivity. Different fabrication methods of graphene-based heterostructure composite are extensively studied, enabling massive progress in developing chemiresistive-type sensors for detecting the nitrogen-containing gas species. With the components of noble metals/metal oxides/metal sulfides/polymers and organic ligands which are composited with graphene, each material has its attractive and unique electrical properties. Consequently, the corresponding composite formed with graphene has different sensing characteristics. Furthermore, working ambient gas and response type can affect gas-sensitive characteristic parameters of graphene-based heterostructure composite sensing materials. Moreover, it requires particular attention in studying gas sensing mechanism of graphene-based heterostructure composite sensing materials for nitrogen-containing gas species. This review focuses on related scientific issues such as material synthesis methods, sensing performance, and gas sensing mechanism to discuss the technical challenges and several perspectives.     

变分自编码器模型综述

变分自编码器（VAE）作为深度隐空间生成模型的一种，近年来其表现性能取得了极大的成功，尤其是在图像生成方面。变分自编码器模型作为无监督式特征学习的重要工具之一，可以通过学习隐编码空间与数据生成空间的特征映射，进而在输出端重构生成输入数据。梳理了传统变分自编码器模型及其衍生变体模型的发展与研究现状，并就此做了总结和对比，最后分析了变分自编码器模型存在的问题与挑战，并就可能的发展趋势做了展望。     

Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

This study focuses on the accurate tracking control and sensorless estimation of external force disturbances on robot manipulators. The proposed approach is based on an adaptive Wavelet Neural Network (WNN), named Adaptive Force-Environment Estimator (WNN-AFEE). Unlike disturbance observers, WNN_AFEE does not require the inverse of the Jacobian transpose for computing the force, thus, it has no computational problem near singular points. In this scheme, WNN estimates the external force disturbance to attenuate its effects on the control system performance by estimating the environment model. A Lyapunov based design is presented to determine adaptive laws for tuning WNN parameters. Another advantage of the proposed approach is that it can estimate the force even when there are some parametric uncertainties in the robot model, because an additional adaptive law is designed to estimate the robot parameters. In a theorem, the stability of the closed loop system is proved and a general condition is presented for identifying the force and robot parameters. Some suggestions are provided for improving the estimation and control performance. Then, a WNN-AFEE is designed for a planar manipulator as an example, and some simulations are performed for different conditions. WNN_AFEE results are compared attentively with the results of an adaptive force estimator and a disturbance estimator. These comparisons show the efficiency of the proposed controller in dealing with different conditions.     

Energy aspects and workpiece surface characteristics in ultrasonic-assisted cylindrical grinding of alumina–zirconia ceramics

Ultrasonic assisted grinding is a novel method for improving the grinding process of difficult-to-cut materials. In the present research a novel setup has been designed and manufactured for utilizing ultrasonic vibrations in external cylindrical grinding. The designed ultrasonic head vibrates a rotating workpiece in axial direction. An alumina–zirconia ceramic (AZ90) has been selected as the workpiece material. Energy aspects and workpiece surface characteristics of ultrasonic assisted cylindrical grinding (UACG) and conventional cylindrical grinding (CG) processes have been analytically modeled and corresponding grinding experiments have been performed. The combined kinematics of the cylindrical plunge grinding process and axial ultrasonic vibrations provide a unique surface treatment conditions, which leads to reduced peak heights and increased valley depths of the surface topography. The main axial vibration mode provides the overlap of the adjacent cutting traces and consequently smoothens the surface topography in cylindrical plunge grinding. It has been analytically and experimentally shown that, applying ultrasonic vibrations, grinding energy can be reduced up to more than 35% depending on the process parameters. The surface characteristics of the ground workpieces have been investigated in terms of four surface roughness parameters and the roundness error.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework

As one of the representative unsupervised data augmentation methods, generative adversarial networks (GANs) have the potential to solve the problem of insufficient samples in fault diagnosis of rotating machinery. However, the existing unsupervised GANs are usually incapable of simultaneously generating multi-mode fault samples and have some shortcomings such as mode collapse and gradient vanishing. To overcome these deficiencies, a supervised model called modified auxiliary classifier GAN (MACGAN) designed with new framework is proposed in this paper. Firstly, a new ACGAN framework is developed by adding an independent classifier to improve the compatibility between the classification and discrimination. Secondly, the Wasserstein distance is introduced in the new loss functions to overcome mode collapse and gradient vanishing. Finally, to achieve stable training, a spectral normalization is used to replace the weight clipping to constrain the weight parameters of discriminator. The proposed method is applied to fault diagnosis of bearing and gear. Compared with the existing GANs, the proposed method can more efficiently generate multi-mode fault samples with higher qualities, which can be used to assist the training of deep learning-based fault diagnosis models with high accuracy and good stability.     

Effect of free stream angle on mixing performance of hydrogen multi-jets in supersonic combustion chamber

The fuel mixing process within the combustion chamber is a critical procedure for advance of scramjet engine. In current study, the influence of free stream angle on the fuel mixing process of multi jets was thoroughly investigated. This research attempts to applied computational technique to disclose the structure of multi-fuel jets when the supersonic air stream is not normal to the jet direction. The effect of both positive and negative supersonic free stream on the diffusion and penetration of multi-hydrogen jets is fully described. The attention of this research is the flow structure of the multi jets and their interaction in the presence of different free stream angle. Our results indicate that the positive free stream angle expands the mixing zone in the downstream while the negative angle of free stream limited jet distribution inside the combustor. Our results show that mixing efficiency increase approximately 50% when the angle of free stream augments from +20° to −20°. According to our findings, mixing efficiency surges up to 17% when the jet spaces are increased from 4Dj to 10 Dj.     

不锈钢网表面润湿性的调控及其油水分离性能

采用简单的化学刻蚀法在 304 不锈钢网上构造微纳米粗糙结构,随后通过自组装技术将不同链长的脂肪酸装饰到粗糙表面,可制备出具有可控润湿性的网膜;利用接触角测量仪对表面润湿性进行测量,利用原子力显微镜、扫描电子显微镜和傅里叶变换红外光谱仪对网膜表面的形貌和成分进行表征分析,根据长链脂肪酸改性后的超疏水超亲油特性测试油水分离性能以及重复利用性。 结果表明:不锈钢滤网表面呈现二维微纳米粗糙结构,并形成有序碳链薄膜。 通过调节脂肪酸的链长可实现亲水到超疏水的转变,接触角范围为 45° ~ 153°;油滴迅速在网膜上渗透,接触角始终为 0°。 链长越长的脂肪酸疏水亲油效果越明显,油水分离效率越高,最高达到 96%;经重复油水分离 50 次测试后,其油水分离效率仍能达到 80%以上。     

超声辅助对激光熔覆Al2O3-ZrO2陶瓷涂层力学性能的影响

目的 探究超声辅助对钛合金表面激光熔覆Al2O3-ZrO2陶瓷涂层力学性能的影响。方法 使用COMSOL Multiphysics仿真软件，探究熔覆工件中有无超声作用下流场的变化，并将超声波直接引入熔池微区，研究超声辅助对Al2O3-ZrO2陶瓷涂层截面形貌、微观组织、元素分布、显微硬度和摩擦磨损性能的影响。结果 仿真结果表明，无超声时熔池横截面涡流大小呈对称状，流速较为缓慢，超声辅助下熔池截面的声速大小瞬时变化，熔池内流体的流速提高。实验结果表明，超声辅助下，熔覆层的截面形貌发生了一些变化，但抑制了熔覆层裂纹的产生。熔覆层晶粒尺寸细化，尤其是当激光功率为1700 W时，晶粒尺寸最小且相对更加致密;熔覆层内元素分布更加均匀，且界面效应降低。熔覆层的力学性能提高，激光功率为1700 W时，熔覆层平均显微硬度值为979.4HV0.2，相同激光功率下，超声辅助的熔覆层具有更大的平均显微硬度值。超声辅助下熔覆层的摩擦系数和波动幅值都较小，激光功率为1700 W时的摩擦系数最小(约为0.329)且波动较为平稳。结论 超声波直接引入熔池微区可以有效提高熔池的流动性，同时细化了晶粒，均匀化了元素分布，提高了熔覆层的力学性能。     

挖掘机动态力学仿真

为方便挖掘机设计中受力分析,便于液压缸选取,提出利用SolidWorks进行模型设计,采用COSMOSMotion进行动态仿真,得到液压缸受力变化曲线,找到最大受力位置状态,以此为依据选取液压缸,缩短了设计周期。