熔石英表面杂质元素在酸蚀过程中的变化

为了探寻熔石英表面痕量杂质元素种类以及随HF酸蚀刻过程中的变化情况,用质量分数为1%HF酸溶液对熔石英进行长达24,48,72,96 h的静态蚀刻实验。结合飞行时间二次离子质谱（TOF-SIMS）和X射线光电子能谱（XPS）测试分析结果发现,熔石英试片表面的痕量杂质元素主要含有B,F,K,Ca,Na,Al,Zn和Cr元素,其中绝大部分都存在于贝氏层中,在亚表面缺陷层检测到有K,Ca元素。所含有的K,Na杂质元素会与氟硅酸反应生成氟硅酸盐化合物。分析表明,在HF酸蚀刻的过程中一部分杂质元素将被消除,一部分杂质元素和生成氟硅酸盐化合物会随着蚀刻液逐渐从熔石英表面向表面纵深方向扩散并被试片吸附沉积,且随着深度的加深各元素的相对含量逐渐减少。     

水声材料插入损失测量中的声波多途效应抑制方法

提出了一种在非自由声场中测量水声材料插入损失时抑制多途效应的方法,称为\     

Co/ZnO/N-C composites obtained by ZIF derived from Co-Zn oxides as highly efficient catalyst for reduction of p-nitrophenol

Journal of Sol-Gel Science and Technology - Zinc oxide (ZnO) and cobalt (Co)-ZnO nanocomposites were first reacted with 2-Methylimidazole to synthesize the zeolitic imidazole frameworks as...     

Infrared-Visible Image Fusion Based on Semantic Guidance and Visual Perception

Infrared-visible fusion has great potential in night-vision enhancement for intelligent vehicles. The fusion performance depends on fusion rules that balance target saliency and visual perception. However, most existing methods do not have explicit and effective rules, which leads to the poor contrast and saliency of the target. In this paper, we propose the SGVPGAN, an adversarial framework for high-quality infrared-visible image fusion, which consists of an infrared-visible image fusion network based on Adversarial Semantic Guidance (ASG) and Adversarial Visual Perception (AVP) modules. Specifically, the ASG module transfers the semantics of the target and background to the fusion process for target highlighting. The AVP module analyzes the visual features from the global structure and local details of the visible and fusion images and then guides the fusion network to adaptively generate a weight map of signal completion so that the resulting fusion images possess a natural and visible appearance. We construct a joint distribution function between the fusion images and the corresponding semantics and use the discriminator to improve the fusion performance in terms of natural appearance and target saliency. Experimental results demonstrate that our proposed ASG and AVP modules can effectively guide the image-fusion process by selectively preserving the details in visible images and the salient information of targets in infrared images. The SGVPGAN exhibits significant improvements over other fusion methods.     

A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network

With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category.     

Non-Destructive Detection of Moldy Walnuts Based on Hyperspectral Imaging Technology

Walnuts with their shells are a popular agricultural product in China. However, mildew from growth can sometimes be processed into foods. It is difficult to visually determine which walnuts have mildew without breaking the shells. A non-destructive method for detecting walnuts with mildew was studied by combining spectral data with image information. A total of 120 “Lüling” walnuts with shells were used for the mildew experiment. The characteristics of the spectral data from six surfaces of all samples were collected in the range of 370–1042 nm on days 0, 15, and 30. The spectrum was pretreated using SNV, and the feature bands were extracted using PCA and modeled using a support vector machine (SVM). The results show that the overall classification accuracy was 93%, with an of accuracy of 100% for INEN walnuts (normal internally and externally). The accuracy for IMEM walnuts (mildew internally and externally) reached 87.29%. There was an accuracy of 78.6% for IMEN walnuts (mildew internally and normal externally). The non-destructive detection of mildewed walnuts can be undertaken using hyperspectral imaging technology, which provides a new technique for exploring the mechanisms of walnuts with mildew.